tools_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Công cụ - pandas**\n",
    "\n",
    "*Thư viện `pandas` cung cấp các công cụ phân tích dữ liệu và cấu trúc dữ liệu hiệu suất cao, dễ sử dụng. Cấu trúc dữ liệu còn được gọi là `DataFrame`, mà bạn có thể xem như bảng 2D trong bộ nhớ (giống như bảng tính, có tên cột và nhãn hàng). Nhiều tính năng có sẵn trong Excel có được hỗ trợ trong pandas, chẳng hạn như tạo bảng tổng hợp, tính toán cột dựa trên các cột khác, vẽ đồ thị, v.v. Bạn cũng có thể nhóm các hàng theo giá trị cột hoặc nối các bảng giống như trong SQL. Pandas cũng rất mạnh mẽ trong việc xử lý chuỗi thời gian.*\n",
    "\n",
    "Điều kiện tiên quyết:\n",
    "* NumPy – nếu bạn chưa quen với NumPy, chúng tôi khuyên bạn nên xem [Hướng dẫn về NumPy](tools_numpy.ipynb) ngay bây giờ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/tools_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://kaggle.com/kernels/welcome?src=https://github.com/ageron/handson-ml2/blob/master/tools_pandas.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" /></a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cài đặt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Đầu tiên, hãy nạp `pandas`. Mọi người thường nạp nó dưới dạng `pd`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Đối tượng `Series`\n",
    "Thư viện `pandas` chứa các cấu trúc dữ liệu hữu ích sau:\n",
    "* Các đối tượng `Series` mà chúng ta sẽ thảo luận ngay bây giờ. Đối tượng `Series` là mảng 1D, tương tự như cột trong bảng tính (có tên cột và nhãn hàng).\n",
    "* Các đối tượng `DataFrame`. Đây là bảng 2D, tương tự như bảng tính (có tên cột và nhãn hàng).\n",
    "* Các đối tượng `Panel`. Bạn có thể xem `Panel` dưới dạng từ điển của `DataFrame`. Chúng ít được sử dụng hơn, vì vậy chúng ta sẽ không thảo luận về chúng ở đây."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tạo `Series`\n",
    "Hãy bắt đầu bằng cách tạo đối tượng `Series` đầu tiên của chúng ta!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1   -1\n",
       "2    3\n",
       "3    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([2,-1,3,5])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tương tự như `ndarray` 1D\n",
    "Các đối tượng `Series` hoạt động giống như các `ndarray` NumPy một chiều và bạn thường có thể chuyển chúng dưới dạng tham số cho các hàm NumPy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      7.389056\n",
       "1      0.367879\n",
       "2     20.085537\n",
       "3    148.413159\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.exp(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Các phép toán số học trên `Series` cũng có thể thực hiện được và chúng áp dụng *theo từng phần tử*, giống như đối với `ndarray`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1002\n",
       "1    1999\n",
       "2    3003\n",
       "3    4005\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s + [1000,2000,3000,4000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tương tự như NumPy, nếu bạn thêm một số vào `Series`, thì số đó sẽ được thêm vào tất cả các mục trong `Series`. Điều này được gọi là *broadcasting*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1002\n",
       "1     999\n",
       "2    1003\n",
       "3    1005\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s + 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Điều này cũng đúng với tất cả các phép toán nhị phân như `*` hoặc `/`, và thậm chí cả các phép toán có điều kiện:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s < 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nhãn chỉ mục\n",
    "Mỗi mục trong đối tượng `Series` có một mã định danh duy nhất được gọi là *nhãn chỉ mục*. Theo mặc định, nó chỉ đơn giản là thứ hạng của mục trong `Series` (bắt đầu từ `0`) nhưng bạn cũng có thể đặt nhãn chỉ mục theo cách thủ công:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice       68\n",
       "bob         83\n",
       "charles    112\n",
       "darwin      68\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = pd.Series([68, 83, 112, 68], index=[\"alice\", \"bob\", \"charles\", \"darwin\"])\n",
    "s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sau đó, bạn có thể sử dụng `Series` giống như `dict`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2[\"bob\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bạn vẫn có thể truy cập các mục theo vị trí số nguyên, giống như trong một mảng thông thường:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Để làm rõ khi bạn đang truy cập theo nhãn hoặc theo vị trí số nguyên, bạn nên luôn sử dụng thuộc tính `loc` khi truy cập theo nhãn và thuộc tính `iloc` khi truy cập theo vị trí số nguyên:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.loc[\"bob\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.iloc[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ta cũng có thể cắt một `Series` sử dụng các nhãn chỉ mục:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bob         83\n",
       "charles    112\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.iloc[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Điều này có thể dẫn đến kết quả không mong muốn khi sử dụng nhãn số mặc định, vì vậy hãy cẩn thận:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1000\n",
       "1    1001\n",
       "2    1002\n",
       "3    1003\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surprise = pd.Series([1000, 1001, 1002, 1003])\n",
    "surprise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1002\n",
       "3    1003\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surprise_slice = surprise[2:]\n",
    "surprise_slice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ồ! Phần tử đầu tiên có nhãn chỉ mục `2`. Phần tử có nhãn chỉ mục `0` không có trong lát cắt:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key error: 0\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    surprise_slice[0]\n",
    "except KeyError as e:\n",
    "    print(\"Key error:\", e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nhưng hãy nhớ rằng bạn có thể truy cập các phần tử theo vị trí số nguyên bằng cách sử dụng thuộc tính `iloc`. Điều này minh họa một lý do khác tại sao việc sử dụng `loc` và `iloc` để truy cập các đối tượng `Series` luôn tốt hơn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1002"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surprise_slice.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Khởi tạo từ `dict`\n",
    "Bạn có thể tạo đối tượng `Series` từ `dict`. Các khóa sẽ được sử dụng làm nhãn chỉ mục:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice     68\n",
       "bob       83\n",
       "colin     86\n",
       "darwin    68\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = {\"alice\": 68, \"bob\": 83, \"colin\": 86, \"darwin\": 68}\n",
    "s3 = pd.Series(weights)\n",
    "s3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bạn có thể kiểm soát những thành phần nào bạn muốn đưa vào `Series` và theo thứ tự nào bằng cách chỉ định rõ ràng `chỉ mục` mong muốn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "colin    86\n",
       "alice    68\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s4 = pd.Series(weights, index = [\"colin\", \"alice\"])\n",
    "s4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Căn chỉnh tự động\n",
    "Khi một thao tác liên quan đến nhiều đối tượng `Series`, `pandas` sẽ tự động căn chỉnh các mục bằng cách khớp các nhãn chỉ mục."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['alice', 'bob', 'charles', 'darwin'], dtype='object')\n",
      "Index(['alice', 'bob', 'colin', 'darwin'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "alice      136.0\n",
       "bob        166.0\n",
       "charles      NaN\n",
       "colin        NaN\n",
       "darwin     136.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(s2.keys())\n",
    "print(s3.keys())\n",
    "\n",
    "s2 + s3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Series` kết quả chứa sự kết hợp của các nhãn chỉ mục từ `s2` và `s3`. Vì `\"colin\"` bị thiếu trong `s2` và `\"charles\"` bị thiếu trong `s3`, nên các mục này có giá trị kết quả `NaN`. (tức là. Not-a-Number có nghĩa là *thiếu số*).\n",
    "\n",
    "Căn chỉnh tự động rất tiện dụng khi làm việc với dữ liệu có thể đến từ nhiều nguồn khác nhau với cấu trúc khác nhau và các mục bị thiếu. Nhưng nếu bạn quên đặt đúng nhãn chỉ mục, bạn có thể nhận được kết quả khá ngạc nhiên:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s2 = [ 68  83 112  68]\n",
      "s5 = [1000 1000 1000 1000]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "alice     NaN\n",
       "bob       NaN\n",
       "charles   NaN\n",
       "darwin    NaN\n",
       "0         NaN\n",
       "1         NaN\n",
       "2         NaN\n",
       "3         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s5 = pd.Series([1000,1000,1000,1000])\n",
    "print(\"s2 =\", s2.values)\n",
    "print(\"s5 =\", s5.values)\n",
    "\n",
    "s2 + s5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas không thể căn chỉnh `Series`, vì nhãn của chúng hoàn toàn không khớp, do đó kết quả chứa đầy `NaN`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Khởi tạo với số vô hướng\n",
    "Bạn cũng có thể khởi tạo đối tượng `Series` bằng cách sử dụng số vô hướng và danh sách các nhãn chỉ mục: tất cả các mục sẽ được đặt thành số vô hướng."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "life          42\n",
       "universe      42\n",
       "everything    42\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meaning = pd.Series(42, [\"life\", \"universe\", \"everything\"])\n",
    "meaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tên `Series`\n",
    "`Series` có thể có `tên`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bob      83\n",
       "alice    68\n",
       "Name: weights, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s6 = pd.Series([83, 68], index=[\"bob\", \"alice\"], name=\"weights\")\n",
    "s6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vẽ đồ thị `Series`\n",
    "Pandas giúp dễ dàng vẽ biểu đồ dữ liệu `Series` bằng matplotlib (để biết thêm chi tiết về matplotlib, hãy xem [hướng dẫn matplotlib](tools_matplotlib.ipynb)). Chỉ cần nhập matplotlib và gọi phương thức `plot()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b5bbe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "temperatures = [4.4,5.1,6.1,6.2,6.1,6.1,5.7,5.2,4.7,4.1,3.9,3.5]\n",
    "s7 = pd.Series(temperatures, name=\"Temperature\")\n",
    "s7.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Có *nhiều* tùy chọn để vẽ biểu đồ dữ liệu của bạn. Không cần thiết phải liệt kê tất cả chúng ở đây: nếu bạn cần một loại biểu đồ cụ thể (biểu đồ tần số, biểu đồ hình tròn, v.v.), chỉ cần tìm nó trong [Trực quan hóa](http://pandas.pydata.org/pandas-docs/stable/visualization.html) trong tài liệu của pandas và xem mã ví dụ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Xử lý thời gian\n",
    "Nhiều bộ dữ liệu có mốc thời gian và pandas rất giỏi trong thao tác với các dữ liệu đó:\n",
    "* nó có thể đại diện cho các khoảng thời gian (chẳng hạn như 2016Q3) và tần suất (chẳng hạn như \"hàng tháng\"),\n",
    "* nó có thể chuyển đổi các khoảng thời gian thành mốc thời gian thực và *ngược lại*,\n",
    "* nó có thể lấy mẫu lại dữ liệu và tổng hợp các giá trị theo bất kỳ cách nào bạn muốn.\n",
    "* nó có thể xử lý các múi giờ.\n",
    "\n",
    "## Khoảng thời gian\n",
    "Hãy bắt đầu bằng cách tạo một chuỗi thời gian bằng cách sử dụng `pd.date_range()`. Thao tác này trả về một `DatetimeIndex` chứa mỗi giờ trong 12 giờ bắt đầu từ ngày 29 tháng 10 năm 2016 lúc 5:30 chiều."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-10-29 17:30:00', '2016-10-29 18:30:00',\n",
       "               '2016-10-29 19:30:00', '2016-10-29 20:30:00',\n",
       "               '2016-10-29 21:30:00', '2016-10-29 22:30:00',\n",
       "               '2016-10-29 23:30:00', '2016-10-30 00:30:00',\n",
       "               '2016-10-30 01:30:00', '2016-10-30 02:30:00',\n",
       "               '2016-10-30 03:30:00', '2016-10-30 04:30:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('2016/10/29 5:30pm', periods=12, freq='H')\n",
    "dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DatetimeIndex` này có thể được sử dụng làm chỉ mục trong `Series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 17:30:00    4.4\n",
       "2016-10-29 18:30:00    5.1\n",
       "2016-10-29 19:30:00    6.1\n",
       "2016-10-29 20:30:00    6.2\n",
       "2016-10-29 21:30:00    6.1\n",
       "2016-10-29 22:30:00    6.1\n",
       "2016-10-29 23:30:00    5.7\n",
       "2016-10-30 00:30:00    5.2\n",
       "2016-10-30 01:30:00    4.7\n",
       "2016-10-30 02:30:00    4.1\n",
       "2016-10-30 03:30:00    3.9\n",
       "2016-10-30 04:30:00    3.5\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series = pd.Series(temperatures, dates)\n",
    "temp_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hãy vẽ đồ thị Series này:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JPCsijo2IE8vLcuDZ5bpKJa4NWl5f28cTOB+4GxiKiD0jYi+K+u4p11XtS8BXgcdExBMi4iDgMcDXKA6BzD0v5Ximfuy2q/OTz0k+cV0P7N5l+R70OIpgB7JSf6qdrLY5Ul/rx3Mm63akvpmsyygv2Ximfuw6L03tKhHF24qJ/lCuq1LqT7VT1gbtr6/t43mbpLcAn4nyjLvyTLxXdORXabWks4DPML6+lwPXtyAv5XimfuzGNHVUycuBdwHfYPwhV88G3hMRn64470l0/+S38k+1U9dWZra2vjkwnnsApzB+P+lmii8rWRkVf5BXnkj0asbXdwfwn8C5EfHbzPOSjWfqx25cdhONG8aKPobxT47LIuLuRjaoQm2uDdLX1/bxNJu2OvfDzLYL8O7Jrud+aXt9bR9P4CmTXa8h7/jJrrcgL9l4pn7sGp/WVdI5k12v2MSTNSo/eaNT4tqg5fW1fTyB/zHF9aodOcX13PNSjmfSx67xry6TtDQ6zn6beD1nba4N0tfX9vE061fjjbtukuZRfDjyl8Bjy8UbKQ6SPzcmzAKXm7bXl1oT41meyLGch+7Dr+uEpqTzjTeQl2w8Uz92o5o6AWehpNMk3Sppi6S7JK0rl+1ecdz/BQ4H3g08t7ycChwGfK7irNS1QcvrmwPjmfqEppMpTnwRcG15EfBFSae0IC/ZeDZwcth2de5An2RH/mXAycC+Hcv2LZd9o+KsnidpTLYuh9rmSH1tH8/UJxj9ANily/JdqWla3sR5KU/uS/rYdV6a+nBycUSsjI6JgiJiU0SspJj3uEpbJP2NihnfgGL2N0kvpjhdtWopa4P219f28Ux9gtEf2L4LqNNjynW556Ucz9SP3ZimzpxMecbRS4CVwFmS7qYY0N0pJrl5ScVZkP5sqrbX1/bxfB+wRlLXE4xqyEs933jqvJTjmfqxG9PUmZOdZxw9ulxc+xlHkvYCiIi76rj/MqOR2srs1tXX9vEsc1Kf0JR6vvHUecnGs6mTw1p/VAmApKcCERHXqZjUfTmwLiIuaXjTKtH2+lJrejwl7Vnzf0g7AUTEH8pT0p8MbKjxBVPSvC75tY5nE1lNHVUiSS8q9yVK0jMl/auk13XuW6wo65+AfwXOlvQB4KPAbsBbJb29yqwyL1ltZV7b62v7eB6t4iiZWyQdJembFJNc3S7pz2rIewHwc4o5zU8A/gv4EHCTpOe1IC/ZeKZ+7MZlN7Sr5CyKt727AvcBD6N463scsDmq/Zb3tRSHdz0M2ATsFxH3SZoPXBMRf1JVVpmXrLYyr+31tX08r6U4bnwBxcRLL4iIq1R8ZdtHIuLoivOuB44F5gM3AkdGxHpJBwJfiYjBzPOSjWfqx65TUx9OPi0ilkjaheLJ8ZiIeFDSFymOi6zStnJf2q8l/Tgi7gOIiAck1fGpdsraoP31tX08d4mItQCSfhERV5V5a8r/LCo3eoSOpJ9FxPpy2W11vINpIC/leCZ/7EY1dTjgNoAozkK7LiIeLK9vo/pDhB6U9Ijy56WjC1Wc8VTHEzFlbdD++to+np3PwbdOWLdrDXl0NMxXdSzbuSV5Kccz+WPXLTilTZIWAETxtVAAqPhC2AcrzvqLiPh1mdX5xNuFYjL3qqWsDdpfX9vH852j/1FExNdGF0p6PPDZGvJWUDaViLi2Y/n+wGktyEs5nqkfuzGz6qgSSbsBu0XEnU1vS9XaXBukr6/t42k2mcande0UEffX+USUdNFk1+tUd23Q/vpS56UeT0krJrteQ967J7vegrxk45n6sWu8cUtaM9n1ir1miuuVSlwbtLy+to8nDz1NutbTpkk/33jqvJTjmfSxm1W7SlKRtHdE/LLp7aiapD0BUp1sYGbNaPwVd90kHSvpp5KuknSEpFuAayTdIemZNWfvIelRNWccIGlY0i+Aa4BrJd1ZLltcZ3YbSXqipEskXSzp8ZI+LekeSdeq+BLhOjKPkXS2pAvLy9mSlk/9m5Vvx7tqut9jJL164t+jpFd1/40dypISnrDVJf/yujOguRNwtgAXAF8ELo8aN0LSDcBLKSYKugg4LiKuLp+En4+Ip1Sc91iKT8tPoDgwf2O56jzgfVHxRPySvgecAXx5dO6H8lCrvwHeGBF/WmXeFNuyNiKWVHyf+1OcabcIuAT40OgYSvpaRLyg4rzvlHkLKB7Hk4EvAcdTjGel/9lLOgM4mOIohDvKxfsBL6OY9rTSE4ym2JafRcQBFd/n+4E/pzjm/nnAGRHxkXLdmhqefylP7rtp4iKKx3L0WPVKT9YaF9RQ414PfISioS4Gvgx8MSKuriFr7I9D0u0RsX/Huhsi4vCK8y4H/jkiVkl6IfA04B0Ux3k+OiIq/dBC0g8j4gnTXbcDeS/stQr4eETsU3HeN4GvAFdTnKW2FHheRNwl6fqIOKLivLH7lPSjiDioY10djeYHEXFwl+WimNO56sfvvl6rgPkRUelJeSrORD0iIrap+OKLLwDrI+JNNT1+a3ucsDUPWFNlM5V0IcV/Du8FHqAYw/+i+I+KiLitqqyJmjpz8v6I+CjwUUkHUEyXeVb5wA5HxNsqzLpH0muBRwF3S3oTcD7wLGBrhTmj9oqIVQARcYGkt0fE/cA7JN1aQ97q8lXGZ9g+teT+FMccX19D3peAz9N9HuKH15C3T0R8vPz5JEl/B3xH0vN7bMOO2rnj5w9PWFfHSRW/kXRkRFw3YfmRwG9qyLuH4rTzzRNXSKpjmtx55clSRMQ9KuYnOUfSv1PPeI6dsCVp3AlbqvjM14h4vqS/BM4B/iUiLpT0uzob9qimGvfYJ64R8TPgg8AHVXw33Ysrzno5xSvePwDPoXiVfxlwG/UcJfCLsrlcAbwQ2ABjr6Dq2Mf2MopXoqeyfWrJOyjmTji3hrybKP5Ib564QtKzasjbRdLDI+I3ABHxOUmbKB7D3WrI+5ikBRGxNSLOGl0o6SDgWzXkvYJiQqtHsn1Xyf7AveW6qn2W4gsoHtK4KV4NV+3Hkp4eEVcClLvzXi3pvcBf1ZC3qePxq/2ErYj4qor5uN8j6dXUfMbkqKZ2lXw4Iv4xeXAC5TuIfwEOBW4A/ndE/FzF3M7LIuIrjW7gDpL0NOC28j/ciesGI2Kk4rw3UbzFvXLC8iOAD0bEs6vMa0rZWMbmdI6Ob/zJmco5OyLigS7rFkXExof+Vi3bUfsJW5IOA/6s4x1ibebk4YCjJL0rIv656e3YUZKOofhA61udb9MkvSoizmtuy/LUMZ7fjogNHctrGU+l/5Z352WY1WnWHQ5Y1yFJPZyYMKuW2spP7d8OLAEul3RSx+o6vhoq6eFdqfNUzME9Op7frns8lf5b3p2XYdZDRI3fRDyTC/Cziu/vvh6XX1FM4ZltbeV9rqX4AAiKQx6/Dvyf8vr1NeS9H/gOxSGIPwZO6li3poa8DyTOSz2eqb/l3XkZZk28NPLh5FSHJFUcl/RT9MS1QfpP7Z/H9sO73g18QdIfRcSbqOc03+MT56Uez9TfFO68PLPGaeqokpTNNPWn6KkPt0r9qX3qxpY6L/V4pv6mcOflmTVOU0eVvBe4MMbPzzu6bmVEnJx8oyqSurbUn9qrmCHvQ/HQozzeC7wtIqr+ztDUecmPglD6b3l3XoZZ43KbaNyWrwb+o5gVh5OZzSZu3GZmmZl1hwOamdnkmvpwMjlJ+1CcVPF74CcRUcc8JY1pe32pNTGeSjyfuvPyzIKGX3FL2kfFHNl/ovILYWvIOFTSt4DvUcxX/W/AWhXzLC+sI7PMrb22MqfV9aXOSz2eSjyfuvPyzHqIOg8Sn+TA9UMpJuz5EcXEL9cAPwU+DSysOOtq4JDy56cCnyl/fg3FHNbZ1jZH6mv7eH6PYmK1nTuW7UwxY+bVzpu9ealrG5dd551PUnCyJwdw44Trazp+XpdzbXOkvraP5w9nss55zeelrq3z0tSukvkRMfotEddSzAtBRPwb8McVZ/1Y0jslHS3pdIoZ+1Ax0Xod9aesDdpfX9vHc7WksyQdJemx5eUoFXOs1zGfuvPyzBqnqRNwLqAo7HKKOav3iIhXlU+OmyPikAqzdgfeRvGW+0bgtIj4Vbm/8klR8bfupKytzGt7fW0fz10p5lM/gfEncVwInBsRv3Xe7MxLXdu47IYad9InR0ptrg0aaWytHk+zmWj9CTgqvjj3RIpDuy6JiO92rHtHRLy3sY2rQNvrSy31eEp6BMV0sUHxPawvppgT5VaK7y6t9DBE51WXl7q2To3s45a0s6TXSnqPpP82Yd07Ko77BPB04C7gI5I6v0ew1xffzlji2qDl9bV9PCmOjhkAHgdcTPFdkx+imF3ubOfN6ryUWePV+cnnJJ+4fpJiZr43AquBD3esq3SOZeCmjp/nUXyx5wXAw6hnfuVktc2R+to+njeU/4riW8nVcf0m583evNS1dV6aOqrkqRHxtxFxBnAUsEDSBZIeRvXz2I5N/RkR2yJiBcWRApcDdZzIkbI2aH99bR/P0awAvl7+O3q9tv2Yzssza1RTjTvlk2NE0vLOBVF8z+SngMUVZ0H6J37b62v7eI6oPBM0Isa+ik3S4ym+pcl5szcvdW1jmjqq5HPA5yLi0gnLTwTOjohdkm9URdpcG6Svr+3jORlJioRPUOflk9X6o0q6kXRO+cqtldpeX2qpx9N5+ealypo107pKOidh3GDCrNS1Qcvra/t4Oi/rvCRZs6Zxk3Zw70yYBen/UNteX9vH03n55iXJmjW7SiRdGhHLp75lftpcG6Svr+3jaTaVWfOKu64noqSFkk6TdKukLZLukrSuXLZ7HZkT1dlk2l5f6rzU4+m8fPOafO41deZkyoLPB+4GlkXEnhGxFzBULju/4qwmHsxW19f28XRe1nmpa9tuR87emekFuAw4Gdi3Y9m+5bJvVJy1fibrcqhtjtTX9vF0XqZ5qWvrvDS1q2RxRKyMiE2jCyJiU0SsBA6sOOs2SW+RNDC6QNKApJOB2yvOgrS1Qfvra/t4Oi/fvNS1jWmqcacs+MXAXsCV5VvtLcAqYE/gRRVnQfoHs+31tX08nZdvXuraxjR15uQewCkUE5A/uly8mWIC8pWR6JuS69Dm2iB9fW0fT7OZmDWHAzZB0isj4lNNb0dd2l5faqnH03n55tWdNesad+LB/VlEHJAiq8xL/Yfa9vraPp7OyzSv7qzZ2LgrLVjSTb1WAQdHxMOqyupjWyp/MNteX+q81OPpvHzzmnzuzavrjiczRcEDPdbN1ABwDMWxlROzvvvQm++YxLVBy+tr+3g6L+u81LWNaaRxk7bgi4AFEXHDxBWSVlWcBekfzLbX1/bxdF6+ealr237/DR1Vci7wqYi4qsu6L0TE3ybfqIq0uTZIX1/bx9NsJmbdPu4UJK2IiNRTgybT9vpSSz2ezss3L1XWrJlkSlLKif//e8Ks1LVBy+tr+3g6L+u8JFmzpnGTdnDr+JLZyaT+Q217fW0fT+flm5ckazY17pSD+7yEWZD+D7Xt9bV9PJ2Xb16SrNnUuGspWNJRkh5V/jxf0qnA2ZJWSlpYR2YXtT2Yba8vdV7q8XRevnlNPveamo87ZcHnAb8ufz4TWAisLJdVftZdAw9mq+tr+3g6L+u81LVtV+ecsb0uwC3AvPLnc4AzgD8H/gm4oOKsdR0/r5mw7oaca5sj9bV9PJ2XaV7q2jovTe0q2SkitpU/D0bEGyPiqog4FfijirNulvTK8ucbJQ0CSDoY+F3FWZC2Nmh/fW0fT+flm5e6tu3q/F9hkv+p/h14ZfnzpyiekAAHA9dVnLUQ+DTwY+CackB/AlwJHJZzbXOkvraPp/MyzUtdW+elqTMnF1LsE3oa8EvgKRST4t8O/ENE3FhD5qOAx1Gc5n9HRGyuOqPMSV5bmdvK+to+ns7LPy91bdDwmZNNFDwhf0FEbK3pvhutrdyG1tTX9vF0Xrvy6s6adae8Jx7c1NOQpv5DbXt9bR9P52WaV3dWU7MDTub7QJXzcf9jr1XAgqpy+lRpbdD++lLnpR5P5+Wb1+Rzr6n5uFMW/H7gQ8C2LusqP6qmgQez1fW1fTydl3Ve6trGNPWKO2XBa4CvRcTqiSsknVhxFqR/MNteX9uGAUfSAAACoUlEQVTH03n55qWubbs6D1mZ5DCa7wJLe6y7veKsQ4B9eqwbyLm2OVJf28fTeZnmpa6t89LU4YCHAFsi4hdd1g1EA0cMVKXNtUH6+to+nmYz0ciZkxGxvtsTsVxX9RN/oaTTJN0qaYukuyStK5ftXmUWpK0N2l9f28fTefnmpa6tU1OTTKUs+HyK7ytcFhF7RsRewFC57PyKs5p4MFtdX9vH03lZ56Wubbs698NMsm/oMuBkYN+OZfuWy75Rcdb6mazLobY5Ul/bx9N5mealrq3z0tQkU4sjYmVEbBpdEBGbImIlcGDFWbdJeoukgdEFkgYknUxx2nTVUtYG7a+v7ePpvHzzUtc2pqnGnbLgFwN7AVeWb7W3AKuAPYEXVZwF6R/MttfX9vF0Xr55qWsb09RRJXsApwAnAI8uF28GLgRWRsSW5BtVkTbXBunra/t4ms3ErJurpA6SnggsAq6OiPs7li+PiEub27JqtL2+1FKPp/PyzWvsuVfnDvQpduw/EXgmsNuE5csrzvkHYD3wNWADcELHujVVZqWubS7U1/bxdF6+eU0898buv847nw0FA2uBBeXPi4ER4A3l9etzrm2O1Nf28XRepnmpa+u8NDVXyWsoTmPeKmkx8GVJiyPiTIrJg6q0U5RTf0bEBknLyrwDa8iCtLVB++tr+3g6L9+81LVtD67zzifL7SwYWAYcK+nDVF/wZkmHj14pc48H9gaWVJwFaWuD9tfX9vF0Xr55qWvbrs6X85O8xbgcOHzCsnnAZ4HfV5y1Hx0nb0xYd3TOtc2R+to+ns7LNC91bZ2Xpg4H3A/YFh0nVXSsOzoi/l/yjapIm2uD9PW1fTzNZmJOHA5oZtYmTe3jNjOzGXLjNjPLjBu3mVlm3LjNzDLz/wEJ5lbocofr5wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b68fda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_series.plot(kind=\"bar\")\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lấy mẫu lại\n",
    "Pandas cho phép chúng ta dễ dàng lấy mẫu lại một chuỗi thời gian. Chỉ cần gọi phương thức `resample()` và chỉ định một tần suất mới:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndexResampler [freq=<2 * Hours>, axis=0, closed=left, label=left, convention=start, base=0]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_freq_2H = temp_series.resample(\"2H\")\n",
    "temp_series_freq_2H"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hoạt động lấy mẫu lại thực ra là một hoạt động bị hoãn, đó là lý do tại sao chúng ta không nhận được đối tượng `Series` mà thay vào đó là đối tượng `DatetimeIndexResampler`. Để  thực sự thực hiện thao tác lấy mẫu lại, chúng ta chỉ cần gọi phương thức `mean()`: Pandas sẽ tính giá trị trung bình của mỗi cặp giờ liên tiếp:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_series_freq_2H = temp_series_freq_2H.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hãy vẽ kết quả:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b739208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_series_freq_2H.plot(kind=\"bar\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lưu ý cách các giá trị tự động được tổng hợp thành các khoảng thời gian 2 giờ. Ví dụ: nếu chúng ta xem xét khoảng thời gian 6-8 giờ tối, thì ta có giá trị là `5,1` lúc 6:30 chiều và `6,1` lúc 7:30 tối. Sau khi lấy mẫu lại, chúng ta chỉ có một giá trị là `5.6`, là giá trị trung bình của `5.1` và `6.1`. Thay vì tính toán giá trị trung bình, chúng ta có thể sử dụng bất kỳ hàm tổng hợp nào khác, ví dụ: chúng ta có thể quyết định giữ giá trị nhỏ nhất của mỗi khoảng thời gian:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 16:00:00    4.4\n",
       "2016-10-29 18:00:00    5.1\n",
       "2016-10-29 20:00:00    6.1\n",
       "2016-10-29 22:00:00    5.7\n",
       "2016-10-30 00:00:00    4.7\n",
       "2016-10-30 02:00:00    3.9\n",
       "2016-10-30 04:00:00    3.5\n",
       "Freq: 2H, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_freq_2H = temp_series.resample(\"2H\").min()\n",
    "temp_series_freq_2H"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hoặc tương tự, chúng ta có thể sử dụng phương thức `apply()` để thay thế:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 16:00:00    4.4\n",
       "2016-10-29 18:00:00    5.1\n",
       "2016-10-29 20:00:00    6.1\n",
       "2016-10-29 22:00:00    5.7\n",
       "2016-10-30 00:00:00    4.7\n",
       "2016-10-30 02:00:00    3.9\n",
       "2016-10-30 04:00:00    3.5\n",
       "Freq: 2H, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_freq_2H = temp_series.resample(\"2H\").apply(np.min)\n",
    "temp_series_freq_2H"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lấy mẫu lên và nội suy\n",
    "Ở trên là một ví dụ về lấy mẫu xuống. Chúng ta cũng có thể  lấy mẫu lên (tức là tăng tần suất), nhưng điều này tạo ra lỗ hổng trong dữ liệu:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 17:30:00    4.4\n",
       "2016-10-29 17:45:00    NaN\n",
       "2016-10-29 18:00:00    NaN\n",
       "2016-10-29 18:15:00    NaN\n",
       "2016-10-29 18:30:00    5.1\n",
       "2016-10-29 18:45:00    NaN\n",
       "2016-10-29 19:00:00    NaN\n",
       "2016-10-29 19:15:00    NaN\n",
       "2016-10-29 19:30:00    6.1\n",
       "2016-10-29 19:45:00    NaN\n",
       "Freq: 15T, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_freq_15min = temp_series.resample(\"15Min\").mean()\n",
    "temp_series_freq_15min.head(n=10) # `head` displays the top n values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Một giải pháp là lấp đầy khoảng trống bằng cách nội suy. Chúng ta chỉ cần gọi phương thức `interpolate()`. Mặc định là sử dụng phép nội suy tuyến tính, nhưng chúng ta cũng có thể chọn một phương pháp khác, chẳng hạn như phép nội suy bậc ba:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 17:30:00    4.400000\n",
       "2016-10-29 17:45:00    4.452911\n",
       "2016-10-29 18:00:00    4.605113\n",
       "2016-10-29 18:15:00    4.829758\n",
       "2016-10-29 18:30:00    5.100000\n",
       "2016-10-29 18:45:00    5.388992\n",
       "2016-10-29 19:00:00    5.669887\n",
       "2016-10-29 19:15:00    5.915839\n",
       "2016-10-29 19:30:00    6.100000\n",
       "2016-10-29 19:45:00    6.203621\n",
       "Freq: 15T, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_freq_15min = temp_series.resample(\"15Min\").interpolate(method=\"cubic\")\n",
    "temp_series_freq_15min.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b8b8710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_series.plot(label=\"Period: 1 hour\")\n",
    "temp_series_freq_15min.plot(label=\"Period: 15 minutes\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Múi giờ\n",
    "Theo mặc định, thời gian dữ liệu là *sơ khai*: chúng không biết múi giờ, vì vậy 2016-10-30 02:30 có thể có nghĩa là ngày 30 tháng 10 năm 2016 lúc 2:30 sáng ở Paris hoặc ở New York. Chúng ta có thể chỉ định múi giờ bằng cách gọi phương thức `tz_localize()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 17:30:00-04:00    4.4\n",
       "2016-10-29 18:30:00-04:00    5.1\n",
       "2016-10-29 19:30:00-04:00    6.1\n",
       "2016-10-29 20:30:00-04:00    6.2\n",
       "2016-10-29 21:30:00-04:00    6.1\n",
       "2016-10-29 22:30:00-04:00    6.1\n",
       "2016-10-29 23:30:00-04:00    5.7\n",
       "2016-10-30 00:30:00-04:00    5.2\n",
       "2016-10-30 01:30:00-04:00    4.7\n",
       "2016-10-30 02:30:00-04:00    4.1\n",
       "2016-10-30 03:30:00-04:00    3.9\n",
       "2016-10-30 04:30:00-04:00    3.5\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_ny = temp_series.tz_localize(\"America/New_York\")\n",
    "temp_series_ny"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lưu ý rằng `-04:00` hiện được thêm vào tất cả các mốc thời gian. Điều này có nghĩa là các mốc thời gian này đề cập đến [UTC](https://en.wikipedia.org/wiki/Coordined_Universal_Time) - 4 giờ.\n",
    "\n",
    "Chúng ta có thể chuyển đổi các ngày giờ này sang giờ Paris như sau:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 23:30:00+02:00    4.4\n",
       "2016-10-30 00:30:00+02:00    5.1\n",
       "2016-10-30 01:30:00+02:00    6.1\n",
       "2016-10-30 02:30:00+02:00    6.2\n",
       "2016-10-30 02:30:00+01:00    6.1\n",
       "2016-10-30 03:30:00+01:00    6.1\n",
       "2016-10-30 04:30:00+01:00    5.7\n",
       "2016-10-30 05:30:00+01:00    5.2\n",
       "2016-10-30 06:30:00+01:00    4.7\n",
       "2016-10-30 07:30:00+01:00    4.1\n",
       "2016-10-30 08:30:00+01:00    3.9\n",
       "2016-10-30 09:30:00+01:00    3.5\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_paris = temp_series_ny.tz_convert(\"Europe/Paris\")\n",
    "temp_series_paris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bạn có thể nhận thấy rằng phần bù UTC thay đổi từ `+02:00` thành `+01:00`: điều này là do Pháp chuyển sang giờ mùa đông lúc 3 giờ sáng vào đêm cụ thể đó (thời gian quay trở lại 2 giờ sáng). Lưu ý rằng 2:30 sáng xảy ra hai lần! Hãy quay lại với một biểu diễn sơ khai (nếu bạn ghi nhật ký một số dữ liệu hàng giờ bằng giờ địa phương mà không lưu trữ múi giờ, bạn có thể nhận được kết quả như sau):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 23:30:00    4.4\n",
       "2016-10-30 00:30:00    5.1\n",
       "2016-10-30 01:30:00    6.1\n",
       "2016-10-30 02:30:00    6.2\n",
       "2016-10-30 02:30:00    6.1\n",
       "2016-10-30 03:30:00    6.1\n",
       "2016-10-30 04:30:00    5.7\n",
       "2016-10-30 05:30:00    5.2\n",
       "2016-10-30 06:30:00    4.7\n",
       "2016-10-30 07:30:00    4.1\n",
       "2016-10-30 08:30:00    3.9\n",
       "2016-10-30 09:30:00    3.5\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_paris_naive = temp_series_paris.tz_localize(None)\n",
    "temp_series_paris_naive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bây giờ `02:30` thực sự mơ hồ. Nếu chúng ta cố gắng bản địa hóa những ngày giờ sơ khai này sang múi giờ Paris, ta sẽ gặp lỗi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pytz.exceptions.AmbiguousTimeError'>\n",
      "Cannot infer dst time from Timestamp('2016-10-30 02:30:00'), try using the 'ambiguous' argument\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    temp_series_paris_naive.tz_localize(\"Europe/Paris\")\n",
    "except Exception as e:\n",
    "    print(type(e))\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "May mắn thay, bằng cách sử dụng đối số `ambiguous`, chúng ta có thể yêu cầu pandas suy ra đúng DST (Daylight Saving Time) dựa trên thứ tự của các dấu thời gian mơ hồ:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 23:30:00+02:00    4.4\n",
       "2016-10-30 00:30:00+02:00    5.1\n",
       "2016-10-30 01:30:00+02:00    6.1\n",
       "2016-10-30 02:30:00+02:00    6.2\n",
       "2016-10-30 02:30:00+01:00    6.1\n",
       "2016-10-30 03:30:00+01:00    6.1\n",
       "2016-10-30 04:30:00+01:00    5.7\n",
       "2016-10-30 05:30:00+01:00    5.2\n",
       "2016-10-30 06:30:00+01:00    4.7\n",
       "2016-10-30 07:30:00+01:00    4.1\n",
       "2016-10-30 08:30:00+01:00    3.9\n",
       "2016-10-30 09:30:00+01:00    3.5\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_paris_naive.tz_localize(\"Europe/Paris\", ambiguous=\"infer\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chu kỳ\n",
    "Hàm `pd.period_range()` trả về `PeriodIndex` thay vì `DatetimeIndex`. Ví dụ: hãy lấy tất cả các quý trong năm 2016 và 2017:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2016Q1', '2016Q2', '2016Q3', '2016Q4', '2017Q1', '2017Q2',\n",
       "             '2017Q3', '2017Q4'],\n",
       "            dtype='period[Q-DEC]', freq='Q-DEC')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarters = pd.period_range('2016Q1', periods=8, freq='Q')\n",
    "quarters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Việc thêm một số `N` vào `PeriodIndex` sẽ dịch chuyển các khoảng thời gian bằng `N` lần tần suất của `PeriodIndex`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2016Q4', '2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1',\n",
       "             '2018Q2', '2018Q3'],\n",
       "            dtype='period[Q-DEC]', freq='Q-DEC')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarters + 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phương thức `asfreq()` cho phép chúng ta thay đổi tần số của `PeriodIndex`. Tất cả các giai đoạn được kéo dài hoặc rút ngắn cho phù hợp. Ví dụ: hãy chuyển đổi tất cả các khoảng thời gian hàng quý thành khoảng thời gian hàng tháng (phóng đại):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2016-03', '2016-06', '2016-09', '2016-12', '2017-03', '2017-06',\n",
       "             '2017-09', '2017-12'],\n",
       "            dtype='period[M]', freq='M')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarters.asfreq(\"M\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Theo mặc định, `asfreq` phóng đại vào cuối mỗi khoảng thời gian. Thay vào đó, chúng ta có thể yêu cầu nó phóng đại vào đầu mỗi khoảng thời gian:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2016-01', '2016-04', '2016-07', '2016-10', '2017-01', '2017-04',\n",
       "             '2017-07', '2017-10'],\n",
       "            dtype='period[M]', freq='M')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarters.asfreq(\"M\", how=\"start\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Và chúng ta có thể thu nhỏ:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2016', '2016', '2016', '2016', '2017', '2017', '2017', '2017'], dtype='period[A-DEC]', freq='A-DEC')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarters.asfreq(\"A\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tất nhiên chúng ta có thể tạo `Series` với `PeriodIndex`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016Q1    300\n",
       "2016Q2    320\n",
       "2016Q3    290\n",
       "2016Q4    390\n",
       "2017Q1    320\n",
       "2017Q2    360\n",
       "2017Q3    310\n",
       "2017Q4    410\n",
       "Freq: Q-DEC, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarterly_revenue = pd.Series([300, 320, 290, 390, 320, 360, 310, 410], index = quarters)\n",
    "quarterly_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d315e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quarterly_revenue.plot(kind=\"line\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chúng ta có thể chuyển đổi các khoảng thời gian thành mốc thời gian bằng cách gọi `to_timestamp`. Theo mặc định, điều này sẽ cung cấp cho chúng ta ngày đầu tiên của mỗi khoảng thời gian, nhưng bằng cách đặt `how` và `freq`, chúng ta có thể nhận được giờ cuối cùng của mỗi khoảng thời gian:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-03-31 23:00:00    300\n",
       "2016-06-30 23:00:00    320\n",
       "2016-09-30 23:00:00    290\n",
       "2016-12-31 23:00:00    390\n",
       "2017-03-31 23:00:00    320\n",
       "2017-06-30 23:00:00    360\n",
       "2017-09-30 23:00:00    310\n",
       "2017-12-31 23:00:00    410\n",
       "Freq: Q-DEC, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_hours = quarterly_revenue.to_timestamp(how=\"end\", freq=\"H\")\n",
    "last_hours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Và quay lại thời gian bằng cách gọi `to_period`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016Q1    300\n",
       "2016Q2    320\n",
       "2016Q3    290\n",
       "2016Q4    390\n",
       "2017Q1    320\n",
       "2017Q2    360\n",
       "2017Q3    310\n",
       "2017Q4    410\n",
       "Freq: Q-DEC, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_hours.to_period()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas cũng cung cấp nhiều chức năng khác liên quan đến thời gian mà chúng tôi khuyên bạn nên xem trong [tài liệu](http://pandas.pydata.org/pandas-docs/stable/timeseries.html). Để kích thích sự tò mò của bạn, đây là một cách để có được ngày làm việc cuối cùng của mỗi tháng trong năm 2016, lúc 9 giờ sáng:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2016-01-29 09:00', '2016-02-29 09:00', '2016-03-31 09:00',\n",
       "             '2016-04-29 09:00', '2016-05-31 09:00', '2016-06-30 09:00',\n",
       "             '2016-07-29 09:00', '2016-08-31 09:00', '2016-09-30 09:00',\n",
       "             '2016-10-31 09:00', '2016-11-30 09:00', '2016-12-30 09:00'],\n",
       "            dtype='period[H]', freq='H')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months_2016 = pd.period_range(\"2016\", periods=12, freq=\"M\")\n",
    "one_day_after_last_days = months_2016.asfreq(\"D\") + 1\n",
    "last_bdays = one_day_after_last_days.to_timestamp() - pd.tseries.offsets.BDay()\n",
    "last_bdays.to_period(\"H\") + 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Đối tượng `DataFrame`\n",
    "Một đối tượng DataFrame đại diện cho một bảng tính, với các giá trị ô, tên cột và nhãn chỉ mục hàng. Bạn có thể xác định các biểu thức để tính toán cột dựa trên các cột khác, tạo bảng tổng hợp, nhóm hàng, vẽ biểu đồ, v.v. Bạn có thể xem `DataFrame` là từ điển của `Series`.\n",
    "\n",
    "## Tạo `DataFrame`\n",
    "Bạn có thể tạo DataFrame bằng cách chuyển từ điển các đối tượng `Series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear  children    hobby  weight\n",
       "alice         1985       NaN   Biking      68\n",
       "bob           1984       3.0  Dancing      83\n",
       "charles       1992       0.0      NaN     112"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people_dict = {\n",
    "    \"weight\": pd.Series([68, 83, 112], index=[\"alice\", \"bob\", \"charles\"]),\n",
    "    \"birthyear\": pd.Series([1984, 1985, 1992], index=[\"bob\", \"alice\", \"charles\"], name=\"year\"),\n",
    "    \"children\": pd.Series([0, 3], index=[\"charles\", \"bob\"]),\n",
    "    \"hobby\": pd.Series([\"Biking\", \"Dancing\"], index=[\"alice\", \"bob\"]),\n",
    "}\n",
    "people = pd.DataFrame(people_dict)\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Một số điều cần lưu ý:\n",
    "* `Series` được căn chỉnh tự động dựa trên chỉ mục của chúng,\n",
    "* các giá trị bị thiếu được biểu thị dưới dạng `NaN`,\n",
    "* Tên `Series` bị bỏ qua (tên `\"year\"` đã bị loại bỏ),\n",
    "* `DataFrame` được hiển thị rõ ràng trong Jupyter Notebook, tuyệt vời!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bạn có thể truy cập các cột một cách dễ dàng. Chúng được trả về dưới dạng các đối tượng `Series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice      1985\n",
       "bob        1984\n",
       "charles    1992\n",
       "Name: birthyear, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people[\"birthyear\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bạn cũng có thể lấy nhiều cột cùng một lúc:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear    hobby\n",
       "alice         1985   Biking\n",
       "bob           1984  Dancing\n",
       "charles       1992      NaN"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people[[\"birthyear\", \"hobby\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nếu bạn chuyển danh sách các cột và/hoặc nhãn hàng chỉ mục cho hàm tạo `DataFrame`, nó sẽ đảm bảo rằng các cột và/hoặc hàng này sẽ tồn tại, theo thứ tự đó và sẽ không tồn tại cột/hàng nào khác. Ví dụ:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eugene</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        birthyear  weight height\n",
       "bob        1984.0    83.0    NaN\n",
       "alice      1985.0    68.0    NaN\n",
       "eugene        NaN     NaN    NaN"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2 = pd.DataFrame(\n",
    "        people_dict,\n",
    "        columns=[\"birthyear\", \"weight\", \"height\"],\n",
    "        index=[\"bob\", \"alice\", \"eugene\"]\n",
    "     )\n",
    "d2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Một cách thuận tiện khác để tạo `DataFrame` là chuyển tất cả các giá trị cho hàm tạo dưới dạng `ndarray` hoặc danh sách các danh sách và chỉ định riêng tên cột và nhãn chỉ mục hàng:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear  children    hobby  weight\n",
       "alice         1985       NaN   Biking      68\n",
       "bob           1984       3.0  Dancing      83\n",
       "charles       1992       0.0      NaN     112"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = [\n",
    "            [1985, np.nan, \"Biking\",   68],\n",
    "            [1984, 3,      \"Dancing\",  83],\n",
    "            [1992, 0,      np.nan,    112]\n",
    "         ]\n",
    "d3 = pd.DataFrame(\n",
    "        values,\n",
    "        columns=[\"birthyear\", \"children\", \"hobby\", \"weight\"],\n",
    "        index=[\"alice\", \"bob\", \"charles\"]\n",
    "     )\n",
    "d3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Để chỉ định các giá trị bị thiếu, bạn có thể sử dụng các mảng được che dấu của `np.nan` hoặc NumPy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        birthyear children    hobby weight\n",
       "alice        1985      NaN   Biking     68\n",
       "bob          1984        3  Dancing     83\n",
       "charles      1992        0      NaN    112"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masked_array = np.ma.asarray(values, dtype=np.object)\n",
    "masked_array[(0, 2), (1, 2)] = np.ma.masked\n",
    "d3 = pd.DataFrame(\n",
    "        masked_array,\n",
    "        columns=[\"birthyear\", \"children\", \"hobby\", \"weight\"],\n",
    "        index=[\"alice\", \"bob\", \"charles\"]\n",
    "     )\n",
    "d3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thay vì `ndarray`, bạn cũng có thể truyền đối tượng `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hobby</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         hobby children\n",
       "alice   Biking      NaN\n",
       "bob    Dancing        3"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d4 = pd.DataFrame(\n",
    "         d3,\n",
    "         columns=[\"hobby\", \"children\"],\n",
    "         index=[\"alice\", \"bob\"]\n",
    "     )\n",
    "d4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cũng có thể tạo `DataFrame` bằng từ điển (hoặc danh sách) lồng trong từ điển (hoặc danh sách):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear  children    hobby  weight\n",
       "alice         1985       NaN   Biking      68\n",
       "bob           1984       3.0  Dancing      83\n",
       "charles       1992       0.0      NaN     112"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people = pd.DataFrame({\n",
    "    \"birthyear\": {\"alice\":1985, \"bob\": 1984, \"charles\": 1992},\n",
    "    \"hobby\": {\"alice\":\"Biking\", \"bob\": \"Dancing\"},\n",
    "    \"weight\": {\"alice\":68, \"bob\": 83, \"charles\": 112},\n",
    "    \"children\": {\"bob\": 3, \"charles\": 0}\n",
    "})\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Đa chỉ mục\n",
    "Nếu tất cả các cột là các tuple có cùng kích thước, thì chúng được hiểu là đa chỉ mục. Điều tương tự cũng xảy ra với nhãn chỉ mục hàng. Ví dụ:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">private</th>\n",
       "      <th colspan=\"2\" halign=\"left\">public</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>weight</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <th>charles</th>\n",
       "      <td>0.0</td>\n",
       "      <td>112</td>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>3.0</td>\n",
       "      <td>83</td>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                private           public         \n",
       "               children weight birthyear    hobby\n",
       "London charles      0.0    112      1992      NaN\n",
       "Paris  alice        NaN     68      1985   Biking\n",
       "       bob          3.0     83      1984  Dancing"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d5 = pd.DataFrame(\n",
    "  {\n",
    "    (\"public\", \"birthyear\"):\n",
    "        {(\"Paris\",\"alice\"):1985, (\"Paris\",\"bob\"): 1984, (\"London\",\"charles\"): 1992},\n",
    "    (\"public\", \"hobby\"):\n",
    "        {(\"Paris\",\"alice\"):\"Biking\", (\"Paris\",\"bob\"): \"Dancing\"},\n",
    "    (\"private\", \"weight\"):\n",
    "        {(\"Paris\",\"alice\"):68, (\"Paris\",\"bob\"): 83, (\"London\",\"charles\"): 112},\n",
    "    (\"private\", \"children\"):\n",
    "        {(\"Paris\", \"alice\"):np.nan, (\"Paris\",\"bob\"): 3, (\"London\",\"charles\"): 0}\n",
    "  }\n",
    ")\n",
    "d5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Giờ đây, bạn có thể nhận được một `DataFrame` chứa tất cả các cột `\"public\"` một cách dễ dàng:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                birthyear    hobby\n",
       "London charles       1992      NaN\n",
       "Paris  alice         1985   Biking\n",
       "       bob           1984  Dancing"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d5[\"public\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "London  charles        NaN\n",
       "Paris   alice       Biking\n",
       "        bob        Dancing\n",
       "Name: (public, hobby), dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d5[\"public\", \"hobby\"]  # Same result as d5[\"public\"][\"hobby\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hạ cấp\n",
    "Hãy xem lại `d5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">private</th>\n",
       "      <th colspan=\"2\" halign=\"left\">public</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>weight</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <th>charles</th>\n",
       "      <td>0.0</td>\n",
       "      <td>112</td>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>3.0</td>\n",
       "      <td>83</td>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                private           public         \n",
       "               children weight birthyear    hobby\n",
       "London charles      0.0    112      1992      NaN\n",
       "Paris  alice        NaN     68      1985   Biking\n",
       "       bob          3.0     83      1984  Dancing"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Có hai cấp độ cột và hai cấp độ chỉ mục. Chúng ta có thể giảm cấp độ cột bằng cách gọi `droplevel()` (tương tự với các chỉ mục):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>weight</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <th>charles</th>\n",
       "      <td>0.0</td>\n",
       "      <td>112</td>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>3.0</td>\n",
       "      <td>83</td>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                children  weight  birthyear    hobby\n",
       "London charles       0.0     112       1992      NaN\n",
       "Paris  alice         NaN      68       1985   Biking\n",
       "       bob           3.0      83       1984  Dancing"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d5.columns = d5.columns.droplevel(level = 0)\n",
    "d5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chuyển vị\n",
    "Bạn có thể hoán đổi các cột và chỉ mục bằng thuộc tính `T`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>London</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Paris</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>charles</th>\n",
       "      <th>alice</th>\n",
       "      <th>bob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>112</td>\n",
       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>birthyear</th>\n",
       "      <td>1992</td>\n",
       "      <td>1985</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hobby</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           London   Paris         \n",
       "          charles   alice      bob\n",
       "children        0     NaN        3\n",
       "weight        112      68       83\n",
       "birthyear    1992    1985     1984\n",
       "hobby         NaN  Biking  Dancing"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d6 = d5.T\n",
    "d6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Xếp chồng và gỡ chồng\n",
    "Gọi phương thức `stack()` sẽ đẩy cột thấp nhất vào sau chỉ mục thấp nhất:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>London</th>\n",
       "      <th>Paris</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">children</th>\n",
       "      <th>bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">weight</th>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>112</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">birthyear</th>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">hobby</th>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  London    Paris\n",
       "children  bob        NaN        3\n",
       "          charles      0      NaN\n",
       "weight    alice      NaN       68\n",
       "          bob        NaN       83\n",
       "          charles    112      NaN\n",
       "birthyear alice      NaN     1985\n",
       "          bob        NaN     1984\n",
       "          charles   1992      NaN\n",
       "hobby     alice      NaN   Biking\n",
       "          bob        NaN  Dancing"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d7 = d6.stack()\n",
    "d7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lưu ý rằng nhiều giá trị `NaN` đã xuất hiện. Điều này có ý nghĩa bởi vì nhiều kết hợp mới không tồn tại trước đây (ví dụ: không có `bob` trong `London`).\n",
    "\n",
    "Việc gọi `unstack()` sẽ làm ngược lại, một lần nữa tạo ra nhiều giá trị `NaN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">London</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Paris</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>alice</th>\n",
       "      <th>bob</th>\n",
       "      <th>charles</th>\n",
       "      <th>alice</th>\n",
       "      <th>bob</th>\n",
       "      <th>charles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>birthyear</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1992</td>\n",
       "      <td>1985</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hobby</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Biking</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          London                Paris                 \n",
       "           alice  bob charles   alice      bob charles\n",
       "children    None  NaN       0    None        3     NaN\n",
       "weight       NaN  NaN     112      68       83     NaN\n",
       "birthyear    NaN  NaN    1992    1985     1984     NaN\n",
       "hobby        NaN  NaN    None  Biking  Dancing    None"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d8 = d7.unstack()\n",
    "d8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nếu chúng ta gọi lại `unstack`, chúng ta sẽ có một đối tượng `Series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "London  alice    children        None\n",
       "                 weight           NaN\n",
       "                 birthyear        NaN\n",
       "                 hobby            NaN\n",
       "        bob      children         NaN\n",
       "                 weight           NaN\n",
       "                 birthyear        NaN\n",
       "                 hobby            NaN\n",
       "        charles  children           0\n",
       "                 weight           112\n",
       "                 birthyear       1992\n",
       "                 hobby           None\n",
       "Paris   alice    children        None\n",
       "                 weight            68\n",
       "                 birthyear       1985\n",
       "                 hobby         Biking\n",
       "        bob      children           3\n",
       "                 weight            83\n",
       "                 birthyear       1984\n",
       "                 hobby        Dancing\n",
       "        charles  children         NaN\n",
       "                 weight           NaN\n",
       "                 birthyear        NaN\n",
       "                 hobby           None\n",
       "dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d9 = d8.unstack()\n",
    "d9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Các phương thức `stack()` và `unstack()` cho phép bạn chọn `level` để xếp chồng/gỡ chồng. Bạn thậm chí có thể xếp chồng/gỡ chồng nhiều cấp độ cùng một lúc:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">London</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Paris</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>alice</th>\n",
       "      <th>bob</th>\n",
       "      <th>charles</th>\n",
       "      <th>alice</th>\n",
       "      <th>bob</th>\n",
       "      <th>charles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>birthyear</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1992</td>\n",
       "      <td>1985</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hobby</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Biking</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          London                Paris                 \n",
       "           alice  bob charles   alice      bob charles\n",
       "children    None  NaN       0    None        3     NaN\n",
       "weight       NaN  NaN     112      68       83     NaN\n",
       "birthyear    NaN  NaN    1992    1985     1984     NaN\n",
       "hobby        NaN  NaN    None  Biking  Dancing    None"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d10 = d9.unstack(level = (0,1))\n",
    "d10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hầu hết các phương thức trả về các bản sao đã sửa đổi\n",
    "Như bạn có thể nhận thấy, các phương thức `stack()` và `unstack()` không sửa đổi đối tượng mà chúng áp dụng. Thay vào đó, chúng làm việc trên một bản sao và trả lại bản sao đó. Điều này đúng với hầu hết các phương thức trong pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Truy cập hàng\n",
    "Hãy quay lại `DataFrame` `people`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear  children    hobby  weight\n",
       "alice         1985       NaN   Biking      68\n",
       "bob           1984       3.0  Dancing      83\n",
       "charles       1992       0.0      NaN     112"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thuộc tính `loc` cho phép bạn truy cập các hàng thay vì các cột. Kết quả là một đối tượng `Series` trong đó tên cột của `DataFrame` được ánh xạ tới nhãn chỉ mục hàng:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "birthyear    1992\n",
       "children        0\n",
       "hobby         NaN\n",
       "weight        112\n",
       "Name: charles, dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.loc[\"charles\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bạn cũng có thể truy cập các hàng theo vị trí số nguyên bằng thuộc tính `iloc`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "birthyear    1992\n",
       "children        0\n",
       "hobby         NaN\n",
       "weight        112\n",
       "Name: charles, dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.iloc[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bạn cũng có thể lấy một lát hàng và điều này trả về một đối tượng `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear  children    hobby  weight\n",
       "bob           1984       3.0  Dancing      83\n",
       "charles       1992       0.0      NaN     112"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.iloc[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuối cùng, bạn có thể chuyển một mảng boolean để nhận các hàng phù hợp:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear  children   hobby  weight\n",
       "alice         1985       NaN  Biking      68\n",
       "charles       1992       0.0     NaN     112"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people[np.array([True, False, True])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Điều này hữu ích nhất khi được kết hợp với các biểu thức boolean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       birthyear  children    hobby  weight\n",
       "alice       1985       NaN   Biking      68\n",
       "bob         1984       3.0  Dancing      83"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people[people[\"birthyear\"] < 1990]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thêm, bớt cột\n",
    "Nhìn chung, bạn có thể xem các đối tượng `DataFrame` giống như từ điển của `Series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear  children    hobby  weight\n",
       "alice         1985       NaN   Biking      68\n",
       "bob           1984       3.0  Dancing      83\n",
       "charles       1992       0.0      NaN     112"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  weight  age  over 30\n",
       "alice     Biking      68   33     True\n",
       "bob      Dancing      83   34     True\n",
       "charles      NaN     112   26    False"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people[\"age\"] = 2018 - people[\"birthyear\"]  # adds a new column \"age\"\n",
    "people[\"over 30\"] = people[\"age\"] > 30      # adds another column \"over 30\"\n",
    "birthyears = people.pop(\"birthyear\")\n",
    "del people[\"children\"]\n",
    "\n",
    "people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice      1985\n",
       "bob        1984\n",
       "charles    1992\n",
       "Name: birthyear, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "birthyears"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Khi bạn thêm một cột mới, cột đó phải có cùng số hàng. Các hàng bị thiếu được lấp đầy bằng `NaN` và các hàng thừa sẽ bị bỏ qua:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  weight  age  over 30  pets\n",
       "alice     Biking      68   33     True   NaN\n",
       "bob      Dancing      83   34     True   0.0\n",
       "charles      NaN     112   26    False   5.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people[\"pets\"] = pd.Series({\"bob\": 0, \"charles\": 5, \"eugene\":1})  # alice is missing, eugene is ignored\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Khi thêm một cột mới, nó sẽ được thêm vào cuối (ở bên phải) theo mặc định. Bạn cũng có thể chèn một cột vào bất kỳ nơi nào khác bằng phương thức `insert()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>185</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  height  weight  age  over 30  pets\n",
       "alice     Biking     172      68   33     True   NaN\n",
       "bob      Dancing     181      83   34     True   0.0\n",
       "charles      NaN     185     112   26    False   5.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.insert(1, \"height\", [172, 181, 185])\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gán cột mới\n",
    "Bạn cũng có thể tạo các cột mới bằng cách gọi phương thức `assign()`. Lưu ý rằng điều này trả về một đối tượng `DataFrame` mới, bản gốc không được sửa đổi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>has_pets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.985398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>185</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.724617</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
       "\n",
       "         has_pets  \n",
       "alice       False  \n",
       "bob         False  \n",
       "charles      True  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.assign(\n",
    "    body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2,\n",
    "    has_pets = people[\"pets\"] > 0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lưu ý rằng bạn không thể truy cập các cột được tạo trong cùng một tác vụ:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key error: 'body_mass_index'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    people.assign(\n",
    "        body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2,\n",
    "        overweight = people[\"body_mass_index\"] > 25\n",
    "    )\n",
    "except KeyError as e:\n",
    "    print(\"Key error:\", e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Giải pháp là chia tác vụ này thành hai tác vụ liên tiếp:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>overweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.985398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>185</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.724617</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
       "\n",
       "         overweight  \n",
       "alice         False  \n",
       "bob            True  \n",
       "charles        True  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d6 = people.assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
    "d6.assign(overweight = d6[\"body_mass_index\"] > 25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Việc phải tạo một biến tạm thời `d6` không thuận tiện lắm. Bạn có thể chỉ muốn tạo một chuỗi các lệnh gọi phân công, nhưng điều này không khả thi vì đối tượng `people` không thực sự được sửa đổi bởi phép gán đầu tiên:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key error: 'body_mass_index'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    (people\n",
    "         .assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
    "         .assign(overweight = people[\"body_mass_index\"] > 25)\n",
    "    )\n",
    "except KeyError as e:\n",
    "    print(\"Key error:\", e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nhưng đừng lo, có một giải pháp đơn giản. Bạn có thể chuyển một hàm cho phương thức `assign()` (thường là hàm `lambda`) và hàm này sẽ được gọi với `DataFrame` làm tham số:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>overweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.985398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>185</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.724617</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
       "\n",
       "         overweight  \n",
       "alice         False  \n",
       "bob            True  \n",
       "charles        True  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(people\n",
    "     .assign(body_mass_index = lambda df: df[\"weight\"] / (df[\"height\"] / 100) ** 2)\n",
    "     .assign(overweight = lambda df: df[\"body_mass_index\"] > 25)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vấn đề đã được giải quyết!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Đánh giá một biểu thức\n",
    "Một tính năng tuyệt vời được hỗ trợ bởi pandas là đánh giá biểu thức. Điều này yêu cầu thư viện `numexpr` phải được cài đặt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice      False\n",
       "bob         True\n",
       "charles     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.eval(\"weight / (height/100) ** 2 > 25\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biểu thức gán cũng được hỗ trợ. Hãy đặt `inplace=True` để sửa đổi trực tiếp `DataFrame` thay vì lấy một bản sao đã sửa đổi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "      <th>body_mass_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.985398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.335002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>185</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.724617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  height  weight  age  over 30  pets  body_mass_index\n",
       "alice     Biking     172      68   33     True   NaN        22.985398\n",
       "bob      Dancing     181      83   34     True   0.0        25.335002\n",
       "charles      NaN     185     112   26    False   5.0        32.724617"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.eval(\"body_mass_index = weight / (height/100) ** 2\", inplace=True)\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bạn có thể sử dụng một biến cục bộ hoặc biến toàn cục trong một biểu thức bằng cách đặt trước nó `'@'`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>overweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.985398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>185</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.724617</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
       "\n",
       "         overweight  \n",
       "alice         False  \n",
       "bob           False  \n",
       "charles        True  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overweight_threshold = 30\n",
    "people.eval(\"overweight = body_mass_index > @overweight_threshold\", inplace=True)\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Truy vấn `DataFrame`\n",
    "Phương thức `query()` cho phép bạn lọc `DataFrame` dựa trên biểu thức truy vấn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>overweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       hobby  height  weight  age  over 30  pets  body_mass_index  overweight\n",
       "bob  Dancing     181      83   34     True   0.0        25.335002       False"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.query(\"age > 30 and pets == 0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sắp xếp `DataFrame`\n",
    "Bạn có thể sắp xếp `DataFrame` bằng cách gọi phương thức `sort_index` của nó. Theo mặc định, nó sắp xếp các hàng theo nhãn chỉ mục của chúng, theo thứ tự tăng dần, nhưng hãy đảo ngược thứ tự:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>overweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>185</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.724617</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.985398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
       "\n",
       "         overweight  \n",
       "charles        True  \n",
       "bob           False  \n",
       "alice         False  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lưu ý rằng `sort_index` đã trả về một *bản sao* đã được sắp xếp của `DataFrame`. Để trực tiếp sửa đổi `people`, chúng ta có thể đặt đối số `inplace` thành `True`. Ngoài ra, chúng ta có thể sắp xếp các cột thay vì các hàng bằng cách đặt `axis=1`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>height</th>\n",
       "      <th>hobby</th>\n",
       "      <th>over 30</th>\n",
       "      <th>overweight</th>\n",
       "      <th>pets</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>33</td>\n",
       "      <td>22.985398</td>\n",
       "      <td>172</td>\n",
       "      <td>Biking</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>34</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>181</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>26</td>\n",
       "      <td>32.724617</td>\n",
       "      <td>185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  body_mass_index  height    hobby  over 30  overweight  pets  \\\n",
       "alice     33        22.985398     172   Biking     True       False   NaN   \n",
       "bob       34        25.335002     181  Dancing     True       False   0.0   \n",
       "charles   26        32.724617     185      NaN    False        True   5.0   \n",
       "\n",
       "         weight  \n",
       "alice        68  \n",
       "bob          83  \n",
       "charles     112  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.sort_index(axis=1, inplace=True)\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Để sắp xếp `DataFrame` theo giá trị thay vì nhãn, chúng ta có thể sử dụng `sort_values` và chỉ định cột để sắp xếp theo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>height</th>\n",
       "      <th>hobby</th>\n",
       "      <th>over 30</th>\n",
       "      <th>overweight</th>\n",
       "      <th>pets</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>26</td>\n",
       "      <td>32.724617</td>\n",
       "      <td>185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>33</td>\n",
       "      <td>22.985398</td>\n",
       "      <td>172</td>\n",
       "      <td>Biking</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>34</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>181</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  body_mass_index  height    hobby  over 30  overweight  pets  \\\n",
       "charles   26        32.724617     185      NaN    False        True   5.0   \n",
       "alice     33        22.985398     172   Biking     True       False   NaN   \n",
       "bob       34        25.335002     181  Dancing     True       False   0.0   \n",
       "\n",
       "         weight  \n",
       "charles     112  \n",
       "alice        68  \n",
       "bob          83  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.sort_values(by=\"age\", inplace=True)\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vẽ đồ thị với `DataFrame`\n",
    "Giống như đối với `Series`, pandas giúp ta dễ dàng vẽ các biểu đồ đẹp dựa trên `DataFrame`.\n",
    "\n",
    "Ví dụ: việc tạo một biểu đồ đường từ dữ liệu của `DataFrame` bằng cách gọi phương thức `plot` của nó là chuyện nhỏ:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b89bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "people.plot(kind = \"line\", x = \"body_mass_index\", y = [\"height\", \"weight\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bạn có thể chuyển các đối số bổ sung được hỗ trợ bởi các hàm của matplotlib. Ví dụ: chúng ta có thể tạo biểu đồ phân tán và chuyển cho nó một danh sách các kích thước bằng cách sử dụng đối số `s` của hàm `scatter()` của matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b89b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "people.plot(kind = \"scatter\", x = \"height\", y = \"weight\", s=[40, 120, 200])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Một lần nữa, có quá nhiều tùy chọn để liệt kê ở đây: tùy chọn tốt nhất là tham khảo trang [Trực quan hóa](http://pandas.pydata.org/pandas-docs/stable/visualization.html) trong tài liệu của pandas, tìm đồ thị mà bạn quan tâm và xem mã ví dụ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Toán tử trên `DataFrame`\n",
    "Mặc dù `DataFrame` không cố gắng bắt chước các mảng NumPy, nhưng có một vài điểm tương đồng. Hãy tạo một `DataFrame` để chứng minh điều này:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sep  oct  nov\n",
       "alice      8    8    9\n",
       "bob       10    9    9\n",
       "charles    4    8    2\n",
       "darwin     9   10   10"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades_array = np.array([[8,8,9],[10,9,9],[4, 8, 2], [9, 10, 10]])\n",
    "grades = pd.DataFrame(grades_array, columns=[\"sep\", \"oct\", \"nov\"], index=[\"alice\",\"bob\",\"charles\",\"darwin\"])\n",
    "grades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bạn có thể áp dụng các hàm toán học NumPy trên `DataFrame`: hàm được áp dụng cho tất cả các giá trị:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>2.828427</td>\n",
       "      <td>2.828427</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>3.162278</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.828427</td>\n",
       "      <td>1.414214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>3.162278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sep       oct       nov\n",
       "alice    2.828427  2.828427  3.000000\n",
       "bob      3.162278  3.000000  3.000000\n",
       "charles  2.000000  2.828427  1.414214\n",
       "darwin   3.000000  3.162278  3.162278"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(grades)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tương tự, việc thêm một giá trị vào `DataFrame` sẽ thêm giá trị đó vào tất cả các phần tử trong `DataFrame`. Điều này được gọi là *broadcasting*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sep  oct  nov\n",
       "alice      9    9   10\n",
       "bob       11   10   10\n",
       "charles    5    9    3\n",
       "darwin    10   11   11"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tất nhiên, điều này cũng đúng với tất cả các phép toán nhị phân khác, bao gồm phép toán số học (`*`,`/`,`**`...) và phép toán có điều kiện (`>`, `==`...):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sep   oct    nov\n",
       "alice     True  True   True\n",
       "bob       True  True   True\n",
       "charles  False  True  False\n",
       "darwin    True  True   True"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades >= 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Các hoạt động tổng hợp, chẳng hạn như tính toán `max`, `sum` hoặc `mean` của `DataFrame`, áp dụng cho từng cột và bạn nhận được một đối tượng `Series`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sep    7.75\n",
       "oct    8.75\n",
       "nov    7.50\n",
       "dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phương thức `all` cũng là một thao tác tổng hợp: nó kiểm tra xem tất cả các giá trị có phải là `True` hay không. Hãy xem trong những tháng nào tất cả học sinh đạt điểm cao hơn `5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sep    False\n",
       "oct     True\n",
       "nov    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(grades > 5).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hầu hết các hàm này đều có tham số `axis` tùy chọn cho phép bạn chỉ định dọc theo trục nào của `DataFrame` mà bạn muốn thao tác được thực thi. Giá trị mặc định là `axis=0`, nghĩa là thao tác được thực hiện theo chiều dọc (trên mỗi cột). Bạn có thể đặt `axis=1` để thực hiện thao tác theo chiều ngang (trên mỗi hàng). Ví dụ: hãy tìm xem học sinh nào có tất cả các điểm lớn hơn `5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice       True\n",
       "bob         True\n",
       "charles    False\n",
       "darwin      True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(grades > 5).all(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phương thức `any` trả về `True` nếu bất kỳ giá trị nào là True. Cùng xem ai đạt ít nhất một điểm 10 nào:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice      False\n",
       "bob         True\n",
       "charles    False\n",
       "darwin      True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(grades == 10).any(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nếu bạn thêm đối tượng `Series` vào `DataFrame` (hoặc thực thi bất kỳ thao tác nhị phân nào khác), pandas sẽ cố gắng broadcast tới tất cả *hàng* trong `DataFrame`. Điều này chỉ hoạt động nếu `Series` có cùng kích thước với các hàng của `DataFrame`. Ví dụ: hãy trừ `giá trị trung bình` của `DataFrame` (một đối tượng `Series`) khỏi `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>0.25</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>2.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>-3.75</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sep   oct  nov\n",
       "alice    0.25 -0.75  1.5\n",
       "bob      2.25  0.25  1.5\n",
       "charles -3.75 -0.75 -5.5\n",
       "darwin   1.25  1.25  2.5"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades - grades.mean()  # equivalent to: grades - [7.75, 8.75, 7.50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chúng ta đã trừ `7,75` cho tất cả các điểm tháng 9, `8,75` cho các điểm tháng 10 và `7,50` cho các điểm tháng 11. Nó tương đương với việc trừ `DataFrame` này:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>7.75</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>7.75</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>7.75</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>7.75</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sep   oct  nov\n",
       "alice    7.75  8.75  7.5\n",
       "bob      7.75  8.75  7.5\n",
       "charles  7.75  8.75  7.5\n",
       "darwin   7.75  8.75  7.5"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([[7.75, 8.75, 7.50]]*4, index=grades.index, columns=grades.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nếu bạn muốn trừ đi giá trị trung bình toàn cầu từ mọi cấp độ, đây là một cách để thực hiện:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sep  oct  nov\n",
       "alice    0.0  0.0  1.0\n",
       "bob      2.0  1.0  1.0\n",
       "charles -4.0  0.0 -6.0\n",
       "darwin   1.0  2.0  2.0"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades - grades.values.mean() # subtracts the global mean (8.00) from all grades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Căn chỉnh tự động\n",
    "Tương tự như `Series`, khi hoạt động trên nhiều `DataFrame`, pandas sẽ tự động sắp xếp chúng theo nhãn chỉ mục hàng cũng như theo tên cột. Hãy tạo `DataFrame` với điểm thưởng cho mỗi người từ tháng 10 đến tháng 12:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         oct  nov  dec\n",
       "bob      0.0  NaN  2.0\n",
       "colin    NaN  1.0  0.0\n",
       "darwin   0.0  1.0  0.0\n",
       "charles  3.0  3.0  0.0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonus_array = np.array([[0,np.nan,2],[np.nan,1,0],[0, 1, 0], [3, 3, 0]])\n",
    "bonus_points = pd.DataFrame(bonus_array, columns=[\"oct\", \"nov\", \"dec\"], index=[\"bob\",\"colin\", \"darwin\", \"charles\"])\n",
    "bonus_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dec</th>\n",
       "      <th>nov</th>\n",
       "      <th>oct</th>\n",
       "      <th>sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dec   nov   oct  sep\n",
       "alice    NaN   NaN   NaN  NaN\n",
       "bob      NaN   NaN   9.0  NaN\n",
       "charles  NaN   5.0  11.0  NaN\n",
       "colin    NaN   NaN   NaN  NaN\n",
       "darwin   NaN  11.0  10.0  NaN"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades + bonus_points"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Có vẻ như phần bổ sung đã hoạt động trong một số trường hợp nhưng có quá nhiều phần tử hiện đang trống. Đó là bởi vì khi căn chỉnh `DataFrame`, một số cột và hàng chỉ xuất hiện ở một bên và do đó chúng được xem là bị thiếu ở phía bên kia (`NaN`). Sau đó, phép cộng với `NaN` cho kết quả `NaN`, dẫn đến kết quả như trên.\n",
    "\n",
    "## Xử lý dữ liệu bị thiếu\n",
    "Xử lý dữ liệu bị thiếu là một nhiệm vụ thường xuyên khi làm việc với dữ liệu thực tế. Pandas cung cấp một số công cụ để xử lý dữ liệu bị thiếu.\n",
    "\n",
    "Hãy thử khắc phục sự cố trên. Ví dụ: chúng ta có thể quyết định rằng dữ liệu bị thiếu sẽ dẫn đến kết quả là 0, thay vì `NaN`. Chúng ta có thể thay thế tất cả các giá trị `NaN` bằng một giá trị bất kỳ bằng cách sử dụng phương thức `fillna()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dec</th>\n",
       "      <th>nov</th>\n",
       "      <th>oct</th>\n",
       "      <th>sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dec   nov   oct  sep\n",
       "alice    0.0   0.0   0.0  0.0\n",
       "bob      0.0   0.0   9.0  0.0\n",
       "charles  0.0   5.0  11.0  0.0\n",
       "colin    0.0   0.0   0.0  0.0\n",
       "darwin   0.0  11.0  10.0  0.0"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(grades + bonus_points).fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuy nhiên, có một chút không công bằng khi chúng ta đặt điểm bằng 0 vào tháng 9. Có lẽ chúng ta nên quyết định rằng điểm thiếu là điểm thiếu, nhưng điểm thưởng bị thiếu nên được thay thế bằng số không:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dec</th>\n",
       "      <th>nov</th>\n",
       "      <th>oct</th>\n",
       "      <th>sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dec   nov   oct   sep\n",
       "alice    NaN   9.0   8.0   8.0\n",
       "bob      NaN   9.0   9.0  10.0\n",
       "charles  NaN   5.0  11.0   4.0\n",
       "colin    NaN   NaN   NaN   NaN\n",
       "darwin   NaN  11.0  10.0   9.0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fixed_bonus_points = bonus_points.fillna(0)\n",
    "fixed_bonus_points.insert(0, \"sep\", 0)\n",
    "fixed_bonus_points.loc[\"alice\"] = 0\n",
    "grades + fixed_bonus_points"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trông đã tốt hơn nhiều: mặc dù chúng ta đã tự tạo một số dữ liệu, nhưng chúng không quá bất công.\n",
    "\n",
    "Một cách khác để xử lý dữ liệu bị thiếu là nội suy. Hãy xem lại `bonus_points` `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         oct  nov  dec\n",
       "bob      0.0  NaN  2.0\n",
       "colin    NaN  1.0  0.0\n",
       "darwin   0.0  1.0  0.0\n",
       "charles  3.0  3.0  0.0"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonus_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bây giờ, hãy gọi phương thức `nội suy`. Theo mặc định, nó nội suy theo chiều dọc (`trục=0`), vì vậy hãy bảo nó nội suy theo chiều ngang (`trục=1`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         oct  nov  dec\n",
       "bob      0.0  1.0  2.0\n",
       "colin    NaN  1.0  0.0\n",
       "darwin   0.0  1.0  0.0\n",
       "charles  3.0  3.0  0.0"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonus_points.interpolate(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bob có 0 điểm thưởng vào tháng 10 và 2 vào tháng 12. Khi chúng ta nội suy cho tháng 11, ta nhận được giá trị trung bình: 1 điểm thưởng. Colin có 1 điểm thưởng trong tháng 11, nhưng chúng ta không biết anh ấy có bao nhiêu điểm thưởng trong tháng 9, vì vậy ta không thể nội suy, đây là lý do tại sao vẫn còn thiếu một giá trị trong tháng 10 sau khi nội suy. Để khắc phục, chúng ta có thể set điểm thưởng tháng 9 bằng 0 trước khi nội suy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sep  oct  nov  dec\n",
       "bob      0.0  0.0  1.0  2.0\n",
       "colin    0.0  0.5  1.0  0.0\n",
       "darwin   0.0  0.0  1.0  0.0\n",
       "charles  0.0  3.0  3.0  0.0\n",
       "alice    0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_bonus_points = bonus_points.copy()\n",
    "better_bonus_points.insert(0, \"sep\", 0)\n",
    "better_bonus_points.loc[\"alice\"] = 0\n",
    "better_bonus_points = better_bonus_points.interpolate(axis=1)\n",
    "better_bonus_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuyệt vời, bây giờ chúng ta đã có điểm thưởng hợp lý ở khắp mọi nơi. Hãy cùng tìm hiểu các lớp cuối cùng:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dec</th>\n",
       "      <th>nov</th>\n",
       "      <th>oct</th>\n",
       "      <th>sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dec   nov   oct   sep\n",
       "alice    NaN   9.0   8.0   8.0\n",
       "bob      NaN  10.0   9.0  10.0\n",
       "charles  NaN   5.0  11.0   4.0\n",
       "colin    NaN   NaN   NaN   NaN\n",
       "darwin   NaN  11.0  10.0   9.0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades + better_bonus_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hơi khó chịu khi cột tháng 9 kết thúc ở bên phải. Điều này là do `DataFrame` mà chúng ta đang thêm không có các cột giống hệt nhau (`DataFrame` `grades` thiếu cột `\"dec\"`), vì vậy, để làm cho mọi thứ có thể dự đoán được, pandas sắp xếp các cột cuối cùng theo thứ tự abc. Để khắc phục điều này, chúng ta chỉ cần thêm cột bị thiếu trước khi thêm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sep   oct   nov  dec\n",
       "alice     8.0   8.0   9.0  NaN\n",
       "bob      10.0   9.0  10.0  NaN\n",
       "charles   4.0  11.0   5.0  NaN\n",
       "colin     NaN   NaN   NaN  NaN\n",
       "darwin    9.0  10.0  11.0  NaN"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades[\"dec\"] = np.nan\n",
    "final_grades = grades + better_bonus_points\n",
    "final_grades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Không có nhiều thứ chúng ta có thể làm về tháng 12 và Colin: thật tệ khi chúng ta tự tạo điểm thưởng, nhưng chúng ta không thể tự tạo điểm một cách hợp lý được (tôi đoán một số giáo viên có thể làm được). Vì vậy, hãy gọi phương thức `dropna()` để loại bỏ các hàng chứa đầy `NaN`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sep   oct   nov  dec\n",
       "alice     8.0   8.0   9.0  NaN\n",
       "bob      10.0   9.0  10.0  NaN\n",
       "charles   4.0  11.0   5.0  NaN\n",
       "darwin    9.0  10.0  11.0  NaN"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_grades_clean = final_grades.dropna(how=\"all\")\n",
    "final_grades_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bây giờ, hãy xóa các cột chứa đầy `NaN` bằng cách đặt đối số `axis` thành `1`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sep   oct   nov\n",
       "alice     8.0   8.0   9.0\n",
       "bob      10.0   9.0  10.0\n",
       "charles   4.0  11.0   5.0\n",
       "darwin    9.0  10.0  11.0"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_grades_clean = final_grades_clean.dropna(axis=1, how=\"all\")\n",
    "final_grades_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tổng hợp với `groupby`\n",
    "Tương tự như ngôn ngữ SQL, pandas cho phép nhóm dữ liệu của bạn thành từng nhóm để chạy tính toán trên từng nhóm.\n",
    "\n",
    "Trước tiên, hãy thêm một số dữ liệu bổ sung về từng người để chúng ta có thể nhóm họ và hãy quay lại `DataFrame` `final_grades` để xem cách các giá trị `NaN` được xử lý:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "      <th>hobby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sep   oct   nov  dec    hobby\n",
       "alice     8.0   8.0   9.0  NaN   Biking\n",
       "bob      10.0   9.0  10.0  NaN  Dancing\n",
       "charles   4.0  11.0   5.0  NaN      NaN\n",
       "colin     NaN   NaN   NaN  NaN  Dancing\n",
       "darwin    9.0  10.0  11.0  NaN   Biking"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_grades[\"hobby\"] = [\"Biking\", \"Dancing\", np.nan, \"Dancing\", \"Biking\"]\n",
    "final_grades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bây giờ hãy nhóm dữ liệu trong `DataFrame` này theo sở thích:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x10b680e10>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_grades = final_grades.groupby(\"hobby\")\n",
    "grouped_grades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chúng ta đã sẵn sàng để tính điểm trung bình cho mỗi sở thích:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hobby</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Biking</th>\n",
       "      <td>8.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dancing</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sep  oct   nov  dec\n",
       "hobby                        \n",
       "Biking    8.5  9.0  10.0  NaN\n",
       "Dancing  10.0  9.0  10.0  NaN"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_grades.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Điều đó thật dễ dàng! Lưu ý rằng các giá trị `NaN` đã bị bỏ qua khi tính rung bình."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bảng tổng hợp\n",
    "Pandas hỗ trợ [bảng tổng hợp](https://en.wikipedia.org/wiki/Pivot_table) dạng bảng tính cho phép tóm tắt dữ liệu nhanh chóng. Để minh họa điều này, hãy tạo một `DataFrame` đơn giản:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         oct  nov  dec\n",
       "bob      0.0  NaN  2.0\n",
       "colin    NaN  1.0  0.0\n",
       "darwin   0.0  1.0  0.0\n",
       "charles  3.0  3.0  0.0"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonus_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>month</th>\n",
       "      <th>grade</th>\n",
       "      <th>bonus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alice</td>\n",
       "      <td>sep</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alice</td>\n",
       "      <td>oct</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alice</td>\n",
       "      <td>nov</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bob</td>\n",
       "      <td>sep</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bob</td>\n",
       "      <td>oct</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bob</td>\n",
       "      <td>nov</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>charles</td>\n",
       "      <td>sep</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>charles</td>\n",
       "      <td>oct</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>charles</td>\n",
       "      <td>nov</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>darwin</td>\n",
       "      <td>sep</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>darwin</td>\n",
       "      <td>oct</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>darwin</td>\n",
       "      <td>nov</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name month  grade  bonus\n",
       "0     alice   sep    8.0    NaN\n",
       "1     alice   oct    8.0    NaN\n",
       "2     alice   nov    9.0    NaN\n",
       "3       bob   sep   10.0    0.0\n",
       "4       bob   oct    9.0    NaN\n",
       "5       bob   nov   10.0    2.0\n",
       "6   charles   sep    4.0    3.0\n",
       "7   charles   oct   11.0    3.0\n",
       "8   charles   nov    5.0    0.0\n",
       "9    darwin   sep    9.0    0.0\n",
       "10   darwin   oct   10.0    1.0\n",
       "11   darwin   nov   11.0    0.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "more_grades = final_grades_clean.stack().reset_index()\n",
    "more_grades.columns = [\"name\", \"month\", \"grade\"]\n",
    "more_grades[\"bonus\"] = [np.nan, np.nan, np.nan, 0, np.nan, 2, 3, 3, 0, 0, 1, 0]\n",
    "more_grades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bây giờ chúng ta có thể gọi hàm `pd.pivot_table()` cho `DataFrame` này, yêu cầu nhóm theo cột `name`. Theo mặc định, `pivot_table()` tính giá trị trung bình của từng cột số:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bonus</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            bonus      grade\n",
       "name                        \n",
       "alice         NaN   8.333333\n",
       "bob      1.000000   9.666667\n",
       "charles  2.000000   6.666667\n",
       "darwin   0.333333  10.000000"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(more_grades, index=\"name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chúng ta có thể thay đổi hàm tổng hợp bằng cách đặt đối số `aggfunc` và chúng ta cũng có thể chỉ định danh sách các cột có giá trị sẽ được tổng hợp:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bonus</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         bonus  grade\n",
       "name                 \n",
       "alice      NaN    9.0\n",
       "bob        2.0   10.0\n",
       "charles    3.0   11.0\n",
       "darwin     1.0   11.0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(more_grades, index=\"name\", values=[\"grade\",\"bonus\"], aggfunc=np.max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chúng ta cũng có thể chỉ định `cột` để tổng hợp theo chiều ngang và yêu cầu tổng cộng cho mỗi hàng và cột bằng cách đặt `margins=True`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>month</th>\n",
       "      <th>nov</th>\n",
       "      <th>oct</th>\n",
       "      <th>sep</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>9.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>10.00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>9.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>5.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>11.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>8.75</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.75</td>\n",
       "      <td>8.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "month      nov   oct    sep        All\n",
       "name                                  \n",
       "alice     9.00   8.0   8.00   8.333333\n",
       "bob      10.00   9.0  10.00   9.666667\n",
       "charles   5.00  11.0   4.00   6.666667\n",
       "darwin   11.00  10.0   9.00  10.000000\n",
       "All       8.75   9.5   7.75   8.666667"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(more_grades, index=\"name\", values=\"grade\", columns=\"month\", margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuối cùng, chúng ta có thể chỉ định nhiều tên chỉ mục hoặc cột và pandas sẽ tạo các chỉ mục đa cấp:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>bonus</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">alice</th>\n",
       "      <th>nov</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oct</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bob</th>\n",
       "      <th>nov</th>\n",
       "      <td>2.000</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oct</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep</th>\n",
       "      <td>0.000</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">charles</th>\n",
       "      <th>nov</th>\n",
       "      <td>0.000</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oct</th>\n",
       "      <td>3.000</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep</th>\n",
       "      <td>3.000</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">darwin</th>\n",
       "      <th>nov</th>\n",
       "      <td>0.000</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oct</th>\n",
       "      <td>1.000</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep</th>\n",
       "      <td>0.000</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <th></th>\n",
       "      <td>1.125</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               bonus  grade\n",
       "name    month              \n",
       "alice   nov      NaN   9.00\n",
       "        oct      NaN   8.00\n",
       "        sep      NaN   8.00\n",
       "bob     nov    2.000  10.00\n",
       "        oct      NaN   9.00\n",
       "        sep    0.000  10.00\n",
       "charles nov    0.000   5.00\n",
       "        oct    3.000  11.00\n",
       "        sep    3.000   4.00\n",
       "darwin  nov    0.000  11.00\n",
       "        oct    1.000  10.00\n",
       "        sep    0.000   9.00\n",
       "All            1.125   8.75"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(more_grades, index=(\"name\", \"month\"), margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Chức năng tổng quan\n",
    "Khi xử lý `DataFrames` lớn, sẽ rất hữu ích nếu bạn có cái nhìn tổng quan nhanh về nội dung của nó. Pandas cung cấp một số chức năng cho việc này. Trước tiên, hãy tạo một `DataFrame` lớn với sự kết hợp của các giá trị số, giá trị bị thiếu và giá trị văn bản. Lưu ý cách Jupyter chỉ hiển thị các góc của `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>some_text</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>I</th>\n",
       "      <th>...</th>\n",
       "      <th>Q</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>T</th>\n",
       "      <th>U</th>\n",
       "      <th>V</th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>143.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>154.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>66.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>165.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>77.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>77.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>88.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>11.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>...</td>\n",
       "      <td>110.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>110.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>22.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>121.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>121.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>33.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>...</td>\n",
       "      <td>132.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>132.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>44.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>...</td>\n",
       "      <td>143.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>143.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>55.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>154.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>154.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>66.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>...</td>\n",
       "      <td>165.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>165.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>77.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>88.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>143.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>55.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>154.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>66.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>165.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>77.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>77.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>88.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>99.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>11.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>...</td>\n",
       "      <td>110.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>110.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>22.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>121.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>121.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>33.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>...</td>\n",
       "      <td>132.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>132.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>44.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>...</td>\n",
       "      <td>143.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9970</th>\n",
       "      <td>88.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9971</th>\n",
       "      <td>99.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>11.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>...</td>\n",
       "      <td>110.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9972</th>\n",
       "      <td>110.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>22.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>121.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9973</th>\n",
       "      <td>121.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>33.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>...</td>\n",
       "      <td>132.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9974</th>\n",
       "      <td>132.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>44.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>...</td>\n",
       "      <td>143.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9975</th>\n",
       "      <td>143.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>55.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>154.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9976</th>\n",
       "      <td>154.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>66.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>...</td>\n",
       "      <td>165.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\n",
       "      <td>165.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>77.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>88.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>143.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>55.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>154.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9985</th>\n",
       "      <td>66.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>165.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>77.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9986</th>\n",
       "      <td>77.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>88.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\n",
       "      <td>99.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>11.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>...</td>\n",
       "      <td>110.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>110.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>22.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>...</td>\n",
       "      <td>121.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>121.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>33.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>...</td>\n",
       "      <td>132.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>132.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>44.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>...</td>\n",
       "      <td>143.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>143.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>55.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>154.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>154.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>66.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>...</td>\n",
       "      <td>165.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>165.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>77.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>88.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          A      B      C some_text      D      E      F      G      H      I  \\\n",
       "0       NaN   11.0   44.0    Blabla   99.0    NaN   88.0   22.0  165.0  143.0   \n",
       "1      11.0   22.0   55.0    Blabla  110.0    NaN   99.0   33.0    NaN  154.0   \n",
       "2      22.0   33.0   66.0    Blabla  121.0   11.0  110.0   44.0    NaN  165.0   \n",
       "3      33.0   44.0   77.0    Blabla  132.0   22.0  121.0   55.0   11.0    NaN   \n",
       "4      44.0   55.0   88.0    Blabla  143.0   33.0  132.0   66.0   22.0    NaN   \n",
       "5      55.0   66.0   99.0    Blabla  154.0   44.0  143.0   77.0   33.0   11.0   \n",
       "6      66.0   77.0  110.0    Blabla  165.0   55.0  154.0   88.0   44.0   22.0   \n",
       "7      77.0   88.0  121.0    Blabla    NaN   66.0  165.0   99.0   55.0   33.0   \n",
       "8      88.0   99.0  132.0    Blabla    NaN   77.0    NaN  110.0   66.0   44.0   \n",
       "9      99.0  110.0  143.0    Blabla   11.0   88.0    NaN  121.0   77.0   55.0   \n",
       "10    110.0  121.0  154.0    Blabla   22.0   99.0   11.0  132.0   88.0   66.0   \n",
       "11    121.0  132.0  165.0    Blabla   33.0  110.0   22.0  143.0   99.0   77.0   \n",
       "12    132.0  143.0    NaN    Blabla   44.0  121.0   33.0  154.0  110.0   88.0   \n",
       "13    143.0  154.0    NaN    Blabla   55.0  132.0   44.0  165.0  121.0   99.0   \n",
       "14    154.0  165.0   11.0    Blabla   66.0  143.0   55.0    NaN  132.0  110.0   \n",
       "15    165.0    NaN   22.0    Blabla   77.0  154.0   66.0    NaN  143.0  121.0   \n",
       "16      NaN    NaN   33.0    Blabla   88.0  165.0   77.0   11.0  154.0  132.0   \n",
       "17      NaN   11.0   44.0    Blabla   99.0    NaN   88.0   22.0  165.0  143.0   \n",
       "18     11.0   22.0   55.0    Blabla  110.0    NaN   99.0   33.0    NaN  154.0   \n",
       "19     22.0   33.0   66.0    Blabla  121.0   11.0  110.0   44.0    NaN  165.0   \n",
       "20     33.0   44.0   77.0    Blabla  132.0   22.0  121.0   55.0   11.0    NaN   \n",
       "21     44.0   55.0   88.0    Blabla  143.0   33.0  132.0   66.0   22.0    NaN   \n",
       "22     55.0   66.0   99.0    Blabla  154.0   44.0  143.0   77.0   33.0   11.0   \n",
       "23     66.0   77.0  110.0    Blabla  165.0   55.0  154.0   88.0   44.0   22.0   \n",
       "24     77.0   88.0  121.0    Blabla    NaN   66.0  165.0   99.0   55.0   33.0   \n",
       "25     88.0   99.0  132.0    Blabla    NaN   77.0    NaN  110.0   66.0   44.0   \n",
       "26     99.0  110.0  143.0    Blabla   11.0   88.0    NaN  121.0   77.0   55.0   \n",
       "27    110.0  121.0  154.0    Blabla   22.0   99.0   11.0  132.0   88.0   66.0   \n",
       "28    121.0  132.0  165.0    Blabla   33.0  110.0   22.0  143.0   99.0   77.0   \n",
       "29    132.0  143.0    NaN    Blabla   44.0  121.0   33.0  154.0  110.0   88.0   \n",
       "...     ...    ...    ...       ...    ...    ...    ...    ...    ...    ...   \n",
       "9970   88.0   99.0  132.0    Blabla    NaN   77.0    NaN  110.0   66.0   44.0   \n",
       "9971   99.0  110.0  143.0    Blabla   11.0   88.0    NaN  121.0   77.0   55.0   \n",
       "9972  110.0  121.0  154.0    Blabla   22.0   99.0   11.0  132.0   88.0   66.0   \n",
       "9973  121.0  132.0  165.0    Blabla   33.0  110.0   22.0  143.0   99.0   77.0   \n",
       "9974  132.0  143.0    NaN    Blabla   44.0  121.0   33.0  154.0  110.0   88.0   \n",
       "9975  143.0  154.0    NaN    Blabla   55.0  132.0   44.0  165.0  121.0   99.0   \n",
       "9976  154.0  165.0   11.0    Blabla   66.0  143.0   55.0    NaN  132.0  110.0   \n",
       "9977  165.0    NaN   22.0    Blabla   77.0  154.0   66.0    NaN  143.0  121.0   \n",
       "9978    NaN    NaN   33.0    Blabla   88.0  165.0   77.0   11.0  154.0  132.0   \n",
       "9979    NaN   11.0   44.0    Blabla   99.0    NaN   88.0   22.0  165.0  143.0   \n",
       "9980   11.0   22.0   55.0    Blabla  110.0    NaN   99.0   33.0    NaN  154.0   \n",
       "9981   22.0   33.0   66.0    Blabla  121.0   11.0  110.0   44.0    NaN  165.0   \n",
       "9982   33.0   44.0   77.0    Blabla  132.0   22.0  121.0   55.0   11.0    NaN   \n",
       "9983   44.0   55.0   88.0    Blabla  143.0   33.0  132.0   66.0   22.0    NaN   \n",
       "9984   55.0   66.0   99.0    Blabla  154.0   44.0  143.0   77.0   33.0   11.0   \n",
       "9985   66.0   77.0  110.0    Blabla  165.0   55.0  154.0   88.0   44.0   22.0   \n",
       "9986   77.0   88.0  121.0    Blabla    NaN   66.0  165.0   99.0   55.0   33.0   \n",
       "9987   88.0   99.0  132.0    Blabla    NaN   77.0    NaN  110.0   66.0   44.0   \n",
       "9988   99.0  110.0  143.0    Blabla   11.0   88.0    NaN  121.0   77.0   55.0   \n",
       "9989  110.0  121.0  154.0    Blabla   22.0   99.0   11.0  132.0   88.0   66.0   \n",
       "9990  121.0  132.0  165.0    Blabla   33.0  110.0   22.0  143.0   99.0   77.0   \n",
       "9991  132.0  143.0    NaN    Blabla   44.0  121.0   33.0  154.0  110.0   88.0   \n",
       "9992  143.0  154.0    NaN    Blabla   55.0  132.0   44.0  165.0  121.0   99.0   \n",
       "9993  154.0  165.0   11.0    Blabla   66.0  143.0   55.0    NaN  132.0  110.0   \n",
       "9994  165.0    NaN   22.0    Blabla   77.0  154.0   66.0    NaN  143.0  121.0   \n",
       "9995    NaN    NaN   33.0    Blabla   88.0  165.0   77.0   11.0  154.0  132.0   \n",
       "9996    NaN   11.0   44.0    Blabla   99.0    NaN   88.0   22.0  165.0  143.0   \n",
       "9997   11.0   22.0   55.0    Blabla  110.0    NaN   99.0   33.0    NaN  154.0   \n",
       "9998   22.0   33.0   66.0    Blabla  121.0   11.0  110.0   44.0    NaN  165.0   \n",
       "9999   33.0   44.0   77.0    Blabla  132.0   22.0  121.0   55.0   11.0    NaN   \n",
       "\n",
       "      ...        Q      R      S      T      U      V      W      X      Y  \\\n",
       "0     ...     11.0    NaN   11.0   44.0   99.0    NaN   88.0   22.0  165.0   \n",
       "1     ...     22.0   11.0   22.0   55.0  110.0    NaN   99.0   33.0    NaN   \n",
       "2     ...     33.0   22.0   33.0   66.0  121.0   11.0  110.0   44.0    NaN   \n",
       "3     ...     44.0   33.0   44.0   77.0  132.0   22.0  121.0   55.0   11.0   \n",
       "4     ...     55.0   44.0   55.0   88.0  143.0   33.0  132.0   66.0   22.0   \n",
       "5     ...     66.0   55.0   66.0   99.0  154.0   44.0  143.0   77.0   33.0   \n",
       "6     ...     77.0   66.0   77.0  110.0  165.0   55.0  154.0   88.0   44.0   \n",
       "7     ...     88.0   77.0   88.0  121.0    NaN   66.0  165.0   99.0   55.0   \n",
       "8     ...     99.0   88.0   99.0  132.0    NaN   77.0    NaN  110.0   66.0   \n",
       "9     ...    110.0   99.0  110.0  143.0   11.0   88.0    NaN  121.0   77.0   \n",
       "10    ...    121.0  110.0  121.0  154.0   22.0   99.0   11.0  132.0   88.0   \n",
       "11    ...    132.0  121.0  132.0  165.0   33.0  110.0   22.0  143.0   99.0   \n",
       "12    ...    143.0  132.0  143.0    NaN   44.0  121.0   33.0  154.0  110.0   \n",
       "13    ...    154.0  143.0  154.0    NaN   55.0  132.0   44.0  165.0  121.0   \n",
       "14    ...    165.0  154.0  165.0   11.0   66.0  143.0   55.0    NaN  132.0   \n",
       "15    ...      NaN  165.0    NaN   22.0   77.0  154.0   66.0    NaN  143.0   \n",
       "16    ...      NaN    NaN    NaN   33.0   88.0  165.0   77.0   11.0  154.0   \n",
       "17    ...     11.0    NaN   11.0   44.0   99.0    NaN   88.0   22.0  165.0   \n",
       "18    ...     22.0   11.0   22.0   55.0  110.0    NaN   99.0   33.0    NaN   \n",
       "19    ...     33.0   22.0   33.0   66.0  121.0   11.0  110.0   44.0    NaN   \n",
       "20    ...     44.0   33.0   44.0   77.0  132.0   22.0  121.0   55.0   11.0   \n",
       "21    ...     55.0   44.0   55.0   88.0  143.0   33.0  132.0   66.0   22.0   \n",
       "22    ...     66.0   55.0   66.0   99.0  154.0   44.0  143.0   77.0   33.0   \n",
       "23    ...     77.0   66.0   77.0  110.0  165.0   55.0  154.0   88.0   44.0   \n",
       "24    ...     88.0   77.0   88.0  121.0    NaN   66.0  165.0   99.0   55.0   \n",
       "25    ...     99.0   88.0   99.0  132.0    NaN   77.0    NaN  110.0   66.0   \n",
       "26    ...    110.0   99.0  110.0  143.0   11.0   88.0    NaN  121.0   77.0   \n",
       "27    ...    121.0  110.0  121.0  154.0   22.0   99.0   11.0  132.0   88.0   \n",
       "28    ...    132.0  121.0  132.0  165.0   33.0  110.0   22.0  143.0   99.0   \n",
       "29    ...    143.0  132.0  143.0    NaN   44.0  121.0   33.0  154.0  110.0   \n",
       "...   ...      ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "9970  ...     99.0   88.0   99.0  132.0    NaN   77.0    NaN  110.0   66.0   \n",
       "9971  ...    110.0   99.0  110.0  143.0   11.0   88.0    NaN  121.0   77.0   \n",
       "9972  ...    121.0  110.0  121.0  154.0   22.0   99.0   11.0  132.0   88.0   \n",
       "9973  ...    132.0  121.0  132.0  165.0   33.0  110.0   22.0  143.0   99.0   \n",
       "9974  ...    143.0  132.0  143.0    NaN   44.0  121.0   33.0  154.0  110.0   \n",
       "9975  ...    154.0  143.0  154.0    NaN   55.0  132.0   44.0  165.0  121.0   \n",
       "9976  ...    165.0  154.0  165.0   11.0   66.0  143.0   55.0    NaN  132.0   \n",
       "9977  ...      NaN  165.0    NaN   22.0   77.0  154.0   66.0    NaN  143.0   \n",
       "9978  ...      NaN    NaN    NaN   33.0   88.0  165.0   77.0   11.0  154.0   \n",
       "9979  ...     11.0    NaN   11.0   44.0   99.0    NaN   88.0   22.0  165.0   \n",
       "9980  ...     22.0   11.0   22.0   55.0  110.0    NaN   99.0   33.0    NaN   \n",
       "9981  ...     33.0   22.0   33.0   66.0  121.0   11.0  110.0   44.0    NaN   \n",
       "9982  ...     44.0   33.0   44.0   77.0  132.0   22.0  121.0   55.0   11.0   \n",
       "9983  ...     55.0   44.0   55.0   88.0  143.0   33.0  132.0   66.0   22.0   \n",
       "9984  ...     66.0   55.0   66.0   99.0  154.0   44.0  143.0   77.0   33.0   \n",
       "9985  ...     77.0   66.0   77.0  110.0  165.0   55.0  154.0   88.0   44.0   \n",
       "9986  ...     88.0   77.0   88.0  121.0    NaN   66.0  165.0   99.0   55.0   \n",
       "9987  ...     99.0   88.0   99.0  132.0    NaN   77.0    NaN  110.0   66.0   \n",
       "9988  ...    110.0   99.0  110.0  143.0   11.0   88.0    NaN  121.0   77.0   \n",
       "9989  ...    121.0  110.0  121.0  154.0   22.0   99.0   11.0  132.0   88.0   \n",
       "9990  ...    132.0  121.0  132.0  165.0   33.0  110.0   22.0  143.0   99.0   \n",
       "9991  ...    143.0  132.0  143.0    NaN   44.0  121.0   33.0  154.0  110.0   \n",
       "9992  ...    154.0  143.0  154.0    NaN   55.0  132.0   44.0  165.0  121.0   \n",
       "9993  ...    165.0  154.0  165.0   11.0   66.0  143.0   55.0    NaN  132.0   \n",
       "9994  ...      NaN  165.0    NaN   22.0   77.0  154.0   66.0    NaN  143.0   \n",
       "9995  ...      NaN    NaN    NaN   33.0   88.0  165.0   77.0   11.0  154.0   \n",
       "9996  ...     11.0    NaN   11.0   44.0   99.0    NaN   88.0   22.0  165.0   \n",
       "9997  ...     22.0   11.0   22.0   55.0  110.0    NaN   99.0   33.0    NaN   \n",
       "9998  ...     33.0   22.0   33.0   66.0  121.0   11.0  110.0   44.0    NaN   \n",
       "9999  ...     44.0   33.0   44.0   77.0  132.0   22.0  121.0   55.0   11.0   \n",
       "\n",
       "          Z  \n",
       "0     143.0  \n",
       "1     154.0  \n",
       "2     165.0  \n",
       "3       NaN  \n",
       "4       NaN  \n",
       "5      11.0  \n",
       "6      22.0  \n",
       "7      33.0  \n",
       "8      44.0  \n",
       "9      55.0  \n",
       "10     66.0  \n",
       "11     77.0  \n",
       "12     88.0  \n",
       "13     99.0  \n",
       "14    110.0  \n",
       "15    121.0  \n",
       "16    132.0  \n",
       "17    143.0  \n",
       "18    154.0  \n",
       "19    165.0  \n",
       "20      NaN  \n",
       "21      NaN  \n",
       "22     11.0  \n",
       "23     22.0  \n",
       "24     33.0  \n",
       "25     44.0  \n",
       "26     55.0  \n",
       "27     66.0  \n",
       "28     77.0  \n",
       "29     88.0  \n",
       "...     ...  \n",
       "9970   44.0  \n",
       "9971   55.0  \n",
       "9972   66.0  \n",
       "9973   77.0  \n",
       "9974   88.0  \n",
       "9975   99.0  \n",
       "9976  110.0  \n",
       "9977  121.0  \n",
       "9978  132.0  \n",
       "9979  143.0  \n",
       "9980  154.0  \n",
       "9981  165.0  \n",
       "9982    NaN  \n",
       "9983    NaN  \n",
       "9984   11.0  \n",
       "9985   22.0  \n",
       "9986   33.0  \n",
       "9987   44.0  \n",
       "9988   55.0  \n",
       "9989   66.0  \n",
       "9990   77.0  \n",
       "9991   88.0  \n",
       "9992   99.0  \n",
       "9993  110.0  \n",
       "9994  121.0  \n",
       "9995  132.0  \n",
       "9996  143.0  \n",
       "9997  154.0  \n",
       "9998  165.0  \n",
       "9999    NaN  \n",
       "\n",
       "[10000 rows x 27 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "much_data = np.fromfunction(lambda x,y: (x+y*y)%17*11, (10000, 26))\n",
    "large_df = pd.DataFrame(much_data, columns=list(\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"))\n",
    "large_df[large_df % 16 == 0] = np.nan\n",
    "large_df.insert(3,\"some_text\", \"Blabla\")\n",
    "large_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phương thức `head()` trả về 5 hàng trên cùng:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>some_text</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>I</th>\n",
       "      <th>...</th>\n",
       "      <th>Q</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>T</th>\n",
       "      <th>U</th>\n",
       "      <th>V</th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>143.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A     B     C some_text      D     E      F     G      H      I  ...    \\\n",
       "0   NaN  11.0  44.0    Blabla   99.0   NaN   88.0  22.0  165.0  143.0  ...     \n",
       "1  11.0  22.0  55.0    Blabla  110.0   NaN   99.0  33.0    NaN  154.0  ...     \n",
       "2  22.0  33.0  66.0    Blabla  121.0  11.0  110.0  44.0    NaN  165.0  ...     \n",
       "3  33.0  44.0  77.0    Blabla  132.0  22.0  121.0  55.0   11.0    NaN  ...     \n",
       "4  44.0  55.0  88.0    Blabla  143.0  33.0  132.0  66.0   22.0    NaN  ...     \n",
       "\n",
       "      Q     R     S     T      U     V      W     X      Y      Z  \n",
       "0  11.0   NaN  11.0  44.0   99.0   NaN   88.0  22.0  165.0  143.0  \n",
       "1  22.0  11.0  22.0  55.0  110.0   NaN   99.0  33.0    NaN  154.0  \n",
       "2  33.0  22.0  33.0  66.0  121.0  11.0  110.0  44.0    NaN  165.0  \n",
       "3  44.0  33.0  44.0  77.0  132.0  22.0  121.0  55.0   11.0    NaN  \n",
       "4  55.0  44.0  55.0  88.0  143.0  33.0  132.0  66.0   22.0    NaN  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tất nhiên, cũng có chức năng `tail()` để xem 5 hàng dưới cùng. Bạn cũng có thể chỉ định số hàng bạn muốn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>some_text</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>I</th>\n",
       "      <th>...</th>\n",
       "      <th>Q</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>T</th>\n",
       "      <th>U</th>\n",
       "      <th>V</th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         A     B     C some_text      D     E      F     G     H      I  \\\n",
       "9998  22.0  33.0  66.0    Blabla  121.0  11.0  110.0  44.0   NaN  165.0   \n",
       "9999  33.0  44.0  77.0    Blabla  132.0  22.0  121.0  55.0  11.0    NaN   \n",
       "\n",
       "      ...       Q     R     S     T      U     V      W     X     Y      Z  \n",
       "9998  ...    33.0  22.0  33.0  66.0  121.0  11.0  110.0  44.0   NaN  165.0  \n",
       "9999  ...    44.0  33.0  44.0  77.0  132.0  22.0  121.0  55.0  11.0    NaN  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_df.tail(n=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phương thức `info()` in ra bản tóm tắt nội dung của từng cột:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 27 columns):\n",
      "A            8823 non-null float64\n",
      "B            8824 non-null float64\n",
      "C            8824 non-null float64\n",
      "some_text    10000 non-null object\n",
      "D            8824 non-null float64\n",
      "E            8822 non-null float64\n",
      "F            8824 non-null float64\n",
      "G            8824 non-null float64\n",
      "H            8822 non-null float64\n",
      "I            8823 non-null float64\n",
      "J            8823 non-null float64\n",
      "K            8822 non-null float64\n",
      "L            8824 non-null float64\n",
      "M            8824 non-null float64\n",
      "N            8822 non-null float64\n",
      "O            8824 non-null float64\n",
      "P            8824 non-null float64\n",
      "Q            8824 non-null float64\n",
      "R            8823 non-null float64\n",
      "S            8824 non-null float64\n",
      "T            8824 non-null float64\n",
      "U            8824 non-null float64\n",
      "V            8822 non-null float64\n",
      "W            8824 non-null float64\n",
      "X            8824 non-null float64\n",
      "Y            8822 non-null float64\n",
      "Z            8823 non-null float64\n",
      "dtypes: float64(26), object(1)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "large_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cuối cùng, phương thức `describe()` cung cấp một cái nhìn tổng quan về các giá trị tổng hợp chính trên mỗi cột:\n",
    "* `count`: số giá trị khác null (không phải NaN)\n",
    "* `mean`: giá trị trung bình của các giá trị khác null\n",
    "* `std`: [độ lệch chuẩn](https://en.wikipedia.org/wiki/Standard_deviation) của các giá trị khác null\n",
    "* `min`: giá trị khác null tối thiểu\n",
    "* `25%`, `50%`, `75%`: [phần trăm thứ 25, 50 và 75](https://en.wikipedia.org/wiki/Percentile) của các giá trị khác null\n",
    "* `max`: giá trị lớn nhất khác null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>I</th>\n",
       "      <th>J</th>\n",
       "      <th>...</th>\n",
       "      <th>Q</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>T</th>\n",
       "      <th>U</th>\n",
       "      <th>V</th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8823.000000</td>\n",
       "      <td>8824.000000</td>\n",
       "      <td>8824.000000</td>\n",
       "      <td>8824.000000</td>\n",
       "      <td>8822.000000</td>\n",
       "      <td>8824.000000</td>\n",
       "      <td>8824.000000</td>\n",
       "      <td>8822.000000</td>\n",
       "      <td>8823.000000</td>\n",
       "      <td>8823.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8824.000000</td>\n",
       "      <td>8823.000000</td>\n",
       "      <td>8824.000000</td>\n",
       "      <td>8824.000000</td>\n",
       "      <td>8824.000000</td>\n",
       "      <td>8822.000000</td>\n",
       "      <td>8824.000000</td>\n",
       "      <td>8824.000000</td>\n",
       "      <td>8822.000000</td>\n",
       "      <td>8823.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>87.977559</td>\n",
       "      <td>87.972575</td>\n",
       "      <td>87.987534</td>\n",
       "      <td>88.012466</td>\n",
       "      <td>87.983791</td>\n",
       "      <td>88.007480</td>\n",
       "      <td>87.977561</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.022441</td>\n",
       "      <td>88.022441</td>\n",
       "      <td>...</td>\n",
       "      <td>87.972575</td>\n",
       "      <td>87.977559</td>\n",
       "      <td>87.972575</td>\n",
       "      <td>87.987534</td>\n",
       "      <td>88.012466</td>\n",
       "      <td>87.983791</td>\n",
       "      <td>88.007480</td>\n",
       "      <td>87.977561</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.022441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47.535911</td>\n",
       "      <td>47.535523</td>\n",
       "      <td>47.521679</td>\n",
       "      <td>47.521679</td>\n",
       "      <td>47.535001</td>\n",
       "      <td>47.519371</td>\n",
       "      <td>47.529755</td>\n",
       "      <td>47.536879</td>\n",
       "      <td>47.535911</td>\n",
       "      <td>47.535911</td>\n",
       "      <td>...</td>\n",
       "      <td>47.535523</td>\n",
       "      <td>47.535911</td>\n",
       "      <td>47.535523</td>\n",
       "      <td>47.521679</td>\n",
       "      <td>47.521679</td>\n",
       "      <td>47.535001</td>\n",
       "      <td>47.519371</td>\n",
       "      <td>47.529755</td>\n",
       "      <td>47.536879</td>\n",
       "      <td>47.535911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 A            B            C            D            E  \\\n",
       "count  8823.000000  8824.000000  8824.000000  8824.000000  8822.000000   \n",
       "mean     87.977559    87.972575    87.987534    88.012466    87.983791   \n",
       "std      47.535911    47.535523    47.521679    47.521679    47.535001   \n",
       "min      11.000000    11.000000    11.000000    11.000000    11.000000   \n",
       "25%      44.000000    44.000000    44.000000    44.000000    44.000000   \n",
       "50%      88.000000    88.000000    88.000000    88.000000    88.000000   \n",
       "75%     132.000000   132.000000   132.000000   132.000000   132.000000   \n",
       "max     165.000000   165.000000   165.000000   165.000000   165.000000   \n",
       "\n",
       "                 F            G            H            I            J  \\\n",
       "count  8824.000000  8824.000000  8822.000000  8823.000000  8823.000000   \n",
       "mean     88.007480    87.977561    88.000000    88.022441    88.022441   \n",
       "std      47.519371    47.529755    47.536879    47.535911    47.535911   \n",
       "min      11.000000    11.000000    11.000000    11.000000    11.000000   \n",
       "25%      44.000000    44.000000    44.000000    44.000000    44.000000   \n",
       "50%      88.000000    88.000000    88.000000    88.000000    88.000000   \n",
       "75%     132.000000   132.000000   132.000000   132.000000   132.000000   \n",
       "max     165.000000   165.000000   165.000000   165.000000   165.000000   \n",
       "\n",
       "          ...                 Q            R            S            T  \\\n",
       "count     ...       8824.000000  8823.000000  8824.000000  8824.000000   \n",
       "mean      ...         87.972575    87.977559    87.972575    87.987534   \n",
       "std       ...         47.535523    47.535911    47.535523    47.521679   \n",
       "min       ...         11.000000    11.000000    11.000000    11.000000   \n",
       "25%       ...         44.000000    44.000000    44.000000    44.000000   \n",
       "50%       ...         88.000000    88.000000    88.000000    88.000000   \n",
       "75%       ...        132.000000   132.000000   132.000000   132.000000   \n",
       "max       ...        165.000000   165.000000   165.000000   165.000000   \n",
       "\n",
       "                 U            V            W            X            Y  \\\n",
       "count  8824.000000  8822.000000  8824.000000  8824.000000  8822.000000   \n",
       "mean     88.012466    87.983791    88.007480    87.977561    88.000000   \n",
       "std      47.521679    47.535001    47.519371    47.529755    47.536879   \n",
       "min      11.000000    11.000000    11.000000    11.000000    11.000000   \n",
       "25%      44.000000    44.000000    44.000000    44.000000    44.000000   \n",
       "50%      88.000000    88.000000    88.000000    88.000000    88.000000   \n",
       "75%     132.000000   132.000000   132.000000   132.000000   132.000000   \n",
       "max     165.000000   165.000000   165.000000   165.000000   165.000000   \n",
       "\n",
       "                 Z  \n",
       "count  8823.000000  \n",
       "mean     88.022441  \n",
       "std      47.535911  \n",
       "min      11.000000  \n",
       "25%      44.000000  \n",
       "50%      88.000000  \n",
       "75%     132.000000  \n",
       "max     165.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lưu & nạp\n",
    "Pandas có thể lưu `DataFrame` vào các chương trình phụ trợ khác nhau, bao gồm các định dạng tệp như CSV, Excel, JSON, HTML và HDF5 hoặc vào cơ sở dữ liệu SQL. Hãy tạo một `DataFrame` để chứng minh điều này:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>68.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>83.1</td>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         hobby  weight  birthyear  children\n",
       "alice   Biking    68.5       1985       NaN\n",
       "bob    Dancing    83.1       1984       3.0"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df = pd.DataFrame(\n",
    "    [[\"Biking\", 68.5, 1985, np.nan], [\"Dancing\", 83.1, 1984, 3]], \n",
    "    columns=[\"hobby\",\"weight\",\"birthyear\",\"children\"],\n",
    "    index=[\"alice\", \"bob\"]\n",
    ")\n",
    "my_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Lưu\n",
    "Hãy lưu nó dưới dạng CSV, HTML và JSON:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_df.to_csv(\"my_df.csv\")\n",
    "my_df.to_html(\"my_df.html\")\n",
    "my_df.to_json(\"my_df.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Xong! Hãy xem qua những gì đã được lưu:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# my_df.csv\n",
      ",hobby,weight,birthyear,children\n",
      "alice,Biking,68.5,1985,\n",
      "bob,Dancing,83.1,1984,3.0\n",
      "\n",
      "\n",
      "# my_df.html\n",
      "<table border=\"1\" class=\"dataframe\">\n",
      "  <thead>\n",
      "    <tr style=\"text-align: right;\">\n",
      "      <th></th>\n",
      "      <th>hobby</th>\n",
      "      <th>weight</th>\n",
      "      <th>birthyear</th>\n",
      "      <th>children</th>\n",
      "    </tr>\n",
      "  </thead>\n",
      "  <tbody>\n",
      "    <tr>\n",
      "      <th>alice</th>\n",
      "      <td>Biking</td>\n",
      "      <td>68.5</td>\n",
      "      <td>1985</td>\n",
      "      <td>NaN</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>bob</th>\n",
      "      <td>Dancing</td>\n",
      "      <td>83.1</td>\n",
      "      <td>1984</td>\n",
      "      <td>3.0</td>\n",
      "    </tr>\n",
      "  </tbody>\n",
      "</table>\n",
      "\n",
      "# my_df.json\n",
      "{\"hobby\":{\"alice\":\"Biking\",\"bob\":\"Dancing\"},\"weight\":{\"alice\":68.5,\"bob\":83.1},\"birthyear\":{\"alice\":1985,\"bob\":1984},\"children\":{\"alice\":null,\"bob\":3.0}}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for filename in (\"my_df.csv\", \"my_df.html\", \"my_df.json\"):\n",
    "    print(\"#\", filename)\n",
    "    with open(filename, \"rt\") as f:\n",
    "        print(f.read())\n",
    "        print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lưu ý rằng chỉ mục được lưu dưới dạng cột đầu tiên (không có tên) trong tệp CSV, dưới dạng thẻ `<th>` trong HTML và dưới dạng khóa trong JSON.\n",
    "\n",
    "Lưu vào các định dạng khác hoạt động rất giống nhau, nhưng một số định dạng yêu cầu cài đặt thêm thư viện. Ví dụ: lưu vào Excel yêu cầu thư viện openpyxl:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No module named 'openpyxl'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    my_df.to_excel(\"my_df.xlsx\", sheet_name='People')\n",
    "except ImportError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nạp\n",
    "Bây giờ, hãy nạp lại tệp CSV của chúng ta vào `DataFrame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>68.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>83.1</td>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         hobby  weight  birthyear  children\n",
       "alice   Biking    68.5       1985       NaN\n",
       "bob    Dancing    83.1       1984       3.0"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df_loaded = pd.read_csv(\"my_df.csv\", index_col=0)\n",
    "my_df_loaded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Như bạn có thể đoán, cũng có các hàm `read_json`, `read_html`, `read_excel` tương tự. Chúng ta cũng có thể đọc dữ liệu trực tiếp từ Internet. Ví dụ: hãy tải 1.000 thành phố nằm đầu của Hoa Kỳ từ github:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Marysville</th>\n",
       "      <td>Washington</td>\n",
       "      <td>63269</td>\n",
       "      <td>48.051764</td>\n",
       "      <td>-122.177082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perris</th>\n",
       "      <td>California</td>\n",
       "      <td>72326</td>\n",
       "      <td>33.782519</td>\n",
       "      <td>-117.228648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleveland</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>390113</td>\n",
       "      <td>41.499320</td>\n",
       "      <td>-81.694361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Worcester</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>182544</td>\n",
       "      <td>42.262593</td>\n",
       "      <td>-71.802293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Columbia</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>133358</td>\n",
       "      <td>34.000710</td>\n",
       "      <td>-81.034814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     State  Population        lat         lon\n",
       "City                                                         \n",
       "Marysville      Washington       63269  48.051764 -122.177082\n",
       "Perris          California       72326  33.782519 -117.228648\n",
       "Cleveland             Ohio      390113  41.499320  -81.694361\n",
       "Worcester    Massachusetts      182544  42.262593  -71.802293\n",
       "Columbia    South Carolina      133358  34.000710  -81.034814"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_cities = None\n",
    "try:\n",
    "    csv_url = \"https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv\"\n",
    "    us_cities = pd.read_csv(csv_url, index_col=0)\n",
    "    us_cities = us_cities.head()\n",
    "except IOError as e:\n",
    "    print(e)\n",
    "us_cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Có nhiều tùy chọn hơn, đặc biệt là về định dạng ngày giờ. Hãy xem [tài liệu](http://pandas.pydata.org/pandas-docs/stable/io.html) để biết thêm chi tiết."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kết hợp `DataFrame`\n",
    "\n",
    "## Phép gộp giống như SQL\n",
    "Một tính năng mạnh mẽ của pandas là khả năng thực hiện các phép gộp giống SQL trên `DataFrame`. Các kiểu gộp khác nhau được hỗ trợ: gộp bên trong, gộp ngoài trái/phải và gộp đầy đủ. Để minh họa điều này, hãy bắt đầu bằng cách tạo một vài `DataFrame` đơn giản:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UT</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>40.755851</td>\n",
       "      <td>-111.896657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state            city        lat         lng\n",
       "0    CA   San Francisco  37.781334 -122.416728\n",
       "1    NY        New York  40.705649  -74.008344\n",
       "2    FL           Miami  25.791100  -80.320733\n",
       "3    OH       Cleveland  41.473508  -81.739791\n",
       "4    UT  Salt Lake City  40.755851 -111.896657"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_loc = pd.DataFrame(\n",
    "    [\n",
    "        [\"CA\", \"San Francisco\", 37.781334, -122.416728],\n",
    "        [\"NY\", \"New York\", 40.705649, -74.008344],\n",
    "        [\"FL\", \"Miami\", 25.791100, -80.320733],\n",
    "        [\"OH\", \"Cleveland\", 41.473508, -81.739791],\n",
    "        [\"UT\", \"Salt Lake City\", 40.755851, -111.896657]\n",
    "    ], columns=[\"state\", \"city\", \"lat\", \"lng\"])\n",
    "city_loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>808976</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8363710</td>\n",
       "      <td>New York</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>413201</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2242193</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population           city       state\n",
       "3      808976  San Francisco  California\n",
       "4     8363710       New York    New-York\n",
       "5      413201          Miami     Florida\n",
       "6     2242193        Houston       Texas"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_pop = pd.DataFrame(\n",
    "    [\n",
    "        [808976, \"San Francisco\", \"California\"],\n",
    "        [8363710, \"New York\", \"New-York\"],\n",
    "        [413201, \"Miami\", \"Florida\"],\n",
    "        [2242193, \"Houston\", \"Texas\"]\n",
    "    ], index=[3,4,5,6], columns=[\"population\", \"city\", \"state\"])\n",
    "city_pop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bây giờ, hãy gộp các `DataFrame` này bằng hàm `merge()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state_x</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>state_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>808976</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>8363710</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>413201</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state_x           city        lat         lng  population     state_y\n",
       "0      CA  San Francisco  37.781334 -122.416728      808976  California\n",
       "1      NY       New York  40.705649  -74.008344     8363710    New-York\n",
       "2      FL          Miami  25.791100  -80.320733      413201     Florida"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left=city_loc, right=city_pop, on=\"city\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lưu ý rằng cả `DataFrame` đều có một cột có tên là `state`, do đó, kết quả là chúng được đổi tên thành `state_x` và `state_y`.\n",
    "\n",
    "Ngoài ra, hãy lưu ý rằng Cleveland, Thành phố Salt Lake và Houston đã bị loại bỏ vì chúng không tồn tại trong *cả hai* `DataFrame`. Điều này tương đương với SQL `INNER JOIN`. Nếu bạn muốn `FULL OUTER JOIN`, trong đó không có thành phố nào bị loại bỏ và các giá trị `NaN` được thêm vào, bạn phải chỉ định `how=\"outer\"`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state_x</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>state_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>808976.0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>8363710.0</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>413201.0</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UT</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>40.755851</td>\n",
       "      <td>-111.896657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Houston</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2242193.0</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state_x            city        lat         lng  population     state_y\n",
       "0      CA   San Francisco  37.781334 -122.416728    808976.0  California\n",
       "1      NY        New York  40.705649  -74.008344   8363710.0    New-York\n",
       "2      FL           Miami  25.791100  -80.320733    413201.0     Florida\n",
       "3      OH       Cleveland  41.473508  -81.739791         NaN         NaN\n",
       "4      UT  Salt Lake City  40.755851 -111.896657         NaN         NaN\n",
       "5     NaN         Houston        NaN         NaN   2242193.0       Texas"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_cities = pd.merge(left=city_loc, right=city_pop, on=\"city\", how=\"outer\")\n",
    "all_cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tất nhiên, `LEFT OUTER JOIN` cũng khả dụng bằng cách đặt `how=\"left\"`: chỉ các thành phố có trong `DataFrame` bên trái mới có kết quả. Tương tự, với `how=\"right\"`, chỉ các thành phố ở bên phải `DataFrame` mới xuất hiện trong kết quả. Ví dụ:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state_x</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>state_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>808976</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>8363710</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>413201</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Houston</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2242193</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state_x           city        lat         lng  population     state_y\n",
       "0      CA  San Francisco  37.781334 -122.416728      808976  California\n",
       "1      NY       New York  40.705649  -74.008344     8363710    New-York\n",
       "2      FL          Miami  25.791100  -80.320733      413201     Florida\n",
       "3     NaN        Houston        NaN         NaN     2242193       Texas"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left=city_loc, right=city_pop, on=\"city\", how=\"right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nếu khóa gộp nằm trong một (hoặc cả hai) chỉ mục của `DataFrame`, thì bạn phải sử dụng `left_index=True` và/hoặc `right_index=True`. Nếu các tên cột chính khác nhau, bạn phải sử dụng `left_on` và `right_on`. Ví dụ:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state_x</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>name</th>\n",
       "      <th>state_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>808976</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>8363710</td>\n",
       "      <td>New York</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>413201</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state_x           city        lat         lng  population           name  \\\n",
       "0      CA  San Francisco  37.781334 -122.416728      808976  San Francisco   \n",
       "1      NY       New York  40.705649  -74.008344     8363710       New York   \n",
       "2      FL          Miami  25.791100  -80.320733      413201          Miami   \n",
       "\n",
       "      state_y  \n",
       "0  California  \n",
       "1    New-York  \n",
       "2     Florida  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_pop2 = city_pop.copy()\n",
    "city_pop2.columns = [\"population\", \"name\", \"state\"]\n",
    "pd.merge(left=city_loc, right=city_pop2, left_on=\"city\", right_on=\"name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nối\n",
    "Thay vì gộp `DataFrame`, chúng ta có thể chỉ muốn nối chúng. Ta có thể sử dụng `concat()` cho mục đích này:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cleveland</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>40.755851</td>\n",
       "      <td>-111.896657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>808976.0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8363710.0</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Miami</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>413201.0</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Houston</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2242193.0</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             city        lat         lng  population       state\n",
       "0   San Francisco  37.781334 -122.416728         NaN          CA\n",
       "1        New York  40.705649  -74.008344         NaN          NY\n",
       "2           Miami  25.791100  -80.320733         NaN          FL\n",
       "3       Cleveland  41.473508  -81.739791         NaN          OH\n",
       "4  Salt Lake City  40.755851 -111.896657         NaN          UT\n",
       "3   San Francisco        NaN         NaN    808976.0  California\n",
       "4        New York        NaN         NaN   8363710.0    New-York\n",
       "5           Miami        NaN         NaN    413201.0     Florida\n",
       "6         Houston        NaN         NaN   2242193.0       Texas"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_concat = pd.concat([city_loc, city_pop])\n",
    "result_concat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lưu ý rằng thao tác này đã căn chỉnh dữ liệu theo chiều ngang (theo cột) nhưng không theo chiều dọc (theo hàng). Trong ví dụ này, chúng ta kết thúc bằng nhiều hàng có cùng chỉ mục (ví dụ: 3). Pandas xử lý việc này khá duyên dáng:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cleveland</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>808976.0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city        lat        lng  population       state\n",
       "3      Cleveland  41.473508 -81.739791         NaN          OH\n",
       "3  San Francisco        NaN        NaN    808976.0  California"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_concat.loc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hoặc bạn có thể yêu cầu pandas bỏ qua chỉ mục:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cleveland</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>40.755851</td>\n",
       "      <td>-111.896657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>808976.0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8363710.0</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Miami</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>413201.0</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Houston</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2242193.0</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             city        lat         lng  population       state\n",
       "0   San Francisco  37.781334 -122.416728         NaN          CA\n",
       "1        New York  40.705649  -74.008344         NaN          NY\n",
       "2           Miami  25.791100  -80.320733         NaN          FL\n",
       "3       Cleveland  41.473508  -81.739791         NaN          OH\n",
       "4  Salt Lake City  40.755851 -111.896657         NaN          UT\n",
       "5   San Francisco        NaN         NaN    808976.0  California\n",
       "6        New York        NaN         NaN   8363710.0    New-York\n",
       "7           Miami        NaN         NaN    413201.0     Florida\n",
       "8         Houston        NaN         NaN   2242193.0       Texas"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([city_loc, city_pop], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lưu ý rằng khi một cột không tồn tại trong `DataFrame`, nó sẽ hoạt động như thể nó chứa đầy các giá trị `NaN`. Nếu chúng ta đặt `join=\"inner\"`, thì chỉ các cột tồn tại trong *cả hai* `DataFrame` được trả về:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UT</td>\n",
       "      <td>Salt Lake City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>California</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New-York</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Miami</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Houston</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state            city\n",
       "0          CA   San Francisco\n",
       "1          NY        New York\n",
       "2          FL           Miami\n",
       "3          OH       Cleveland\n",
       "4          UT  Salt Lake City\n",
       "3  California   San Francisco\n",
       "4    New-York        New York\n",
       "5     Florida           Miami\n",
       "6       Texas         Houston"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([city_loc, city_pop], join=\"inner\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bạn có thể nối `DataFrame` theo chiều ngang thay vì theo chiều dọc bằng cách đặt `axis=1`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "      <td>808976.0</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UT</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>40.755851</td>\n",
       "      <td>-111.896657</td>\n",
       "      <td>8363710.0</td>\n",
       "      <td>New York</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>413201.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2242193.0</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state            city        lat         lng  population           city  \\\n",
       "0    CA   San Francisco  37.781334 -122.416728         NaN            NaN   \n",
       "1    NY        New York  40.705649  -74.008344         NaN            NaN   \n",
       "2    FL           Miami  25.791100  -80.320733         NaN            NaN   \n",
       "3    OH       Cleveland  41.473508  -81.739791    808976.0  San Francisco   \n",
       "4    UT  Salt Lake City  40.755851 -111.896657   8363710.0       New York   \n",
       "5   NaN             NaN        NaN         NaN    413201.0          Miami   \n",
       "6   NaN             NaN        NaN         NaN   2242193.0        Houston   \n",
       "\n",
       "        state  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3  California  \n",
       "4    New-York  \n",
       "5     Florida  \n",
       "6       Texas  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([city_loc, city_pop], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trong trường hợp này, nó thực sự không có nhiều ý nghĩa bởi vì các chỉ số không thẳng hàng (ví dụ: Cleveland và San Francisco nằm trên cùng một hàng, vì chúng có chung nhãn chỉ mục `3`). Vì vậy, hãy lập chỉ mục lại `DataFrame` theo tên thành phố trước khi nối:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cleveland</th>\n",
       "      <td>OH</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Houston</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2242193.0</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miami</th>\n",
       "      <td>FL</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>413201.0</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>NY</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>8363710.0</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salt Lake City</th>\n",
       "      <td>UT</td>\n",
       "      <td>40.755851</td>\n",
       "      <td>-111.896657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco</th>\n",
       "      <td>CA</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>808976.0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               state        lat         lng  population       state\n",
       "Cleveland         OH  41.473508  -81.739791         NaN         NaN\n",
       "Houston          NaN        NaN         NaN   2242193.0       Texas\n",
       "Miami             FL  25.791100  -80.320733    413201.0     Florida\n",
       "New York          NY  40.705649  -74.008344   8363710.0    New-York\n",
       "Salt Lake City    UT  40.755851 -111.896657         NaN         NaN\n",
       "San Francisco     CA  37.781334 -122.416728    808976.0  California"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([city_loc.set_index(\"city\"), city_pop.set_index(\"city\")], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cái này trông rất giống `FULL OUTER JOIN`, ngoại trừ việc các cột `state` không được đổi tên thành `state_x` và `state_y`, và cột `city` hiện là chỉ mục."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phương thức `append()` là một cách viết tắt hữu ích để nối các `DataFrame` theo chiều dọc:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cleveland</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>40.755851</td>\n",
       "      <td>-111.896657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>UT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>808976.0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8363710.0</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Miami</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>413201.0</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Houston</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2242193.0</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             city        lat         lng  population       state\n",
       "0   San Francisco  37.781334 -122.416728         NaN          CA\n",
       "1        New York  40.705649  -74.008344         NaN          NY\n",
       "2           Miami  25.791100  -80.320733         NaN          FL\n",
       "3       Cleveland  41.473508  -81.739791         NaN          OH\n",
       "4  Salt Lake City  40.755851 -111.896657         NaN          UT\n",
       "3   San Francisco        NaN         NaN    808976.0  California\n",
       "4        New York        NaN         NaN   8363710.0    New-York\n",
       "5           Miami        NaN         NaN    413201.0     Florida\n",
       "6         Houston        NaN         NaN   2242193.0       Texas"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_loc.append(city_pop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Như mọi khi trong pandas, phương thức `append()` *không* thực sự sửa đổi `city_loc`: nó hoạt động trên một bản sao và trả về bản sao đã sửa đổi."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Danh mục\n",
    "Sẽ khá thường xuyên ta có các giá trị đại diện cho các danh mục, ví dụ: `1` cho nữ và `2` cho nam hoặc `\"A\"` cho Tốt, `\"B\"` cho Trung bình, `\"C\"` cho Kém. Các giá trị phân loại này có thể khó đọc và khó xử lý, nhưng may mắn thay, pandas làm cho nó trở nên dễ dàng. Để minh họa điều này, hãy lấy `DataFrame` `city_pop` mà chúng ta đã tạo trước đó và thêm một cột đại diện cho một danh mục:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>eco_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>808976</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8363710</td>\n",
       "      <td>New York</td>\n",
       "      <td>New-York</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>413201</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2242193</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population           city       state  eco_code\n",
       "3      808976  San Francisco  California        17\n",
       "4     8363710       New York    New-York        17\n",
       "5      413201          Miami     Florida        34\n",
       "6     2242193        Houston       Texas        20"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_eco = city_pop.copy()\n",
    "city_eco[\"eco_code\"] = [17, 17, 34, 20]\n",
    "city_eco"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hiện tại, cột `eco_code` chứa đầy các mã rõ ràng là vô nghĩa. Hãy khắc phục điều đó. Đầu tiên, chúng ta sẽ tạo một cột phân loại mới dựa trên `eco_code`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([17, 20, 34], dtype='int64')"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_eco[\"economy\"] = city_eco[\"eco_code\"].astype('category')\n",
    "city_eco[\"economy\"].cat.categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bây giờ chúng ta có thể đặt cho mỗi danh mục một cái tên có ý nghĩa:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>eco_code</th>\n",
       "      <th>economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>808976</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>17</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8363710</td>\n",
       "      <td>New York</td>\n",
       "      <td>New-York</td>\n",
       "      <td>17</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>413201</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "      <td>34</td>\n",
       "      <td>Tourism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2242193</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>20</td>\n",
       "      <td>Energy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population           city       state  eco_code  economy\n",
       "3      808976  San Francisco  California        17  Finance\n",
       "4     8363710       New York    New-York        17  Finance\n",
       "5      413201          Miami     Florida        34  Tourism\n",
       "6     2242193        Houston       Texas        20   Energy"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_eco[\"economy\"].cat.categories = [\"Finance\", \"Energy\", \"Tourism\"]\n",
    "city_eco"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lưu ý rằng các giá trị phân loại được sắp xếp theo thứ tự phân loại của chúng, *không phải* thứ tự bảng chữ cái:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>eco_code</th>\n",
       "      <th>economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>413201</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "      <td>34</td>\n",
       "      <td>Tourism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2242193</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>20</td>\n",
       "      <td>Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8363710</td>\n",
       "      <td>New York</td>\n",
       "      <td>New-York</td>\n",
       "      <td>17</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>808976</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>17</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population           city       state  eco_code  economy\n",
       "5      413201          Miami     Florida        34  Tourism\n",
       "6     2242193        Houston       Texas        20   Energy\n",
       "4     8363710       New York    New-York        17  Finance\n",
       "3      808976  San Francisco  California        17  Finance"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_eco.sort_values(by=\"economy\", ascending=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tiếp theo là gì?\n",
    "Như bạn có thể nhận thấy, pandas là một thư viện lớn với *nhiều* tính năng. Mặc dù chúng ta đã xem qua các tính năng quan trọng nhất nhưng vẫn còn rất nhiều điều cần khám phá. Có lẽ cách tốt nhất để tìm hiểu thêm là thực hành với một số dữ liệu thực tế. Bạn cũng nên tham khảo [tài liệu](http://pandas.pydata.org/pandas-docs/stable/index.html) tuyệt vời này của pandas, đặc biệt là [cuốn sách này](http://pandas.pydata.org/pandas-docs/stable/cookbook.htm)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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