6_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>table {align:left;display:block} </style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "table_css = 'table {align:left;display:block} '\n",
    "HTML('<style>{}</style>'.format(table_css))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **ASI WA Python Workshop**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## **Jupyter Notebook Tips**\n",
    "\n",
    "Outside of a cell:\n",
    "\n",
    "| key combo | action |\n",
    "| :--: | ---- |\n",
    "| shift+enter |  run a cell |\n",
    "| b |  insert cell below | \n",
    "| a |  insert cell above |\n",
    "| dd | delete selected cell |\n",
    "\n",
    "<br>\n",
    "Within a cell:\n",
    "\n",
    "| key combo | action |\n",
    "| :--: | ---- |\n",
    "| Command + / |  comment out line(s) |\n",
    "| Alt + Cursor | vertical selection |\n",
    "\n",
    "**Control = Command\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# **Pandas**\n",
    "\n",
    "Pandas is a very popular Python package that is like the Excel or Tidyverse for Python, allowing you to easily manipulate and analyze tabular data, perform calculations, and apply data transformations using a familiar interface. "
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![pandas_datastruct.png](attachment:fc0be153-7004-4013-b557-efb4b3818007.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas, similar to NumPy, is typically imported using the shorthand pd for convenience and brevity in code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Series**\n",
    "Like an array or a list, a Pandas Series is a one-dimensional labeled array that can hold different data types and is often compared to a column in a spreadsheet or a single column from a database table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create a pandas series using a list or numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    400\n",
       "1    200\n",
       "2    300\n",
       "3    400\n",
       "4    500\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strengths = pd.Series([400, 200, 300, 400, 500])\n",
    "strengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Batman\n",
       "1         Robin\n",
       "2     Spiderman\n",
       "3       Robocop\n",
       "4    Terminator\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "names = pd.Series(np.array([\"Batman\", \"Robin\", \"Spiderman\", \"Robocop\", \"Terminator\"]))\n",
    "names"
   ]
  },
  {
   "attachments": {
    "e25c73a3-0281-4d78-a590-7c613395add4.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "## **DataFrame**\n",
    "\n",
    "A Pandas DataFrame is a two-dimensional labeled data structure that can store heterogeneous data types. It can be thought of as a table or a spreadsheet, where rows represent observations and columns represent variables or features.\n",
    "\n",
    "![series-and-dataframe.width-1200.png](attachment:e25c73a3-0281-4d78-a590-7c613395add4.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating\n",
    "At its core, a DataFrame in Pandas is a two-dimensional data structure built on top of a NumPy ndarray, with the added feature of labels for both rows and columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      col1  col2  col3\n",
      "row1     1     2     3\n",
      "row2     4     5     6\n",
      "row3     7     8     9\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Create a NumPy ndarray\n",
    "data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
    "\n",
    "# Create a DataFrame from the ndarray\n",
    "df1 = pd.DataFrame(data)\n",
    "# add column labels\n",
    "df1.columns = ['col1','col2','col3']\n",
    "# add index labels\n",
    "df1.index = ['row1','row2','row3']\n",
    "\n",
    "# Print the DataFrame\n",
    "print(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also set the labels within the dataframe function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      col1  col2  col3\n",
      "row1    32    38    34\n",
      "row2    32    40    14\n",
      "row3    63    45    46\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame( np.random.randint(0, 100, (3,3)), index=['row1','row2','row3'], columns=['col1','col2','col3'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily add another column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>new_col</th>\n",
       "      <th>rm_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>32</td>\n",
       "      <td>38</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>63</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      col1  col2  col3  new_col  rm_col\n",
       "row1    32    38    34        1       4\n",
       "row2    32    40    14        2       5\n",
       "row3    63    45    46        3       6"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['new_col'] = [1,2,3]\n",
    "df['rm_col'] = [4,5,6]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also easily drop columns with `.drop()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "      <th>new_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>32</td>\n",
       "      <td>38</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>63</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      col1  col2  col3  new_col\n",
       "row1    32    38    34        1\n",
       "row2    32    40    14        2\n",
       "row3    63    45    46        3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop( columns='rm_col', inplace=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And rename the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>apple</th>\n",
       "      <th>bee</th>\n",
       "      <th>cat</th>\n",
       "      <th>dice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>32</td>\n",
       "      <td>38</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>63</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      apple  bee  cat  dice\n",
       "row1     32   38   34     1\n",
       "row2     32   40   14     2\n",
       "row3     63   45   46     3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['apple','bee','cat','dice']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Meta data\n",
    "When working with a Pandas DataFrame, you can access important metadata attributes such as columns, index, and shape. These attributes provide valuable information about the structure, labels, and dimensions of the DataFrame, allowing you to better understand and analyze the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>apple</th>\n",
       "      <th>bee</th>\n",
       "      <th>cat</th>\n",
       "      <th>dice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>32</td>\n",
       "      <td>38</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>63</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      apple  bee  cat  dice\n",
       "row1     32   38   34     1\n",
       "row2     32   40   14     2\n",
       "row3     63   45   46     3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['apple', 'bee', 'cat', 'dice'], dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['row1', 'row2', 'row3'], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3 entries, row1 to row3\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   apple   3 non-null      int64\n",
      " 1   bee     3 non-null      int64\n",
      " 2   cat     3 non-null      int64\n",
      " 3   dice    3 non-null      int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 120.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pulling Columns\n",
    "We can index a column with `[]` string to return a series\n",
    "\n",
    "```series = dataframe[string]```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>apple</th>\n",
       "      <th>bee</th>\n",
       "      <th>cat</th>\n",
       "      <th>dice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>32</td>\n",
       "      <td>38</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>63</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      apple  bee  cat  dice\n",
       "row1     32   38   34     1\n",
       "row2     32   40   14     2\n",
       "row3     63   45   46     3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row1    34\n",
       "row2    14\n",
       "row3    46\n",
       "Name: cat, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['cat']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we can return a column using `.` and a column name\n",
    "\n",
    "```series = dataframe.string```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row1    34\n",
       "row2    14\n",
       "row3    46\n",
       "Name: cat, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also pass a list of column names as strings\n",
    "\n",
    "```sub-dataframe = dataframe[[string1,string2,string3]]```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>bee</th>\n",
       "      <th>dice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>row1</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row2</th>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row3</th>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      bee  dice\n",
       "row1   38     1\n",
       "row2   40     2\n",
       "row3   45     3"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[ ['bee','dice'] ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "#### *Task 6.1: Create a pandas dataframe*\n",
    "\n",
    "1. Create DataFrame presented below, and index the 'movie_title' column.\n",
    "```\n",
    "                                         movie_title  imdb_score\n",
    "0                                            Avatar          7.9\n",
    "1          Pirates of the Caribbean: At World's End          7.1\n",
    "2                                           Spectre          6.8\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "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>movie_title</th>\n",
       "      <th>imdb_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spectre</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                movie_title  imdb_score\n",
       "0                                    Avatar         7.9\n",
       "1  Pirates of the Caribbean: At World's End         7.1\n",
       "2                                   Spectre         6.8"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 6.1.1\n",
    "movie_titles = ['Avatar',\"Pirates of the Caribbean: At World's End\",'Spectre']\n",
    "scores = [7.9, 7.1, 6.8]\n",
    "df = pd.DataFrame( [], columns=['movie_title','imdb_score'])\n",
    "\n",
    "df['movie_title'] = movie_titles\n",
    "df['imdb_score'] = scores\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>movie_title</th>\n",
       "      <th>imdb_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spectre</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                movie_title  imdb_score\n",
       "0                                    Avatar         7.9\n",
       "1  Pirates of the Caribbean: At World's End         7.1\n",
       "2                                   Spectre         6.8"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_titles = pd.Series( ['Avatar',\"Pirates of the Caribbean: At World's End\",'Spectre'])\n",
    "scores = pd.Series( [7.9, 7.1, 6.8])\n",
    "\n",
    "df = pd.DataFrame( {'movie_title':movie_titles, 'imdb_score':scores})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. display the column imdb_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7.9\n",
       "1    7.1\n",
       "2    6.8\n",
       "Name: imdb_score, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 6.1.2\n",
    "\n",
    "df['imdb_score']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Change the columns to col1 and col2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spectre</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       col1  col2\n",
       "0                                    Avatar   7.9\n",
       "1  Pirates of the Caribbean: At World's End   7.1\n",
       "2                                   Spectre   6.8"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 6.1.3\n",
    "\n",
    "df.columns = ['col1','col2']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                       col1  col2\n",
      "0                                    Avatar   7.9\n",
      "1  Pirates of the Caribbean: At World's End   7.1\n",
      "2                                   Spectre   6.8\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<br>\n",
    "\n",
    "## Reading in Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the key functionalities of pandas is the ability to read in data from various file formats, such as CSV, Excel, SQL databases, and more.\n",
    "\n",
    "To read in data using pandas, you can use the `read_*` functions provided by the library. These functions allow you to import data from different file formats into pandas data structures, such as DataFrames and Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Uncomment and press tab..\n",
    "# pd.read_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# pd.read_csv?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "#### *Task 6.2: Load .csv Data*\n",
    "1. Load \"./data/movies.csv\" to variable called `movies` using pandas `.read_csv()`. Use the dataframe functions `.head()` and `.tail()` to take a peak at the imported data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "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>movie_title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>num_voted_users</th>\n",
       "      <th>...</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>color</th>\n",
       "      <th>duration</th>\n",
       "      <th>plot_keywords</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>facenumber_in_poster</th>\n",
       "      <th>movie_imdb_link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5041</th>\n",
       "      <td>Shanghai Calling</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10443.0</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>660</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1255</td>\n",
       "      <td>...</td>\n",
       "      <td>Eliza Coupe</td>\n",
       "      <td>489.0</td>\n",
       "      <td>2386</td>\n",
       "      <td>Color</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2.35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>http://www.imdb.com/title/tt2070597/?ref_=fn_t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5042</th>\n",
       "      <td>My Date with Drew</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>85222.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>456</td>\n",
       "      <td>6.6</td>\n",
       "      <td>4285</td>\n",
       "      <td>...</td>\n",
       "      <td>Jon Gunn</td>\n",
       "      <td>16.0</td>\n",
       "      <td>163</td>\n",
       "      <td>Color</td>\n",
       "      <td>90.0</td>\n",
       "      <td>actress name in title|crush|date|four word tit...</td>\n",
       "      <td>PG</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0378407/?ref_=fn_t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            movie_title  title_year  budget    gross                genres  \\\n",
       "5041   Shanghai Calling      2012.0     NaN  10443.0  Comedy|Drama|Romance   \n",
       "5042  My Date with Drew      2004.0  1100.0  85222.0           Documentary   \n",
       "\n",
       "     language country  movie_facebook_likes  imdb_score  num_voted_users  ...  \\\n",
       "5041  English     USA                   660         6.3             1255  ...   \n",
       "5042  English     USA                   456         6.6             4285  ...   \n",
       "\n",
       "      actor_3_name  actor_3_facebook_likes cast_total_facebook_likes  color  \\\n",
       "5041   Eliza Coupe                   489.0                      2386  Color   \n",
       "5042      Jon Gunn                    16.0                       163  Color   \n",
       "\n",
       "     duration                                      plot_keywords  \\\n",
       "5041    100.0                                                NaN   \n",
       "5042     90.0  actress name in title|crush|date|four word tit...   \n",
       "\n",
       "     content_rating  aspect_ratio facenumber_in_poster  \\\n",
       "5041          PG-13          2.35                  5.0   \n",
       "5042             PG          1.85                  0.0   \n",
       "\n",
       "                                        movie_imdb_link  \n",
       "5041  http://www.imdb.com/title/tt2070597/?ref_=fn_t...  \n",
       "5042  http://www.imdb.com/title/tt0378407/?ref_=fn_t...  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 6.2.1\n",
    "\n",
    "movies = pd.read_csv( 'data/movies.csv')\n",
    "movies.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Pull the shape dimensions and then columns it has."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5043, 28)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['movie_title', 'title_year', 'budget', 'gross', 'genres',\n",
       "       'language', 'country', 'movie_facebook_likes', 'imdb_score',\n",
       "       'num_voted_users', 'num_critic_for_reviews',\n",
       "       'num_user_for_reviews', 'director_name', 'director_facebook_likes',\n",
       "       'actor_1_name', 'actor_1_facebook_likes', 'actor_2_name',\n",
       "       'actor_2_facebook_likes', 'actor_3_name', 'actor_3_facebook_likes',\n",
       "       'cast_total_facebook_likes', 'color', 'duration', 'plot_keywords',\n",
       "       'content_rating', 'aspect_ratio', 'facenumber_in_poster',\n",
       "       'movie_imdb_link'], dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 6.2.2\n",
    "\n",
    "print( movies.shape)\n",
    "movies.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'describe()' function in pandas calculates various statistical measures of the **numrical data** and presents them in a structured output.\n",
    "\n",
    "Here's an overview of what the describe() function does:\n",
    "\n",
    "- Count: It returns the count of non-null values in each column, providing an idea of the completeness of the data.\n",
    "\n",
    "- Mean: It calculates the mean (average) of the values in each column, which represents the central tendency of the data.\n",
    "\n",
    "- Standard Deviation: It computes the standard deviation, which measures the spread or dispersion of the data around the mean.\n",
    "\n",
    "- Minimum: It gives the minimum value in each column, representing the smallest observed value.\n",
    "\n",
    "- 25th Percentile (Q1): It calculates the value below which 25% of the data falls, also known as the first quartile or lower quartile.\n",
    "\n",
    "- Median (Q2): It provides the median value, which is the middle value in a sorted dataset. It divides the data into two halves, with 50% falling below and 50% above the median.\n",
    "\n",
    "- 75th Percentile (Q3): It calculates the value below which 75% of the data falls, also known as the third quartile or upper quartile.\n",
    "\n",
    "- Maximum: It gives the maximum value in each column, representing the largest observed value.\n",
    "\n",
    "3. Run the describe function on the movies dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>title_year</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>num_voted_users</th>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>duration</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>facenumber_in_poster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4935.000000</td>\n",
       "      <td>4.551000e+03</td>\n",
       "      <td>4.159000e+03</td>\n",
       "      <td>5043.000000</td>\n",
       "      <td>5043.000000</td>\n",
       "      <td>5.043000e+03</td>\n",
       "      <td>4993.000000</td>\n",
       "      <td>5022.000000</td>\n",
       "      <td>4939.000000</td>\n",
       "      <td>5036.000000</td>\n",
       "      <td>5030.000000</td>\n",
       "      <td>5020.000000</td>\n",
       "      <td>5043.000000</td>\n",
       "      <td>5028.000000</td>\n",
       "      <td>4714.000000</td>\n",
       "      <td>5030.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2002.470517</td>\n",
       "      <td>3.975262e+07</td>\n",
       "      <td>4.846841e+07</td>\n",
       "      <td>7525.964505</td>\n",
       "      <td>6.442138</td>\n",
       "      <td>8.366816e+04</td>\n",
       "      <td>140.194272</td>\n",
       "      <td>272.770808</td>\n",
       "      <td>686.509212</td>\n",
       "      <td>6560.047061</td>\n",
       "      <td>1651.754473</td>\n",
       "      <td>645.009761</td>\n",
       "      <td>9699.063851</td>\n",
       "      <td>107.201074</td>\n",
       "      <td>2.220403</td>\n",
       "      <td>1.371173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.474599</td>\n",
       "      <td>2.061149e+08</td>\n",
       "      <td>6.845299e+07</td>\n",
       "      <td>19320.445110</td>\n",
       "      <td>1.125116</td>\n",
       "      <td>1.384853e+05</td>\n",
       "      <td>121.601675</td>\n",
       "      <td>377.982886</td>\n",
       "      <td>2813.328607</td>\n",
       "      <td>15020.759120</td>\n",
       "      <td>4042.438863</td>\n",
       "      <td>1665.041728</td>\n",
       "      <td>18163.799124</td>\n",
       "      <td>25.197441</td>\n",
       "      <td>1.385113</td>\n",
       "      <td>2.013576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1916.000000</td>\n",
       "      <td>2.180000e+02</td>\n",
       "      <td>1.620000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.180000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1999.000000</td>\n",
       "      <td>6.000000e+06</td>\n",
       "      <td>5.340988e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>8.593500e+03</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>1411.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2005.000000</td>\n",
       "      <td>2.000000e+07</td>\n",
       "      <td>2.551750e+07</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>3.435900e+04</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>988.000000</td>\n",
       "      <td>595.000000</td>\n",
       "      <td>371.500000</td>\n",
       "      <td>3090.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>2.350000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2011.000000</td>\n",
       "      <td>4.500000e+07</td>\n",
       "      <td>6.230944e+07</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>9.630900e+04</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>194.500000</td>\n",
       "      <td>11000.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>636.000000</td>\n",
       "      <td>13756.500000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>2.350000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.221550e+10</td>\n",
       "      <td>7.605058e+08</td>\n",
       "      <td>349000.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>1.689764e+06</td>\n",
       "      <td>813.000000</td>\n",
       "      <td>5060.000000</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>640000.000000</td>\n",
       "      <td>137000.000000</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>656730.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        title_year        budget         gross  movie_facebook_likes  \\\n",
       "count  4935.000000  4.551000e+03  4.159000e+03           5043.000000   \n",
       "mean   2002.470517  3.975262e+07  4.846841e+07           7525.964505   \n",
       "std      12.474599  2.061149e+08  6.845299e+07          19320.445110   \n",
       "min    1916.000000  2.180000e+02  1.620000e+02              0.000000   \n",
       "25%    1999.000000  6.000000e+06  5.340988e+06              0.000000   \n",
       "50%    2005.000000  2.000000e+07  2.551750e+07            166.000000   \n",
       "75%    2011.000000  4.500000e+07  6.230944e+07           3000.000000   \n",
       "max    2016.000000  1.221550e+10  7.605058e+08         349000.000000   \n",
       "\n",
       "        imdb_score  num_voted_users  num_critic_for_reviews  \\\n",
       "count  5043.000000     5.043000e+03             4993.000000   \n",
       "mean      6.442138     8.366816e+04              140.194272   \n",
       "std       1.125116     1.384853e+05              121.601675   \n",
       "min       1.600000     5.000000e+00                1.000000   \n",
       "25%       5.800000     8.593500e+03               50.000000   \n",
       "50%       6.600000     3.435900e+04              110.000000   \n",
       "75%       7.200000     9.630900e+04              195.000000   \n",
       "max       9.500000     1.689764e+06              813.000000   \n",
       "\n",
       "       num_user_for_reviews  director_facebook_likes  actor_1_facebook_likes  \\\n",
       "count           5022.000000              4939.000000             5036.000000   \n",
       "mean             272.770808               686.509212             6560.047061   \n",
       "std              377.982886              2813.328607            15020.759120   \n",
       "min                1.000000                 0.000000                0.000000   \n",
       "25%               65.000000                 7.000000              614.000000   \n",
       "50%              156.000000                49.000000              988.000000   \n",
       "75%              326.000000               194.500000            11000.000000   \n",
       "max             5060.000000             23000.000000           640000.000000   \n",
       "\n",
       "       actor_2_facebook_likes  actor_3_facebook_likes  \\\n",
       "count             5030.000000             5020.000000   \n",
       "mean              1651.754473              645.009761   \n",
       "std               4042.438863             1665.041728   \n",
       "min                  0.000000                0.000000   \n",
       "25%                281.000000              133.000000   \n",
       "50%                595.000000              371.500000   \n",
       "75%                918.000000              636.000000   \n",
       "max             137000.000000            23000.000000   \n",
       "\n",
       "       cast_total_facebook_likes     duration  aspect_ratio  \\\n",
       "count                5043.000000  5028.000000   4714.000000   \n",
       "mean                 9699.063851   107.201074      2.220403   \n",
       "std                 18163.799124    25.197441      1.385113   \n",
       "min                     0.000000     7.000000      1.180000   \n",
       "25%                  1411.000000    93.000000      1.850000   \n",
       "50%                  3090.000000   103.000000      2.350000   \n",
       "75%                 13756.500000   118.000000      2.350000   \n",
       "max                656730.000000   511.000000     16.000000   \n",
       "\n",
       "       facenumber_in_poster  \n",
       "count           5030.000000  \n",
       "mean               1.371173  \n",
       "std                2.013576  \n",
       "min                0.000000  \n",
       "25%                0.000000  \n",
       "50%                1.000000  \n",
       "75%                2.000000  \n",
       "max               43.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 6.2.3\n",
    "movies.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<br>\n",
    "\n",
    "## Indexing\n",
    "There are two primary indexing methods in pandas DataFrame:\n",
    "\n",
    "- `iloc`: Like we have seen before, the `.iloc` (or *integer location*) indexer is used for integer-based indexing, allowing you to access data based on the integer positions of rows and columns. It uses zero-based indexing, where the first row or column has a position of 0. You can use integer slices, lists, or boolean arrays to select specific rows or columns using iloc.\n",
    "\n",
    "- `loc`: The `.loc` (or *location*) indexer is used for label-based indexing, allowing you to access data based on the row and column labels. With loc, you can refer to rows or columns using their specific labels instead of integer positions. You can use label slices, lists, or boolean arrays to select specific rows or columns using loc.\n",
    "\n",
    "Both of these indexing methods use the same `[,]` notation we have seen before:\n",
    "\n",
    "`dataframe.iloc[row_index,column_index]`\n",
    "\n",
    "`dataframe.loc[row_name,column_name]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>Bob</td>\n",
       "      <td>30</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>35</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>David</td>\n",
       "      <td>40</td>\n",
       "      <td>Tokyo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>Eve</td>\n",
       "      <td>45</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age      City\n",
       "A    Alice   25  New York\n",
       "B      Bob   30    London\n",
       "C  Charlie   35     Paris\n",
       "D    David   40     Tokyo\n",
       "E      Eve   45    Sydney"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a DataFrame\n",
    "data = {'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],\n",
    "        'Age': [25, 30, 35, 40, 45],\n",
    "        'City': ['New York', 'London', 'Paris', 'Tokyo', 'Sydney']}\n",
    "df = pd.DataFrame(data, index=['A', 'B', 'C', 'D', 'E'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name    Charlie\n",
       "Age          35\n",
       "City      Paris\n",
       "Name: C, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using iloc to access data by integer positions\n",
    "df.iloc[2]  # Access the third row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name    Charlie\n",
       "Age          35\n",
       "City      Paris\n",
       "Name: C, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name    Charlie\n",
      "Age          35\n",
      "City      Paris\n",
      "Name: C, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Using loc to access data by label names\n",
    "print(df.loc['C'])  # Access the row with label 'C'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A    25\n",
      "B    30\n",
      "C    35\n",
      "D    40\n",
      "E    45\n",
      "Name: Age, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[:,1])  # Access the second column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A    25\n",
      "B    30\n",
      "C    35\n",
      "D    40\n",
      "E    45\n",
      "Name: Age, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df.loc[:,'Age'])  # Access the column with label 'Age'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "#### *Task 6.3: Retrieve specific subsets of data from the DataFrame*\n",
    "1. Use `iloc` to select the first three rows and the last two columns from df (above)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Age</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>25</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>30</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>35</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age      City\n",
       "A   25  New York\n",
       "B   30    London\n",
       "C   35     Paris"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 6.3.1\n",
    "# print(df)\n",
    "df.iloc[:3,-2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Use `loc` to select rows with Age greater than 30 and the 'Name' and 'City' columns from df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Name</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>David</td>\n",
       "      <td>Tokyo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>Eve</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name    City\n",
       "C  Charlie   Paris\n",
       "D    David   Tokyo\n",
       "E      Eve  Sydney"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 6.3.2\n",
    "df.loc[df['Age']>30,['Name','City']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<br>\n",
    "\n",
    "## Filtering Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets create a dataframe to filter. We will create it from the two pandas series we created at the start, strengths and names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>strength</th>\n",
       "      <th>hero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>400</td>\n",
       "      <td>Batman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>Robin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300</td>\n",
       "      <td>Spiderman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>400</td>\n",
       "      <td>Robocop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "      <td>Terminator</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strength        hero\n",
       "0       400      Batman\n",
       "1       200       Robin\n",
       "2       300   Spiderman\n",
       "3       400     Robocop\n",
       "4       500  Terminator"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes = pd.DataFrame(\n",
    "        {'strength' : strengths,\n",
    "         'hero' : names\n",
    "        })\n",
    "heroes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boolean indexing\n",
    "Like numpy, pandas supports boolean indexing\n",
    "\n",
    "Conditioning a column will create a bool mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "Name: strength, dtype: bool"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes['strength']==400"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which we can use the subselect our dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>strength</th>\n",
       "      <th>hero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>400</td>\n",
       "      <td>Batman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>400</td>\n",
       "      <td>Robocop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strength     hero\n",
       "0       400   Batman\n",
       "3       400  Robocop"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes[heroes['strength']==400]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can of course use any conditional operator we like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>strength</th>\n",
       "      <th>hero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "      <td>Terminator</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strength        hero\n",
       "4       500  Terminator"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes[heroes['strength']>400]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple conditions\n",
    "**Pandas boolean indexing allows you to filter data based on multiple conditions, but like numpy, the comparisons are performed element-wise (element-to-element). Logical combinations using `&` and `|` are used for performing the logical AND and OR operations between elements. We can not use `and` or `or`.** \n",
    "\n",
    "The full syntax is:\n",
    "\n",
    "`(condition1) & (condition2)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>strength</th>\n",
       "      <th>hero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300</td>\n",
       "      <td>Spiderman</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strength       hero\n",
       "2       300  Spiderman"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# two conditions with AND operator\n",
    "heroes[(heroes['strength'] > 200) & (heroes['strength'] < 400)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>strength</th>\n",
       "      <th>hero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>400</td>\n",
       "      <td>Batman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>Robin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>400</td>\n",
       "      <td>Robocop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "      <td>Terminator</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strength        hero\n",
       "0       400      Batman\n",
       "1       200       Robin\n",
       "3       400     Robocop\n",
       "4       500  Terminator"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# two conditions with OR operator\n",
    "heroes[(heroes['strength'] <= 200) | (heroes['strength'] >= 400)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Negation\n",
    "\n",
    "As with numpy, pandas recognizes the `~` is a negation operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "Name: strength, dtype: bool"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "~(heroes['strength'] == 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "Name: strength, dtype: bool"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes['strength'] != 400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>strength</th>\n",
       "      <th>hero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300</td>\n",
       "      <td>Spiderman</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strength       hero\n",
       "2       300  Spiderman"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes[~((heroes['strength'] <= 200) | (heroes['strength'] >= 400))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `.isin()`\n",
    "The `isin()` function is a convenient method for filtering data based on whether values from a column/row is present in a given list or other iterable. It acts like a conditional statement and returns a bool mask. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>strength</th>\n",
       "      <th>hero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>400</td>\n",
       "      <td>Batman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>Robin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strength    hero\n",
       "0       400  Batman\n",
       "1       200   Robin"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes[heroes['hero'].isin(['Batman', 'Robin'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "#### *Task 6.4: Filtering Data*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Using the movies dataframe we read in previously, find how many movies were directed by Clint Eastwood or James Cameron?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27, 28)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 6.4.1\n",
    "dir_mask = movies['director_name'].isin(['Clint Eastwood','James Cameron'])\n",
    "movies[dir_mask].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. What movies have earned above $500m?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>num_voted_users</th>\n",
       "      <th>...</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
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       "      <th>plot_keywords</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>aspect_ratio</th>\n",
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       "      <th>movie_imdb_link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>237000000.0</td>\n",
       "      <td>760505847.0</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>33000</td>\n",
       "      <td>7.9</td>\n",
       "      <td>886204</td>\n",
       "      <td>...</td>\n",
       "      <td>Wes Studi</td>\n",
       "      <td>855.0</td>\n",
       "      <td>4834</td>\n",
       "      <td>Color</td>\n",
       "      <td>178.0</td>\n",
       "      <td>avatar|future|marine|native|paraplegic</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0499549/?ref_=fn_t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>The Avengers</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>220000000.0</td>\n",
       "      <td>623279547.0</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>123000</td>\n",
       "      <td>8.1</td>\n",
       "      <td>995415</td>\n",
       "      <td>...</td>\n",
       "      <td>Scarlett Johansson</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>87697</td>\n",
       "      <td>Color</td>\n",
       "      <td>173.0</td>\n",
       "      <td>alien invasion|assassin|battle|iron man|soldier</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>1.85</td>\n",
       "      <td>3.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0848228/?ref_=fn_t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Titanic</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>200000000.0</td>\n",
       "      <td>658672302.0</td>\n",
       "      <td>Drama|Romance</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>26000</td>\n",
       "      <td>7.7</td>\n",
       "      <td>793059</td>\n",
       "      <td>...</td>\n",
       "      <td>Gloria Stuart</td>\n",
       "      <td>794.0</td>\n",
       "      <td>45223</td>\n",
       "      <td>Color</td>\n",
       "      <td>194.0</td>\n",
       "      <td>artist|love|ship|titanic|wet</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0120338/?ref_=fn_t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>150000000.0</td>\n",
       "      <td>652177271.0</td>\n",
       "      <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>150000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>418214</td>\n",
       "      <td>...</td>\n",
       "      <td>Omar Sy</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>8458</td>\n",
       "      <td>Color</td>\n",
       "      <td>124.0</td>\n",
       "      <td>dinosaur|disaster film|experiment gone wrong|j...</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0369610/?ref_=fn_t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>185000000.0</td>\n",
       "      <td>533316061.0</td>\n",
       "      <td>Action|Crime|Drama|Thriller</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>37000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1676169</td>\n",
       "      <td>...</td>\n",
       "      <td>Morgan Freeman</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>57802</td>\n",
       "      <td>Color</td>\n",
       "      <td>152.0</td>\n",
       "      <td>based on comic book|dc comics|psychopath|star ...</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0468569/?ref_=fn_t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>The Avengers</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>220000000.0</td>\n",
       "      <td>623279547.0</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>123000</td>\n",
       "      <td>8.1</td>\n",
       "      <td>995415</td>\n",
       "      <td>...</td>\n",
       "      <td>Scarlett Johansson</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>87697</td>\n",
       "      <td>Color</td>\n",
       "      <td>173.0</td>\n",
       "      <td>alien invasion|assassin|battle|iron man|soldier</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>1.85</td>\n",
       "      <td>3.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0848228/?ref_=fn_t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_title  title_year       budget        gross  \\\n",
       "0             Avatar      2009.0  237000000.0  760505847.0   \n",
       "17      The Avengers      2012.0  220000000.0  623279547.0   \n",
       "26           Titanic      1997.0  200000000.0  658672302.0   \n",
       "29    Jurassic World      2015.0  150000000.0  652177271.0   \n",
       "66   The Dark Knight      2008.0  185000000.0  533316061.0   \n",
       "794     The Avengers      2012.0  220000000.0  623279547.0   \n",
       "\n",
       "                               genres language country  movie_facebook_likes  \\\n",
       "0     Action|Adventure|Fantasy|Sci-Fi  English     USA                 33000   \n",
       "17            Action|Adventure|Sci-Fi  English     USA                123000   \n",
       "26                      Drama|Romance  English     USA                 26000   \n",
       "29   Action|Adventure|Sci-Fi|Thriller  English     USA                150000   \n",
       "66        Action|Crime|Drama|Thriller  English     USA                 37000   \n",
       "794           Action|Adventure|Sci-Fi  English     USA                123000   \n",
       "\n",
       "     imdb_score  num_voted_users  ...        actor_3_name  \\\n",
       "0           7.9           886204  ...           Wes Studi   \n",
       "17          8.1           995415  ...  Scarlett Johansson   \n",
       "26          7.7           793059  ...       Gloria Stuart   \n",
       "29          7.0           418214  ...             Omar Sy   \n",
       "66          9.0          1676169  ...      Morgan Freeman   \n",
       "794         8.1           995415  ...  Scarlett Johansson   \n",
       "\n",
       "     actor_3_facebook_likes cast_total_facebook_likes  color duration  \\\n",
       "0                     855.0                      4834  Color    178.0   \n",
       "17                  19000.0                     87697  Color    173.0   \n",
       "26                    794.0                     45223  Color    194.0   \n",
       "29                   1000.0                      8458  Color    124.0   \n",
       "66                  11000.0                     57802  Color    152.0   \n",
       "794                 19000.0                     87697  Color    173.0   \n",
       "\n",
       "                                         plot_keywords content_rating  \\\n",
       "0               avatar|future|marine|native|paraplegic          PG-13   \n",
       "17     alien invasion|assassin|battle|iron man|soldier          PG-13   \n",
       "26                        artist|love|ship|titanic|wet          PG-13   \n",
       "29   dinosaur|disaster film|experiment gone wrong|j...          PG-13   \n",
       "66   based on comic book|dc comics|psychopath|star ...          PG-13   \n",
       "794    alien invasion|assassin|battle|iron man|soldier          PG-13   \n",
       "\n",
       "     aspect_ratio facenumber_in_poster  \\\n",
       "0            1.78                  0.0   \n",
       "17           1.85                  3.0   \n",
       "26           2.35                  0.0   \n",
       "29           2.00                  0.0   \n",
       "66           2.35                  0.0   \n",
       "794          1.85                  3.0   \n",
       "\n",
       "                                       movie_imdb_link  \n",
       "0    http://www.imdb.com/title/tt0499549/?ref_=fn_t...  \n",
       "17   http://www.imdb.com/title/tt0848228/?ref_=fn_t...  \n",
       "26   http://www.imdb.com/title/tt0120338/?ref_=fn_t...  \n",
       "29   http://www.imdb.com/title/tt0369610/?ref_=fn_t...  \n",
       "66   http://www.imdb.com/title/tt0468569/?ref_=fn_t...  \n",
       "794  http://www.imdb.com/title/tt0848228/?ref_=fn_t...  \n",
       "\n",
       "[6 rows x 28 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 6.4.2\n",
    "gross_mk = movies['gross']>500_000_000\n",
    "movies[gross_mk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Are there any Polish movies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <td>Polish</td>\n",
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       "      <td>0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>12591</td>\n",
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       "      <td>2.0</td>\n",
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       "      <th>4170</th>\n",
       "      <td>Ida</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3826455.0</td>\n",
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       "      <td>46.0</td>\n",
       "      <td>205</td>\n",
       "      <td>Black and White</td>\n",
       "      <td>82.0</td>\n",
       "      <td>convent|jew|jewish|nun|poland</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://www.imdb.com/title/tt2718492/?ref_=fn_t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4213</th>\n",
       "      <td>Thr3e</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2400000.0</td>\n",
       "      <td>978908.0</td>\n",
       "      <td>Drama|Horror|Mystery|Thriller</td>\n",
       "      <td>English</td>\n",
       "      <td>Poland</td>\n",
       "      <td>250</td>\n",
       "      <td>5.1</td>\n",
       "      <td>4057</td>\n",
       "      <td>...</td>\n",
       "      <td>Justine Waddell</td>\n",
       "      <td>201.0</td>\n",
       "      <td>2306</td>\n",
       "      <td>Color</td>\n",
       "      <td>101.0</td>\n",
       "      <td>bound with duct tape|flash forward|number|ridd...</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0486028/?ref_=fn_t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 movie_title  title_year     budget      gross  \\\n",
       "2824     Dekalog                     NaN        NaN   447093.0   \n",
       "2828  The Border                     NaN        NaN        NaN   \n",
       "3207     Dekalog                     NaN        NaN   447093.0   \n",
       "4170                     Ida      2013.0        NaN  3826455.0   \n",
       "4213                   Thr3e      2006.0  2400000.0   978908.0   \n",
       "\n",
       "                             genres language country  movie_facebook_likes  \\\n",
       "2824                          Drama   Polish  Poland                     0   \n",
       "2828    Action|Crime|Drama|Thriller   Polish  Poland                    64   \n",
       "3207                          Drama   Polish  Poland                     0   \n",
       "4170                          Drama   Polish  Poland                 17000   \n",
       "4213  Drama|Horror|Mystery|Thriller  English  Poland                   250   \n",
       "\n",
       "      imdb_score  num_voted_users  ...         actor_3_name  \\\n",
       "2824         9.1            12590  ...  Olgierd Lukaszewicz   \n",
       "2828         7.4              271  ...     Jaroslaw Boberek   \n",
       "3207         9.1            12591  ...  Olgierd Lukaszewicz   \n",
       "4170         7.4            34643  ...        Agata Kulesza   \n",
       "4213         5.1             4057  ...      Justine Waddell   \n",
       "\n",
       "      actor_3_facebook_likes cast_total_facebook_likes             color  \\\n",
       "2824                     2.0                        25             Color   \n",
       "2828                     2.0                        74             Color   \n",
       "3207                     2.0                        25             Color   \n",
       "4170                    46.0                       205   Black and White   \n",
       "4213                   201.0                      2306             Color   \n",
       "\n",
       "     duration                                      plot_keywords  \\\n",
       "2824     55.0  meaning of life|moral challenge|morality|searc...   \n",
       "2828     41.0                                                NaN   \n",
       "3207     55.0  meaning of life|moral challenge|morality|searc...   \n",
       "4170     82.0                      convent|jew|jewish|nun|poland   \n",
       "4213    101.0  bound with duct tape|flash forward|number|ridd...   \n",
       "\n",
       "     content_rating  aspect_ratio facenumber_in_poster  \\\n",
       "2824          TV-MA          1.33                  0.0   \n",
       "2828            NaN           NaN                  4.0   \n",
       "3207          TV-MA          1.33                  0.0   \n",
       "4170          PG-13          1.33                  1.0   \n",
       "4213          PG-13          2.35                  0.0   \n",
       "\n",
       "                                        movie_imdb_link  \n",
       "2824  http://www.imdb.com/title/tt0092337/?ref_=fn_t...  \n",
       "2828  http://www.imdb.com/title/tt4048942/?ref_=fn_t...  \n",
       "3207  http://www.imdb.com/title/tt0092337/?ref_=fn_t...  \n",
       "4170  http://www.imdb.com/title/tt2718492/?ref_=fn_t...  \n",
       "4213  http://www.imdb.com/title/tt0486028/?ref_=fn_t...  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 6.4.3\n",
    "lang_mk = movies['language']=='Polish'\n",
    "movies[lang_mk]\n",
    "\n",
    "movies[movies['country']=='Poland']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. What are really popular great movies? (> 100k FB likes, > 8.5 IMDB score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "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>movie_title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>num_voted_users</th>\n",
       "      <th>...</th>\n",
       "      <th>actor_3_name</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>color</th>\n",
       "      <th>duration</th>\n",
       "      <th>plot_keywords</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>facenumber_in_poster</th>\n",
       "      <th>movie_imdb_link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Interstellar</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>165000000.0</td>\n",
       "      <td>187991439.0</td>\n",
       "      <td>Adventure|Drama|Sci-Fi</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>349000</td>\n",
       "      <td>8.6</td>\n",
       "      <td>928227</td>\n",
       "      <td>...</td>\n",
       "      <td>Mackenzie Foy</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>31488</td>\n",
       "      <td>Color</td>\n",
       "      <td>169.0</td>\n",
       "      <td>black hole|father daughter relationship|saving...</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0816692/?ref_=fn_t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Inception</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>160000000.0</td>\n",
       "      <td>292568851.0</td>\n",
       "      <td>Action|Adventure|Sci-Fi|Thriller</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>175000</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1468200</td>\n",
       "      <td>...</td>\n",
       "      <td>Joseph Gordon-Levitt</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>81115</td>\n",
       "      <td>Color</td>\n",
       "      <td>148.0</td>\n",
       "      <td>ambiguous ending|corporate espionage|dream|sub...</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt1375666/?ref_=fn_t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>25000000.0</td>\n",
       "      <td>28341469.0</td>\n",
       "      <td>Crime|Drama</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>108000</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1689764</td>\n",
       "      <td>...</td>\n",
       "      <td>Bob Gunton</td>\n",
       "      <td>461.0</td>\n",
       "      <td>13495</td>\n",
       "      <td>Color</td>\n",
       "      <td>142.0</td>\n",
       "      <td>escape from prison|first person narration|pris...</td>\n",
       "      <td>R</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0111161/?ref_=fn_t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   movie_title  title_year       budget        gross  \\\n",
       "96                Interstellar      2014.0  165000000.0  187991439.0   \n",
       "97                   Inception      2010.0  160000000.0  292568851.0   \n",
       "1937  The Shawshank Redemption      1994.0   25000000.0   28341469.0   \n",
       "\n",
       "                                genres language country  movie_facebook_likes  \\\n",
       "96              Adventure|Drama|Sci-Fi  English     USA                349000   \n",
       "97    Action|Adventure|Sci-Fi|Thriller  English     USA                175000   \n",
       "1937                       Crime|Drama  English     USA                108000   \n",
       "\n",
       "      imdb_score  num_voted_users  ...          actor_3_name  \\\n",
       "96           8.6           928227  ...         Mackenzie Foy   \n",
       "97           8.8          1468200  ...  Joseph Gordon-Levitt   \n",
       "1937         9.3          1689764  ...            Bob Gunton   \n",
       "\n",
       "      actor_3_facebook_likes cast_total_facebook_likes  color duration  \\\n",
       "96                    6000.0                     31488  Color    169.0   \n",
       "97                   23000.0                     81115  Color    148.0   \n",
       "1937                   461.0                     13495  Color    142.0   \n",
       "\n",
       "                                          plot_keywords content_rating  \\\n",
       "96    black hole|father daughter relationship|saving...          PG-13   \n",
       "97    ambiguous ending|corporate espionage|dream|sub...          PG-13   \n",
       "1937  escape from prison|first person narration|pris...              R   \n",
       "\n",
       "      aspect_ratio facenumber_in_poster  \\\n",
       "96            2.35                  1.0   \n",
       "97            2.35                  0.0   \n",
       "1937          1.85                  0.0   \n",
       "\n",
       "                                        movie_imdb_link  \n",
       "96    http://www.imdb.com/title/tt0816692/?ref_=fn_t...  \n",
       "97    http://www.imdb.com/title/tt1375666/?ref_=fn_t...  \n",
       "1937  http://www.imdb.com/title/tt0111161/?ref_=fn_t...  \n",
       "\n",
       "[3 rows x 28 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 6.4.4\n",
    "movies[(movies['movie_facebook_likes']>100_000) & (movies['imdb_score']>8.5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "---\n",
    "\n",
    "<br>\n",
    "\n",
    "## Exporting data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can convert the contents of the dataframe to a numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[400, 'Batman'],\n",
       "       [200, 'Robin'],\n",
       "       [300, 'Spiderman'],\n",
       "       [400, 'Robocop'],\n",
       "       [500, 'Terminator']], dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can save the dataframe as a csv or other files types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "heroes.to_csv('data/heroes.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<br>\n",
    "\n",
    "#### *Task 6.5: Saving a Dataframe*\n",
    "Create a csv with movie titles and cast (actors) of movies with budget above $200m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200000000.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "200e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Task 6.5\n",
    "# this is bit tricky in that we need to get all the actor columns\n",
    "actor_cols = []\n",
    "for ind_itr in movies.columns:\n",
    "    if 'actor' in ind_itr and 'name' in ind_itr:\n",
    "        actor_cols.append(ind_itr)\n",
    "# append the movie_titles column\n",
    "actor_cols.append('movie_title')\n",
    "\n",
    "new_df = movies.loc[movies['budget']>200e6,actor_cols]\n",
    "new_df.to_csv(\"./big_buget_movies.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<br>\n",
    "\n",
    "## **Vector operations**\n",
    "Mathematical operators work much like numpy operators, element wise. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>strength</th>\n",
       "      <th>hero</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>400</td>\n",
       "      <td>Batman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>Robin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300</td>\n",
       "      <td>Spiderman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>400</td>\n",
       "      <td>Robocop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "      <td>Terminator</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strength        hero\n",
       "0       400      Batman\n",
       "1       200       Robin\n",
       "2       300   Spiderman\n",
       "3       400     Robocop\n",
       "4       500  Terminator"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     800\n",
       "1     400\n",
       "2     600\n",
       "3     800\n",
       "4    1000\n",
       "Name: strength, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes['strength'] * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "heroes['height'] = [180, 170, 175, 190, 185]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.222222\n",
       "1    1.176471\n",
       "2    1.714286\n",
       "3    2.105263\n",
       "4    2.702703\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes['strength'] / heroes['height']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>strength</th>\n",
       "      <th>hero</th>\n",
       "      <th>height</th>\n",
       "      <th>strength_per_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>400</td>\n",
       "      <td>Batman</td>\n",
       "      <td>180</td>\n",
       "      <td>2.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>Robin</td>\n",
       "      <td>170</td>\n",
       "      <td>1.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300</td>\n",
       "      <td>Spiderman</td>\n",
       "      <td>175</td>\n",
       "      <td>1.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>400</td>\n",
       "      <td>Robocop</td>\n",
       "      <td>190</td>\n",
       "      <td>2.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "      <td>Terminator</td>\n",
       "      <td>185</td>\n",
       "      <td>2.702703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strength        hero  height  strength_per_cm\n",
       "0       400      Batman     180         2.222222\n",
       "1       200       Robin     170         1.176471\n",
       "2       300   Spiderman     175         1.714286\n",
       "3       400     Robocop     190         2.105263\n",
       "4       500  Terminator     185         2.702703"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes['strength_per_cm'] = heroes['strength'] / heroes['height']\n",
    "heroes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting and value counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400    2\n",
       "200    1\n",
       "300    1\n",
       "500    1\n",
       "Name: strength, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes['strength'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>strength</th>\n",
       "      <th>hero</th>\n",
       "      <th>height</th>\n",
       "      <th>strength_per_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>Robin</td>\n",
       "      <td>170</td>\n",
       "      <td>1.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300</td>\n",
       "      <td>Spiderman</td>\n",
       "      <td>175</td>\n",
       "      <td>1.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>400</td>\n",
       "      <td>Batman</td>\n",
       "      <td>180</td>\n",
       "      <td>2.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>400</td>\n",
       "      <td>Robocop</td>\n",
       "      <td>190</td>\n",
       "      <td>2.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "      <td>Terminator</td>\n",
       "      <td>185</td>\n",
       "      <td>2.702703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strength        hero  height  strength_per_cm\n",
       "1       200       Robin     170         1.176471\n",
       "2       300   Spiderman     175         1.714286\n",
       "0       400      Batman     180         2.222222\n",
       "3       400     Robocop     190         2.105263\n",
       "4       500  Terminator     185         2.702703"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes.sort_values('strength')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>strength</th>\n",
       "      <th>hero</th>\n",
       "      <th>height</th>\n",
       "      <th>strength_per_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "      <td>Terminator</td>\n",
       "      <td>185</td>\n",
       "      <td>2.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>400</td>\n",
       "      <td>Batman</td>\n",
       "      <td>180</td>\n",
       "      <td>2.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>400</td>\n",
       "      <td>Robocop</td>\n",
       "      <td>190</td>\n",
       "      <td>2.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300</td>\n",
       "      <td>Spiderman</td>\n",
       "      <td>175</td>\n",
       "      <td>1.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>Robin</td>\n",
       "      <td>170</td>\n",
       "      <td>1.176471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strength        hero  height  strength_per_cm\n",
       "4       500  Terminator     185         2.702703\n",
       "0       400      Batman     180         2.222222\n",
       "3       400     Robocop     190         2.105263\n",
       "2       300   Spiderman     175         1.714286\n",
       "1       200       Robin     170         1.176471"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes.sort_values(\n",
    "    ['strength','height'],\n",
    "    ascending=[False, True]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<br>\n",
    "\n",
    "#### *Task 6.5: Sorting a Dataframe*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are 10 most profitable movies? (ratio between gross and budget)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "tags": []
   },
   "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>movie_title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>num_voted_users</th>\n",
       "      <th>...</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>cast_total_facebook_likes</th>\n",
       "      <th>color</th>\n",
       "      <th>duration</th>\n",
       "      <th>plot_keywords</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>facenumber_in_poster</th>\n",
       "      <th>movie_imdb_link</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4793</th>\n",
       "      <td>Paranormal Activity</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>107917283.0</td>\n",
       "      <td>Horror</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>12000</td>\n",
       "      <td>6.3</td>\n",
       "      <td>184824</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>330</td>\n",
       "      <td>Color</td>\n",
       "      <td>84.0</td>\n",
       "      <td>dark force|entity|evil force|found footage|par...</td>\n",
       "      <td>R</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt1179904/?ref_=fn_t...</td>\n",
       "      <td>7194.485533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4799</th>\n",
       "      <td>Tarnation</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>592014.0</td>\n",
       "      <td>Biography|Documentary</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>754</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5709</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78</td>\n",
       "      <td>Color</td>\n",
       "      <td>88.0</td>\n",
       "      <td>answering machine|home movie|lithium|schizophr...</td>\n",
       "      <td>Unrated</td>\n",
       "      <td>1.37</td>\n",
       "      <td>3.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0390538/?ref_=fn_t...</td>\n",
       "      <td>2715.660550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4707</th>\n",
       "      <td>The Blair Witch Project</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>140530114.0</td>\n",
       "      <td>Horror</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>186786</td>\n",
       "      <td>...</td>\n",
       "      <td>39.0</td>\n",
       "      <td>399</td>\n",
       "      <td>Black and White</td>\n",
       "      <td>81.0</td>\n",
       "      <td>found footage|looking at the camera|lost in th...</td>\n",
       "      <td>R</td>\n",
       "      <td>1.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0185937/?ref_=fn_t...</td>\n",
       "      <td>2342.168567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4984</th>\n",
       "      <td>The Brothers McMullen</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>10246600.0</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>265</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6375</td>\n",
       "      <td>...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>388</td>\n",
       "      <td>Color</td>\n",
       "      <td>98.0</td>\n",
       "      <td>abusive father|critically acclaimed|loss of fa...</td>\n",
       "      <td>R</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0112585/?ref_=fn_t...</td>\n",
       "      <td>409.864000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4936</th>\n",
       "      <td>The Texas Chain Saw Massacre</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>83532.0</td>\n",
       "      <td>30859000.0</td>\n",
       "      <td>Horror|Thriller</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>96411</td>\n",
       "      <td>...</td>\n",
       "      <td>177.0</td>\n",
       "      <td>1094</td>\n",
       "      <td>Color</td>\n",
       "      <td>88.0</td>\n",
       "      <td>cannibal|chainsaw|hitchhiker|independent film|...</td>\n",
       "      <td>R</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0072271/?ref_=fn_t...</td>\n",
       "      <td>369.427285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3278</th>\n",
       "      <td>The Texas Chain Saw Massacre</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>83532.0</td>\n",
       "      <td>30859000.0</td>\n",
       "      <td>Horror|Thriller</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>96410</td>\n",
       "      <td>...</td>\n",
       "      <td>177.0</td>\n",
       "      <td>1094</td>\n",
       "      <td>Color</td>\n",
       "      <td>88.0</td>\n",
       "      <td>cannibal|chainsaw|hitchhiker|independent film|...</td>\n",
       "      <td>R</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0072271/?ref_=fn_t...</td>\n",
       "      <td>369.427285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5035</th>\n",
       "      <td>El Mariachi</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>2040920.0</td>\n",
       "      <td>Action|Crime|Drama|Romance|Thriller</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>52055</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>147</td>\n",
       "      <td>Color</td>\n",
       "      <td>81.0</td>\n",
       "      <td>assassin|death|guitar|gun|mariachi</td>\n",
       "      <td>R</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0104815/?ref_=fn_t...</td>\n",
       "      <td>291.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4956</th>\n",
       "      <td>The Gallows</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>22757819.0</td>\n",
       "      <td>Horror|Thriller</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>13521</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>276</td>\n",
       "      <td>Color</td>\n",
       "      <td>81.0</td>\n",
       "      <td>breaking and entering|gallows|hanging|high sch...</td>\n",
       "      <td>R</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt2309260/?ref_=fn_t...</td>\n",
       "      <td>227.578190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4977</th>\n",
       "      <td>Super Size Me</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>11529368.0</td>\n",
       "      <td>Comedy|Documentary|Drama</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>85028</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Color</td>\n",
       "      <td>100.0</td>\n",
       "      <td>experiment|fast food|food industry|meal|overea...</td>\n",
       "      <td>PG</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0390521/?ref_=fn_t...</td>\n",
       "      <td>177.374892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2619</th>\n",
       "      <td>Halloween</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>47000000.0</td>\n",
       "      <td>Horror|Thriller</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>12000</td>\n",
       "      <td>7.9</td>\n",
       "      <td>157857</td>\n",
       "      <td>...</td>\n",
       "      <td>598.0</td>\n",
       "      <td>4400</td>\n",
       "      <td>Color</td>\n",
       "      <td>101.0</td>\n",
       "      <td>halloween|masked killer|michael myers|slasher|...</td>\n",
       "      <td>R</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.0</td>\n",
       "      <td>http://www.imdb.com/title/tt0077651/?ref_=fn_t...</td>\n",
       "      <td>156.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       movie_title  title_year    budget        gross  \\\n",
       "4793           Paranormal Activity      2007.0   15000.0  107917283.0   \n",
       "4799                     Tarnation      2003.0     218.0     592014.0   \n",
       "4707       The Blair Witch Project      1999.0   60000.0  140530114.0   \n",
       "4984         The Brothers McMullen      1995.0   25000.0   10246600.0   \n",
       "4936  The Texas Chain Saw Massacre      1974.0   83532.0   30859000.0   \n",
       "3278  The Texas Chain Saw Massacre      1974.0   83532.0   30859000.0   \n",
       "5035                   El Mariachi      1992.0    7000.0    2040920.0   \n",
       "4956                   The Gallows      2015.0  100000.0   22757819.0   \n",
       "4977                 Super Size Me      2004.0   65000.0   11529368.0   \n",
       "2619                     Halloween      1978.0  300000.0   47000000.0   \n",
       "\n",
       "                                   genres language country  \\\n",
       "4793                               Horror  English     USA   \n",
       "4799                Biography|Documentary  English     USA   \n",
       "4707                               Horror  English     USA   \n",
       "4984                 Comedy|Drama|Romance  English     USA   \n",
       "4936                      Horror|Thriller  English     USA   \n",
       "3278                      Horror|Thriller  English     USA   \n",
       "5035  Action|Crime|Drama|Romance|Thriller  Spanish     USA   \n",
       "4956                      Horror|Thriller  English     USA   \n",
       "4977             Comedy|Documentary|Drama  English     USA   \n",
       "2619                      Horror|Thriller  English     USA   \n",
       "\n",
       "      movie_facebook_likes  imdb_score  num_voted_users  ...  \\\n",
       "4793                 12000         6.3           184824  ...   \n",
       "4799                   754         7.2             5709  ...   \n",
       "4707                     0         6.4           186786  ...   \n",
       "4984                   265         6.6             6375  ...   \n",
       "4936                     0         7.5            96411  ...   \n",
       "3278                     0         7.5            96410  ...   \n",
       "5035                     0         6.9            52055  ...   \n",
       "4956                     0         4.2            13521  ...   \n",
       "4977                     0         7.3            85028  ...   \n",
       "2619                 12000         7.9           157857  ...   \n",
       "\n",
       "      actor_3_facebook_likes  cast_total_facebook_likes             color  \\\n",
       "4793                    21.0                        330             Color   \n",
       "4799                     0.0                         78             Color   \n",
       "4707                    39.0                        399   Black and White   \n",
       "4984                    73.0                        388             Color   \n",
       "4936                   177.0                       1094             Color   \n",
       "3278                   177.0                       1094             Color   \n",
       "5035                     6.0                        147             Color   \n",
       "4956                     7.0                        276             Color   \n",
       "4977                     0.0                          0             Color   \n",
       "2619                   598.0                       4400             Color   \n",
       "\n",
       "      duration                                      plot_keywords  \\\n",
       "4793      84.0  dark force|entity|evil force|found footage|par...   \n",
       "4799      88.0  answering machine|home movie|lithium|schizophr...   \n",
       "4707      81.0  found footage|looking at the camera|lost in th...   \n",
       "4984      98.0  abusive father|critically acclaimed|loss of fa...   \n",
       "4936      88.0  cannibal|chainsaw|hitchhiker|independent film|...   \n",
       "3278      88.0  cannibal|chainsaw|hitchhiker|independent film|...   \n",
       "5035      81.0                 assassin|death|guitar|gun|mariachi   \n",
       "4956      81.0  breaking and entering|gallows|hanging|high sch...   \n",
       "4977     100.0  experiment|fast food|food industry|meal|overea...   \n",
       "2619     101.0  halloween|masked killer|michael myers|slasher|...   \n",
       "\n",
       "      content_rating aspect_ratio  facenumber_in_poster  \\\n",
       "4793               R         1.85                   0.0   \n",
       "4799         Unrated         1.37                   3.0   \n",
       "4707               R         1.33                   0.0   \n",
       "4984               R         1.85                   0.0   \n",
       "4936               R         1.85                   0.0   \n",
       "3278               R         1.85                   0.0   \n",
       "5035               R         1.37                   0.0   \n",
       "4956               R         1.85                   0.0   \n",
       "4977              PG         1.78                   2.0   \n",
       "2619               R         2.35                   0.0   \n",
       "\n",
       "                                        movie_imdb_link       profit  \n",
       "4793  http://www.imdb.com/title/tt1179904/?ref_=fn_t...  7194.485533  \n",
       "4799  http://www.imdb.com/title/tt0390538/?ref_=fn_t...  2715.660550  \n",
       "4707  http://www.imdb.com/title/tt0185937/?ref_=fn_t...  2342.168567  \n",
       "4984  http://www.imdb.com/title/tt0112585/?ref_=fn_t...   409.864000  \n",
       "4936  http://www.imdb.com/title/tt0072271/?ref_=fn_t...   369.427285  \n",
       "3278  http://www.imdb.com/title/tt0072271/?ref_=fn_t...   369.427285  \n",
       "5035  http://www.imdb.com/title/tt0104815/?ref_=fn_t...   291.560000  \n",
       "4956  http://www.imdb.com/title/tt2309260/?ref_=fn_t...   227.578190  \n",
       "4977  http://www.imdb.com/title/tt0390521/?ref_=fn_t...   177.374892  \n",
       "2619  http://www.imdb.com/title/tt0077651/?ref_=fn_t...   156.666667  \n",
       "\n",
       "[10 rows x 29 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 6.5\n",
    "\n",
    "movies['profit'] = movies['gross'] / movies['budget']\n",
    "movies.sort_values('profit', ascending=False).iloc[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "---\n",
    "# A quick glance at visualizing and grouping data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>strength</th>\n",
       "      <th>hero</th>\n",
       "      <th>height</th>\n",
       "      <th>strength_per_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>400</td>\n",
       "      <td>Batman</td>\n",
       "      <td>180</td>\n",
       "      <td>2.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>Robin</td>\n",
       "      <td>170</td>\n",
       "      <td>1.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300</td>\n",
       "      <td>Spiderman</td>\n",
       "      <td>175</td>\n",
       "      <td>1.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>400</td>\n",
       "      <td>Robocop</td>\n",
       "      <td>190</td>\n",
       "      <td>2.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "      <td>Terminator</td>\n",
       "      <td>185</td>\n",
       "      <td>2.702703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strength        hero  height  strength_per_cm\n",
       "0       400      Batman     180         2.222222\n",
       "1       200       Robin     170         1.176471\n",
       "2       300   Spiderman     175         1.714286\n",
       "3       400     Robocop     190         2.105263\n",
       "4       500  Terminator     185         2.702703"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series([1, 2, 3]).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series([1, 2, 3], index=['Batman', 'Robin', 'Rambo']).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series([1, 2, 3], index=['Batman', 'Robin', 'Rambo']).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series([1, 2, 3], index=['Batman', 'Robin', 'Rambo']).plot(\n",
    "    kind='bar',\n",
    "    figsize=(15, 6)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='None'>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series([1, 2, 3], index=['Batman', 'Robin', 'Rambo']).plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>strength</th>\n",
       "      <th>hero</th>\n",
       "      <th>height</th>\n",
       "      <th>strength_per_cm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>400</td>\n",
       "      <td>Batman</td>\n",
       "      <td>180</td>\n",
       "      <td>2.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>Robin</td>\n",
       "      <td>170</td>\n",
       "      <td>1.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300</td>\n",
       "      <td>Spiderman</td>\n",
       "      <td>175</td>\n",
       "      <td>1.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>400</td>\n",
       "      <td>Robocop</td>\n",
       "      <td>190</td>\n",
       "      <td>2.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "      <td>Terminator</td>\n",
       "      <td>185</td>\n",
       "      <td>2.702703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strength        hero  height  strength_per_cm\n",
       "0       400      Batman     180         2.222222\n",
       "1       200       Robin     170         1.176471\n",
       "2       300   Spiderman     175         1.714286\n",
       "3       400     Robocop     190         2.105263\n",
       "4       500  Terminator     185         2.702703"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heroes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heroes.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>strength</th>\n",
       "      <th>height</th>\n",
       "      <th>strength_per_cm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hero</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Batman</th>\n",
       "      <td>400</td>\n",
       "      <td>180</td>\n",
       "      <td>2.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robin</th>\n",
       "      <td>200</td>\n",
       "      <td>170</td>\n",
       "      <td>1.176471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spiderman</th>\n",
       "      <td>300</td>\n",
       "      <td>175</td>\n",
       "      <td>1.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robocop</th>\n",
       "      <td>400</td>\n",
       "      <td>190</td>\n",
       "      <td>2.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terminator</th>\n",
       "      <td>500</td>\n",
       "      <td>185</td>\n",
       "      <td>2.702703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            strength  height  strength_per_cm\n",
       "hero                                         \n",
       "Batman           400     180         2.222222\n",
       "Robin            200     170         1.176471\n",
       "Spiderman        300     175         1.714286\n",
       "Robocop          400     190         2.105263\n",
       "Terminator       500     185         2.702703"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexed_heroes = heroes.set_index('hero')\n",
    "indexed_heroes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='hero'>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "indexed_heroes.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='hero'>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "indexed_heroes.plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "indexed_heroes.plot(kind='bar', subplots=True, figsize=(15, 15));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='hero'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zaeAJYEpXxx/e0GrVsfO27ufzvdi/55OkZvDvTg1cwyPi/rr12Zk5e4NtLgLOBLasa9sxM5cDZObyiNihat8FuLtuu6VVW6e67YGLiMsjYmVELK5rOy8ifh0RC6ufD9S9t1FjuJIkSYVpy8zJdT/rhbeIOAJYmZkLeni86KAtu9qhJz1wVwD/Anxrg/avZeYF6519/THcnYGbI2KPzFzbg/NIkiQNBgcCH6o6uEYCW0XEt4EVEbFT1fu2E7Cy2n4psGvd/mOAZV2doNseuMy8DXiuhwVv9BiuJEnSYJKZZ2XmmMzcjVrH1k8y8wTgemB6tdl04Lpq+XrguIjYLCLGAuOAe7s6R19uYjg9Ih6shljbb4PdBXi2bptux3AlSZKGiFnAoRHxOHBotU5mPgxcDTwC3Aic1t3oZW8D3L8CbwUmAcuBr1btPR7DjYgZEXF/RNzf1tbWyzIkSZIGrsz8aWYeUS2vysypmTmuen2ubrvzM/OtmflHmfkf3R23VwEuM1dk5trM/D3wTV4fJu3xGG5mzm6/+G/4cG+GlSRJ6qleBbjqwrt2RwPtd6hu9BiuJEmSNk63XV8RcSVwMPCmiFgKnAscHBGTqA2PLgE+BbUx3IhoH8NtowdjuJIkSdo43Qa4zDy+g+bLutj+fOD8vhQlSZKkzjmVliRJUmEMcJIkSYUxwEmSJBXGACdJklQYA5wkSVJhDHCSJEmFMcBJkiQVxgAnSZJUGAOcJElSYQxwkiRJhTHASZIkFcYAJ0mSVBgDnCRJUmEMcJIkSYUxwEmSJBXGACdJklQYA5wkSVJhDHCSJEmFMcBJkiQVxgAnSZJUGAOcJElSYQxwkiRJhTHASZIkFcYAJ0mSVBgDnCRJUmEMcJIkSYUxwEmSJBXGACdJklQYA5wkSVJhDHCSJEmFMcBJkiQVxgAnSZJUGAOcJElSYQxwkiRJhTHASZIkFcYAJ0mSVBgDnCRJUmEMcJIkSYUxwEmSJBXGACdJklQYA5wkSVJhDHCSJEmFMcBJkiQVxgAnSZJUGAOcJElSYQxwkiRJhek2wEXE5RGxMiIW17X9Y0Q8FhEPRsT3I2Kbqn23iFgdEQurn0uaWLskSdKQ1JMeuCuAaRu03QRMyMx9gF8CZ9W992RmTqp+Pt2YMiVJktSu2wCXmbcBz23Q9uPMbKtW7wbGNKE2SZIkdaAR18CdDPxH3frYiPh5RNwaEQd1tlNEzIiI+yPi/ra2ts42kyRJ0gaG92XniPg80AbMrZqWA2/JzFURsS/wg4jYKzN/u+G+mTkbmA0wevTo7EsdkiRJQ0mve+AiYjpwBPCJzEyAzFyTmauq5QXAk8AejShUkiRJNb0KcBExDfgc8KHMfKWu/c0RMaxa3h0YBzzViEIlSZJU0+0QakRcCRwMvCkilgLnUrvrdDPgpogAuLu64/RdwBcjog1YC3w6M5/r8MCSJEnqlW4DXGYe30HzZZ1sey1wbV+LkiRJUueciUGSJKkwBjhJkqTCGOAkSZIKY4CTJEkqjAFOkiSpMAY4SZKkwhjgJEmSCmOAkyRJKowBTpIkqTAGOEmSpMIY4CRJkgpjgJMkSSqMAU6SJKkwBjhJkqTCGOAkSZIKY4CTJElqoIgYGRH3RsSiiHg4Ir5QtW8XETdFxOPV67Z1+5wVEU9ExC8i4n3dncMAJ0mS1FhrgPdk5kRgEjAtIvYHZgLzM3McML9aJyLGA8cBewHTgIsjYlhXJzDASZIkNVDW/K5aHVH9JHAkMKdqnwMcVS0fCVyVmWsy82ngCWBKV+cY3uiiJWnAOG/rfj7fi/17PkmtMjwi7q9bn52Zs+s3qHrQFgD/A/hGZt4TETtm5nKAzFweETtUm+8C3F23+9KqrfMC+vonkCRJGmLaMnNyVxtk5lpgUkRsA3w/IiZ0sXl0dIiuju8QqiRJUpNk5gvAT6ld27YiInYCqF5XVpstBXat220MsKyr4xrgJEmSGigi3lz1vBERo4D3Ao8B1wPTq82mA9dVy9cDx0XEZhExFhgH3NvVORxClSRJaqydgDnVdXCbAFdn5g0RcRdwdUScAjwDHAOQmQ9HxNXAI0AbcFo1BNspA5wkSVIDZeaDwNs6aF8FTO1kn/OB83t6DodQJUmSCmOAkyRJKowBTpIkqTAGOEmSpMIY4CRJkgpjgJMkSSqMAU6SJKkwBjhJkqTCGOAkSZIKY4CTJEkqjAFOkiSpMAY4SZKkwhjgJEmSCmOAkyRJKowBTpIkqTAGOEmSpMIY4CRJkgpjgJMkSSqMAU6SJKkwBjhJkqTCGOAkSZIK022Ai4jLI2JlRCyua9suIm6KiMer123r3jsrIp6IiF9ExPuaVbgkSdJQ1ZMeuCuAaRu0zQTmZ+Y4YH61TkSMB44D9qr2uTgihjWsWkmSJHUf4DLzNuC5DZqPBOZUy3OAo+rar8rMNZn5NPAEMKUxpUqSJAl6fw3cjpm5HKB63aFq3wV4tm67pVWbJEmSGmR4g48XHbRlhxtGzABmAGy66aYNLkOSJGnw6m0P3IqI2Amgel1ZtS8Fdq3bbgywrKMDZObszJycmZOHD290jpQkSRq8ehvgrgemV8vTgevq2o+LiM0iYiwwDri3byVKkiSpXrddXxFxJXAw8KaIWAqcC8wCro6IU4BngGMAMvPhiLgaeARoA07LzLVNql2SJGlI6jbAZebxnbw1tZPtzwfO70tRkiRJ6pwzMUiSJBXGACdJklQYA5wkSVJhDHCSJEmFMcBJkiQVxgAnSZJUGAOcJElSYQxwkiRJhTHASZIkFcYAJ0mSVBgDnCRJUmEMcJIkSYUxwEmSJBXGACdJklQYA5wkSVJhDHCSJEmFMcBJkiQVxgAnSZJUGAOcJElSYQxwkiRJhTHASZIkFcYAJ0mSVBgDnCRJUmEMcJIkSYUxwEmSJBXGACdJklQYA5wkSVJhDHCSJEmFMcBJkiQVxgAnSZJUGAOcJElSYQxwkiRJhTHASZIkFcYAJ0mSVBgDnCRJUmEMcJIkSYUxwEmSJBXGACdJklQYA5wkSVJhDHCSJEmFMcBJkiQVxgAnSZJUGAOcJElSYQxwkiRJDRQRu0bELRHxaEQ8HBFnVO3bRcRNEfF49bpt3T5nRcQTEfGLiHhfd+cwwEmSJDVWG/BXmbknsD9wWkSMB2YC8zNzHDC/Wqd67zhgL2AacHFEDOvqBAY4SZKkBsrM5Zn5QLX8EvAosAtwJDCn2mwOcFS1fCRwVWauycyngSeAKV2dY3hvi4uIPwLm1TXtDpwDbAOcCvymaj87M3/U2/NIkiSVKiJ2A94G3APsmJnLoRbyImKHarNdgLvrdltatXWq1wEuM38BTKqKGwb8Gvg+8Enga5l5QW+PLQ0o523dz+d7sX/PJ0naWMMj4v669dmZOXvDjSJiC+Ba4C8y87cR0dnxOnojuyygp5V2YyrwZGb+qoviJEmSBoO2zJzc1QYRMYJaeJubmd+rmldExE5V79tOwMqqfSmwa93uY4BlXR2/UdfAHQdcWbd+ekQ8GBGX199hUS8iZkTE/RFxf1tbW4PKkCRJaq2o9WZdBjyamRfWvXU9ML1ang5cV9d+XERsFhFjgXHAvV2do88BLiI2BT4EfLdq+lfgrdSGV5cDX+1ov8ycnZmTM3Py8OGN6giUJElquQOBE4H3RMTC6ucDwCzg0Ih4HDi0WiczHwauBh4BbgROy8y1XZ2gEcnp/cADmbmiKmJF+xsR8U3ghgacQ5IkqQiZeQcdX9cGtcvOOtrnfOD8np6jEUOox1M3fFqN6bY7GljcgHNIkiSp0qceuIjYnFoX4Kfqmv8hIiZRu3tiyQbvSZIkqY/6FOAy8xVg+w3aTuxTRZIkSeqSMzFIkiQVxgAnSZJUGAOcJElSYQxwkiRJhTHASZIkFcYAJ0mSVBgDnCRJUmEMcJIkSYUxwEmSJBXGACdJklQYA5wkSVJhDHCSJEmFMcBJkiQVxgAnSZJUGAOcJElSYQxwkiRJhTHASZIkFcYAJ0mSVBgDnCRJUmEMcJIkSYUxwEmSJBXGACdJklQYA5wkSVJhDHCSJEmFMcBJkiQVxgAnSZJUGAOcJElSYQxwkiRJhTHASZIkFcYAJ0mSVBgDnCRJUmEMcJIkSYUxwEmSJBXGACdJklQYA5wkSVJhDHCSJEmFMcBJkiQVxgAnSZJUGAOcJElSYQxwkiRJhTHASZIkFcYAJ0mSVBgDnCRJUmEMcJIkSYUxwEmSJBVmeF92joglwEvAWqAtMydHxHbAPGA3YAnwscx8vm9lSpIkqV0jeuAOycxJmTm5Wp8JzM/MccD8al2SJEkN0owh1COBOdXyHOCoJpxDkiRpyOprgEvgxxGxICJmVG07ZuZygOp1hz6eQ5IkSXX6dA0ccGBmLouIHYCbIuKxnu5YBb4ZAJtuumkfy5AkSRo6+tQDl5nLqteVwPeBKcCKiNgJoHpd2cm+szNzcmZOHj68rzlSkiRp6Oh1gIuI0RGxZfsycBiwGLgemF5tNh24rq9FSpIk6XV96fraEfh+RLQf5zuZeWNE3AdcHRGnAM8Ax/S9TEmSJLXrdYDLzKeAiR20rwKm9qUoSZIkdc6ZGCRJkgpjgJMkSSqMAU6SJKkwBjhJkqTCGOAkSZIKY4CTJEkqjAFOkiSpMAY4SZKkwhjgJEmSCmOAkyRJKowBTpIkqTAGOEmSpMIY4CRJkgpjgJMkSSqMAU6SJKkwBjhJkqTCGOAkSZIKY4CTJElqoIi4PCJWRsTiurbtIuKmiHi8et227r2zIuKJiPhFRLyvJ+cwwEmSJDXWFcC0DdpmAvMzcxwwv1onIsYDxwF7VftcHBHDujuBAU6SJKmBMvM24LkNmo8E5lTLc4Cj6tqvysw1mfk08AQwpbtzGOAkSZKab8fMXA5Qve5Qte8CPFu33dKqrUvDG16eJEnS4DY8Iu6vW5+dmbN7eazooC27LaCXJ5MkSRqq2jJz8kbusyIidsrM5RGxE7Cyal8K7Fq33RhgWXcHcwhVkiSp+a4HplfL04Hr6tqPi4jNImIsMA64t7uD2QMnSZLUQBFxJXAw8KaIWAqcC8wCro6IU4BngGMAMvPhiLgaeARoA07LzLXdncMAJ0mS1ECZeXwnb03tZPvzgfM35hwOoUqSJBXGACdJklQYA5wkSVJhDHCSJEmFMcBJkiQVxgAnSZJUGAOcJElSYQxwkiRJhTHASZIkFcYAJ0mSVBgDnCRJUmEMcJIkSYUxwEmSJBXGACdJklQYA5wkSVJhDHCSJEmFMcBJkiQVxgAnSZJUGAOcJElSYQxwkiRJhel1gIuIXSPiloh4NCIejogzqvbzIuLXEbGw+vlA48qVJEnS8D7s2wb8VWY+EBFbAgsi4qbqva9l5gV9L0+SJEkb6nWAy8zlwPJq+aWIeBTYpVGFSZIkqWMNuQYuInYD3gbcUzWdHhEPRsTlEbFtJ/vMiIj7I+L+tra2RpQhSZI0JPQ5wEXEFsC1wF9k5m+BfwXeCkyi1kP31Y72y8zZmTk5MycPH96XkVxJkqShpU8BLiJGUAtvczPzewCZuSIz12bm74FvAlP6XqYkSZLa9eUu1AAuAx7NzAvr2neq2+xoYHHvy5MkSdKG+jJ2eSBwIvBQRCys2s4Gjo+ISUACS4BP9eEckiRJ2kBf7kK9A4gO3vpR78uRJElSd5yJQZIkqTAGOEmSpMIY4CRJkgpjgJMkSSqMAU6SJKkwBjhJkqTCGOAkSZIKY4CTJEkqjAFOkiSpMAY4SZKkwhjgJEmSCmOAkyRJKowBTpIkqTAGOEmSpMIY4CRJkgpjgJMkSSqMAU6SJKkwBjhJkqTCGOAkSZIKY4CTJEkqjAFOkiSpMAY4SZKkwhjgJEmSCmOAkyRJKowBTpIkqTAGOEmSpMIY4CRJkgpjgJMkSSqMAU6SJKkwBjhJkqTCGOAkSZIKY4CTJEkqjAFOkiSpMAY4SZKkwhjgJEmSCmOAkyRJKowBTpIkqTAGOEmSpMIY4CRJkgpjgJMkSSqMAU6SJKkwBjhJkqTCGOAkSZIKY4CTJEkqjAFOkiSpME0LcBExLSJ+ERFPRMTMZp1HkiRpoGl2DmpKgIuIYcA3gPcD44HjI2J8M84lSZI0kPRHDmpWD9wU4InMfCoz/xu4CjiySeeSJEkaSJqeg5oV4HYBnq1bX1q1SZIkDXZNz0HDG3mwOtFBW663QcQMYEb7exGxukm1tFzU/ju39dsJv9DRf371lp9fufzsyubnV7Z+/fz6/7MbFRH3163PzszZdevd5qC+alaAWwrsWrc+BlhWv0H1B63/ww5aEXF/Zk5udR3qHT+/cvnZlc3Pr2xD/PPrNgf1VbOGUO8DxkXE2IjYFDgOuL5J55IkSRpImp6DmtIDl5ltEXE68J/AMODyzHy4GeeSJEkaSPojBzVrCJXM/BHwo2YdvzBDYqh4EPPzK5efXdn8/Mo2pD+/ZuegyGzoNXWSJElqMqfSkiRJKowBTpIk9VpEbBIRH2t1HUONAU6SJPVaZv4eOL3VdQw1XgPXRNVcaDtSd7NIZj7TuoqkocHvXrkiYnvgPOBAag8+vQP4YmauamVd6lpE/C2wGpgHvNzenpnPtayoQc4A1yQR8efAucAK4PdVc2bmPq2rSj0VER8G/h7YgdoTtYPa57dVSwtTt/zulS0ibgJuA75dNX0CODgz39u6qtSdiHi6g+bMzN37vZghwgDXJBHxBPB2f2ssU/X5fTAzH211Ldo4fvfKFhELMnPfDdqG8hP9pQ55DVzzPAu82Ooi1GsrDG/F8rtXtlsi4rjqwvj2i+N/2Oqi1LWIGBERn4mIa6qf0yNiRKvrGszsgWuSiLgM+CNqf/GsaW/PzAtbVpR6LCL+CfgD4Aes//l9r1U1qWf87pUtIl4CRvP68PcmvH5NlZcxDFARcSkwAphTNZ0IrM3MP2ldVYNb02ZiEM9UP5tWPyrLVsArwGF1bQkY4AY+v3sFy8wtW12DemW/zJxYt/6TiFjUsmqGAHvgJEkDSkR8CHhXtfrTzLyhlfWoexHxAHBMZj5Zre8OXJOZf9zaygYve+CaJCLeDJwJ7AWMbG/PzPe0rCh1KyLOzMx/iIivU+txW09mfqYFZWkj+N0rW0TMAvYD5lZNZ0TEOzNzZgvLUvf+mtr1i09Ru2v/D4GTW1vS4GaAa5651J6HcwTwaWA68JuWVqSeaL9x4f6WVqG+8LtXtg8Ak6qHwxIRc4CfAwa4ge0OYBy1608DeKy15Qx+DqE2Sfut8BHxYPvzpyLi1sx8d6trU89FxFbULpx+qdW1qGf87pUtIh6k9ty356r17agNo/ocvwEsIh7YcLi0ozY1jj1wzfNa9bo8Ig4HlgFjWliPNkJETAb+L7BlbTVeAE7OzAUtLUw94XevbF8Bfh4Rt1DryXkXcFZrS1JnIuIPgF2AURHxNmqfGdRuBNu8ZYUNAfbANUlEHAHcDuwKfJ3a/8xfyMzrW1qYeqTqBTgtM2+v1t8JXGwvwMDnd698EbETtevgArgnM/9fi0tSJyJiOnASMJn1Lz15CbjCRy81jwFO6kBE3JmZB3bXJqmxIuJo4CeZ+WK1vg21IdUftLIudS0iPpKZ17a6jqHEANckETEW+HNgN9afUPtDrapJ3YuI9us1TqTW/X8ltbtRjwWez8zPt6o29YzfvbJFxMLMnLRB288z820tKkk9VF2ysOHd319sXUWDm9fANc8PgMuAf+f1J4pr4PvqBuvn1i37204ZfoDfvZJ1NMWj/1YNcBFxCbVfeg8BLgU+Ctzb0qIGOXvgmiQi7snMt7e6Dmmo8btXtoi4HHgB+Aa1X5r+HNg2M09qYVnqRvtd33WvWwDfy8zDut1ZvWKAa5KI+Di1Z+L8mPXnY3ygZUWpxyJia2q9b+1Pg78V+GL7dTkauPzulS0iRgN/C7yX2k0MPwb+LjNf7nJHtVT7L04RcTfwYWAVsDgzx7W4tEHLbunm2ZvadVTv4fVhnKzWNfBdDiwGPlatn0jtsSIfbllF6im/ewWrgtrM6hmMv8/M37W6JvXIDdUNJ/8IPEDtO3dpSysa5OyBa5KIeAzYJzP/u9W1aON1ciH1G9o08PjdK1tE7A18C9iuavovYHpmLm5dVdoYEbEZMNIRi+ayB655FgHbACtbXId6Z3U1/+IdABFxILC6xTWpZ/zule3/AP87M28BiIiDgdnAAS2sST0QEQdQd/d3RJCZ32ppUYOYAa55dgQei4j7WP86HB9lUIY/BeZU18IF8By1OTU18PndK9vo9vAGkJk/ra6L0wAWEf8GvBVYCKytmpNab6qawADXPOd2v4kGqsxcCEysrsMBeIXas+AebFlR6im/e2V7KiL+Fvi3av0E4OkW1qOemQyMT6/L6jcGuOb5QGZ+rr4hIv6e2t2MGqCqwHYatbn9rgNurtY/S21obm7rqlN3ImIT4BuZOaHVtajXTga+AHyPWu/3bcAnW1qRemIx8AfA8lYXMlR4E0OTRMQDmfnHG7Q96FyaA1tEXAc8D9wFTAW2BTYFzqh65TTARcRc4KzMfKbVtaj3qssXfp+ZL7W6FnUvIm4BJlF7eK+XLvQDe+AaLCL+FPgzYPdqQvR2WwI/a01V2gi7Z+beABFxKbU74N7iPyJF2Ql4OCLuBdY9O8x/SMoQEftRe4zPltX6i8DJmbmgpYWpO+e1uoChxh64Bqt+a9wW+Aows+6tlzLzudZUpZ7asOe0o55UDWwR8e6O2jPTyxcKUP3ie1pm3l6tvxO42NELaX0GuCaLiB1Yf2Jfh3UGsIhYy+u9NgGMonYDQwCZmVt1tq8Gjoj4Q2BcZt4cEZsDw+xFLUNE3JmZB3bXpoEhIu7IzHdGxEusP1+0f2c2mQGuSSLig8CFwM7Unkf1h8CjmblXSwuTBrmIOBWYAWyXmW+NiHHAJZk5tcWlqQsR0d7TfSK1SdGvpBYIjgWez8zPt6o2aSAywDVJRCyiNnXPzZn5tog4BDg+M2e0uDRpUIuIhcAU4J7MfFvV9lD7tY0amKqL4DuTmelUaANcRGwL7Erd9fXOQdw83sTQPK9l5qqI2CQiNsnMW6rHiEhqrjWZ+d8RAUBEDGf9oR0NQJl5SKtrUO9FxJeAk4CncA7ifmGAa54XImILas8wmhsRK4G2FtckDQW3RsTZwKiIOJTaXeH/3uKa1EPVjWDnAu+qmm4Fvui8mgPex4C3Ogdx/3EItUmqqV9WA5sAnwC2BuZm5qqWFiYNctXDfE8BDqN2IfV/Apf6hPgyRMS11B4KO6dqOhGYmJkfbl1V6k71uf1pZjoHcT8xwPWDiHgTsMp/QCSpaxGxMDMnddemgSUiJlObvWYxPsi3XziE2mARsT8wi9rk51+iNp/fm4BNIuJ/ZeaNraxPGqwi4iG6uNbN54gVY3VEvDMz7wCIiAOpjWZoYJsD/D3wEK9fA6cmMsA13r8AZ1MbMv0J8P7MvDsi/ie12+INcFJzHFG9nla9tk+G/glqz/JTGf4UmFNdCxfUfhme3tqS1AP/lZn/3OoihhKHUBusvqs/Ih7NzD3r3vt5+2MNJDWHD4IdHCKi/QGwrwDHZubcVtajrkXEhdSGTq9n/SFUHyPSJPbANV591/GG3f6mZan5Rm8wBHcAMLrFNakbVWA7DdiF2rVUN1frnwUWAQa4ga29c2L/ujYfI9JE9sA1WN1UTPXTMFGtj8zMEa2qTRoKImJfapOhb101vUBtMnR7AgawiLgOeB64C5hKbU7pTYEzMnNhC0tTNyJiGPCZzPxaq2sZSgxwkgalqkcnfH5YGepny6gCwX8Bb3EO2zJExC0+jLl/OYQqaVCIiBMy89sR8b83aAcgMy9sSWHqqdfaFzJzbUQ8bXgrys8i4l+AedRGoQCvgWsmA5ykwaL9OrctW1qFemtiRPy2Wg5qM2n8tlrOzNyq8101ABxQvX6xrs1r4JrIIVRJkqTCbNLqAiSpkSJi94j494j4TUSsjIjrImL3VtclDWYRsWNEXBYR/1Gtj4+IU1pd12BmgJM02HwHuBrYCdgZ+C61h2hLap4rqM07vHO1/kvgL1pVzFBggJM02ERm/ltmtlU/38ZnMEpNERHt19K/KTOvpnoWama2AWtbVtgQYICTNNjcEhEzI2K3iPjDiDgT+GFEbBcR27W6OGmQubd6fTkitqf6ZamaF9xH+DSRNzFIGlQi4ulqsf0vt6h7OzPT6+GkBmmfIjIi/hj4OjABWAy8GfhoZj7Y0gIHMQOcpEEhIvYDns3M/1etTwc+AiwBzsvM51pYnjQoRcRSoP0Zi5sAm1H7pWkNsNbnLzaPQ6iSBov/A/w3QES8C/gKMIfaMM7sFtYlDWbDgC2oPX9xNLXnyw4DNsdnMjaVPXCSBoWIWJSZE6vlbwC/yczzqvWFmTmpheVJg1JEPJCZf9zqOoYie+AkDRbD6u6Imwr8pO49Z52RmiO630TN4F9qkgaLK4FbI+K/gNXA7QAR8T/wbjipWaa2uoChyiFUSYNG9eiCnYAfZ+bLVdsewBZOqi1pMDHASZIkFcZr4CRJkgpjgJMkSSqMAU5Scappsha3ug5JahUDnKQhpe5RI5JULP8ik1SqYRHxTeAA4NfAkcDOwDeozcP4CnBqZj4WEVcAzwFvAx6IiH8DLqH2tPgngZMz8/n+/yNIUu/YAyepVOOAb2TmXsAL1OY9nQ38eWbuC3wWuLhu+z2A92bmXwHfAj6XmfsADwHn9mfhktRX9sBJKtXTmbmwWl4A7EatN+67EeseDr9Z3fbfzcy1EbE1sE1m3lq1zwG+2/xyJalxDHCSSrWmbnktsCPwQhdznr7c9IokqZ84hCppsPgt8HREHAMQNRM33CgzXwSej4iDqqYTgVs33E6SBjIDnKTB5BPAKRGxCHiY2o0NHZkO/GNEPAhMAr7YP+VJUmM4lZYkSVJh7IGTJEkqjAFOkiSpMAY4SZKkwhjgJEmSCmOAkyRJKowBTpIkqTAGOEmSpMIY4CRJkgrz/wESWg4DaYUbSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# alternative to subplots\n",
    "heroes.plot(\n",
    "    x='hero',\n",
    "    y=['height', 'strength'],\n",
    "    kind='bar',\n",
    "    secondary_y='strength',\n",
    "    figsize=(10,8)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Super plot of super heroes'}, xlabel='hero'>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heroes.plot(\n",
    "    x='hero',\n",
    "    y=['height', 'strength'],\n",
    "    kind='bar',\n",
    "    secondary_y='strength',\n",
    "    title='Super plot of super heroes',\n",
    "    figsize=(10,8)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heroes.hist(figsize=(10, 10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrames everywhere.. are easy to plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "heroes.describe()['strength'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregation by Grouping\n",
    "GroupBy operations in pandas involve splitting a DataFrame into groups based on one or more categorical variables, applying a function to each group, and then combining the results into a new DataFrame.\n",
    "\n",
    "By using the groupby() function in pandas, you can perform various aggregation and transformation operations on groups of data within a DataFrame. Some common operations include calculating summary statistics (such as mean, sum, count), applying custom functions, and performing data transformations specific to each group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Group  Value\n",
      "0     A     10\n",
      "1     B     15\n",
      "2     A     20\n",
      "3     B     25\n",
      "4     A     30\n",
      "5     B     35\n",
      "       Value\n",
      "Group       \n",
      "A       20.0\n",
      "B       25.0\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame\n",
    "data = {'Group': ['A', 'B', 'A', 'B', 'A', 'B'],\n",
    "        'Value': [10, 15, 20, 25, 30, 35]}\n",
    "df = pd.DataFrame(data)\n",
    "print( df)\n",
    "\n",
    "# Perform GroupBy and calculate the mean for each group\n",
    "grouped_df = df.groupby('Group').mean()\n",
    "\n",
    "# Print the resulting grouped DataFrame\n",
    "print(grouped_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More detailed examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>movie</th>\n",
       "      <th>strength</th>\n",
       "      <th>speed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hero</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Batman</th>\n",
       "      <td>Batman</td>\n",
       "      <td>400</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robin</th>\n",
       "      <td>Batman</td>\n",
       "      <td>100</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spiderman</th>\n",
       "      <td>Spiderman</td>\n",
       "      <td>400</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robocop</th>\n",
       "      <td>Robocop</td>\n",
       "      <td>560</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lex Luthor</th>\n",
       "      <td>Spiderman</td>\n",
       "      <td>89</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr Octopus</th>\n",
       "      <td>Spiderman</td>\n",
       "      <td>300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                movie  strength  speed\n",
       "hero                                  \n",
       "Batman         Batman       400  100.0\n",
       "Robin          Batman       100   10.0\n",
       "Spiderman   Spiderman       400  200.0\n",
       "Robocop       Robocop       560    1.0\n",
       "Lex Luthor  Spiderman        89   20.0\n",
       "Dr Octopus  Spiderman       300    NaN"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_heroes = pd.DataFrame({\n",
    "    'hero': ['Batman', 'Robin', 'Spiderman', 'Robocop', 'Lex Luthor', 'Dr Octopus'],\n",
    "    'movie': ['Batman', 'Batman', 'Spiderman', 'Robocop', 'Spiderman', 'Spiderman'],\n",
    "    'strength': [400, 100, 400, 560, 89, 300],\n",
    "    'speed': [100, 10, 200, 1, 20, None],\n",
    "})\n",
    "movie_heroes = movie_heroes.set_index('hero')\n",
    "movie_heroes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie\n",
       "Batman       2\n",
       "Robocop      1\n",
       "Spiderman    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_heroes.groupby('movie').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>strength</th>\n",
       "      <th>speed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Batman</th>\n",
       "      <td>500</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robocop</th>\n",
       "      <td>560</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spiderman</th>\n",
       "      <td>789</td>\n",
       "      <td>220.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           strength  speed\n",
       "movie                     \n",
       "Batman          500  110.0\n",
       "Robocop         560    1.0\n",
       "Spiderman       789  220.0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_heroes.groupby('movie').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Batman</th>\n",
       "      <td>250.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robocop</th>\n",
       "      <td>560.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spiderman</th>\n",
       "      <td>263.0</td>\n",
       "      <td>110.0</td>\n",
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      "text/plain": [
       "           strength  speed\n",
       "movie                     \n",
       "Batman        250.0   55.0\n",
       "Robocop       560.0    1.0\n",
       "Spiderman     263.0  110.0"
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     "execution_count": 103,
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    "movie_heroes.groupby('movie').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>hero</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Robin</td>\n",
       "      <td>Batman</td>\n",
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       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spiderman</td>\n",
       "      <td>Spiderman</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lex Luthor</td>\n",
       "      <td>Spiderman</td>\n",
       "      <td>89</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dr Octopus</td>\n",
       "      <td>Spiderman</td>\n",
       "      <td>300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         hero      movie  strength  speed\n",
       "0      Batman     Batman       400  100.0\n",
       "1       Robin     Batman       100   10.0\n",
       "2   Spiderman  Spiderman       400  200.0\n",
       "3     Robocop    Robocop       560    1.0\n",
       "4  Lex Luthor  Spiderman        89   20.0\n",
       "5  Dr Octopus  Spiderman       300    NaN"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_heroes = movie_heroes.reset_index()\n",
    "movie_heroes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Batman</th>\n",
       "      <th>Batman</th>\n",
       "      <td>400.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robin</th>\n",
       "      <td>100.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Robocop</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Spiderman</th>\n",
       "      <th>Dr Octopus</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Lex Luthor</th>\n",
       "      <td>89.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spiderman</th>\n",
       "      <td>400.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                      strength  speed\n",
       "movie     hero                       \n",
       "Batman    Batman         400.0  100.0\n",
       "          Robin          100.0   10.0\n",
       "Robocop   Robocop        560.0    1.0\n",
       "Spiderman Dr Octopus     300.0    NaN\n",
       "          Lex Luthor      89.0   20.0\n",
       "          Spiderman      400.0  200.0"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_heroes.groupby(['movie', 'hero']).mean()"
   ]
  }
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