Pandas, Data Frame and Data Series.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b3f7f9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6dd1458",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "      <th>Col5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>R1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R2</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R3</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R4</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "    Col1  Col2  Col3  Col4  Col5\n",
       "R1     0     1     2     3     4\n",
       "R2     5     6     7     8     9\n",
       "R3    10    11    12    13    14\n",
       "R4    15    16    17    18    19"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(np.arange(0,20).reshape(4,5),index=[\"R1\",\"R2\",\"R3\",\"R4\"],columns=[\"Col1\",\"Col2\",\"Col3\",\"Col4\",\"Col5\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "25de4c62",
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving the output in csv file\n",
    "df.to_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "265e01a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Col1    0\n",
       "Col2    1\n",
       "Col3    2\n",
       "Col4    3\n",
       "Col5    4\n",
       "Name: R1, dtype: int32"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accessing the data from dataframe using loc=lable based access\n",
    "df.loc[\"R1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "69749783",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.loc[\"R1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fbbcf934",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Col1    0\n",
       "Col2    1\n",
       "Col3    2\n",
       "Col4    3\n",
       "Col5    4\n",
       "Name: R1, dtype: int32"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#index position based accessing\n",
    "df.iloc[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c8693917",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.iloc[0,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "95054818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert dataframes into array\n",
    "df.iloc[:,:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "67a4bbfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Col1    0\n",
       "Col2    0\n",
       "Col3    0\n",
       "Col4    0\n",
       "Col5    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check null values in DF\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f0dea721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Col1\n",
       "0     1\n",
       "5     1\n",
       "10    1\n",
       "15    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check number of occurances in col\n",
    "df.value_counts(\"Col1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8a8fe5be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  5, 10, 15])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check unique values\n",
    "df[\"Col1\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "790250df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R1     0\n",
       "R2     5\n",
       "R3    10\n",
       "R4    15\n",
       "Name: Col1, dtype: int32"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Col1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d35baa27",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R3</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R4</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
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       "  Unnamed: 0  Col1  Col2  Col3  Col4  Col5\n",
       "0         R1     0     1     2     3     4\n",
       "1         R2     5     6     7     8     9\n",
       "2         R3    10    11    12    13    14\n",
       "3         R4    15    16    17    18    19"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading data from csv\n",
    "data=pd.read_csv(\"test.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c41beeb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Unnamed: 0  4 non-null      object\n",
      " 1   Col1        4 non-null      int64 \n",
      " 2   Col2        4 non-null      int64 \n",
      " 3   Col3        4 non-null      int64 \n",
      " 4   Col4        4 non-null      int64 \n",
      " 5   Col5        4 non-null      int64 \n",
      "dtypes: int64(5), object(1)\n",
      "memory usage: 320.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3f65fea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Col1</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>11.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.454972</td>\n",
       "      <td>6.454972</td>\n",
       "      <td>6.454972</td>\n",
       "      <td>6.454972</td>\n",
       "      <td>6.454972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.750000</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>7.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>11.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.250000</td>\n",
       "      <td>12.250000</td>\n",
       "      <td>13.250000</td>\n",
       "      <td>14.250000</td>\n",
       "      <td>15.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            Col1       Col2       Col3       Col4       Col5\n",
       "count   4.000000   4.000000   4.000000   4.000000   4.000000\n",
       "mean    7.500000   8.500000   9.500000  10.500000  11.500000\n",
       "std     6.454972   6.454972   6.454972   6.454972   6.454972\n",
       "min     0.000000   1.000000   2.000000   3.000000   4.000000\n",
       "25%     3.750000   4.750000   5.750000   6.750000   7.750000\n",
       "50%     7.500000   8.500000   9.500000  10.500000  11.500000\n",
       "75%    11.250000  12.250000  13.250000  14.250000  15.250000\n",
       "max    15.000000  16.000000  17.000000  18.000000  19.000000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cec93fdc",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-52-b9d0aa569a2c>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-52-b9d0aa569a2c>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    import StringIO,BytesIO from io\u001b[0m\n\u001b[1;37m                            ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "from io import StringIO,BytesIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a10e6235",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=('c1,c2,c3\\n'\n",
    "          '1,2,3\\n'\n",
    "          '4,5,6\\n'\n",
    "          '7,8,9')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3b56a3e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3de29500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.StringIO at 0x1ac0ea9ce50>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "StringIO()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ab61c2a4",
   "metadata": {},
   "outputs": [
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       "   c1  c2  c3\n",
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   "source": [
    "data2=pd.read_csv(StringIO(data1))\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8f9bd97f",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>c1</th>\n",
       "      <th>c3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  c1 c3\n",
       "0  1  3\n",
       "1  4  6\n",
       "2  7  9"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3=pd.read_csv(StringIO(data1),usecols=[\"c1\",\"c3\"],dtype=object)\n",
    "data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1b97fbfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d610147e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3[\"c1\"][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "547c00d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data3[\"c1\"][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c58477d2",
   "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>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   c1  c2   c3\n",
       "0   1   2  3.0\n",
       "1   4   5  6.0\n",
       "2   7   8  9.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4=pd.read_csv(StringIO(data1),dtype={\"c1\":int,\"c3\":float})\n",
    "data4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "69b244e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data4[\"c3\"][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "135801ce",
   "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>c1</th>\n",
       "      <th>c2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c3</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    c1  c2\n",
       "c3        \n",
       "3    1   2\n",
       "6    4   5\n",
       "9    7   8"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5=pd.read_csv(StringIO(data1),index_col=2)\n",
    "data5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "258a08f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data6=('c1,c2,c3\\n'\n",
    "          '1,rocky,sdsd,\\n'\n",
    "          '4,ddd,ffff,\\n'\n",
    "          '7,bbbb,kkk,')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3055dc2c",
   "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>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>rocky</td>\n",
       "      <td>sdsd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>ddd</td>\n",
       "      <td>ffff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>bbbb</td>\n",
       "      <td>kkk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   c1     c2    c3\n",
       "0   1  rocky  sdsd\n",
       "1   4    ddd  ffff\n",
       "2   7   bbbb   kkk"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data7=pd.read_csv(StringIO(data6),index_col=False)\n",
    "data7"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "661efb31",
   "metadata": {},
   "source": [
    "# reading json "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "5ddad3af",
   "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>myself</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'name': 'rocky', 'age': 27, 'hobbies': [{'tit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'name': 'rocky', 'age': 27, 'hobbies': [{'tit...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              myself\n",
       "0  {'name': 'rocky', 'age': 27, 'hobbies': [{'tit...\n",
       "1  {'name': 'rocky', 'age': 27, 'hobbies': [{'tit..."
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data8='{\"myself\":[{\"name\":\"rocky\",\"age\":27,\"hobbies\":[{\"title1\":\"youtube\",\"title2\":\"cricket\"}]},{\"name\":\"rocky\",\"age\":27,\"hobbies\":[{\"title1\":\"youtube\",\"title2\":\"cricket\"}]}]}'\n",
    "df8=pd.read_json(data8)\n",
    "df8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "b978b2c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"myself\":{\"name\":\"rocky\",\"age\":27,\"hobbies\":[{\"title1\":\"youtube\",\"title2\":\"cricket\"}]}},{\"myself\":{\"name\":\"rocky\",\"age\":27,\"hobbies\":[{\"title1\":\"youtube\",\"title2\":\"cricket\"}]}}]'"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8.to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "6614c7da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df8.to_json())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d81a4d05",
   "metadata": {},
   "source": [
    "# reading html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "89708045",
   "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>MCC</th>\n",
       "      <th>MNC</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Operator</th>\n",
       "      <th>Status</th>\n",
       "      <th>Bands (MHz)</th>\n",
       "      <th>References and notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>TEST</td>\n",
       "      <td>Test network</td>\n",
       "      <td>Operational</td>\n",
       "      <td>any</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>TEST</td>\n",
       "      <td>Test network</td>\n",
       "      <td>Operational</td>\n",
       "      <td>any</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>999</td>\n",
       "      <td>99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Internal use</td>\n",
       "      <td>Operational</td>\n",
       "      <td>any</td>\n",
       "      <td>Internal use in private networks, no roaming[4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>999</td>\n",
       "      <td>999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Internal use</td>\n",
       "      <td>Operational</td>\n",
       "      <td>any</td>\n",
       "      <td>Internal use in private networks, no roaming[4]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MCC  MNC Brand      Operator       Status Bands (MHz)  \\\n",
       "0    1    1  TEST  Test network  Operational         any   \n",
       "1    1    1  TEST  Test network  Operational         any   \n",
       "2  999   99   NaN  Internal use  Operational         any   \n",
       "3  999  999   NaN  Internal use  Operational         any   \n",
       "\n",
       "                              References and notes  \n",
       "0                                              NaN  \n",
       "1                                              NaN  \n",
       "2  Internal use in private networks, no roaming[4]  \n",
       "3  Internal use in private networks, no roaming[4]  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url=\"https://en.wikipedia.org/wiki/Mobile_country_code\"\n",
    "mobile_code=pd.read_html(url,match=\"Operator\",header=0)\n",
    "mobile_code[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bab9dd5f",
   "metadata": {},
   "source": [
    "# reading excel nad pickling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "6eee9045",
   "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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>City</th>\n",
       "      <th>...</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2019-152156</td>\n",
       "      <td>2019-11-08</td>\n",
       "      <td>2019-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2019-152156</td>\n",
       "      <td>2019-11-08</td>\n",
       "      <td>2019-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2019-138688</td>\n",
       "      <td>2019-06-12</td>\n",
       "      <td>2019-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2018-108966</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>2018-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2018-108966</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>2018-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0       1  CA-2019-152156 2019-11-08 2019-11-11    Second Class    CG-12520   \n",
       "1       2  CA-2019-152156 2019-11-08 2019-11-11    Second Class    CG-12520   \n",
       "2       3  CA-2019-138688 2019-06-12 2019-06-16    Second Class    DV-13045   \n",
       "3       4  US-2018-108966 2018-10-11 2018-10-18  Standard Class    SO-20335   \n",
       "4       5  US-2018-108966 2018-10-11 2018-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment Country/Region             City  ...  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "1      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  ...   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "\n",
       "  Postal Code  Region       Product ID         Category Sub-Category  \\\n",
       "0     42420.0   South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1     42420.0   South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2     90036.0    West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3     33311.0   South  FUR-TA-10000577        Furniture       Tables   \n",
       "4     33311.0   South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  Quantity  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600         2   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400         3   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200         2   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775         5   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680         2   \n",
       "\n",
       "   Discount    Profit  \n",
       "0      0.00   41.9136  \n",
       "1      0.00  219.5820  \n",
       "2      0.00    6.8714  \n",
       "3      0.45 -383.0310  \n",
       "4      0.20    2.5164  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=\"Sample - Superstore.xls\"\n",
    "excel=pd.read_excel(path)\n",
    "excel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "19dbf9e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "excel.to_pickle(\"excel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "4e3daf55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>City</th>\n",
       "      <th>...</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2019-152156</td>\n",
       "      <td>2019-11-08</td>\n",
       "      <td>2019-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2019-152156</td>\n",
       "      <td>2019-11-08</td>\n",
       "      <td>2019-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>...</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2019-138688</td>\n",
       "      <td>2019-06-12</td>\n",
       "      <td>2019-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2018-108966</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>2018-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2018-108966</td>\n",
       "      <td>2018-10-11</td>\n",
       "      <td>2018-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>...</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0       1  CA-2019-152156 2019-11-08 2019-11-11    Second Class    CG-12520   \n",
       "1       2  CA-2019-152156 2019-11-08 2019-11-11    Second Class    CG-12520   \n",
       "2       3  CA-2019-138688 2019-06-12 2019-06-16    Second Class    DV-13045   \n",
       "3       4  US-2018-108966 2018-10-11 2018-10-18  Standard Class    SO-20335   \n",
       "4       5  US-2018-108966 2018-10-11 2018-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment Country/Region             City  ...  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "1      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  ...   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "\n",
       "  Postal Code  Region       Product ID         Category Sub-Category  \\\n",
       "0     42420.0   South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1     42420.0   South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2     90036.0    West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3     33311.0   South  FUR-TA-10000577        Furniture       Tables   \n",
       "4     33311.0   South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  Quantity  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600         2   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400         3   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200         2   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775         5   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680         2   \n",
       "\n",
       "   Discount    Profit  \n",
       "0      0.00   41.9136  \n",
       "1      0.00  219.5820  \n",
       "2      0.00    6.8714  \n",
       "3      0.45 -383.0310  \n",
       "4      0.20    2.5164  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickle=pd.read_pickle(\"excel\")\n",
    "pickle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60a84fe3",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60ebb0b6",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c17c8e6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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