Pandas-Reading JSON,HTML,PICKEL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading JSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from io import StringIO,BytesIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data='{\"name\":\"shantanu\",\"email\":\"shaan4u36@gmail\",\"job profile\":[{\"title1\":\"team lead\",\"title 2\":\"analyst\"}]}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"name\":\"shantanu\",\"email\":\"shaan4u36@gmail\",\"job profile\":[{\"title1\":\"team lead\",\"title 2\":\"analyst\"}]}'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>email</th>\n",
       "      <th>job profile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>shantanu</td>\n",
       "      <td>shaan4u36@gmail</td>\n",
       "      <td>{'title1': 'team lead', 'title 2': 'analyst'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name            email                                    job profile\n",
       "0  shantanu  shaan4u36@gmail  {'title1': 'team lead', 'title 2': 'analyst'}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_json(Data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data',header=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of      0      1     2     3     4    5     6     7     8     9      10    11  \\\n",
       "0     1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29   5.64  1.04   \n",
       "1     1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28   4.38  1.05   \n",
       "2     1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81   5.68  1.03   \n",
       "3     1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18   7.80  0.86   \n",
       "4     1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82   4.32  1.04   \n",
       "..   ..    ...   ...   ...   ...  ...   ...   ...   ...   ...    ...   ...   \n",
       "173   3  13.71  5.65  2.45  20.5   95  1.68  0.61  0.52  1.06   7.70  0.64   \n",
       "174   3  13.40  3.91  2.48  23.0  102  1.80  0.75  0.43  1.41   7.30  0.70   \n",
       "175   3  13.27  4.28  2.26  20.0  120  1.59  0.69  0.43  1.35  10.20  0.59   \n",
       "176   3  13.17  2.59  2.37  20.0  120  1.65  0.68  0.53  1.46   9.30  0.60   \n",
       "177   3  14.13  4.10  2.74  24.5   96  2.05  0.76  0.56  1.35   9.20  0.61   \n",
       "\n",
       "       12    13  \n",
       "0    3.92  1065  \n",
       "1    3.40  1050  \n",
       "2    3.17  1185  \n",
       "3    3.45  1480  \n",
       "4    2.93   735  \n",
       "..    ...   ...  \n",
       "173  1.74   740  \n",
       "174  1.56   750  \n",
       "175  1.56   835  \n",
       "176  1.62   840  \n",
       "177  1.60   560  \n",
       "\n",
       "[178 rows x 14 columns]>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.to_csv('wine.csv') # saving data on a computer in .csv format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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130\":0.76,\"131\":0.74,\"132\":0.66,\"133\":0.78,\"134\":0.75,\"135\":0.73,\"136\":0.75,\"137\":0.82,\"138\":0.81,\"139\":0.89,\"140\":0.77,\"141\":0.7,\"142\":0.89,\"143\":0.91,\"144\":0.65,\"145\":0.6,\"146\":0.58,\"147\":0.54,\"148\":0.55,\"149\":0.57,\"150\":0.59,\"151\":0.48,\"152\":0.61,\"153\":0.56,\"154\":0.58,\"155\":0.6,\"156\":0.57,\"157\":0.67,\"158\":0.57,\"159\":0.57,\"160\":0.56,\"161\":0.96,\"162\":0.87,\"163\":0.68,\"164\":0.7,\"165\":0.78,\"166\":0.85,\"167\":0.72,\"168\":0.74,\"169\":0.67,\"170\":0.66,\"171\":0.57,\"172\":0.62,\"173\":0.64,\"174\":0.7,\"175\":0.59,\"176\":0.6,\"177\":0.61},\"12\":{\"0\":3.92,\"1\":3.4,\"2\":3.17,\"3\":3.45,\"4\":2.93,\"5\":2.85,\"6\":3.58,\"7\":3.58,\"8\":2.85,\"9\":3.55,\"10\":3.17,\"11\":2.82,\"12\":2.9,\"13\":2.73,\"14\":3.0,\"15\":2.88,\"16\":2.65,\"17\":2.57,\"18\":2.82,\"19\":3.36,\"20\":3.71,\"21\":3.52,\"22\":4.0,\"23\":3.63,\"24\":3.82,\"25\":3.2,\"26\":3.22,\"27\":2.77,\"28\":3.4,\"29\":3.59,\"30\":2.71,\"31\":2.88,\"32\":2.87,\"33\":3.0,\"34\":2.87,\"35\":3.47,\"36\":2.78,\"37\":2.51,\"38\":2.69,\"39\":3.53,\"40\":3.38,\"41\":3.0,\"42\":3.56,\"43\":3.0,\"44\":3.35,\"45\":3.33,\"46\":3.44,\"47\":3.33,\"48\":2.75,\"49\":3.1,\"50\":2.91,\"51\":3.37,\"52\":3.26,\"53\":2.93,\"54\":3.2,\"55\":3.03,\"56\":3.31,\"57\":2.84,\"58\":2.87,\"59\":1.82,\"60\":1.67,\"61\":1.59,\"62\":2.46,\"63\":2.87,\"64\":2.23,\"65\":2.3,\"66\":3.18,\"67\":3.48,\"68\":1.93,\"69\":3.07,\"70\":1.82,\"71\":3.16,\"72\":2.78,\"73\":3.5,\"74\":3.13,\"75\":2.14,\"76\":2.48,\"77\":2.52,\"78\":2.31,\"79\":3.13,\"80\":3.12,\"81\":3.14,\"82\":2.72,\"83\":2.01,\"84\":3.08,\"85\":3.16,\"86\":2.26,\"87\":3.21,\"88\":2.75,\"89\":3.21,\"90\":2.27,\"91\":2.65,\"92\":2.06,\"93\":3.3,\"94\":2.96,\"95\":2.63,\"96\":2.26,\"97\":2.74,\"98\":2.77,\"99\":2.83,\"100\":2.96,\"101\":2.77,\"102\":3.38,\"103\":2.44,\"104\":3.57,\"105\":3.3,\"106\":3.17,\"107\":2.42,\"108\":3.02,\"109\":3.26,\"110\":2.81,\"111\":2.78,\"112\":2.5,\"113\":2.31,\"114\":3.19,\"115\":2.87,\"116\":3.33,\"117\":2.96,\"118\":2.12,\"119\":3.05,\"120\":3.39,\"121\":3.69,\"122\":3.12,\"123\":3.1,\"124\":3.64,\"125\":3.28,\"126\":2.84,\"127\":2.44,\"128\":2.78,\"129\":2.57,\"130\":1.29,\"131\":1.42,\"132\":1.36,\"133\":1.29,\"134\":1.51,\"135\":1.58,\"136\":1.27,\"137\":1.69,\"138\":1.82,\"139\":2.15,\"140\":2.31,\"141\":2.47,\"142\":2.06,\"143\":2.05,\"144\":2.0,\"145\":1.68,\"146\":1.33,\"147\":1.86,\"148\":1.62,\"149\":1.33,\"150\":1.3,\"151\":1.47,\"152\":1.33,\"153\":1.51,\"154\":1.55,\"155\":1.48,\"156\":1.64,\"157\":1.73,\"158\":1.96,\"159\":1.78,\"160\":1.58,\"161\":1.82,\"162\":2.11,\"163\":1.75,\"164\":1.68,\"165\":1.75,\"166\":1.56,\"167\":1.75,\"168\":1.8,\"169\":1.92,\"170\":1.83,\"171\":1.63,\"172\":1.71,\"173\":1.74,\"174\":1.56,\"175\":1.56,\"176\":1.62,\"177\":1.6},\"13\":{\"0\":1065,\"1\":1050,\"2\":1185,\"3\":1480,\"4\":735,\"5\":1450,\"6\":1290,\"7\":1295,\"8\":1045,\"9\":1045,\"10\":1510,\"11\":1280,\"12\":1320,\"13\":1150,\"14\":1547,\"15\":1310,\"16\":1280,\"17\":1130,\"18\":1680,\"19\":845,\"20\":780,\"21\":770,\"22\":1035,\"23\":1015,\"24\":845,\"25\":830,\"26\":1195,\"27\":1285,\"28\":915,\"29\":1035,\"30\":1285,\"31\":1515,\"32\":990,\"33\":1235,\"34\":1095,\"35\":920,\"36\":880,\"37\":1105,\"38\":1020,\"39\":760,\"40\":795,\"41\":1035,\"42\":1095,\"43\":680,\"44\":885,\"45\":1080,\"46\":1065,\"47\":985,\"48\":1060,\"49\":1260,\"50\":1150,\"51\":1265,\"52\":1190,\"53\":1375,\"54\":1060,\"55\":1120,\"56\":970,\"57\":1270,\"58\":1285,\"59\":520,\"60\":680,\"61\":450,\"62\":630,\"63\":420,\"64\":355,\"65\":678,\"66\":502,\"67\":510,\"68\":750,\"69\":718,\"70\":870,\"71\":410,\"72\":472,\"73\":985,\"74\":886,\"75\":428,\"76\":392,\"77\":500,\"78\":750,\"79\":463,\"80\":278,\"81\":714,\"82\":630,\"83\":515,\"84\":520,\"85\":450,\"86\":495,\"87\":562,\"88\":680,\"89\":625,\"90\":480,\"91\":450,\"92\":495,\"93\":290,\"94\":345,\"95\":937,\"96\":625,\"97\":428,\"98\":660,\"99\":406,\"100\":710,\"101\":562,\"102\":438,\"103\":415,\"104\":672,\"105\":315,\"106\":510,\"107\":488,\"108\":312,\"109\":680,\"110\":562,\"111\":325,\"112\":607,\"113\":434,\"114\":385,\"115\":407,\"116\":495,\"117\":345,\"118\":372,\"119\":564,\"120\":625,\"121\":465,\"122\":365,\"123\":380,\"124\":380,\"125\":378,\"126\":352,\"127\":466,\"128\":342,\"129\":580,\"130\":630,\"131\":530,\"132\":560,\"133\":600,\"134\":650,\"135\":695,\"136\":720,\"137\":515,\"138\":580,\"139\":590,\"140\":600,\"141\":780,\"142\":520,\"143\":550,\"144\":855,\"145\":830,\"146\":415,\"147\":625,\"148\":650,\"149\":550,\"150\":500,\"151\":480,\"152\":425,\"153\":675,\"154\":640,\"155\":725,\"156\":480,\"157\":880,\"158\":660,\"159\":620,\"160\":520,\"161\":680,\"162\":570,\"163\":675,\"164\":615,\"165\":520,\"166\":695,\"167\":685,\"168\":750,\"169\":630,\"170\":510,\"171\":470,\"172\":660,\"173\":740,\"174\":750,\"175\":835,\"176\":840,\"177\":560}}'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.to_json() # convertingf wine data into jason format, we can see the key value structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"name\":{\"0\":\"shantanu\"},\"email\":{\"0\":\"shaan4u36@gmail\"},\"job profile\":{\"0\":{\"title1\":\"team lead\",\"title 2\":\"analyst\"}}}'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_json() # converting data we created  at the top nto json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"name\":\"shantanu\",\"email\":\"shaan4u36@gmail\",\"job profile\":{\"title1\":\"team lead\",\"title 2\":\"analyst\"}}]'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_json(orient=\"records\") # see the difference from above and below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading HTML \n",
    ". A web scrapping technique\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "url='https://en.wikipedia.org/wiki/Mobile_country_code'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4=pd.read_html(url,match='Country',header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Mobile country code</th>\n",
       "      <th>Country</th>\n",
       "      <th>ISO 3166</th>\n",
       "      <th>Mobile network codes</th>\n",
       "      <th>National MNC authority</th>\n",
       "      <th>Remarks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>289</td>\n",
       "      <td>A Abkhazia</td>\n",
       "      <td>GE-AB</td>\n",
       "      <td>List of mobile network codes in Abkhazia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MCC is not listed by ITU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>412</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AF</td>\n",
       "      <td>List of mobile network codes in Afghanistan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>276</td>\n",
       "      <td>Albania</td>\n",
       "      <td>AL</td>\n",
       "      <td>List of mobile network codes in Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>603</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZ</td>\n",
       "      <td>List of mobile network codes in Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>544</td>\n",
       "      <td>American Samoa (United States of America)</td>\n",
       "      <td>AS</td>\n",
       "      <td>List of mobile network codes in American Samoa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>452</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>VN</td>\n",
       "      <td>List of mobile network codes in the Vietnam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>543</td>\n",
       "      <td>W Wallis and Futuna</td>\n",
       "      <td>WF</td>\n",
       "      <td>List of mobile network codes in Wallis and Futuna</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>421</td>\n",
       "      <td>Y Yemen</td>\n",
       "      <td>YE</td>\n",
       "      <td>List of mobile network codes in the Yemen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>645</td>\n",
       "      <td>Z Zambia</td>\n",
       "      <td>ZM</td>\n",
       "      <td>List of mobile network codes in Zambia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>648</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZW</td>\n",
       "      <td>List of mobile network codes in Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mobile country code                                    Country ISO 3166  \\\n",
       "0                    289                                 A Abkhazia    GE-AB   \n",
       "1                    412                                Afghanistan       AF   \n",
       "2                    276                                    Albania       AL   \n",
       "3                    603                                    Algeria       DZ   \n",
       "4                    544  American Samoa (United States of America)       AS   \n",
       "..                   ...                                        ...      ...   \n",
       "247                  452                                    Vietnam       VN   \n",
       "248                  543                        W Wallis and Futuna       WF   \n",
       "249                  421                                    Y Yemen       YE   \n",
       "250                  645                                   Z Zambia       ZM   \n",
       "251                  648                                   Zimbabwe       ZW   \n",
       "\n",
       "                                  Mobile network codes National MNC authority  \\\n",
       "0             List of mobile network codes in Abkhazia                    NaN   \n",
       "1          List of mobile network codes in Afghanistan                    NaN   \n",
       "2              List of mobile network codes in Albania                    NaN   \n",
       "3              List of mobile network codes in Algeria                    NaN   \n",
       "4       List of mobile network codes in American Samoa                    NaN   \n",
       "..                                                 ...                    ...   \n",
       "247        List of mobile network codes in the Vietnam                    NaN   \n",
       "248  List of mobile network codes in Wallis and Futuna                    NaN   \n",
       "249          List of mobile network codes in the Yemen                    NaN   \n",
       "250             List of mobile network codes in Zambia                    NaN   \n",
       "251           List of mobile network codes in Zimbabwe                    NaN   \n",
       "\n",
       "                      Remarks  \n",
       "0    MCC is not listed by ITU  \n",
       "1                         NaN  \n",
       "2                         NaN  \n",
       "3                         NaN  \n",
       "4                         NaN  \n",
       "..                        ...  \n",
       "247                       NaN  \n",
       "248                       NaN  \n",
       "249                       NaN  \n",
       "250                       NaN  \n",
       "251                       NaN  \n",
       "\n",
       "[252 rows x 6 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading EXCEL file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_excel=pd.read_excel('C:/Users/Shanty/Desktop/Python Start to End/samplexcel.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>Age</th>\n",
       "      <th>Date</th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Dulce</td>\n",
       "      <td>Abril</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>32</td>\n",
       "      <td>15/10/2017</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Mara</td>\n",
       "      <td>Hashimoto</td>\n",
       "      <td>Female</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>25</td>\n",
       "      <td>16/08/2016</td>\n",
       "      <td>1582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Philip</td>\n",
       "      <td>Gent</td>\n",
       "      <td>Male</td>\n",
       "      <td>France</td>\n",
       "      <td>36</td>\n",
       "      <td>21/05/2015</td>\n",
       "      <td>2587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Kathleen</td>\n",
       "      <td>Hanner</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>25</td>\n",
       "      <td>15/10/2017</td>\n",
       "      <td>3549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Nereida</td>\n",
       "      <td>Magwood</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>58</td>\n",
       "      <td>16/08/2016</td>\n",
       "      <td>2468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Gaston</td>\n",
       "      <td>Brumm</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
       "      <td>24</td>\n",
       "      <td>21/05/2015</td>\n",
       "      <td>2554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Etta</td>\n",
       "      <td>Hurn</td>\n",
       "      <td>Female</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>56</td>\n",
       "      <td>15/10/2017</td>\n",
       "      <td>3598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Earlean</td>\n",
       "      <td>Melgar</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>27</td>\n",
       "      <td>16/08/2016</td>\n",
       "      <td>2456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Vincenza</td>\n",
       "      <td>Weiland</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>40</td>\n",
       "      <td>21/05/2015</td>\n",
       "      <td>6548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0 First Name  Last Name  Gender        Country  Age        Date    Id\n",
       "0  1      Dulce      Abril  Female  United States   32  15/10/2017  1562\n",
       "1  2       Mara  Hashimoto  Female  Great Britain   25  16/08/2016  1582\n",
       "2  3     Philip       Gent    Male         France   36  21/05/2015  2587\n",
       "3  4   Kathleen     Hanner  Female  United States   25  15/10/2017  3549\n",
       "4  5    Nereida    Magwood  Female  United States   58  16/08/2016  2468\n",
       "5  6     Gaston      Brumm    Male  United States   24  21/05/2015  2554\n",
       "6  7       Etta       Hurn  Female  Great Britain   56  15/10/2017  3598\n",
       "7  8    Earlean     Melgar  Female  United States   27  16/08/2016  2456\n",
       "8  9   Vincenza    Weiland  Female  United States   40  21/05/2015  6548"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_excel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pickling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pickles are used in order to save ongoing files on a computer in order to prevent loss of data in case of any failure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_excel.to_pickle('excel_pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df6=pd.read_pickle('excel_pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <td>Kathleen</td>\n",
       "      <td>Hanner</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>25</td>\n",
       "      <td>15/10/2017</td>\n",
       "      <td>3549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Nereida</td>\n",
       "      <td>Magwood</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>58</td>\n",
       "      <td>16/08/2016</td>\n",
       "      <td>2468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Gaston</td>\n",
       "      <td>Brumm</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States</td>\n",
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       "      <td>21/05/2015</td>\n",
       "      <td>2554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Etta</td>\n",
       "      <td>Hurn</td>\n",
       "      <td>Female</td>\n",
       "      <td>Great Britain</td>\n",
       "      <td>56</td>\n",
       "      <td>15/10/2017</td>\n",
       "      <td>3598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Earlean</td>\n",
       "      <td>Melgar</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>27</td>\n",
       "      <td>16/08/2016</td>\n",
       "      <td>2456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Vincenza</td>\n",
       "      <td>Weiland</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States</td>\n",
       "      <td>40</td>\n",
       "      <td>21/05/2015</td>\n",
       "      <td>6548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0 First Name  Last Name  Gender        Country  Age        Date    Id\n",
       "0  1      Dulce      Abril  Female  United States   32  15/10/2017  1562\n",
       "1  2       Mara  Hashimoto  Female  Great Britain   25  16/08/2016  1582\n",
       "2  3     Philip       Gent    Male         France   36  21/05/2015  2587\n",
       "3  4   Kathleen     Hanner  Female  United States   25  15/10/2017  3549\n",
       "4  5    Nereida    Magwood  Female  United States   58  16/08/2016  2468\n",
       "5  6     Gaston      Brumm    Male  United States   24  21/05/2015  2554\n",
       "6  7       Etta       Hurn  Female  Great Britain   56  15/10/2017  3598\n",
       "7  8    Earlean     Melgar  Female  United States   27  16/08/2016  2456\n",
       "8  9   Vincenza    Weiland  Female  United States   40  21/05/2015  6548"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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