Week6_Wednesday.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading and Writing Files in Pandas\n",
    "Pandas doc: https://pandas.pydata.org/pandas-docs/stable/reference/io.html<br/>\n",
    "Tutorial: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data source: https://databank.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG/1ff4a498/Popular-Indicators#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Series Name</th>\n",
       "      <th>Series Code</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>2000 [YR2000]</th>\n",
       "      <th>2001 [YR2001]</th>\n",
       "      <th>2002 [YR2002]</th>\n",
       "      <th>2003 [YR2003]</th>\n",
       "      <th>2004 [YR2004]</th>\n",
       "      <th>2005 [YR2005]</th>\n",
       "      <th>2006 [YR2006]</th>\n",
       "      <th>2007 [YR2007]</th>\n",
       "      <th>2008 [YR2008]</th>\n",
       "      <th>2009 [YR2009]</th>\n",
       "      <th>2010 [YR2010]</th>\n",
       "      <th>2011 [YR2011]</th>\n",
       "      <th>2012 [YR2012]</th>\n",
       "      <th>2013 [YR2013]</th>\n",
       "      <th>2014 [YR2014]</th>\n",
       "      <th>2015 [YR2015]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>20779953</td>\n",
       "      <td>21606988</td>\n",
       "      <td>22600770</td>\n",
       "      <td>23680871</td>\n",
       "      <td>24726684</td>\n",
       "      <td>25654277</td>\n",
       "      <td>26433049</td>\n",
       "      <td>27100536</td>\n",
       "      <td>27722276</td>\n",
       "      <td>28394813</td>\n",
       "      <td>29185507</td>\n",
       "      <td>30117413</td>\n",
       "      <td>31161376</td>\n",
       "      <td>32269589</td>\n",
       "      <td>33370794</td>\n",
       "      <td>34413603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>3089027</td>\n",
       "      <td>3060173</td>\n",
       "      <td>3051010</td>\n",
       "      <td>3039616</td>\n",
       "      <td>3026939</td>\n",
       "      <td>3011487</td>\n",
       "      <td>2992547</td>\n",
       "      <td>2970017</td>\n",
       "      <td>2947314</td>\n",
       "      <td>2927519</td>\n",
       "      <td>2913021</td>\n",
       "      <td>2905195</td>\n",
       "      <td>2900401</td>\n",
       "      <td>2895092</td>\n",
       "      <td>2889104</td>\n",
       "      <td>2880703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZA</td>\n",
       "      <td>31042235</td>\n",
       "      <td>31451514</td>\n",
       "      <td>31855109</td>\n",
       "      <td>32264157</td>\n",
       "      <td>32692163</td>\n",
       "      <td>33149724</td>\n",
       "      <td>33641002</td>\n",
       "      <td>34166972</td>\n",
       "      <td>34730608</td>\n",
       "      <td>35333881</td>\n",
       "      <td>35977455</td>\n",
       "      <td>36661444</td>\n",
       "      <td>37383887</td>\n",
       "      <td>38140132</td>\n",
       "      <td>38923687</td>\n",
       "      <td>39728025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>ASM</td>\n",
       "      <td>57821</td>\n",
       "      <td>58494</td>\n",
       "      <td>59080</td>\n",
       "      <td>59504</td>\n",
       "      <td>59681</td>\n",
       "      <td>59562</td>\n",
       "      <td>59107</td>\n",
       "      <td>58365</td>\n",
       "      <td>57492</td>\n",
       "      <td>56683</td>\n",
       "      <td>56079</td>\n",
       "      <td>55759</td>\n",
       "      <td>55667</td>\n",
       "      <td>55713</td>\n",
       "      <td>55791</td>\n",
       "      <td>55812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>AND</td>\n",
       "      <td>65390</td>\n",
       "      <td>67341</td>\n",
       "      <td>70049</td>\n",
       "      <td>73182</td>\n",
       "      <td>76244</td>\n",
       "      <td>78867</td>\n",
       "      <td>80993</td>\n",
       "      <td>82684</td>\n",
       "      <td>83862</td>\n",
       "      <td>84463</td>\n",
       "      <td>84449</td>\n",
       "      <td>83747</td>\n",
       "      <td>82427</td>\n",
       "      <td>80774</td>\n",
       "      <td>79213</td>\n",
       "      <td>78011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Series Name  Series Code    Country Name Country Code 2000 [YR2000]  \\\n",
       "0  Population, total  SP.POP.TOTL     Afghanistan          AFG      20779953   \n",
       "1  Population, total  SP.POP.TOTL         Albania          ALB       3089027   \n",
       "2  Population, total  SP.POP.TOTL         Algeria          DZA      31042235   \n",
       "3  Population, total  SP.POP.TOTL  American Samoa          ASM         57821   \n",
       "4  Population, total  SP.POP.TOTL         Andorra          AND         65390   \n",
       "\n",
       "  2001 [YR2001] 2002 [YR2002] 2003 [YR2003] 2004 [YR2004] 2005 [YR2005]  \\\n",
       "0      21606988      22600770      23680871      24726684      25654277   \n",
       "1       3060173       3051010       3039616       3026939       3011487   \n",
       "2      31451514      31855109      32264157      32692163      33149724   \n",
       "3         58494         59080         59504         59681         59562   \n",
       "4         67341         70049         73182         76244         78867   \n",
       "\n",
       "  2006 [YR2006] 2007 [YR2007] 2008 [YR2008] 2009 [YR2009] 2010 [YR2010]  \\\n",
       "0      26433049      27100536      27722276      28394813      29185507   \n",
       "1       2992547       2970017       2947314       2927519       2913021   \n",
       "2      33641002      34166972      34730608      35333881      35977455   \n",
       "3         59107         58365         57492         56683         56079   \n",
       "4         80993         82684         83862         84463         84449   \n",
       "\n",
       "  2011 [YR2011] 2012 [YR2012] 2013 [YR2013] 2014 [YR2014] 2015 [YR2015]  \n",
       "0      30117413      31161376      32269589      33370794      34413603  \n",
       "1       2905195       2900401       2895092       2889104       2880703  \n",
       "2      36661444      37383887      38140132      38923687      39728025  \n",
       "3         55759         55667         55713         55791         55812  \n",
       "4         83747         82427         80774         79213         78011  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#read the csv file by pointing its directory\n",
    "df = pd.read_csv(\"C:\\\\Users\\\\yasin.unlu\\\\Documents\\\\Original Docs\\\\Documents1\\\\Docs\\\\Teaching\\\\PythonForDataScienceSummer2020\\\\Week-6\\Popular_Indicators\\\\Popular_Indicators_CSV.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yasin.unlu\\Documents\\Original Docs\\Documents1\\Docs\\Teaching\\PythonForDataScienceSummer2020\\Week-6\\Small_data\n"
     ]
    }
   ],
   "source": [
    "%cd \"C:\\\\Users\\\\yasin.unlu\\\\Documents\\\\Original Docs\\\\Documents1\\\\Docs\\\\Teaching\\\\PythonForDataScienceSummer2020\\\\Week-6\\\\Small_data\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read CSV File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>POP</th>\n",
       "      <th>AREA</th>\n",
       "      <th>GDP</th>\n",
       "      <th>CONT</th>\n",
       "      <th>IND_DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>1398.72</td>\n",
       "      <td>9596.96</td>\n",
       "      <td>12234.80</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>1351.16</td>\n",
       "      <td>3287.26</td>\n",
       "      <td>2575.67</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/15/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>329.74</td>\n",
       "      <td>9833.52</td>\n",
       "      <td>19485.40</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1776-07-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDN</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>268.07</td>\n",
       "      <td>1910.93</td>\n",
       "      <td>1015.54</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/17/1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>210.32</td>\n",
       "      <td>8515.77</td>\n",
       "      <td>2055.51</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1822-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PAK</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>205.71</td>\n",
       "      <td>881.91</td>\n",
       "      <td>302.14</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/14/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>200.96</td>\n",
       "      <td>923.77</td>\n",
       "      <td>375.77</td>\n",
       "      <td>Africa</td>\n",
       "      <td>10/1/1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BGD</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>167.09</td>\n",
       "      <td>147.57</td>\n",
       "      <td>245.63</td>\n",
       "      <td>Asia</td>\n",
       "      <td>3/26/1971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RUS</td>\n",
       "      <td>Russia</td>\n",
       "      <td>146.79</td>\n",
       "      <td>17098.20</td>\n",
       "      <td>1530.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/12/1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MEX</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>126.58</td>\n",
       "      <td>1964.38</td>\n",
       "      <td>1158.23</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1810-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>126.22</td>\n",
       "      <td>377.97</td>\n",
       "      <td>4872.42</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>83.02</td>\n",
       "      <td>357.11</td>\n",
       "      <td>3693.20</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>67.02</td>\n",
       "      <td>640.68</td>\n",
       "      <td>2582.49</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1789-07-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GBR</td>\n",
       "      <td>UK</td>\n",
       "      <td>66.44</td>\n",
       "      <td>242.50</td>\n",
       "      <td>2631.23</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "      <td>60.36</td>\n",
       "      <td>301.34</td>\n",
       "      <td>1943.84</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>44.94</td>\n",
       "      <td>2780.40</td>\n",
       "      <td>637.49</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1816-07-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>43.38</td>\n",
       "      <td>2381.74</td>\n",
       "      <td>167.56</td>\n",
       "      <td>Africa</td>\n",
       "      <td>7/5/1962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>37.59</td>\n",
       "      <td>9984.67</td>\n",
       "      <td>1647.12</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1867-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>25.47</td>\n",
       "      <td>7692.02</td>\n",
       "      <td>1408.68</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KAZ</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>18.53</td>\n",
       "      <td>2724.90</td>\n",
       "      <td>159.41</td>\n",
       "      <td>Asia</td>\n",
       "      <td>12/16/1991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID     COUNTRY      POP      AREA       GDP       CONT     IND_DAY\n",
       "0   CHN       China  1398.72   9596.96  12234.80       Asia         NaN\n",
       "1   IND       India  1351.16   3287.26   2575.67       Asia   8/15/1947\n",
       "2   USA          US   329.74   9833.52  19485.40  N.America  1776-07-04\n",
       "3   IDN   Indonesia   268.07   1910.93   1015.54       Asia   8/17/1945\n",
       "4   BRA      Brazil   210.32   8515.77   2055.51  S.America  1822-09-07\n",
       "5   PAK    Pakistan   205.71    881.91    302.14       Asia   8/14/1947\n",
       "6   NGA     Nigeria   200.96    923.77    375.77     Africa   10/1/1960\n",
       "7   BGD  Bangladesh   167.09    147.57    245.63       Asia   3/26/1971\n",
       "8   RUS      Russia   146.79  17098.20   1530.75        NaN   6/12/1992\n",
       "9   MEX      Mexico   126.58   1964.38   1158.23  N.America  1810-09-16\n",
       "10  JPN       Japan   126.22    377.97   4872.42       Asia         NaN\n",
       "11  DEU     Germany    83.02    357.11   3693.20     Europe         NaN\n",
       "12  FRA      France    67.02    640.68   2582.49     Europe  1789-07-14\n",
       "13  GBR          UK    66.44    242.50   2631.23     Europe         NaN\n",
       "14  ITA       Italy    60.36    301.34   1943.84     Europe         NaN\n",
       "15  ARG   Argentina    44.94   2780.40    637.49  S.America  1816-07-09\n",
       "16  DZA     Algeria    43.38   2381.74    167.56     Africa    7/5/1962\n",
       "17  CAN      Canada    37.59   9984.67   1647.12  N.America  1867-07-01\n",
       "18  AUS   Australia    25.47   7692.02   1408.68    Oceania         NaN\n",
       "19  KAZ  Kazakhstan    18.53   2724.90    159.41       Asia  12/16/1991"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#read the csv file \n",
    "df = pd.read_csv(\"Countries_CSV.csv\") #this will read the data into dataframe.\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function read_csv in module pandas.io.parsers:\n",
      "\n",
      "read_csv(filepath_or_buffer: Union[str, pathlib.Path, IO[~AnyStr]], sep=',', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal: str = '.', lineterminator=None, quotechar='\"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, dialect=None, error_bad_lines=True, warn_bad_lines=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None)\n",
      "    Read a comma-separated values (csv) file into DataFrame.\n",
      "    \n",
      "    Also supports optionally iterating or breaking of the file\n",
      "    into chunks.\n",
      "    \n",
      "    Additional help can be found in the online docs for\n",
      "    `IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    filepath_or_buffer : str, path object or file-like object\n",
      "        Any valid string path is acceptable. The string could be a URL. Valid\n",
      "        URL schemes include http, ftp, s3, and file. For file URLs, a host is\n",
      "        expected. A local file could be: file://localhost/path/to/table.csv.\n",
      "    \n",
      "        If you want to pass in a path object, pandas accepts any ``os.PathLike``.\n",
      "    \n",
      "        By file-like object, we refer to objects with a ``read()`` method, such as\n",
      "        a file handler (e.g. via builtin ``open`` function) or ``StringIO``.\n",
      "    sep : str, default ','\n",
      "        Delimiter to use. If sep is None, the C engine cannot automatically detect\n",
      "        the separator, but the Python parsing engine can, meaning the latter will\n",
      "        be used and automatically detect the separator by Python's builtin sniffer\n",
      "        tool, ``csv.Sniffer``. In addition, separators longer than 1 character and\n",
      "        different from ``'\\s+'`` will be interpreted as regular expressions and\n",
      "        will also force the use of the Python parsing engine. Note that regex\n",
      "        delimiters are prone to ignoring quoted data. Regex example: ``'\\r\\t'``.\n",
      "    delimiter : str, default ``None``\n",
      "        Alias for sep.\n",
      "    header : int, list of int, default 'infer'\n",
      "        Row number(s) to use as the column names, and the start of the\n",
      "        data.  Default behavior is to infer the column names: if no names\n",
      "        are passed the behavior is identical to ``header=0`` and column\n",
      "        names are inferred from the first line of the file, if column\n",
      "        names are passed explicitly then the behavior is identical to\n",
      "        ``header=None``. Explicitly pass ``header=0`` to be able to\n",
      "        replace existing names. The header can be a list of integers that\n",
      "        specify row locations for a multi-index on the columns\n",
      "        e.g. [0,1,3]. Intervening rows that are not specified will be\n",
      "        skipped (e.g. 2 in this example is skipped). Note that this\n",
      "        parameter ignores commented lines and empty lines if\n",
      "        ``skip_blank_lines=True``, so ``header=0`` denotes the first line of\n",
      "        data rather than the first line of the file.\n",
      "    names : array-like, optional\n",
      "        List of column names to use. If the file contains a header row,\n",
      "        then you should explicitly pass ``header=0`` to override the column names.\n",
      "        Duplicates in this list are not allowed.\n",
      "    index_col : int, str, sequence of int / str, or False, default ``None``\n",
      "      Column(s) to use as the row labels of the ``DataFrame``, either given as\n",
      "      string name or column index. If a sequence of int / str is given, a\n",
      "      MultiIndex is used.\n",
      "    \n",
      "      Note: ``index_col=False`` can be used to force pandas to *not* use the first\n",
      "      column as the index, e.g. when you have a malformed file with delimiters at\n",
      "      the end of each line.\n",
      "    usecols : list-like or callable, optional\n",
      "        Return a subset of the columns. If list-like, all elements must either\n",
      "        be positional (i.e. integer indices into the document columns) or strings\n",
      "        that correspond to column names provided either by the user in `names` or\n",
      "        inferred from the document header row(s). For example, a valid list-like\n",
      "        `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.\n",
      "        Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.\n",
      "        To instantiate a DataFrame from ``data`` with element order preserved use\n",
      "        ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns\n",
      "        in ``['foo', 'bar']`` order or\n",
      "        ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``\n",
      "        for ``['bar', 'foo']`` order.\n",
      "    \n",
      "        If callable, the callable function will be evaluated against the column\n",
      "        names, returning names where the callable function evaluates to True. An\n",
      "        example of a valid callable argument would be ``lambda x: x.upper() in\n",
      "        ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster\n",
      "        parsing time and lower memory usage.\n",
      "    squeeze : bool, default False\n",
      "        If the parsed data only contains one column then return a Series.\n",
      "    prefix : str, optional\n",
      "        Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...\n",
      "    mangle_dupe_cols : bool, default True\n",
      "        Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than\n",
      "        'X'...'X'. Passing in False will cause data to be overwritten if there\n",
      "        are duplicate names in the columns.\n",
      "    dtype : Type name or dict of column -> type, optional\n",
      "        Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32,\n",
      "        'c': 'Int64'}\n",
      "        Use `str` or `object` together with suitable `na_values` settings\n",
      "        to preserve and not interpret dtype.\n",
      "        If converters are specified, they will be applied INSTEAD\n",
      "        of dtype conversion.\n",
      "    engine : {'c', 'python'}, optional\n",
      "        Parser engine to use. The C engine is faster while the python engine is\n",
      "        currently more feature-complete.\n",
      "    converters : dict, optional\n",
      "        Dict of functions for converting values in certain columns. Keys can either\n",
      "        be integers or column labels.\n",
      "    true_values : list, optional\n",
      "        Values to consider as True.\n",
      "    false_values : list, optional\n",
      "        Values to consider as False.\n",
      "    skipinitialspace : bool, default False\n",
      "        Skip spaces after delimiter.\n",
      "    skiprows : list-like, int or callable, optional\n",
      "        Line numbers to skip (0-indexed) or number of lines to skip (int)\n",
      "        at the start of the file.\n",
      "    \n",
      "        If callable, the callable function will be evaluated against the row\n",
      "        indices, returning True if the row should be skipped and False otherwise.\n",
      "        An example of a valid callable argument would be ``lambda x: x in [0, 2]``.\n",
      "    skipfooter : int, default 0\n",
      "        Number of lines at bottom of file to skip (Unsupported with engine='c').\n",
      "    nrows : int, optional\n",
      "        Number of rows of file to read. Useful for reading pieces of large files.\n",
      "    na_values : scalar, str, list-like, or dict, optional\n",
      "        Additional strings to recognize as NA/NaN. If dict passed, specific\n",
      "        per-column NA values.  By default the following values are interpreted as\n",
      "        NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',\n",
      "        '1.#IND', '1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'n/a',\n",
      "        'nan', 'null'.\n",
      "    keep_default_na : bool, default True\n",
      "        Whether or not to include the default NaN values when parsing the data.\n",
      "        Depending on whether `na_values` is passed in, the behavior is as follows:\n",
      "    \n",
      "        * If `keep_default_na` is True, and `na_values` are specified, `na_values`\n",
      "          is appended to the default NaN values used for parsing.\n",
      "        * If `keep_default_na` is True, and `na_values` are not specified, only\n",
      "          the default NaN values are used for parsing.\n",
      "        * If `keep_default_na` is False, and `na_values` are specified, only\n",
      "          the NaN values specified `na_values` are used for parsing.\n",
      "        * If `keep_default_na` is False, and `na_values` are not specified, no\n",
      "          strings will be parsed as NaN.\n",
      "    \n",
      "        Note that if `na_filter` is passed in as False, the `keep_default_na` and\n",
      "        `na_values` parameters will be ignored.\n",
      "    na_filter : bool, default True\n",
      "        Detect missing value markers (empty strings and the value of na_values). In\n",
      "        data without any NAs, passing na_filter=False can improve the performance\n",
      "        of reading a large file.\n",
      "    verbose : bool, default False\n",
      "        Indicate number of NA values placed in non-numeric columns.\n",
      "    skip_blank_lines : bool, default True\n",
      "        If True, skip over blank lines rather than interpreting as NaN values.\n",
      "    parse_dates : bool or list of int or names or list of lists or dict, default False\n",
      "        The behavior is as follows:\n",
      "    \n",
      "        * boolean. If True -> try parsing the index.\n",
      "        * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3\n",
      "          each as a separate date column.\n",
      "        * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as\n",
      "          a single date column.\n",
      "        * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call\n",
      "          result 'foo'\n",
      "    \n",
      "        If a column or index cannot be represented as an array of datetimes,\n",
      "        say because of an unparseable value or a mixture of timezones, the column\n",
      "        or index will be returned unaltered as an object data type. For\n",
      "        non-standard datetime parsing, use ``pd.to_datetime`` after\n",
      "        ``pd.read_csv``. To parse an index or column with a mixture of timezones,\n",
      "        specify ``date_parser`` to be a partially-applied\n",
      "        :func:`pandas.to_datetime` with ``utc=True``. See\n",
      "        :ref:`io.csv.mixed_timezones` for more.\n",
      "    \n",
      "        Note: A fast-path exists for iso8601-formatted dates.\n",
      "    infer_datetime_format : bool, default False\n",
      "        If True and `parse_dates` is enabled, pandas will attempt to infer the\n",
      "        format of the datetime strings in the columns, and if it can be inferred,\n",
      "        switch to a faster method of parsing them. In some cases this can increase\n",
      "        the parsing speed by 5-10x.\n",
      "    keep_date_col : bool, default False\n",
      "        If True and `parse_dates` specifies combining multiple columns then\n",
      "        keep the original columns.\n",
      "    date_parser : function, optional\n",
      "        Function to use for converting a sequence of string columns to an array of\n",
      "        datetime instances. The default uses ``dateutil.parser.parser`` to do the\n",
      "        conversion. Pandas will try to call `date_parser` in three different ways,\n",
      "        advancing to the next if an exception occurs: 1) Pass one or more arrays\n",
      "        (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the\n",
      "        string values from the columns defined by `parse_dates` into a single array\n",
      "        and pass that; and 3) call `date_parser` once for each row using one or\n",
      "        more strings (corresponding to the columns defined by `parse_dates`) as\n",
      "        arguments.\n",
      "    dayfirst : bool, default False\n",
      "        DD/MM format dates, international and European format.\n",
      "    cache_dates : bool, default True\n",
      "        If True, use a cache of unique, converted dates to apply the datetime\n",
      "        conversion. May produce significant speed-up when parsing duplicate\n",
      "        date strings, especially ones with timezone offsets.\n",
      "    \n",
      "        .. versionadded:: 0.25.0\n",
      "    iterator : bool, default False\n",
      "        Return TextFileReader object for iteration or getting chunks with\n",
      "        ``get_chunk()``.\n",
      "    chunksize : int, optional\n",
      "        Return TextFileReader object for iteration.\n",
      "        See the `IO Tools docs\n",
      "        <https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_\n",
      "        for more information on ``iterator`` and ``chunksize``.\n",
      "    compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'\n",
      "        For on-the-fly decompression of on-disk data. If 'infer' and\n",
      "        `filepath_or_buffer` is path-like, then detect compression from the\n",
      "        following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no\n",
      "        decompression). If using 'zip', the ZIP file must contain only one data\n",
      "        file to be read in. Set to None for no decompression.\n",
      "    thousands : str, optional\n",
      "        Thousands separator.\n",
      "    decimal : str, default '.'\n",
      "        Character to recognize as decimal point (e.g. use ',' for European data).\n",
      "    lineterminator : str (length 1), optional\n",
      "        Character to break file into lines. Only valid with C parser.\n",
      "    quotechar : str (length 1), optional\n",
      "        The character used to denote the start and end of a quoted item. Quoted\n",
      "        items can include the delimiter and it will be ignored.\n",
      "    quoting : int or csv.QUOTE_* instance, default 0\n",
      "        Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of\n",
      "        QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).\n",
      "    doublequote : bool, default ``True``\n",
      "       When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate\n",
      "       whether or not to interpret two consecutive quotechar elements INSIDE a\n",
      "       field as a single ``quotechar`` element.\n",
      "    escapechar : str (length 1), optional\n",
      "        One-character string used to escape other characters.\n",
      "    comment : str, optional\n",
      "        Indicates remainder of line should not be parsed. If found at the beginning\n",
      "        of a line, the line will be ignored altogether. This parameter must be a\n",
      "        single character. Like empty lines (as long as ``skip_blank_lines=True``),\n",
      "        fully commented lines are ignored by the parameter `header` but not by\n",
      "        `skiprows`. For example, if ``comment='#'``, parsing\n",
      "        ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being\n",
      "        treated as the header.\n",
      "    encoding : str, optional\n",
      "        Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python\n",
      "        standard encodings\n",
      "        <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ .\n",
      "    dialect : str or csv.Dialect, optional\n",
      "        If provided, this parameter will override values (default or not) for the\n",
      "        following parameters: `delimiter`, `doublequote`, `escapechar`,\n",
      "        `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to\n",
      "        override values, a ParserWarning will be issued. See csv.Dialect\n",
      "        documentation for more details.\n",
      "    error_bad_lines : bool, default True\n",
      "        Lines with too many fields (e.g. a csv line with too many commas) will by\n",
      "        default cause an exception to be raised, and no DataFrame will be returned.\n",
      "        If False, then these \"bad lines\" will dropped from the DataFrame that is\n",
      "        returned.\n",
      "    warn_bad_lines : bool, default True\n",
      "        If error_bad_lines is False, and warn_bad_lines is True, a warning for each\n",
      "        \"bad line\" will be output.\n",
      "    delim_whitespace : bool, default False\n",
      "        Specifies whether or not whitespace (e.g. ``' '`` or ``'    '``) will be\n",
      "        used as the sep. Equivalent to setting ``sep='\\s+'``. If this option\n",
      "        is set to True, nothing should be passed in for the ``delimiter``\n",
      "        parameter.\n",
      "    low_memory : bool, default True\n",
      "        Internally process the file in chunks, resulting in lower memory use\n",
      "        while parsing, but possibly mixed type inference.  To ensure no mixed\n",
      "        types either set False, or specify the type with the `dtype` parameter.\n",
      "        Note that the entire file is read into a single DataFrame regardless,\n",
      "        use the `chunksize` or `iterator` parameter to return the data in chunks.\n",
      "        (Only valid with C parser).\n",
      "    memory_map : bool, default False\n",
      "        If a filepath is provided for `filepath_or_buffer`, map the file object\n",
      "        directly onto memory and access the data directly from there. Using this\n",
      "        option can improve performance because there is no longer any I/O overhead.\n",
      "    float_precision : str, optional\n",
      "        Specifies which converter the C engine should use for floating-point\n",
      "        values. The options are `None` for the ordinary converter,\n",
      "        `high` for the high-precision converter, and `round_trip` for the\n",
      "        round-trip converter.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    DataFrame or TextParser\n",
      "        A comma-separated values (csv) file is returned as two-dimensional\n",
      "        data structure with labeled axes.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    to_csv : Write DataFrame to a comma-separated values (csv) file.\n",
      "    read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
      "    read_fwf : Read a table of fixed-width formatted lines into DataFrame.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> pd.read_csv('data.csv')  # doctest: +SKIP\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pd.read_csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can provide variety of arguments as options. Let's try a couple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
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       "    }\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>1398.72</td>\n",
       "      <td>9596.96</td>\n",
       "      <td>12234.80</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>1351.16</td>\n",
       "      <td>3287.26</td>\n",
       "      <td>2575.67</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/15/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>329.74</td>\n",
       "      <td>9833.52</td>\n",
       "      <td>19485.40</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1776-07-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDN</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>268.07</td>\n",
       "      <td>1910.93</td>\n",
       "      <td>1015.54</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/17/1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>210.32</td>\n",
       "      <td>8515.77</td>\n",
       "      <td>2055.51</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1822-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PAK</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>205.71</td>\n",
       "      <td>881.91</td>\n",
       "      <td>302.14</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/14/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>200.96</td>\n",
       "      <td>923.77</td>\n",
       "      <td>375.77</td>\n",
       "      <td>Africa</td>\n",
       "      <td>10/1/1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BGD</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>167.09</td>\n",
       "      <td>147.57</td>\n",
       "      <td>245.63</td>\n",
       "      <td>Asia</td>\n",
       "      <td>3/26/1971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RUS</td>\n",
       "      <td>Russia</td>\n",
       "      <td>146.79</td>\n",
       "      <td>17098.20</td>\n",
       "      <td>1530.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/12/1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MEX</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>126.58</td>\n",
       "      <td>1964.38</td>\n",
       "      <td>1158.23</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1810-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>126.22</td>\n",
       "      <td>377.97</td>\n",
       "      <td>4872.42</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>83.02</td>\n",
       "      <td>357.11</td>\n",
       "      <td>3693.20</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>67.02</td>\n",
       "      <td>640.68</td>\n",
       "      <td>2582.49</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1789-07-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GBR</td>\n",
       "      <td>UK</td>\n",
       "      <td>66.44</td>\n",
       "      <td>242.50</td>\n",
       "      <td>2631.23</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "      <td>60.36</td>\n",
       "      <td>301.34</td>\n",
       "      <td>1943.84</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>44.94</td>\n",
       "      <td>2780.40</td>\n",
       "      <td>637.49</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1816-07-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>43.38</td>\n",
       "      <td>2381.74</td>\n",
       "      <td>167.56</td>\n",
       "      <td>Africa</td>\n",
       "      <td>7/5/1962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>37.59</td>\n",
       "      <td>9984.67</td>\n",
       "      <td>1647.12</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1867-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>25.47</td>\n",
       "      <td>7692.02</td>\n",
       "      <td>1408.68</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KAZ</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>18.53</td>\n",
       "      <td>2724.90</td>\n",
       "      <td>159.41</td>\n",
       "      <td>Asia</td>\n",
       "      <td>12/16/1991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0           1        2         3         4          5           6\n",
       "0   CHN       China  1398.72   9596.96  12234.80       Asia         NaN\n",
       "1   IND       India  1351.16   3287.26   2575.67       Asia   8/15/1947\n",
       "2   USA          US   329.74   9833.52  19485.40  N.America  1776-07-04\n",
       "3   IDN   Indonesia   268.07   1910.93   1015.54       Asia   8/17/1945\n",
       "4   BRA      Brazil   210.32   8515.77   2055.51  S.America  1822-09-07\n",
       "5   PAK    Pakistan   205.71    881.91    302.14       Asia   8/14/1947\n",
       "6   NGA     Nigeria   200.96    923.77    375.77     Africa   10/1/1960\n",
       "7   BGD  Bangladesh   167.09    147.57    245.63       Asia   3/26/1971\n",
       "8   RUS      Russia   146.79  17098.20   1530.75        NaN   6/12/1992\n",
       "9   MEX      Mexico   126.58   1964.38   1158.23  N.America  1810-09-16\n",
       "10  JPN       Japan   126.22    377.97   4872.42       Asia         NaN\n",
       "11  DEU     Germany    83.02    357.11   3693.20     Europe         NaN\n",
       "12  FRA      France    67.02    640.68   2582.49     Europe  1789-07-14\n",
       "13  GBR          UK    66.44    242.50   2631.23     Europe         NaN\n",
       "14  ITA       Italy    60.36    301.34   1943.84     Europe         NaN\n",
       "15  ARG   Argentina    44.94   2780.40    637.49  S.America  1816-07-09\n",
       "16  DZA     Algeria    43.38   2381.74    167.56     Africa    7/5/1962\n",
       "17  CAN      Canada    37.59   9984.67   1647.12  N.America  1867-07-01\n",
       "18  AUS   Australia    25.47   7692.02   1408.68    Oceania         NaN\n",
       "19  KAZ  Kazakhstan    18.53   2724.90    159.41       Asia  12/16/1991"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#read the csv file \n",
    "df = pd.read_csv(\"Countries_CSV.csv\", skiprows = 1, header = None)  #this will read skip the header\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "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>ID</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>POP</th>\n",
       "      <th>AREA</th>\n",
       "      <th>GDP</th>\n",
       "      <th>CONT</th>\n",
       "      <th>IND_DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>1398.72</td>\n",
       "      <td>9596.96</td>\n",
       "      <td>12234.80</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>1351.16</td>\n",
       "      <td>3287.26</td>\n",
       "      <td>2575.67</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/15/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>329.74</td>\n",
       "      <td>9833.52</td>\n",
       "      <td>19485.40</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1776-07-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDN</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>268.07</td>\n",
       "      <td>1910.93</td>\n",
       "      <td>1015.54</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/17/1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>210.32</td>\n",
       "      <td>8515.77</td>\n",
       "      <td>2055.51</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1822-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PAK</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>205.71</td>\n",
       "      <td>881.91</td>\n",
       "      <td>302.14</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/14/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>200.96</td>\n",
       "      <td>923.77</td>\n",
       "      <td>375.77</td>\n",
       "      <td>Africa</td>\n",
       "      <td>10/1/1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BGD</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>167.09</td>\n",
       "      <td>147.57</td>\n",
       "      <td>245.63</td>\n",
       "      <td>Asia</td>\n",
       "      <td>3/26/1971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RUS</td>\n",
       "      <td>Russia</td>\n",
       "      <td>146.79</td>\n",
       "      <td>17098.20</td>\n",
       "      <td>1530.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/12/1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MEX</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>126.58</td>\n",
       "      <td>1964.38</td>\n",
       "      <td>1158.23</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1810-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>126.22</td>\n",
       "      <td>377.97</td>\n",
       "      <td>4872.42</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>83.02</td>\n",
       "      <td>357.11</td>\n",
       "      <td>3693.20</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>67.02</td>\n",
       "      <td>640.68</td>\n",
       "      <td>2582.49</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1789-07-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GBR</td>\n",
       "      <td>UK</td>\n",
       "      <td>66.44</td>\n",
       "      <td>242.50</td>\n",
       "      <td>2631.23</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "      <td>60.36</td>\n",
       "      <td>301.34</td>\n",
       "      <td>1943.84</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>44.94</td>\n",
       "      <td>2780.40</td>\n",
       "      <td>637.49</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1816-07-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>43.38</td>\n",
       "      <td>2381.74</td>\n",
       "      <td>167.56</td>\n",
       "      <td>Africa</td>\n",
       "      <td>7/5/1962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>37.59</td>\n",
       "      <td>9984.67</td>\n",
       "      <td>1647.12</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1867-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>25.47</td>\n",
       "      <td>7692.02</td>\n",
       "      <td>1408.68</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KAZ</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>18.53</td>\n",
       "      <td>2724.90</td>\n",
       "      <td>159.41</td>\n",
       "      <td>Asia</td>\n",
       "      <td>12/16/1991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID     COUNTRY      POP      AREA       GDP       CONT     IND_DAY\n",
       "0   CHN       China  1398.72   9596.96  12234.80       Asia         NaN\n",
       "1   IND       India  1351.16   3287.26   2575.67       Asia   8/15/1947\n",
       "2   USA          US   329.74   9833.52  19485.40  N.America  1776-07-04\n",
       "3   IDN   Indonesia   268.07   1910.93   1015.54       Asia   8/17/1945\n",
       "4   BRA      Brazil   210.32   8515.77   2055.51  S.America  1822-09-07\n",
       "5   PAK    Pakistan   205.71    881.91    302.14       Asia   8/14/1947\n",
       "6   NGA     Nigeria   200.96    923.77    375.77     Africa   10/1/1960\n",
       "7   BGD  Bangladesh   167.09    147.57    245.63       Asia   3/26/1971\n",
       "8   RUS      Russia   146.79  17098.20   1530.75        NaN   6/12/1992\n",
       "9   MEX      Mexico   126.58   1964.38   1158.23  N.America  1810-09-16\n",
       "10  JPN       Japan   126.22    377.97   4872.42       Asia         NaN\n",
       "11  DEU     Germany    83.02    357.11   3693.20     Europe         NaN\n",
       "12  FRA      France    67.02    640.68   2582.49     Europe  1789-07-14\n",
       "13  GBR          UK    66.44    242.50   2631.23     Europe         NaN\n",
       "14  ITA       Italy    60.36    301.34   1943.84     Europe         NaN\n",
       "15  ARG   Argentina    44.94   2780.40    637.49  S.America  1816-07-09\n",
       "16  DZA     Algeria    43.38   2381.74    167.56     Africa    7/5/1962\n",
       "17  CAN      Canada    37.59   9984.67   1647.12  N.America  1867-07-01\n",
       "18  AUS   Australia    25.47   7692.02   1408.68    Oceania         NaN\n",
       "19  KAZ  Kazakhstan    18.53   2724.90    159.41       Asia  12/16/1991"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#read the csv file \n",
    "df = pd.read_csv(\"Countries_CSV.csv\", sep = \",\")  #this is already a CSV file and \",\" is the default input\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read txt File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>POP</th>\n",
       "      <th>AREA</th>\n",
       "      <th>GDP</th>\n",
       "      <th>CONT</th>\n",
       "      <th>IND_DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>1398.72</td>\n",
       "      <td>9596.96</td>\n",
       "      <td>12234.80</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>1351.16</td>\n",
       "      <td>3287.26</td>\n",
       "      <td>2575.67</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/15/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>329.74</td>\n",
       "      <td>9833.52</td>\n",
       "      <td>19485.40</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1776-07-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDN</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>268.07</td>\n",
       "      <td>1910.93</td>\n",
       "      <td>1015.54</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/17/1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>210.32</td>\n",
       "      <td>8515.77</td>\n",
       "      <td>2055.51</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1822-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PAK</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>205.71</td>\n",
       "      <td>881.91</td>\n",
       "      <td>302.14</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/14/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>200.96</td>\n",
       "      <td>923.77</td>\n",
       "      <td>375.77</td>\n",
       "      <td>Africa</td>\n",
       "      <td>10/1/1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BGD</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>167.09</td>\n",
       "      <td>147.57</td>\n",
       "      <td>245.63</td>\n",
       "      <td>Asia</td>\n",
       "      <td>3/26/1971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RUS</td>\n",
       "      <td>Russia</td>\n",
       "      <td>146.79</td>\n",
       "      <td>17098.20</td>\n",
       "      <td>1530.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/12/1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MEX</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>126.58</td>\n",
       "      <td>1964.38</td>\n",
       "      <td>1158.23</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1810-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>126.22</td>\n",
       "      <td>377.97</td>\n",
       "      <td>4872.42</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>83.02</td>\n",
       "      <td>357.11</td>\n",
       "      <td>3693.20</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>67.02</td>\n",
       "      <td>640.68</td>\n",
       "      <td>2582.49</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1789-07-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GBR</td>\n",
       "      <td>UK</td>\n",
       "      <td>66.44</td>\n",
       "      <td>242.50</td>\n",
       "      <td>2631.23</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "      <td>60.36</td>\n",
       "      <td>301.34</td>\n",
       "      <td>1943.84</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>44.94</td>\n",
       "      <td>2780.40</td>\n",
       "      <td>637.49</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1816-07-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>43.38</td>\n",
       "      <td>2381.74</td>\n",
       "      <td>167.56</td>\n",
       "      <td>Africa</td>\n",
       "      <td>7/5/1962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>37.59</td>\n",
       "      <td>9984.67</td>\n",
       "      <td>1647.12</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1867-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>25.47</td>\n",
       "      <td>7692.02</td>\n",
       "      <td>1408.68</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KAZ</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>18.53</td>\n",
       "      <td>2724.90</td>\n",
       "      <td>159.41</td>\n",
       "      <td>Asia</td>\n",
       "      <td>12/16/1991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID     COUNTRY      POP      AREA       GDP       CONT     IND_DAY\n",
       "0   CHN       China  1398.72   9596.96  12234.80       Asia         NaN\n",
       "1   IND       India  1351.16   3287.26   2575.67       Asia   8/15/1947\n",
       "2   USA          US   329.74   9833.52  19485.40  N.America  1776-07-04\n",
       "3   IDN   Indonesia   268.07   1910.93   1015.54       Asia   8/17/1945\n",
       "4   BRA      Brazil   210.32   8515.77   2055.51  S.America  1822-09-07\n",
       "5   PAK    Pakistan   205.71    881.91    302.14       Asia   8/14/1947\n",
       "6   NGA     Nigeria   200.96    923.77    375.77     Africa   10/1/1960\n",
       "7   BGD  Bangladesh   167.09    147.57    245.63       Asia   3/26/1971\n",
       "8   RUS      Russia   146.79  17098.20   1530.75        NaN   6/12/1992\n",
       "9   MEX      Mexico   126.58   1964.38   1158.23  N.America  1810-09-16\n",
       "10  JPN       Japan   126.22    377.97   4872.42       Asia         NaN\n",
       "11  DEU     Germany    83.02    357.11   3693.20     Europe         NaN\n",
       "12  FRA      France    67.02    640.68   2582.49     Europe  1789-07-14\n",
       "13  GBR          UK    66.44    242.50   2631.23     Europe         NaN\n",
       "14  ITA       Italy    60.36    301.34   1943.84     Europe         NaN\n",
       "15  ARG   Argentina    44.94   2780.40    637.49  S.America  1816-07-09\n",
       "16  DZA     Algeria    43.38   2381.74    167.56     Africa    7/5/1962\n",
       "17  CAN      Canada    37.59   9984.67   1647.12  N.America  1867-07-01\n",
       "18  AUS   Australia    25.47   7692.02   1408.68    Oceania         NaN\n",
       "19  KAZ  Kazakhstan    18.53   2724.90    159.41       Asia  12/16/1991"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#read the csv file \n",
    "df = pd.read_csv(\"countries - comma.txt\")  #we can read comma separated values in txt files as well. Notice that sep=\",\" is default.\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>ID\\tCOUNTRY\\tPOP\\tAREA\\tGDP\\tCONT\\tIND_DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHN\\tChina\\t1398.72\\t9596.96\\t12234.8\\tAsia\\tNaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IND\\tIndia\\t1351.16\\t3287.26\\t2575.67\\tAsia\\t8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA\\tUS\\t329.74\\t9833.52\\t19485.4\\tN.America\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDN\\tIndonesia\\t268.07\\t1910.93\\t1015.54\\tAsia...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRA\\tBrazil\\t210.32\\t8515.77\\t2055.51\\tS.Ameri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PAK\\tPakistan\\t205.71\\t881.91\\t302.14\\tAsia\\t8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NGA\\tNigeria\\t200.96\\t923.77\\t375.77\\tAfrica\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BGD\\tBangladesh\\t167.09\\t147.57\\t245.63\\tAsia\\...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RUS\\tRussia\\t146.79\\t17098.2\\t1530.75\\tNaN\\t6/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MEX\\tMexico\\t126.58\\t1964.38\\t1158.23\\tN.Ameri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>JPN\\tJapan\\t126.22\\t377.97\\t4872.42\\tAsia\\tNaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DEU\\tGermany\\t83.02\\t357.11\\t3693.2\\tEurope\\tNaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FRA\\tFrance\\t67.02\\t640.68\\t2582.49\\tEurope\\t1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GBR\\tUK\\t66.44\\t242.5\\t2631.23\\tEurope\\tNaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ITA\\tItaly\\t60.36\\t301.34\\t1943.84\\tEurope\\tNaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ARG\\tArgentina\\t44.94\\t2780.4\\t637.49\\tS.Ameri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DZA\\tAlgeria\\t43.38\\t2381.74\\t167.56\\tAfrica\\t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAN\\tCanada\\t37.59\\t9984.67\\t1647.12\\tN.Americ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AUS\\tAustralia\\t25.47\\t7692.02\\t1408.68\\tOcean...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KAZ\\tKazakhstan\\t18.53\\t2724.9\\t159.41\\tAsia\\t...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID\\tCOUNTRY\\tPOP\\tAREA\\tGDP\\tCONT\\tIND_DAY\n",
       "0    CHN\\tChina\\t1398.72\\t9596.96\\t12234.8\\tAsia\\tNaN\n",
       "1   IND\\tIndia\\t1351.16\\t3287.26\\t2575.67\\tAsia\\t8...\n",
       "2   USA\\tUS\\t329.74\\t9833.52\\t19485.4\\tN.America\\t...\n",
       "3   IDN\\tIndonesia\\t268.07\\t1910.93\\t1015.54\\tAsia...\n",
       "4   BRA\\tBrazil\\t210.32\\t8515.77\\t2055.51\\tS.Ameri...\n",
       "5   PAK\\tPakistan\\t205.71\\t881.91\\t302.14\\tAsia\\t8...\n",
       "6   NGA\\tNigeria\\t200.96\\t923.77\\t375.77\\tAfrica\\t...\n",
       "7   BGD\\tBangladesh\\t167.09\\t147.57\\t245.63\\tAsia\\...\n",
       "8   RUS\\tRussia\\t146.79\\t17098.2\\t1530.75\\tNaN\\t6/...\n",
       "9   MEX\\tMexico\\t126.58\\t1964.38\\t1158.23\\tN.Ameri...\n",
       "10     JPN\\tJapan\\t126.22\\t377.97\\t4872.42\\tAsia\\tNaN\n",
       "11   DEU\\tGermany\\t83.02\\t357.11\\t3693.2\\tEurope\\tNaN\n",
       "12  FRA\\tFrance\\t67.02\\t640.68\\t2582.49\\tEurope\\t1...\n",
       "13        GBR\\tUK\\t66.44\\t242.5\\t2631.23\\tEurope\\tNaN\n",
       "14    ITA\\tItaly\\t60.36\\t301.34\\t1943.84\\tEurope\\tNaN\n",
       "15  ARG\\tArgentina\\t44.94\\t2780.4\\t637.49\\tS.Ameri...\n",
       "16  DZA\\tAlgeria\\t43.38\\t2381.74\\t167.56\\tAfrica\\t...\n",
       "17  CAN\\tCanada\\t37.59\\t9984.67\\t1647.12\\tN.Americ...\n",
       "18  AUS\\tAustralia\\t25.47\\t7692.02\\t1408.68\\tOcean...\n",
       "19  KAZ\\tKazakhstan\\t18.53\\t2724.9\\t159.41\\tAsia\\t..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#read the csv file \n",
    "df = pd.read_csv(\"Countries_TSV.txt\")  #seperator is not comma any more.\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>ID</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>POP</th>\n",
       "      <th>AREA</th>\n",
       "      <th>GDP</th>\n",
       "      <th>CONT</th>\n",
       "      <th>IND_DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>1398.72</td>\n",
       "      <td>9596.96</td>\n",
       "      <td>12234.80</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>1351.16</td>\n",
       "      <td>3287.26</td>\n",
       "      <td>2575.67</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/15/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>329.74</td>\n",
       "      <td>9833.52</td>\n",
       "      <td>19485.40</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1776-07-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDN</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>268.07</td>\n",
       "      <td>1910.93</td>\n",
       "      <td>1015.54</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/17/1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>210.32</td>\n",
       "      <td>8515.77</td>\n",
       "      <td>2055.51</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1822-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PAK</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>205.71</td>\n",
       "      <td>881.91</td>\n",
       "      <td>302.14</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/14/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>200.96</td>\n",
       "      <td>923.77</td>\n",
       "      <td>375.77</td>\n",
       "      <td>Africa</td>\n",
       "      <td>10/1/1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BGD</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>167.09</td>\n",
       "      <td>147.57</td>\n",
       "      <td>245.63</td>\n",
       "      <td>Asia</td>\n",
       "      <td>3/26/1971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RUS</td>\n",
       "      <td>Russia</td>\n",
       "      <td>146.79</td>\n",
       "      <td>17098.20</td>\n",
       "      <td>1530.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6/12/1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MEX</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>126.58</td>\n",
       "      <td>1964.38</td>\n",
       "      <td>1158.23</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1810-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>126.22</td>\n",
       "      <td>377.97</td>\n",
       "      <td>4872.42</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>83.02</td>\n",
       "      <td>357.11</td>\n",
       "      <td>3693.20</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>67.02</td>\n",
       "      <td>640.68</td>\n",
       "      <td>2582.49</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1789-07-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GBR</td>\n",
       "      <td>UK</td>\n",
       "      <td>66.44</td>\n",
       "      <td>242.50</td>\n",
       "      <td>2631.23</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "      <td>60.36</td>\n",
       "      <td>301.34</td>\n",
       "      <td>1943.84</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>44.94</td>\n",
       "      <td>2780.40</td>\n",
       "      <td>637.49</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1816-07-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>43.38</td>\n",
       "      <td>2381.74</td>\n",
       "      <td>167.56</td>\n",
       "      <td>Africa</td>\n",
       "      <td>7/5/1962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>37.59</td>\n",
       "      <td>9984.67</td>\n",
       "      <td>1647.12</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1867-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>25.47</td>\n",
       "      <td>7692.02</td>\n",
       "      <td>1408.68</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KAZ</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>18.53</td>\n",
       "      <td>2724.90</td>\n",
       "      <td>159.41</td>\n",
       "      <td>Asia</td>\n",
       "      <td>12/16/1991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID     COUNTRY      POP      AREA       GDP       CONT     IND_DAY\n",
       "0   CHN       China  1398.72   9596.96  12234.80       Asia         NaN\n",
       "1   IND       India  1351.16   3287.26   2575.67       Asia   8/15/1947\n",
       "2   USA          US   329.74   9833.52  19485.40  N.America  1776-07-04\n",
       "3   IDN   Indonesia   268.07   1910.93   1015.54       Asia   8/17/1945\n",
       "4   BRA      Brazil   210.32   8515.77   2055.51  S.America  1822-09-07\n",
       "5   PAK    Pakistan   205.71    881.91    302.14       Asia   8/14/1947\n",
       "6   NGA     Nigeria   200.96    923.77    375.77     Africa   10/1/1960\n",
       "7   BGD  Bangladesh   167.09    147.57    245.63       Asia   3/26/1971\n",
       "8   RUS      Russia   146.79  17098.20   1530.75        NaN   6/12/1992\n",
       "9   MEX      Mexico   126.58   1964.38   1158.23  N.America  1810-09-16\n",
       "10  JPN       Japan   126.22    377.97   4872.42       Asia         NaN\n",
       "11  DEU     Germany    83.02    357.11   3693.20     Europe         NaN\n",
       "12  FRA      France    67.02    640.68   2582.49     Europe  1789-07-14\n",
       "13  GBR          UK    66.44    242.50   2631.23     Europe         NaN\n",
       "14  ITA       Italy    60.36    301.34   1943.84     Europe         NaN\n",
       "15  ARG   Argentina    44.94   2780.40    637.49  S.America  1816-07-09\n",
       "16  DZA     Algeria    43.38   2381.74    167.56     Africa    7/5/1962\n",
       "17  CAN      Canada    37.59   9984.67   1647.12  N.America  1867-07-01\n",
       "18  AUS   Australia    25.47   7692.02   1408.68    Oceania         NaN\n",
       "19  KAZ  Kazakhstan    18.53   2724.90    159.41       Asia  12/16/1991"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#read the csv file \n",
    "df = pd.read_csv(\"Countries_TSV.txt\", sep = \"\\t\")  #explicitly define the sep argument\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read Excel File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "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>ID</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>POP</th>\n",
       "      <th>AREA</th>\n",
       "      <th>GDP</th>\n",
       "      <th>CONT</th>\n",
       "      <th>IND_DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>1398.72</td>\n",
       "      <td>9596.96</td>\n",
       "      <td>12234.80</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>1351.16</td>\n",
       "      <td>3287.26</td>\n",
       "      <td>2575.67</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1947-08-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>329.74</td>\n",
       "      <td>9833.52</td>\n",
       "      <td>19485.40</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1776-07-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDN</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>268.07</td>\n",
       "      <td>1910.93</td>\n",
       "      <td>1015.54</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1945-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>210.32</td>\n",
       "      <td>8515.77</td>\n",
       "      <td>2055.51</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1822-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PAK</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>205.71</td>\n",
       "      <td>881.91</td>\n",
       "      <td>302.14</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1947-08-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>200.96</td>\n",
       "      <td>923.77</td>\n",
       "      <td>375.77</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1960-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BGD</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>167.09</td>\n",
       "      <td>147.57</td>\n",
       "      <td>245.63</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1971-03-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RUS</td>\n",
       "      <td>Russia</td>\n",
       "      <td>146.79</td>\n",
       "      <td>17098.20</td>\n",
       "      <td>1530.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1992-06-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MEX</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>126.58</td>\n",
       "      <td>1964.38</td>\n",
       "      <td>1158.23</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1810-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>126.22</td>\n",
       "      <td>377.97</td>\n",
       "      <td>4872.42</td>\n",
       "      <td>Asia</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>83.02</td>\n",
       "      <td>357.11</td>\n",
       "      <td>3693.20</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>67.02</td>\n",
       "      <td>640.68</td>\n",
       "      <td>2582.49</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1789-07-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GBR</td>\n",
       "      <td>UK</td>\n",
       "      <td>66.44</td>\n",
       "      <td>242.50</td>\n",
       "      <td>2631.23</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "      <td>60.36</td>\n",
       "      <td>301.34</td>\n",
       "      <td>1943.84</td>\n",
       "      <td>Europe</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>44.94</td>\n",
       "      <td>2780.40</td>\n",
       "      <td>637.49</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1816-07-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>43.38</td>\n",
       "      <td>2381.74</td>\n",
       "      <td>167.56</td>\n",
       "      <td>Africa</td>\n",
       "      <td>1962-07-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>37.59</td>\n",
       "      <td>9984.67</td>\n",
       "      <td>1647.12</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1867-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>25.47</td>\n",
       "      <td>7692.02</td>\n",
       "      <td>1408.68</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KAZ</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>18.53</td>\n",
       "      <td>2724.90</td>\n",
       "      <td>159.41</td>\n",
       "      <td>Asia</td>\n",
       "      <td>1991-12-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID     COUNTRY      POP      AREA       GDP       CONT    IND_DAY\n",
       "0   CHN       China  1398.72   9596.96  12234.80       Asia        NaT\n",
       "1   IND       India  1351.16   3287.26   2575.67       Asia 1947-08-15\n",
       "2   USA          US   329.74   9833.52  19485.40  N.America 1776-07-04\n",
       "3   IDN   Indonesia   268.07   1910.93   1015.54       Asia 1945-08-17\n",
       "4   BRA      Brazil   210.32   8515.77   2055.51  S.America 1822-09-07\n",
       "5   PAK    Pakistan   205.71    881.91    302.14       Asia 1947-08-14\n",
       "6   NGA     Nigeria   200.96    923.77    375.77     Africa 1960-10-01\n",
       "7   BGD  Bangladesh   167.09    147.57    245.63       Asia 1971-03-26\n",
       "8   RUS      Russia   146.79  17098.20   1530.75        NaN 1992-06-12\n",
       "9   MEX      Mexico   126.58   1964.38   1158.23  N.America 1810-09-16\n",
       "10  JPN       Japan   126.22    377.97   4872.42       Asia        NaT\n",
       "11  DEU     Germany    83.02    357.11   3693.20     Europe        NaT\n",
       "12  FRA      France    67.02    640.68   2582.49     Europe 1789-07-14\n",
       "13  GBR          UK    66.44    242.50   2631.23     Europe        NaT\n",
       "14  ITA       Italy    60.36    301.34   1943.84     Europe        NaT\n",
       "15  ARG   Argentina    44.94   2780.40    637.49  S.America 1816-07-09\n",
       "16  DZA     Algeria    43.38   2381.74    167.56     Africa 1962-07-05\n",
       "17  CAN      Canada    37.59   9984.67   1647.12  N.America 1867-07-01\n",
       "18  AUS   Australia    25.47   7692.02   1408.68    Oceania        NaT\n",
       "19  KAZ  Kazakhstan    18.53   2724.90    159.41       Asia 1991-12-16"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#read the Excel file \n",
    "df = pd.read_excel(\"Countries_EXCEL.xlsx\")  #read from an excel data source. This will read from the first sheet.\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Series Name</th>\n",
       "      <th>Series Code</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>2000 [YR2000]</th>\n",
       "      <th>2001 [YR2001]</th>\n",
       "      <th>2002 [YR2002]</th>\n",
       "      <th>2003 [YR2003]</th>\n",
       "      <th>2004 [YR2004]</th>\n",
       "      <th>2005 [YR2005]</th>\n",
       "      <th>2006 [YR2006]</th>\n",
       "      <th>2007 [YR2007]</th>\n",
       "      <th>2008 [YR2008]</th>\n",
       "      <th>2009 [YR2009]</th>\n",
       "      <th>2010 [YR2010]</th>\n",
       "      <th>2011 [YR2011]</th>\n",
       "      <th>2012 [YR2012]</th>\n",
       "      <th>2013 [YR2013]</th>\n",
       "      <th>2014 [YR2014]</th>\n",
       "      <th>2015 [YR2015]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>20779953</td>\n",
       "      <td>21606988</td>\n",
       "      <td>22600770</td>\n",
       "      <td>23680871</td>\n",
       "      <td>24726684</td>\n",
       "      <td>25654277</td>\n",
       "      <td>26433049</td>\n",
       "      <td>27100536</td>\n",
       "      <td>27722276</td>\n",
       "      <td>28394813</td>\n",
       "      <td>29185507</td>\n",
       "      <td>30117413</td>\n",
       "      <td>31161376</td>\n",
       "      <td>32269589</td>\n",
       "      <td>33370794</td>\n",
       "      <td>34413603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>3089027</td>\n",
       "      <td>3060173</td>\n",
       "      <td>3051010</td>\n",
       "      <td>3039616</td>\n",
       "      <td>3026939</td>\n",
       "      <td>3011487</td>\n",
       "      <td>2992547</td>\n",
       "      <td>2970017</td>\n",
       "      <td>2947314</td>\n",
       "      <td>2927519</td>\n",
       "      <td>2913021</td>\n",
       "      <td>2905195</td>\n",
       "      <td>2900401</td>\n",
       "      <td>2895092</td>\n",
       "      <td>2889104</td>\n",
       "      <td>2880703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>DZA</td>\n",
       "      <td>31042235</td>\n",
       "      <td>31451514</td>\n",
       "      <td>31855109</td>\n",
       "      <td>32264157</td>\n",
       "      <td>32692163</td>\n",
       "      <td>33149724</td>\n",
       "      <td>33641002</td>\n",
       "      <td>34166972</td>\n",
       "      <td>34730608</td>\n",
       "      <td>35333881</td>\n",
       "      <td>35977455</td>\n",
       "      <td>36661444</td>\n",
       "      <td>37383887</td>\n",
       "      <td>38140132</td>\n",
       "      <td>38923687</td>\n",
       "      <td>39728025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>ASM</td>\n",
       "      <td>57821</td>\n",
       "      <td>58494</td>\n",
       "      <td>59080</td>\n",
       "      <td>59504</td>\n",
       "      <td>59681</td>\n",
       "      <td>59562</td>\n",
       "      <td>59107</td>\n",
       "      <td>58365</td>\n",
       "      <td>57492</td>\n",
       "      <td>56683</td>\n",
       "      <td>56079</td>\n",
       "      <td>55759</td>\n",
       "      <td>55667</td>\n",
       "      <td>55713</td>\n",
       "      <td>55791</td>\n",
       "      <td>55812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>AND</td>\n",
       "      <td>65390</td>\n",
       "      <td>67341</td>\n",
       "      <td>70049</td>\n",
       "      <td>73182</td>\n",
       "      <td>76244</td>\n",
       "      <td>78867</td>\n",
       "      <td>80993</td>\n",
       "      <td>82684</td>\n",
       "      <td>83862</td>\n",
       "      <td>84463</td>\n",
       "      <td>84449</td>\n",
       "      <td>83747</td>\n",
       "      <td>82427</td>\n",
       "      <td>80774</td>\n",
       "      <td>79213</td>\n",
       "      <td>78011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>16395473</td>\n",
       "      <td>16945753</td>\n",
       "      <td>17519417</td>\n",
       "      <td>18121479</td>\n",
       "      <td>18758145</td>\n",
       "      <td>19433602</td>\n",
       "      <td>20149901</td>\n",
       "      <td>20905363</td>\n",
       "      <td>21695634</td>\n",
       "      <td>22514281</td>\n",
       "      <td>23356246</td>\n",
       "      <td>24220661</td>\n",
       "      <td>25107931</td>\n",
       "      <td>26015780</td>\n",
       "      <td>26941779</td>\n",
       "      <td>27884381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>ATG</td>\n",
       "      <td>76016</td>\n",
       "      <td>77212</td>\n",
       "      <td>78295</td>\n",
       "      <td>79300</td>\n",
       "      <td>80336</td>\n",
       "      <td>81465</td>\n",
       "      <td>82704</td>\n",
       "      <td>84026</td>\n",
       "      <td>85397</td>\n",
       "      <td>86746</td>\n",
       "      <td>88028</td>\n",
       "      <td>89253</td>\n",
       "      <td>90409</td>\n",
       "      <td>91516</td>\n",
       "      <td>92562</td>\n",
       "      <td>93566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>36870787</td>\n",
       "      <td>37275652</td>\n",
       "      <td>37681749</td>\n",
       "      <td>38087868</td>\n",
       "      <td>38491972</td>\n",
       "      <td>38892931</td>\n",
       "      <td>39289878</td>\n",
       "      <td>39684295</td>\n",
       "      <td>40080160</td>\n",
       "      <td>40482788</td>\n",
       "      <td>40788453</td>\n",
       "      <td>41261490</td>\n",
       "      <td>41733271</td>\n",
       "      <td>42202935</td>\n",
       "      <td>42669500</td>\n",
       "      <td>43131966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>ARM</td>\n",
       "      <td>3069591</td>\n",
       "      <td>3050687</td>\n",
       "      <td>3033978</td>\n",
       "      <td>3017932</td>\n",
       "      <td>3000720</td>\n",
       "      <td>2981269</td>\n",
       "      <td>2958307</td>\n",
       "      <td>2932618</td>\n",
       "      <td>2907618</td>\n",
       "      <td>2888092</td>\n",
       "      <td>2877319</td>\n",
       "      <td>2876538</td>\n",
       "      <td>2884229</td>\n",
       "      <td>2897584</td>\n",
       "      <td>2912403</td>\n",
       "      <td>2925553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>90853</td>\n",
       "      <td>92898</td>\n",
       "      <td>94992</td>\n",
       "      <td>97017</td>\n",
       "      <td>98737</td>\n",
       "      <td>100031</td>\n",
       "      <td>100834</td>\n",
       "      <td>101222</td>\n",
       "      <td>101358</td>\n",
       "      <td>101455</td>\n",
       "      <td>101669</td>\n",
       "      <td>102046</td>\n",
       "      <td>102560</td>\n",
       "      <td>103159</td>\n",
       "      <td>103774</td>\n",
       "      <td>104341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Australia</td>\n",
       "      <td>AUS</td>\n",
       "      <td>19153000</td>\n",
       "      <td>19413000</td>\n",
       "      <td>19651400</td>\n",
       "      <td>19895400</td>\n",
       "      <td>20127400</td>\n",
       "      <td>20394800</td>\n",
       "      <td>20697900</td>\n",
       "      <td>20827600</td>\n",
       "      <td>21249200</td>\n",
       "      <td>21691700</td>\n",
       "      <td>22031750</td>\n",
       "      <td>22340024</td>\n",
       "      <td>22733465</td>\n",
       "      <td>23128129</td>\n",
       "      <td>23475686</td>\n",
       "      <td>23815995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Austria</td>\n",
       "      <td>AUT</td>\n",
       "      <td>8011566</td>\n",
       "      <td>8042293</td>\n",
       "      <td>8081957</td>\n",
       "      <td>8121423</td>\n",
       "      <td>8171966</td>\n",
       "      <td>8227829</td>\n",
       "      <td>8268641</td>\n",
       "      <td>8295487</td>\n",
       "      <td>8321496</td>\n",
       "      <td>8343323</td>\n",
       "      <td>8363404</td>\n",
       "      <td>8391643</td>\n",
       "      <td>8429991</td>\n",
       "      <td>8479823</td>\n",
       "      <td>8546356</td>\n",
       "      <td>8642699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>AZE</td>\n",
       "      <td>8048600</td>\n",
       "      <td>8111200</td>\n",
       "      <td>8171950</td>\n",
       "      <td>8234100</td>\n",
       "      <td>8306500</td>\n",
       "      <td>8391850</td>\n",
       "      <td>8484550</td>\n",
       "      <td>8581300</td>\n",
       "      <td>8763400</td>\n",
       "      <td>8947243</td>\n",
       "      <td>9054332</td>\n",
       "      <td>9173082</td>\n",
       "      <td>9295784</td>\n",
       "      <td>9416801</td>\n",
       "      <td>9535079</td>\n",
       "      <td>9649341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Bahamas, The</td>\n",
       "      <td>BHS</td>\n",
       "      <td>298051</td>\n",
       "      <td>302618</td>\n",
       "      <td>307660</td>\n",
       "      <td>313129</td>\n",
       "      <td>318894</td>\n",
       "      <td>324859</td>\n",
       "      <td>331033</td>\n",
       "      <td>337389</td>\n",
       "      <td>343681</td>\n",
       "      <td>349604</td>\n",
       "      <td>354942</td>\n",
       "      <td>359577</td>\n",
       "      <td>363584</td>\n",
       "      <td>367168</td>\n",
       "      <td>370633</td>\n",
       "      <td>374206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Population, total</td>\n",
       "      <td>SP.POP.TOTL</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>BHR</td>\n",
       "      <td>664611</td>\n",
       "      <td>697545</td>\n",
       "      <td>735146</td>\n",
       "      <td>778708</td>\n",
       "      <td>829844</td>\n",
       "      <td>889164</td>\n",
       "      <td>958418</td>\n",
       "      <td>1035919</td>\n",
       "      <td>1114641</td>\n",
       "      <td>1185076</td>\n",
       "      <td>1240860</td>\n",
       "      <td>1278151</td>\n",
       "      <td>1299943</td>\n",
       "      <td>1315029</td>\n",
       "      <td>1336075</td>\n",
       "      <td>1371851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Series Name  Series Code         Country Name Country Code  \\\n",
       "0   Population, total  SP.POP.TOTL          Afghanistan          AFG   \n",
       "1   Population, total  SP.POP.TOTL              Albania          ALB   \n",
       "2   Population, total  SP.POP.TOTL              Algeria          DZA   \n",
       "3   Population, total  SP.POP.TOTL       American Samoa          ASM   \n",
       "4   Population, total  SP.POP.TOTL              Andorra          AND   \n",
       "5   Population, total  SP.POP.TOTL               Angola          AGO   \n",
       "6   Population, total  SP.POP.TOTL  Antigua and Barbuda          ATG   \n",
       "7   Population, total  SP.POP.TOTL            Argentina          ARG   \n",
       "8   Population, total  SP.POP.TOTL              Armenia          ARM   \n",
       "9   Population, total  SP.POP.TOTL                Aruba          ABW   \n",
       "10  Population, total  SP.POP.TOTL            Australia          AUS   \n",
       "11  Population, total  SP.POP.TOTL              Austria          AUT   \n",
       "12  Population, total  SP.POP.TOTL           Azerbaijan          AZE   \n",
       "13  Population, total  SP.POP.TOTL         Bahamas, The          BHS   \n",
       "14  Population, total  SP.POP.TOTL              Bahrain          BHR   \n",
       "\n",
       "    2000 [YR2000]  2001 [YR2001]  2002 [YR2002]  2003 [YR2003]  2004 [YR2004]  \\\n",
       "0        20779953       21606988       22600770       23680871       24726684   \n",
       "1         3089027        3060173        3051010        3039616        3026939   \n",
       "2        31042235       31451514       31855109       32264157       32692163   \n",
       "3           57821          58494          59080          59504          59681   \n",
       "4           65390          67341          70049          73182          76244   \n",
       "5        16395473       16945753       17519417       18121479       18758145   \n",
       "6           76016          77212          78295          79300          80336   \n",
       "7        36870787       37275652       37681749       38087868       38491972   \n",
       "8         3069591        3050687        3033978        3017932        3000720   \n",
       "9           90853          92898          94992          97017          98737   \n",
       "10       19153000       19413000       19651400       19895400       20127400   \n",
       "11        8011566        8042293        8081957        8121423        8171966   \n",
       "12        8048600        8111200        8171950        8234100        8306500   \n",
       "13         298051         302618         307660         313129         318894   \n",
       "14         664611         697545         735146         778708         829844   \n",
       "\n",
       "    2005 [YR2005]  2006 [YR2006]  2007 [YR2007]  2008 [YR2008]  2009 [YR2009]  \\\n",
       "0        25654277       26433049       27100536       27722276       28394813   \n",
       "1         3011487        2992547        2970017        2947314        2927519   \n",
       "2        33149724       33641002       34166972       34730608       35333881   \n",
       "3           59562          59107          58365          57492          56683   \n",
       "4           78867          80993          82684          83862          84463   \n",
       "5        19433602       20149901       20905363       21695634       22514281   \n",
       "6           81465          82704          84026          85397          86746   \n",
       "7        38892931       39289878       39684295       40080160       40482788   \n",
       "8         2981269        2958307        2932618        2907618        2888092   \n",
       "9          100031         100834         101222         101358         101455   \n",
       "10       20394800       20697900       20827600       21249200       21691700   \n",
       "11        8227829        8268641        8295487        8321496        8343323   \n",
       "12        8391850        8484550        8581300        8763400        8947243   \n",
       "13         324859         331033         337389         343681         349604   \n",
       "14         889164         958418        1035919        1114641        1185076   \n",
       "\n",
       "    2010 [YR2010]  2011 [YR2011]  2012 [YR2012]  2013 [YR2013]  2014 [YR2014]  \\\n",
       "0        29185507       30117413       31161376       32269589       33370794   \n",
       "1         2913021        2905195        2900401        2895092        2889104   \n",
       "2        35977455       36661444       37383887       38140132       38923687   \n",
       "3           56079          55759          55667          55713          55791   \n",
       "4           84449          83747          82427          80774          79213   \n",
       "5        23356246       24220661       25107931       26015780       26941779   \n",
       "6           88028          89253          90409          91516          92562   \n",
       "7        40788453       41261490       41733271       42202935       42669500   \n",
       "8         2877319        2876538        2884229        2897584        2912403   \n",
       "9          101669         102046         102560         103159         103774   \n",
       "10       22031750       22340024       22733465       23128129       23475686   \n",
       "11        8363404        8391643        8429991        8479823        8546356   \n",
       "12        9054332        9173082        9295784        9416801        9535079   \n",
       "13         354942         359577         363584         367168         370633   \n",
       "14        1240860        1278151        1299943        1315029        1336075   \n",
       "\n",
       "    2015 [YR2015]  \n",
       "0        34413603  \n",
       "1         2880703  \n",
       "2        39728025  \n",
       "3           55812  \n",
       "4           78011  \n",
       "5        27884381  \n",
       "6           93566  \n",
       "7        43131966  \n",
       "8         2925553  \n",
       "9          104341  \n",
       "10       23815995  \n",
       "11        8642699  \n",
       "12        9649341  \n",
       "13         374206  \n",
       "14        1371851  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#read from an excel with a sheet name.\n",
    "df = pd.read_excel(\"Countries_EXCEL.xlsx\", sheet_name = 'Population')  \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function read_excel in module pandas.io.excel._base:\n",
      "\n",
      "read_excel(io, sheet_name=0, header=0, names=None, index_col=None, usecols=None, squeeze=False, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, keep_default_na=True, verbose=False, parse_dates=False, date_parser=None, thousands=None, comment=None, skipfooter=0, convert_float=True, mangle_dupe_cols=True, **kwds)\n",
      "    Read an Excel file into a pandas DataFrame.\n",
      "    \n",
      "    Supports `xls`, `xlsx`, `xlsm`, `xlsb`, and `odf` file extensions\n",
      "    read from a local filesystem or URL. Supports an option to read\n",
      "    a single sheet or a list of sheets.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    io : str, bytes, ExcelFile, xlrd.Book, path object, or file-like object\n",
      "        Any valid string path is acceptable. The string could be a URL. Valid\n",
      "        URL schemes include http, ftp, s3, and file. For file URLs, a host is\n",
      "        expected. A local file could be: ``file://localhost/path/to/table.xlsx``.\n",
      "    \n",
      "        If you want to pass in a path object, pandas accepts any ``os.PathLike``.\n",
      "    \n",
      "        By file-like object, we refer to objects with a ``read()`` method,\n",
      "        such as a file handler (e.g. via builtin ``open`` function)\n",
      "        or ``StringIO``.\n",
      "    sheet_name : str, int, list, or None, default 0\n",
      "        Strings are used for sheet names. Integers are used in zero-indexed\n",
      "        sheet positions. Lists of strings/integers are used to request\n",
      "        multiple sheets. Specify None to get all sheets.\n",
      "    \n",
      "        Available cases:\n",
      "    \n",
      "        * Defaults to ``0``: 1st sheet as a `DataFrame`\n",
      "        * ``1``: 2nd sheet as a `DataFrame`\n",
      "        * ``\"Sheet1\"``: Load sheet with name \"Sheet1\"\n",
      "        * ``[0, 1, \"Sheet5\"]``: Load first, second and sheet named \"Sheet5\"\n",
      "          as a dict of `DataFrame`\n",
      "        * None: All sheets.\n",
      "    \n",
      "    header : int, list of int, default 0\n",
      "        Row (0-indexed) to use for the column labels of the parsed\n",
      "        DataFrame. If a list of integers is passed those row positions will\n",
      "        be combined into a ``MultiIndex``. Use None if there is no header.\n",
      "    names : array-like, default None\n",
      "        List of column names to use. If file contains no header row,\n",
      "        then you should explicitly pass header=None.\n",
      "    index_col : int, list of int, default None\n",
      "        Column (0-indexed) to use as the row labels of the DataFrame.\n",
      "        Pass None if there is no such column.  If a list is passed,\n",
      "        those columns will be combined into a ``MultiIndex``.  If a\n",
      "        subset of data is selected with ``usecols``, index_col\n",
      "        is based on the subset.\n",
      "    usecols : int, str, list-like, or callable default None\n",
      "        * If None, then parse all columns.\n",
      "        * If str, then indicates comma separated list of Excel column letters\n",
      "          and column ranges (e.g. \"A:E\" or \"A,C,E:F\"). Ranges are inclusive of\n",
      "          both sides.\n",
      "        * If list of int, then indicates list of column numbers to be parsed.\n",
      "        * If list of string, then indicates list of column names to be parsed.\n",
      "    \n",
      "          .. versionadded:: 0.24.0\n",
      "    \n",
      "        * If callable, then evaluate each column name against it and parse the\n",
      "          column if the callable returns ``True``.\n",
      "    \n",
      "        Returns a subset of the columns according to behavior above.\n",
      "    \n",
      "          .. versionadded:: 0.24.0\n",
      "    \n",
      "    squeeze : bool, default False\n",
      "        If the parsed data only contains one column then return a Series.\n",
      "    dtype : Type name or dict of column -> type, default None\n",
      "        Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}\n",
      "        Use `object` to preserve data as stored in Excel and not interpret dtype.\n",
      "        If converters are specified, they will be applied INSTEAD\n",
      "        of dtype conversion.\n",
      "    engine : str, default None\n",
      "        If io is not a buffer or path, this must be set to identify io.\n",
      "        Acceptable values are None, \"xlrd\", \"openpyxl\" or \"odf\".\n",
      "    converters : dict, default None\n",
      "        Dict of functions for converting values in certain columns. Keys can\n",
      "        either be integers or column labels, values are functions that take one\n",
      "        input argument, the Excel cell content, and return the transformed\n",
      "        content.\n",
      "    true_values : list, default None\n",
      "        Values to consider as True.\n",
      "    false_values : list, default None\n",
      "        Values to consider as False.\n",
      "    skiprows : list-like\n",
      "        Rows to skip at the beginning (0-indexed).\n",
      "    nrows : int, default None\n",
      "        Number of rows to parse.\n",
      "    \n",
      "        .. versionadded:: 0.23.0\n",
      "    \n",
      "    na_values : scalar, str, list-like, or dict, default None\n",
      "        Additional strings to recognize as NA/NaN. If dict passed, specific\n",
      "        per-column NA values. By default the following values are interpreted\n",
      "        as NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',\n",
      "        '1.#IND', '1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'n/a',\n",
      "        'nan', 'null'.\n",
      "    keep_default_na : bool, default True\n",
      "        Whether or not to include the default NaN values when parsing the data.\n",
      "        Depending on whether `na_values` is passed in, the behavior is as follows:\n",
      "    \n",
      "        * If `keep_default_na` is True, and `na_values` are specified, `na_values`\n",
      "          is appended to the default NaN values used for parsing.\n",
      "        * If `keep_default_na` is True, and `na_values` are not specified, only\n",
      "          the default NaN values are used for parsing.\n",
      "        * If `keep_default_na` is False, and `na_values` are specified, only\n",
      "          the NaN values specified `na_values` are used for parsing.\n",
      "        * If `keep_default_na` is False, and `na_values` are not specified, no\n",
      "          strings will be parsed as NaN.\n",
      "    \n",
      "        Note that if `na_filter` is passed in as False, the `keep_default_na` and\n",
      "        `na_values` parameters will be ignored.\n",
      "    na_filter : bool, default True\n",
      "        Detect missing value markers (empty strings and the value of na_values). In\n",
      "        data without any NAs, passing na_filter=False can improve the performance\n",
      "        of reading a large file.\n",
      "    verbose : bool, default False\n",
      "        Indicate number of NA values placed in non-numeric columns.\n",
      "    parse_dates : bool, list-like, or dict, default False\n",
      "        The behavior is as follows:\n",
      "    \n",
      "        * bool. If True -> try parsing the index.\n",
      "        * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3\n",
      "          each as a separate date column.\n",
      "        * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as\n",
      "          a single date column.\n",
      "        * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call\n",
      "          result 'foo'\n",
      "    \n",
      "        If a column or index contains an unparseable date, the entire column or\n",
      "        index will be returned unaltered as an object data type. If you don`t want to\n",
      "        parse some cells as date just change their type in Excel to \"Text\".\n",
      "        For non-standard datetime parsing, use ``pd.to_datetime`` after ``pd.read_excel``.\n",
      "    \n",
      "        Note: A fast-path exists for iso8601-formatted dates.\n",
      "    date_parser : function, optional\n",
      "        Function to use for converting a sequence of string columns to an array of\n",
      "        datetime instances. The default uses ``dateutil.parser.parser`` to do the\n",
      "        conversion. Pandas will try to call `date_parser` in three different ways,\n",
      "        advancing to the next if an exception occurs: 1) Pass one or more arrays\n",
      "        (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the\n",
      "        string values from the columns defined by `parse_dates` into a single array\n",
      "        and pass that; and 3) call `date_parser` once for each row using one or\n",
      "        more strings (corresponding to the columns defined by `parse_dates`) as\n",
      "        arguments.\n",
      "    thousands : str, default None\n",
      "        Thousands separator for parsing string columns to numeric.  Note that\n",
      "        this parameter is only necessary for columns stored as TEXT in Excel,\n",
      "        any numeric columns will automatically be parsed, regardless of display\n",
      "        format.\n",
      "    comment : str, default None\n",
      "        Comments out remainder of line. Pass a character or characters to this\n",
      "        argument to indicate comments in the input file. Any data between the\n",
      "        comment string and the end of the current line is ignored.\n",
      "    skipfooter : int, default 0\n",
      "        Rows at the end to skip (0-indexed).\n",
      "    convert_float : bool, default True\n",
      "        Convert integral floats to int (i.e., 1.0 --> 1). If False, all numeric\n",
      "        data will be read in as floats: Excel stores all numbers as floats\n",
      "        internally.\n",
      "    mangle_dupe_cols : bool, default True\n",
      "        Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than\n",
      "        'X'...'X'. Passing in False will cause data to be overwritten if there\n",
      "        are duplicate names in the columns.\n",
      "    **kwds : optional\n",
      "            Optional keyword arguments can be passed to ``TextFileReader``.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    DataFrame or dict of DataFrames\n",
      "        DataFrame from the passed in Excel file. See notes in sheet_name\n",
      "        argument for more information on when a dict of DataFrames is returned.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    to_excel : Write DataFrame to an Excel file.\n",
      "    to_csv : Write DataFrame to a comma-separated values (csv) file.\n",
      "    read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
      "    read_fwf : Read a table of fixed-width formatted lines into DataFrame.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    The file can be read using the file name as string or an open file object:\n",
      "    \n",
      "    >>> pd.read_excel('tmp.xlsx', index_col=0)  # doctest: +SKIP\n",
      "           Name  Value\n",
      "    0   string1      1\n",
      "    1   string2      2\n",
      "    2  #Comment      3\n",
      "    \n",
      "    >>> pd.read_excel(open('tmp.xlsx', 'rb'),\n",
      "    ...               sheet_name='Sheet3')  # doctest: +SKIP\n",
      "       Unnamed: 0      Name  Value\n",
      "    0           0   string1      1\n",
      "    1           1   string2      2\n",
      "    2           2  #Comment      3\n",
      "    \n",
      "    Index and header can be specified via the `index_col` and `header` arguments\n",
      "    \n",
      "    >>> pd.read_excel('tmp.xlsx', index_col=None, header=None)  # doctest: +SKIP\n",
      "         0         1      2\n",
      "    0  NaN      Name  Value\n",
      "    1  0.0   string1      1\n",
      "    2  1.0   string2      2\n",
      "    3  2.0  #Comment      3\n",
      "    \n",
      "    Column types are inferred but can be explicitly specified\n",
      "    \n",
      "    >>> pd.read_excel('tmp.xlsx', index_col=0,\n",
      "    ...               dtype={'Name': str, 'Value': float})  # doctest: +SKIP\n",
      "           Name  Value\n",
      "    0   string1    1.0\n",
      "    1   string2    2.0\n",
      "    2  #Comment    3.0\n",
      "    \n",
      "    True, False, and NA values, and thousands separators have defaults,\n",
      "    but can be explicitly specified, too. Supply the values you would like\n",
      "    as strings or lists of strings!\n",
      "    \n",
      "    >>> pd.read_excel('tmp.xlsx', index_col=0,\n",
      "    ...               na_values=['string1', 'string2'])  # doctest: +SKIP\n",
      "           Name  Value\n",
      "    0       NaN      1\n",
      "    1       NaN      2\n",
      "    2  #Comment      3\n",
      "    \n",
      "    Comment lines in the excel input file can be skipped using the `comment` kwarg\n",
      "    \n",
      "    >>> pd.read_excel('tmp.xlsx', index_col=0, comment='#')  # doctest: +SKIP\n",
      "          Name  Value\n",
      "    0  string1    1.0\n",
      "    1  string2    2.0\n",
      "    2     None    NaN\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pd.read_excel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read JSON File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "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>ID</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>POP</th>\n",
       "      <th>AREA</th>\n",
       "      <th>GDP</th>\n",
       "      <th>CONT</th>\n",
       "      <th>IND_DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>1398.72</td>\n",
       "      <td>9596.96</td>\n",
       "      <td>12234.80</td>\n",
       "      <td>Asia</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>1351.16</td>\n",
       "      <td>3287.26</td>\n",
       "      <td>2575.67</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/15/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>329.74</td>\n",
       "      <td>9833.52</td>\n",
       "      <td>19485.40</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1776-07-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDN</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>268.07</td>\n",
       "      <td>1910.93</td>\n",
       "      <td>1015.54</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/17/1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>210.32</td>\n",
       "      <td>8515.77</td>\n",
       "      <td>2055.51</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1822-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PAK</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>205.71</td>\n",
       "      <td>881.91</td>\n",
       "      <td>302.14</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/14/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>200.96</td>\n",
       "      <td>923.77</td>\n",
       "      <td>375.77</td>\n",
       "      <td>Africa</td>\n",
       "      <td>10/1/1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BGD</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>167.09</td>\n",
       "      <td>147.57</td>\n",
       "      <td>245.63</td>\n",
       "      <td>Asia</td>\n",
       "      <td>3/26/1971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RUS</td>\n",
       "      <td>Russia</td>\n",
       "      <td>146.79</td>\n",
       "      <td>17098.20</td>\n",
       "      <td>1530.75</td>\n",
       "      <td>None</td>\n",
       "      <td>6/12/1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MEX</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>126.58</td>\n",
       "      <td>1964.38</td>\n",
       "      <td>1158.23</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1810-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>126.22</td>\n",
       "      <td>377.97</td>\n",
       "      <td>4872.42</td>\n",
       "      <td>Asia</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>83.02</td>\n",
       "      <td>357.11</td>\n",
       "      <td>3693.20</td>\n",
       "      <td>Europe</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>67.02</td>\n",
       "      <td>640.68</td>\n",
       "      <td>2582.49</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1789-07-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GBR</td>\n",
       "      <td>UK</td>\n",
       "      <td>66.44</td>\n",
       "      <td>242.50</td>\n",
       "      <td>2631.23</td>\n",
       "      <td>Europe</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "      <td>60.36</td>\n",
       "      <td>301.34</td>\n",
       "      <td>1943.84</td>\n",
       "      <td>Europe</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>44.94</td>\n",
       "      <td>2780.40</td>\n",
       "      <td>637.49</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1816-07-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>43.38</td>\n",
       "      <td>2381.74</td>\n",
       "      <td>167.56</td>\n",
       "      <td>Africa</td>\n",
       "      <td>7/5/1962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>37.59</td>\n",
       "      <td>9984.67</td>\n",
       "      <td>1647.12</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1867-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>25.47</td>\n",
       "      <td>7692.02</td>\n",
       "      <td>1408.68</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KAZ</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>18.53</td>\n",
       "      <td>2724.90</td>\n",
       "      <td>159.41</td>\n",
       "      <td>Asia</td>\n",
       "      <td>12/16/1991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID     COUNTRY      POP      AREA       GDP       CONT     IND_DAY\n",
       "0   CHN       China  1398.72   9596.96  12234.80       Asia        None\n",
       "1   IND       India  1351.16   3287.26   2575.67       Asia   8/15/1947\n",
       "2   USA          US   329.74   9833.52  19485.40  N.America  1776-07-04\n",
       "3   IDN   Indonesia   268.07   1910.93   1015.54       Asia   8/17/1945\n",
       "4   BRA      Brazil   210.32   8515.77   2055.51  S.America  1822-09-07\n",
       "5   PAK    Pakistan   205.71    881.91    302.14       Asia   8/14/1947\n",
       "6   NGA     Nigeria   200.96    923.77    375.77     Africa   10/1/1960\n",
       "7   BGD  Bangladesh   167.09    147.57    245.63       Asia   3/26/1971\n",
       "8   RUS      Russia   146.79  17098.20   1530.75       None   6/12/1992\n",
       "9   MEX      Mexico   126.58   1964.38   1158.23  N.America  1810-09-16\n",
       "10  JPN       Japan   126.22    377.97   4872.42       Asia        None\n",
       "11  DEU     Germany    83.02    357.11   3693.20     Europe        None\n",
       "12  FRA      France    67.02    640.68   2582.49     Europe  1789-07-14\n",
       "13  GBR          UK    66.44    242.50   2631.23     Europe        None\n",
       "14  ITA       Italy    60.36    301.34   1943.84     Europe        None\n",
       "15  ARG   Argentina    44.94   2780.40    637.49  S.America  1816-07-09\n",
       "16  DZA     Algeria    43.38   2381.74    167.56     Africa    7/5/1962\n",
       "17  CAN      Canada    37.59   9984.67   1647.12  N.America  1867-07-01\n",
       "18  AUS   Australia    25.47   7692.02   1408.68    Oceania        None\n",
       "19  KAZ  Kazakhstan    18.53   2724.90    159.41       Asia  12/16/1991"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#read the csv file \n",
    "df = pd.read_json(\"Countries_JSON.json\")  #read from a json data source. it converts that json format into dataframe format.\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read from a SQL Database\n",
    "Let's first take a look at the \"flights.db\" on online database processor: https://sqliteonline.com/<br/>\n",
    "There are 3 tables in this database: \"airlines\", \"airport\" and \"routes\".<br/>\n",
    "Database Link: https://sites.google.com/site/yasinunlu/home/research/new1/flights.db<br/>\n",
    "This is a SQLite database file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sqlite3.Connection"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import sqlite3\n",
    "db_file = \"C:\\\\Users\\\\yasin.unlu\\\\Documents\\\\flights.db\"\n",
    "conn = None\n",
    "try:\n",
    "    conn = sqlite3.connect(db_file)\n",
    "except Error as e:\n",
    "        print(e)\n",
    "\n",
    "type(conn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, '1', 'Private flight', '\\\\N', '-', None, None, None, 'Y')\n",
      "(1, '2', '135 Airways', '\\\\N', None, 'GNL', 'GENERAL', 'United States', 'N')\n",
      "(2, '3', '1Time Airline', '\\\\N', '1T', 'RNX', 'NEXTIME', 'South Africa', 'Y')\n",
      "(3, '4', '2 Sqn No 1 Elementary Flying Training School', '\\\\N', None, 'WYT', None, 'United Kingdom', 'N')\n",
      "(4, '5', '213 Flight Unit', '\\\\N', None, 'TFU', None, 'Russia', 'N')\n"
     ]
    }
   ],
   "source": [
    "cur = conn.cursor()\n",
    "cur.execute(\"select * from airlines limit 5;\") #execute a simple query\n",
    "\n",
    "rows = cur.fetchall()\n",
    "\n",
    "for row in rows:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(rows) #list of tuples!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's read the database table into a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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>index</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>alias</th>\n",
       "      <th>iata</th>\n",
       "      <th>icao</th>\n",
       "      <th>callsign</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Private flight</td>\n",
       "      <td>\\N</td>\n",
       "      <td>-</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>135 Airways</td>\n",
       "      <td>\\N</td>\n",
       "      <td>None</td>\n",
       "      <td>GNL</td>\n",
       "      <td>GENERAL</td>\n",
       "      <td>United States</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1Time Airline</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1T</td>\n",
       "      <td>RNX</td>\n",
       "      <td>NEXTIME</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2 Sqn No 1 Elementary Flying Training School</td>\n",
       "      <td>\\N</td>\n",
       "      <td>None</td>\n",
       "      <td>WYT</td>\n",
       "      <td>None</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>213 Flight Unit</td>\n",
       "      <td>\\N</td>\n",
       "      <td>None</td>\n",
       "      <td>TFU</td>\n",
       "      <td>None</td>\n",
       "      <td>Russia</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index id                                          name alias  iata  icao  \\\n",
       "0      0  1                                Private flight    \\N     -  None   \n",
       "1      1  2                                   135 Airways    \\N  None   GNL   \n",
       "2      2  3                                 1Time Airline    \\N    1T   RNX   \n",
       "3      3  4  2 Sqn No 1 Elementary Flying Training School    \\N  None   WYT   \n",
       "4      4  5                               213 Flight Unit    \\N  None   TFU   \n",
       "\n",
       "  callsign         country active  \n",
       "0     None            None      Y  \n",
       "1  GENERAL   United States      N  \n",
       "2  NEXTIME    South Africa      Y  \n",
       "3     None  United Kingdom      N  \n",
       "4     None          Russia      N  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import sqlite3\n",
    "conn = sqlite3.connect(\"C:\\\\Users\\\\yasin.unlu\\\\Documents\\\\flights.db\")\n",
    "df_airlines = pd.read_sql_query(\"select * from airlines limit 5;\", conn) #this will select the first 5 rows in airlines table in the database\n",
    "df_airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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>index</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>alias</th>\n",
       "      <th>iata</th>\n",
       "      <th>icao</th>\n",
       "      <th>callsign</th>\n",
       "      <th>country</th>\n",
       "      <th>active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Private flight</td>\n",
       "      <td>\\N</td>\n",
       "      <td>-</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>135 Airways</td>\n",
       "      <td>\\N</td>\n",
       "      <td>None</td>\n",
       "      <td>GNL</td>\n",
       "      <td>GENERAL</td>\n",
       "      <td>United States</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1Time Airline</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1T</td>\n",
       "      <td>RNX</td>\n",
       "      <td>NEXTIME</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2 Sqn No 1 Elementary Flying Training School</td>\n",
       "      <td>\\N</td>\n",
       "      <td>None</td>\n",
       "      <td>WYT</td>\n",
       "      <td>None</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>213 Flight Unit</td>\n",
       "      <td>\\N</td>\n",
       "      <td>None</td>\n",
       "      <td>TFU</td>\n",
       "      <td>None</td>\n",
       "      <td>Russia</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index id                                          name alias  iata  icao  \\\n",
       "0      0  1                                Private flight    \\N     -  None   \n",
       "1      1  2                                   135 Airways    \\N  None   GNL   \n",
       "2      2  3                                 1Time Airline    \\N    1T   RNX   \n",
       "3      3  4  2 Sqn No 1 Elementary Flying Training School    \\N  None   WYT   \n",
       "4      4  5                               213 Flight Unit    \\N  None   TFU   \n",
       "\n",
       "  callsign         country active  \n",
       "0     None            None      Y  \n",
       "1  GENERAL   United States      N  \n",
       "2  NEXTIME    South Africa      Y  \n",
       "3     None  United Kingdom      N  \n",
       "4     None          Russia      N  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_airlines = pd.read_sql_query(\"select * from airlines;\", conn) #this will select the all records in airlines table in the database.\n",
    "df_airlines.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Write CSV File\n",
    "Let's change our directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yasin.unlu\\Documents\\Original Docs\\Documents1\\Docs\\Teaching\\PythonForDataScienceSummer2020\\Week-6\\flight_DB\n"
     ]
    }
   ],
   "source": [
    "%cd \"C:\\\\Users\\\\yasin.unlu\\\\Documents\\\\Original Docs\\\\Documents1\\\\Docs\\\\Teaching\\\\PythonForDataScienceSummer2020\\\\Week-6\\\\flight_DB\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the dataframe into a csv file with .csv extension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_airlines.to_csv(\"airlines_CSV.csv\") #write the dataframe into a csv file with .csv extension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function to_csv in module pandas.core.generic:\n",
      "\n",
      "to_csv(self, path_or_buf: Union[str, pathlib.Path, IO[~AnyStr], NoneType] = None, sep: str = ',', na_rep: str = '', float_format: Union[str, NoneType] = None, columns: Union[Sequence[Union[Hashable, NoneType]], NoneType] = None, header: Union[bool, List[str]] = True, index: bool = True, index_label: Union[bool, str, Sequence[Union[Hashable, NoneType]], NoneType] = None, mode: str = 'w', encoding: Union[str, NoneType] = None, compression: Union[str, Mapping[str, str], NoneType] = 'infer', quoting: Union[int, NoneType] = None, quotechar: str = '\"', line_terminator: Union[str, NoneType] = None, chunksize: Union[int, NoneType] = None, date_format: Union[str, NoneType] = None, doublequote: bool = True, escapechar: Union[str, NoneType] = None, decimal: Union[str, NoneType] = '.') -> Union[str, NoneType]\n",
      "    Write object to a comma-separated values (csv) file.\n",
      "    \n",
      "    .. versionchanged:: 0.24.0\n",
      "        The order of arguments for Series was changed.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    path_or_buf : str or file handle, default None\n",
      "        File path or object, if None is provided the result is returned as\n",
      "        a string.  If a file object is passed it should be opened with\n",
      "        `newline=''`, disabling universal newlines.\n",
      "    \n",
      "        .. versionchanged:: 0.24.0\n",
      "    \n",
      "           Was previously named \"path\" for Series.\n",
      "    \n",
      "    sep : str, default ','\n",
      "        String of length 1. Field delimiter for the output file.\n",
      "    na_rep : str, default ''\n",
      "        Missing data representation.\n",
      "    float_format : str, default None\n",
      "        Format string for floating point numbers.\n",
      "    columns : sequence, optional\n",
      "        Columns to write.\n",
      "    header : bool or list of str, default True\n",
      "        Write out the column names. If a list of strings is given it is\n",
      "        assumed to be aliases for the column names.\n",
      "    \n",
      "        .. versionchanged:: 0.24.0\n",
      "    \n",
      "           Previously defaulted to False for Series.\n",
      "    \n",
      "    index : bool, default True\n",
      "        Write row names (index).\n",
      "    index_label : str or sequence, or False, default None\n",
      "        Column label for index column(s) if desired. If None is given, and\n",
      "        `header` and `index` are True, then the index names are used. A\n",
      "        sequence should be given if the object uses MultiIndex. If\n",
      "        False do not print fields for index names. Use index_label=False\n",
      "        for easier importing in R.\n",
      "    mode : str\n",
      "        Python write mode, default 'w'.\n",
      "    encoding : str, optional\n",
      "        A string representing the encoding to use in the output file,\n",
      "        defaults to 'utf-8'.\n",
      "    compression : str or dict, default 'infer'\n",
      "        If str, represents compression mode. If dict, value at 'method' is\n",
      "        the compression mode. Compression mode may be any of the following\n",
      "        possible values: {'infer', 'gzip', 'bz2', 'zip', 'xz', None}. If\n",
      "        compression mode is 'infer' and `path_or_buf` is path-like, then\n",
      "        detect compression mode from the following extensions: '.gz',\n",
      "        '.bz2', '.zip' or '.xz'. (otherwise no compression). If dict given\n",
      "        and mode is 'zip' or inferred as 'zip', other entries passed as\n",
      "        additional compression options.\n",
      "    \n",
      "        .. versionchanged:: 1.0.0\n",
      "    \n",
      "           May now be a dict with key 'method' as compression mode\n",
      "           and other entries as additional compression options if\n",
      "           compression mode is 'zip'.\n",
      "    \n",
      "    quoting : optional constant from csv module\n",
      "        Defaults to csv.QUOTE_MINIMAL. If you have set a `float_format`\n",
      "        then floats are converted to strings and thus csv.QUOTE_NONNUMERIC\n",
      "        will treat them as non-numeric.\n",
      "    quotechar : str, default '\\\"'\n",
      "        String of length 1. Character used to quote fields.\n",
      "    line_terminator : str, optional\n",
      "        The newline character or character sequence to use in the output\n",
      "        file. Defaults to `os.linesep`, which depends on the OS in which\n",
      "        this method is called ('\\n' for linux, '\\r\\n' for Windows, i.e.).\n",
      "    \n",
      "        .. versionchanged:: 0.24.0\n",
      "    chunksize : int or None\n",
      "        Rows to write at a time.\n",
      "    date_format : str, default None\n",
      "        Format string for datetime objects.\n",
      "    doublequote : bool, default True\n",
      "        Control quoting of `quotechar` inside a field.\n",
      "    escapechar : str, default None\n",
      "        String of length 1. Character used to escape `sep` and `quotechar`\n",
      "        when appropriate.\n",
      "    decimal : str, default '.'\n",
      "        Character recognized as decimal separator. E.g. use ',' for\n",
      "        European data.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    None or str\n",
      "        If path_or_buf is None, returns the resulting csv format as a\n",
      "        string. Otherwise returns None.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    read_csv : Load a CSV file into a DataFrame.\n",
      "    to_excel : Write DataFrame to an Excel file.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],\n",
      "    ...                    'mask': ['red', 'purple'],\n",
      "    ...                    'weapon': ['sai', 'bo staff']})\n",
      "    >>> df.to_csv(index=False)\n",
      "    'name,mask,weapon\\nRaphael,red,sai\\nDonatello,purple,bo staff\\n'\n",
      "    \n",
      "    Create 'out.zip' containing 'out.csv'\n",
      "    \n",
      "    >>> compression_opts = dict(method='zip',\n",
      "    ...                         archive_name='out.csv')  # doctest: +SKIP\n",
      "    >>> df.to_csv('out.zip', index=False,\n",
      "    ...           compression=compression_opts)  # doctest: +SKIP\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pd.DataFrame.to_csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Default separator is comma."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the dataframe into as a CSV format with .txt extension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_airlines.to_csv(\"airlines_CSV.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can change the separator to '\\t' or any deliminator we'd like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_airlines.to_csv(\"airlines_TSV.txt\", sep = '\\t') #this will place tab between records."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Write JSON File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_airlines.to_json(\"airlines.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>alias</th>\n",
       "      <th>iata</th>\n",
       "      <th>icao</th>\n",
       "      <th>callsign</th>\n",
       "      <th>country</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
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       "      <td>135 Airways</td>\n",
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       "      <td>N</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1Time Airline</td>\n",
       "      <td>\\N</td>\n",
       "      <td>1T</td>\n",
       "      <td>RNX</td>\n",
       "      <td>NEXTIME</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2 Sqn No 1 Elementary Flying Training School</td>\n",
       "      <td>\\N</td>\n",
       "      <td>None</td>\n",
       "      <td>WYT</td>\n",
       "      <td>None</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>213 Flight Unit</td>\n",
       "      <td>\\N</td>\n",
       "      <td>None</td>\n",
       "      <td>TFU</td>\n",
       "      <td>None</td>\n",
       "      <td>Russia</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  id                                          name alias  iata  icao  \\\n",
       "0      0   1                                Private flight    \\N     -  None   \n",
       "1      1   2                                   135 Airways    \\N  None   GNL   \n",
       "2      2   3                                 1Time Airline    \\N    1T   RNX   \n",
       "3      3   4  2 Sqn No 1 Elementary Flying Training School    \\N  None   WYT   \n",
       "4      4   5                               213 Flight Unit    \\N  None   TFU   \n",
       "\n",
       "  callsign         country active  \n",
       "0     None            None      Y  \n",
       "1  GENERAL   United States      N  \n",
       "2  NEXTIME    South Africa      Y  \n",
       "3     None  United Kingdom      N  \n",
       "4     None          Russia      N  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_json = pd.read_json(\"airlines.json\") #let's read it as a dataframe.\n",
    "my_json.head() #in df format. But not in json format!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises:\n",
    "When we look into airlines.json file, we notice that it is not in an indented format. We can convert this file into an indented file format. It will still be in .json file. But we will work on a smaller file \"countries.json\" to see how it actually works.<br/>\n",
    "1) Read each .json file as a single string by using `read()` function in Python.<br/>\n",
    "2) You must notice this is not in a nice format (i.e. no indentation). Create a JSON object by using `json.loads()`.<br/> \n",
    "3) Convert the JSON object into an indented format by using `json.dumps()` function.<br/>\n",
    "4) Write the formatted data into a file with .json extension. Use `write()` function. For example, the JSON formatted data of df_tracks dataframe should be stored under a name \"countries_.json\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Read each .json file as a single string by using read() function in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"ID\":{\"0\":\"CHN\",\"1\":\"IND\",\"2\":\"USA\",\"3\":\"IDN\",\"4\":\"BRA\",\"5\":\"PAK\",\"6\":\"NGA\",\"7\":\"BGD\",\"8\":\"RUS\",\"9\":\"MEX\",\"10\":\"JPN\",\"11\":\"DEU\",\"12\":\"FRA\",\"13\":\"GBR\",\"14\":\"ITA\",\"15\":\"ARG\",\"16\":\"DZA\",\"17\":\"CAN\",\"18\":\"AUS\",\"19\":\"KAZ\"},\"COUNTRY\":{\"0\":\"China\",\"1\":\"India\",\"2\":\"US\",\"3\":\"Indonesia\",\"4\":\"Brazil\",\"5\":\"Pakistan\",\"6\":\"Nigeria\",\"7\":\"Bangladesh\",\"8\":\"Russia\",\"9\":\"Mexico\",\"10\":\"Japan\",\"11\":\"Germany\",\"12\":\"France\",\"13\":\"UK\",\"14\":\"Italy\",\"15\":\"Argentina\",\"16\":\"Algeria\",\"17\":\"Canada\",\"18\":\"Australia\",\"19\":\"Kazakhstan\"},\"POP\":{\"0\":1398.72,\"1\":1351.16,\"2\":329.74,\"3\":268.07,\"4\":210.32,\"5\":205.71,\"6\":200.96,\"7\":167.09,\"8\":146.79,\"9\":126.58,\"10\":126.22,\"11\":83.02,\"12\":67.02,\"13\":66.44,\"14\":60.36,\"15\":44.94,\"16\":43.38,\"17\":37.59,\"18\":25.47,\"19\":18.53},\"AREA\":{\"0\":9596.96,\"1\":3287.26,\"2\":9833.52,\"3\":1910.93,\"4\":8515.77,\"5\":881.91,\"6\":923.77,\"7\":147.57,\"8\":17098.2,\"9\":1964.38,\"10\":377.97,\"11\":357.11,\"12\":640.68,\"13\":242.5,\"14\":301.34,\"15\":2780.4,\"16\":2381.74,\"17\":9984.67,\"18\":7692.02,\"19\":2724.9},\"GDP\":{\"0\":12234.8,\"1\":2575.67,\"2\":19485.4,\"3\":1015.54,\"4\":2055.51,\"5\":302.14,\"6\":375.77,\"7\":245.63,\"8\":1530.75,\"9\":1158.23,\"10\":4872.42,\"11\":3693.2,\"12\":2582.49,\"13\":2631.23,\"14\":1943.84,\"15\":637.49,\"16\":167.56,\"17\":1647.12,\"18\":1408.68,\"19\":159.41},\"CONT\":{\"0\":\"Asia\",\"1\":\"Asia\",\"2\":\"N.America\",\"3\":\"Asia\",\"4\":\"S.America\",\"5\":\"Asia\",\"6\":\"Africa\",\"7\":\"Asia\",\"8\":null,\"9\":\"N.America\",\"10\":\"Asia\",\"11\":\"Europe\",\"12\":\"Europe\",\"13\":\"Europe\",\"14\":\"Europe\",\"15\":\"S.America\",\"16\":\"Africa\",\"17\":\"N.America\",\"18\":\"Oceania\",\"19\":\"Asia\"},\"IND_DAY\":{\"0\":null,\"1\":\"8\\\\/15\\\\/1947\",\"2\":\"1776-07-04\",\"3\":\"8\\\\/17\\\\/1945\",\"4\":\"1822-09-07\",\"5\":\"8\\\\/14\\\\/1947\",\"6\":\"10\\\\/1\\\\/1960\",\"7\":\"3\\\\/26\\\\/1971\",\"8\":\"6\\\\/12\\\\/1992\",\"9\":\"1810-09-16\",\"10\":null,\"11\":null,\"12\":\"1789-07-14\",\"13\":null,\"14\":null,\"15\":\"1816-07-09\",\"16\":\"7\\\\/5\\\\/1962\",\"17\":\"1867-07-01\",\"18\":null,\"19\":\"12\\\\/16\\\\/1991\"}}'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_file = open(\"countries.json\", \"r\")\n",
    "my_data = my_file.read()\n",
    "my_file.close()\n",
    "my_data #not a readable format!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) You must notice this is not in a nice format (i.e. no indentation). Create a JSON object by using json.loads()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ID': {'0': 'CHN', '1': 'IND', '2': 'USA', '3': 'IDN', '4': 'BRA', '5': 'PAK', '6': 'NGA', '7': 'BGD', '8': 'RUS', '9': 'MEX', '10': 'JPN', '11': 'DEU', '12': 'FRA', '13': 'GBR', '14': 'ITA', '15': 'ARG', '16': 'DZA', '17': 'CAN', '18': 'AUS', '19': 'KAZ'}, 'COUNTRY': {'0': 'China', '1': 'India', '2': 'US', '3': 'Indonesia', '4': 'Brazil', '5': 'Pakistan', '6': 'Nigeria', '7': 'Bangladesh', '8': 'Russia', '9': 'Mexico', '10': 'Japan', '11': 'Germany', '12': 'France', '13': 'UK', '14': 'Italy', '15': 'Argentina', '16': 'Algeria', '17': 'Canada', '18': 'Australia', '19': 'Kazakhstan'}, 'POP': {'0': 1398.72, '1': 1351.16, '2': 329.74, '3': 268.07, '4': 210.32, '5': 205.71, '6': 200.96, '7': 167.09, '8': 146.79, '9': 126.58, '10': 126.22, '11': 83.02, '12': 67.02, '13': 66.44, '14': 60.36, '15': 44.94, '16': 43.38, '17': 37.59, '18': 25.47, '19': 18.53}, 'AREA': {'0': 9596.96, '1': 3287.26, '2': 9833.52, '3': 1910.93, '4': 8515.77, '5': 881.91, '6': 923.77, '7': 147.57, '8': 17098.2, '9': 1964.38, '10': 377.97, '11': 357.11, '12': 640.68, '13': 242.5, '14': 301.34, '15': 2780.4, '16': 2381.74, '17': 9984.67, '18': 7692.02, '19': 2724.9}, 'GDP': {'0': 12234.8, '1': 2575.67, '2': 19485.4, '3': 1015.54, '4': 2055.51, '5': 302.14, '6': 375.77, '7': 245.63, '8': 1530.75, '9': 1158.23, '10': 4872.42, '11': 3693.2, '12': 2582.49, '13': 2631.23, '14': 1943.84, '15': 637.49, '16': 167.56, '17': 1647.12, '18': 1408.68, '19': 159.41}, 'CONT': {'0': 'Asia', '1': 'Asia', '2': 'N.America', '3': 'Asia', '4': 'S.America', '5': 'Asia', '6': 'Africa', '7': 'Asia', '8': None, '9': 'N.America', '10': 'Asia', '11': 'Europe', '12': 'Europe', '13': 'Europe', '14': 'Europe', '15': 'S.America', '16': 'Africa', '17': 'N.America', '18': 'Oceania', '19': 'Asia'}, 'IND_DAY': {'0': None, '1': '8/15/1947', '2': '1776-07-04', '3': '8/17/1945', '4': '1822-09-07', '5': '8/14/1947', '6': '10/1/1960', '7': '3/26/1971', '8': '6/12/1992', '9': '1810-09-16', '10': None, '11': None, '12': '1789-07-14', '13': None, '14': None, '15': '1816-07-09', '16': '7/5/1962', '17': '1867-07-01', '18': None, '19': '12/16/1991'}}\n"
     ]
    }
   ],
   "source": [
    "import json as JSON\n",
    "json_object = JSON.loads(my_data)\n",
    "print(json_object) #still not pretty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Convert the JSON object into an indented format by using json.dumps() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"ID\": {\n",
      "    \"0\": \"CHN\",\n",
      "    \"1\": \"IND\",\n",
      "    \"2\": \"USA\",\n",
      "    \"3\": \"IDN\",\n",
      "    \"4\": \"BRA\",\n",
      "    \"5\": \"PAK\",\n",
      "    \"6\": \"NGA\",\n",
      "    \"7\": \"BGD\",\n",
      "    \"8\": \"RUS\",\n",
      "    \"9\": \"MEX\",\n",
      "    \"10\": \"JPN\",\n",
      "    \"11\": \"DEU\",\n",
      "    \"12\": \"FRA\",\n",
      "    \"13\": \"GBR\",\n",
      "    \"14\": \"ITA\",\n",
      "    \"15\": \"ARG\",\n",
      "    \"16\": \"DZA\",\n",
      "    \"17\": \"CAN\",\n",
      "    \"18\": \"AUS\",\n",
      "    \"19\": \"KAZ\"\n",
      "  },\n",
      "  \"COUNTRY\": {\n",
      "    \"0\": \"China\",\n",
      "    \"1\": \"India\",\n",
      "    \"2\": \"US\",\n",
      "    \"3\": \"Indonesia\",\n",
      "    \"4\": \"Brazil\",\n",
      "    \"5\": \"Pakistan\",\n",
      "    \"6\": \"Nigeria\",\n",
      "    \"7\": \"Bangladesh\",\n",
      "    \"8\": \"Russia\",\n",
      "    \"9\": \"Mexico\",\n",
      "    \"10\": \"Japan\",\n",
      "    \"11\": \"Germany\",\n",
      "    \"12\": \"France\",\n",
      "    \"13\": \"UK\",\n",
      "    \"14\": \"Italy\",\n",
      "    \"15\": \"Argentina\",\n",
      "    \"16\": \"Algeria\",\n",
      "    \"17\": \"Canada\",\n",
      "    \"18\": \"Australia\",\n",
      "    \"19\": \"Kazakhstan\"\n",
      "  },\n",
      "  \"POP\": {\n",
      "    \"0\": 1398.72,\n",
      "    \"1\": 1351.16,\n",
      "    \"2\": 329.74,\n",
      "    \"3\": 268.07,\n",
      "    \"4\": 210.32,\n",
      "    \"5\": 205.71,\n",
      "    \"6\": 200.96,\n",
      "    \"7\": 167.09,\n",
      "    \"8\": 146.79,\n",
      "    \"9\": 126.58,\n",
      "    \"10\": 126.22,\n",
      "    \"11\": 83.02,\n",
      "    \"12\": 67.02,\n",
      "    \"13\": 66.44,\n",
      "    \"14\": 60.36,\n",
      "    \"15\": 44.94,\n",
      "    \"16\": 43.38,\n",
      "    \"17\": 37.59,\n",
      "    \"18\": 25.47,\n",
      "    \"19\": 18.53\n",
      "  },\n",
      "  \"AREA\": {\n",
      "    \"0\": 9596.96,\n",
      "    \"1\": 3287.26,\n",
      "    \"2\": 9833.52,\n",
      "    \"3\": 1910.93,\n",
      "    \"4\": 8515.77,\n",
      "    \"5\": 881.91,\n",
      "    \"6\": 923.77,\n",
      "    \"7\": 147.57,\n",
      "    \"8\": 17098.2,\n",
      "    \"9\": 1964.38,\n",
      "    \"10\": 377.97,\n",
      "    \"11\": 357.11,\n",
      "    \"12\": 640.68,\n",
      "    \"13\": 242.5,\n",
      "    \"14\": 301.34,\n",
      "    \"15\": 2780.4,\n",
      "    \"16\": 2381.74,\n",
      "    \"17\": 9984.67,\n",
      "    \"18\": 7692.02,\n",
      "    \"19\": 2724.9\n",
      "  },\n",
      "  \"GDP\": {\n",
      "    \"0\": 12234.8,\n",
      "    \"1\": 2575.67,\n",
      "    \"2\": 19485.4,\n",
      "    \"3\": 1015.54,\n",
      "    \"4\": 2055.51,\n",
      "    \"5\": 302.14,\n",
      "    \"6\": 375.77,\n",
      "    \"7\": 245.63,\n",
      "    \"8\": 1530.75,\n",
      "    \"9\": 1158.23,\n",
      "    \"10\": 4872.42,\n",
      "    \"11\": 3693.2,\n",
      "    \"12\": 2582.49,\n",
      "    \"13\": 2631.23,\n",
      "    \"14\": 1943.84,\n",
      "    \"15\": 637.49,\n",
      "    \"16\": 167.56,\n",
      "    \"17\": 1647.12,\n",
      "    \"18\": 1408.68,\n",
      "    \"19\": 159.41\n",
      "  },\n",
      "  \"CONT\": {\n",
      "    \"0\": \"Asia\",\n",
      "    \"1\": \"Asia\",\n",
      "    \"2\": \"N.America\",\n",
      "    \"3\": \"Asia\",\n",
      "    \"4\": \"S.America\",\n",
      "    \"5\": \"Asia\",\n",
      "    \"6\": \"Africa\",\n",
      "    \"7\": \"Asia\",\n",
      "    \"8\": null,\n",
      "    \"9\": \"N.America\",\n",
      "    \"10\": \"Asia\",\n",
      "    \"11\": \"Europe\",\n",
      "    \"12\": \"Europe\",\n",
      "    \"13\": \"Europe\",\n",
      "    \"14\": \"Europe\",\n",
      "    \"15\": \"S.America\",\n",
      "    \"16\": \"Africa\",\n",
      "    \"17\": \"N.America\",\n",
      "    \"18\": \"Oceania\",\n",
      "    \"19\": \"Asia\"\n",
      "  },\n",
      "  \"IND_DAY\": {\n",
      "    \"0\": null,\n",
      "    \"1\": \"8/15/1947\",\n",
      "    \"2\": \"1776-07-04\",\n",
      "    \"3\": \"8/17/1945\",\n",
      "    \"4\": \"1822-09-07\",\n",
      "    \"5\": \"8/14/1947\",\n",
      "    \"6\": \"10/1/1960\",\n",
      "    \"7\": \"3/26/1971\",\n",
      "    \"8\": \"6/12/1992\",\n",
      "    \"9\": \"1810-09-16\",\n",
      "    \"10\": null,\n",
      "    \"11\": null,\n",
      "    \"12\": \"1789-07-14\",\n",
      "    \"13\": null,\n",
      "    \"14\": null,\n",
      "    \"15\": \"1816-07-09\",\n",
      "    \"16\": \"7/5/1962\",\n",
      "    \"17\": \"1867-07-01\",\n",
      "    \"18\": null,\n",
      "    \"19\": \"12/16/1991\"\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "json_formatted_str = JSON.dumps(json_object, indent = 2) #we can print it in a nice format\n",
    "\n",
    "print(json_formatted_str) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) Write the formatted data into a file with .json extension. Use write() function. For example, the JSON formatted data of df_tracks dataframe should be stored under a name \"tracks_formatted.json\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_file = open(\"countries_.json\",\"w\")\n",
    "my_file.write(json_formatted_str)\n",
    "my_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>ID</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>POP</th>\n",
       "      <th>AREA</th>\n",
       "      <th>GDP</th>\n",
       "      <th>CONT</th>\n",
       "      <th>IND_DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>1398.72</td>\n",
       "      <td>9596.96</td>\n",
       "      <td>12234.80</td>\n",
       "      <td>Asia</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>1351.16</td>\n",
       "      <td>3287.26</td>\n",
       "      <td>2575.67</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/15/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>329.74</td>\n",
       "      <td>9833.52</td>\n",
       "      <td>19485.40</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1776-07-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDN</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>268.07</td>\n",
       "      <td>1910.93</td>\n",
       "      <td>1015.54</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/17/1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>210.32</td>\n",
       "      <td>8515.77</td>\n",
       "      <td>2055.51</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1822-09-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PAK</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>205.71</td>\n",
       "      <td>881.91</td>\n",
       "      <td>302.14</td>\n",
       "      <td>Asia</td>\n",
       "      <td>8/14/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>200.96</td>\n",
       "      <td>923.77</td>\n",
       "      <td>375.77</td>\n",
       "      <td>Africa</td>\n",
       "      <td>10/1/1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BGD</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>167.09</td>\n",
       "      <td>147.57</td>\n",
       "      <td>245.63</td>\n",
       "      <td>Asia</td>\n",
       "      <td>3/26/1971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RUS</td>\n",
       "      <td>Russia</td>\n",
       "      <td>146.79</td>\n",
       "      <td>17098.20</td>\n",
       "      <td>1530.75</td>\n",
       "      <td>None</td>\n",
       "      <td>6/12/1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MEX</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>126.58</td>\n",
       "      <td>1964.38</td>\n",
       "      <td>1158.23</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1810-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>126.22</td>\n",
       "      <td>377.97</td>\n",
       "      <td>4872.42</td>\n",
       "      <td>Asia</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>83.02</td>\n",
       "      <td>357.11</td>\n",
       "      <td>3693.20</td>\n",
       "      <td>Europe</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>67.02</td>\n",
       "      <td>640.68</td>\n",
       "      <td>2582.49</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1789-07-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GBR</td>\n",
       "      <td>UK</td>\n",
       "      <td>66.44</td>\n",
       "      <td>242.50</td>\n",
       "      <td>2631.23</td>\n",
       "      <td>Europe</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Italy</td>\n",
       "      <td>60.36</td>\n",
       "      <td>301.34</td>\n",
       "      <td>1943.84</td>\n",
       "      <td>Europe</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>44.94</td>\n",
       "      <td>2780.40</td>\n",
       "      <td>637.49</td>\n",
       "      <td>S.America</td>\n",
       "      <td>1816-07-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>43.38</td>\n",
       "      <td>2381.74</td>\n",
       "      <td>167.56</td>\n",
       "      <td>Africa</td>\n",
       "      <td>7/5/1962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>37.59</td>\n",
       "      <td>9984.67</td>\n",
       "      <td>1647.12</td>\n",
       "      <td>N.America</td>\n",
       "      <td>1867-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>25.47</td>\n",
       "      <td>7692.02</td>\n",
       "      <td>1408.68</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KAZ</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>18.53</td>\n",
       "      <td>2724.90</td>\n",
       "      <td>159.41</td>\n",
       "      <td>Asia</td>\n",
       "      <td>12/16/1991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID     COUNTRY      POP      AREA       GDP       CONT     IND_DAY\n",
       "0   CHN       China  1398.72   9596.96  12234.80       Asia        None\n",
       "1   IND       India  1351.16   3287.26   2575.67       Asia   8/15/1947\n",
       "2   USA          US   329.74   9833.52  19485.40  N.America  1776-07-04\n",
       "3   IDN   Indonesia   268.07   1910.93   1015.54       Asia   8/17/1945\n",
       "4   BRA      Brazil   210.32   8515.77   2055.51  S.America  1822-09-07\n",
       "5   PAK    Pakistan   205.71    881.91    302.14       Asia   8/14/1947\n",
       "6   NGA     Nigeria   200.96    923.77    375.77     Africa   10/1/1960\n",
       "7   BGD  Bangladesh   167.09    147.57    245.63       Asia   3/26/1971\n",
       "8   RUS      Russia   146.79  17098.20   1530.75       None   6/12/1992\n",
       "9   MEX      Mexico   126.58   1964.38   1158.23  N.America  1810-09-16\n",
       "10  JPN       Japan   126.22    377.97   4872.42       Asia        None\n",
       "11  DEU     Germany    83.02    357.11   3693.20     Europe        None\n",
       "12  FRA      France    67.02    640.68   2582.49     Europe  1789-07-14\n",
       "13  GBR          UK    66.44    242.50   2631.23     Europe        None\n",
       "14  ITA       Italy    60.36    301.34   1943.84     Europe        None\n",
       "15  ARG   Argentina    44.94   2780.40    637.49  S.America  1816-07-09\n",
       "16  DZA     Algeria    43.38   2381.74    167.56     Africa    7/5/1962\n",
       "17  CAN      Canada    37.59   9984.67   1647.12  N.America  1867-07-01\n",
       "18  AUS   Australia    25.47   7692.02   1408.68    Oceania        None\n",
       "19  KAZ  Kazakhstan    18.53   2724.90    159.41       Asia  12/16/1991"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ = pd.read_json(\"countries_.json\")\n",
    "df_"
   ]
  }
 ],
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