Week6-Part2.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": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\nvanb\\\\Documents\\\\NathanPC\\\\USF\\\\python4DS'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check your current working directory\n",
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C is Windows\n",
      " Volume Serial Number is 7A3E-B0C7\n",
      "\n",
      " Directory of C:\\Users\\nvanb\\Documents\\NathanPC\\USF\\python4DS\\data\n",
      "\n",
      "06/24/2020  12:04 PM    <DIR>          .\n",
      "06/24/2020  12:04 PM    <DIR>          ..\n",
      "06/24/2020  10:21 AM    <DIR>          chinook_db\n",
      "06/22/2020  09:18 PM             1,032 countries - comma.txt\n",
      "06/22/2020  09:18 PM             2,242 countries - JSON.txt\n",
      "06/22/2020  09:18 PM             1,032 countries - tab.txt\n",
      "06/22/2020  09:18 PM             1,032 countries.csv\n",
      "06/24/2020  12:04 PM    <DIR>          flights_db\n",
      "06/22/2020  09:18 PM             3,330 json_file.json\n",
      "06/24/2020  12:04 PM    <DIR>          Small_data\n",
      "06/22/2020  09:18 PM               148 thisFile.txt\n",
      "               6 File(s)          8,816 bytes\n",
      "               5 Dir(s)  254,711,250,944 bytes free\n"
     ]
    }
   ],
   "source": [
    "%ls \"data\\\\\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yasin.unlu\\Documents\\Original Docs\\Documents1\\Docs\\Teaching\\PythonForDataScienceSummer2020\\Week-6\\Small_data\n"
     ]
    }
   ],
   "source": [
    "#change your your working directory to the directory where your files are located.\n",
    "%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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "df =  pd.read_csv(\"data\\\\Small_data\\\\Countries_CSV.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </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"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "import pandas as pd\n",
    "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": [],
   "source": [
    "# import pandas as pd\n",
    "# df = pd.read_csv(\"data\\\\countries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read txt File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>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": [
    "df = pd.read_csv(\"data\\\\Small_data\\\\Countries_CSV.txt\")\n",
    "df.hea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data\\\\Small_data\\\\Countries_TSV.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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\\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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # only one col per row because looking for \",\" delim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>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",
       "  </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"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data\\\\Small_data\\\\Countries_TSV.txt\", sep = \"\\t\") \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read Excel File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"data\\\\Small_data\\\\Countries_EXCEL.xlsx\") # will read first sheet by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </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"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"data\\\\Small_data\\\\Countries_EXCEL.xlsx\", sheet_name = 'Population') # read a different sheet "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read JSON File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    " df = pd.read_json(\"data\\\\Small_data\\\\Countries_JSON.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </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"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets try reading it in with the built in `read()` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"data\\\\Small_data\\\\Countries_JSON.json\", \"r\") as json_file:\n",
    "    text = json_file.read()\n",
    "    \n",
    "json_file.closed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[\\n  {\\n    \"ID\": \"CHN\",\\n    \"COUNTRY\": \"China\",\\n    \"POP\": 1398.72,\\n    \"AREA\": 9596.96,\\n    \"GDP\": 12234.8,\\n    \"CONT\": \"Asia\",\\n    \"IND_DAY\": null\\n  },\\n  {\\n    \"ID\": \"IND\",\\n    \"COUNTRY\": \"India\",\\n    \"POP\": 1351.16,\\n    \"AREA\": 3287.26,\\n    \"GDP\": 2575.67,\\n    \"CONT\": \"Asia\",\\n    \"IND_DAY\": \"8/15/1947\"\\n  },\\n  {\\n    \"ID\": \"USA\",\\n    \"COUNTRY\": \"US\",\\n    \"POP\": 329.74,\\n    \"AREA\": 9833.52,\\n    \"GDP\": 19485.4,\\n    \"CONT\": \"N.America\",\\n    \"IND_DAY\": \"1776-07-04\"\\n  },\\n  {\\n    \"ID\": \"IDN\",\\n    \"COUNTRY\": \"Indonesia\",\\n    \"POP\": 268.07,\\n    \"AREA\": 1910.93,\\n    \"GDP\": 1015.54,\\n    \"CONT\": \"Asia\",\\n    \"IND_DAY\": \"8/17/1945\"\\n  },\\n  {\\n    \"ID\": \"BRA\",\\n    \"COUNTRY\": \"Brazil\",\\n    \"POP\": 210.32,\\n    \"AREA\": 8515.77,\\n    \"GDP\": 2055.51,\\n    \"CONT\": \"S.America\",\\n    \"IND_DAY\": \"1822-09-07\"\\n  },\\n  {\\n    \"ID\": \"PAK\",\\n    \"COUNTRY\": \"Pakistan\",\\n    \"POP\": 205.71,\\n    \"AREA\": 881.91,\\n    \"GDP\": 302.14,\\n    \"CONT\": \"Asia\",\\n    \"IND_DAY\": \"8/14/1947\"\\n  },\\n  {\\n    \"ID\": \"NGA\",\\n    \"COUNTRY\": \"Nigeria\",\\n    \"POP\": 200.96,\\n    \"AREA\": 923.77,\\n    \"GDP\": 375.77,\\n    \"CONT\": \"Africa\",\\n    \"IND_DAY\": \"10/1/1960\"\\n  },\\n  {\\n    \"ID\": \"BGD\",\\n    \"COUNTRY\": \"Bangladesh\",\\n    \"POP\": 167.09,\\n    \"AREA\": 147.57,\\n    \"GDP\": 245.63,\\n    \"CONT\": \"Asia\",\\n    \"IND_DAY\": \"3/26/1971\"\\n  },\\n  {\\n    \"ID\": \"RUS\",\\n    \"COUNTRY\": \"Russia\",\\n    \"POP\": 146.79,\\n    \"AREA\": 17098.2,\\n    \"GDP\": 1530.75,\\n    \"CONT\": null,\\n    \"IND_DAY\": \"6/12/1992\"\\n  },\\n  {\\n    \"ID\": \"MEX\",\\n    \"COUNTRY\": \"Mexico\",\\n    \"POP\": 126.58,\\n    \"AREA\": 1964.38,\\n    \"GDP\": 1158.23,\\n    \"CONT\": \"N.America\",\\n    \"IND_DAY\": \"1810-09-16\"\\n  },\\n  {\\n    \"ID\": \"JPN\",\\n    \"COUNTRY\": \"Japan\",\\n    \"POP\": 126.22,\\n    \"AREA\": 377.97,\\n    \"GDP\": 4872.42,\\n    \"CONT\": \"Asia\",\\n    \"IND_DAY\": null\\n  },\\n  {\\n    \"ID\": \"DEU\",\\n    \"COUNTRY\": \"Germany\",\\n    \"POP\": 83.02,\\n    \"AREA\": 357.11,\\n    \"GDP\": 3693.2,\\n    \"CONT\": \"Europe\",\\n    \"IND_DAY\": null\\n  },\\n  {\\n    \"ID\": \"FRA\",\\n    \"COUNTRY\": \"France\",\\n    \"POP\": 67.02,\\n    \"AREA\": 640.68,\\n    \"GDP\": 2582.49,\\n    \"CONT\": \"Europe\",\\n    \"IND_DAY\": \"1789-07-14\"\\n  },\\n  {\\n    \"ID\": \"GBR\",\\n    \"COUNTRY\": \"UK\",\\n    \"POP\": 66.44,\\n    \"AREA\": 242.5,\\n    \"GDP\": 2631.23,\\n    \"CONT\": \"Europe\",\\n    \"IND_DAY\": null\\n  },\\n  {\\n    \"ID\": \"ITA\",\\n    \"COUNTRY\": \"Italy\",\\n    \"POP\": 60.36,\\n    \"AREA\": 301.34,\\n    \"GDP\": 1943.84,\\n    \"CONT\": \"Europe\",\\n    \"IND_DAY\": null\\n  },\\n  {\\n    \"ID\": \"ARG\",\\n    \"COUNTRY\": \"Argentina\",\\n    \"POP\": 44.94,\\n    \"AREA\": 2780.4,\\n    \"GDP\": 637.49,\\n    \"CONT\": \"S.America\",\\n    \"IND_DAY\": \"1816-07-09\"\\n  },\\n  {\\n    \"ID\": \"DZA\",\\n    \"COUNTRY\": \"Algeria\",\\n    \"POP\": 43.38,\\n    \"AREA\": 2381.74,\\n    \"GDP\": 167.56,\\n    \"CONT\": \"Africa\",\\n    \"IND_DAY\": \"7/5/1962\"\\n  },\\n  {\\n    \"ID\": \"CAN\",\\n    \"COUNTRY\": \"Canada\",\\n    \"POP\": 37.59,\\n    \"AREA\": 9984.67,\\n    \"GDP\": 1647.12,\\n    \"CONT\": \"N.America\",\\n    \"IND_DAY\": \"1867-07-01\"\\n  },\\n  {\\n    \"ID\": \"AUS\",\\n    \"COUNTRY\": \"Australia\",\\n    \"POP\": 25.47,\\n    \"AREA\": 7692.02,\\n    \"GDP\": 1408.68,\\n    \"CONT\": \"Oceania\",\\n    \"IND_DAY\": null\\n  },\\n  {\\n    \"ID\": \"KAZ\",\\n    \"COUNTRY\": \"Kazakhstan\",\\n    \"POP\": 18.53,\\n    \"AREA\": 2724.9,\\n    \"GDP\": 159.41,\\n    \"CONT\": \"Asia\",\\n    \"IND_DAY\": \"12/16/1991\"\\n  }\\n]'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text  # messy, more tedious, and only get back a single string object"
   ]
  },
  {
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sqlite3.Connection"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sqlite3\n",
    "db_file = \"data\\\\flights_db\\\\flights.db\"\n",
    "\n",
    "conn = None\n",
    "try:\n",
    "    conn = sqlite3.connect(db_file)\n",
    "except Error as e:\n",
    "    print(e)\n",
    "    \n",
    "type(conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cursor object\n",
    "\n",
    "A cursor is a temporaty work-space/area for SQL commands (alocates a little bit of memory to work and then will return some data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "<class 'tuple'>\n",
      "(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": [
    "# create cursor object\n",
    "cur = conn.cursor()\n",
    "\n",
    "# execute some SQL\n",
    "cur.execute(\"SELECT * FROM airlines LIMIT 5;\")\n",
    "\n",
    "# # Iterate over the cursor\n",
    "# for result in cur:\n",
    "#     print(result)\n",
    "\n",
    "# Fetch one result\n",
    "# print(cur.fetchone())\n",
    "\n",
    "# Fetch all results as list\n",
    "rows = cur.fetchall()  # returns a list of tuples\n",
    "\n",
    "print(type(rows))\n",
    "print(type(rows[0]))\n",
    "\n",
    "\n",
    "for row in rows:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's read the database table into a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6048, 9)\n"
     ]
    },
    {
     "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we already have a connection\n",
    "df_airlines = pd.read_sql_query(\"SELECT * FROM airlines;\", con = conn)\n",
    "\n",
    "print(df_airlines.shape)\n",
    "\n",
    "df_airlines.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Write CSV File\n",
    "Let's change our directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_airlines.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_airlines.to_csv(\"data\\\\flights_db\\\\My_Airlines_CSV.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_airlines.to_csv(\"data\\\\flights_db\\\\My_Airlines_TSV.txt\", sep = \"\\t\", index = False) # w/o saving pandas index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Write JSON File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_airlines.to_json(\"data\\\\flights_db\\\\My_Airlines_JSON.json\")   # writes as 1 long string "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read file back into Pandas to convert into dataframe format again. no formatting necessary!\n",
    "my_json_df = pd.read_json(\"data\\\\flights_db\\\\My_Airlines_JSON.json\")\n",
    "my_json_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question\n",
    "\n",
    "What happens if we accidentally leave a connection open (either to db or open file)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise:\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": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json as JSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "<class 'str'>\n"
     ]
    },
    {
     "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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Read each .json file as a single string by using read() function in Python.\n",
    "with open(\"data\\\\flights_db\\\\countries.json\", \"r\") as json_file:\n",
    "    json_data = json_file.read()\n",
    "\n",
    "print(json_file.closed)\n",
    "print(type(json_data))\n",
    "json_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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\":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\"}}\n"
     ]
    }
   ],
   "source": [
    "print(json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'ID': {'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",
       " 'COUNTRY': {'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",
       " 'POP': {'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",
       " 'AREA': {'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",
       " 'GDP': {'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",
       " 'CONT': {'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': None,\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",
       " 'IND_DAY': {'0': None,\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': None,\n",
       "  '11': None,\n",
       "  '12': '1789-07-14',\n",
       "  '13': None,\n",
       "  '14': None,\n",
       "  '15': '1816-07-09',\n",
       "  '16': '7/5/1962',\n",
       "  '17': '1867-07-01',\n",
       "  '18': None,\n",
       "  '19': '12/16/1991'}}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. You must notice this is not in a nice format (i.e. no indentation). Create a JSON object by using json.loads().\n",
    "json_object = JSON.loads(json_data)\n",
    "print(json_object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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": [
    "# 3. Convert the JSON object into an indented format by using json.dumps() function.\n",
    "json_formatted = JSON.dumps(json_object, indent = 2)\n",
    "\n",
    "print(json_formatted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(json_formatted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Write the formatted data into a file with .json extension. Use write() function. \n",
    "# For example, the JSON formatted data of df_tracks dataframe should be stored under a name \"countries_.json\".\n",
    "\n",
    "with open(\"data\\\\flights_db\\\\countries_JSON.json\", \"w\") as file:\n",
    "    file.write(json_formatted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file.closed"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}