Importation des donnees avec Pandas.ipynb

{
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   "cell_type": "markdown",
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   },
   "source": [
    "# <center>**IMPORTATION DES DONNEES AVEC PANDAS**\n",
    "\n",
    "Par [Josué AFOUDA](https://afouda-datascience.com/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uFP92ljElOL4"
   },
   "source": [
    "Avant d'analyser vos données et construire des modèles de prédiction à partir de ces données, vous devez nécessairement importer vos données. \n",
    "\n",
    "Dans ce tutoriel, vous apprendrez à importer des données provenant de fichiers textes comme ***.txt***, ***.csv***, fichiers excels (***.xlsx***).\n",
    "\n",
    "Le principal outil que nous utiliserons dans ce tutorial est la librairie **Pandas**."
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![pandas.png](attachment:pandas.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Pandas](https://pandas.pydata.org/) permet de réaliser l'intégralité d'une analyse de données en manipulant des dataframes. La dataframe Pandas est l'analogue pythonique de la dataframe du langage R. Dans une dataframe, les données se présentent sous forme de tableau (lignes et colonnes). Plus précisément, dans une dataframe on parle d'observations (pour désigner les lignes) et de variables (pour désigner les colonnes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importation de Pandas\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "voy7zco-rOXG"
   },
   "source": [
    "# <font color=blue> Importation des fichiers textes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour importer des fichiers textes en tant que dataframe, vous utiliserez principalement la fonction [read_csv](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) de Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "DEQE7k1KqRJ4"
   },
   "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, gs, 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",
      "    DataFrame.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": [
    "# Aide sur la fonction read_csv\n",
    "\n",
    "help(pd.read_csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JP7rewebf_Sw"
   },
   "source": [
    "Importons le célèbre jeu de données *titanic*. Le seul argument de la fonction ***read_csv*** que vous devez absolument spécifié est : ***filepath_or_buffer***. Cet argument indique le chemin de votre fichier. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "executionInfo": {
     "elapsed": 1073,
     "status": "ok",
     "timestamp": 1608789955896,
     "user": {
      "displayName": "J.A DATATECH CONSULTING",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64",
      "userId": "06410945177748020218"
     },
     "user_tz": -60
    },
    "id": "KTw_jAdohH_Y",
    "outputId": "8eb51128-f52c-4b99-b668-847bf490f99e"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
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       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importation des données du titanic ('titanic.csv')\n",
    "\n",
    "df = pd.read_csv(filepath_or_buffer = 'titanic.csv') # pd.read_csv('titanic.csv')\n",
    "\n",
    "# Affichage des cinq premières lignes de la dataframe df\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Etant donné que le fichier *titanic.csv* est dans le même emplacement (dossier) que le notebook actuel, alors vous avez eu juste besoin de mettre **titanic.csv** (nom du fichier + extension) comme valeur de l'argument *filepath_or_buffer* de la fonction *read_csv*.\n",
    "\n",
    "Un cas souvent rencontré est le fait de disposer d'une adresse url de voytre fichier texte."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1068,
     "status": "ok",
     "timestamp": 1608789955897,
     "user": {
      "displayName": "J.A DATATECH CONSULTING",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gi9wsjK7psnF8z3uOCEcA2yFkP2ckMODUtZxShO=s64",
      "userId": "06410945177748020218"
     },
     "user_tz": -60
    },
    "id": "jFiFM-4mh7FD",
    "outputId": "2241bcc3-a071-4b56-fadd-1437316256af"
   },
   "outputs": [],
   "source": [
    "# Importaion d'un fichier csv à partir d'une url\n",
    "\n",
    "titanic_url = 'https://raw.githubusercontent.com/JosueAfouda/TUTORIALS/main/titanic.csv'\n",
    "\n",
    "df = pd.read_csv(titanic_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Par défaut, Pandas donne des numéros pour identifier de manière unique chaque ligne de la dataframe. C'est ce qu'on appelle les indices de lignes. La première ligne est d'indice 0. Il arrive parfois qu'on veuille définir les valeurs d'une colonne de la dataframe comme étant les indices de ligne."
   ]
  },
  {
   "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>Unnamed: 0</th>\n",
       "      <th>country</th>\n",
       "      <th>social_support</th>\n",
       "      <th>freedom</th>\n",
       "      <th>corruption</th>\n",
       "      <th>generosity</th>\n",
       "      <th>gdp_per_cap</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>happiness_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Finland</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>42400</td>\n",
       "      <td>81.8</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>48300</td>\n",
       "      <td>81.0</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>66300</td>\n",
       "      <td>82.6</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>47900</td>\n",
       "      <td>83.0</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>50500</td>\n",
       "      <td>81.8</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      country  social_support  freedom  corruption  generosity  \\\n",
       "0           1      Finland             2.0      5.0         4.0        47.0   \n",
       "1           2      Denmark             4.0      6.0         3.0        22.0   \n",
       "2           3       Norway             3.0      3.0         8.0        11.0   \n",
       "3           4      Iceland             1.0      7.0        45.0         3.0   \n",
       "4           5  Netherlands            15.0     19.0        12.0         7.0   \n",
       "\n",
       "   gdp_per_cap  life_exp  happiness_score  \n",
       "0        42400      81.8              155  \n",
       "1        48300      81.0              154  \n",
       "2        66300      82.6              153  \n",
       "3        47900      83.0              152  \n",
       "4        50500      81.8              151  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importation du fichier 'world_happiness.csv'\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/JosueAfouda/Statistics-with-Python/main/world_happiness.csv'\n",
    "\n",
    "wh = pd.read_csv(url)\n",
    "\n",
    "wh.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(143, 9)\n",
      "143\n"
     ]
    }
   ],
   "source": [
    "# shape de la dataframe wh et nombre de valeurs distinctes de la variable 'Unnamed: 0'\n",
    "\n",
    "print(wh.shape)\n",
    "\n",
    "print(wh['Unnamed: 0'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La dataframe *wh* contient 143 lignes et la variable 'Unnamed: 0' contient 143 valeurs uniques. Donc cette variable peut servir à identifier de manière unique chaque observation de la dataframe *wh*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>social_support</th>\n",
       "      <th>freedom</th>\n",
       "      <th>corruption</th>\n",
       "      <th>generosity</th>\n",
       "      <th>gdp_per_cap</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>happiness_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Finland</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>42400</td>\n",
       "      <td>81.8</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>48300</td>\n",
       "      <td>81.0</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>66300</td>\n",
       "      <td>82.6</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>47900</td>\n",
       "      <td>83.0</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>50500</td>\n",
       "      <td>81.8</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  social_support  freedom  corruption  generosity  gdp_per_cap  \\\n",
       "1      Finland             2.0      5.0         4.0        47.0        42400   \n",
       "2      Denmark             4.0      6.0         3.0        22.0        48300   \n",
       "3       Norway             3.0      3.0         8.0        11.0        66300   \n",
       "4      Iceland             1.0      7.0        45.0         3.0        47900   \n",
       "5  Netherlands            15.0     19.0        12.0         7.0        50500   \n",
       "\n",
       "   life_exp  happiness_score  \n",
       "1      81.8              155  \n",
       "2      81.0              154  \n",
       "3      82.6              153  \n",
       "4      83.0              152  \n",
       "5      81.8              151  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (Re)importation du fichier 'world_happiness.csv'\n",
    "\n",
    "wh = pd.read_csv(url, index_col = 0)\n",
    "\n",
    "wh.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'argument ***index_col*** permet de spécifier la colonne dont les valeurs seront utilisées comme étiquettes de lignes. Ici en mettant, *index_col = 0*, on a spécifié la première colonne comme inices de ligne.\n",
    "\n",
    "Cet argument est très utile surtout lors de l'importation de séries temporelles. Il peut aussi prendre comme valeur, le nom de la variable qu'on veut transformer en index de lignes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>34.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>55.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>36.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>35.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>62.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Total\n",
       "0  2019  34.52\n",
       "1  2018  55.97\n",
       "2  2017  36.62\n",
       "3  2016  35.48\n",
       "4  2015  62.61"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importation des données du fichier 'annual_rainfall_dallas.csv'\n",
    "\n",
    "url_rainfall = 'https://raw.githubusercontent.com/JosueAfouda/Statistics-with-Python/main/annual_rainfall_dallas.csv'\n",
    "\n",
    "rainfall = pd.read_csv(url_rainfall)\n",
    "\n",
    "rainfall.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>34.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>55.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>36.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>35.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>62.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Total\n",
       "Year       \n",
       "2019  34.52\n",
       "2018  55.97\n",
       "2017  36.62\n",
       "2016  35.48\n",
       "2015  62.61"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rainfall = pd.read_csv(url_rainfall, index_col = 'Year')\n",
    "\n",
    "rainfall.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'argument ***parse_dates*** est aussi très utile pour l'importation de séries temporelles."
   ]
  },
  {
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-12-03</td>\n",
       "      <td>13368.219727</td>\n",
       "      <td>13407.240234</td>\n",
       "      <td>13296.280273</td>\n",
       "      <td>13314.570313</td>\n",
       "      <td>13314.570313</td>\n",
       "      <td>212170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-12-04</td>\n",
       "      <td>13311.240234</td>\n",
       "      <td>13316.280273</td>\n",
       "      <td>13237.589844</td>\n",
       "      <td>13248.730469</td>\n",
       "      <td>13248.730469</td>\n",
       "      <td>204940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-12-05</td>\n",
       "      <td>13244.009766</td>\n",
       "      <td>13460.240234</td>\n",
       "      <td>13244.009766</td>\n",
       "      <td>13444.959961</td>\n",
       "      <td>13444.959961</td>\n",
       "      <td>256800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-12-06</td>\n",
       "      <td>13445.849609</td>\n",
       "      <td>13632.900391</td>\n",
       "      <td>13426.179688</td>\n",
       "      <td>13619.889648</td>\n",
       "      <td>13619.889648</td>\n",
       "      <td>197270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-12-07</td>\n",
       "      <td>13618.269531</td>\n",
       "      <td>13668.099609</td>\n",
       "      <td>13601.360352</td>\n",
       "      <td>13625.580078</td>\n",
       "      <td>13625.580078</td>\n",
       "      <td>179420000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date          Open          High           Low         Close  \\\n",
       "0  2007-12-03  13368.219727  13407.240234  13296.280273  13314.570313   \n",
       "1  2007-12-04  13311.240234  13316.280273  13237.589844  13248.730469   \n",
       "2  2007-12-05  13244.009766  13460.240234  13244.009766  13444.959961   \n",
       "3  2007-12-06  13445.849609  13632.900391  13426.179688  13619.889648   \n",
       "4  2007-12-07  13618.269531  13668.099609  13601.360352  13625.580078   \n",
       "\n",
       "      Adj Close     Volume  \n",
       "0  13314.570313  212170000  \n",
       "1  13248.730469  204940000  \n",
       "2  13444.959961  256800000  \n",
       "3  13619.889648  197270000  \n",
       "4  13625.580078  179420000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importation des données du fichier 'dow_jones_2007_2017.csv'\n",
    "\n",
    "url_dowjones = 'https://raw.githubusercontent.com/JosueAfouda/Stock-Market-Price-Prediction/master/dow_jones_2007_2017.csv'\n",
    "\n",
    "dowjones = pd.read_csv(url_dowjones)\n",
    "\n",
    "dowjones.head()"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-12-03</th>\n",
       "      <td>13368.219727</td>\n",
       "      <td>13407.240234</td>\n",
       "      <td>13296.280273</td>\n",
       "      <td>13314.570313</td>\n",
       "      <td>13314.570313</td>\n",
       "      <td>212170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-04</th>\n",
       "      <td>13311.240234</td>\n",
       "      <td>13316.280273</td>\n",
       "      <td>13237.589844</td>\n",
       "      <td>13248.730469</td>\n",
       "      <td>13248.730469</td>\n",
       "      <td>204940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-05</th>\n",
       "      <td>13244.009766</td>\n",
       "      <td>13460.240234</td>\n",
       "      <td>13244.009766</td>\n",
       "      <td>13444.959961</td>\n",
       "      <td>13444.959961</td>\n",
       "      <td>256800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-06</th>\n",
       "      <td>13445.849609</td>\n",
       "      <td>13632.900391</td>\n",
       "      <td>13426.179688</td>\n",
       "      <td>13619.889648</td>\n",
       "      <td>13619.889648</td>\n",
       "      <td>197270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-07</th>\n",
       "      <td>13618.269531</td>\n",
       "      <td>13668.099609</td>\n",
       "      <td>13601.360352</td>\n",
       "      <td>13625.580078</td>\n",
       "      <td>13625.580078</td>\n",
       "      <td>179420000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Open          High           Low         Close  \\\n",
       "Date                                                                 \n",
       "2007-12-03  13368.219727  13407.240234  13296.280273  13314.570313   \n",
       "2007-12-04  13311.240234  13316.280273  13237.589844  13248.730469   \n",
       "2007-12-05  13244.009766  13460.240234  13244.009766  13444.959961   \n",
       "2007-12-06  13445.849609  13632.900391  13426.179688  13619.889648   \n",
       "2007-12-07  13618.269531  13668.099609  13601.360352  13625.580078   \n",
       "\n",
       "               Adj Close     Volume  \n",
       "Date                                 \n",
       "2007-12-03  13314.570313  212170000  \n",
       "2007-12-04  13248.730469  204940000  \n",
       "2007-12-05  13444.959961  256800000  \n",
       "2007-12-06  13619.889648  197270000  \n",
       "2007-12-07  13625.580078  179420000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Les indices de ligne sont maintenant au format datetime\n",
    "\n",
    "dowjones = pd.read_csv(url_dowjones, index_col = 0, parse_dates=True)\n",
    "\n",
    "dowjones.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.datetimes.DatetimeIndex"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dowjones.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un cas parfois rencontré est le fait que le fichier de données n'a pas d'en-têtes c'est-à-dire pas de noms de variables. "
   ]
  },
  {
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>5.1</th>\n",
       "      <th>3.5</th>\n",
       "      <th>1.4</th>\n",
       "      <th>0.2</th>\n",
       "      <th>Iris-setosa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   5.1  3.5  1.4  0.2  Iris-setosa\n",
       "0  4.9  3.0  1.4  0.2  Iris-setosa\n",
       "1  4.7  3.2  1.3  0.2  Iris-setosa\n",
       "2  4.6  3.1  1.5  0.2  Iris-setosa\n",
       "3  5.0  3.6  1.4  0.2  Iris-setosa\n",
       "4  5.4  3.9  1.7  0.4  Iris-setosa"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importation du fichier 'iris.data'\n",
    "\n",
    "url_iris = 'https://raw.githubusercontent.com/JosueAfouda/Machine-Learning-par-la-pratique-avec-Python/master/iris.data'\n",
    "\n",
    "iris = pd.read_csv(url_iris)\n",
    "\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La fonction *read_csv* considère par défaut, la première ligne du fichier comme la ligne d'en-têtes. Etant donné que le fichier 'iris.data' ne contient pas d'en-têtes, remarquez que la première observation a été définie comme noms de variabes. Pour régler ce genre de problème, vous devez spécifier l'argument ***header***."
   ]
  },
  {
   "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",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3            4\n",
       "0  5.1  3.5  1.4  0.2  Iris-setosa\n",
       "1  4.9  3.0  1.4  0.2  Iris-setosa\n",
       "2  4.7  3.2  1.3  0.2  Iris-setosa\n",
       "3  4.6  3.1  1.5  0.2  Iris-setosa\n",
       "4  5.0  3.6  1.4  0.2  Iris-setosa"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (Re)Importation du fichier 'iris.data'\n",
    "\n",
    "iris = pd.read_csv(url_iris, header = None)\n",
    "\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En mettant, ***header = None***, vous dites à Pandas que ce fichier ne contient pas d'en-têtes. Ainsi Pandas attribue des numéros à chaque colonne. La première colonne est la colonne 0.\n",
    "\n",
    "Supposons que vous connaissez les noms de variables. Voici comment vous pouvez spécifier ces noms dans votre imporation en utilisant l'argument ***names*** :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width        class\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (Re)Importation du fichier 'iris.data'\n",
    "\n",
    "iris_names = ['sepal_length', 'sepal_width', 'petal_length' ,'petal_width' ,'class']\n",
    "\n",
    "iris = pd.read_csv(url_iris, header = None, names = iris_names)\n",
    "\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans certains cas, vous devez spécifier le délimiteur utilisé dans votre fichier texte (souvent avec les formats *.txt*). Pour ce faire, utilisez l'argument ***sep***."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>1      1     242.0     23.2    25.4    30.0    38.4   13.4   NA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2      1     290.0     24.0    26.3    31....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3      1     340.0     23.9    26.5    31....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4      1     363.0     26.3    29.0    33....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5      1     430.0     26.5    29.0    34....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6      1     450.0     26.8    29.7    34....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      1      1     242.0     23.2    25.4    30.0    38.4   13.4   NA\n",
       "0      2      1     290.0     24.0    26.3    31....                 \n",
       "1      3      1     340.0     23.9    26.5    31....                 \n",
       "2      4      1     363.0     26.3    29.0    33....                 \n",
       "3      5      1     430.0     26.5    29.0    34....                 \n",
       "4      6      1     450.0     26.8    29.7    34....                 "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importons le fichier fishcatch.dat.txt\n",
    "\n",
    "fishes_url = 'http://jse.amstat.org/datasets/fishcatch.dat.txt'\n",
    "\n",
    "fishes = pd.read_csv(fishes_url)\n",
    "\n",
    "fishes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>1.1</th>\n",
       "      <th>242.0</th>\n",
       "      <th>23.2</th>\n",
       "      <th>25.4</th>\n",
       "      <th>30.0</th>\n",
       "      <th>38.4</th>\n",
       "      <th>13.4</th>\n",
       "      <th>NA</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2</td>\n",
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       "      <td>26.3</td>\n",
       "      <td>31.2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "      <td>39.8</td>\n",
       "      <td>15.1</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>4</td>\n",
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       "      <td>363.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>5</td>\n",
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       "      <td>26.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>15.1</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>450.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>34.7</td>\n",
       "      <td>39.2</td>\n",
       "      <td>14.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1  1.1  242.0  23.2  25.4  30.0  38.4  13.4  NA\n",
       "0  2    1  290.0  24.0  26.3  31.2  40.0  13.8 NaN\n",
       "1  3    1  340.0  23.9  26.5  31.1  39.8  15.1 NaN\n",
       "2  4    1  363.0  26.3  29.0  33.5  38.0  13.3 NaN\n",
       "3  5    1  430.0  26.5  29.0  34.0  36.6  15.1 NaN\n",
       "4  6    1  450.0  26.8  29.7  34.7  39.2  14.2 NaN"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (Re)Importons le fichier fishcatch.dat.txt\n",
    "\n",
    "fishes = pd.read_csv(fishes_url, sep = '\\s+')\n",
    "\n",
    "fishes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Species</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Length1</th>\n",
       "      <th>Length2</th>\n",
       "      <th>Length3</th>\n",
       "      <th>Height%</th>\n",
       "      <th>Width%</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>242.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>25.4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>38.4</td>\n",
       "      <td>13.4</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>290.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>31.2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>13.8</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>340.0</td>\n",
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       "      <td>26.5</td>\n",
       "      <td>31.1</td>\n",
       "      <td>39.8</td>\n",
       "      <td>15.1</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>363.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>13.3</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>430.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>36.6</td>\n",
       "      <td>15.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Species  Weight  Length1  Length2  Length3  Height%  Width%  Sex\n",
       "1        1   242.0     23.2     25.4     30.0     38.4    13.4  NaN\n",
       "2        1   290.0     24.0     26.3     31.2     40.0    13.8  NaN\n",
       "3        1   340.0     23.9     26.5     31.1     39.8    15.1  NaN\n",
       "4        1   363.0     26.3     29.0     33.5     38.0    13.3  NaN\n",
       "5        1   430.0     26.5     29.0     34.0     36.6    15.1  NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (Re)Importons le fichier fishcatch.dat.txt\n",
    "\n",
    "columns = ['Species', 'Weight', 'Length1', 'Length2', 'Length3', 'Height%', 'Width%', 'Sex']\n",
    "\n",
    "\n",
    "fishes = pd.read_csv(fishes_url, sep = '\\s+', names = columns)\n",
    "\n",
    "fishes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il y a d'autres cas possibles pouvant être rencontrés lors de l'importation de l'importation de fichiers textes. N'hésitez pas à toujours regarder la documentation web de la fonction [read_csv](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) ou à utiliser l'aide avec la fonction *help()*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "22sX2siEjM7W"
   },
   "source": [
    "# <font color=blue> Importation des fichiers Excel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excel est omniprésent dans notre quotidien. De ce fait, vous aurez à traiter des feuilles de calcul Excel à un moment donné.\n",
    "\n",
    "Pour importer les fichiers excel en tant que dataframe dans Python, vous utiliserez pincipalement la fonction [read_excel](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html) de Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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, gs, 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",
      "    DataFrame.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": [
    "# Aide sur la fonction read_excel\n",
    "\n",
    "help(pd.read_csv)"
   ]
  },
  {
   "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>FreeCodeCamp New Developer Survey Responses, 2016</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 88</th>\n",
       "      <th>Unnamed: 89</th>\n",
       "      <th>Unnamed: 90</th>\n",
       "      <th>Unnamed: 91</th>\n",
       "      <th>Unnamed: 92</th>\n",
       "      <th>Unnamed: 93</th>\n",
       "      <th>Unnamed: 94</th>\n",
       "      <th>Unnamed: 95</th>\n",
       "      <th>Unnamed: 96</th>\n",
       "      <th>Unnamed: 97</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Source: https://www.kaggle.com/freecodecamp/20...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Age</td>\n",
       "      <td>AttendedBootcamp</td>\n",
       "      <td>BootcampFinish</td>\n",
       "      <td>BootcampLoanYesNo</td>\n",
       "      <td>BootcampName</td>\n",
       "      <td>BootcampRecommend</td>\n",
       "      <td>ChildrenNumber</td>\n",
       "      <td>CityPopulation</td>\n",
       "      <td>CodeEventConferences</td>\n",
       "      <td>CodeEventDjangoGirls</td>\n",
       "      <td>...</td>\n",
       "      <td>ResourcePluralSight</td>\n",
       "      <td>ResourceSkillCrush</td>\n",
       "      <td>ResourceStackOverflow</td>\n",
       "      <td>ResourceTreehouse</td>\n",
       "      <td>ResourceUdacity</td>\n",
       "      <td>ResourceUdemy</td>\n",
       "      <td>ResourceW3Schools</td>\n",
       "      <td>SchoolDegree</td>\n",
       "      <td>SchoolMajor</td>\n",
       "      <td>StudentDebtOwe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>high school diploma or equivalent (GED)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FreeCodeCamp New Developer Survey Responses, 2016        Unnamed: 1  \\\n",
       "0  Source: https://www.kaggle.com/freecodecamp/20...               NaN   \n",
       "1                                                Age  AttendedBootcamp   \n",
       "2                                                 28                 0   \n",
       "3                                                 22                 0   \n",
       "4                                                 19                 0   \n",
       "\n",
       "       Unnamed: 2         Unnamed: 3    Unnamed: 4         Unnamed: 5  \\\n",
       "0             NaN                NaN           NaN                NaN   \n",
       "1  BootcampFinish  BootcampLoanYesNo  BootcampName  BootcampRecommend   \n",
       "2             NaN                NaN           NaN                NaN   \n",
       "3             NaN                NaN           NaN                NaN   \n",
       "4             NaN                NaN           NaN                NaN   \n",
       "\n",
       "       Unnamed: 6                     Unnamed: 7            Unnamed: 8  \\\n",
       "0             NaN                            NaN                   NaN   \n",
       "1  ChildrenNumber                 CityPopulation  CodeEventConferences   \n",
       "2             NaN  between 100,000 and 1 million                   NaN   \n",
       "3             NaN  between 100,000 and 1 million                   NaN   \n",
       "4             NaN            more than 1 million                   NaN   \n",
       "\n",
       "             Unnamed: 9  ...          Unnamed: 88         Unnamed: 89  \\\n",
       "0                   NaN  ...                  NaN                 NaN   \n",
       "1  CodeEventDjangoGirls  ...  ResourcePluralSight  ResourceSkillCrush   \n",
       "2                   NaN  ...                  NaN                 NaN   \n",
       "3                   NaN  ...                  NaN                 NaN   \n",
       "4                   NaN  ...                  NaN                 NaN   \n",
       "\n",
       "             Unnamed: 90        Unnamed: 91      Unnamed: 92    Unnamed: 93  \\\n",
       "0                    NaN                NaN              NaN            NaN   \n",
       "1  ResourceStackOverflow  ResourceTreehouse  ResourceUdacity  ResourceUdemy   \n",
       "2                    NaN                NaN              NaN            NaN   \n",
       "3                    NaN                NaN              NaN              1   \n",
       "4                    NaN                NaN              NaN            NaN   \n",
       "\n",
       "         Unnamed: 94                              Unnamed: 95  Unnamed: 96  \\\n",
       "0                NaN                                      NaN          NaN   \n",
       "1  ResourceW3Schools                             SchoolDegree  SchoolMajor   \n",
       "2                NaN           some college credit, no degree          NaN   \n",
       "3                NaN           some college credit, no degree          NaN   \n",
       "4                NaN  high school diploma or equivalent (GED)          NaN   \n",
       "\n",
       "      Unnamed: 97  \n",
       "0             NaN  \n",
       "1  StudentDebtOwe  \n",
       "2           20000  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "\n",
       "[5 rows x 98 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importation du fichier 'dev-coder-survey.xlsx'\n",
    "\n",
    "sondage = pd.read_excel('dev-coder-survey.xlsx')\n",
    "\n",
    "sondage.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il est conseillé de lire préalablement le fichier avec le logiciel Excel afin de savoir comment bien l'importer dans Pandas.\n",
    "\n",
    "L'argument ***skiprows*** indique le nombre de lignes à sauter en comptant à partir d la première ligne d'indice 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>CityPopulation</th>\n",
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       "      <th>...</th>\n",
       "      <th>ResourcePluralSight</th>\n",
       "      <th>ResourceSkillCrush</th>\n",
       "      <th>ResourceStackOverflow</th>\n",
       "      <th>ResourceTreehouse</th>\n",
       "      <th>ResourceUdacity</th>\n",
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       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>high school diploma or equivalent (GED)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>Cinematography And Film</td>\n",
       "      <td>7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  AttendedBootcamp  BootcampFinish  BootcampLoanYesNo BootcampName  \\\n",
       "0  28.0               0.0             NaN                NaN          NaN   \n",
       "1  22.0               0.0             NaN                NaN          NaN   \n",
       "2  19.0               0.0             NaN                NaN          NaN   \n",
       "3  26.0               0.0             NaN                NaN          NaN   \n",
       "4  20.0               0.0             NaN                NaN          NaN   \n",
       "\n",
       "   BootcampRecommend  ChildrenNumber                 CityPopulation  \\\n",
       "0                NaN             NaN  between 100,000 and 1 million   \n",
       "1                NaN             NaN  between 100,000 and 1 million   \n",
       "2                NaN             NaN            more than 1 million   \n",
       "3                NaN             NaN            more than 1 million   \n",
       "4                NaN             NaN  between 100,000 and 1 million   \n",
       "\n",
       "   CodeEventConferences  CodeEventDjangoGirls  ...  ResourcePluralSight  \\\n",
       "0                   NaN                   NaN  ...                  NaN   \n",
       "1                   NaN                   NaN  ...                  NaN   \n",
       "2                   NaN                   NaN  ...                  NaN   \n",
       "3                   NaN                   NaN  ...                  NaN   \n",
       "4                   NaN                   NaN  ...                  NaN   \n",
       "\n",
       "   ResourceSkillCrush  ResourceStackOverflow  ResourceTreehouse  \\\n",
       "0                 NaN                    NaN                NaN   \n",
       "1                 NaN                    NaN                NaN   \n",
       "2                 NaN                    NaN                NaN   \n",
       "3                 NaN                    NaN                NaN   \n",
       "4                 NaN                    NaN                NaN   \n",
       "\n",
       "   ResourceUdacity  ResourceUdemy  ResourceW3Schools  \\\n",
       "0              NaN            NaN                NaN   \n",
       "1              NaN            1.0                NaN   \n",
       "2              NaN            NaN                NaN   \n",
       "3              NaN            NaN                NaN   \n",
       "4              NaN            NaN                NaN   \n",
       "\n",
       "                              SchoolDegree              SchoolMajor  \\\n",
       "0           some college credit, no degree                      NaN   \n",
       "1           some college credit, no degree                      NaN   \n",
       "2  high school diploma or equivalent (GED)                      NaN   \n",
       "3                        bachelor's degree  Cinematography And Film   \n",
       "4           some college credit, no degree                      NaN   \n",
       "\n",
       "   StudentDebtOwe  \n",
       "0           20000  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3            7000  \n",
       "4             NaN  \n",
       "\n",
       "[5 rows x 98 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (Re)Importation du fichier 'dev-coder-survey.xlsx'\n",
    "\n",
    "sondage2 = pd.read_excel('dev-coder-survey.xlsx', skiprows=2)\n",
    "\n",
    "sondage2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En mettant *skiprows = 2*, vous importez les données à partir de la ligne d'indice 2 ce qui veut dire que vous n'importez pas les deux premières lignes.\n",
    "\n",
    "Par défaut, Pandas importe la première feuille du fichier Excel (feuille d'indice 0). Vous pouvez préciser la feuille à importer en utilisant l'argument ***sheet_name***."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>AttendedBootcamp</th>\n",
       "      <th>BootcampFinish</th>\n",
       "      <th>BootcampLoanYesNo</th>\n",
       "      <th>BootcampName</th>\n",
       "      <th>BootcampRecommend</th>\n",
       "      <th>ChildrenNumber</th>\n",
       "      <th>CityPopulation</th>\n",
       "      <th>CodeEventConferences</th>\n",
       "      <th>CodeEventDjangoGirls</th>\n",
       "      <th>...</th>\n",
       "      <th>ResourcePluralSight</th>\n",
       "      <th>ResourceSkillCrush</th>\n",
       "      <th>ResourceStackOverflow</th>\n",
       "      <th>ResourceTreehouse</th>\n",
       "      <th>ResourceUdacity</th>\n",
       "      <th>ResourceUdemy</th>\n",
       "      <th>ResourceW3Schools</th>\n",
       "      <th>SchoolDegree</th>\n",
       "      <th>SchoolMajor</th>\n",
       "      <th>StudentDebtOwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>less than 100,000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>high school diploma or equivalent (GED)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  AttendedBootcamp  BootcampFinish  BootcampLoanYesNo BootcampName  \\\n",
       "0  27.0               0.0             NaN                NaN          NaN   \n",
       "1  34.0               0.0             NaN                NaN          NaN   \n",
       "2  21.0               0.0             NaN                NaN          NaN   \n",
       "3  26.0               0.0             NaN                NaN          NaN   \n",
       "4  20.0               0.0             NaN                NaN          NaN   \n",
       "\n",
       "   BootcampRecommend  ChildrenNumber                 CityPopulation  \\\n",
       "0                NaN             NaN            more than 1 million   \n",
       "1                NaN             NaN              less than 100,000   \n",
       "2                NaN             NaN            more than 1 million   \n",
       "3                NaN             NaN  between 100,000 and 1 million   \n",
       "4                NaN             NaN  between 100,000 and 1 million   \n",
       "\n",
       "   CodeEventConferences  CodeEventDjangoGirls  ...  ResourcePluralSight  \\\n",
       "0                   NaN                   NaN  ...                  NaN   \n",
       "1                   NaN                   NaN  ...                  NaN   \n",
       "2                   NaN                   NaN  ...                  NaN   \n",
       "3                   NaN                   NaN  ...                  NaN   \n",
       "4                   NaN                   NaN  ...                  NaN   \n",
       "\n",
       "   ResourceSkillCrush  ResourceStackOverflow  ResourceTreehouse  \\\n",
       "0                 NaN                    NaN                NaN   \n",
       "1                 NaN                    1.0                NaN   \n",
       "2                 NaN                    NaN                NaN   \n",
       "3                 NaN                    1.0                NaN   \n",
       "4                 NaN                    1.0                NaN   \n",
       "\n",
       "   ResourceUdacity  ResourceUdemy  ResourceW3Schools  \\\n",
       "0              NaN            1.0                1.0   \n",
       "1              NaN            1.0                1.0   \n",
       "2              1.0            1.0                NaN   \n",
       "3              NaN            NaN                NaN   \n",
       "4              NaN            NaN                NaN   \n",
       "\n",
       "                              SchoolDegree             SchoolMajor  \\\n",
       "0           some college credit, no degree                     NaN   \n",
       "1           some college credit, no degree                     NaN   \n",
       "2  high school diploma or equivalent (GED)                     NaN   \n",
       "3           some college credit, no degree                     NaN   \n",
       "4                        bachelor's degree  Information Technology   \n",
       "\n",
       "   StudentDebtOwe  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "\n",
       "[5 rows x 98 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importation de la deuxième feuille du fichier 'dev-coder-survey.xlsx'\n",
    "\n",
    "sondage3 = pd.read_excel('dev-coder-survey.xlsx', skiprows=2, sheet_name=1)\n",
    "\n",
    "sondage3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Au lieu de spécifier l'indice de la feuille que vous voulez iporter, vous pouvez sépcifier son nom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>AttendedBootcamp</th>\n",
       "      <th>BootcampFinish</th>\n",
       "      <th>BootcampLoanYesNo</th>\n",
       "      <th>BootcampName</th>\n",
       "      <th>BootcampRecommend</th>\n",
       "      <th>ChildrenNumber</th>\n",
       "      <th>CityPopulation</th>\n",
       "      <th>CodeEventConferences</th>\n",
       "      <th>CodeEventDjangoGirls</th>\n",
       "      <th>...</th>\n",
       "      <th>ResourcePluralSight</th>\n",
       "      <th>ResourceSkillCrush</th>\n",
       "      <th>ResourceStackOverflow</th>\n",
       "      <th>ResourceTreehouse</th>\n",
       "      <th>ResourceUdacity</th>\n",
       "      <th>ResourceUdemy</th>\n",
       "      <th>ResourceW3Schools</th>\n",
       "      <th>SchoolDegree</th>\n",
       "      <th>SchoolMajor</th>\n",
       "      <th>StudentDebtOwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>less than 100,000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>more than 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>high school diploma or equivalent (GED)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>between 100,000 and 1 million</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>some college credit, no degree</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 98 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age  AttendedBootcamp  BootcampFinish  BootcampLoanYesNo BootcampName  \\\n",
       "0  27.0               0.0             NaN                NaN          NaN   \n",
       "1  34.0               0.0             NaN                NaN          NaN   \n",
       "2  21.0               0.0             NaN                NaN          NaN   \n",
       "3  26.0               0.0             NaN                NaN          NaN   \n",
       "4  20.0               0.0             NaN                NaN          NaN   \n",
       "\n",
       "   BootcampRecommend  ChildrenNumber                 CityPopulation  \\\n",
       "0                NaN             NaN            more than 1 million   \n",
       "1                NaN             NaN              less than 100,000   \n",
       "2                NaN             NaN            more than 1 million   \n",
       "3                NaN             NaN  between 100,000 and 1 million   \n",
       "4                NaN             NaN  between 100,000 and 1 million   \n",
       "\n",
       "   CodeEventConferences  CodeEventDjangoGirls  ...  ResourcePluralSight  \\\n",
       "0                   NaN                   NaN  ...                  NaN   \n",
       "1                   NaN                   NaN  ...                  NaN   \n",
       "2                   NaN                   NaN  ...                  NaN   \n",
       "3                   NaN                   NaN  ...                  NaN   \n",
       "4                   NaN                   NaN  ...                  NaN   \n",
       "\n",
       "   ResourceSkillCrush  ResourceStackOverflow  ResourceTreehouse  \\\n",
       "0                 NaN                    NaN                NaN   \n",
       "1                 NaN                    1.0                NaN   \n",
       "2                 NaN                    NaN                NaN   \n",
       "3                 NaN                    1.0                NaN   \n",
       "4                 NaN                    1.0                NaN   \n",
       "\n",
       "   ResourceUdacity  ResourceUdemy  ResourceW3Schools  \\\n",
       "0              NaN            1.0                1.0   \n",
       "1              NaN            1.0                1.0   \n",
       "2              1.0            1.0                NaN   \n",
       "3              NaN            NaN                NaN   \n",
       "4              NaN            NaN                NaN   \n",
       "\n",
       "                              SchoolDegree             SchoolMajor  \\\n",
       "0           some college credit, no degree                     NaN   \n",
       "1           some college credit, no degree                     NaN   \n",
       "2  high school diploma or equivalent (GED)                     NaN   \n",
       "3           some college credit, no degree                     NaN   \n",
       "4                        bachelor's degree  Information Technology   \n",
       "\n",
       "   StudentDebtOwe  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "\n",
       "[5 rows x 98 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (Re)Importation de la deuxième feuille du fichier 'dev-coder-survey.xlsx'\n",
    "\n",
    "sondage3 = pd.read_excel('dev-coder-survey.xlsx', skiprows=2, sheet_name='2017')\n",
    "\n",
    "sondage3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Certains arguments présents au niveau de la fonction ***read_csv*** sont aussi présents au niveau de la fonction ***read_excel***. On peut citer par exemple : *header*, *names*, *parse_dates*, etc."
   ]
  }
 ],
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   "name": "12_Correlation.ipynb",
   "provenance": []
  },
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