Python and Data Analysis Library

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Init signature:
pd.Series(
    data=None,
    index=None,
    dtype: 'Dtype | None' = None,
    name=None,
    copy: 'bool | None' = None,
    fastpath: 'bool | lib.NoDefault' = <no_default>,
) -> 'None'
Docstring:     
One-dimensional ndarray with axis labels (including time series).

Labels need not be unique but must be a hashable type. The object
supports both integer- and label-based indexing and provides a host of
methods for performing operations involving the index. Statistical
methods from ndarray have been overridden to automatically exclude
missing data (currently represented as NaN).

Operations between Series (+, -, /, \*, \*\*) align values based on their
associated index values-- they need not be the same length. The result
index will be the sorted union of the two indexes.

Parameters
----------
data : array-like, Iterable, dict, or scalar value
    Contains data stored in Series. If data is a dict, argument order is
    maintained.
index : array-like or Index (1d)
    Values must be hashable and have the same length as `data`.
    Non-unique index values are allowed. Will default to
    RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like
    and index is None, then the keys in the data are used as the index. If the
    index is not None, the resulting Series is reindexed with the index values.
dtype : str, numpy.dtype, or ExtensionDtype, optional
    Data type for the output Series. If not specified, this will be
    inferred from `data`.
    See the :ref:`user guide <basics.dtypes>` for more usages.
name : Hashable, default None
    The name to give to the Series.
copy : bool, default False
    Copy input data. Only affects Series or 1d ndarray input. See examples.

Notes
-----
Please reference the :ref:`User Guide <basics.series>` for more information.

Examples
--------
Constructing Series from a dictionary with an Index specified

>>> d = {'a': 1, 'b': 2, 'c': 3}
>>> ser = pd.Series(data=d, index=['a', 'b', 'c'])
>>> ser
a   1
b   2
c   3
dtype: int64

The keys of the dictionary match with the Index values, hence the Index
values have no effect.

>>> d = {'a': 1, 'b': 2, 'c': 3}
>>> ser = pd.Series(data=d, index=['x', 'y', 'z'])
>>> ser
x   NaN
y   NaN
z   NaN
dtype: float64

Note that the Index is first build with the keys from the dictionary.
After this the Series is reindexed with the given Index values, hence we
get all NaN as a result.

Constructing Series from a list with `copy=False`.

>>> r = [1, 2]
>>> ser = pd.Series(r, copy=False)
>>> ser.iloc[0] = 999
>>> r
[1, 2]
>>> ser
0    999
1      2
dtype: int64

Due to input data type the Series has a `copy` of
the original data even though `copy=False`, so
the data is unchanged.

Constructing Series from a 1d ndarray with `copy=False`.

>>> r = np.array([1, 2])
>>> ser = pd.Series(r, copy=False)
>>> ser.iloc[0] = 999
>>> r
array([999,   2])
>>> ser
0    999
1      2
dtype: int64

Due to input data type the Series has a `view` on
the original data, so
the data is changed as well.
File:           c:\users\phili\anaconda3\envs\vscode-env\lib\site-packages\pandas\core\series.py
Type:           type
Subclasses:     SubclassedSeries

Init signature:
pd.DataFrame(
    data=None,
    index: 'Axes | None' = None,
    columns: 'Axes | None' = None,
    dtype: 'Dtype | None' = None,
    copy: 'bool | None' = None,
) -> 'None'
Docstring:     
Two-dimensional, size-mutable, potentially heterogeneous tabular data.

Data structure also contains labeled axes (rows and columns).
Arithmetic operations align on both row and column labels. Can be
thought of as a dict-like container for Series objects. The primary
pandas data structure.

Parameters
----------
data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame
    Dict can contain Series, arrays, constants, dataclass or list-like objects. If
    data is a dict, column order follows insertion-order. If a dict contains Series
    which have an index defined, it is aligned by its index. This alignment also
    occurs if data is a Series or a DataFrame itself. Alignment is done on
    Series/DataFrame inputs.

    If data is a list of dicts, column order follows insertion-order.

index : Index or array-like
    Index to use for resulting frame. Will default to RangeIndex if
    no indexing information part of input data and no index provided.
columns : Index or array-like
    Column labels to use for resulting frame when data does not have them,
    defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,
    will perform column selection instead.
dtype : dtype, default None
    Data type to force. Only a single dtype is allowed. If None, infer.
copy : bool or None, default None
    Copy data from inputs.
    For dict data, the default of None behaves like ``copy=True``.  For DataFrame
    or 2d ndarray input, the default of None behaves like ``copy=False``.
    If data is a dict containing one or more Series (possibly of different dtypes),
    ``copy=False`` will ensure that these inputs are not copied.

    .. versionchanged:: 1.3.0

See Also
--------
DataFrame.from_records : Constructor from tuples, also record arrays.
DataFrame.from_dict : From dicts of Series, arrays, or dicts.
read_csv : Read a comma-separated values (csv) file into DataFrame.
read_table : Read general delimited file into DataFrame.
read_clipboard : Read text from clipboard into DataFrame.

Notes
-----
Please reference the :ref:`User Guide <basics.dataframe>` for more information.

Examples
--------
Constructing DataFrame from a dictionary.

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df
   col1  col2
0     1     3
1     2     4

Notice that the inferred dtype is int64.

>>> df.dtypes
col1    int64
col2    int64
dtype: object

To enforce a single dtype:

>>> df = pd.DataFrame(data=d, dtype=np.int8)
>>> df.dtypes
col1    int8
col2    int8
dtype: object

Constructing DataFrame from a dictionary including Series:

>>> d = {'col1': [0, 1, 2, 3], 'col2': pd.Series([2, 3], index=[2, 3])}
>>> pd.DataFrame(data=d, index=[0, 1, 2, 3])
   col1  col2
0     0   NaN
1     1   NaN
2     2   2.0
3     3   3.0

Constructing DataFrame from numpy ndarray:

>>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
...                    columns=['a', 'b', 'c'])
>>> df2
   a  b  c
0  1  2  3
1  4  5  6
2  7  8  9

Constructing DataFrame from a numpy ndarray that has labeled columns:

>>> data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
...                 dtype=[("a", "i4"), ("b", "i4"), ("c", "i4")])
>>> df3 = pd.DataFrame(data, columns=['c', 'a'])
...
>>> df3
   c  a
0  3  1
1  6  4
2  9  7

Constructing DataFrame from dataclass:

>>> from dataclasses import make_dataclass
>>> Point = make_dataclass("Point", [("x", int), ("y", int)])
>>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])
   x  y
0  0  0
1  0  3
2  2  3

Constructing DataFrame from Series/DataFrame:

>>> ser = pd.Series([1, 2, 3], index=["a", "b", "c"])
>>> df = pd.DataFrame(data=ser, index=["a", "c"])
>>> df
   0
a  1
c  3

>>> df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"], columns=["x"])
>>> df2 = pd.DataFrame(data=df1, index=["a", "c"])
>>> df2
   x
a  1
c  3
File:           c:\users\phili\anaconda3\envs\vscode-env\lib\site-packages\pandas\core\frame.py
Type:           type
Subclasses:     SubclassedDataFrame

read_clipboard
read_csv
read_excel
read_feather
read_fwf
read_gbq
read_hdf
read_html
read_json
read_orc
read_parquet
read_pickle
read_sas
read_spss
read_sql
read_sql_query
read_sql_table
read_stata
read_table
read_xml

Overwriting ./files/Book1.csv

Signature:
pd.read_csv(
    filepath_or_buffer: 'FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str]',
    *,
    sep: 'str | None | lib.NoDefault' = <no_default>,
    delimiter: 'str | None | lib.NoDefault' = None,
    header: "int | Sequence[int] | None | Literal['infer']" = 'infer',
    names: 'Sequence[Hashable] | None | lib.NoDefault' = <no_default>,
    index_col: 'IndexLabel | Literal[False] | None' = None,
    usecols: 'UsecolsArgType' = None,
    dtype: 'DtypeArg | None' = None,
    engine: 'CSVEngine | None' = None,
    converters: 'Mapping[Hashable, Callable] | None' = None,
    true_values: 'list | None' = None,
    false_values: 'list | None' = None,
    skipinitialspace: 'bool' = False,
    skiprows: 'list[int] | int | Callable[[Hashable], bool] | None' = None,
    skipfooter: 'int' = 0,
    nrows: 'int | None' = None,
    na_values: 'Hashable | Iterable[Hashable] | Mapping[Hashable, Iterable[Hashable]] | None' = None,
    keep_default_na: 'bool' = True,
    na_filter: 'bool' = True,
    verbose: 'bool | lib.NoDefault' = <no_default>,
    skip_blank_lines: 'bool' = True,
    parse_dates: 'bool | Sequence[Hashable] | None' = None,
    infer_datetime_format: 'bool | lib.NoDefault' = <no_default>,
    keep_date_col: 'bool | lib.NoDefault' = <no_default>,
    date_parser: 'Callable | lib.NoDefault' = <no_default>,
    date_format: 'str | dict[Hashable, str] | None' = None,
    dayfirst: 'bool' = False,
    cache_dates: 'bool' = True,
    iterator: 'bool' = False,
    chunksize: 'int | None' = None,
    compression: 'CompressionOptions' = 'infer',
    thousands: 'str | None' = None,
    decimal: 'str' = '.',
    lineterminator: 'str | None' = None,
    quotechar: 'str' = '"',
    quoting: 'int' = 0,
    doublequote: 'bool' = True,
    escapechar: 'str | None' = None,
    comment: 'str | None' = None,
    encoding: 'str | None' = None,
    encoding_errors: 'str | None' = 'strict',
    dialect: 'str | csv.Dialect | None' = None,
    on_bad_lines: 'str' = 'error',
    delim_whitespace: 'bool | lib.NoDefault' = <no_default>,
    low_memory: 'bool' = True,
    memory_map: 'bool' = False,
    float_precision: "Literal['high', 'legacy'] | None" = None,
    storage_options: 'StorageOptions | None' = None,
    dtype_backend: 'DtypeBackend | lib.NoDefault' = <no_default>,
) -> 'DataFrame | TextFileReader'
Docstring:
Read a comma-separated values (csv) file into DataFrame.

Also supports optionally iterating or breaking of the file
into chunks.

Additional help can be found in the online docs for
`IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.

Parameters
----------
filepath_or_buffer : str, path object or file-like object
    Any valid string path is acceptable. The string could be a URL. Valid
    URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is
    expected. A local file could be: file://localhost/path/to/table.csv.

    If you want to pass in a path object, pandas accepts any ``os.PathLike``.

    By file-like object, we refer to objects with a ``read()`` method, such as
    a file handle (e.g. via builtin ``open`` function) or ``StringIO``.
sep : str, default ','
    Character or regex pattern to treat as the delimiter. If ``sep=None``, the
    C engine cannot automatically detect
    the separator, but the Python parsing engine can, meaning the latter will
    be used and automatically detect the separator from only the first valid
    row of the file by Python's builtin sniffer tool, ``csv.Sniffer``.
    In addition, separators longer than 1 character and different from
    ``'\s+'`` will be interpreted as regular expressions and will also force
    the use of the Python parsing engine. Note that regex delimiters are prone
    to ignoring quoted data. Regex example: ``'\r\t'``.
delimiter : str, optional
    Alias for ``sep``.
header : int, Sequence of int, 'infer' or None, default 'infer'
    Row number(s) containing column labels and marking the start of the
    data (zero-indexed). Default behavior is to infer the column names: if no ``names``
    are passed the behavior is identical to ``header=0`` and column
    names are inferred from the first line of the file, if column
    names are passed explicitly to ``names`` then the behavior is identical to
    ``header=None``. Explicitly pass ``header=0`` to be able to
    replace existing names. The header can be a list of integers that
    specify row locations for a :class:`~pandas.MultiIndex` on the columns
    e.g. ``[0, 1, 3]``. Intervening rows that are not specified will be
    skipped (e.g. 2 in this example is skipped). Note that this
    parameter ignores commented lines and empty lines if
    ``skip_blank_lines=True``, so ``header=0`` denotes the first line of
    data rather than the first line of the file.
names : Sequence of Hashable, optional
    Sequence of column labels to apply. If the file contains a header row,
    then you should explicitly pass ``header=0`` to override the column names.
    Duplicates in this list are not allowed.
index_col : Hashable, Sequence of Hashable or False, optional
  Column(s) to use as row label(s), denoted either by column labels or column
  indices.  If a sequence of labels or indices is given, :class:`~pandas.MultiIndex`
  will be formed for the row labels.

  Note: ``index_col=False`` can be used to force pandas to *not* use the first
  column as the index, e.g., when you have a malformed file with delimiters at
  the end of each line.
usecols : Sequence of Hashable or Callable, optional
    Subset of columns to select, denoted either by column labels or column indices.
    If list-like, all elements must either
    be positional (i.e. integer indices into the document columns) or strings
    that correspond to column names provided either by the user in ``names`` or
    inferred from the document header row(s). If ``names`` are given, the document
    header row(s) are not taken into account. For example, a valid list-like
    ``usecols`` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.
    Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
    To instantiate a :class:`~pandas.DataFrame` from ``data`` with element order
    preserved use ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]``
    for columns in ``['foo', 'bar']`` order or
    ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
    for ``['bar', 'foo']`` order.

    If callable, the callable function will be evaluated against the column
    names, returning names where the callable function evaluates to ``True``. An
    example of a valid callable argument would be ``lambda x: x.upper() in
    ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
    parsing time and lower memory usage.
dtype : dtype or dict of {Hashable : dtype}, optional
    Data type(s) to apply to either the whole dataset or individual columns.
    E.g., ``{'a': np.float64, 'b': np.int32, 'c': 'Int64'}``
    Use ``str`` or ``object`` together with suitable ``na_values`` settings
    to preserve and not interpret ``dtype``.
    If ``converters`` are specified, they will be applied INSTEAD
    of ``dtype`` conversion.

    .. versionadded:: 1.5.0

        Support for ``defaultdict`` was added. Specify a ``defaultdict`` as input where
        the default determines the ``dtype`` of the columns which are not explicitly
        listed.
engine : {'c', 'python', 'pyarrow'}, optional
    Parser engine to use. The C and pyarrow engines are faster, while the python engine
    is currently more feature-complete. Multithreading is currently only supported by
    the pyarrow engine.

    .. versionadded:: 1.4.0

        The 'pyarrow' engine was added as an *experimental* engine, and some features
        are unsupported, or may not work correctly, with this engine.
converters : dict of {Hashable : Callable}, optional
    Functions for converting values in specified columns. Keys can either
    be column labels or column indices.
true_values : list, optional
    Values to consider as ``True`` in addition to case-insensitive variants of 'True'.
false_values : list, optional
    Values to consider as ``False`` in addition to case-insensitive variants of 'False'.
skipinitialspace : bool, default False
    Skip spaces after delimiter.
skiprows : int, list of int or Callable, optional
    Line numbers to skip (0-indexed) or number of lines to skip (``int``)
    at the start of the file.

    If callable, the callable function will be evaluated against the row
    indices, returning ``True`` if the row should be skipped and ``False`` otherwise.
    An example of a valid callable argument would be ``lambda x: x in [0, 2]``.
skipfooter : int, default 0
    Number of lines at bottom of file to skip (Unsupported with ``engine='c'``).
nrows : int, optional
    Number of rows of file to read. Useful for reading pieces of large files.
na_values : Hashable, Iterable of Hashable or dict of {Hashable : Iterable}, optional
    Additional strings to recognize as ``NA``/``NaN``. If ``dict`` passed, specific
    per-column ``NA`` values.  By default the following values are interpreted as
    ``NaN``: " ", "#N/A", "#N/A N/A", "#NA", "-1.#IND", "-1.#QNAN", "-NaN", "-nan",
    "1.#IND", "1.#QNAN", "<NA>", "N/A", "NA", "NULL", "NaN", "None",
    "n/a", "nan", "null ".

keep_default_na : bool, default True
    Whether or not to include the default ``NaN`` values when parsing the data.
    Depending on whether ``na_values`` is passed in, the behavior is as follows:

    * If ``keep_default_na`` is ``True``, and ``na_values`` are specified, ``na_values``
      is appended to the default ``NaN`` values used for parsing.
    * If ``keep_default_na`` is ``True``, and ``na_values`` are not specified, only
      the default ``NaN`` values are used for parsing.
    * If ``keep_default_na`` is ``False``, and ``na_values`` are specified, only
      the ``NaN`` values specified ``na_values`` are used for parsing.
    * If ``keep_default_na`` is ``False``, and ``na_values`` are not specified, no
      strings will be parsed as ``NaN``.

    Note that if ``na_filter`` is passed in as ``False``, the ``keep_default_na`` and
    ``na_values`` parameters will be ignored.
na_filter : bool, default True
    Detect missing value markers (empty strings and the value of ``na_values``). In
    data without any ``NA`` values, passing ``na_filter=False`` can improve the
    performance of reading a large file.
verbose : bool, default False
    Indicate number of ``NA`` values placed in non-numeric columns.
skip_blank_lines : bool, default True
    If ``True``, skip over blank lines rather than interpreting as ``NaN`` values.
parse_dates : bool, list of Hashable, list of lists or dict of {Hashable : list}, default False
    The behavior is as follows:

    * ``bool``. If ``True`` -> try parsing the index. Note: Automatically set to
      ``True`` if ``date_format`` or ``date_parser`` arguments have been passed.
    * ``list`` of ``int`` or names. e.g. If ``[1, 2, 3]`` -> try parsing columns 1, 2, 3
      each as a separate date column.
    * ``list`` of ``list``. e.g.  If ``[[1, 3]]`` -> combine columns 1 and 3 and parse
      as a single date column. Values are joined with a space before parsing.
    * ``dict``, e.g. ``{'foo' : [1, 3]}`` -> parse columns 1, 3 as date and call
      result 'foo'. Values are joined with a space before parsing.

    If a column or index cannot be represented as an array of ``datetime``,
    say because of an unparsable value or a mixture of timezones, the column
    or index will be returned unaltered as an ``object`` data type. For
    non-standard ``datetime`` parsing, use :func:`~pandas.to_datetime` after
    :func:`~pandas.read_csv`.

    Note: A fast-path exists for iso8601-formatted dates.
infer_datetime_format : bool, default False
    If ``True`` and ``parse_dates`` is enabled, pandas will attempt to infer the
    format of the ``datetime`` strings in the columns, and if it can be inferred,
    switch to a faster method of parsing them. In some cases this can increase
    the parsing speed by 5-10x.

    .. deprecated:: 2.0.0
        A strict version of this argument is now the default, passing it has no effect.

keep_date_col : bool, default False
    If ``True`` and ``parse_dates`` specifies combining multiple columns then
    keep the original columns.
date_parser : Callable, optional
    Function to use for converting a sequence of string columns to an array of
    ``datetime`` instances. The default uses ``dateutil.parser.parser`` to do the
    conversion. pandas will try to call ``date_parser`` in three different ways,
    advancing to the next if an exception occurs: 1) Pass one or more arrays
    (as defined by ``parse_dates``) as arguments; 2) concatenate (row-wise) the
    string values from the columns defined by ``parse_dates`` into a single array
    and pass that; and 3) call ``date_parser`` once for each row using one or
    more strings (corresponding to the columns defined by ``parse_dates``) as
    arguments.

    .. deprecated:: 2.0.0
       Use ``date_format`` instead, or read in as ``object`` and then apply
       :func:`~pandas.to_datetime` as-needed.
date_format : str or dict of column -> format, optional
    Format to use for parsing dates when used in conjunction with ``parse_dates``.
    The strftime to parse time, e.g. :const:`"%d/%m/%Y"`. See
    `strftime documentation
    <https://docs.python.org/3/library/datetime.html
    #strftime-and-strptime-behavior>`_ for more information on choices, though
    note that :const:`"%f"` will parse all the way up to nanoseconds.
    You can also pass:

    - "ISO8601", to parse any `ISO8601 <https://en.wikipedia.org/wiki/ISO_8601>`_
        time string (not necessarily in exactly the same format);
    - "mixed", to infer the format for each element individually. This is risky,
        and you should probably use it along with `dayfirst`.

    .. versionadded:: 2.0.0
dayfirst : bool, default False
    DD/MM format dates, international and European format.
cache_dates : bool, default True
    If ``True``, use a cache of unique, converted dates to apply the ``datetime``
    conversion. May produce significant speed-up when parsing duplicate
    date strings, especially ones with timezone offsets.

iterator : bool, default False
    Return ``TextFileReader`` object for iteration or getting chunks with
    ``get_chunk()``.
chunksize : int, optional
    Number of lines to read from the file per chunk. Passing a value will cause the
    function to return a ``TextFileReader`` object for iteration.
    See the `IO Tools docs
    <https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
    for more information on ``iterator`` and ``chunksize``.

compression : str or dict, default 'infer'
    For on-the-fly decompression of on-disk data. If 'infer' and 'filepath_or_buffer' is
    path-like, then detect compression from the following extensions: '.gz',
    '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
    (otherwise no compression).
    If using 'zip' or 'tar', the ZIP file must contain only one data file to be read in.
    Set to ``None`` for no decompression.
    Can also be a dict with key ``'method'`` set
    to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and
    other key-value pairs are forwarded to
    ``zipfile.ZipFile``, ``gzip.GzipFile``,
    ``bz2.BZ2File``, ``zstandard.ZstdDecompressor``, ``lzma.LZMAFile`` or
    ``tarfile.TarFile``, respectively.
    As an example, the following could be passed for Zstandard decompression using a
    custom compression dictionary:
    ``compression={'method': 'zstd', 'dict_data': my_compression_dict}``.

    .. versionadded:: 1.5.0
        Added support for `.tar` files.

    .. versionchanged:: 1.4.0 Zstandard support.

thousands : str (length 1), optional
    Character acting as the thousands separator in numerical values.
decimal : str (length 1), default '.'
    Character to recognize as decimal point (e.g., use ',' for European data).
lineterminator : str (length 1), optional
    Character used to denote a line break. Only valid with C parser.
quotechar : str (length 1), optional
    Character used to denote the start and end of a quoted item. Quoted
    items can include the ``delimiter`` and it will be ignored.
quoting : {0 or csv.QUOTE_MINIMAL, 1 or csv.QUOTE_ALL, 2 or csv.QUOTE_NONNUMERIC, 3 or csv.QUOTE_NONE}, default csv.QUOTE_MINIMAL
    Control field quoting behavior per ``csv.QUOTE_*`` constants. Default is
    ``csv.QUOTE_MINIMAL`` (i.e., 0) which implies that only fields containing special
    characters are quoted (e.g., characters defined in ``quotechar``, ``delimiter``,
    or ``lineterminator``.
doublequote : bool, default True
   When ``quotechar`` is specified and ``quoting`` is not ``QUOTE_NONE``, indicate
   whether or not to interpret two consecutive ``quotechar`` elements INSIDE a
   field as a single ``quotechar`` element.
escapechar : str (length 1), optional
    Character used to escape other characters.
comment : str (length 1), optional
    Character indicating that the remainder of line should not be parsed.
    If found at the beginning
    of a line, the line will be ignored altogether. This parameter must be a
    single character. Like empty lines (as long as ``skip_blank_lines=True``),
    fully commented lines are ignored by the parameter ``header`` but not by
    ``skiprows``. For example, if ``comment='#'``, parsing
    ``#empty\na,b,c\n1,2,3`` with ``header=0`` will result in ``'a,b,c'`` being
    treated as the header.
encoding : str, optional, default 'utf-8'
    Encoding to use for UTF when reading/writing (ex. ``'utf-8'``). `List of Python
    standard encodings
    <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ .

encoding_errors : str, optional, default 'strict'
    How encoding errors are treated. `List of possible values
    <https://docs.python.org/3/library/codecs.html#error-handlers>`_ .

    .. versionadded:: 1.3.0

dialect : str or csv.Dialect, optional
    If provided, this parameter will override values (default or not) for the
    following parameters: ``delimiter``, ``doublequote``, ``escapechar``,
    ``skipinitialspace``, ``quotechar``, and ``quoting``. If it is necessary to
    override values, a ``ParserWarning`` will be issued. See ``csv.Dialect``
    documentation for more details.
on_bad_lines : {'error', 'warn', 'skip'} or Callable, default 'error'
    Specifies what to do upon encountering a bad line (a line with too many fields).
    Allowed values are :

    - ``'error'``, raise an Exception when a bad line is encountered.
    - ``'warn'``, raise a warning when a bad line is encountered and skip that line.
    - ``'skip'``, skip bad lines without raising or warning when they are encountered.

    .. versionadded:: 1.3.0

    .. versionadded:: 1.4.0

        - Callable, function with signature
          ``(bad_line: list[str]) -> list[str] | None`` that will process a single
          bad line. ``bad_line`` is a list of strings split by the ``sep``.
          If the function returns ``None``, the bad line will be ignored.
          If the function returns a new ``list`` of strings with more elements than
          expected, a ``ParserWarning`` will be emitted while dropping extra elements.
          Only supported when ``engine='python'``

    .. versionchanged:: 2.2.0

        - Callable, function with signature
          as described in `pyarrow documentation
          <https://arrow.apache.org/docs/python/generated/pyarrow.csv.ParseOptions.html
          #pyarrow.csv.ParseOptions.invalid_row_handler>`_ when ``engine='pyarrow'``

delim_whitespace : bool, default False
    Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be
    used as the ``sep`` delimiter. Equivalent to setting ``sep='\s+'``. If this option
    is set to ``True``, nothing should be passed in for the ``delimiter``
    parameter.

    .. deprecated:: 2.2.0
        Use ``sep="\s+"`` instead.
low_memory : bool, default True
    Internally process the file in chunks, resulting in lower memory use
    while parsing, but possibly mixed type inference.  To ensure no mixed
    types either set ``False``, or specify the type with the ``dtype`` parameter.
    Note that the entire file is read into a single :class:`~pandas.DataFrame`
    regardless, use the ``chunksize`` or ``iterator`` parameter to return the data in
    chunks. (Only valid with C parser).
memory_map : bool, default False
    If a filepath is provided for ``filepath_or_buffer``, map the file object
    directly onto memory and access the data directly from there. Using this
    option can improve performance because there is no longer any I/O overhead.
float_precision : {'high', 'legacy', 'round_trip'}, optional
    Specifies which converter the C engine should use for floating-point
    values. The options are ``None`` or ``'high'`` for the ordinary converter,
    ``'legacy'`` for the original lower precision pandas converter, and
    ``'round_trip'`` for the round-trip converter.

storage_options : dict, optional
    Extra options that make sense for a particular storage connection, e.g.
    host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
    are forwarded to ``urllib.request.Request`` as header options. For other
    URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
    forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
    details, and for more examples on storage options refer `here
    <https://pandas.pydata.org/docs/user_guide/io.html?
    highlight=storage_options#reading-writing-remote-files>`_.

dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
    Back-end data type applied to the resultant :class:`DataFrame`
    (still experimental). Behaviour is as follows:

    * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
      (default).
    * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
      DataFrame.

    .. versionadded:: 2.0

Returns
-------
DataFrame or TextFileReader
    A comma-separated values (csv) file is returned as two-dimensional
    data structure with labeled axes.

See Also
--------
DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
read_table : Read general delimited file into DataFrame.
read_fwf : Read a table of fixed-width formatted lines into DataFrame.

Examples
--------
>>> pd.read_csv('data.csv')  # doctest: +SKIP
File:      c:\users\phili\anaconda3\envs\vscode-env\lib\site-packages\pandas\io\parsers\readers.py
Type:      function

Overwriting ./files/Book2.txt

Signature:
pd.read_excel(
    io,
    sheet_name: 'str | int | list[IntStrT] | None' = 0,
    *,
    header: 'int | Sequence[int] | None' = 0,
    names: 'SequenceNotStr[Hashable] | range | None' = None,
    index_col: 'int | str | Sequence[int] | None' = None,
    usecols: 'int | str | Sequence[int] | Sequence[str] | Callable[[str], bool] | None' = None,
    dtype: 'DtypeArg | None' = None,
    engine: "Literal['xlrd', 'openpyxl', 'odf', 'pyxlsb', 'calamine'] | None" = None,
    converters: 'dict[str, Callable] | dict[int, Callable] | None' = None,
    true_values: 'Iterable[Hashable] | None' = None,
    false_values: 'Iterable[Hashable] | None' = None,
    skiprows: 'Sequence[int] | int | Callable[[int], object] | None' = None,
    nrows: 'int | None' = None,
    na_values=None,
    keep_default_na: 'bool' = True,
    na_filter: 'bool' = True,
    verbose: 'bool' = False,
    parse_dates: 'list | dict | bool' = False,
    date_parser: 'Callable | lib.NoDefault' = <no_default>,
    date_format: 'dict[Hashable, str] | str | None' = None,
    thousands: 'str | None' = None,
    decimal: 'str' = '.',
    comment: 'str | None' = None,
    skipfooter: 'int' = 0,
    storage_options: 'StorageOptions | None' = None,
    dtype_backend: 'DtypeBackend | lib.NoDefault' = <no_default>,
    engine_kwargs: 'dict | None' = None,
) -> 'DataFrame | dict[IntStrT, DataFrame]'
Docstring:
Read an Excel file into a ``pandas`` ``DataFrame``.

Supports `xls`, `xlsx`, `xlsm`, `xlsb`, `odf`, `ods` and `odt` file extensions
read from a local filesystem or URL. Supports an option to read
a single sheet or a list of sheets.

Parameters
----------
io : str, bytes, ExcelFile, xlrd.Book, path object, or file-like object
    Any valid string path is acceptable. The string could be a URL. Valid
    URL schemes include http, ftp, s3, and file. For file URLs, a host is
    expected. A local file could be: ``file://localhost/path/to/table.xlsx``.

    If you want to pass in a path object, pandas accepts any ``os.PathLike``.

    By file-like object, we refer to objects with a ``read()`` method,
    such as a file handle (e.g. via builtin ``open`` function)
    or ``StringIO``.

    .. deprecated:: 2.1.0
        Passing byte strings is deprecated. To read from a
        byte string, wrap it in a ``BytesIO`` object.
sheet_name : str, int, list, or None, default 0
    Strings are used for sheet names. Integers are used in zero-indexed
    sheet positions (chart sheets do not count as a sheet position).
    Lists of strings/integers are used to request multiple sheets.
    Specify ``None`` to get all worksheets.

    Available cases:

    * Defaults to ``0``: 1st sheet as a `DataFrame`
    * ``1``: 2nd sheet as a `DataFrame`
    * ``"Sheet1"``: Load sheet with name "Sheet1"
    * ``[0, 1, "Sheet5"]``: Load first, second and sheet named "Sheet5"
      as a dict of `DataFrame`
    * ``None``: All worksheets.

header : int, list of int, default 0
    Row (0-indexed) to use for the column labels of the parsed
    DataFrame. If a list of integers is passed those row positions will
    be combined into a ``MultiIndex``. Use None if there is no header.
names : array-like, default None
    List of column names to use. If file contains no header row,
    then you should explicitly pass header=None.
index_col : int, str, list of int, default None
    Column (0-indexed) to use as the row labels of the DataFrame.
    Pass None if there is no such column.  If a list is passed,
    those columns will be combined into a ``MultiIndex``.  If a
    subset of data is selected with ``usecols``, index_col
    is based on the subset.

    Missing values will be forward filled to allow roundtripping with
    ``to_excel`` for ``merged_cells=True``. To avoid forward filling the
    missing values use ``set_index`` after reading the data instead of
    ``index_col``.
usecols : str, list-like, or callable, default None
    * If None, then parse all columns.
    * If str, then indicates comma separated list of Excel column letters
      and column ranges (e.g. "A:E" or "A,C,E:F"). Ranges are inclusive of
      both sides.
    * If list of int, then indicates list of column numbers to be parsed
      (0-indexed).
    * If list of string, then indicates list of column names to be parsed.
    * If callable, then evaluate each column name against it and parse the
      column if the callable returns ``True``.

    Returns a subset of the columns according to behavior above.
dtype : Type name or dict of column -> type, default None
    Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}
    Use ``object`` to preserve data as stored in Excel and not interpret dtype,
    which will necessarily result in ``object`` dtype.
    If converters are specified, they will be applied INSTEAD
    of dtype conversion.
    If you use ``None``, it will infer the dtype of each column based on the data.
engine : {'openpyxl', 'calamine', 'odf', 'pyxlsb', 'xlrd'}, default None
    If io is not a buffer or path, this must be set to identify io.
    Engine compatibility :

    - ``openpyxl`` supports newer Excel file formats.
    - ``calamine`` supports Excel (.xls, .xlsx, .xlsm, .xlsb)
      and OpenDocument (.ods) file formats.
    - ``odf`` supports OpenDocument file formats (.odf, .ods, .odt).
    - ``pyxlsb`` supports Binary Excel files.
    - ``xlrd`` supports old-style Excel files (.xls).

    When ``engine=None``, the following logic will be used to determine the engine:

    - If ``path_or_buffer`` is an OpenDocument format (.odf, .ods, .odt),
      then `odf <https://pypi.org/project/odfpy/>`_ will be used.
    - Otherwise if ``path_or_buffer`` is an xls format, ``xlrd`` will be used.
    - Otherwise if ``path_or_buffer`` is in xlsb format, ``pyxlsb`` will be used.
    - Otherwise ``openpyxl`` will be used.
converters : dict, default None
    Dict of functions for converting values in certain columns. Keys can
    either be integers or column labels, values are functions that take one
    input argument, the Excel cell content, and return the transformed
    content.
true_values : list, default None
    Values to consider as True.
false_values : list, default None
    Values to consider as False.
skiprows : list-like, int, or callable, optional
    Line numbers to skip (0-indexed) or number of lines to skip (int) at the
    start of the file. If callable, the callable function will be evaluated
    against the row indices, returning True if the row should be skipped and
    False otherwise. An example of a valid callable argument would be ``lambda
    x: x in [0, 2]``.
nrows : int, default None
    Number of rows to parse.
na_values : scalar, str, list-like, or dict, default None
    Additional strings to recognize as NA/NaN. If dict passed, specific
    per-column NA values. By default the following values are interpreted
    as NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',
    '1.#IND', '1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'None',
    'n/a', 'nan', 'null'.
keep_default_na : bool, default True
    Whether or not to include the default NaN values when parsing the data.
    Depending on whether ``na_values`` is passed in, the behavior is as follows:

    * If ``keep_default_na`` is True, and ``na_values`` are specified,
      ``na_values`` is appended to the default NaN values used for parsing.
    * If ``keep_default_na`` is True, and ``na_values`` are not specified, only
      the default NaN values are used for parsing.
    * If ``keep_default_na`` is False, and ``na_values`` are specified, only
      the NaN values specified ``na_values`` are used for parsing.
    * If ``keep_default_na`` is False, and ``na_values`` are not specified, no
      strings will be parsed as NaN.

    Note that if `na_filter` is passed in as False, the ``keep_default_na`` and
    ``na_values`` parameters will be ignored.
na_filter : bool, default True
    Detect missing value markers (empty strings and the value of na_values). In
    data without any NAs, passing ``na_filter=False`` can improve the
    performance of reading a large file.
verbose : bool, default False
    Indicate number of NA values placed in non-numeric columns.
parse_dates : bool, list-like, or dict, default False
    The behavior is as follows:

    * ``bool``. If True -> try parsing the index.
    * ``list`` of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
      each as a separate date column.
    * ``list`` of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
      a single date column.
    * ``dict``, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call
      result 'foo'

    If a column or index contains an unparsable date, the entire column or
    index will be returned unaltered as an object data type. If you don`t want to
    parse some cells as date just change their type in Excel to "Text".
    For non-standard datetime parsing, use ``pd.to_datetime`` after ``pd.read_excel``.

    Note: A fast-path exists for iso8601-formatted dates.
date_parser : function, optional
    Function to use for converting a sequence of string columns to an array of
    datetime instances. The default uses ``dateutil.parser.parser`` to do the
    conversion. Pandas will try to call `date_parser` in three different ways,
    advancing to the next if an exception occurs: 1) Pass one or more arrays
    (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
    string values from the columns defined by `parse_dates` into a single array
    and pass that; and 3) call `date_parser` once for each row using one or
    more strings (corresponding to the columns defined by `parse_dates`) as
    arguments.

    .. deprecated:: 2.0.0
       Use ``date_format`` instead, or read in as ``object`` and then apply
       :func:`to_datetime` as-needed.
date_format : str or dict of column -> format, default ``None``
   If used in conjunction with ``parse_dates``, will parse dates according to this
   format. For anything more complex,
   please read in as ``object`` and then apply :func:`to_datetime` as-needed.

   .. versionadded:: 2.0.0
thousands : str, default None
    Thousands separator for parsing string columns to numeric.  Note that
    this parameter is only necessary for columns stored as TEXT in Excel,
    any numeric columns will automatically be parsed, regardless of display
    format.
decimal : str, default '.'
    Character to recognize as decimal point for parsing string columns to numeric.
    Note that this parameter is only necessary for columns stored as TEXT in Excel,
    any numeric columns will automatically be parsed, regardless of display
    format.(e.g. use ',' for European data).

    .. versionadded:: 1.4.0

comment : str, default None
    Comments out remainder of line. Pass a character or characters to this
    argument to indicate comments in the input file. Any data between the
    comment string and the end of the current line is ignored.
skipfooter : int, default 0
    Rows at the end to skip (0-indexed).
storage_options : dict, optional
    Extra options that make sense for a particular storage connection, e.g.
    host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
    are forwarded to ``urllib.request.Request`` as header options. For other
    URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
    forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
    details, and for more examples on storage options refer `here
    <https://pandas.pydata.org/docs/user_guide/io.html?
    highlight=storage_options#reading-writing-remote-files>`_.

dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
    Back-end data type applied to the resultant :class:`DataFrame`
    (still experimental). Behaviour is as follows:

    * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
      (default).
    * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
      DataFrame.

    .. versionadded:: 2.0

engine_kwargs : dict, optional
    Arbitrary keyword arguments passed to excel engine.

Returns
-------
DataFrame or dict of DataFrames
    DataFrame from the passed in Excel file. See notes in sheet_name
    argument for more information on when a dict of DataFrames is returned.

See Also
--------
DataFrame.to_excel : Write DataFrame to an Excel file.
DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
read_csv : Read a comma-separated values (csv) file into DataFrame.
read_fwf : Read a table of fixed-width formatted lines into DataFrame.

Notes
-----
For specific information on the methods used for each Excel engine, refer to the pandas
:ref:`user guide <io.excel_reader>`

Examples
--------
The file can be read using the file name as string or an open file object:

>>> pd.read_excel('tmp.xlsx', index_col=0)  # doctest: +SKIP
       Name  Value
0   string1      1
1   string2      2
2  #Comment      3

>>> pd.read_excel(open('tmp.xlsx', 'rb'),
...               sheet_name='Sheet3')  # doctest: +SKIP
   Unnamed: 0      Name  Value
0           0   string1      1
1           1   string2      2
2           2  #Comment      3

Index and header can be specified via the `index_col` and `header` arguments

>>> pd.read_excel('tmp.xlsx', index_col=None, header=None)  # doctest: +SKIP
     0         1      2
0  NaN      Name  Value
1  0.0   string1      1
2  1.0   string2      2
3  2.0  #Comment      3

Column types are inferred but can be explicitly specified

>>> pd.read_excel('tmp.xlsx', index_col=0,
...               dtype={'Name': str, 'Value': float})  # doctest: +SKIP
       Name  Value
0   string1    1.0
1   string2    2.0
2  #Comment    3.0

True, False, and NA values, and thousands separators have defaults,
but can be explicitly specified, too. Supply the values you would like
as strings or lists of strings!

>>> pd.read_excel('tmp.xlsx', index_col=0,
...               na_values=['string1', 'string2'])  # doctest: +SKIP
       Name  Value
0       NaN      1
1       NaN      2
2  #Comment      3

Comment lines in the excel input file can be skipped using the
``comment`` kwarg.

>>> pd.read_excel('tmp.xlsx', index_col=0, comment='#')  # doctest: +SKIP
      Name  Value
0  string1    1.0
1  string2    2.0
2     None    NaN
File:      c:\users\phili\anaconda3\envs\vscode-env\lib\site-packages\pandas\io\excel\_base.py
Type:      function

to_clipboard
to_csv
to_dict
to_excel
to_feather
to_gbq
to_hdf
to_html
to_json
to_latex
to_markdown
to_numpy
to_orc
to_parquet
to_period
to_pickle
to_records
to_sql
to_stata
to_string
to_timestamp
to_xarray
to_xml

Signature:
df.to_csv(
    path_or_buf: 'FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None' = None,
    *,
    sep: 'str' = ',',
    na_rep: 'str' = '',
    float_format: 'str | Callable | None' = None,
    columns: 'Sequence[Hashable] | None' = None,
    header: 'bool_t | list[str]' = True,
    index: 'bool_t' = True,
    index_label: 'IndexLabel | None' = None,
    mode: 'str' = 'w',
    encoding: 'str | None' = None,
    compression: 'CompressionOptions' = 'infer',
    quoting: 'int | None' = None,
    quotechar: 'str' = '"',
    lineterminator: 'str | None' = None,
    chunksize: 'int | None' = None,
    date_format: 'str | None' = None,
    doublequote: 'bool_t' = True,
    escapechar: 'str | None' = None,
    decimal: 'str' = '.',
    errors: 'OpenFileErrors' = 'strict',
    storage_options: 'StorageOptions | None' = None,
) -> 'str | None'
Docstring:
Write object to a comma-separated values (csv) file.

Parameters
----------
path_or_buf : str, path object, file-like object, or None, default None
    String, path object (implementing os.PathLike[str]), or file-like
    object implementing a write() function. If None, the result is
    returned as a string. If a non-binary file object is passed, it should
    be opened with `newline=''`, disabling universal newlines. If a binary
    file object is passed, `mode` might need to contain a `'b'`.
sep : str, default ','
    String of length 1. Field delimiter for the output file.
na_rep : str, default ''
    Missing data representation.
float_format : str, Callable, default None
    Format string for floating point numbers. If a Callable is given, it takes
    precedence over other numeric formatting parameters, like decimal.
columns : sequence, optional
    Columns to write.
header : bool or list of str, default True
    Write out the column names. If a list of strings is given it is
    assumed to be aliases for the column names.
index : bool, default True
    Write row names (index).
index_label : str or sequence, or False, default None
    Column label for index column(s) if desired. If None is given, and
    `header` and `index` are True, then the index names are used. A
    sequence should be given if the object uses MultiIndex. If
    False do not print fields for index names. Use index_label=False
    for easier importing in R.
mode : {'w', 'x', 'a'}, default 'w'
    Forwarded to either `open(mode=)` or `fsspec.open(mode=)` to control
    the file opening. Typical values include:

    - 'w', truncate the file first.
    - 'x', exclusive creation, failing if the file already exists.
    - 'a', append to the end of file if it exists.

encoding : str, optional
    A string representing the encoding to use in the output file,
    defaults to 'utf-8'. `encoding` is not supported if `path_or_buf`
    is a non-binary file object.
compression : str or dict, default 'infer'
    For on-the-fly compression of the output data. If 'infer' and 'path_or_buf' is
    path-like, then detect compression from the following extensions: '.gz',
    '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
    (otherwise no compression).
    Set to ``None`` for no compression.
    Can also be a dict with key ``'method'`` set
    to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'xz'``, ``'tar'``} and
    other key-value pairs are forwarded to
    ``zipfile.ZipFile``, ``gzip.GzipFile``,
    ``bz2.BZ2File``, ``zstandard.ZstdCompressor``, ``lzma.LZMAFile`` or
    ``tarfile.TarFile``, respectively.
    As an example, the following could be passed for faster compression and to create
    a reproducible gzip archive:
    ``compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}``.

    .. versionadded:: 1.5.0
        Added support for `.tar` files.

       May be a dict with key 'method' as compression mode
       and other entries as additional compression options if
       compression mode is 'zip'.

       Passing compression options as keys in dict is
       supported for compression modes 'gzip', 'bz2', 'zstd', and 'zip'.
quoting : optional constant from csv module
    Defaults to csv.QUOTE_MINIMAL. If you have set a `float_format`
    then floats are converted to strings and thus csv.QUOTE_NONNUMERIC
    will treat them as non-numeric.
quotechar : str, default '\"'
    String of length 1. Character used to quote fields.
lineterminator : str, optional
    The newline character or character sequence to use in the output
    file. Defaults to `os.linesep`, which depends on the OS in which
    this method is called ('\\n' for linux, '\\r\\n' for Windows, i.e.).

    .. versionchanged:: 1.5.0

        Previously was line_terminator, changed for consistency with
        read_csv and the standard library 'csv' module.

chunksize : int or None
    Rows to write at a time.
date_format : str, default None
    Format string for datetime objects.
doublequote : bool, default True
    Control quoting of `quotechar` inside a field.
escapechar : str, default None
    String of length 1. Character used to escape `sep` and `quotechar`
    when appropriate.
decimal : str, default '.'
    Character recognized as decimal separator. E.g. use ',' for
    European data.
errors : str, default 'strict'
    Specifies how encoding and decoding errors are to be handled.
    See the errors argument for :func:`open` for a full list
    of options.

storage_options : dict, optional
    Extra options that make sense for a particular storage connection, e.g.
    host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
    are forwarded to ``urllib.request.Request`` as header options. For other
    URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
    forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
    details, and for more examples on storage options refer `here
    <https://pandas.pydata.org/docs/user_guide/io.html?
    highlight=storage_options#reading-writing-remote-files>`_.

Returns
-------
None or str
    If path_or_buf is None, returns the resulting csv format as a
    string. Otherwise returns None.

See Also
--------
read_csv : Load a CSV file into a DataFrame.
to_excel : Write DataFrame to an Excel file.

Examples
--------
Create 'out.csv' containing 'df' without indices

>>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],
...                    'mask': ['red', 'purple'],
...                    'weapon': ['sai', 'bo staff']})
>>> df.to_csv('out.csv', index=False)  # doctest: +SKIP

Create 'out.zip' containing 'out.csv'

>>> df.to_csv(index=False)
'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'
>>> compression_opts = dict(method='zip',
...                         archive_name='out.csv')  # doctest: +SKIP
>>> df.to_csv('out.zip', index=False,
...           compression=compression_opts)  # doctest: +SKIP

To write a csv file to a new folder or nested folder you will first
need to create it using either Pathlib or os:

>>> from pathlib import Path  # doctest: +SKIP
>>> filepath = Path('folder/subfolder/out.csv')  # doctest: +SKIP
>>> filepath.parent.mkdir(parents=True, exist_ok=True)  # doctest: +SKIP
>>> df.to_csv(filepath)  # doctest: +SKIP

>>> import os  # doctest: +SKIP
>>> os.makedirs('folder/subfolder', exist_ok=True)  # doctest: +SKIP
>>> df.to_csv('folder/subfolder/out.csv')  # doctest: +SKIP
File:      c:\users\phili\anaconda3\envs\vscode-env\lib\site-packages\pandas\core\generic.py
Type:      method

Signature:
df.to_excel(
    excel_writer: 'FilePath | WriteExcelBuffer | ExcelWriter',
    *,
    sheet_name: 'str' = 'Sheet1',
    na_rep: 'str' = '',
    float_format: 'str | None' = None,
    columns: 'Sequence[Hashable] | None' = None,
    header: 'Sequence[Hashable] | bool_t' = True,
    index: 'bool_t' = True,
    index_label: 'IndexLabel | None' = None,
    startrow: 'int' = 0,
    startcol: 'int' = 0,
    engine: "Literal['openpyxl', 'xlsxwriter'] | None" = None,
    merge_cells: 'bool_t' = True,
    inf_rep: 'str' = 'inf',
    freeze_panes: 'tuple[int, int] | None' = None,
    storage_options: 'StorageOptions | None' = None,
    engine_kwargs: 'dict[str, Any] | None' = None,
) -> 'None'
Docstring:
Write object to an Excel sheet.

To write a single object to an Excel .xlsx file it is only necessary to
specify a target file name. To write to multiple sheets it is necessary to
create an `ExcelWriter` object with a target file name, and specify a sheet
in the file to write to.

Multiple sheets may be written to by specifying unique `sheet_name`.
With all data written to the file it is necessary to save the changes.
Note that creating an `ExcelWriter` object with a file name that already
exists will result in the contents of the existing file being erased.

Parameters
----------
excel_writer : path-like, file-like, or ExcelWriter object
    File path or existing ExcelWriter.
sheet_name : str, default 'Sheet1'
    Name of sheet which will contain DataFrame.
na_rep : str, default ''
    Missing data representation.
float_format : str, optional
    Format string for floating point numbers. For example
    ``float_format="%.2f"`` will format 0.1234 to 0.12.
columns : sequence or list of str, optional
    Columns to write.
header : bool or list of str, default True
    Write out the column names. If a list of string is given it is
    assumed to be aliases for the column names.
index : bool, default True
    Write row names (index).
index_label : str or sequence, optional
    Column label for index column(s) if desired. If not specified, and
    `header` and `index` are True, then the index names are used. A
    sequence should be given if the DataFrame uses MultiIndex.
startrow : int, default 0
    Upper left cell row to dump data frame.
startcol : int, default 0
    Upper left cell column to dump data frame.
engine : str, optional
    Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set this
    via the options ``io.excel.xlsx.writer`` or
    ``io.excel.xlsm.writer``.

merge_cells : bool, default True
    Write MultiIndex and Hierarchical Rows as merged cells.
inf_rep : str, default 'inf'
    Representation for infinity (there is no native representation for
    infinity in Excel).
freeze_panes : tuple of int (length 2), optional
    Specifies the one-based bottommost row and rightmost column that
    is to be frozen.
storage_options : dict, optional
    Extra options that make sense for a particular storage connection, e.g.
    host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
    are forwarded to ``urllib.request.Request`` as header options. For other
    URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
    forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
    details, and for more examples on storage options refer `here
    <https://pandas.pydata.org/docs/user_guide/io.html?
    highlight=storage_options#reading-writing-remote-files>`_.

    .. versionadded:: 1.2.0
engine_kwargs : dict, optional
    Arbitrary keyword arguments passed to excel engine.

See Also
--------
to_csv : Write DataFrame to a comma-separated values (csv) file.
ExcelWriter : Class for writing DataFrame objects into excel sheets.
read_excel : Read an Excel file into a pandas DataFrame.
read_csv : Read a comma-separated values (csv) file into DataFrame.
io.formats.style.Styler.to_excel : Add styles to Excel sheet.

Notes
-----
For compatibility with :meth:`~DataFrame.to_csv`,
to_excel serializes lists and dicts to strings before writing.

Once a workbook has been saved it is not possible to write further
data without rewriting the whole workbook.

Examples
--------

Create, write to and save a workbook:

>>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']],
...                    index=['row 1', 'row 2'],
...                    columns=['col 1', 'col 2'])
>>> df1.to_excel("output.xlsx")  # doctest: +SKIP

To specify the sheet name:

>>> df1.to_excel("output.xlsx",
...              sheet_name='Sheet_name_1')  # doctest: +SKIP

If you wish to write to more than one sheet in the workbook, it is
necessary to specify an ExcelWriter object:

>>> df2 = df1.copy()
>>> with pd.ExcelWriter('output.xlsx') as writer:  # doctest: +SKIP
...     df1.to_excel(writer, sheet_name='Sheet_name_1')
...     df2.to_excel(writer, sheet_name='Sheet_name_2')

ExcelWriter can also be used to append to an existing Excel file:

>>> with pd.ExcelWriter('output.xlsx',
...                     mode='a') as writer:  # doctest: +SKIP
...     df1.to_excel(writer, sheet_name='Sheet_name_3')

To set the library that is used to write the Excel file,
you can pass the `engine` keyword (the default engine is
automatically chosen depending on the file extension):

>>> df1.to_excel('output1.xlsx', engine='xlsxwriter')  # doctest: +SKIP
File:      c:\users\phili\anaconda3\envs\vscode-env\lib\site-packages\pandas\core\generic.py
Type:      method

{'attribute': ['book',
               'date_format',
               'datetime_format',
               'engine',
               'if_sheet_exists',
               'sheets',
               'supported_extensions'],
 'method': ['check_extension', 'close'],
 'datamodel_attribute': ['__annotations__',
                         '__dict__',
                         '__doc__',
                         '__module__',
                         '__orig_bases__',
                         '__parameters__',
                         '__weakref__'],
 'datamodel_method': ['__class__',
                      '__class_getitem__',
                      '__delattr__',
                      '__dir__',
                      '__enter__',
                      '__eq__',
                      '__exit__',
                      '__format__',
                      '__fspath__',
                      '__ge__',
                      '__getattribute__',
                      '__getstate__',
                      '__gt__',
                      '__hash__',
                      '__init__',
                      '__init_subclass__',
                      '__le__',
                      '__lt__',
                      '__ne__',
                      '__new__',
                      '__reduce__',
                      '__reduce_ex__',
                      '__repr__',
                      '__setattr__',
                      '__sizeof__',
                      '__str__',
                      '__subclasshook__']}

|    | string            |   integer | boolean   |   floatingpoint | date                | time            | category   |
|---:|:------------------|----------:|:----------|----------------:|:--------------------|:----------------|:-----------|
|  0 | the fat black cat |         4 | True      |            0.86 | 2023-07-24 11:30:00 | 0 days 11:36:00 | A          |
|  1 | sat on the mat    |         4 | True      |            0.86 | 2023-07-25 00:00:00 | 0 days 12:36:00 | A          |
|  2 | twinkle, twinkle  |         2 | True      |           -1.14 | 2023-07-26 00:00:00 | 0 days 13:36:00 | B          |
|  3 | little star       |         2 | True      |           -1.14 | 2023-07-27 00:00:00 | 0 days 14:36:00 | B          |
|  4 | how I wonder      |         3 | False     |           -0.14 | 2023-07-28 00:00:00 | 0 days 15:36:00 | B          |
|  5 | what you are      |         4 | True      |            0.86 | 2023-07-29 00:00:00 | 0 days 16:36:00 | B          |

{'attribute': ['array',
               'at',
               'attrs',
               'axes',
               'dtype',
               'dtypes',
               'empty',
               'flags',
               'hasnans',
               'iat',
               'iloc',
               'index',
               'is_monotonic_decreasing',
               'is_monotonic_increasing',
               'is_unique',
               'loc',
               'name',
               'nbytes',
               'ndim',
               'shape',
               'size',
               'values'],
 'constant': ['T'],
 'method': ['abs',
            'add',
            'add_prefix',
            'add_suffix',
            'agg',
            'aggregate',
            'align',
            'all',
            'any',
            'apply',
            'argmax',
            'argmin',
            'argsort',
            'asfreq',
            'asof',
            'astype',
            'at_time',
            'autocorr',
            'between',
            'between_time',
            'bfill',
            'case_when',
            'clip',
            'combine',
            'combine_first',
            'compare',
            'convert_dtypes',
            'copy',
            'corr',
            'count',
            'cov',
            'cummax',
            'cummin',
            'cumprod',
            'cumsum',
            'describe',
            'diff',
            'div',
            'divide',
            'divmod',
            'dot',
            'drop',
            'drop_duplicates',
            'droplevel',
            'dropna',
            'duplicated',
            'eq',
            'equals',
            'ewm',
            'expanding',
            'explode',
            'factorize',
            'ffill',
            'fillna',
            'filter',
            'first',
            'first_valid_index',
            'floordiv',
            'ge',
            'get',
            'groupby',
            'gt',
            'head',
            'hist',
            'idxmax',
            'idxmin',
            'infer_objects',
            'info',
            'interpolate',
            'isin',
            'isna',
            'isnull',
            'item',
            'items',
            'keys',
            'kurt',
            'kurtosis',
            'last',
            'last_valid_index',
            'le',
            'lt',
            'map',
            'mask',
            'max',
            'mean',
            'median',
            'memory_usage',
            'min',
            'mod',
            'mode',
            'mul',
            'multiply',
            'ne',
            'nlargest',
            'notna',
            'notnull',
            'nsmallest',
            'nunique',
            'pad',
            'pct_change',
            'pipe',
            'pop',
            'pow',
            'prod',
            'product',
            'quantile',
            'radd',
            'rank',
            'rdiv',
            'rdivmod',
            'reindex',
            'reindex_like',
            'rename',
            'rename_axis',
            'reorder_levels',
            'repeat',
            'replace',
            'resample',
            'reset_index',
            'rfloordiv',
            'rmod',
            'rmul',
            'rolling',
            'round',
            'rpow',
            'rsub',
            'rtruediv',
            'sample',
            'searchsorted',
            'sem',
            'set_axis',
            'set_flags',
            'shift',
            'skew',
            'sort_index',
            'sort_values',
            'squeeze',
            'std',
            'sub',
            'subtract',
            'sum',
            'swaplevel',
            'tail',
            'take',
            'to_clipboard',
            'to_csv',
            'to_dict',
            'to_excel',
            'to_frame',
            'to_hdf',
            'to_json',
            'to_latex',
            'to_list',
            'to_markdown',
            'to_numpy',
            'to_period',
            'to_pickle',
            'to_sql',
            'to_string',
            'to_timestamp',
            'to_xarray',
            'tolist',
            'transform',
            'transpose',
            'truediv',
            'truncate',
            'tz_convert',
            'tz_localize',
            'unique',
            'unstack',
            'update',
            'value_counts',
            'var',
            'where',
            'xs'],
 'lower_class': ['cat', 'dt', 'list', 'plot', 'sparse', 'str', 'struct'],
 'datamodel_attribute': ['__annotations__',
                         '__array_priority__',
                         '__dict__',
                         '__doc__',
                         '__hash__',
                         '__module__',
                         '__pandas_priority__',
                         '__weakref__'],
 'datamodel_method': ['__abs__',
                      '__add__',
                      '__and__',
                      '__array__',
                      '__array_ufunc__',
                      '__bool__',
                      '__class__',
                      '__column_consortium_standard__',
                      '__contains__',
                      '__copy__',
                      '__deepcopy__',
                      '__delattr__',
                      '__delitem__',
                      '__dir__',
                      '__divmod__',
                      '__eq__',
                      '__finalize__',
                      '__float__',
                      '__floordiv__',
                      '__format__',
                      '__ge__',
                      '__getattr__',
                      '__getattribute__',
                      '__getitem__',
                      '__getstate__',
                      '__gt__',
                      '__iadd__',
                      '__iand__',
                      '__ifloordiv__',
                      '__imod__',
                      '__imul__',
                      '__init__',
                      '__init_subclass__',
                      '__int__',
                      '__invert__',
                      '__ior__',
                      '__ipow__',
                      '__isub__',
                      '__iter__',
                      '__itruediv__',
                      '__ixor__',
                      '__le__',
                      '__len__',
                      '__lt__',
                      '__matmul__',
                      '__mod__',
                      '__mul__',
                      '__ne__',
                      '__neg__',
                      '__new__',
                      '__nonzero__',
                      '__or__',
                      '__pos__',
                      '__pow__',
                      '__radd__',
                      '__rand__',
                      '__rdivmod__',
                      '__reduce__',
                      '__reduce_ex__',
                      '__repr__',
                      '__rfloordiv__',
                      '__rmatmul__',
                      '__rmod__',
                      '__rmul__',
                      '__ror__',
                      '__round__',
                      '__rpow__',
                      '__rsub__',
                      '__rtruediv__',
                      '__rxor__',
                      '__setattr__',
                      '__setitem__',
                      '__setstate__',
                      '__sizeof__',
                      '__str__',
                      '__sub__',
                      '__subclasshook__',
                      '__truediv__',
                      '__xor__']}

Init signature:
pd.Series(
    data=None,
    index=None,
    dtype: 'Dtype | None' = None,
    name=None,
    copy: 'bool | None' = None,
    fastpath: 'bool | lib.NoDefault' = <no_default>,
) -> 'None'
Docstring:     
One-dimensional ndarray with axis labels (including time series).

Labels need not be unique but must be a hashable type. The object
supports both integer- and label-based indexing and provides a host of
methods for performing operations involving the index. Statistical
methods from ndarray have been overridden to automatically exclude
missing data (currently represented as NaN).

Operations between Series (+, -, /, \*, \*\*) align values based on their
associated index values-- they need not be the same length. The result
index will be the sorted union of the two indexes.

Parameters
----------
data : array-like, Iterable, dict, or scalar value
    Contains data stored in Series. If data is a dict, argument order is
    maintained.
index : array-like or Index (1d)
    Values must be hashable and have the same length as `data`.
    Non-unique index values are allowed. Will default to
    RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like
    and index is None, then the keys in the data are used as the index. If the
    index is not None, the resulting Series is reindexed with the index values.
dtype : str, numpy.dtype, or ExtensionDtype, optional
    Data type for the output Series. If not specified, this will be
    inferred from `data`.
    See the :ref:`user guide <basics.dtypes>` for more usages.
name : Hashable, default None
    The name to give to the Series.
copy : bool, default False
    Copy input data. Only affects Series or 1d ndarray input. See examples.

Notes
-----
Please reference the :ref:`User Guide <basics.series>` for more information.

Examples
--------
Constructing Series from a dictionary with an Index specified

>>> d = {'a': 1, 'b': 2, 'c': 3}
>>> ser = pd.Series(data=d, index=['a', 'b', 'c'])
>>> ser
a   1
b   2
c   3
dtype: int64

The keys of the dictionary match with the Index values, hence the Index
values have no effect.

>>> d = {'a': 1, 'b': 2, 'c': 3}
>>> ser = pd.Series(data=d, index=['x', 'y', 'z'])
>>> ser
x   NaN
y   NaN
z   NaN
dtype: float64

Note that the Index is first build with the keys from the dictionary.
After this the Series is reindexed with the given Index values, hence we
get all NaN as a result.

Constructing Series from a list with `copy=False`.

>>> r = [1, 2]
>>> ser = pd.Series(r, copy=False)
>>> ser.iloc[0] = 999
>>> r
[1, 2]
>>> ser
0    999
1      2
dtype: int64

Due to input data type the Series has a `copy` of
the original data even though `copy=False`, so
the data is unchanged.

Constructing Series from a 1d ndarray with `copy=False`.

>>> r = np.array([1, 2])
>>> ser = pd.Series(r, copy=False)
>>> ser.iloc[0] = 999
>>> r
array([999,   2])
>>> ser
0    999
1      2
dtype: int64

Due to input data type the Series has a `view` on
the original data, so
the data is changed as well.
File:           c:\users\phili\anaconda3\envs\vscode-env\lib\site-packages\pandas\core\series.py
Type:           type
Subclasses:     SubclassedSeries