From 0c80f4ebaf6b57d877ded7084c4c7c9286066754 Mon Sep 17 00:00:00 2001 From: tpaxman <39451402+tpaxman@users.noreply.github.com> Date: Fri, 23 Jun 2023 10:45:32 -0600 Subject: [PATCH] DOC: Add code-formatting and cross-reference links to `read_csv` docstring (#53735) * apply appropriate code-formatting to `read_csv` docstring using backticks * escape curly braces for `dict` examples * fix formatting under `delim_whitespace` so that tab character appears properly * fix formatting issue in read_csv description for dtype_backend * test linking for :class:`~pandas.DataFrame` * add spinx references to pandas classes and functions * remove code formatting from initial parameter description lines in read_csv * replaced "default None" with "optional" in read_csv docstring * add additional code formatting changes to read_csv after review * shorten docstring lines to 88 characters max * fix formatting causing docstring error --------- Co-authored-by: tpaxman --- pandas/io/parsers/readers.py | 225 ++++++++++++++++++----------------- 1 file changed, 113 insertions(+), 112 deletions(-) diff --git a/pandas/io/parsers/readers.py b/pandas/io/parsers/readers.py index ad9e5b646371e..153899e023137 100644 --- a/pandas/io/parsers/readers.py +++ b/pandas/io/parsers/readers.py @@ -96,12 +96,12 @@ 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``. + 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 {_default_sep} - Delimiter to use. If sep is None, the C engine cannot automatically detect + Delimiter to use. 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``. @@ -109,18 +109,18 @@ ``'\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, default ``None`` - Alias for sep. +delimiter : str, optional + Alias for ``sep``. header : int, list of int, None, default 'infer' Row number(s) to use as the column names, and the start of the - data. Default behavior is to infer the column names: if no names + data. 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 then the behavior is identical to + 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 multi-index on the columns - e.g. [0,1,3]. Intervening rows that are not specified will be + 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 @@ -129,45 +129,45 @@ List of column names to use. 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 : int, str, sequence of int / str, or False, optional, default ``None`` - Column(s) to use as the row labels of the ``DataFrame``, either given as - string name or column index. If a sequence of int / str is given, a - MultiIndex is used. +index_col : int, str, sequence of int / str, or False, optional + Column(s) to use as the row labels of the :class:`~pandas.DataFrame`, either given as + string name or column index. If a sequence of ``int`` / ``str`` is given, a + :class:`~pandas.MultiIndex` is used. - Note: ``index_col=False`` can be used to force pandas to *not* use the first + 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 : list-like or callable, optional Return a subset of the columns. 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 + 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']``. + ``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 DataFrame from ``data`` with element order preserved use - ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns - in ``['foo', 'bar']`` order or + 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 + 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 : Type name or dict of column -> type, optional - Data type for data or 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. + Data type for data or 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 + 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 @@ -179,76 +179,76 @@ The "pyarrow" engine was added as an *experimental* engine, and some features are unsupported, or may not work correctly, with this engine. converters : dict, optional - Dict of functions for converting values in certain columns. Keys can either + ``dict`` of functions for converting values in certain columns. Keys can either be integers or column labels. true_values : list, optional - Values to consider as True in addition to case-insensitive variants of "True". + 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". + Values to consider as ``False`` in addition to case-insensitive variants of "False". skipinitialspace : bool, default False Skip spaces after delimiter. skiprows : list-like, int or callable, optional - Line numbers to skip (0-indexed) or number of lines to skip (int) + 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. + 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'). + 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 : scalar, str, list-like, or dict, 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: '""" + Additional strings to recognize as ``NA``/``NaN``. If ``dict`` passed, specific + per-column ``NA`` values. By default the following values are interpreted as + ``NaN``: '""" + fill("', '".join(sorted(STR_NA_VALUES)), 70, subsequent_indent=" ") + """'. 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. + 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. + 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. + 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. + If ``True``, skip over blank lines rather than interpreting as ``NaN`` values. parse_dates : bool or list of int or names or list of lists or dict, \ default False The behavior is as follows: - * boolean. If True -> try parsing the index. - * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 + * ``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 + * ``list`` of ``list``. 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 cannot be represented as an array of datetimes, + 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 ``pd.to_datetime`` after - ``pd.read_csv``. + 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, + 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. @@ -256,44 +256,44 @@ 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 + If ``True`` and ``parse_dates`` specifies combining multiple columns then keep the original columns. 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, + ``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 + (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`` + :func:`~pandas.to_datetime` as-needed. +date_format : str or dict of column -> format, optional 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. + please read in as ``object`` and then apply :func:`~pandas.to_datetime` as-needed. .. 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 + 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 + Return ``TextFileReader`` object for iteration or getting chunks with ``get_chunk()``. .. versionchanged:: 1.2 ``TextFileReader`` is a context manager. chunksize : int, optional - Return TextFileReader object for iteration. + Return ``TextFileReader`` object for iteration. See the `IO Tools docs `_ for more information on ``iterator`` and ``chunksize``. @@ -313,13 +313,14 @@ Character to break file into lines. Only valid with C parser. quotechar : str (length 1), optional The character used to denote the start and end of a quoted item. Quoted - items can include the delimiter and it will be ignored. + items can include the ``delimiter`` and it will be ignored. quoting : int or csv.QUOTE_* instance, default 0 Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of - QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3). -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 + ``QUOTE_MINIMAL`` (0), ``QUOTE_ALL`` (1), ``QUOTE_NONNUMERIC`` (2) or + ``QUOTE_NONE`` (3). +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 One-character string used to escape other characters. @@ -327,12 +328,12 @@ Indicates 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 + 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 + Encoding to use for UTF when reading/writing (ex. ``'utf-8'``). `List of Python standard encodings `_ . @@ -355,51 +356,51 @@ 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 + 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. + - ``'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 + - 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 + 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"`` delim_whitespace : bool, default False - Specifies whether or not whitespace (e.g. ``' '`` or ``'\t'``) will be + Specifies whether or not whitespace (e.g. ``' '`` or ``'\\t'``) will be used as the sep. Equivalent to setting ``sep='\\s+'``. If this option - is set to True, nothing should be passed in for the ``delimiter`` + is set to ``True``, nothing should be passed in for the ``delimiter`` parameter. 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 DataFrame regardless, - use the `chunksize` or `iterator` parameter to return the data in chunks. - (Only valid with C parser). + 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 + 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 : str, 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. + 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. .. versionchanged:: 1.2 @@ -407,13 +408,13 @@ .. versionadded:: 1.2 -dtype_backend : {{"numpy_nullable", "pyarrow"}}, defaults to NumPy backed DataFrames - Which dtype_backend to use, e.g. whether a DataFrame should have NumPy - arrays, nullable dtypes are used for all dtypes that have a nullable - implementation when "numpy_nullable" is set, pyarrow is used for all - dtypes if "pyarrow" is set. +dtype_backend : {{"numpy_nullable", "pyarrow"}}, defaults to NumPy backed DataFrame + Which ``dtype_backend`` to use, e.g. whether a :class:`~pandas.DataFrame` should + have NumPy arrays, nullable ``dtypes`` are used for all ``dtypes`` that have a + nullable implementation when ``"numpy_nullable"`` is set, pyarrow is used for all + dtypes if ``"pyarrow"`` is set. - The dtype_backends are still experimential. + The ``dtype_backends`` are still experimential. .. versionadded:: 2.0