R(read_系列1)： Function26~35
Types['Function'][25:35] ['read_clipboard', 'read_csv', 'read_excel', 'read_feather', 'read_fwf', 'read_gbq', 'read_hdf', 'read_html', 'read_json', 'read_orc']
Function26 read_clipboard()
Help on function read_clipboard in module pandas.io.clipboards: read_clipboard(sep='\\s+', **kwargs) Read text from clipboard and pass to read_csv. Parameters ---------- sep : str, default '\s+' A string or regex delimiter. The default of '\s+' denotes one or more whitespace characters. **kwargs See read_csv for the full argument list. Returns ------- DataFrame A parsed DataFrame object.
Function27 read_csv()
Help on function read_csv in module pandas.io.parsers.readers: read_csv(filepath_or_buffer: 'FilePathOrBuffer', sep=<no_default>, delimiter=None, header='infer', names=<no_default>, index_col=None, usecols=None, squeeze=False, prefix=<no_default>, mangle_dupe_cols=True, dtype: 'DtypeArg | None' = None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal: 'str' = '.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, encoding_errors: 'str | None' = 'strict', dialect=None, error_bad_lines=None, warn_bad_lines=None, on_bad_lines=None, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options: 'StorageOptions' = None) 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 ',' Delimiter to use. If sep is 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 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, default ``None`` Alias for sep. header : int, list of int, 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 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 ``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 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 : array-like, optional 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, 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. 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 inferred from the document header row(s). 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 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. squeeze : bool, default False If the parsed data only contains one column then return a Series. prefix : str, optional Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ... mangle_dupe_cols : bool, default True Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than 'X'...'X'. Passing in False will cause data to be overwritten if there are duplicate names in the columns. 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. engine : {'c', 'python'}, optional Parser engine to use. The C engine is faster while the python engine is currently more feature-complete. converters : dict, optional 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. false_values : list, optional Values to consider as 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) 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 : 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: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan', '1.#IND', '1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', '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. skip_blank_lines : bool, default True 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 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 cannot be represented as an array of datetimes, 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``. To parse an index or column with a mixture of timezones, specify ``date_parser`` to be a partially-applied :func:`pandas.to_datetime` with ``utc=True``. See :ref:`io.csv.mixed_timezones` for more. 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. keep_date_col : bool, default False 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, 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. 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. .. versionadded:: 0.25.0 iterator : bool, default False 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. See the `IO Tools docs <https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_ for more information on ``iterator`` and ``chunksize``. .. versionchanged:: 1.2 ``TextFileReader`` is a context manager. compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, 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', or '.xz' (otherwise no decompression). If using 'zip', the ZIP file must contain only one data file to be read in. Set to None for no decompression. thousands : str, optional Thousands separator. decimal : str, default '.' Character to recognize as decimal point (e.g. use ',' for European data). lineterminator : str (length 1), optional 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. 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 field as a single ``quotechar`` element. escapechar : str (length 1), optional One-character string used to escape other characters. comment : str, optional 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 treated as the header. encoding : str, optional 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>`_ . .. versionchanged:: 1.2 When ``encoding`` is ``None``, ``errors="replace"`` is passed to ``open()``. Otherwise, ``errors="strict"`` is passed to ``open()``. This behavior was previously only the case for ``engine="python"``. .. versionchanged:: 1.3.0 ``encoding_errors`` is a new argument. ``encoding`` has no longer an influence on how encoding errors are handled. 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. error_bad_lines : bool, default ``None`` Lines with too many fields (e.g. a csv line with too many commas) will by default cause an exception to be raised, and no DataFrame will be returned. If False, then these "bad lines" will be dropped from the DataFrame that is returned. .. deprecated:: 1.3.0 The ``on_bad_lines`` parameter should be used instead to specify behavior upon encountering a bad line instead. warn_bad_lines : bool, default ``None`` If error_bad_lines is False, and warn_bad_lines is True, a warning for each "bad line" will be output. .. deprecated:: 1.3.0 The ``on_bad_lines`` parameter should be used instead to specify behavior upon encountering a bad line instead. on_bad_lines : {'error', 'warn', 'skip'}, 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 delim_whitespace : bool, default False Specifies whether or not whitespace (e.g. ``' '`` or ``' '``) 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`` 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). 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 : 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. .. versionchanged:: 1.2 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`` as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to ``fsspec``. Please see ``fsspec`` and ``urllib`` for more details. .. versionadded:: 1.2 Returns ------- DataFrame or TextParser 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_csv : Read a comma-separated values (csv) file into DataFrame. read_fwf : Read a table of fixed-width formatted lines into DataFrame. Examples -------- >>> pd.read_csv('data.csv') # doctest: +SKIP
Function28 read_excel()
Help on function read_excel in module pandas.io.excel._base: read_excel(io, sheet_name=0, header=0, names=None, index_col=None, usecols=None, squeeze=False, dtype: 'DtypeArg | None' = None, engine=None, converters=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, parse_dates=False, date_parser=None, thousands=None, comment=None, skipfooter=0, convert_float=None, mangle_dupe_cols=True, storage_options: 'StorageOptions' = None) 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``. sheet_name : str, int, list, or None, default 0 Strings are used for sheet names. Integers are used in zero-indexed sheet positions. Lists of strings/integers are used to request multiple sheets. Specify None to get all sheets. 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 sheets. 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, 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. usecols : int, 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. * 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. squeeze : bool, default False If the parsed data only contains one column then return a Series. 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. If converters are specified, they will be applied INSTEAD of dtype conversion. engine : str, default None If io is not a buffer or path, this must be set to identify io. Supported engines: "xlrd", "openpyxl", "odf", "pyxlsb". Engine compatibility : - "xlrd" supports old-style Excel files (.xls). - "openpyxl" supports newer Excel file formats. - "odf" supports OpenDocument file formats (.odf, .ods, .odt). - "pyxlsb" supports Binary Excel files. .. versionchanged:: 1.2.0 The engine `xlrd <https://xlrd.readthedocs.io/en/latest/>`_ now only supports old-style ``.xls`` files. 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. .. versionadded:: 1.3.0 - Otherwise ``openpyxl`` will be used. .. versionchanged:: 1.3.0 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', '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. 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. 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). convert_float : bool, default True Convert integral floats to int (i.e., 1.0 --> 1). If False, all numeric data will be read in as floats: Excel stores all numbers as floats internally. .. deprecated:: 1.3.0 convert_float will be removed in a future version mangle_dupe_cols : bool, default True Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than 'X'...'X'. Passing in False will cause data to be overwritten if there are duplicate names in the columns. storage_options : dict, optional Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc., if using a URL that will be parsed by ``fsspec``, e.g., starting "s3://", "gcs://". An error will be raised if providing this argument with a local path or a file-like buffer. See the fsspec and backend storage implementation docs for the set of allowed keys and values. .. versionadded:: 1.2.0 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. 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
Function29 read_feather()
Help on function read_feather in module pandas.io.feather_format: read_feather(path, columns=None, use_threads: bool = True, storage_options: Union[Dict[str, Any], NoneType] = None) Load a feather-format object from the file path. Parameters ---------- path : 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, and file. For file URLs, a host is expected. A local file could be: ``file://localhost/path/to/table.feather``. 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``. columns : sequence, default None If not provided, all columns are read. use_threads : bool, default True Whether to parallelize reading using multiple threads. 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`` as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to ``fsspec``. Please see ``fsspec`` and ``urllib`` for more details. .. versionadded:: 1.2.0 Returns ------- type of object stored in file
Function30 read_fwf()
Help on function read_fwf in module pandas.io.parsers.readers: read_fwf(filepath_or_buffer: 'FilePathOrBuffer', colspecs='infer', widths=None, infer_nrows=100, **kwds) Read a table of fixed-width formatted lines 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, 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``. colspecs : list of tuple (int, int) or 'infer'. optional A list of tuples giving the extents of the fixed-width fields of each line as half-open intervals (i.e., [from, to[ ). String value 'infer' can be used to instruct the parser to try detecting the column specifications from the first 100 rows of the data which are not being skipped via skiprows (default='infer'). widths : list of int, optional A list of field widths which can be used instead of 'colspecs' if the intervals are contiguous. infer_nrows : int, default 100 The number of rows to consider when letting the parser determine the `colspecs`. **kwds : optional Optional keyword arguments can be passed to ``TextFileReader``. Returns ------- DataFrame or TextParser 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_csv : Read a comma-separated values (csv) file into DataFrame. Examples -------- >>> pd.read_fwf('data.csv') # doctest: +SKIP
Function31 read_gbq()
Help on function read_gbq in module pandas.io.gbq: read_gbq(query: 'str', project_id: 'str | None' = None, index_col: 'str | None' = None, col_order: 'list[str] | None' = None, reauth: 'bool' = False, auth_local_webserver: 'bool' = False, dialect: 'str | None' = None, location: 'str | None' = None, configuration: 'dict[str, Any] | None' = None, credentials=None, use_bqstorage_api: 'bool | None' = None, max_results: 'int | None' = None, progress_bar_type: 'str | None' = None) -> 'DataFrame' Load data from Google BigQuery. This function requires the `pandas-gbq package <https://pandas-gbq.readthedocs.io>`__. See the `How to authenticate with Google BigQuery <https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__ guide for authentication instructions. Parameters ---------- query : str SQL-Like Query to return data values. project_id : str, optional Google BigQuery Account project ID. Optional when available from the environment. index_col : str, optional Name of result column to use for index in results DataFrame. col_order : list(str), optional List of BigQuery column names in the desired order for results DataFrame. reauth : bool, default False Force Google BigQuery to re-authenticate the user. This is useful if multiple accounts are used. auth_local_webserver : bool, default False Use the `local webserver flow`_ instead of the `console flow`_ when getting user credentials. .. _local webserver flow: https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server .. _console flow: https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console *New in version 0.2.0 of pandas-gbq*. dialect : str, default 'legacy' Note: The default value is changing to 'standard' in a future version. SQL syntax dialect to use. Value can be one of: ``'legacy'`` Use BigQuery's legacy SQL dialect. For more information see `BigQuery Legacy SQL Reference <https://cloud.google.com/bigquery/docs/reference/legacy-sql>`__. ``'standard'`` Use BigQuery's standard SQL, which is compliant with the SQL 2011 standard. For more information see `BigQuery Standard SQL Reference <https://cloud.google.com/bigquery/docs/reference/standard-sql/>`__. location : str, optional Location where the query job should run. See the `BigQuery locations documentation <https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a list of available locations. The location must match that of any datasets used in the query. *New in version 0.5.0 of pandas-gbq*. configuration : dict, optional Query config parameters for job processing. For example: configuration = {'query': {'useQueryCache': False}} For more information see `BigQuery REST API Reference <https://cloud.google.com/bigquery/docs/reference/rest/v2/jobs#configuration.query>`__. credentials : google.auth.credentials.Credentials, optional Credentials for accessing Google APIs. Use this parameter to override default credentials, such as to use Compute Engine :class:`google.auth.compute_engine.Credentials` or Service Account :class:`google.oauth2.service_account.Credentials` directly. *New in version 0.8.0 of pandas-gbq*. use_bqstorage_api : bool, default False Use the `BigQuery Storage API <https://cloud.google.com/bigquery/docs/reference/storage/>`__ to download query results quickly, but at an increased cost. To use this API, first `enable it in the Cloud Console <https://console.cloud.google.com/apis/library/bigquerystorage.googleapis.com>`__. You must also have the `bigquery.readsessions.create <https://cloud.google.com/bigquery/docs/access-control#roles>`__ permission on the project you are billing queries to. This feature requires version 0.10.0 or later of the ``pandas-gbq`` package. It also requires the ``google-cloud-bigquery-storage`` and ``fastavro`` packages. .. versionadded:: 0.25.0 max_results : int, optional If set, limit the maximum number of rows to fetch from the query results. *New in version 0.12.0 of pandas-gbq*. .. versionadded:: 1.1.0 progress_bar_type : Optional, str If set, use the `tqdm <https://tqdm.github.io/>`__ library to display a progress bar while the data downloads. Install the ``tqdm`` package to use this feature. Possible values of ``progress_bar_type`` include: ``None`` No progress bar. ``'tqdm'`` Use the :func:`tqdm.tqdm` function to print a progress bar to :data:`sys.stderr`. ``'tqdm_notebook'`` Use the :func:`tqdm.tqdm_notebook` function to display a progress bar as a Jupyter notebook widget. ``'tqdm_gui'`` Use the :func:`tqdm.tqdm_gui` function to display a progress bar as a graphical dialog box. Note that this feature requires version 0.12.0 or later of the ``pandas-gbq`` package. And it requires the ``tqdm`` package. Slightly different than ``pandas-gbq``, here the default is ``None``. .. versionadded:: 1.0.0 Returns ------- df: DataFrame DataFrame representing results of query. See Also -------- pandas_gbq.read_gbq : This function in the pandas-gbq library. DataFrame.to_gbq : Write a DataFrame to Google BigQuery.
Function32 read_hdf()
Help on function read_hdf in module pandas.io.pytables: read_hdf(path_or_buf, key=None, mode: 'str' = 'r', errors: 'str' = 'strict', where=None, start: 'int | None' = None, stop: 'int | None' = None, columns=None, iterator=False, chunksize: 'int | None' = None, **kwargs) Read from the store, close it if we opened it. Retrieve pandas object stored in file, optionally based on where criteria. .. warning:: Pandas uses PyTables for reading and writing HDF5 files, which allows serializing object-dtype data with pickle when using the "fixed" format. Loading pickled data received from untrusted sources can be unsafe. See: https://docs.python.org/3/library/pickle.html for more. Parameters ---------- path_or_buf : str, path object, pandas.HDFStore Any valid string path is acceptable. Only supports the local file system, remote URLs and file-like objects are not supported. If you want to pass in a path object, pandas accepts any ``os.PathLike``. Alternatively, pandas accepts an open :class:`pandas.HDFStore` object. key : object, optional The group identifier in the store. Can be omitted if the HDF file contains a single pandas object. mode : {'r', 'r+', 'a'}, default 'r' Mode to use when opening the file. Ignored if path_or_buf is a :class:`pandas.HDFStore`. Default is 'r'. 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. where : list, optional A list of Term (or convertible) objects. start : int, optional Row number to start selection. stop : int, optional Row number to stop selection. columns : list, optional A list of columns names to return. iterator : bool, optional Return an iterator object. chunksize : int, optional Number of rows to include in an iteration when using an iterator. **kwargs Additional keyword arguments passed to HDFStore. Returns ------- item : object The selected object. Return type depends on the object stored. See Also -------- DataFrame.to_hdf : Write a HDF file from a DataFrame. HDFStore : Low-level access to HDF files. Examples -------- >>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z']) >>> df.to_hdf('./store.h5', 'data') >>> reread = pd.read_hdf('./store.h5')
Function33 read_html()
Help on function read_html in module pandas.io.html: read_html(io: 'FilePathOrBuffer', match: 'str | Pattern' = '.+', flavor: 'str | None' = None, header: 'int | Sequence[int] | None' = None, index_col: 'int | Sequence[int] | None' = None, skiprows: 'int | Sequence[int] | slice | None' = None, attrs: 'dict[str, str] | None' = None, parse_dates: 'bool' = False, thousands: 'str | None' = ',', encoding: 'str | None' = None, decimal: 'str' = '.', converters: 'dict | None' = None, na_values=None, keep_default_na: 'bool' = True, displayed_only: 'bool' = True) -> 'list[DataFrame]' Read HTML tables into a ``list`` of ``DataFrame`` objects. Parameters ---------- io : str, path object or file-like object A URL, a file-like object, or a raw string containing HTML. Note that lxml only accepts the http, ftp and file url protocols. If you have a URL that starts with ``'https'`` you might try removing the ``'s'``. match : str or compiled regular expression, optional The set of tables containing text matching this regex or string will be returned. Unless the HTML is extremely simple you will probably need to pass a non-empty string here. Defaults to '.+' (match any non-empty string). The default value will return all tables contained on a page. This value is converted to a regular expression so that there is consistent behavior between Beautiful Soup and lxml. flavor : str, optional The parsing engine to use. 'bs4' and 'html5lib' are synonymous with each other, they are both there for backwards compatibility. The default of ``None`` tries to use ``lxml`` to parse and if that fails it falls back on ``bs4`` + ``html5lib``. header : int or list-like, optional The row (or list of rows for a :class:`~pandas.MultiIndex`) to use to make the columns headers. index_col : int or list-like, optional The column (or list of columns) to use to create the index. skiprows : int, list-like or slice, optional Number of rows to skip after parsing the column integer. 0-based. If a sequence of integers or a slice is given, will skip the rows indexed by that sequence. Note that a single element sequence means 'skip the nth row' whereas an integer means 'skip n rows'. attrs : dict, optional This is a dictionary of attributes that you can pass to use to identify the table in the HTML. These are not checked for validity before being passed to lxml or Beautiful Soup. However, these attributes must be valid HTML table attributes to work correctly. For example, :: attrs = {'id': 'table'} is a valid attribute dictionary because the 'id' HTML tag attribute is a valid HTML attribute for *any* HTML tag as per `this document <https://html.spec.whatwg.org/multipage/dom.html#global-attributes>`__. :: attrs = {'asdf': 'table'} is *not* a valid attribute dictionary because 'asdf' is not a valid HTML attribute even if it is a valid XML attribute. Valid HTML 4.01 table attributes can be found `here <http://www.w3.org/TR/REC-html40/struct/tables.html#h-11.2>`__. A working draft of the HTML 5 spec can be found `here <https://html.spec.whatwg.org/multipage/tables.html>`__. It contains the latest information on table attributes for the modern web. parse_dates : bool, optional See :func:`~read_csv` for more details. thousands : str, optional Separator to use to parse thousands. Defaults to ``','``. encoding : str, optional The encoding used to decode the web page. Defaults to ``None``.``None`` preserves the previous encoding behavior, which depends on the underlying parser library (e.g., the parser library will try to use the encoding provided by the document). decimal : str, default '.' Character to recognize as decimal point (e.g. use ',' for European data). 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 cell (not column) content, and return the transformed content. na_values : iterable, default None Custom NA values. keep_default_na : bool, default True If na_values are specified and keep_default_na is False the default NaN values are overridden, otherwise they're appended to. displayed_only : bool, default True Whether elements with "display: none" should be parsed. Returns ------- dfs A list of DataFrames. See Also -------- read_csv : Read a comma-separated values (csv) file into DataFrame. Notes ----- Before using this function you should read the :ref:`gotchas about the HTML parsing libraries <io.html.gotchas>`. Expect to do some cleanup after you call this function. For example, you might need to manually assign column names if the column names are converted to NaN when you pass the `header=0` argument. We try to assume as little as possible about the structure of the table and push the idiosyncrasies of the HTML contained in the table to the user. This function searches for ``<table>`` elements and only for ``<tr>`` and ``<th>`` rows and ``<td>`` elements within each ``<tr>`` or ``<th>`` element in the table. ``<td>`` stands for "table data". This function attempts to properly handle ``colspan`` and ``rowspan`` attributes. If the function has a ``<thead>`` argument, it is used to construct the header, otherwise the function attempts to find the header within the body (by putting rows with only ``<th>`` elements into the header). Similar to :func:`~read_csv` the `header` argument is applied **after** `skiprows` is applied. This function will *always* return a list of :class:`DataFrame` *or* it will fail, e.g., it will *not* return an empty list. Examples -------- See the :ref:`read_html documentation in the IO section of the docs <io.read_html>` for some examples of reading in HTML tables.
Function34 read_json()
Help on function read_json in module pandas.io.json._json: read_json(path_or_buf=None, orient=None, typ='frame', dtype: 'DtypeArg | None' = None, convert_axes=None, convert_dates=True, keep_default_dates: 'bool' = True, numpy: 'bool' = False, precise_float: 'bool' = False, date_unit=None, encoding=None, encoding_errors: 'str | None' = 'strict', lines: 'bool' = False, chunksize: 'int | None' = None, compression: 'CompressionOptions' = 'infer', nrows: 'int | None' = None, storage_options: 'StorageOptions' = None) Convert a JSON string to pandas object. Parameters ---------- path_or_buf : a valid JSON 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, and file. For file URLs, a host is expected. A local file could be: ``file://localhost/path/to/table.json``. 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``. orient : str Indication of expected JSON string format. Compatible JSON strings can be produced by ``to_json()`` with a corresponding orient value. The set of possible orients is: - ``'split'`` : dict like ``{index -> [index], columns -> [columns], data -> [values]}`` - ``'records'`` : list like ``[{column -> value}, ... , {column -> value}]`` - ``'index'`` : dict like ``{index -> {column -> value}}`` - ``'columns'`` : dict like ``{column -> {index -> value}}`` - ``'values'`` : just the values array The allowed and default values depend on the value of the `typ` parameter. * when ``typ == 'series'``, - allowed orients are ``{'split','records','index'}`` - default is ``'index'`` - The Series index must be unique for orient ``'index'``. * when ``typ == 'frame'``, - allowed orients are ``{'split','records','index', 'columns','values', 'table'}`` - default is ``'columns'`` - The DataFrame index must be unique for orients ``'index'`` and ``'columns'``. - The DataFrame columns must be unique for orients ``'index'``, ``'columns'``, and ``'records'``. typ : {'frame', 'series'}, default 'frame' The type of object to recover. dtype : bool or dict, default None If True, infer dtypes; if a dict of column to dtype, then use those; if False, then don't infer dtypes at all, applies only to the data. For all ``orient`` values except ``'table'``, default is True. .. versionchanged:: 0.25.0 Not applicable for ``orient='table'``. convert_axes : bool, default None Try to convert the axes to the proper dtypes. For all ``orient`` values except ``'table'``, default is True. .. versionchanged:: 0.25.0 Not applicable for ``orient='table'``. convert_dates : bool or list of str, default True If True then default datelike columns may be converted (depending on keep_default_dates). If False, no dates will be converted. If a list of column names, then those columns will be converted and default datelike columns may also be converted (depending on keep_default_dates). keep_default_dates : bool, default True If parsing dates (convert_dates is not False), then try to parse the default datelike columns. A column label is datelike if * it ends with ``'_at'``, * it ends with ``'_time'``, * it begins with ``'timestamp'``, * it is ``'modified'``, or * it is ``'date'``. numpy : bool, default False Direct decoding to numpy arrays. Supports numeric data only, but non-numeric column and index labels are supported. Note also that the JSON ordering MUST be the same for each term if numpy=True. .. deprecated:: 1.0.0 precise_float : bool, default False Set to enable usage of higher precision (strtod) function when decoding string to double values. Default (False) is to use fast but less precise builtin functionality. date_unit : str, default None The timestamp unit to detect if converting dates. The default behaviour is to try and detect the correct precision, but if this is not desired then pass one of 's', 'ms', 'us' or 'ns' to force parsing only seconds, milliseconds, microseconds or nanoseconds respectively. encoding : str, default is 'utf-8' The encoding to use to decode py3 bytes. 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 lines : bool, default False Read the file as a json object per line. chunksize : int, optional Return JsonReader object for iteration. See the `line-delimited json docs <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#line-delimited-json>`_ for more information on ``chunksize``. This can only be passed if `lines=True`. If this is None, the file will be read into memory all at once. .. versionchanged:: 1.2 ``JsonReader`` is a context manager. compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer' For on-the-fly decompression of on-disk data. If 'infer', then use gzip, bz2, zip or xz if path_or_buf is a string ending in '.gz', '.bz2', '.zip', or 'xz', respectively, and no decompression otherwise. If using 'zip', the ZIP file must contain only one data file to be read in. Set to None for no decompression. nrows : int, optional The number of lines from the line-delimited jsonfile that has to be read. This can only be passed if `lines=True`. If this is None, all the rows will be returned. .. versionadded:: 1.1 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`` as header options. For other URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are forwarded to ``fsspec``. Please see ``fsspec`` and ``urllib`` for more details. .. versionadded:: 1.2.0 Returns ------- Series or DataFrame The type returned depends on the value of `typ`. See Also -------- DataFrame.to_json : Convert a DataFrame to a JSON string. Series.to_json : Convert a Series to a JSON string. Notes ----- Specific to ``orient='table'``, if a :class:`DataFrame` with a literal :class:`Index` name of `index` gets written with :func:`to_json`, the subsequent read operation will incorrectly set the :class:`Index` name to ``None``. This is because `index` is also used by :func:`DataFrame.to_json` to denote a missing :class:`Index` name, and the subsequent :func:`read_json` operation cannot distinguish between the two. The same limitation is encountered with a :class:`MultiIndex` and any names beginning with ``'level_'``. Examples -------- >>> df = pd.DataFrame([['a', 'b'], ['c', 'd']], ... index=['row 1', 'row 2'], ... columns=['col 1', 'col 2']) Encoding/decoding a Dataframe using ``'split'`` formatted JSON: >>> df.to_json(orient='split') '{"columns":["col 1","col 2"],"index":["row 1","row 2"],"data":[["a","b"],["c","d"]]}' >>> pd.read_json(_, orient='split') col 1 col 2 row 1 a b row 2 c d Encoding/decoding a Dataframe using ``'index'`` formatted JSON: >>> df.to_json(orient='index') '{"row 1":{"col 1":"a","col 2":"b"},"row 2":{"col 1":"c","col 2":"d"}}' >>> pd.read_json(_, orient='index') col 1 col 2 row 1 a b row 2 c d Encoding/decoding a Dataframe using ``'records'`` formatted JSON. Note that index labels are not preserved with this encoding. >>> df.to_json(orient='records') '[{"col 1":"a","col 2":"b"},{"col 1":"c","col 2":"d"}]' >>> pd.read_json(_, orient='records') col 1 col 2 0 a b 1 c d Encoding with Table Schema >>> df.to_json(orient='table') '{"schema":{"fields":[{"name":"index","type":"string"},{"name":"col 1","type":"string"},{"name":"col 2","type":"string"}],"primaryKey":["index"],"pandas_version":"0.20.0"},"data":[{"index":"row 1","col 1":"a","col 2":"b"},{"index":"row 2","col 1":"c","col 2":"d"}]}'
Function35 read_orc()
Help on function read_orc in module pandas.io.orc: read_orc(path: 'FilePathOrBuffer', columns: 'list[str] | None' = None, **kwargs) -> 'DataFrame' Load an ORC object from the file path, returning a DataFrame. .. versionadded:: 1.0.0 Parameters ---------- path : 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, and file. For file URLs, a host is expected. A local file could be: ``file://localhost/path/to/table.orc``. 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``. columns : list, default None If not None, only these columns will be read from the file. **kwargs Any additional kwargs are passed to pyarrow. Returns ------- DataFrame Notes ------- Before using this function you should read the :ref:`user guide about ORC <io.orc>` and :ref:`install optional dependencies <install.warn_orc>`.
