pandas.read_table — pandas 2.2.2 documentation (2024)

Read general delimited file into DataFrame.

Also supports optionally iterating or breaking of the fileinto chunks.

Additional help can be found in the online docs forIO Tools.

Parameters:

filepath_or_bufferstr, path object or file-like object

Any valid string path is acceptable. The string could be a URL. ValidURL schemes include http, ftp, s3, gs, and file. For file URLs, a host isexpected. 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 asa file handle (e.g. via builtin open function) or StringIO.

sepstr, default ‘\t’ (tab-stop)

Character or regex pattern to treat as the delimiter. If sep=None, theC engine cannot automatically detectthe separator, but the Python parsing engine can, meaning the latter willbe used and automatically detect the separator from only the first validrow 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 forcethe use of the Python parsing engine. Note that regex delimiters are proneto ignoring quoted data. Regex example: '\r\t'.

delimiterstr, optional

Alias for sep.

headerint, Sequence of int, ‘infer’ or None, default ‘infer’

Row number(s) containing column labels and marking the start of thedata (zero-indexed). Default behavior is to infer the column names: if no namesare passed the behavior is identical to header=0 and columnnames are inferred from the first line of the file, if columnnames are passed explicitly to names then the behavior is identical toheader=None. Explicitly pass header=0 to be able toreplace existing names. The header can be a list of integers thatspecify row locations for a MultiIndex on the columnse.g. [0, 1, 3]. Intervening rows that are not specified will beskipped (e.g. 2 in this example is skipped). Note that thisparameter ignores commented lines and empty lines ifskip_blank_lines=True, so header=0 denotes the first line ofdata rather than the first line of the file.

namesSequence 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_colHashable, Sequence of Hashable or False, optional

Column(s) to use as row label(s), denoted either by column labels or columnindices. If a sequence of labels or indices is given, MultiIndexwill be formed for the row labels.

Note: index_col=False can be used to force pandas to not use the firstcolumn as the index, e.g., when you have a malformed file with delimiters atthe end of each line.

usecolsSequence of Hashable or Callable, optional

Subset of columns to select, denoted either by column labels or column indices.If list-like, all elements must eitherbe positional (i.e. integer indices into the document columns) or stringsthat correspond to column names provided either by the user in names orinferred from the document header row(s). If names are given, the documentheader row(s) are not taken into account. For example, a valid list-likeusecols 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 orderpreserved use pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]for columns in ['foo', 'bar'] order orpd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]for ['bar', 'foo'] order.

If callable, the callable function will be evaluated against the columnnames, returning names where the callable function evaluates to True. Anexample of a valid callable argument would be lambda x: x.upper() in['AAA', 'BBB', 'DDD']. Using this parameter results in much fasterparsing time and lower memory usage.

dtypedtype or dict of {Hashabledtype}, 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 settingsto preserve and not interpret dtype.If converters are specified, they will be applied INSTEADof dtype conversion.

New in version 1.5.0: Support for defaultdict was added. Specify a defaultdict as input wherethe default determines the dtype of the columns which are not explicitlylisted.

engine{‘c’, ‘python’, ‘pyarrow’}, optional

Parser engine to use. The C and pyarrow engines are faster, while the python engineis currently more feature-complete. Multithreading is currently only supported bythe pyarrow engine.

New in version 1.4.0: The ‘pyarrow’ engine was added as an experimental engine, and some featuresare unsupported, or may not work correctly, with this engine.

convertersdict of {HashableCallable}, optional

Functions for converting values in specified columns. Keys can eitherbe column labels or column indices.

true_valueslist, optional

Values to consider as True in addition to case-insensitive variants of ‘True’.

false_valueslist, optional

Values to consider as False in addition to case-insensitive variants of ‘False’.

skipinitialspacebool, default False

Skip spaces after delimiter.

skiprowsint, 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 rowindices, 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].

skipfooterint, default 0

Number of lines at bottom of file to skip (Unsupported with engine='c').

nrowsint, optional

Number of rows of file to read. Useful for reading pieces of large files.

See Also

Create table from file - MATLAB readtableImport Data from Text File to Table - MATLAB & SimulinkCreate Tables and Assign Data to Them - MATLAB & Simulink - MathWorks Deutschlandread.table function - RDocumentation

na_valuesHashable, Iterable of Hashable or dict of {HashableIterable}, optional

Additional strings to recognize as NA/NaN. If dict passed, specificper-column NA values. By default the following values are interpreted asNaN: “ “, “#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_nabool, 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_valuesis appended to the default NaN values used for parsing.

• If keep_default_na is True, and na_values are not specified, onlythe default NaN values are used for parsing.

• If keep_default_na is False, and na_values are specified, onlythe NaN values specified na_values are used for parsing.

• If keep_default_na is False, and na_values are not specified, nostrings will be parsed as NaN.

Note that if na_filter is passed in as False, the keep_default_na andna_values parameters will be ignored.

na_filterbool, default True

Detect missing value markers (empty strings and the value of na_values). Indata without any NA values, passing na_filter=False can improve theperformance of reading a large file.

verbosebool, default False

Indicate number of NA values placed in non-numeric columns.

Deprecated since version 2.2.0.

skip_blank_linesbool, default True

If True, skip over blank lines rather than interpreting as NaN values.

parse_datesbool, list of Hashable, list of lists or dict of {Hashablelist}, default False

The behavior is as follows:

• bool. If True -> try parsing the index. Note: Automatically set toTrue 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, 3each as a separate date column.

• list of list. e.g. If [[1, 3]] -> combine columns 1 and 3 and parseas 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 callresult ‘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 columnor index will be returned unaltered as an object data type. Fornon-standard datetime parsing, use to_datetime() afterread_csv().

Note: A fast-path exists for iso8601-formatted dates.

infer_datetime_formatbool, default False

If True and parse_dates is enabled, pandas will attempt to infer theformat 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 increasethe parsing speed by 5-10x.

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

keep_date_colbool, default False

If True and parse_dates specifies combining multiple columns thenkeep the original columns.

date_parserCallable, optional

Function to use for converting a sequence of string columns to an array ofdatetime instances. The default uses dateutil.parser.parser to do theconversion. 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) thestring values from the columns defined by parse_dates into a single arrayand pass that; and 3) call date_parser once for each row using one ormore strings (corresponding to the columns defined by parse_dates) asarguments.

Deprecated since version 2.0.0: Use date_format instead, or read in as object and then applyto_datetime() as-needed.

date_formatstr 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. "%d/%m/%Y". Seestrftime documentation for more information on choices, thoughnote that "%f" will parse all the way up to nanoseconds.You can also pass:

• “ISO8601”, to parse any ISO8601

  time string (not necessarily in exactly the same format);

  See Also

  Read Spreadsheet Data into Table - MATLAB & Simulink

• “mixed”, to infer the format for each element individually. This is risky,

  and you should probably use it along with dayfirst.

New in version 2.0.0.

dayfirstbool, default False

DD/MM format dates, international and European format.

cache_datesbool, default True

If True, use a cache of unique, converted dates to apply the datetimeconversion. May produce significant speed-up when parsing duplicatedate strings, especially ones with timezone offsets.

iteratorbool, default False

Return TextFileReader object for iteration or getting chunks withget_chunk().

chunksizeint, optional

Number of lines to read from the file per chunk. Passing a value will cause thefunction to return a TextFileReader object for iteration.See the IO Tools docsfor more information on iterator and chunksize.

compressionstr or dict, default ‘infer’

For on-the-fly decompression of on-disk data. If ‘infer’ and ‘filepath_or_buffer’ ispath-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' setto one of {'zip', 'gzip', 'bz2', 'zstd', 'xz', 'tar'} andother key-value pairs are forwarded tozipfile.ZipFile, gzip.GzipFile,bz2.BZ2File, zstandard.ZstdDecompressor, lzma.LZMAFile ortarfile.TarFile, respectively.As an example, the following could be passed for Zstandard decompression using acustom compression dictionary:compression={'method': 'zstd', 'dict_data': my_compression_dict}.

New in version 1.5.0: Added support for .tar files.

Changed in version 1.4.0: Zstandard support.

thousandsstr (length 1), optional

Character acting as the thousands separator in numerical values.

decimalstr (length 1), default ‘.’

Character to recognize as decimal point (e.g., use ‘,’ for European data).

lineterminatorstr (length 1), optional

Character used to denote a line break. Only valid with C parser.

quotecharstr (length 1), optional

Character used to denote the start and end of a quoted item. Quoteditems 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 iscsv.QUOTE_MINIMAL (i.e., 0) which implies that only fields containing specialcharacters are quoted (e.g., characters defined in quotechar, delimiter,or lineterminator.

doublequotebool, default True

When quotechar is specified and quoting is not QUOTE_NONE, indicatewhether or not to interpret two consecutive quotechar elements INSIDE afield as a single quotechar element.

escapecharstr (length 1), optional

Character used to escape other characters.

commentstr (length 1), optional

Character indicating that the remainder of line should not be parsed.If found at the beginningof a line, the line will be ignored altogether. This parameter must be asingle character. Like empty lines (as long as skip_blank_lines=True),fully commented lines are ignored by the parameter header but not byskiprows. For example, if comment='#', parsing#empty\na,b,c\n1,2,3 with header=0 will result in 'a,b,c' beingtreated as the header.

encodingstr, optional, default ‘utf-8’

Encoding to use for UTF when reading/writing (ex. 'utf-8'). List of Pythonstandard encodings .

encoding_errorsstr, optional, default ‘strict’

How encoding errors are treated. List of possible values .

New in version 1.3.0.

dialectstr or csv.Dialect, optional

If provided, this parameter will override values (default or not) for thefollowing parameters: delimiter, doublequote, escapechar,skipinitialspace, quotechar, and quoting. If it is necessary tooverride values, a ParserWarning will be issued. See csv.Dialectdocumentation 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.

New in version 1.3.0.

New in version 1.4.0:

• Callable, function with signature(bad_line: list[str]) -> list[str] | None that will process a singlebad 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 thanexpected, a ParserWarning will be emitted while dropping extra elements.Only supported when engine='python'

Changed in version 2.2.0:

• Callable, function with signatureas described in pyarrow documentation when engine='pyarrow'

delim_whitespacebool, default False

Specifies whether or not whitespace (e.g. ' ' or '\t') will beused as the sep delimiter. Equivalent to setting sep='\s+'. If this optionis set to True, nothing should be passed in for the delimiterparameter.

Deprecated since version 2.2.0: Use sep="\s+" instead.

low_memorybool, default True

Internally process the file in chunks, resulting in lower memory usewhile parsing, but possibly mixed type inference. To ensure no mixedtypes either set False, or specify the type with the dtype parameter.Note that the entire file is read into a single DataFrameregardless, use the chunksize or iterator parameter to return the data inchunks. (Only valid with C parser).

memory_mapbool, default False

If a filepath is provided for filepath_or_buffer, map the file objectdirectly onto memory and access the data directly from there. Using thisoption 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-pointvalues. 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_optionsdict, 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 pairsare forwarded to urllib.request.Request as header options. For otherURLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs areforwarded to fsspec.open. Please see fsspec and urllib for moredetails, and for more examples on storage options refer here.

dtype_backend{‘numpy_nullable’, ‘pyarrow’}, default ‘numpy_nullable’

Back-end data type applied to the resultant DataFrame(still experimental). Behaviour is as follows:

• "numpy_nullable": returns nullable-dtype-backed DataFrame(default).

• "pyarrow": returns pyarrow-backed nullable ArrowDtypeDataFrame.

New in version 2.0.

Returns:

DataFrame or TextFileReader

A comma-separated values (csv) file is returned as two-dimensionaldata structure with labeled axes.

Examples

>>> pd.read_table('data.csv')