read_csv(filepath_or_buffer: 'FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str]', sep=<no_default>, delimiter=None, header='infer', names=<no_default>, index_col=None, usecols=None, squeeze=None, prefix=<no_default>, mangle_dupe_cols=True, dtype: 'DtypeArg | None' = None, engine: 'CSVEngine | None' = 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=None, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression: 'CompressionOptions' = '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)
Also supports optionally iterating or breaking of the file into chunks.
Additional help can be found in the online docs for IO Tools.
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
.
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'
.
Alias for sep.
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.
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.
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.
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 :None:None:`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 :None:None:`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.
If the parsed data only contains one column then return a Series.
Append .squeeze("columns")
to the call to read_csv
to squeeze the data.
Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
Use a list comprehension on the DataFrame's columns after calling read_csv
.
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.
Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32, 'c': 'Int64'} Use :None:None:`str` or :None:None:`object` together with suitable :None:None:`na_values` settings to preserve and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion.
Parser engine to use. The C and pyarrow engines are faster, while the python engine is currently more feature-complete. Multithreading is currently only supported by the pyarrow engine.
The "pyarrow" engine was added as an experimental engine, and some features are unsupported, or may not work correctly, with this engine.
Dict of functions for converting values in certain columns. Keys can either be integers or column labels.
Values to consider as True.
Values to consider as False.
Skip spaces after delimiter.
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]
.
Number of lines at bottom of file to skip (Unsupported with engine='c').
Number of rows of file to read. Useful for reading pieces of large files.
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'.
Whether or not to include the default NaN values when parsing the data. Depending on whether :None:None:`na_values` is passed in, the behavior is as follows:
If :None:None:`keep_default_na` is True, and :None:None:`na_values` are specified, :None:None:`na_values` is appended to the default NaN values used for parsing.
If :None:None:`keep_default_na` is True, and :None:None:`na_values` are not specified, only the default NaN values are used for parsing.
If :None:None:`keep_default_na` is False, and :None:None:`na_values` are specified, only the NaN values specified :None:None:`na_values` are used for parsing.
If :None:None:`keep_default_na` is False, and :None:None:`na_values` are not specified, no strings will be parsed as NaN.
Note that if :None:None:`na_filter` is passed in as False, the :None:None:`keep_default_na` and :None:None:`na_values` parameters will be ignored.
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.
Indicate number of NA values placed in non-numeric columns.
If True, skip over blank lines rather than interpreting as NaN values.
The behavior is as follows:
If True and :None:None:`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.
If True and :None:None:`parse_dates` specifies combining multiple columns then keep the original columns.
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 :None:None:`date_parser` in three different ways, advancing to the next if an exception occurs: 1) Pass one or more arrays (as defined by :None:None:`parse_dates`) as arguments; 2) concatenate (row-wise) the string values from the columns defined by :None:None:`parse_dates` into a single array and pass that; and 3) call :None:None:`date_parser` once for each row using one or more strings (corresponding to the columns defined by :None:None:`parse_dates`) as arguments.
DD/MM format dates, international and European format.
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.
Return TextFileReader object for iteration or getting chunks with get_chunk()
.
TextFileReader
is a context manager.
Return TextFileReader object for iteration. See the :None:None:`IO Tools docs
<https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>` for more information on iterator
and chunksize
.
TextFileReader
is a context manager.
For on-the-fly decompression of on-disk data. If 'infer' and '%s' is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', or '.zst' (otherwise no compression). If using 'zip', the ZIP file must contain only one data file to be read in. Set to None
for no decompression. Can also be a dict with key 'method'
set to one of { 'zip'
, 'gzip'
, 'bz2'
, 'zstd'
} and other key-value pairs are forwarded to zipfile.ZipFile
, gzip.GzipFile
, bz2.BZ2File
, or zstandard.ZstdDecompressor
, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary: compression={'method': 'zstd', 'dict_data': my_compression_dict}
.
Thousands separator.
Character to recognize as decimal point (e.g. use ',' for European data).
Character to break file into lines. Only valid with C parser.
The character used to denote the start and end of a quoted item. Quoted items can include the delimiter and it will be ignored.
Control field quoting behavior per csv.QUOTE_*
constants. Use one of QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
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.
One-character string used to escape other characters.
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 :None:None:`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 to use for UTF when reading/writing (ex. 'utf-8'). :None:None:`List of Python
standard encodings
<https://docs.python.org/3/library/codecs.html#standard-encodings>` .
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"
.
encoding_errors
is a new argument. encoding
has no longer an influence on how encoding errors are handled.
How encoding errors are treated. :None:None:`List of possible values
<https://docs.python.org/3/library/codecs.html#error-handlers>` .
If provided, this parameter will override values (default or not) for the following parameters: :None:None:`delimiter`, :None:None:`doublequote`, :None:None:`escapechar`, :None:None:`skipinitialspace`, :None:None:`quotechar`, and :None:None:`quoting`. If it is necessary to override values, a ParserWarning will be issued. See csv.Dialect documentation for more details.
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.
The on_bad_lines
parameter should be used instead to specify behavior upon encountering a bad line instead.
If error_bad_lines is False, and warn_bad_lines is True, a warning for each "bad line" will be output.
The on_bad_lines
parameter should be used instead to specify behavior upon encountering a bad line instead.
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.
callable, function with signature (bad_line: list[str]) -> list[str] | None
that will process a single bad line. bad_line
is a list of strings split by the sep
. If the function returns None
, the bad line will be ignored. If the function returns a new list of strings with more elements than expected, a ParserWarning
will be emitted while dropping extra elements. Only supported when engine="python"
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.
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 :None:None:`chunksize` or :None:None:`iterator` parameter to return the data in chunks. (Only valid with C parser).
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.
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.
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.
A comma-separated values (csv) file is returned as two-dimensional data structure with labeled axes.
Read a comma-separated values (csv) file into DataFrame.
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.
>>> pd.read_csv('data.csv') # doctest: +SKIPSee :
The following pages refer to to this document either explicitly or contain code examples using this.
dask.blockwise.BlockwiseDepDict
pandas.core.frame.DataFrame
pandas.core.generic.NDFrame.to_excel
pandas.io.parsers.readers.read_table
pandas.io.parsers.readers.read_fwf
pandas.errors.ParserError
pandas.core.generic.NDFrame.to_csv
pandas.io.html.read_html
pandas.io.excel._base.read_excel
pandas.io.parsers.readers.read_csv
pandas.errors.DtypeWarning
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