# pandas.read_csv

pandas.read_csv(filepath_or_buffer, sep=NoDefault.no_default, delimiter=None, header='infer', names=NoDefault.no_default, index_col=None, usecols=None, squeeze=False, prefix=NoDefault.no_default, mangle_dupe_cols=True, dtype=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='.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, encoding_errors='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=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.

  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 `pandas.to_datetime()` with `utc=True`. See Parsing a CSV with 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.

      New in version 0.25.0.

    iterator bool, default False

      Return TextFileReader object for iteration or getting chunks with `get_chunk()`.

      Changed in version 1.2: `TextFileReader` is a context manager.

    chunksize int, optional

      Return TextFileReader object for iteration. See the IO Tools docs for more information on `iterator` and `chunksize`.

      Changed in version 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 .

      Changed in version 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"`.

      Changed in version 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 .

      New in version 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 since version 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 since version 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.

      New in version 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.

      Changed in version 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.

      New in version 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')