# pandas.read_csv

`pandas.``read_csv`(filepath_or_buffer, sep=', ', dialect=None, compression='infer', doublequote=True, escapechar=None, quotechar='"', quoting=0, skipinitialspace=False, lineterminator=None, header='infer', index_col=None, names=None, prefix=None, skiprows=None, skipfooter=None, skip_footer=0, na_values=None, true_values=None, false_values=None, delimiter=None, converters=None, dtype=None, usecols=None, engine=None, delim_whitespace=False, as_recarray=False, na_filter=True, compact_ints=False, use_unsigned=False, low_memory=True, buffer_lines=None, warn_bad_lines=True, error_bad_lines=True, keep_default_na=True, thousands=None, comment=None, decimal='.', parse_dates=False, keep_date_col=False, dayfirst=False, date_parser=None, memory_map=False, float_precision=None, nrows=None, iterator=False, chunksize=None, verbose=False, encoding=None, squeeze=False, mangle_dupe_cols=True, tupleize_cols=False, infer_datetime_format=False, skip_blank_lines=True)

  Read CSV (comma-separated) 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 : string or file handle / StringIO

  > The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be file ://localhost/path/to/table.csv

  sep : string, default ‘,’

  > Delimiter to use. If sep is None, will try to automatically determine this. Regular expressions are accepted.

  engine : {‘c’, ‘python’}

  > Parser engine to use. The C engine is faster while the python engine is currently more feature-complete.

  lineterminator : string (length 1), default None

  > Character to break file into lines. Only valid with C parser

  quotechar : string (length 1)

  > 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 None

  > Control field quoting behavior per `csv.QUOTE_*` constants. Use one of QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3). Default (None) results in QUOTE_MINIMAL behavior.

  skipinitialspace : boolean, default False

  > Skip spaces after delimiter

  escapechar : string (length 1), default None

  > One-character string used to escape delimiter when quoting is QUOTE_NONE.

  dtype : Type name or dict of column -> type, default None

  > Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32} (Unsupported with engine=’python’)

  compression : {‘gzip’, ‘bz2’, ‘infer’, None}, default ‘infer’

  > For on-the-fly decompression of on-disk data. If ‘infer’, then use gzip or bz2 if filepath_or_buffer is a string ending in ‘.gz’ or ‘.bz2’, respectively, and no decompression otherwise. Set to None for no decompression.

  dialect : string or csv.Dialect instance, default None

  > If None defaults to Excel dialect. Ignored if sep longer than 1 char See csv.Dialect documentation for more details

  header : int, list of ints, default ‘infer’

  > Row number(s) to use as the column names, and the start of the data. Defaults to 0 if no `names` passed, otherwise `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 are 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.

  skiprows : list-like or integer, default None

  > Line numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file

  index_col : int or sequence or False, default None

  > Column to use as the row labels of the DataFrame. If a sequence is given, a MultiIndex is used. If you have a malformed file with delimiters at the end of each line, you might consider index_col=False to force pandas to _not_ use the first column as the index (row names)

  names : array-like, default None

  > List of column names to use. If file contains no header row, then you should explicitly pass header=None

  prefix : string, default None

  > Prefix to add to column numbers when no header, e.g ‘X’ for X0, X1, ...

  na_values : str, list-like or dict, default None

  > Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values

  true_values : list, default None

  > Values to consider as True

  false_values : list, default None

  > Values to consider as False

  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

  parse_dates : boolean, list of ints or names, list of lists, or dict, default False

  > If True -> try parsing the index. If [1, 2, 3] -> try parsing columns 1, 2, 3 each as a separate date column. If [[1, 3]] -> combine columns 1 and 3 and parse as a single date column. {‘foo’ : [1, 3]} -> parse columns 1, 3 as date and call result ‘foo’ A fast-path exists for iso8601-formatted dates.

  keep_date_col : boolean, default False

  > If True and parse_dates specifies combining multiple columns then keep the original columns.

  date_parser : function, default None

  > 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 : boolean, default False

  > DD/MM format dates, international and European format

  thousands : str, default None

  > Thousands separator

  comment : str, default None

  > 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 ‘#emptyna,b,cn1,2,3’ with header=0 will result in ‘a,b,c’ being treated as the header.

  decimal : str, default ‘.’

  > Character to recognize as decimal point. E.g. use ‘,’ for European data

  nrows : int, default None

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

  iterator : boolean, default False

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

  chunksize : int, default None

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

  skipfooter : int, default 0

  > Number of lines at bottom of file to skip (Unsupported with engine=’c’)

  converters : dict, default None

  > Dict of functions for converting values in certain columns. Keys can either be integers or column labels

  verbose : boolean, default False

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

  delimiter : string, default None

  > Alternative argument name for sep. Regular expressions are accepted.

  encoding : string, default None

  > Encoding to use for UTF when reading/writing (ex. ‘utf-8’). List of Python standard encodings

  squeeze : boolean, default False

  > If the parsed data only contains one column then return a Series

  na_filter : boolean, 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

  usecols : array-like, default None

  > Return a subset of the columns. Results in much faster parsing time and lower memory usage.

  mangle_dupe_cols : boolean, default True

  > Duplicate columns will be specified as ‘X.0’...’X.N’, rather than ‘X’...’X’

  tupleize_cols : boolean, default False

  > Leave a list of tuples on columns as is (default is to convert to a Multi Index on the columns)

  error_bad_lines : boolean, default True

  > 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 dropped from the DataFrame that is returned. (Only valid with C parser)

  warn_bad_lines : boolean, default True

  > If error_bad_lines is False, and warn_bad_lines is True, a warning for each “bad line” will be output. (Only valid with C parser).

  infer_datetime_format : boolean, default False

  > If True and parse_dates is enabled for a column, attempt to infer the datetime format to speed up the processing

  skip_blank_lines : boolean, default True

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

  Returns: | result : DataFrame or TextParser