pandas.io.parsers.read_csv¶
- pandas.io.parsers.read_csv(filepath_or_buffer, sep=', ', dialect=None, compression=None, 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, na_fvalues=None, true_values=None, false_values=None, delimiter=None, converters=None, dtype=None, usecols=None, engine='c', 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, nrows=None, iterator=False, chunksize=None, verbose=False, encoding=None, squeeze=False, mangle_dupe_cols=True, tupleize_cols=False, infer_datetime_format=False)¶
Read CSV (comma-separated) file into DataFrame
Also supports optionally iterating or breaking of the file into chunks.
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.
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
dtype : Type name or dict of column -> type
Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32}
compression : {‘gzip’, ‘bz2’, None}, default None
For on-the-fly decompression of on-disk data
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 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)
skiprows : list-like or integer
Row numbers to skip (0-indexed) or number of rows 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
List of column names to use. If file contains no header row, then you should explicitly pass header=None
prefix : string or None (default)
Prefix to add to column numbers when no header, e.g ‘X’ for X0, X1, ...
na_values : list-like or dict, default None
Additional strings to recognize as NA/NaN. If dict passed, specific per-column NA values
true_values : list
Values to consider as True
false_values : list
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
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
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.
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 Does not support line commenting (will return empty line)
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
chunksize : int, default None
Return TextFileReader object for iteration
skipfooter : int, default 0
Number of line at bottom of file to skip
converters : dict. optional
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’)
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
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
Returns : result : DataFrame or TextParser