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pandas.read_csv(filepath_or_buffer, sep=', ', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, 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, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='"', quoting=0, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=False, error_bad_lines=True, warn_bad_lines=True, skipfooter=0, skip_footer=0, doublequote=True, delim_whitespace=False, as_recarray=False, compact_ints=False, use_unsigned=False, low_memory=True, buffer_lines=None, memory_map=False, float_precision=None)
[source] -
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 : str, pathlib.Path, py._path.local.LocalPath or any object with a read() method (such as a file handle or 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 : str, default ?,?
Delimiter to use. If sep is None, will try to automatically determine this. Separators longer than 1 character and different from
'\s+'
will be interpreted as regular expressions, will force use of the python parsing engine and will ignore quotes in the data. Regex example:'\r\t'
delimiter : str, default
None
Alternative argument name for sep.
delim_whitespace : boolean, default False
Specifies whether or not whitespace (e.g.
' '
or' '
) will be used as the sep. Equivalent to settingsep='\s+'
. If this option is set to True, nothing should be passed in for thedelimiter
parameter.New in version 0.18.1: support for the Python parser.
header : int or list of ints, default ?infer?
Row number(s) to use as the column names, and the start of the data. Default behavior is as if set to 0 if no
names
passed, otherwiseNone
. Explicitly passheader=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 ifskip_blank_lines=True
, so header=0 denotes the first line of data rather than the first line of the file.names : array-like, default None
List of column names to use. If file contains no header row, then you should explicitly pass header=None. Duplicates in this list are not allowed unless mangle_dupe_cols=True, which is the default.
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)
usecols : array-like, default None
Return a subset of the columns. All elements in this array 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 validusecols
parameter would be [0, 1, 2] or [?foo?, ?bar?, ?baz?]. Using this parameter results in much faster parsing time and lower memory usage.as_recarray : boolean, default False
DEPRECATED: this argument will be removed in a future version. Please call
pd.read_csv(...).to_records()
instead.Return a NumPy recarray instead of a DataFrame after parsing the data. If set to True, this option takes precedence over the
squeeze
parameter. In addition, as row indices are not available in such a format, theindex_col
parameter will be ignored.squeeze : boolean, default False
If the parsed data only contains one column then return a Series
prefix : str, default None
Prefix to add to column numbers when no header, e.g. ?X? for X0, X1, ...
mangle_dupe_cols : boolean, default True
Duplicate columns will be specified as ?X.0?...?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, default None
Data type for data or columns. E.g. {?a?: np.float64, ?b?: np.int32} (Unsupported with engine=?python?). Use
str
orobject
to preserve and not interpret dtype.engine : {?c?, ?python?}, optional
Parser engine to use. The C engine is faster while the python engine is currently more feature-complete.
converters : dict, default None
Dict of functions for converting values in certain columns. Keys can either be integers or column labels
true_values : list, default None
Values to consider as True
false_values : list, default None
Values to consider as False
skipinitialspace : boolean, default False
Skip spaces after delimiter.
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
skipfooter : int, default 0
Number of lines at bottom of file to skip (Unsupported with engine=?c?)
skip_footer : int, default 0
DEPRECATED: use the
skipfooter
parameter instead, as they are identicalnrows : int, default None
Number of rows of file to read. Useful for reading pieces of large files
na_values : scalar, str, list-like, or dict, default None
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?, ?N/A?, ?NA?, ?NULL?, ?NaN?, ?nan?`.
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.
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
verbose : boolean, default False
Indicate number of NA values placed in non-numeric columns
skip_blank_lines : boolean, default True
If True, skip over blank lines rather than interpreting as NaN values
parse_dates : boolean or list of ints or names or list of lists or dict, default False
- boolean. If True -> try parsing the index.
- list of ints 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?
Note: A fast-path exists for iso8601-formatted dates.
infer_datetime_format : boolean, 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 : 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
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
andchunksize
.compression : {?infer?, ?gzip?, ?bz2?, ?zip?, ?xz?, None}, default ?infer?
For on-the-fly decompression of on-disk data. If ?infer?, then use gzip, bz2, zip or xz if filepath_or_buffer is a string ending in ?.gz?, ?.bz2?, ?.zip?, or ?xz?, respectively, and no decompression otherwise. If using ?zip?, the ZIP file must contain only one data file to be read in. Set to None for no decompression.
New in version 0.18.1: support for ?zip? and ?xz? compression.
thousands : str, default None
Thousands separator
decimal : str, default ?.?
Character to recognize as decimal point (e.g. use ?,? for European data).
float_precision : string, default None
Specifies which converter the C engine should use for floating-point values. The options are
None
for the ordinary converter,high
for the high-precision converter, andround_trip
for the round-trip converter.lineterminator : str (length 1), default None
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 : boolean, 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 singlequotechar
element.escapechar : str (length 1), default None
One-character string used to escape delimiter when quoting is QUOTE_NONE.
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 parameterheader
but not byskiprows
. For example, if comment=?#?, parsing ?#emptyna,b,cn1,2,3? withheader=0
will result in ?a,b,c? being treated as the header.encoding : str, default None
Encoding to use for UTF when reading/writing (ex. ?utf-8?). List of Python standard encodings
dialect : str 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
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).
low_memory : boolean, 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 thechunksize
oriterator
parameter to return the data in chunks. (Only valid with C parser)buffer_lines : int, default None
DEPRECATED: this argument will be removed in a future version because its value is not respected by the parser
compact_ints : boolean, default False
DEPRECATED: this argument will be removed in a future version
If compact_ints is True, then for any column that is of integer dtype, the parser will attempt to cast it as the smallest integer dtype possible, either signed or unsigned depending on the specification from the
use_unsigned
parameter.use_unsigned : boolean, default False
DEPRECATED: this argument will be removed in a future version
If integer columns are being compacted (i.e.
compact_ints=True
), specify whether the column should be compacted to the smallest signed or unsigned integer dtype.memory_map : boolean, 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.Returns: result : DataFrame or TextParser
pandas.read_csv()
2017-01-12 04:50:31
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