pandas.read_csv¶
-
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, 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, iterator=False, chunksize=None, compression='infer', thousands=None, decimal=b'.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=None, error_bad_lines=True, warn_bad_lines=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None)[source]¶ 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, 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 either
pathlib.Path
orpy._path.local.LocalPath
.By file-like object, we refer to objects with a
read()
method, such as a file handler (e.g. via builtinopen
function) orStringIO
.- 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 toheader=None
. 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
, soheader=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 file contains no header row, then you should explicitly pass
header=None
. Duplicates in this list will cause aUserWarning
to be issued.- index_col : int, sequence or bool, optional
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 : 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, sousecols=[0, 1]
is the same as[1, 0]
. To instantiate a DataFrame fromdata
with element order preserved usepd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]
for columns in['foo', 'bar']
order orpd.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’, ‘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 unparseable 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
afterpd.read_csv
. To parse an index or column with a mixture of timezones, specifydate_parser
to be a partially-appliedpandas.to_datetime()
withutc=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.
- iterator : bool, default False
Return TextFileReader object for iteration or getting chunks with
get_chunk()
.- chunksize : int, optional
Return TextFileReader object for iteration. See the 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’ 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.
New in version 0.18.1: support for ‘zip’ and ‘xz’ compression.
- 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 singlequotechar
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, ifcomment='#'
, parsing#empty\na,b,c\n1,2,3
withheader=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 .
- 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.
- tupleize_cols : bool, default False
Leave a list of tuples on columns as is (default is to convert to a MultiIndex on the columns).
Deprecated since version 0.21.0: This argument will be removed and will always convert to MultiIndex
- error_bad_lines : bool, 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.
- warn_bad_lines : bool, default True
If error_bad_lines is False, and warn_bad_lines is True, a warning for each “bad line” will be output.
- delim_whitespace : bool, 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.
- 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 for the ordinary converter, high for the high-precision converter, and round_trip for the round-trip converter.
Returns: - DataFrame or TextParser
A comma-separated values (csv) file is returned as two-dimensional data structure with labeled axes.
See also
Examples
>>> pd.read_csv('data.csv') # doctest: +SKIP