pandas.read_csv¶

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.

param 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 any os.PathLike.

By file-like object, we refer to objects with a read() method, such as a file handler (e.g. via builtin open function) or StringIO.

param 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'.

param delimiter

str, default None: Alias for sep.

param 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.

param 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 are not allowed.

param 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.

param 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.

param squeeze

bool, default False: If the parsed data only contains one column then return a Series.

param prefix

str, optional: Prefix to add to column numbers when no header, e.g. ‘X’ for X0, X1, …

param 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.

param 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.

param engine

{‘c’, ‘python’}, optional: Parser engine to use. The C engine is faster while the python engine is currently more feature-complete.

param converters

dict, optional: Dict of functions for converting values in certain columns. Keys can either be integers or column labels.

param true_values

list, optional: Values to consider as True.

param false_values

list, optional: Values to consider as False.

param skipinitialspace

bool, default False: Skip spaces after delimiter.

param 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].

param skipfooter

int, default 0: Number of lines at bottom of file to skip (Unsupported with engine=’c’).

param nrows

int, optional: Number of rows of file to read. Useful for reading pieces of large files.

param 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’.

param 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.

param 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.

param verbose

bool, default False: Indicate number of NA values placed in non-numeric columns.

param skip_blank_lines

bool, default True: If True, skip over blank lines rather than interpreting as NaN values.

param 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 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.

param 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.

param keep_date_col

bool, default False: If True and parse_dates specifies combining multiple columns then keep the original columns.

param 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.

param dayfirst

bool, default False: DD/MM format dates, international and European format.

param cache_dates

boolean, 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.

param iterator

bool, default False: Return TextFileReader object for iteration or getting chunks with get_chunk().

param chunksize

int, optional: Return TextFileReader object for iteration. See the IO Tools docs for more information on iterator and chunksize.

param 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.

param thousands

str, optional: Thousands separator.

param decimal

str, default ‘.’: Character to recognize as decimal point (e.g. use ‘,’ for European data).

param lineterminator

str (length 1), optional: Character to break file into lines. Only valid with C parser.

param 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.

param 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).

param 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.

param escapechar

str (length 1), optional: One-character string used to escape other characters.

param 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.

param encoding

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

param 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.

param 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.

param 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.

param 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.

New in version 0.18.1: support for the Python parser.

param 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).

param 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.

param 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.

return

DataFrame or TextParser A comma-separated values (csv) file is returned as two-dimensional data structure with labeled axes.

Limitations¶

Parameters
header, index_col, squeeze, prefix, mangle_dupe_cols, engine, converters, true_values, false_values, skipinitialspace, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map and float_precision are currently unsupported by Intel Scalable Dataframe Compiler.
Resulting DataFrame type could be inferred from constant file name of from parameters.
filepath_or_buffer could be constant for inferencing from file. filepath_or_buffer could be variable for inferencing from parameters if dtype is constant. If both filepath_or_buffer and dtype are constants then default is inferencing from parameters.
For inferring from parameters names or usecols should be provided additionally to dtype.
For inferring from file sep, delimiter and skiprows should be constants or omitted.
names and usecols should be constants or omitted for both types of inferrencing.
usecols with list of ints is unsupported by Intel Scalable Dataframe Compiler.

Examples¶

Inference from file. File name is constant. Resulting DataFrame depends on CSV file content at the moment of compilation.

>>> pd.read_csv('data.csv')  

Inference from file. File name, names, usecols, delimiter and skiprow are constants. Resulting DataFrame contains one column A with type of column depending on CSV file content at the moment of compilation.

>>> pd.read_csv('data.csv', names=['A','B'], usecols=['A'], delimiter=';', skiprows=2)  

Inference from parameters. File name, delimiter and skiprow are variables. names, usecols and dtype are constants. Resulting DataFrame contains column A with type np.float64.

>>> pd.read_csv(file_name, names=['A','B'], usecols=['A'], dtype={'A': np.float64}, \
                delimiter=some_char, skiprows=some_int)  

Navigation

pandas.read_csv¶

Limitations¶

Examples¶