polars.DataFrame.write_csv#

Write to comma-separated values (CSV) file.

Parameters:

file

File path or writable file-like object to which the result will be written. If set to None (default), the output is returned as a string instead.

include_bom

Whether to include UTF-8 BOM in the CSV output.

include_header

Whether to include header in the CSV output.

separator

Separate CSV fields with this symbol.

line_terminator

String used to end each row.

quote_char

Byte to use as quoting character.

batch_size

Number of rows that will be processed per thread.

datetime_format

A format string, with the specifiers defined by the chrono Rust crate. If no format specified, the default fractional-second precision is inferred from the maximum timeunit found in the frame’s Datetime cols (if any).

date_format

A format string, with the specifiers defined by the chrono Rust crate.

time_format

A format string, with the specifiers defined by the chrono Rust crate.

float_precision

Number of decimal places to write, applied to both Float32 and Float64 datatypes.

null_value

A string representing null values (defaulting to the empty string).

quote_style{‘necessary’, ‘always’, ‘non_numeric’, ‘never’}

Determines the quoting strategy used.

necessary (default): This puts quotes around fields only when necessary. They are necessary when fields contain a quote, separator or record terminator. Quotes are also necessary when writing an empty record (which is indistinguishable from a record with one empty field). This is the default.
always: This puts quotes around every field. Always.
never: This never puts quotes around fields, even if that results in invalid CSV data (e.g.: by not quoting strings containing the separator).
non_numeric: This puts quotes around all fields that are non-numeric. Namely, when writing a field that does not parse as a valid float or integer, then quotes will be used even if they aren`t strictly necessary.

Examples

>>> import pathlib
>>>
>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3, 4, 5],
...         "bar": [6, 7, 8, 9, 10],
...         "ham": ["a", "b", "c", "d", "e"],
...     }
... )
>>> path: pathlib.Path = dirpath / "new_file.csv"
>>> df.write_csv(path, separator=",")