polars.read_csv#

polars.read_csv( source: str | TextIO | BytesIO | Path | BinaryIO | bytes, *, has_header: bool = True, columns: Sequence[int] | Sequence[str] | None = None, new_columns: Sequence[str] | None = None, separator: str = ',', comment_char: str | None = None, quote_char: str | None = '"', skip_rows: int = 0, dtypes: Mapping[str, PolarsDataType] | Sequence[PolarsDataType] | None = None, null_values: str | Sequence[str] | dict[str, str] | None = None, missing_utf8_is_empty_string: bool = False, ignore_errors: bool = False, try_parse_dates: bool = False, n_threads: int | None = None, infer_schema_length: int | None = 100, batch_size: int = 8192, n_rows: int | None = None, encoding: CsvEncoding | str = 'utf8', low_memory: bool = False, rechunk: bool = True, use_pyarrow: bool = False, storage_options: dict[str, Any] | None = None, skip_rows_after_header: int = 0, row_count_name: str | None = None, row_count_offset: int = 0, sample_size: int = 1024, eol_char: str = '\n', raise_if_empty: bool = True, ) → DataFrame[source]#

Read a CSV file into a DataFrame.

Parameters:

source

Path to a file or a file-like object (by file-like object, we refer to objects that have a read() method, such as a file handler (e.g. via builtin open function) or BytesIO). If fsspec is installed, it will be used to open remote files.

has_header

Indicate if the first row of dataset is a header or not. If set to False, column names will be autogenerated in the following format: column_x, with x being an enumeration over every column in the dataset starting at 1.

columns

Columns to select. Accepts a list of column indices (starting at zero) or a list of column names.

new_columns

Rename columns right after parsing the CSV file. If the given list is shorter than the width of the DataFrame the remaining columns will have their original name.

separator

Single byte character to use as delimiter in the file.

comment_char

Single byte character that indicates the start of a comment line, for instance #.

quote_char

Single byte character used for csv quoting, default = ". Set to None to turn off special handling and escaping of quotes.

skip_rows

Start reading after skip_rows lines.

dtypes

Overwrite dtypes for specific or all columns during schema inference.

null_values

Values to interpret as null values. You can provide a:

str: All values equal to this string will be null.
List[str]: All values equal to any string in this list will be null.
Dict[str, str]: A dictionary that maps column name to a null value string.

missing_utf8_is_empty_string

By default a missing value is considered to be null; if you would prefer missing utf8 values to be treated as the empty string you can set this param True.

ignore_errors

Try to keep reading lines if some lines yield errors. Before using this option, try to increase the number of lines used for schema inference with e.g infer_schema_length=10000 or override automatic dtype inference for specific columns with the dtypes option or use infer_schema_length=0 to read all columns as pl.Utf8 to check which values might cause an issue.

try_parse_dates

Try to automatically parse dates. Most ISO8601-like formats can be inferred, as well as a handful of others. If this does not succeed, the column remains of data type pl.Utf8. If use_pyarrow=True, dates will always be parsed.

n_threads

Number of threads to use in csv parsing. Defaults to the number of physical cpu’s of your system.

infer_schema_length

Maximum number of lines to read to infer schema. If schema is inferred wrongly (e.g. as pl.Int64 instead of pl.Float64), try to increase the number of lines used to infer the schema or override inferred dtype for those columns with dtypes. If set to 0, all columns will be read as pl.Utf8. If set to None, a full table scan will be done (slow).

batch_size

Number of lines to read into the buffer at once. Modify this to change performance.

n_rows

Stop reading from CSV file after reading n_rows. During multi-threaded parsing, an upper bound of n_rows rows cannot be guaranteed.

encoding{‘utf8’, ‘utf8-lossy’, …}

Lossy means that invalid utf8 values are replaced with � characters. When using other encodings than utf8 or utf8-lossy, the input is first decoded in memory with python. Defaults to utf8.

low_memory

Reduce memory usage at expense of performance.

rechunk

Make sure that all columns are contiguous in memory by aggregating the chunks into a single array.

use_pyarrow

Try to use pyarrow’s native CSV parser. This will always parse dates, even if try_parse_dates=False. This is not always possible. The set of arguments given to this function determines if it is possible to use pyarrow’s native parser. Note that pyarrow and polars may have a different strategy regarding type inference.

storage_options

Extra options that make sense for fsspec.open() or a particular storage connection. e.g. host, port, username, password, etc.

skip_rows_after_header

Skip this number of rows when the header is parsed.

row_count_name

If not None, this will insert a row count column with the given name into the DataFrame.

row_count_offset

Offset to start the row_count column (only used if the name is set).

sample_size

Set the sample size. This is used to sample statistics to estimate the allocation needed.

eol_char

Single byte end of line character.

raise_if_empty

When there is no data in the source,``NoDataError`` is raised. If this parameter is set to False, an empty DataFrame (with no columns) is returned instead.

Returns:

DataFrame