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,
schema: SchemaDict | 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,
truncate_ragged_lines: bool = False,
) DataFrame[source]#

Read a CSV file into a DataFrame.


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.


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


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.


Single byte character to use as delimiter in the file.


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


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


Start reading after skip_rows lines.


Overwrite dtypes for specific or all columns during schema inference.


Provide the schema. This means that polars doesn’t do schema inference. This argument expects the complete schema, whereas dtypes can be used to partially overwrite a schema.


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.


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.


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 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.


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


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).


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


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.


Reduce memory usage at expense of performance.


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


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.


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


Skip this number of rows when the header is parsed.


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


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


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


Single byte end of line character.


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.


Truncate lines that are longer than the schema.


See also


Lazily read from a CSV file or multiple files via glob patterns.


This operation defaults to a rechunk operation at the end, meaning that all data will be stored continuously in memory. Set rechunk=False if you are benchmarking the csv-reader. A rechunk is an expensive operation.