source: str | Path,
has_header: bool = True,
columns: Sequence[int] | Sequence[str] | None = None,
new_columns: Sequence[str] | None = None,
separator: str = ',',
comment_prefix: str | None = None,
quote_char: str | None = '"',
skip_rows: int = 0,
schema_overrides: 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 = 50000,
n_rows: int | None = None,
encoding: CsvEncoding | str = 'utf8',
low_memory: bool = False,
rechunk: bool = False,
skip_rows_after_header: int = 0,
row_index_name: str | None = None,
row_index_offset: int = 0,
sample_size: int = 1024,
eol_char: str = '\n',
raise_if_empty: bool = True,
truncate_ragged_lines: bool = False,
decimal_comma: bool = False,
) BatchedCsvReader[source]#

Read a CSV file in batches.

Upon creation of the BatchedCsvReader, Polars will gather statistics and determine the file chunks. After that, work will only be done if next_batches is called, which will return a list of n frames of the given batch size.


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 like the builtin open function, or a BytesIO instance). If fsspec is installed, it will be used to open remote files.


Indicate if the first row of the 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 separator in the file.


A string used to indicate the start of a comment line. Comment lines are skipped during parsing. Common examples of comment prefixes are # and //.


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 during inference.


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. First try infer_schema_length=0 to read all columns as pl.String 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.String.


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


The maximum number of rows to scan for schema inference. If set to 0, all columns will be read as pl.String. If set to None, the full data may be scanned (this is 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 pressure at the expense of performance.


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


Skip this number of rows when the header is parsed.


Insert a row index column with the given name into the DataFrame as the first column. If set to None (default), no row index column is created.


Start the row index at this offset. Cannot be negative. Only used if row_index_name is set.


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


Single byte end of line character (default: n). When encountering a file with windows line endings (rn), one can go with the default n. The extra r will be removed when processed.


When there is no data in the source,`NoDataError` is raised. If this parameter is set to False, None will be returned from next_batches(n) instead.


Truncate lines that are longer than the schema.


Parse floats using a comma as the decimal separator instead of a period.


See also


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


>>> reader = pl.read_csv_batched(
...     "./tpch/tables_scale_100/lineitem.tbl",
...     separator="|",
...     try_parse_dates=True,
... )
>>> batches = reader.next_batches(5)
>>> for df in batches:
...     print(df)

Read big CSV file in batches and write a CSV file for each “group” of interest.

>>> seen_groups = set()
>>> reader = pl.read_csv_batched("big_file.csv")
>>> batches = reader.next_batches(100)
>>> while batches:
...     df_current_batches = pl.concat(batches)
...     partition_dfs = df_current_batches.partition_by("group", as_dict=True)
...     for group, df in partition_dfs.items():
...         if group in seen_groups:
...             with open(f"./data/{group}.csv", "a") as fh:
...                 fh.write(df.write_csv(file=None, include_header=False))
...         else:
...             df.write_csv(file=f"./data/{group}.csv", include_header=True)
...         seen_groups.add(group)
...     batches = reader.next_batches(100)