polars.read_csv_batched#

polars.read_csv_batched(
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.

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

has_header

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

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 separator in the file.

comment_prefix

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

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.

schema_overrides

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

n_threads

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

infer_schema_length

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).

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 pressure at the expense of performance.

rechunk

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

skip_rows_after_header

Skip this number of rows when the header is parsed.

row_index_name

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.

row_index_offset

Start the row index at this offset. Cannot be negative. Only used if row_index_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 (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.

raise_if_empty

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_ragged_lines

Truncate lines that are longer than the schema.

decimal_comma

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

Returns:
BatchedCsvReader

See also

scan_csv

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

Examples

>>> 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)