polars.DataFrame.glimpse#

DataFrame.glimpse( *, max_items_per_column: int = 10, max_colname_length: int = 50, return_as_string: Literal[False], ) → None[source]#

DataFrame.glimpse( *, max_items_per_column: int = 10, max_colname_length: int = 50, return_as_string: Literal[True], ) → str

Return a dense preview of the DataFrame.

The formatting shows one line per column so that wide dataframes display cleanly. Each line shows the column name, the data type, and the first few values.

Parameters:

max_items_per_column: Maximum number of items to show per column.
max_colname_length: Maximum length of the displayed column names; values that exceed this value are truncated with a trailing ellipsis.
return_as_string: If True, return the preview as a string instead of printing to stdout.

See also

describe, head, tail

Examples

>>> from datetime import date
>>> df = pl.DataFrame(
...     {
...         "a": [1.0, 2.8, 3.0],
...         "b": [4, 5, None],
...         "c": [True, False, True],
...         "d": [None, "b", "c"],
...         "e": ["usd", "eur", None],
...         "f": [date(2020, 1, 1), date(2021, 1, 2), date(2022, 1, 1)],
...     }
... )
>>> df.glimpse()
Rows: 3
Columns: 6
$ a  <f64> 1.0, 2.8, 3.0
$ b  <i64> 4, 5, None
$ c <bool> True, False, True
$ d  <str> None, 'b', 'c'
$ e  <str> 'usd', 'eur', None
$ f <date> 2020-01-01, 2021-01-02, 2022-01-01