polars.DataFrame.glimpse#

DataFrame.glimpse(
*,
max_items_per_column: int = 10,
max_colname_length: int = 50,
return_type: Literal['frame', 'self', 'string'] | None = None,
) → str | DataFrame | None[source]#

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.

Changed in version 1.35.0: The return_as_string parameter was renamed return_type and now accepts string values 'string' and 'frame' instead of boolean True or False.

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_type

Modify the return format:

  • None (default): Print the glimpse output to stdout, returning None.

  • "self": Print the glimpse output to stdout, returning the original frame.

  • "frame": Return the glimpse output as a new DataFrame.

  • "string": Return the glimpse output as a string.

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)],
...     }
... )

Print glimpse-formatted output to stdout, returning None:

>>> res = df.glimpse()
Rows: 3
Columns: 6
$ a  <f64> 1.0, 2.8, 3.0
$ b  <i64> 4, 5, null
$ c <bool> True, False, True
$ d  <str> null, 'b', 'c'
$ e  <str> 'usd', 'eur', null
$ f <date> 2020-01-01, 2021-01-02, 2022-01-01
>>> res is None
True

Return the glimpse output as a string:

>>> res = df.glimpse(return_type="string")
>>> isinstance(res, str)
True

Return the glimpse output as a DataFrame:

>>> df.glimpse(return_type="frame")
shape: (6, 3)
┌────────┬───────┬─────────────────────────────────┐
│ column ┆ dtype ┆ values                          │
│ ---    ┆ ---   ┆ ---                             │
│ str    ┆ str   ┆ list[str]                       │
╞════════╪═══════╪═════════════════════════════════╡
│ a      ┆ f64   ┆ ["1.0", "2.8", "3.0"]           │
│ b      ┆ i64   ┆ ["4", "5", null]                │
│ c      ┆ bool  ┆ ["True", "False", "True"]       │
│ d      ┆ str   ┆ [null, "'b'", "'c'"]            │
│ e      ┆ str   ┆ ["'usd'", "'eur'", null]        │
│ f      ┆ date  ┆ ["2020-01-01", "2021-01-02", "… │
└────────┴───────┴─────────────────────────────────┘

Print glimpse-formatted output to stdout, returning the original frame:

>>> res = df.glimpse(return_type="self")
Rows: 3
Columns: 6
$ a  <f64> 1.0, 2.8, 3.0
$ b  <i64> 4, 5, null
$ c <bool> True, False, True
$ d  <str> null, 'b', 'c'
$ e  <str> 'usd', 'eur', null
$ f <date> 2020-01-01, 2021-01-02, 2022-01-01
>>> res
shape: (3, 6)
┌─────┬──────┬───────┬──────┬──────┬────────────┐
│ a   ┆ b    ┆ c     ┆ d    ┆ e    ┆ f          │
│ --- ┆ ---  ┆ ---   ┆ ---  ┆ ---  ┆ ---        │
│ f64 ┆ i64  ┆ bool  ┆ str  ┆ str  ┆ date       │
╞═════╪══════╪═══════╪══════╪══════╪════════════╡
│ 1.0 ┆ 4    ┆ true  ┆ null ┆ usd  ┆ 2020-01-01 │
│ 2.8 ┆ 5    ┆ false ┆ b    ┆ eur  ┆ 2021-01-02 │
│ 3.0 ┆ null ┆ true  ┆ c    ┆ null ┆ 2022-01-01 │
└─────┴──────┴───────┴──────┴──────┴────────────┘
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