polars.DataFrame.__getitem__#

DataFrame.__getitem__(
key: SingleIndexSelector | SingleColSelector | MultiColSelector | MultiIndexSelector | tuple[SingleIndexSelector, SingleColSelector] | tuple[SingleIndexSelector, MultiColSelector] | tuple[MultiIndexSelector, SingleColSelector] | tuple[MultiIndexSelector, MultiColSelector],
) → DataFrame | Series | Any[source]#

Get part of the DataFrame as a new DataFrame, Series, or scalar.

Parameters:
key

Rows / columns to select. This is easiest to explain via example. Suppose we have a DataFrame with columns 'a', 'd', 'c', 'd'. Here is what various types of key would do:

  • df[0, 'a'] extracts the first element of column 'a' and returns a scalar.

  • df[0] extracts the first row and returns a Dataframe.

  • df['a'] extracts column 'a' and returns a Series.

  • df[0:2] extracts the first two rows and returns a Dataframe.

  • df[0:2, 'a'] extracts the first two rows from column 'a' and returns a Series.

  • df[0:2, 0] extracts the first two rows from the first column and returns a Series.

  • df[[0, 1], [0, 1, 2]] extracts the first two rows and the first three columns and returns a Dataframe.

  • df[0: 2, ['a', 'c']] extracts the first two rows from columns 'a' and 'c' and returns a Dataframe.

  • df[:, 0: 2] extracts all rows from the first two columns and returns a Dataframe.

  • df[:, 'a': 'c'] extracts all rows and all columns positioned between 'a' and 'c' inclusive and returns a Dataframe. In our example, that would extract columns 'a', 'd', and 'c'.

Returns:
DataFrame, Series, or scalar, depending on key.

Examples

>>> df = pl.DataFrame(
...     {"a": [1, 2, 3], "d": [4, 5, 6], "c": [1, 3, 2], "b": [7, 8, 9]}
... )
>>> df[0]
shape: (1, 4)
┌─────┬─────┬─────┬─────┐
│ a   ┆ d   ┆ c   ┆ b   │
│ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╪═════╡
│ 1   ┆ 4   ┆ 1   ┆ 7   │
└─────┴─────┴─────┴─────┘
>>> df[0, "a"]
1
>>> df["a"]
shape: (3,)
Series: 'a' [i64]
[
    1
    2
    3
]
>>> df[0:2]
shape: (2, 4)
┌─────┬─────┬─────┬─────┐
│ a   ┆ d   ┆ c   ┆ b   │
│ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╪═════╡
│ 1   ┆ 4   ┆ 1   ┆ 7   │
│ 2   ┆ 5   ┆ 3   ┆ 8   │
└─────┴─────┴─────┴─────┘
>>> df[0:2, "a"]
shape: (2,)
Series: 'a' [i64]
[
    1
    2
]
>>> df[0:2, 0]
shape: (2,)
Series: 'a' [i64]
[
    1
    2
]
>>> df[[0, 1], [0, 1, 2]]
shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ d   ┆ c   │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1   ┆ 4   ┆ 1   │
│ 2   ┆ 5   ┆ 3   │
└─────┴─────┴─────┘
>>> df[0:2, ["a", "c"]]
shape: (2, 2)
┌─────┬─────┐
│ a   ┆ c   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 1   │
│ 2   ┆ 3   │
└─────┴─────┘
>>> df[:, 0:2]
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ d   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 4   │
│ 2   ┆ 5   │
│ 3   ┆ 6   │
└─────┴─────┘
>>> df[:, "a":"c"]
shape: (3, 3)
┌─────┬─────┬─────┐
│ a   ┆ d   ┆ c   │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1   ┆ 4   ┆ 1   │
│ 2   ┆ 5   ┆ 3   │
│ 3   ┆ 6   ┆ 2   │
└─────┴─────┴─────┘