DataFrame#

This page gives an overview of all public DataFrame methods.

class polars.DataFrame(
data: FrameInitTypes | None = None,
schema: SchemaDefinition | None = None,
*,
schema_overrides: SchemaDict | None = None,
strict: bool = True,
orient: Orientation | None = None,
infer_schema_length: int | None = 100,
nan_to_null: bool = False,
)[source]

Two-dimensional data structure representing data as a table with rows and columns.

Parameters:
datadict, Sequence, ndarray, Series, or pandas.DataFrame

Two-dimensional data in various forms; dict input must contain Sequences, Generators, or a range. Sequence may contain Series or other Sequences.

schemaSequence of str, (str,DataType) pairs, or a {str:DataType,} dict

The schema of the resulting DataFrame. The schema may be declared in several ways:

  • As a dict of {name:type} pairs; if type is None, it will be auto-inferred.

  • As a list of column names; in this case types are automatically inferred.

  • As a list of (name,type) pairs; this is equivalent to the dictionary form.

If you supply a list of column names that does not match the names in the underlying data, the names given here will overwrite them. The number of names given in the schema should match the underlying data dimensions.

If set to None (default), the schema is inferred from the data.

schema_overridesdict, default None

Support type specification or override of one or more columns; note that any dtypes inferred from the schema param will be overridden.

The number of entries in the schema should match the underlying data dimensions, unless a sequence of dictionaries is being passed, in which case a partial schema can be declared to prevent specific fields from being loaded.

strictbool, default True

Throw an error if any data value does not exactly match the given or inferred data type for that column. If set to False, values that do not match the data type are cast to that data type or, if casting is not possible, set to null instead.

orient{‘col’, ‘row’}, default None

Whether to interpret two-dimensional data as columns or as rows. If None, the orientation is inferred by matching the columns and data dimensions. If this does not yield conclusive results, column orientation is used.

infer_schema_lengthint or None

The maximum number of rows to scan for schema inference. If set to None, the full data may be scanned (this can be slow). This parameter only applies if the input data is a sequence or generator of rows; other input is read as-is.

nan_to_nullbool, default False

If the data comes from one or more numpy arrays, can optionally convert input data np.nan values to null instead. This is a no-op for all other input data.

Notes

Polars explicitly does not support subclassing of its core data types. See the following GitHub issue for possible workarounds: pola-rs/polars#2846

Examples

Constructing a DataFrame from a dictionary:

>>> data = {"a": [1, 2], "b": [3, 4]}
>>> df = pl.DataFrame(data)
>>> df
shape: (2, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 3   │
│ 2   ┆ 4   │
└─────┴─────┘

Notice that the dtypes are automatically inferred as polars Int64:

>>> df.dtypes
[Int64, Int64]

To specify a more detailed/specific frame schema you can supply the schema parameter with a dictionary of (name,dtype) pairs…

>>> data = {"col1": [0, 2], "col2": [3, 7]}
>>> df2 = pl.DataFrame(data, schema={"col1": pl.Float32, "col2": pl.Int64})
>>> df2
shape: (2, 2)
┌──────┬──────┐
│ col1 ┆ col2 │
│ ---  ┆ ---  │
│ f32  ┆ i64  │
╞══════╪══════╡
│ 0.0  ┆ 3    │
│ 2.0  ┆ 7    │
└──────┴──────┘

…a sequence of (name,dtype) pairs…

>>> data = {"col1": [1, 2], "col2": [3, 4]}
>>> df3 = pl.DataFrame(data, schema=[("col1", pl.Float32), ("col2", pl.Int64)])
>>> df3
shape: (2, 2)
┌──────┬──────┐
│ col1 ┆ col2 │
│ ---  ┆ ---  │
│ f32  ┆ i64  │
╞══════╪══════╡
│ 1.0  ┆ 3    │
│ 2.0  ┆ 4    │
└──────┴──────┘

…or a list of typed Series.

>>> data = [
...     pl.Series("col1", [1, 2], dtype=pl.Float32),
...     pl.Series("col2", [3, 4], dtype=pl.Int64),
... ]
>>> df4 = pl.DataFrame(data)
>>> df4
shape: (2, 2)
┌──────┬──────┐
│ col1 ┆ col2 │
│ ---  ┆ ---  │
│ f32  ┆ i64  │
╞══════╪══════╡
│ 1.0  ┆ 3    │
│ 2.0  ┆ 4    │
└──────┴──────┘

Constructing a DataFrame from a numpy ndarray, specifying column names:

>>> import numpy as np
>>> data = np.array([(1, 2), (3, 4)], dtype=np.int64)
>>> df5 = pl.DataFrame(data, schema=["a", "b"], orient="col")
>>> df5
shape: (2, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 3   │
│ 2   ┆ 4   │
└─────┴─────┘

Constructing a DataFrame from a list of lists, row orientation inferred:

>>> data = [[1, 2, 3], [4, 5, 6]]
>>> df6 = pl.DataFrame(data, schema=["a", "b", "c"])
>>> df6
shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1   ┆ 2   ┆ 3   │
│ 4   ┆ 5   ┆ 6   │
└─────┴─────┴─────┘

Methods:

apply

Apply a custom/user-defined function (UDF) over the rows of the DataFrame.

approx_n_unique

Approximate count of unique values.

bottom_k

Return the k smallest elements.

cast

Cast DataFrame column(s) to the specified dtype(s).

clear

Create an empty (n=0) or n-row null-filled (n>0) copy of the DataFrame.

clone

Create a copy of this DataFrame.

corr

Return pairwise Pearson product-moment correlation coefficients between columns.

count

Return the number of non-null elements for each column.

describe

Summary statistics for a DataFrame.

drop

Remove columns from the dataframe.

drop_in_place

Drop a single column in-place and return the dropped column.

drop_nulls

Drop all rows that contain null values.

equals

Check whether the DataFrame is equal to another DataFrame.

estimated_size

Return an estimation of the total (heap) allocated size of the DataFrame.

explode

Explode the dataframe to long format by exploding the given columns.

extend

Extend the memory backed by this DataFrame with the values from other.

fill_nan

Fill floating point NaN values by an Expression evaluation.

fill_null

Fill null values using the specified value or strategy.

filter

Filter the rows in the DataFrame based on one or more predicate expressions.

find_idx_by_name

Find the index of a column by name.

fold

Apply a horizontal reduction on a DataFrame.

frame_equal

Check whether the DataFrame is equal to another DataFrame.

gather_every

Take every nth row in the DataFrame and return as a new DataFrame.

get_column

Get a single column by name.

get_column_index

Find the index of a column by name.

get_columns

Get the DataFrame as a List of Series.

glimpse

Return a dense preview of the DataFrame.

group_by

Start a group by operation.

group_by_dynamic

Group based on a time value (or index value of type Int32, Int64).

group_by_rolling

Create rolling groups based on a time, Int32, or Int64 column.

groupby

Start a group by operation.

groupby_dynamic

Group based on a time value (or index value of type Int32, Int64).

groupby_rolling

Create rolling groups based on a time, Int32, or Int64 column.

hash_rows

Hash and combine the rows in this DataFrame.

head

Get the first n rows.

hstack

Return a new DataFrame grown horizontally by stacking multiple Series to it.

insert_at_idx

Insert a Series at a certain column index.

insert_column

Insert a Series at a certain column index.

interpolate

Interpolate intermediate values.

is_duplicated

Get a mask of all duplicated rows in this DataFrame.

is_empty

Check if the dataframe is empty.

is_unique

Get a mask of all unique rows in this DataFrame.

item

Return the DataFrame as a scalar, or return the element at the given row/column.

iter_columns

Returns an iterator over the columns of this DataFrame.

iter_rows

Returns an iterator over the DataFrame of rows of python-native values.

iter_slices

Returns a non-copying iterator of slices over the underlying DataFrame.

join

Join in SQL-like fashion.

join_asof

Perform an asof join.

lazy

Start a lazy query from this point.

limit

Get the first n rows.

map_rows

Apply a custom/user-defined function (UDF) over the rows of the DataFrame.

max

Aggregate the columns of this DataFrame to their maximum value.

max_horizontal

Get the maximum value horizontally across columns.

mean

Aggregate the columns of this DataFrame to their mean value.

mean_horizontal

Take the mean of all values horizontally across columns.

median

Aggregate the columns of this DataFrame to their median value.

melt

Unpivot a DataFrame from wide to long format.

merge_sorted

Take two sorted DataFrames and merge them by the sorted key.

min

Aggregate the columns of this DataFrame to their minimum value.

min_horizontal

Get the minimum value horizontally across columns.

n_chunks

Get number of chunks used by the ChunkedArrays of this DataFrame.

n_unique

Return the number of unique rows, or the number of unique row-subsets.

null_count

Create a new DataFrame that shows the null counts per column.

partition_by

Group by the given columns and return the groups as separate dataframes.

pipe

Offers a structured way to apply a sequence of user-defined functions (UDFs).

pivot

Create a spreadsheet-style pivot table as a DataFrame.

product

Aggregate the columns of this DataFrame to their product values.

quantile

Aggregate the columns of this DataFrame to their quantile value.

rechunk

Rechunk the data in this DataFrame to a contiguous allocation.

rename

Rename column names.

replace

Replace a column by a new Series.

replace_at_idx

Replace a column at an index location.

replace_column

Replace a column at an index location.

reverse

Reverse the DataFrame.

rolling

Create rolling groups based on a temporal or integer column.

row

Get the values of a single row, either by index or by predicate.

rows

Returns all data in the DataFrame as a list of rows of python-native values.

rows_by_key

Returns all data as a dictionary of python-native values keyed by some column.

sample

Sample from this DataFrame.

select

Select columns from this DataFrame.

select_seq

Select columns from this DataFrame.

set_sorted

Indicate that one or multiple columns are sorted.

shift

Shift values by the given number of indices.

shift_and_fill

Shift values by the given number of places and fill the resulting null values.

shrink_to_fit

Shrink DataFrame memory usage.

slice

Get a slice of this DataFrame.

sort

Sort the dataframe by the given columns.

std

Aggregate the columns of this DataFrame to their standard deviation value.

sum

Aggregate the columns of this DataFrame to their sum value.

sum_horizontal

Sum all values horizontally across columns.

tail

Get the last n rows.

take_every

Take every nth row in the DataFrame and return as a new DataFrame.

to_arrow

Collect the underlying arrow arrays in an Arrow Table.

to_dict

Convert DataFrame to a dictionary mapping column name to values.

to_dicts

Convert every row to a dictionary of Python-native values.

to_dummies

Convert categorical variables into dummy/indicator variables.

to_init_repr

Convert DataFrame to instantiatable string representation.

to_numpy

Convert this DataFrame to a NumPy ndarray.

to_pandas

Convert this DataFrame to a pandas DataFrame.

to_series

Select column as Series at index location.

to_struct

Convert a DataFrame to a Series of type Struct.

top_k

Return the k largest elements.

transpose

Transpose a DataFrame over the diagonal.

unique

Drop duplicate rows from this dataframe.

unnest

Decompose struct columns into separate columns for each of their fields.

unstack

Unstack a long table to a wide form without doing an aggregation.

update

Update the values in this DataFrame with the values in other.

upsample

Upsample a DataFrame at a regular frequency.

var

Aggregate the columns of this DataFrame to their variance value.

vstack

Grow this DataFrame vertically by stacking a DataFrame to it.

with_columns

Add columns to this DataFrame.

with_columns_seq

Add columns to this DataFrame.

with_row_count

Add a column at index 0 that counts the rows.

with_row_index

Add a row index as the first column in the DataFrame.

write_avro

Write to Apache Avro file.

write_csv

Write to comma-separated values (CSV) file.

write_database

Write a polars frame to a database.

write_delta

Write DataFrame as delta table.

write_excel

Write frame data to a table in an Excel workbook/worksheet.

write_ipc

Write to Arrow IPC binary stream or Feather file.

write_ipc_stream

Write to Arrow IPC record batch stream.

write_json

Serialize to JSON representation.

write_ndjson

Serialize to newline delimited JSON representation.

write_parquet

Write to Apache Parquet file.

Attributes:

columns

Get or set column names.

dtypes

Get the datatypes of the columns of this DataFrame.

flags

Get flags that are set on the columns of this DataFrame.

height

Get the height of the DataFrame.

plot

Create a plot namespace.

schema

Get a dict[column name, DataType].

shape

Get the shape of the DataFrame.

width

Get the width of the DataFrame.

apply(
function: Callable[[tuple[Any, ...]], Any],
return_dtype: PolarsDataType | None = None,
*,
inference_size: int = 256,
) DataFrame[source]

Apply a custom/user-defined function (UDF) over the rows of the DataFrame.

Deprecated since version 0.19.0: This method has been renamed to DataFrame.map_rows().

Parameters:
function

Custom function or lambda.

return_dtype

Output type of the operation. If none given, Polars tries to infer the type.

inference_size

Only used in the case when the custom function returns rows. This uses the first n rows to determine the output schema

approx_n_unique() DataFrame[source]

Approximate count of unique values.

Deprecated since version 0.20.11: Use select(pl.all().approx_n_unique()) instead.

This is done using the HyperLogLog++ algorithm for cardinality estimation.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3, 4],
...         "b": [1, 2, 1, 1],
...     }
... )
>>> df.approx_n_unique()  
shape: (1, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═════╪═════╡
│ 4   ┆ 2   │
└─────┴─────┘
bottom_k(
k: int,
*,
by: IntoExpr | Iterable[IntoExpr],
descending: bool | Sequence[bool] = False,
nulls_last: bool = False,
maintain_order: bool = False,
) DataFrame[source]

Return the k smallest elements.

If descending=True the largest elements will be given.

Parameters:
k

Number of rows to return.

by

Column(s) included in sort order. Accepts expression input. Strings are parsed as column names.

descending

Return the k largest. Bottom-k by multiple columns can be specified per column by passing a sequence of booleans.

nulls_last

Place null values last.

maintain_order

Whether the order should be maintained if elements are equal. Note that if true streaming is not possible and performance might be worse since this requires a stable search.

See also

top_k

Examples

>>> df = pl.DataFrame(
...     {
...         "a": ["a", "b", "a", "b", "b", "c"],
...         "b": [2, 1, 1, 3, 2, 1],
...     }
... )

Get the rows which contain the 4 smallest values in column b.

>>> df.bottom_k(4, by="b")
shape: (4, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════╪═════╡
│ b   ┆ 1   │
│ a   ┆ 1   │
│ c   ┆ 1   │
│ a   ┆ 2   │
└─────┴─────┘

Get the rows which contain the 4 smallest values when sorting on column a and b.

>>> df.bottom_k(4, by=["a", "b"])
shape: (4, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════╪═════╡
│ a   ┆ 1   │
│ a   ┆ 2   │
│ b   ┆ 1   │
│ b   ┆ 2   │
└─────┴─────┘
cast(
dtypes: Mapping[ColumnNameOrSelector | PolarsDataType, PolarsDataType] | PolarsDataType,
*,
strict: bool = True,
) DataFrame[source]

Cast DataFrame column(s) to the specified dtype(s).

Parameters:
dtypes

Mapping of column names (or selector) to dtypes, or a single dtype to which all columns will be cast.

strict

Throw an error if a cast could not be done (for instance, due to an overflow).

Examples

>>> from datetime import date
>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6.0, 7.0, 8.0],
...         "ham": [date(2020, 1, 2), date(2021, 3, 4), date(2022, 5, 6)],
...     }
... )

Cast specific frame columns to the specified dtypes:

>>> df.cast({"foo": pl.Float32, "bar": pl.UInt8})
shape: (3, 3)
┌─────┬─────┬────────────┐
│ foo ┆ bar ┆ ham        │
│ --- ┆ --- ┆ ---        │
│ f32 ┆ u8  ┆ date       │
╞═════╪═════╪════════════╡
│ 1.0 ┆ 6   ┆ 2020-01-02 │
│ 2.0 ┆ 7   ┆ 2021-03-04 │
│ 3.0 ┆ 8   ┆ 2022-05-06 │
└─────┴─────┴────────────┘

Cast all frame columns matching one dtype (or dtype group) to another dtype:

>>> df.cast({pl.Date: pl.Datetime})
shape: (3, 3)
┌─────┬─────┬─────────────────────┐
│ foo ┆ bar ┆ ham                 │
│ --- ┆ --- ┆ ---                 │
│ i64 ┆ f64 ┆ datetime[μs]        │
╞═════╪═════╪═════════════════════╡
│ 1   ┆ 6.0 ┆ 2020-01-02 00:00:00 │
│ 2   ┆ 7.0 ┆ 2021-03-04 00:00:00 │
│ 3   ┆ 8.0 ┆ 2022-05-06 00:00:00 │
└─────┴─────┴─────────────────────┘

Use selectors to define the columns being cast:

>>> import polars.selectors as cs
>>> df.cast({cs.numeric(): pl.UInt32, cs.temporal(): pl.String})
shape: (3, 3)
┌─────┬─────┬────────────┐
│ foo ┆ bar ┆ ham        │
│ --- ┆ --- ┆ ---        │
│ u32 ┆ u32 ┆ str        │
╞═════╪═════╪════════════╡
│ 1   ┆ 6   ┆ 2020-01-02 │
│ 2   ┆ 7   ┆ 2021-03-04 │
│ 3   ┆ 8   ┆ 2022-05-06 │
└─────┴─────┴────────────┘

Cast all frame columns to the specified dtype:

>>> df.cast(pl.String).to_dict(as_series=False)
{'foo': ['1', '2', '3'],
 'bar': ['6.0', '7.0', '8.0'],
 'ham': ['2020-01-02', '2021-03-04', '2022-05-06']}
clear(n: int = 0) Self[source]

Create an empty (n=0) or n-row null-filled (n>0) copy of the DataFrame.

Returns a n-row null-filled DataFrame with an identical schema. n can be greater than the current number of rows in the DataFrame.

Parameters:
n

Number of (null-filled) rows to return in the cleared frame.

See also

clone

Cheap deepcopy/clone.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [None, 2, 3, 4],
...         "b": [0.5, None, 2.5, 13],
...         "c": [True, True, False, None],
...     }
... )
>>> df.clear()
shape: (0, 3)
┌─────┬─────┬──────┐
│ a   ┆ b   ┆ c    │
│ --- ┆ --- ┆ ---  │
│ i64 ┆ f64 ┆ bool │
╞═════╪═════╪══════╡
└─────┴─────┴──────┘
>>> df.clear(n=2)
shape: (2, 3)
┌──────┬──────┬──────┐
│ a    ┆ b    ┆ c    │
│ ---  ┆ ---  ┆ ---  │
│ i64  ┆ f64  ┆ bool │
╞══════╪══════╪══════╡
│ null ┆ null ┆ null │
│ null ┆ null ┆ null │
└──────┴──────┴──────┘
clone() Self[source]

Create a copy of this DataFrame.

This is a cheap operation that does not copy data.

See also

clear

Create an empty copy of the current DataFrame, with identical schema but no data.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3, 4],
...         "b": [0.5, 4, 10, 13],
...         "c": [True, True, False, True],
...     }
... )
>>> df.clone()
shape: (4, 3)
┌─────┬──────┬───────┐
│ a   ┆ b    ┆ c     │
│ --- ┆ ---  ┆ ---   │
│ i64 ┆ f64  ┆ bool  │
╞═════╪══════╪═══════╡
│ 1   ┆ 0.5  ┆ true  │
│ 2   ┆ 4.0  ┆ true  │
│ 3   ┆ 10.0 ┆ false │
│ 4   ┆ 13.0 ┆ true  │
└─────┴──────┴───────┘
property columns: list[str][source]

Get or set column names.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.columns
['foo', 'bar', 'ham']

Set column names:

>>> df.columns = ["apple", "banana", "orange"]
>>> df
shape: (3, 3)
┌───────┬────────┬────────┐
│ apple ┆ banana ┆ orange │
│ ---   ┆ ---    ┆ ---    │
│ i64   ┆ i64    ┆ str    │
╞═══════╪════════╪════════╡
│ 1     ┆ 6      ┆ a      │
│ 2     ┆ 7      ┆ b      │
│ 3     ┆ 8      ┆ c      │
└───────┴────────┴────────┘
corr(**kwargs: Any) DataFrame[source]

Return pairwise Pearson product-moment correlation coefficients between columns.

See numpy corrcoef for more information: https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html

Parameters:
**kwargs

Keyword arguments are passed to numpy corrcoef.

Notes

This functionality requires numpy to be installed.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3], "bar": [3, 2, 1], "ham": [7, 8, 9]})
>>> df.corr()
shape: (3, 3)
┌──────┬──────┬──────┐
│ foo  ┆ bar  ┆ ham  │
│ ---  ┆ ---  ┆ ---  │
│ f64  ┆ f64  ┆ f64  │
╞══════╪══════╪══════╡
│ 1.0  ┆ -1.0 ┆ 1.0  │
│ -1.0 ┆ 1.0  ┆ -1.0 │
│ 1.0  ┆ -1.0 ┆ 1.0  │
└──────┴──────┴──────┘
count() DataFrame[source]

Return the number of non-null elements for each column.

Examples

>>> df = pl.DataFrame(
...     {"a": [1, 2, 3, 4], "b": [1, 2, 1, None], "c": [None, None, None, None]}
... )
>>> df.count()
shape: (1, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ u32 ┆ u32 ┆ u32 │
╞═════╪═════╪═════╡
│ 4   ┆ 3   ┆ 0   │
└─────┴─────┴─────┘
describe(
percentiles: Sequence[float] | float | None = (0.25, 0.5, 0.75),
*,
interpolation: RollingInterpolationMethod = 'nearest',
) DataFrame[source]

Summary statistics for a DataFrame.

Parameters:
percentiles

One or more percentiles to include in the summary statistics. All values must be in the range [0, 1].

interpolation{‘nearest’, ‘higher’, ‘lower’, ‘midpoint’, ‘linear’}

Interpolation method used when calculating percentiles.

Warning

We do not guarantee the output of describe to be stable. It will show statistics that we deem informative, and may be updated in the future. Using describe programmatically (versus interactive exploration) is not recommended for this reason.

See also

glimpse

Notes

The median is included by default as the 50% percentile.

Examples

>>> from datetime import date, time
>>> df = pl.DataFrame(
...     {
...         "float": [1.0, 2.8, 3.0],
...         "int": [40, 50, None],
...         "bool": [True, False, True],
...         "str": ["zz", "xx", "yy"],
...         "date": [date(2020, 1, 1), date(2021, 7, 5), date(2022, 12, 31)],
...         "time": [time(10, 20, 30), time(14, 45, 50), time(23, 15, 10)],
...     }
... )

Show default frame statistics:

>>> df.describe()
shape: (9, 7)
┌────────────┬──────────┬──────────┬──────────┬──────┬────────────┬──────────┐
│ statistic  ┆ float    ┆ int      ┆ bool     ┆ str  ┆ date       ┆ time     │
│ ---        ┆ ---      ┆ ---      ┆ ---      ┆ ---  ┆ ---        ┆ ---      │
│ str        ┆ f64      ┆ f64      ┆ f64      ┆ str  ┆ str        ┆ str      │
╞════════════╪══════════╪══════════╪══════════╪══════╪════════════╪══════════╡
│ count      ┆ 3.0      ┆ 2.0      ┆ 3.0      ┆ 3    ┆ 3          ┆ 3        │
│ null_count ┆ 0.0      ┆ 1.0      ┆ 0.0      ┆ 0    ┆ 0          ┆ 0        │
│ mean       ┆ 2.266667 ┆ 45.0     ┆ 0.666667 ┆ null ┆ 2021-07-02 ┆ 16:07:10 │
│ std        ┆ 1.101514 ┆ 7.071068 ┆ null     ┆ null ┆ null       ┆ null     │
│ min        ┆ 1.0      ┆ 40.0     ┆ 0.0      ┆ xx   ┆ 2020-01-01 ┆ 10:20:30 │
│ 25%        ┆ 2.8      ┆ 40.0     ┆ null     ┆ null ┆ 2021-07-05 ┆ 14:45:50 │
│ 50%        ┆ 2.8      ┆ 50.0     ┆ null     ┆ null ┆ 2021-07-05 ┆ 14:45:50 │
│ 75%        ┆ 3.0      ┆ 50.0     ┆ null     ┆ null ┆ 2022-12-31 ┆ 23:15:10 │
│ max        ┆ 3.0      ┆ 50.0     ┆ 1.0      ┆ zz   ┆ 2022-12-31 ┆ 23:15:10 │
└────────────┴──────────┴──────────┴──────────┴──────┴────────────┴──────────┘

Customize which percentiles are displayed, applying linear interpolation:

>>> with pl.Config(tbl_rows=12):
...     df.describe(
...         percentiles=[0.1, 0.3, 0.5, 0.7, 0.9],
...         interpolation="linear",
...     )
shape: (11, 7)
┌────────────┬──────────┬──────────┬──────────┬──────┬────────────┬──────────┐
│ statistic  ┆ float    ┆ int      ┆ bool     ┆ str  ┆ date       ┆ time     │
│ ---        ┆ ---      ┆ ---      ┆ ---      ┆ ---  ┆ ---        ┆ ---      │
│ str        ┆ f64      ┆ f64      ┆ f64      ┆ str  ┆ str        ┆ str      │
╞════════════╪══════════╪══════════╪══════════╪══════╪════════════╪══════════╡
│ count      ┆ 3.0      ┆ 2.0      ┆ 3.0      ┆ 3    ┆ 3          ┆ 3        │
│ null_count ┆ 0.0      ┆ 1.0      ┆ 0.0      ┆ 0    ┆ 0          ┆ 0        │
│ mean       ┆ 2.266667 ┆ 45.0     ┆ 0.666667 ┆ null ┆ 2021-07-02 ┆ 16:07:10 │
│ std        ┆ 1.101514 ┆ 7.071068 ┆ null     ┆ null ┆ null       ┆ null     │
│ min        ┆ 1.0      ┆ 40.0     ┆ 0.0      ┆ xx   ┆ 2020-01-01 ┆ 10:20:30 │
│ 10%        ┆ 1.36     ┆ 41.0     ┆ null     ┆ null ┆ 2020-04-20 ┆ 11:13:34 │
│ 30%        ┆ 2.08     ┆ 43.0     ┆ null     ┆ null ┆ 2020-11-26 ┆ 12:59:42 │
│ 50%        ┆ 2.8      ┆ 45.0     ┆ null     ┆ null ┆ 2021-07-05 ┆ 14:45:50 │
│ 70%        ┆ 2.88     ┆ 47.0     ┆ null     ┆ null ┆ 2022-02-07 ┆ 18:09:34 │
│ 90%        ┆ 2.96     ┆ 49.0     ┆ null     ┆ null ┆ 2022-09-13 ┆ 21:33:18 │
│ max        ┆ 3.0      ┆ 50.0     ┆ 1.0      ┆ zz   ┆ 2022-12-31 ┆ 23:15:10 │
└────────────┴──────────┴──────────┴──────────┴──────┴────────────┴──────────┘
drop(
*columns: ColumnNameOrSelector | Iterable[ColumnNameOrSelector],
) DataFrame[source]

Remove columns from the dataframe.

Parameters:
*columns

Names of the columns that should be removed from the dataframe. Accepts column selector input.

Examples

Drop a single column by passing the name of that column.

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6.0, 7.0, 8.0],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.drop("ham")
shape: (3, 2)
┌─────┬─────┐
│ foo ┆ bar │
│ --- ┆ --- │
│ i64 ┆ f64 │
╞═════╪═════╡
│ 1   ┆ 6.0 │
│ 2   ┆ 7.0 │
│ 3   ┆ 8.0 │
└─────┴─────┘

Drop multiple columns by passing a list of column names.

>>> df.drop(["bar", "ham"])
shape: (3, 1)
┌─────┐
│ foo │
│ --- │
│ i64 │
╞═════╡
│ 1   │
│ 2   │
│ 3   │
└─────┘

Drop multiple columns by passing a selector.

>>> import polars.selectors as cs
>>> df.drop(cs.numeric())
shape: (3, 1)
┌─────┐
│ ham │
│ --- │
│ str │
╞═════╡
│ a   │
│ b   │
│ c   │
└─────┘

Use positional arguments to drop multiple columns.

>>> df.drop("foo", "ham")
shape: (3, 1)
┌─────┐
│ bar │
│ --- │
│ f64 │
╞═════╡
│ 6.0 │
│ 7.0 │
│ 8.0 │
└─────┘
drop_in_place(name: str) Series[source]

Drop a single column in-place and return the dropped column.

Parameters:
name

Name of the column to drop.

Returns:
Series

The dropped column.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.drop_in_place("ham")
shape: (3,)
Series: 'ham' [str]
[
    "a"
    "b"
    "c"
]
drop_nulls(
subset: ColumnNameOrSelector | Collection[ColumnNameOrSelector] | None = None,
) DataFrame[source]

Drop all rows that contain null values.

The original order of the remaining rows is preserved.

Parameters:
subset

Column name(s) for which null values are considered. If set to None (default), use all columns.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, None, 8],
...         "ham": ["a", "b", None],
...     }
... )

The default behavior of this method is to drop rows where any single value of the row is null.

>>> df.drop_nulls()
shape: (1, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 6   ┆ a   │
└─────┴─────┴─────┘

This behaviour can be constrained to consider only a subset of columns, as defined by name or with a selector. For example, dropping rows if there is a null in any of the integer columns:

>>> import polars.selectors as cs
>>> df.drop_nulls(subset=cs.integer())
shape: (2, 3)
┌─────┬─────┬──────┐
│ foo ┆ bar ┆ ham  │
│ --- ┆ --- ┆ ---  │
│ i64 ┆ i64 ┆ str  │
╞═════╪═════╪══════╡
│ 1   ┆ 6   ┆ a    │
│ 3   ┆ 8   ┆ null │
└─────┴─────┴──────┘

Below are some additional examples that show how to drop null values based on other conditions.

>>> df = pl.DataFrame(
...     {
...         "a": [None, None, None, None],
...         "b": [1, 2, None, 1],
...         "c": [1, None, None, 1],
...     }
... )
>>> df
shape: (4, 3)
┌──────┬──────┬──────┐
│ a    ┆ b    ┆ c    │
│ ---  ┆ ---  ┆ ---  │
│ null ┆ i64  ┆ i64  │
╞══════╪══════╪══════╡
│ null ┆ 1    ┆ 1    │
│ null ┆ 2    ┆ null │
│ null ┆ null ┆ null │
│ null ┆ 1    ┆ 1    │
└──────┴──────┴──────┘

Drop a row only if all values are null:

>>> df.filter(~pl.all_horizontal(pl.all().is_null()))
shape: (3, 3)
┌──────┬─────┬──────┐
│ a    ┆ b   ┆ c    │
│ ---  ┆ --- ┆ ---  │
│ null ┆ i64 ┆ i64  │
╞══════╪═════╪══════╡
│ null ┆ 1   ┆ 1    │
│ null ┆ 2   ┆ null │
│ null ┆ 1   ┆ 1    │
└──────┴─────┴──────┘

Drop a column if all values are null:

>>> df[[s.name for s in df if not (s.null_count() == df.height)]]
shape: (4, 2)
┌──────┬──────┐
│ b    ┆ c    │
│ ---  ┆ ---  │
│ i64  ┆ i64  │
╞══════╪══════╡
│ 1    ┆ 1    │
│ 2    ┆ null │
│ null ┆ null │
│ 1    ┆ 1    │
└──────┴──────┘
property dtypes: list[DataType][source]

Get the datatypes of the columns of this DataFrame.

The datatypes can also be found in column headers when printing the DataFrame.

See also

schema

Returns a {colname:dtype} mapping.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6.0, 7.0, 8.0],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.dtypes
[Int64, Float64, String]
>>> df
shape: (3, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 6.0 ┆ a   │
│ 2   ┆ 7.0 ┆ b   │
│ 3   ┆ 8.0 ┆ c   │
└─────┴─────┴─────┘
equals(
other: DataFrame,
*,
null_equal: bool = True,
) bool[source]

Check whether the DataFrame is equal to another DataFrame.

Parameters:
other

DataFrame to compare with.

null_equal

Consider null values as equal.

See also

assert_frame_equal

Examples

>>> df1 = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6.0, 7.0, 8.0],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df2 = pl.DataFrame(
...     {
...         "foo": [3, 2, 1],
...         "bar": [8.0, 7.0, 6.0],
...         "ham": ["c", "b", "a"],
...     }
... )
>>> df1.equals(df1)
True
>>> df1.equals(df2)
False
estimated_size(unit: SizeUnit = 'b') int | float[source]

Return an estimation of the total (heap) allocated size of the DataFrame.

Estimated size is given in the specified unit (bytes by default).

This estimation is the sum of the size of its buffers, validity, including nested arrays. Multiple arrays may share buffers and bitmaps. Therefore, the size of 2 arrays is not the sum of the sizes computed from this function. In particular, [StructArray]’s size is an upper bound.

When an array is sliced, its allocated size remains constant because the buffer unchanged. However, this function will yield a smaller number. This is because this function returns the visible size of the buffer, not its total capacity.

FFI buffers are included in this estimation.

Parameters:
unit{‘b’, ‘kb’, ‘mb’, ‘gb’, ‘tb’}

Scale the returned size to the given unit.

Examples

>>> df = pl.DataFrame(
...     {
...         "x": list(reversed(range(1_000_000))),
...         "y": [v / 1000 for v in range(1_000_000)],
...         "z": [str(v) for v in range(1_000_000)],
...     },
...     schema=[("x", pl.UInt32), ("y", pl.Float64), ("z", pl.String)],
... )
>>> df.estimated_size()
17888890
>>> df.estimated_size("mb")
17.0601749420166
explode(
columns: str | Expr | Sequence[str | Expr],
*more_columns: str | Expr,
) DataFrame[source]

Explode the dataframe to long format by exploding the given columns.

Parameters:
columns

Column names, expressions, or a selector defining them. The underlying columns being exploded must be of the List or Array data type.

*more_columns

Additional names of columns to explode, specified as positional arguments.

Returns:
DataFrame

Examples

>>> df = pl.DataFrame(
...     {
...         "letters": ["a", "a", "b", "c"],
...         "numbers": [[1], [2, 3], [4, 5], [6, 7, 8]],
...     }
... )
>>> df
shape: (4, 2)
┌─────────┬───────────┐
│ letters ┆ numbers   │
│ ---     ┆ ---       │
│ str     ┆ list[i64] │
╞═════════╪═══════════╡
│ a       ┆ [1]       │
│ a       ┆ [2, 3]    │
│ b       ┆ [4, 5]    │
│ c       ┆ [6, 7, 8] │
└─────────┴───────────┘
>>> df.explode("numbers")
shape: (8, 2)
┌─────────┬─────────┐
│ letters ┆ numbers │
│ ---     ┆ ---     │
│ str     ┆ i64     │
╞═════════╪═════════╡
│ a       ┆ 1       │
│ a       ┆ 2       │
│ a       ┆ 3       │
│ b       ┆ 4       │
│ b       ┆ 5       │
│ c       ┆ 6       │
│ c       ┆ 7       │
│ c       ┆ 8       │
└─────────┴─────────┘
extend(other: DataFrame) Self[source]

Extend the memory backed by this DataFrame with the values from other.

Different from vstack which adds the chunks from other to the chunks of this DataFrame, extend appends the data from other to the underlying memory locations and thus may cause a reallocation.

If this does not cause a reallocation, the resulting data structure will not have any extra chunks and thus will yield faster queries.

Prefer extend over vstack when you want to do a query after a single append. For instance, during online operations where you add n rows and rerun a query.

Prefer vstack over extend when you want to append many times before doing a query. For instance, when you read in multiple files and want to store them in a single DataFrame. In the latter case, finish the sequence of vstack operations with a rechunk.

Parameters:
other

DataFrame to vertically add.

Warning

This method modifies the dataframe in-place. The dataframe is returned for convenience only.

See also

vstack

Examples

>>> df1 = pl.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
>>> df2 = pl.DataFrame({"foo": [10, 20, 30], "bar": [40, 50, 60]})
>>> df1.extend(df2)
shape: (6, 2)
┌─────┬─────┐
│ foo ┆ bar │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 4   │
│ 2   ┆ 5   │
│ 3   ┆ 6   │
│ 10  ┆ 40  │
│ 20  ┆ 50  │
│ 30  ┆ 60  │
└─────┴─────┘
fill_nan(value: Expr | int | float | None) DataFrame[source]

Fill floating point NaN values by an Expression evaluation.

Parameters:
value

Value with which to replace NaN values.

Returns:
DataFrame

DataFrame with NaN values replaced by the given value.

Warning

Note that floating point NaNs (Not a Number) are not missing values! To replace missing values, use fill_null().

See also

fill_null

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1.5, 2, float("nan"), 4],
...         "b": [0.5, 4, float("nan"), 13],
...     }
... )
>>> df.fill_nan(99)
shape: (4, 2)
┌──────┬──────┐
│ a    ┆ b    │
│ ---  ┆ ---  │
│ f64  ┆ f64  │
╞══════╪══════╡
│ 1.5  ┆ 0.5  │
│ 2.0  ┆ 4.0  │
│ 99.0 ┆ 99.0 │
│ 4.0  ┆ 13.0 │
└──────┴──────┘
fill_null(
value: Any | None = None,
strategy: FillNullStrategy | None = None,
limit: int | None = None,
*,
matches_supertype: bool = True,
) DataFrame[source]

Fill null values using the specified value or strategy.

Parameters:
value

Value used to fill null values.

strategy{None, ‘forward’, ‘backward’, ‘min’, ‘max’, ‘mean’, ‘zero’, ‘one’}

Strategy used to fill null values.

limit

Number of consecutive null values to fill when using the ‘forward’ or ‘backward’ strategy.

matches_supertype

Fill all matching supertype of the fill value.

Returns:
DataFrame

DataFrame with None values replaced by the filling strategy.

See also

fill_nan

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, None, 4],
...         "b": [0.5, 4, None, 13],
...     }
... )
>>> df.fill_null(99)
shape: (4, 2)
┌─────┬──────┐
│ a   ┆ b    │
│ --- ┆ ---  │
│ i64 ┆ f64  │
╞═════╪══════╡
│ 1   ┆ 0.5  │
│ 2   ┆ 4.0  │
│ 99  ┆ 99.0 │
│ 4   ┆ 13.0 │
└─────┴──────┘
>>> df.fill_null(strategy="forward")
shape: (4, 2)
┌─────┬──────┐
│ a   ┆ b    │
│ --- ┆ ---  │
│ i64 ┆ f64  │
╞═════╪══════╡
│ 1   ┆ 0.5  │
│ 2   ┆ 4.0  │
│ 2   ┆ 4.0  │
│ 4   ┆ 13.0 │
└─────┴──────┘
>>> df.fill_null(strategy="max")
shape: (4, 2)
┌─────┬──────┐
│ a   ┆ b    │
│ --- ┆ ---  │
│ i64 ┆ f64  │
╞═════╪══════╡
│ 1   ┆ 0.5  │
│ 2   ┆ 4.0  │
│ 4   ┆ 13.0 │
│ 4   ┆ 13.0 │
└─────┴──────┘
>>> df.fill_null(strategy="zero")
shape: (4, 2)
┌─────┬──────┐
│ a   ┆ b    │
│ --- ┆ ---  │
│ i64 ┆ f64  │
╞═════╪══════╡
│ 1   ┆ 0.5  │
│ 2   ┆ 4.0  │
│ 0   ┆ 0.0  │
│ 4   ┆ 13.0 │
└─────┴──────┘
filter(
*predicates: IntoExprColumn | Iterable[IntoExprColumn] | bool | list[bool] | np.ndarray[Any, Any],
**constraints: Any,
) DataFrame[source]

Filter the rows in the DataFrame based on one or more predicate expressions.

The original order of the remaining rows is preserved.

Parameters:
predicates

Expression(s) that evaluates to a boolean Series.

constraints

Column filters; use name = value to filter columns by the supplied value. Each constraint will behave the same as pl.col(name).eq(value), and will be implicitly joined with the other filter conditions using &.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )

Filter on one condition:

>>> df.filter(pl.col("foo") > 1)
shape: (2, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 2   ┆ 7   ┆ b   │
│ 3   ┆ 8   ┆ c   │
└─────┴─────┴─────┘

Filter on multiple conditions, combined with and/or operators:

>>> df.filter((pl.col("foo") < 3) & (pl.col("ham") == "a"))
shape: (1, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 6   ┆ a   │
└─────┴─────┴─────┘
>>> df.filter((pl.col("foo") == 1) | (pl.col("ham") == "c"))
shape: (2, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 6   ┆ a   │
│ 3   ┆ 8   ┆ c   │
└─────┴─────┴─────┘

Provide multiple filters using *args syntax:

>>> df.filter(
...     pl.col("foo") <= 2,
...     ~pl.col("ham").is_in(["b", "c"]),
... )
shape: (1, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 6   ┆ a   │
└─────┴─────┴─────┘

Provide multiple filters using **kwargs syntax:

>>> df.filter(foo=2, ham="b")
shape: (1, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 2   ┆ 7   ┆ b   │
└─────┴─────┴─────┘
find_idx_by_name(name: str) int[source]

Find the index of a column by name.

Deprecated since version 0.19.14: This method has been renamed to get_column_index().

Parameters:
name

Name of the column to find.

property flags: dict[str, dict[str, bool]][source]

Get flags that are set on the columns of this DataFrame.

Returns:
dict

Mapping from column names to column flags.

fold(operation: Callable[[Series, Series], Series]) Series[source]

Apply a horizontal reduction on a DataFrame.

This can be used to effectively determine aggregations on a row level, and can be applied to any DataType that can be supercasted (casted to a similar parent type).

An example of the supercast rules when applying an arithmetic operation on two DataTypes are for instance:

  • Int8 + String = String

  • Float32 + Int64 = Float32

  • Float32 + Float64 = Float64

Parameters:
operation

function that takes two Series and returns a Series.

Examples

A horizontal sum operation:

>>> df = pl.DataFrame(
...     {
...         "a": [2, 1, 3],
...         "b": [1, 2, 3],
...         "c": [1.0, 2.0, 3.0],
...     }
... )
>>> df.fold(lambda s1, s2: s1 + s2)
shape: (3,)
Series: 'a' [f64]
[
    4.0
    5.0
    9.0
]

A horizontal minimum operation:

>>> df = pl.DataFrame({"a": [2, 1, 3], "b": [1, 2, 3], "c": [1.0, 2.0, 3.0]})
>>> df.fold(lambda s1, s2: s1.zip_with(s1 < s2, s2))
shape: (3,)
Series: 'a' [f64]
[
    1.0
    1.0
    3.0
]

A horizontal string concatenation:

>>> df = pl.DataFrame(
...     {
...         "a": ["foo", "bar", 2],
...         "b": [1, 2, 3],
...         "c": [1.0, 2.0, 3.0],
...     }
... )
>>> df.fold(lambda s1, s2: s1 + s2)
shape: (3,)
Series: 'a' [str]
[
    "foo11.0"
    "bar22.0"
    null
]

A horizontal boolean or, similar to a row-wise .any():

>>> df = pl.DataFrame(
...     {
...         "a": [False, False, True],
...         "b": [False, True, False],
...     }
... )
>>> df.fold(lambda s1, s2: s1 | s2)
shape: (3,)
Series: 'a' [bool]
[
        false
        true
        true
]
frame_equal(
other: DataFrame,
*,
null_equal: bool = True,
) bool[source]

Check whether the DataFrame is equal to another DataFrame.

Deprecated since version 0.19.16: This method has been renamed to equals().

Parameters:
other

DataFrame to compare with.

null_equal

Consider null values as equal.

gather_every(n: int, offset: int = 0) DataFrame[source]

Take every nth row in the DataFrame and return as a new DataFrame.

Parameters:
n

Gather every n-th row.

offset

Starting index.

Examples

>>> s = pl.DataFrame({"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]})
>>> s.gather_every(2)
shape: (2, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 5   │
│ 3   ┆ 7   │
└─────┴─────┘
>>> s.gather_every(2, offset=1)
shape: (2, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 2   ┆ 6   │
│ 4   ┆ 8   │
└─────┴─────┘
get_column(name: str) Series[source]

Get a single column by name.

Parameters:
namestr

Name of the column to retrieve.

Returns:
Series

See also

to_series

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
>>> df.get_column("foo")
shape: (3,)
Series: 'foo' [i64]
[
        1
        2
        3
]
get_column_index(name: str) int[source]

Find the index of a column by name.

Parameters:
name

Name of the column to find.

Examples

>>> df = pl.DataFrame(
...     {"foo": [1, 2, 3], "bar": [6, 7, 8], "ham": ["a", "b", "c"]}
... )
>>> df.get_column_index("ham")
2
get_columns() list[Series][source]

Get the DataFrame as a List of Series.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
>>> df.get_columns()
[shape: (3,)
Series: 'foo' [i64]
[
        1
        2
        3
], shape: (3,)
Series: 'bar' [i64]
[
        4
        5
        6
]]
>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3, 4],
...         "b": [0.5, 4, 10, 13],
...         "c": [True, True, False, True],
...     }
... )
>>> df.get_columns()
[shape: (4,)
Series: 'a' [i64]
[
    1
    2
    3
    4
], shape: (4,)
Series: 'b' [f64]
[
    0.5
    4.0
    10.0
    13.0
], shape: (4,)
Series: 'c' [bool]
[
    true
    true
    false
    true
]]
glimpse(
*,
max_items_per_column: int = 10,
max_colname_length: int = 50,
return_as_string: bool = False,
) str | 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.

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
group_by(
*by: IntoExpr | Iterable[IntoExpr],
maintain_order: bool = False,
**named_by: IntoExpr,
) GroupBy[source]

Start a group by operation.

Parameters:
*by

Column(s) to group by. Accepts expression input. Strings are parsed as column names.

maintain_order

Ensure that the order of the groups is consistent with the input data. This is slower than a default group by. Settings this to True blocks the possibility to run on the streaming engine.

Note

Within each group, the order of rows is always preserved, regardless of this argument.

**named_by

Additional columns to group by, specified as keyword arguments. The columns will be renamed to the keyword used.

Returns:
GroupBy

Object which can be used to perform aggregations.

Examples

Group by one column and call agg to compute the grouped sum of another column.

>>> df = pl.DataFrame(
...     {
...         "a": ["a", "b", "a", "b", "c"],
...         "b": [1, 2, 1, 3, 3],
...         "c": [5, 4, 3, 2, 1],
...     }
... )
>>> df.group_by("a").agg(pl.col("b").sum())  
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════╪═════╡
│ a   ┆ 2   │
│ b   ┆ 5   │
│ c   ┆ 3   │
└─────┴─────┘

Set maintain_order=True to ensure the order of the groups is consistent with the input.

>>> df.group_by("a", maintain_order=True).agg(pl.col("c"))
shape: (3, 2)
┌─────┬───────────┐
│ a   ┆ c         │
│ --- ┆ ---       │
│ str ┆ list[i64] │
╞═════╪═══════════╡
│ a   ┆ [5, 3]    │
│ b   ┆ [4, 2]    │
│ c   ┆ [1]       │
└─────┴───────────┘

Group by multiple columns by passing a list of column names.

>>> df.group_by(["a", "b"]).agg(pl.max("c"))  
shape: (4, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ a   ┆ 1   ┆ 5   │
│ b   ┆ 2   ┆ 4   │
│ b   ┆ 3   ┆ 2   │
│ c   ┆ 3   ┆ 1   │
└─────┴─────┴─────┘

Or use positional arguments to group by multiple columns in the same way. Expressions are also accepted.

>>> df.group_by("a", pl.col("b") // 2).agg(pl.col("c").mean())  
shape: (3, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ f64 │
╞═════╪═════╪═════╡
│ a   ┆ 0   ┆ 4.0 │
│ b   ┆ 1   ┆ 3.0 │
│ c   ┆ 1   ┆ 1.0 │
└─────┴─────┴─────┘

The GroupBy object returned by this method is iterable, returning the name and data of each group.

>>> for name, data in df.group_by("a"):  
...     print(name)
...     print(data)
a
shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ a   ┆ 1   ┆ 5   │
│ a   ┆ 1   ┆ 3   │
└─────┴─────┴─────┘
b
shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ b   ┆ 2   ┆ 4   │
│ b   ┆ 3   ┆ 2   │
└─────┴─────┴─────┘
c
shape: (1, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ c   ┆ 3   ┆ 1   │
└─────┴─────┴─────┘
group_by_dynamic(
index_column: IntoExpr,
*,
every: str | timedelta,
period: str | timedelta | None = None,
offset: str | timedelta | None = None,
truncate: bool | None = None,
include_boundaries: bool = False,
closed: ClosedInterval = 'left',
label: Label = 'left',
by: IntoExpr | Iterable[IntoExpr] | None = None,
start_by: StartBy = 'window',
check_sorted: bool = True,
) DynamicGroupBy[source]

Group based on a time value (or index value of type Int32, Int64).

Time windows are calculated and rows are assigned to windows. Different from a normal group by is that a row can be member of multiple groups. By default, the windows look like:

  • [start, start + period)

  • [start + every, start + every + period)

  • [start + 2*every, start + 2*every + period)

where start is determined by start_by, offset, and every (see parameter descriptions below).

Warning

The index column must be sorted in ascending order. If by is passed, then the index column must be sorted in ascending order within each group.

Parameters:
index_column

Column used to group based on the time window. Often of type Date/Datetime. This column must be sorted in ascending order (or, if by is specified, then it must be sorted in ascending order within each group).

In case of a dynamic group by on indices, dtype needs to be one of {Int32, Int64}. Note that Int32 gets temporarily cast to Int64, so if performance matters use an Int64 column.

every

interval of the window

period

length of the window, if None it will equal ‘every’

offset

offset of the window, only takes effect if start_by is 'window'. Defaults to negative every.

truncate

truncate the time value to the window lower bound

Deprecated since version 0.19.4: Use label instead.

include_boundaries

Add the lower and upper bound of the window to the “_lower_boundary” and “_upper_boundary” columns. This will impact performance because it’s harder to parallelize

closed{‘left’, ‘right’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive).

label{‘left’, ‘right’, ‘datapoint’}

Define which label to use for the window:

  • ‘left’: lower boundary of the window

  • ‘right’: upper boundary of the window

  • ‘datapoint’: the first value of the index column in the given window. If you don’t need the label to be at one of the boundaries, choose this option for maximum performance

by

Also group by this column/these columns

start_by{‘window’, ‘datapoint’, ‘monday’, ‘tuesday’, ‘wednesday’, ‘thursday’, ‘friday’, ‘saturday’, ‘sunday’}

The strategy to determine the start of the first window by.

  • ‘window’: Start by taking the earliest timestamp, truncating it with every, and then adding offset. Note that weekly windows start on Monday.

  • ‘datapoint’: Start from the first encountered data point.

  • a day of the week (only takes effect if every contains 'w'):

    • ‘monday’: Start the window on the Monday before the first data point.

    • ‘tuesday’: Start the window on the Tuesday before the first data point.

    • ‘sunday’: Start the window on the Sunday before the first data point.

check_sorted

When the by argument is given, polars can not check sortedness by the metadata and has to do a full scan on the index column to verify data is sorted. This is expensive. If you are sure the data within the by groups is sorted, you can set this to False. Doing so incorrectly will lead to incorrect output

Returns:
DynamicGroupBy

Object you can call .agg on to aggregate by groups, the result of which will be sorted by index_column (but note that if by columns are passed, it will only be sorted within each by group).

See also

rolling

Notes

  1. If you’re coming from pandas, then

    # polars
    df.group_by_dynamic("ts", every="1d").agg(pl.col("value").sum())
    

    is equivalent to

    # pandas
    df.set_index("ts").resample("D")["value"].sum().reset_index()
    

    though note that, unlike pandas, polars doesn’t add extra rows for empty windows. If you need index_column to be evenly spaced, then please combine with DataFrame.upsample().

  2. The every, period and offset arguments are created with the following string language:

    • 1ns (1 nanosecond)

    • 1us (1 microsecond)

    • 1ms (1 millisecond)

    • 1s (1 second)

    • 1m (1 minute)

    • 1h (1 hour)

    • 1d (1 calendar day)

    • 1w (1 calendar week)

    • 1mo (1 calendar month)

    • 1q (1 calendar quarter)

    • 1y (1 calendar year)

    • 1i (1 index count)

    Or combine them: “3d12h4m25s” # 3 days, 12 hours, 4 minutes, and 25 seconds

    By “calendar day”, we mean the corresponding time on the next day (which may not be 24 hours, due to daylight savings). Similarly for “calendar week”, “calendar month”, “calendar quarter”, and “calendar year”.

    In case of a group_by_dynamic on an integer column, the windows are defined by:

    • “1i” # length 1

    • “10i” # length 10

Examples

>>> from datetime import datetime
>>> df = pl.DataFrame(
...     {
...         "time": pl.datetime_range(
...             start=datetime(2021, 12, 16),
...             end=datetime(2021, 12, 16, 3),
...             interval="30m",
...             eager=True,
...         ),
...         "n": range(7),
...     }
... )
>>> df
shape: (7, 2)
┌─────────────────────┬─────┐
│ time                ┆ n   │
│ ---                 ┆ --- │
│ datetime[μs]        ┆ i64 │
╞═════════════════════╪═════╡
│ 2021-12-16 00:00:00 ┆ 0   │
│ 2021-12-16 00:30:00 ┆ 1   │
│ 2021-12-16 01:00:00 ┆ 2   │
│ 2021-12-16 01:30:00 ┆ 3   │
│ 2021-12-16 02:00:00 ┆ 4   │
│ 2021-12-16 02:30:00 ┆ 5   │
│ 2021-12-16 03:00:00 ┆ 6   │
└─────────────────────┴─────┘

Group by windows of 1 hour starting at 2021-12-16 00:00:00.

>>> df.group_by_dynamic("time", every="1h", closed="right").agg(pl.col("n"))
shape: (4, 2)
┌─────────────────────┬───────────┐
│ time                ┆ n         │
│ ---                 ┆ ---       │
│ datetime[μs]        ┆ list[i64] │
╞═════════════════════╪═══════════╡
│ 2021-12-15 23:00:00 ┆ [0]       │
│ 2021-12-16 00:00:00 ┆ [1, 2]    │
│ 2021-12-16 01:00:00 ┆ [3, 4]    │
│ 2021-12-16 02:00:00 ┆ [5, 6]    │
└─────────────────────┴───────────┘

The window boundaries can also be added to the aggregation result

>>> df.group_by_dynamic(
...     "time", every="1h", include_boundaries=True, closed="right"
... ).agg(pl.col("n").mean())
shape: (4, 4)
┌─────────────────────┬─────────────────────┬─────────────────────┬─────┐
│ _lower_boundary     ┆ _upper_boundary     ┆ time                ┆ n   │
│ ---                 ┆ ---                 ┆ ---                 ┆ --- │
│ datetime[μs]        ┆ datetime[μs]        ┆ datetime[μs]        ┆ f64 │
╞═════════════════════╪═════════════════════╪═════════════════════╪═════╡
│ 2021-12-15 23:00:00 ┆ 2021-12-16 00:00:00 ┆ 2021-12-15 23:00:00 ┆ 0.0 │
│ 2021-12-16 00:00:00 ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 00:00:00 ┆ 1.5 │
│ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ 3.5 │
│ 2021-12-16 02:00:00 ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 02:00:00 ┆ 5.5 │
└─────────────────────┴─────────────────────┴─────────────────────┴─────┘

When closed=”left”, the window excludes the right end of interval: [lower_bound, upper_bound)

>>> df.group_by_dynamic("time", every="1h", closed="left").agg(pl.col("n"))
shape: (4, 2)
┌─────────────────────┬───────────┐
│ time                ┆ n         │
│ ---                 ┆ ---       │
│ datetime[μs]        ┆ list[i64] │
╞═════════════════════╪═══════════╡
│ 2021-12-16 00:00:00 ┆ [0, 1]    │
│ 2021-12-16 01:00:00 ┆ [2, 3]    │
│ 2021-12-16 02:00:00 ┆ [4, 5]    │
│ 2021-12-16 03:00:00 ┆ [6]       │
└─────────────────────┴───────────┘

When closed=”both” the time values at the window boundaries belong to 2 groups.

>>> df.group_by_dynamic("time", every="1h", closed="both").agg(pl.col("n"))
shape: (5, 2)
┌─────────────────────┬───────────┐
│ time                ┆ n         │
│ ---                 ┆ ---       │
│ datetime[μs]        ┆ list[i64] │
╞═════════════════════╪═══════════╡
│ 2021-12-15 23:00:00 ┆ [0]       │
│ 2021-12-16 00:00:00 ┆ [0, 1, 2] │
│ 2021-12-16 01:00:00 ┆ [2, 3, 4] │
│ 2021-12-16 02:00:00 ┆ [4, 5, 6] │
│ 2021-12-16 03:00:00 ┆ [6]       │
└─────────────────────┴───────────┘

Dynamic group bys can also be combined with grouping on normal keys

>>> df = df.with_columns(groups=pl.Series(["a", "a", "a", "b", "b", "a", "a"]))
>>> df
shape: (7, 3)
┌─────────────────────┬─────┬────────┐
│ time                ┆ n   ┆ groups │
│ ---                 ┆ --- ┆ ---    │
│ datetime[μs]        ┆ i64 ┆ str    │
╞═════════════════════╪═════╪════════╡
│ 2021-12-16 00:00:00 ┆ 0   ┆ a      │
│ 2021-12-16 00:30:00 ┆ 1   ┆ a      │
│ 2021-12-16 01:00:00 ┆ 2   ┆ a      │
│ 2021-12-16 01:30:00 ┆ 3   ┆ b      │
│ 2021-12-16 02:00:00 ┆ 4   ┆ b      │
│ 2021-12-16 02:30:00 ┆ 5   ┆ a      │
│ 2021-12-16 03:00:00 ┆ 6   ┆ a      │
└─────────────────────┴─────┴────────┘
>>> df.group_by_dynamic(
...     "time",
...     every="1h",
...     closed="both",
...     by="groups",
...     include_boundaries=True,
... ).agg(pl.col("n"))
shape: (7, 5)
┌────────┬─────────────────────┬─────────────────────┬─────────────────────┬───────────┐
│ groups ┆ _lower_boundary     ┆ _upper_boundary     ┆ time                ┆ n         │
│ ---    ┆ ---                 ┆ ---                 ┆ ---                 ┆ ---       │
│ str    ┆ datetime[μs]        ┆ datetime[μs]        ┆ datetime[μs]        ┆ list[i64] │
╞════════╪═════════════════════╪═════════════════════╪═════════════════════╪═══════════╡
│ a      ┆ 2021-12-15 23:00:00 ┆ 2021-12-16 00:00:00 ┆ 2021-12-15 23:00:00 ┆ [0]       │
│ a      ┆ 2021-12-16 00:00:00 ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 00:00:00 ┆ [0, 1, 2] │
│ a      ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ [2]       │
│ a      ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 02:00:00 ┆ [5, 6]    │
│ a      ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 04:00:00 ┆ 2021-12-16 03:00:00 ┆ [6]       │
│ b      ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ [3, 4]    │
│ b      ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 02:00:00 ┆ [4]       │
└────────┴─────────────────────┴─────────────────────┴─────────────────────┴───────────┘

Dynamic group by on an index column

>>> df = pl.DataFrame(
...     {
...         "idx": pl.int_range(0, 6, eager=True),
...         "A": ["A", "A", "B", "B", "B", "C"],
...     }
... )
>>> (
...     df.group_by_dynamic(
...         "idx",
...         every="2i",
...         period="3i",
...         include_boundaries=True,
...         closed="right",
...     ).agg(pl.col("A").alias("A_agg_list"))
... )
shape: (4, 4)
┌─────────────────┬─────────────────┬─────┬─────────────────┐
│ _lower_boundary ┆ _upper_boundary ┆ idx ┆ A_agg_list      │
│ ---             ┆ ---             ┆ --- ┆ ---             │
│ i64             ┆ i64             ┆ i64 ┆ list[str]       │
╞═════════════════╪═════════════════╪═════╪═════════════════╡
│ -2              ┆ 1               ┆ -2  ┆ ["A", "A"]      │
│ 0               ┆ 3               ┆ 0   ┆ ["A", "B", "B"] │
│ 2               ┆ 5               ┆ 2   ┆ ["B", "B", "C"] │
│ 4               ┆ 7               ┆ 4   ┆ ["C"]           │
└─────────────────┴─────────────────┴─────┴─────────────────┘
group_by_rolling(
index_column: IntoExpr,
*,
period: str | timedelta,
offset: str | timedelta | None = None,
closed: ClosedInterval = 'right',
by: IntoExpr | Iterable[IntoExpr] | None = None,
check_sorted: bool = True,
) RollingGroupBy[source]

Create rolling groups based on a time, Int32, or Int64 column.

Deprecated since version 0.19.9: This method has been renamed to DataFrame.rolling().

Parameters:
index_column

Column used to group based on the time window. Often of type Date/Datetime. This column must be sorted in ascending order (or, if by is specified, then it must be sorted in ascending order within each group).

In case of a rolling group by on indices, dtype needs to be one of {Int32, Int64}. Note that Int32 gets temporarily cast to Int64, so if performance matters use an Int64 column.

period

length of the window - must be non-negative

offset

offset of the window. Default is -period

closed{‘right’, ‘left’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive).

by

Also group by this column/these columns

check_sorted

When the by argument is given, polars can not check sortedness by the metadata and has to do a full scan on the index column to verify data is sorted. This is expensive. If you are sure the data within the by groups is sorted, you can set this to False. Doing so incorrectly will lead to incorrect output

groupby(
by: IntoExpr | Iterable[IntoExpr],
*more_by: IntoExpr,
maintain_order: bool = False,
) GroupBy[source]

Start a group by operation.

Deprecated since version 0.19.0: This method has been renamed to DataFrame.group_by().

Parameters:
by

Column(s) to group by. Accepts expression input. Strings are parsed as column names.

*more_by

Additional columns to group by, specified as positional arguments.

maintain_order

Ensure that the order of the groups is consistent with the input data. This is slower than a default group by. Settings this to True blocks the possibility to run on the streaming engine.

Note

Within each group, the order of rows is always preserved, regardless of this argument.

Returns:
GroupBy

Object which can be used to perform aggregations.

groupby_dynamic(
index_column: IntoExpr,
*,
every: str | timedelta,
period: str | timedelta | None = None,
offset: str | timedelta | None = None,
truncate: bool = True,
include_boundaries: bool = False,
closed: ClosedInterval = 'left',
by: IntoExpr | Iterable[IntoExpr] | None = None,
start_by: StartBy = 'window',
check_sorted: bool = True,
) DynamicGroupBy[source]

Group based on a time value (or index value of type Int32, Int64).

Deprecated since version 0.19.0: This method has been renamed to DataFrame.group_by_dynamic().

Parameters:
index_column

Column used to group based on the time window. Often of type Date/Datetime. This column must be sorted in ascending order (or, if by is specified, then it must be sorted in ascending order within each group).

In case of a dynamic group by on indices, dtype needs to be one of {Int32, Int64}. Note that Int32 gets temporarily cast to Int64, so if performance matters use an Int64 column.

every

interval of the window

period

length of the window, if None it will equal ‘every’

offset

offset of the window, only takes effect if start_by is 'window'. Defaults to negative every.

truncate

truncate the time value to the window lower bound

include_boundaries

Add the lower and upper bound of the window to the “_lower_bound” and “_upper_bound” columns. This will impact performance because it’s harder to parallelize

closed{‘left’, ‘right’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive).

by

Also group by this column/these columns

start_by{‘window’, ‘datapoint’, ‘monday’, ‘tuesday’, ‘wednesday’, ‘thursday’, ‘friday’, ‘saturday’, ‘sunday’}

The strategy to determine the start of the first window by.

  • ‘window’: Start by taking the earliest timestamp, truncating it with every, and then adding offset. Note that weekly windows start on Monday.

  • ‘datapoint’: Start from the first encountered data point.

  • a day of the week (only takes effect if every contains 'w'):

    • ‘monday’: Start the window on the Monday before the first data point.

    • ‘tuesday’: Start the window on the Tuesday before the first data point.

    • ‘sunday’: Start the window on the Sunday before the first data point.

check_sorted

When the by argument is given, polars can not check sortedness by the metadata and has to do a full scan on the index column to verify data is sorted. This is expensive. If you are sure the data within the by groups is sorted, you can set this to False. Doing so incorrectly will lead to incorrect output

Returns:
DynamicGroupBy

Object you can call .agg on to aggregate by groups, the result of which will be sorted by index_column (but note that if by columns are passed, it will only be sorted within each by group).

groupby_rolling(
index_column: IntoExpr,
*,
period: str | timedelta,
offset: str | timedelta | None = None,
closed: ClosedInterval = 'right',
by: IntoExpr | Iterable[IntoExpr] | None = None,
check_sorted: bool = True,
) RollingGroupBy[source]

Create rolling groups based on a time, Int32, or Int64 column.

Deprecated since version 0.19.0: This method has been renamed to DataFrame.rolling().

Parameters:
index_column

Column used to group based on the time window. Often of type Date/Datetime. This column must be sorted in ascending order (or, if by is specified, then it must be sorted in ascending order within each group).

In case of a rolling group by on indices, dtype needs to be one of {Int32, Int64}. Note that Int32 gets temporarily cast to Int64, so if performance matters use an Int64 column.

period

length of the window - must be non-negative

offset

offset of the window. Default is -period

closed{‘right’, ‘left’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive).

by

Also group by this column/these columns

check_sorted

When the by argument is given, polars can not check sortedness by the metadata and has to do a full scan on the index column to verify data is sorted. This is expensive. If you are sure the data within the by groups is sorted, you can set this to False. Doing so incorrectly will lead to incorrect output

hash_rows(
seed: int = 0,
seed_1: int | None = None,
seed_2: int | None = None,
seed_3: int | None = None,
) Series[source]

Hash and combine the rows in this DataFrame.

The hash value is of type UInt64.

Parameters:
seed

Random seed parameter. Defaults to 0.

seed_1

Random seed parameter. Defaults to seed if not set.

seed_2

Random seed parameter. Defaults to seed if not set.

seed_3

Random seed parameter. Defaults to seed if not set.

Notes

This implementation of hash_rows does not guarantee stable results across different Polars versions. Its stability is only guaranteed within a single version.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, None, 3, 4],
...         "ham": ["a", "b", None, "d"],
...     }
... )
>>> df.hash_rows(seed=42)  
shape: (4,)
Series: '' [u64]
[
    10783150408545073287
    1438741209321515184
    10047419486152048166
    2047317070637311557
]
head(n: int = 5) Self[source]

Get the first n rows.

Parameters:
n

Number of rows to return. If a negative value is passed, return all rows except the last abs(n).

See also

tail, glimpse, slice

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3, 4, 5],
...         "bar": [6, 7, 8, 9, 10],
...         "ham": ["a", "b", "c", "d", "e"],
...     }
... )
>>> df.head(3)
shape: (3, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 6   ┆ a   │
│ 2   ┆ 7   ┆ b   │
│ 3   ┆ 8   ┆ c   │
└─────┴─────┴─────┘

Pass a negative value to get all rows except the last abs(n).

>>> df.head(-3)
shape: (2, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 6   ┆ a   │
│ 2   ┆ 7   ┆ b   │
└─────┴─────┴─────┘
property height: int[source]

Get the height of the DataFrame.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5]})
>>> df.height
5
hstack(columns: list[Series] | DataFrame, *, in_place: bool = False) Self[source]

Return a new DataFrame grown horizontally by stacking multiple Series to it.

Parameters:
columns

Series to stack.

in_place

Modify in place.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> x = pl.Series("apple", [10, 20, 30])
>>> df.hstack([x])
shape: (3, 4)
┌─────┬─────┬─────┬───────┐
│ foo ┆ bar ┆ ham ┆ apple │
│ --- ┆ --- ┆ --- ┆ ---   │
│ i64 ┆ i64 ┆ str ┆ i64   │
╞═════╪═════╪═════╪═══════╡
│ 1   ┆ 6   ┆ a   ┆ 10    │
│ 2   ┆ 7   ┆ b   ┆ 20    │
│ 3   ┆ 8   ┆ c   ┆ 30    │
└─────┴─────┴─────┴───────┘
insert_at_idx(index: int, column: Series) Self[source]

Insert a Series at a certain column index. This operation is in place.

Deprecated since version 0.19.14: This method has been renamed to insert_column().

Parameters:
index

Column to insert the new Series column.

column

Series to insert.

insert_column(index: int, column: Series) Self[source]

Insert a Series at a certain column index.

This operation is in place.

Parameters:
index

Index at which to insert the new Series column.

column

Series to insert.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
>>> s = pl.Series("baz", [97, 98, 99])
>>> df.insert_column(1, s)
shape: (3, 3)
┌─────┬─────┬─────┐
│ foo ┆ baz ┆ bar │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1   ┆ 97  ┆ 4   │
│ 2   ┆ 98  ┆ 5   │
│ 3   ┆ 99  ┆ 6   │
└─────┴─────┴─────┘
>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3, 4],
...         "b": [0.5, 4, 10, 13],
...         "c": [True, True, False, True],
...     }
... )
>>> s = pl.Series("d", [-2.5, 15, 20.5, 0])
>>> df.insert_column(3, s)
shape: (4, 4)
┌─────┬──────┬───────┬──────┐
│ a   ┆ b    ┆ c     ┆ d    │
│ --- ┆ ---  ┆ ---   ┆ ---  │
│ i64 ┆ f64  ┆ bool  ┆ f64  │
╞═════╪══════╪═══════╪══════╡
│ 1   ┆ 0.5  ┆ true  ┆ -2.5 │
│ 2   ┆ 4.0  ┆ true  ┆ 15.0 │
│ 3   ┆ 10.0 ┆ false ┆ 20.5 │
│ 4   ┆ 13.0 ┆ true  ┆ 0.0  │
└─────┴──────┴───────┴──────┘
interpolate() DataFrame[source]

Interpolate intermediate values. The interpolation method is linear.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, None, 9, 10],
...         "bar": [6, 7, 9, None],
...         "baz": [1, None, None, 9],
...     }
... )
>>> df.interpolate()
shape: (4, 3)
┌──────┬──────┬──────────┐
│ foo  ┆ bar  ┆ baz      │
│ ---  ┆ ---  ┆ ---      │
│ f64  ┆ f64  ┆ f64      │
╞══════╪══════╪══════════╡
│ 1.0  ┆ 6.0  ┆ 1.0      │
│ 5.0  ┆ 7.0  ┆ 3.666667 │
│ 9.0  ┆ 9.0  ┆ 6.333333 │
│ 10.0 ┆ null ┆ 9.0      │
└──────┴──────┴──────────┘
is_duplicated() Series[source]

Get a mask of all duplicated rows in this DataFrame.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3, 1],
...         "b": ["x", "y", "z", "x"],
...     }
... )
>>> df.is_duplicated()
shape: (4,)
Series: '' [bool]
[
        true
        false
        false
        true
]

This mask can be used to visualize the duplicated lines like this:

>>> df.filter(df.is_duplicated())
shape: (2, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ str │
╞═════╪═════╡
│ 1   ┆ x   │
│ 1   ┆ x   │
└─────┴─────┘
is_empty() bool[source]

Check if the dataframe is empty.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
>>> df.is_empty()
False
>>> df.filter(pl.col("foo") > 99).is_empty()
True
is_unique() Series[source]

Get a mask of all unique rows in this DataFrame.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3, 1],
...         "b": ["x", "y", "z", "x"],
...     }
... )
>>> df.is_unique()
shape: (4,)
Series: '' [bool]
[
        false
        true
        true
        false
]

This mask can be used to visualize the unique lines like this:

>>> df.filter(df.is_unique())
shape: (2, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ str │
╞═════╪═════╡
│ 2   ┆ y   │
│ 3   ┆ z   │
└─────┴─────┘
item(row: int | None = None, column: int | str | None = None) Any[source]

Return the DataFrame as a scalar, or return the element at the given row/column.

Parameters:
row

Optional row index.

column

Optional column index or name.

See also

row

Get the values of a single row, either by index or by predicate.

Notes

If row/col not provided, this is equivalent to df[0,0], with a check that the shape is (1,1). With row/col, this is equivalent to df[row,col].

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df.select((pl.col("a") * pl.col("b")).sum()).item()
32
>>> df.item(1, 1)
5
>>> df.item(2, "b")
6
iter_columns() Iterator[Series][source]

Returns an iterator over the columns of this DataFrame.

Yields:
Series

Notes

Consider whether you can use all() instead. If you can, it will be more efficient.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 3, 5],
...         "b": [2, 4, 6],
...     }
... )
>>> [s.name for s in df.iter_columns()]
['a', 'b']

If you’re using this to modify a dataframe’s columns, e.g.

>>> # Do NOT do this
>>> pl.DataFrame(column * 2 for column in df.iter_columns())
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 2   ┆ 4   │
│ 6   ┆ 8   │
│ 10  ┆ 12  │
└─────┴─────┘

then consider whether you can use all() instead:

>>> df.select(pl.all() * 2)
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 2   ┆ 4   │
│ 6   ┆ 8   │
│ 10  ┆ 12  │
└─────┴─────┘
iter_rows(
*,
named: bool = False,
buffer_size: int = 512,
) Iterator[tuple[Any, ...]] | Iterator[dict[str, Any]][source]

Returns an iterator over the DataFrame of rows of python-native values.

Parameters:
named

Return dictionaries instead of tuples. The dictionaries are a mapping of column name to row value. This is more expensive than returning a regular tuple, but allows for accessing values by column name.

buffer_size

Determines the number of rows that are buffered internally while iterating over the data; you should only modify this in very specific cases where the default value is determined not to be a good fit to your access pattern, as the speedup from using the buffer is significant (~2-4x). Setting this value to zero disables row buffering (not recommended).

Returns:
iterator of tuples (default) or dictionaries (if named) of python row values

Warning

Row iteration is not optimal as the underlying data is stored in columnar form; where possible, prefer export via one of the dedicated export/output methods that deals with columnar data.

See also

rows

Materialises all frame data as a list of rows (potentially expensive).

rows_by_key

Materialises frame data as a key-indexed dictionary.

Notes

If you have ns-precision temporal values you should be aware that Python natively only supports up to μs-precision; ns-precision values will be truncated to microseconds on conversion to Python. If this matters to your use-case you should export to a different format (such as Arrow or NumPy).

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 3, 5],
...         "b": [2, 4, 6],
...     }
... )
>>> [row[0] for row in df.iter_rows()]
[1, 3, 5]
>>> [row["b"] for row in df.iter_rows(named=True)]
[2, 4, 6]
iter_slices(
n_rows: int = 10000,
) Iterator[DataFrame][source]

Returns a non-copying iterator of slices over the underlying DataFrame.

Parameters:
n_rows

Determines the number of rows contained in each DataFrame slice.

See also

iter_rows

Row iterator over frame data (does not materialise all rows).

partition_by

Split into multiple DataFrames, partitioned by groups.

Examples

>>> from datetime import date
>>> df = pl.DataFrame(
...     data={
...         "a": range(17_500),
...         "b": date(2023, 1, 1),
...         "c": "klmnoopqrstuvwxyz",
...     },
...     schema_overrides={"a": pl.Int32},
... )
>>> for idx, frame in enumerate(df.iter_slices()):
...     print(f"{type(frame).__name__}:[{idx}]:{len(frame)}")
DataFrame:[0]:10000
DataFrame:[1]:7500

Using iter_slices is an efficient way to chunk-iterate over DataFrames and any supported frame export/conversion types; for example, as RecordBatches:

>>> for frame in df.iter_slices(n_rows=15_000):
...     record_batch = frame.to_arrow().to_batches()[0]
...     print(f"{record_batch.schema}\n<< {len(record_batch)}")
a: int32
b: date32[day]
c: large_string
<< 15000
a: int32
b: date32[day]
c: large_string
<< 2500
join(
other: DataFrame,
on: str | Expr | Sequence[str | Expr] | None = None,
how: JoinStrategy = 'inner',
*,
left_on: str | Expr | Sequence[str | Expr] | None = None,
right_on: str | Expr | Sequence[str | Expr] | None = None,
suffix: str = '_right',
validate: JoinValidation = 'm:m',
join_nulls: bool = False,
) DataFrame[source]

Join in SQL-like fashion.

Parameters:
other

DataFrame to join with.

on

Name(s) of the join columns in both DataFrames.

how{‘inner’, ‘left’, ‘outer’, ‘semi’, ‘anti’, ‘cross’, ‘outer_coalesce’}

Join strategy.

  • inner

    Returns rows that have matching values in both tables

  • left

    Returns all rows from the left table, and the matched rows from the right table

  • outer

    Returns all rows when there is a match in either left or right table

  • outer_coalesce

    Same as ‘outer’, but coalesces the key columns

  • cross

    Returns the Cartesian product of rows from both tables

  • semi

    Filter rows that have a match in the right table.

  • anti

    Filter rows that not have a match in the right table.

Note

A left join preserves the row order of the left DataFrame.

left_on

Name(s) of the left join column(s).

right_on

Name(s) of the right join column(s).

suffix

Suffix to append to columns with a duplicate name.

validate: {‘m:m’, ‘m:1’, ‘1:m’, ‘1:1’}

Checks if join is of specified type.

  • many_to_many

    “m:m”: default, does not result in checks

  • one_to_one

    “1:1”: check if join keys are unique in both left and right datasets

  • one_to_many

    “1:m”: check if join keys are unique in left dataset

  • many_to_one

    “m:1”: check if join keys are unique in right dataset

Note

  • This is currently not supported the streaming engine.

  • This is only supported when joined by single columns.

join_nulls

Join on null values. By default null values will never produce matches.

Returns:
DataFrame

See also

join_asof

Notes

For joining on columns with categorical data, see polars.StringCache.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6.0, 7.0, 8.0],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> other_df = pl.DataFrame(
...     {
...         "apple": ["x", "y", "z"],
...         "ham": ["a", "b", "d"],
...     }
... )
>>> df.join(other_df, on="ham")
shape: (2, 4)
┌─────┬─────┬─────┬───────┐
│ foo ┆ bar ┆ ham ┆ apple │
│ --- ┆ --- ┆ --- ┆ ---   │
│ i64 ┆ f64 ┆ str ┆ str   │
╞═════╪═════╪═════╪═══════╡
│ 1   ┆ 6.0 ┆ a   ┆ x     │
│ 2   ┆ 7.0 ┆ b   ┆ y     │
└─────┴─────┴─────┴───────┘
>>> df.join(other_df, on="ham", how="outer")
shape: (4, 5)
┌──────┬──────┬──────┬───────┬───────────┐
│ foo  ┆ bar  ┆ ham  ┆ apple ┆ ham_right │
│ ---  ┆ ---  ┆ ---  ┆ ---   ┆ ---       │
│ i64  ┆ f64  ┆ str  ┆ str   ┆ str       │
╞══════╪══════╪══════╪═══════╪═══════════╡
│ 1    ┆ 6.0  ┆ a    ┆ x     ┆ a         │
│ 2    ┆ 7.0  ┆ b    ┆ y     ┆ b         │
│ null ┆ null ┆ null ┆ z     ┆ d         │
│ 3    ┆ 8.0  ┆ c    ┆ null  ┆ null      │
└──────┴──────┴──────┴───────┴───────────┘
>>> df.join(other_df, on="ham", how="left")
shape: (3, 4)
┌─────┬─────┬─────┬───────┐
│ foo ┆ bar ┆ ham ┆ apple │
│ --- ┆ --- ┆ --- ┆ ---   │
│ i64 ┆ f64 ┆ str ┆ str   │
╞═════╪═════╪═════╪═══════╡
│ 1   ┆ 6.0 ┆ a   ┆ x     │
│ 2   ┆ 7.0 ┆ b   ┆ y     │
│ 3   ┆ 8.0 ┆ c   ┆ null  │
└─────┴─────┴─────┴───────┘
>>> df.join(other_df, on="ham", how="semi")
shape: (2, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 6.0 ┆ a   │
│ 2   ┆ 7.0 ┆ b   │
└─────┴─────┴─────┘
>>> df.join(other_df, on="ham", how="anti")
shape: (1, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ str │
╞═════╪═════╪═════╡
│ 3   ┆ 8.0 ┆ c   │
└─────┴─────┴─────┘
join_asof(
other: DataFrame,
*,
left_on: str | None | Expr = None,
right_on: str | None | Expr = None,
on: str | None | Expr = None,
by_left: str | Sequence[str] | None = None,
by_right: str | Sequence[str] | None = None,
by: str | Sequence[str] | None = None,
strategy: AsofJoinStrategy = 'backward',
suffix: str = '_right',
tolerance: str | int | float | timedelta | None = None,
allow_parallel: bool = True,
force_parallel: bool = False,
) DataFrame[source]

Perform an asof join.

This is similar to a left-join except that we match on nearest key rather than equal keys.

Both DataFrames must be sorted by the asof_join key.

For each row in the left DataFrame:

  • A “backward” search selects the last row in the right DataFrame whose ‘on’ key is less than or equal to the left’s key.

  • A “forward” search selects the first row in the right DataFrame whose ‘on’ key is greater than or equal to the left’s key.

  • A “nearest” search selects the last row in the right DataFrame whose value is nearest to the left’s key. String keys are not currently supported for a nearest search.

The default is “backward”.

Parameters:
other

Lazy DataFrame to join with.

left_on

Join column of the left DataFrame.

right_on

Join column of the right DataFrame.

on

Join column of both DataFrames. If set, left_on and right_on should be None.

by

join on these columns before doing asof join

by_left

join on these columns before doing asof join

by_right

join on these columns before doing asof join

strategy{‘backward’, ‘forward’, ‘nearest’}

Join strategy.

suffix

Suffix to append to columns with a duplicate name.

tolerance

Numeric tolerance. By setting this the join will only be done if the near keys are within this distance. If an asof join is done on columns of dtype “Date”, “Datetime”, “Duration” or “Time”, use either a datetime.timedelta object or the following string language:

  • 1ns (1 nanosecond)

  • 1us (1 microsecond)

  • 1ms (1 millisecond)

  • 1s (1 second)

  • 1m (1 minute)

  • 1h (1 hour)

  • 1d (1 calendar day)

  • 1w (1 calendar week)

  • 1mo (1 calendar month)

  • 1q (1 calendar quarter)

  • 1y (1 calendar year)

  • 1i (1 index count)

Or combine them: “3d12h4m25s” # 3 days, 12 hours, 4 minutes, and 25 seconds

By “calendar day”, we mean the corresponding time on the next day (which may not be 24 hours, due to daylight savings). Similarly for “calendar week”, “calendar month”, “calendar quarter”, and “calendar year”.

allow_parallel

Allow the physical plan to optionally evaluate the computation of both DataFrames up to the join in parallel.

force_parallel

Force the physical plan to evaluate the computation of both DataFrames up to the join in parallel.

Examples

>>> from datetime import date
>>> gdp = pl.DataFrame(
...     {
...         "date": pl.date_range(
...             date(2016, 1, 1),
...             date(2020, 1, 1),
...             "1y",
...             eager=True,
...         ),
...         "gdp": [4164, 4411, 4566, 4696, 4827],
...     }
... )
>>> gdp
shape: (5, 2)
┌────────────┬──────┐
│ date       ┆ gdp  │
│ ---        ┆ ---  │
│ date       ┆ i64  │
╞════════════╪══════╡
│ 2016-01-01 ┆ 4164 │
│ 2017-01-01 ┆ 4411 │
│ 2018-01-01 ┆ 4566 │
│ 2019-01-01 ┆ 4696 │
│ 2020-01-01 ┆ 4827 │
└────────────┴──────┘
>>> population = pl.DataFrame(
...     {
...         "date": [date(2016, 3, 1), date(2018, 8, 1), date(2019, 1, 1)],
...         "population": [82.19, 82.66, 83.12],
...     }
... ).sort("date")
>>> population
shape: (3, 2)
┌────────────┬────────────┐
│ date       ┆ population │
│ ---        ┆ ---        │
│ date       ┆ f64        │
╞════════════╪════════════╡
│ 2016-03-01 ┆ 82.19      │
│ 2018-08-01 ┆ 82.66      │
│ 2019-01-01 ┆ 83.12      │
└────────────┴────────────┘

Note how the dates don’t quite match. If we join them using join_asof and strategy='backward', then each date from population which doesn’t have an exact match is matched with the closest earlier date from gdp:

>>> population.join_asof(gdp, on="date", strategy="backward")
shape: (3, 3)
┌────────────┬────────────┬──────┐
│ date       ┆ population ┆ gdp  │
│ ---        ┆ ---        ┆ ---  │
│ date       ┆ f64        ┆ i64  │
╞════════════╪════════════╪══════╡
│ 2016-03-01 ┆ 82.19      ┆ 4164 │
│ 2018-08-01 ┆ 82.66      ┆ 4566 │
│ 2019-01-01 ┆ 83.12      ┆ 4696 │
└────────────┴────────────┴──────┘

Note how:

  • date 2016-03-01 from population is matched with 2016-01-01 from gdp;

  • date 2018-08-01 from population is matched with 2018-01-01 from gdp.

If we instead use strategy='forward', then each date from population which doesn’t have an exact match is matched with the closest later date from gdp:

>>> population.join_asof(gdp, on="date", strategy="forward")
shape: (3, 3)
┌────────────┬────────────┬──────┐
│ date       ┆ population ┆ gdp  │
│ ---        ┆ ---        ┆ ---  │
│ date       ┆ f64        ┆ i64  │
╞════════════╪════════════╪══════╡
│ 2016-03-01 ┆ 82.19      ┆ 4411 │
│ 2018-08-01 ┆ 82.66      ┆ 4696 │
│ 2019-01-01 ┆ 83.12      ┆ 4696 │
└────────────┴────────────┴──────┘

Note how:

  • date 2016-03-01 from population is matched with 2017-01-01 from gdp;

  • date 2018-08-01 from population is matched with 2019-01-01 from gdp.

Finally, strategy='nearest' gives us a mix of the two results above, as each date from population which doesn’t have an exact match is matched with the closest date from gdp, regardless of whether it’s earlier or later:

>>> population.join_asof(gdp, on="date", strategy="nearest")
shape: (3, 3)
┌────────────┬────────────┬──────┐
│ date       ┆ population ┆ gdp  │
│ ---        ┆ ---        ┆ ---  │
│ date       ┆ f64        ┆ i64  │
╞════════════╪════════════╪══════╡
│ 2016-03-01 ┆ 82.19      ┆ 4164 │
│ 2018-08-01 ┆ 82.66      ┆ 4696 │
│ 2019-01-01 ┆ 83.12      ┆ 4696 │
└────────────┴────────────┴──────┘

Note how:

  • date 2016-03-01 from population is matched with 2016-01-01 from gdp;

  • date 2018-08-01 from population is matched with 2019-01-01 from gdp.

lazy() LazyFrame[source]

Start a lazy query from this point. This returns a LazyFrame object.

Operations on a LazyFrame are not executed until this is requested by either calling:

  • .fetch()

    (run on a small number of rows)

  • .collect()

    (run on all data)

  • .describe_plan()

    (print unoptimized query plan)

  • .describe_optimized_plan()

    (print optimized query plan)

  • .show_graph()

    (show (un)optimized query plan as graphviz graph)

Lazy operations are advised because they allow for query optimization and more parallelization.

Returns:
LazyFrame

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [None, 2, 3, 4],
...         "b": [0.5, None, 2.5, 13],
...         "c": [True, True, False, None],
...     }
... )
>>> df.lazy()  
<LazyFrame [3 cols, {"a": Int64 … "c": Boolean}] at ...>
limit(n: int = 5) Self[source]

Get the first n rows.

Alias for DataFrame.head().

Parameters:
n

Number of rows to return. If a negative value is passed, return all rows except the last abs(n).

See also

head
map_rows(
function: Callable[[tuple[Any, ...]], Any],
return_dtype: PolarsDataType | None = None,
*,
inference_size: int = 256,
) DataFrame[source]

Apply a custom/user-defined function (UDF) over the rows of the DataFrame.

Warning

This method is much slower than the native expressions API. Only use it if you cannot implement your logic otherwise.

The UDF will receive each row as a tuple of values: udf(row).

Implementing logic using a Python function is almost always significantly slower and more memory intensive than implementing the same logic using the native expression API because:

  • The native expression engine runs in Rust; UDFs run in Python.

  • Use of Python UDFs forces the DataFrame to be materialized in memory.

  • Polars-native expressions can be parallelised (UDFs typically cannot).

  • Polars-native expressions can be logically optimised (UDFs cannot).

Wherever possible you should strongly prefer the native expression API to achieve the best performance.

Parameters:
function

Custom function or lambda.

return_dtype

Output type of the operation. If none given, Polars tries to infer the type.

inference_size

Only used in the case when the custom function returns rows. This uses the first n rows to determine the output schema.

Notes

  • The frame-level apply cannot track column names (as the UDF is a black-box that may arbitrarily drop, rearrange, transform, or add new columns); if you want to apply a UDF such that column names are preserved, you should use the expression-level apply syntax instead.

  • If your function is expensive and you don’t want it to be called more than once for a given input, consider applying an @lru_cache decorator to it. If your data is suitable you may achieve significant speedups.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3], "bar": [-1, 5, 8]})

Return a DataFrame by mapping each row to a tuple:

>>> df.map_rows(lambda t: (t[0] * 2, t[1] * 3))
shape: (3, 2)
┌──────────┬──────────┐
│ column_0 ┆ column_1 │
│ ---      ┆ ---      │
│ i64      ┆ i64      │
╞══════════╪══════════╡
│ 2        ┆ -3       │
│ 4        ┆ 15       │
│ 6        ┆ 24       │
└──────────┴──────────┘

However, it is much better to implement this with a native expression:

>>> df.select(
...     pl.col("foo") * 2,
...     pl.col("bar") * 3,
... )  

Return a DataFrame with a single column by mapping each row to a scalar:

>>> df.map_rows(lambda t: (t[0] * 2 + t[1]))  
shape: (3, 1)
┌───────┐
│ apply │
│ ---   │
│ i64   │
╞═══════╡
│ 1     │
│ 9     │
│ 14    │
└───────┘

In this case it is better to use the following native expression:

>>> df.select(pl.col("foo") * 2 + pl.col("bar"))  
max(axis: int | None = None) Self | Series[source]

Aggregate the columns of this DataFrame to their maximum value.

Parameters:
axis

Either 0 (vertical) or 1 (horizontal).

Deprecated since version 0.19.14: This argument will be removed in a future version. This method will only support vertical aggregation, as if axis were set to 0. To perform horizontal aggregation, use max_horizontal().

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.max()
shape: (1, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 3   ┆ 8   ┆ c   │
└─────┴─────┴─────┘
max_horizontal() Series[source]

Get the maximum value horizontally across columns.

Returns:
Series

A Series named "max".

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [4.0, 5.0, 6.0],
...     }
... )
>>> df.max_horizontal()
shape: (3,)
Series: 'max' [f64]
[
        4.0
        5.0
        6.0
]
mean(
*,
axis: int | None = None,
null_strategy: NullStrategy = 'ignore',
) Self | Series[source]

Aggregate the columns of this DataFrame to their mean value.

Parameters:
axis

Either 0 (vertical) or 1 (horizontal).

Deprecated since version 0.19.14: This argument will be removed in a future version. This method will only support vertical aggregation, as if axis were set to 0. To perform horizontal aggregation, use mean_horizontal().

null_strategy{‘ignore’, ‘propagate’}

This argument is only used if axis == 1.

Deprecated since version 0.19.14: This argument will be removed in a future version.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...         "spam": [True, False, None],
...     }
... )
>>> df.mean()
shape: (1, 4)
┌─────┬─────┬──────┬──────┐
│ foo ┆ bar ┆ ham  ┆ spam │
│ --- ┆ --- ┆ ---  ┆ ---  │
│ f64 ┆ f64 ┆ str  ┆ f64  │
╞═════╪═════╪══════╪══════╡
│ 2.0 ┆ 7.0 ┆ null ┆ 0.5  │
└─────┴─────┴──────┴──────┘
mean_horizontal(*, ignore_nulls: bool = True) Series[source]

Take the mean of all values horizontally across columns.

Parameters:
ignore_nulls

Ignore null values (default). If set to False, any null value in the input will lead to a null output.

Returns:
Series

A Series named "mean".

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [4.0, 5.0, 6.0],
...     }
... )
>>> df.mean_horizontal()
shape: (3,)
Series: 'mean' [f64]
[
        2.5
        3.5
        4.5
]
median() Self[source]

Aggregate the columns of this DataFrame to their median value.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.median()
shape: (1, 3)
┌─────┬─────┬──────┐
│ foo ┆ bar ┆ ham  │
│ --- ┆ --- ┆ ---  │
│ f64 ┆ f64 ┆ str  │
╞═════╪═════╪══════╡
│ 2.0 ┆ 7.0 ┆ null │
└─────┴─────┴──────┘
melt(
id_vars: ColumnNameOrSelector | Sequence[ColumnNameOrSelector] | None = None,
value_vars: ColumnNameOrSelector | Sequence[ColumnNameOrSelector] | None = None,
variable_name: str | None = None,
value_name: str | None = None,
) Self[source]

Unpivot a DataFrame from wide to long format.

Optionally leaves identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars) while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis leaving just two non-identifier columns, ‘variable’ and ‘value’.

Parameters:
id_vars

Column(s) or selector(s) to use as identifier variables.

value_vars

Column(s) or selector(s) to use as values variables; if value_vars is empty all columns that are not in id_vars will be used.

variable_name

Name to give to the variable column. Defaults to “variable”

value_name

Name to give to the value column. Defaults to “value”

Examples

>>> df = pl.DataFrame(
...     {
...         "a": ["x", "y", "z"],
...         "b": [1, 3, 5],
...         "c": [2, 4, 6],
...     }
... )
>>> import polars.selectors as cs
>>> df.melt(id_vars="a", value_vars=cs.numeric())
shape: (6, 3)
┌─────┬──────────┬───────┐
│ a   ┆ variable ┆ value │
│ --- ┆ ---      ┆ ---   │
│ str ┆ str      ┆ i64   │
╞═════╪══════════╪═══════╡
│ x   ┆ b        ┆ 1     │
│ y   ┆ b        ┆ 3     │
│ z   ┆ b        ┆ 5     │
│ x   ┆ c        ┆ 2     │
│ y   ┆ c        ┆ 4     │
│ z   ┆ c        ┆ 6     │
└─────┴──────────┴───────┘
merge_sorted(
other: DataFrame,
key: str,
) DataFrame[source]

Take two sorted DataFrames and merge them by the sorted key.

The output of this operation will also be sorted. It is the callers responsibility that the frames are sorted by that key otherwise the output will not make sense.

The schemas of both DataFrames must be equal.

Parameters:
other

Other DataFrame that must be merged

key

Key that is sorted.

Examples

>>> df0 = pl.DataFrame(
...     {"name": ["steve", "elise", "bob"], "age": [42, 44, 18]}
... ).sort("age")
>>> df0
shape: (3, 2)
┌───────┬─────┐
│ name  ┆ age │
│ ---   ┆ --- │
│ str   ┆ i64 │
╞═══════╪═════╡
│ bob   ┆ 18  │
│ steve ┆ 42  │
│ elise ┆ 44  │
└───────┴─────┘
>>> df1 = pl.DataFrame(
...     {"name": ["anna", "megan", "steve", "thomas"], "age": [21, 33, 42, 20]}
... ).sort("age")
>>> df1
shape: (4, 2)
┌────────┬─────┐
│ name   ┆ age │
│ ---    ┆ --- │
│ str    ┆ i64 │
╞════════╪═════╡
│ thomas ┆ 20  │
│ anna   ┆ 21  │
│ megan  ┆ 33  │
│ steve  ┆ 42  │
└────────┴─────┘
>>> df0.merge_sorted(df1, key="age")
shape: (7, 2)
┌────────┬─────┐
│ name   ┆ age │
│ ---    ┆ --- │
│ str    ┆ i64 │
╞════════╪═════╡
│ bob    ┆ 18  │
│ thomas ┆ 20  │
│ anna   ┆ 21  │
│ megan  ┆ 33  │
│ steve  ┆ 42  │
│ steve  ┆ 42  │
│ elise  ┆ 44  │
└────────┴─────┘
min(axis: int | None = None) Self | Series[source]

Aggregate the columns of this DataFrame to their minimum value.

Parameters:
axis

Either 0 (vertical) or 1 (horizontal).

Deprecated since version 0.19.14: This argument will be removed in a future version. This method will only support vertical aggregation, as if axis were set to 0. To perform horizontal aggregation, use min_horizontal().

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.min()
shape: (1, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 6   ┆ a   │
└─────┴─────┴─────┘
min_horizontal() Series[source]

Get the minimum value horizontally across columns.

Returns:
Series

A Series named "min".

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [4.0, 5.0, 6.0],
...     }
... )
>>> df.min_horizontal()
shape: (3,)
Series: 'min' [f64]
[
        1.0
        2.0
        3.0
]
n_chunks(strategy: str = 'first') int | list[int][source]

Get number of chunks used by the ChunkedArrays of this DataFrame.

Parameters:
strategy{‘first’, ‘all’}

Return the number of chunks of the ‘first’ column, or ‘all’ columns in this DataFrame.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3, 4],
...         "b": [0.5, 4, 10, 13],
...         "c": [True, True, False, True],
...     }
... )
>>> df.n_chunks()
1
>>> df.n_chunks(strategy="all")
[1, 1, 1]
n_unique(subset: str | Expr | Sequence[str | Expr] | None = None) int[source]

Return the number of unique rows, or the number of unique row-subsets.

Parameters:
subset

One or more columns/expressions that define what to count; omit to return the count of unique rows.

Notes

This method operates at the DataFrame level; to operate on subsets at the expression level you can make use of struct-packing instead, for example:

>>> expr_unique_subset = pl.struct(["a", "b"]).n_unique()

If instead you want to count the number of unique values per-column, you can also use expression-level syntax to return a new frame containing that result:

>>> df = pl.DataFrame([[1, 2, 3], [1, 2, 4]], schema=["a", "b", "c"])
>>> df_nunique = df.select(pl.all().n_unique())

In aggregate context there is also an equivalent method for returning the unique values per-group:

>>> df_agg_nunique = df.group_by(["a"]).n_unique()

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 1, 2, 3, 4, 5],
...         "b": [0.5, 0.5, 1.0, 2.0, 3.0, 3.0],
...         "c": [True, True, True, False, True, True],
...     }
... )
>>> df.n_unique()
5

Simple columns subset.

>>> df.n_unique(subset=["b", "c"])
4

Expression subset.

>>> df.n_unique(
...     subset=[
...         (pl.col("a") // 2),
...         (pl.col("c") | (pl.col("b") >= 2)),
...     ],
... )
3
null_count() Self[source]

Create a new DataFrame that shows the null counts per column.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, None, 3],
...         "bar": [6, 7, None],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.null_count()
shape: (1, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ u32 ┆ u32 ┆ u32 │
╞═════╪═════╪═════╡
│ 1   ┆ 1   ┆ 0   │
└─────┴─────┴─────┘
partition_by(
by: ColumnNameOrSelector | Sequence[ColumnNameOrSelector],
*more_by: ColumnNameOrSelector,
maintain_order: bool = True,
include_key: bool = True,
as_dict: bool = False,
) list[Self] | dict[Any, Self][source]

Group by the given columns and return the groups as separate dataframes.

Parameters:
by

Column name(s) or selector(s) to group by.

*more_by

Additional names of columns to group by, specified as positional arguments.

maintain_order

Ensure that the order of the groups is consistent with the input data. This is slower than a default partition by operation.

include_key

Include the columns used to partition the DataFrame in the output.

as_dict

Return a dictionary instead of a list. The dictionary keys are tuples of the distinct group values that identify each group. If a single string was passed to by, the keys are a single value instead of a tuple.

Examples

Pass a single column name to partition by that column.

>>> df = pl.DataFrame(
...     {
...         "a": ["a", "b", "a", "b", "c"],
...         "b": [1, 2, 1, 3, 3],
...         "c": [5, 4, 3, 2, 1],
...     }
... )
>>> df.partition_by("a")  
[shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ a   ┆ 1   ┆ 5   │
│ a   ┆ 1   ┆ 3   │
└─────┴─────┴─────┘,
shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ b   ┆ 2   ┆ 4   │
│ b   ┆ 3   ┆ 2   │
└─────┴─────┴─────┘,
shape: (1, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ c   ┆ 3   ┆ 1   │
└─────┴─────┴─────┘]

Partition by multiple columns by either passing a list of column names, or by specifying each column name as a positional argument.

>>> df.partition_by("a", "b")  
[shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ a   ┆ 1   ┆ 5   │
│ a   ┆ 1   ┆ 3   │
└─────┴─────┴─────┘,
shape: (1, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ b   ┆ 2   ┆ 4   │
└─────┴─────┴─────┘,
shape: (1, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ b   ┆ 3   ┆ 2   │
└─────┴─────┴─────┘,
shape: (1, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ c   ┆ 3   ┆ 1   │
└─────┴─────┴─────┘]

Return the partitions as a dictionary by specifying as_dict=True.

>>> import polars.selectors as cs
>>> df.partition_by(cs.string(), as_dict=True)  
{('a',): shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ a   ┆ 1   ┆ 5   │
│ a   ┆ 1   ┆ 3   │
└─────┴─────┴─────┘,
('b',): shape: (2, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ b   ┆ 2   ┆ 4   │
│ b   ┆ 3   ┆ 2   │
└─────┴─────┴─────┘,
('c',): shape: (1, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ c   ┆ 3   ┆ 1   │
└─────┴─────┴─────┘}
pipe(
function: Callable[Concatenate[DataFrame, P], T],
*args: P.args,
**kwargs: P.kwargs,
) T[source]

Offers a structured way to apply a sequence of user-defined functions (UDFs).

Parameters:
function

Callable; will receive the frame as the first parameter, followed by any given args/kwargs.

*args

Arguments to pass to the UDF.

**kwargs

Keyword arguments to pass to the UDF.

Notes

It is recommended to use LazyFrame when piping operations, in order to fully take advantage of query optimization and parallelization. See df.lazy().

Examples

>>> def cast_str_to_int(data, col_name):
...     return data.with_columns(pl.col(col_name).cast(pl.Int64))
>>> df = pl.DataFrame({"a": [1, 2, 3, 4], "b": ["10", "20", "30", "40"]})
>>> df.pipe(cast_str_to_int, col_name="b")
shape: (4, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 10  │
│ 2   ┆ 20  │
│ 3   ┆ 30  │
│ 4   ┆ 40  │
└─────┴─────┘
>>> df = pl.DataFrame({"b": [1, 2], "a": [3, 4]})
>>> df
shape: (2, 2)
┌─────┬─────┐
│ b   ┆ a   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 3   │
│ 2   ┆ 4   │
└─────┴─────┘
>>> df.pipe(lambda tdf: tdf.select(sorted(tdf.columns)))
shape: (2, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 3   ┆ 1   │
│ 4   ┆ 2   │
└─────┴─────┘
pivot(
*,
values: ColumnNameOrSelector | Sequence[ColumnNameOrSelector] | None,
index: ColumnNameOrSelector | Sequence[ColumnNameOrSelector] | None,
columns: ColumnNameOrSelector | Sequence[ColumnNameOrSelector] | None,
aggregate_function: PivotAgg | Expr | None = None,
maintain_order: bool = True,
sort_columns: bool = False,
separator: str = '_',
) Self[source]

Create a spreadsheet-style pivot table as a DataFrame.

Only available in eager mode. See “Examples” section below for how to do a “lazy pivot” if you know the unique column values in advance.

Parameters:
values

Column values to aggregate. Can be multiple columns if the columns arguments contains multiple columns as well. If None, all remaining columns will be used.

index

One or multiple keys to group by.

columns

Name of the column(s) whose values will be used as the header of the output DataFrame.

aggregate_function

Choose from:

  • None: no aggregation takes place, will raise error if multiple values are in group.

  • A predefined aggregate function string, one of {‘min’, ‘max’, ‘first’, ‘last’, ‘sum’, ‘mean’, ‘median’, ‘len’}

  • An expression to do the aggregation.

maintain_order

Sort the grouped keys so that the output order is predictable.

sort_columns

Sort the transposed columns by name. Default is by order of discovery.

separator

Used as separator/delimiter in generated column names.

Returns:
DataFrame

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": ["one", "one", "two", "two", "one", "two"],
...         "bar": ["y", "y", "y", "x", "x", "x"],
...         "baz": [1, 2, 3, 4, 5, 6],
...     }
... )
>>> df.pivot(index="foo", columns="bar", values="baz", aggregate_function="sum")
shape: (2, 3)
┌─────┬─────┬─────┐
│ foo ┆ y   ┆ x   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ one ┆ 3   ┆ 5   │
│ two ┆ 3   ┆ 10  │
└─────┴─────┴─────┘

Pivot using selectors to determine the index/values/columns:

>>> import polars.selectors as cs
>>> df.pivot(
...     index=cs.string(),
...     columns=cs.string(),
...     values=cs.numeric(),
...     aggregate_function="sum",
...     sort_columns=True,
... ).sort(
...     by=cs.string(),
... )
shape: (4, 6)
┌─────┬─────┬─────────────┬─────────────┬─────────────┬─────────────┐
│ foo ┆ bar ┆ {"one","x"} ┆ {"one","y"} ┆ {"two","x"} ┆ {"two","y"} │
│ --- ┆ --- ┆ ---         ┆ ---         ┆ ---         ┆ ---         │
│ str ┆ str ┆ i64         ┆ i64         ┆ i64         ┆ i64         │
╞═════╪═════╪═════════════╪═════════════╪═════════════╪═════════════╡
│ one ┆ x   ┆ 5           ┆ null        ┆ null        ┆ null        │
│ one ┆ y   ┆ null        ┆ 3           ┆ null        ┆ null        │
│ two ┆ x   ┆ null        ┆ null        ┆ 10          ┆ null        │
│ two ┆ y   ┆ null        ┆ null        ┆ null        ┆ 3           │
└─────┴─────┴─────────────┴─────────────┴─────────────┴─────────────┘

Run an expression as aggregation function

>>> df = pl.DataFrame(
...     {
...         "col1": ["a", "a", "a", "b", "b", "b"],
...         "col2": ["x", "x", "x", "x", "y", "y"],
...         "col3": [6, 7, 3, 2, 5, 7],
...     }
... )
>>> df.pivot(
...     index="col1",
...     columns="col2",
...     values="col3",
...     aggregate_function=pl.element().tanh().mean(),
... )
shape: (2, 3)
┌──────┬──────────┬──────────┐
│ col1 ┆ x        ┆ y        │
│ ---  ┆ ---      ┆ ---      │
│ str  ┆ f64      ┆ f64      │
╞══════╪══════════╪══════════╡
│ a    ┆ 0.998347 ┆ null     │
│ b    ┆ 0.964028 ┆ 0.999954 │
└──────┴──────────┴──────────┘

Note that pivot is only available in eager mode. If you know the unique column values in advance, you can use polars.LazyFrame.groupby() to get the same result as above in lazy mode:

>>> index = pl.col("col1")
>>> columns = pl.col("col2")
>>> values = pl.col("col3")
>>> unique_column_values = ["x", "y"]
>>> aggregate_function = lambda col: col.tanh().mean()
>>> df.lazy().group_by(index).agg(
...     aggregate_function(values.filter(columns == value)).alias(value)
...     for value in unique_column_values
... ).collect()  
shape: (2, 3)
┌──────┬──────────┬──────────┐
│ col1 ┆ x        ┆ y        │
│ ---  ┆ ---      ┆ ---      │
│ str  ┆ f64      ┆ f64      │
╞══════╪══════════╪══════════╡
│ a    ┆ 0.998347 ┆ null     │
│ b    ┆ 0.964028 ┆ 0.999954 │
└──────┴──────────┴──────────┘
property plot: hvPlotTabularPolars[source]

Create a plot namespace.

Polars does not implement plotting logic itself, but instead defers to hvplot. Please see the hvplot reference gallery for more information and documentation.

Examples

Scatter plot:

>>> df = pl.DataFrame(
...     {
...         "length": [1, 4, 6],
...         "width": [4, 5, 6],
...         "species": ["setosa", "setosa", "versicolor"],
...     }
... )
>>> df.plot.scatter(x="length", y="width", by="species")  

Line plot:

>>> from datetime import date
>>> df = pl.DataFrame(
...     {
...         "date": [date(2020, 1, 2), date(2020, 1, 3), date(2020, 1, 4)],
...         "stock_1": [1, 4, 6],
...         "stock_2": [1, 5, 2],
...     }
... )
>>> df.plot.line(x="date", y=["stock_1", "stock_2"])  

For more info on what you can pass, you can use hvplot.help:

>>> import hvplot  
>>> hvplot.help("scatter")  
product() DataFrame[source]

Aggregate the columns of this DataFrame to their product values.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3],
...         "b": [0.5, 4, 10],
...         "c": [True, True, False],
...     }
... )
>>> df.product()
shape: (1, 3)
┌─────┬──────┬─────┐
│ a   ┆ b    ┆ c   │
│ --- ┆ ---  ┆ --- │
│ i64 ┆ f64  ┆ i64 │
╞═════╪══════╪═════╡
│ 6   ┆ 20.0 ┆ 0   │
└─────┴──────┴─────┘
quantile(
quantile: float,
interpolation: RollingInterpolationMethod = 'nearest',
) Self[source]

Aggregate the columns of this DataFrame to their quantile value.

Parameters:
quantile

Quantile between 0.0 and 1.0.

interpolation{‘nearest’, ‘higher’, ‘lower’, ‘midpoint’, ‘linear’}

Interpolation method.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.quantile(0.5, "nearest")
shape: (1, 3)
┌─────┬─────┬──────┐
│ foo ┆ bar ┆ ham  │
│ --- ┆ --- ┆ ---  │
│ f64 ┆ f64 ┆ str  │
╞═════╪═════╪══════╡
│ 2.0 ┆ 7.0 ┆ null │
└─────┴─────┴──────┘
rechunk() Self[source]

Rechunk the data in this DataFrame to a contiguous allocation.

This will make sure all subsequent operations have optimal and predictable performance.

rename(
mapping: dict[str, str] | Callable[[str], str],
) DataFrame[source]

Rename column names.

Parameters:
mapping

Key value pairs that map from old name to new name, or a function that takes the old name as input and returns the new name.

Examples

>>> df = pl.DataFrame(
...     {"foo": [1, 2, 3], "bar": [6, 7, 8], "ham": ["a", "b", "c"]}
... )
>>> df.rename({"foo": "apple"})
shape: (3, 3)
┌───────┬─────┬─────┐
│ apple ┆ bar ┆ ham │
│ ---   ┆ --- ┆ --- │
│ i64   ┆ i64 ┆ str │
╞═══════╪═════╪═════╡
│ 1     ┆ 6   ┆ a   │
│ 2     ┆ 7   ┆ b   │
│ 3     ┆ 8   ┆ c   │
└───────┴─────┴─────┘
>>> df.rename(lambda column_name: "c" + column_name[1:])
shape: (3, 3)
┌─────┬─────┬─────┐
│ coo ┆ car ┆ cam │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 6   ┆ a   │
│ 2   ┆ 7   ┆ b   │
│ 3   ┆ 8   ┆ c   │
└─────┴─────┴─────┘
replace(column: str, new_column: Series) Self[source]

Replace a column by a new Series.

Parameters:
column

Column to replace.

new_column

New column to insert.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
>>> s = pl.Series([10, 20, 30])
>>> df.replace("foo", s)  # works in-place!  
shape: (3, 2)
┌─────┬─────┐
│ foo ┆ bar │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 10  ┆ 4   │
│ 20  ┆ 5   │
│ 30  ┆ 6   │
└─────┴─────┘
replace_at_idx(index: int, new_column: Series) Self[source]

Replace a column at an index location.

Deprecated since version 0.19.14: This method has been renamed to replace_column().

Parameters:
index

Column index.

new_column

Series that will replace the column.

replace_column(index: int, column: Series) Self[source]

Replace a column at an index location.

This operation is in place.

Parameters:
index

Column index.

column

Series that will replace the column.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> s = pl.Series("apple", [10, 20, 30])
>>> df.replace_column(0, s)
shape: (3, 3)
┌───────┬─────┬─────┐
│ apple ┆ bar ┆ ham │
│ ---   ┆ --- ┆ --- │
│ i64   ┆ i64 ┆ str │
╞═══════╪═════╪═════╡
│ 10    ┆ 6   ┆ a   │
│ 20    ┆ 7   ┆ b   │
│ 30    ┆ 8   ┆ c   │
└───────┴─────┴─────┘
reverse() DataFrame[source]

Reverse the DataFrame.

Examples

>>> df = pl.DataFrame(
...     {
...         "key": ["a", "b", "c"],
...         "val": [1, 2, 3],
...     }
... )
>>> df.reverse()
shape: (3, 2)
┌─────┬─────┐
│ key ┆ val │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════╪═════╡
│ c   ┆ 3   │
│ b   ┆ 2   │
│ a   ┆ 1   │
└─────┴─────┘
rolling(
index_column: IntoExpr,
*,
period: str | timedelta,
offset: str | timedelta | None = None,
closed: ClosedInterval = 'right',
by: IntoExpr | Iterable[IntoExpr] | None = None,
check_sorted: bool = True,
) RollingGroupBy[source]

Create rolling groups based on a temporal or integer column.

Different from a group_by_dynamic the windows are now determined by the individual values and are not of constant intervals. For constant intervals use DataFrame.group_by_dynamic().

If you have a time series <t_0, t_1, ..., t_n>, then by default the windows created will be

  • (t_0 - period, t_0]

  • (t_1 - period, t_1]

  • (t_n - period, t_n]

whereas if you pass a non-default offset, then the windows will be

  • (t_0 + offset, t_0 + offset + period]

  • (t_1 + offset, t_1 + offset + period]

  • (t_n + offset, t_n + offset + period]

The period and offset arguments are created either from a timedelta, or by using the following string language:

  • 1ns (1 nanosecond)

  • 1us (1 microsecond)

  • 1ms (1 millisecond)

  • 1s (1 second)

  • 1m (1 minute)

  • 1h (1 hour)

  • 1d (1 calendar day)

  • 1w (1 calendar week)

  • 1mo (1 calendar month)

  • 1q (1 calendar quarter)

  • 1y (1 calendar year)

  • 1i (1 index count)

Or combine them: “3d12h4m25s” # 3 days, 12 hours, 4 minutes, and 25 seconds

By “calendar day”, we mean the corresponding time on the next day (which may not be 24 hours, due to daylight savings). Similarly for “calendar week”, “calendar month”, “calendar quarter”, and “calendar year”.

Parameters:
index_column

Column used to group based on the time window. Often of type Date/Datetime. This column must be sorted in ascending order (or, if by is specified, then it must be sorted in ascending order within each group).

In case of a rolling operation on indices, dtype needs to be one of {UInt32, UInt64, Int32, Int64}. Note that the first three get temporarily cast to Int64, so if performance matters use an Int64 column.

period

length of the window - must be non-negative

offset

offset of the window. Default is -period

closed{‘right’, ‘left’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive).

by

Also group by this column/these columns

check_sorted

When the by argument is given, polars can not check sortedness by the metadata and has to do a full scan on the index column to verify data is sorted. This is expensive. If you are sure the data within the by groups is sorted, you can set this to False. Doing so incorrectly will lead to incorrect output

Returns:
RollingGroupBy

Object you can call .agg on to aggregate by groups, the result of which will be sorted by index_column (but note that if by columns are passed, it will only be sorted within each by group).

See also

group_by_dynamic

Examples

>>> dates = [
...     "2020-01-01 13:45:48",
...     "2020-01-01 16:42:13",
...     "2020-01-01 16:45:09",
...     "2020-01-02 18:12:48",
...     "2020-01-03 19:45:32",
...     "2020-01-08 23:16:43",
... ]
>>> df = pl.DataFrame({"dt": dates, "a": [3, 7, 5, 9, 2, 1]}).with_columns(
...     pl.col("dt").str.strptime(pl.Datetime).set_sorted()
... )
>>> out = df.rolling(index_column="dt", period="2d").agg(
...     [
...         pl.sum("a").alias("sum_a"),
...         pl.min("a").alias("min_a"),
...         pl.max("a").alias("max_a"),
...     ]
... )
>>> assert out["sum_a"].to_list() == [3, 10, 15, 24, 11, 1]
>>> assert out["max_a"].to_list() == [3, 7, 7, 9, 9, 1]
>>> assert out["min_a"].to_list() == [3, 3, 3, 3, 2, 1]
>>> out
shape: (6, 4)
┌─────────────────────┬───────┬───────┬───────┐
│ dt                  ┆ sum_a ┆ min_a ┆ max_a │
│ ---                 ┆ ---   ┆ ---   ┆ ---   │
│ datetime[μs]        ┆ i64   ┆ i64   ┆ i64   │
╞═════════════════════╪═══════╪═══════╪═══════╡
│ 2020-01-01 13:45:48 ┆ 3     ┆ 3     ┆ 3     │
│ 2020-01-01 16:42:13 ┆ 10    ┆ 3     ┆ 7     │
│ 2020-01-01 16:45:09 ┆ 15    ┆ 3     ┆ 7     │
│ 2020-01-02 18:12:48 ┆ 24    ┆ 3     ┆ 9     │
│ 2020-01-03 19:45:32 ┆ 11    ┆ 2     ┆ 9     │
│ 2020-01-08 23:16:43 ┆ 1     ┆ 1     ┆ 1     │
└─────────────────────┴───────┴───────┴───────┘
row(
index: int | None = None,
*,
by_predicate: Expr | None = None,
named: bool = False,
) tuple[Any, ...] | dict[str, Any][source]

Get the values of a single row, either by index or by predicate.

Parameters:
index

Row index.

by_predicate

Select the row according to a given expression/predicate.

named

Return a dictionary instead of a tuple. The dictionary is a mapping of column name to row value. This is more expensive than returning a regular tuple, but allows for accessing values by column name.

Returns:
tuple (default) or dictionary of row values

Warning

You should NEVER use this method to iterate over a DataFrame; if you require row-iteration you should strongly prefer use of iter_rows() instead.

See also

iter_rows

Row iterator over frame data (does not materialise all rows).

rows

Materialise all frame data as a list of rows (potentially expensive).

item

Return dataframe element as a scalar.

Notes

The index and by_predicate params are mutually exclusive. Additionally, to ensure clarity, the by_predicate parameter must be supplied by keyword.

When using by_predicate it is an error condition if anything other than one row is returned; more than one row raises TooManyRowsReturnedError, and zero rows will raise NoRowsReturnedError (both inherit from RowsError).

Examples

Specify an index to return the row at the given index as a tuple.

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.row(2)
(3, 8, 'c')

Specify named=True to get a dictionary instead with a mapping of column names to row values.

>>> df.row(2, named=True)
{'foo': 3, 'bar': 8, 'ham': 'c'}

Use by_predicate to return the row that matches the given predicate.

>>> df.row(by_predicate=(pl.col("ham") == "b"))
(2, 7, 'b')
rows(
*,
named: bool = False,
) list[tuple[Any, ...]] | list[dict[str, Any]][source]

Returns all data in the DataFrame as a list of rows of python-native values.

By default, each row is returned as a tuple of values given in the same order as the frame columns. Setting named=True will return rows of dictionaries instead.

Parameters:
named

Return dictionaries instead of tuples. The dictionaries are a mapping of column name to row value. This is more expensive than returning a regular tuple, but allows for accessing values by column name.

Returns:
list of row value tuples (default), or list of dictionaries (if named=True).

Warning

Row-iteration is not optimal as the underlying data is stored in columnar form; where possible, prefer export via one of the dedicated export/output methods. You should also consider using iter_rows instead, to avoid materialising all the data at once; there is little performance difference between the two, but peak memory can be reduced if processing rows in batches.

See also

iter_rows

Row iterator over frame data (does not materialise all rows).

rows_by_key

Materialises frame data as a key-indexed dictionary.

Notes

If you have ns-precision temporal values you should be aware that Python natively only supports up to μs-precision; ns-precision values will be truncated to microseconds on conversion to Python. If this matters to your use-case you should export to a different format (such as Arrow or NumPy).

Examples

>>> df = pl.DataFrame(
...     {
...         "x": ["a", "b", "b", "a"],
...         "y": [1, 2, 3, 4],
...         "z": [0, 3, 6, 9],
...     }
... )
>>> df.rows()
[('a', 1, 0), ('b', 2, 3), ('b', 3, 6), ('a', 4, 9)]
>>> df.rows(named=True)
[{'x': 'a', 'y': 1, 'z': 0},
 {'x': 'b', 'y': 2, 'z': 3},
 {'x': 'b', 'y': 3, 'z': 6},
 {'x': 'a', 'y': 4, 'z': 9}]
rows_by_key(
key: ColumnNameOrSelector | Sequence[ColumnNameOrSelector],
*,
named: bool = False,
include_key: bool = False,
unique: bool = False,
) dict[Any, Iterable[Any]][source]

Returns all data as a dictionary of python-native values keyed by some column.

This method is like rows, but instead of returning rows in a flat list, rows are grouped by the values in the key column(s) and returned as a dictionary.

Note that this method should not be used in place of native operations, due to the high cost of materializing all frame data out into a dictionary; it should be used only when you need to move the values out into a Python data structure or other object that cannot operate directly with Polars/Arrow.

Parameters:
key

The column(s) to use as the key for the returned dictionary. If multiple columns are specified, the key will be a tuple of those values, otherwise it will be a string.

named

Return dictionary rows instead of tuples, mapping column name to row value.

include_key

Include key values inline with the associated data (by default the key values are omitted as a memory/performance optimisation, as they can be reoconstructed from the key).

unique

Indicate that the key is unique; this will result in a 1:1 mapping from key to a single associated row. Note that if the key is not actually unique the last row with the given key will be returned.

See also

rows

Materialize all frame data as a list of rows (potentially expensive).

iter_rows

Row iterator over frame data (does not materialize all rows).

Notes

If you have ns-precision temporal values you should be aware that Python natively only supports up to μs-precision; ns-precision values will be truncated to microseconds on conversion to Python. If this matters to your use-case you should export to a different format (such as Arrow or NumPy).

Examples

>>> df = pl.DataFrame(
...     {
...         "w": ["a", "b", "b", "a"],
...         "x": ["q", "q", "q", "k"],
...         "y": [1.0, 2.5, 3.0, 4.5],
...         "z": [9, 8, 7, 6],
...     }
... )

Group rows by the given key column(s):

>>> df.rows_by_key(key=["w"])
defaultdict(<class 'list'>,
    {'a': [('q', 1.0, 9), ('k', 4.5, 6)],
     'b': [('q', 2.5, 8), ('q', 3.0, 7)]})

Return the same row groupings as dictionaries:

>>> df.rows_by_key(key=["w"], named=True)
defaultdict(<class 'list'>,
    {'a': [{'x': 'q', 'y': 1.0, 'z': 9},
           {'x': 'k', 'y': 4.5, 'z': 6}],
     'b': [{'x': 'q', 'y': 2.5, 'z': 8},
           {'x': 'q', 'y': 3.0, 'z': 7}]})

Return row groupings, assuming keys are unique:

>>> df.rows_by_key(key=["z"], unique=True)
{9: ('a', 'q', 1.0),
 8: ('b', 'q', 2.5),
 7: ('b', 'q', 3.0),
 6: ('a', 'k', 4.5)}

Return row groupings as dictionaries, assuming keys are unique:

>>> df.rows_by_key(key=["z"], named=True, unique=True)
{9: {'w': 'a', 'x': 'q', 'y': 1.0},
 8: {'w': 'b', 'x': 'q', 'y': 2.5},
 7: {'w': 'b', 'x': 'q', 'y': 3.0},
 6: {'w': 'a', 'x': 'k', 'y': 4.5}}

Return dictionary rows grouped by a compound key, including key values:

>>> df.rows_by_key(key=["w", "x"], named=True, include_key=True)
defaultdict(<class 'list'>,
    {('a', 'q'): [{'w': 'a', 'x': 'q', 'y': 1.0, 'z': 9}],
     ('b', 'q'): [{'w': 'b', 'x': 'q', 'y': 2.5, 'z': 8},
                  {'w': 'b', 'x': 'q', 'y': 3.0, 'z': 7}],
     ('a', 'k'): [{'w': 'a', 'x': 'k', 'y': 4.5, 'z': 6}]})
sample(
n: int | Series | None = None,
*,
fraction: float | Series | None = None,
with_replacement: bool = False,
shuffle: bool = False,
seed: int | None = None,
) Self[source]

Sample from this DataFrame.

Parameters:
n

Number of items to return. Cannot be used with fraction. Defaults to 1 if fraction is None.

fraction

Fraction of items to return. Cannot be used with n.

with_replacement

Allow values to be sampled more than once.

shuffle

If set to True, the order of the sampled rows will be shuffled. If set to False (default), the order of the returned rows will be neither stable nor fully random.

seed

Seed for the random number generator. If set to None (default), a random seed is generated for each sample operation.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.sample(n=2, seed=0)  
shape: (2, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 3   ┆ 8   ┆ c   │
│ 2   ┆ 7   ┆ b   │
└─────┴─────┴─────┘
property schema: OrderedDict[str, DataType][source]

Get a dict[column name, DataType].

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6.0, 7.0, 8.0],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.schema
OrderedDict({'foo': Int64, 'bar': Float64, 'ham': String})
select(
*exprs: IntoExpr | Iterable[IntoExpr],
**named_exprs: IntoExpr,
) DataFrame[source]

Select columns from this DataFrame.

Parameters:
*exprs

Column(s) to select, specified as positional arguments. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.

**named_exprs

Additional columns to select, specified as keyword arguments. The columns will be renamed to the keyword used.

Examples

Pass the name of a column to select that column.

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.select("foo")
shape: (3, 1)
┌─────┐
│ foo │
│ --- │
│ i64 │
╞═════╡
│ 1   │
│ 2   │
│ 3   │
└─────┘

Multiple columns can be selected by passing a list of column names.

>>> df.select(["foo", "bar"])
shape: (3, 2)
┌─────┬─────┐
│ foo ┆ bar │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 6   │
│ 2   ┆ 7   │
│ 3   ┆ 8   │
└─────┴─────┘

Multiple columns can also be selected using positional arguments instead of a list. Expressions are also accepted.

>>> df.select(pl.col("foo"), pl.col("bar") + 1)
shape: (3, 2)
┌─────┬─────┐
│ foo ┆ bar │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 7   │
│ 2   ┆ 8   │
│ 3   ┆ 9   │
└─────┴─────┘

Use keyword arguments to easily name your expression inputs.

>>> df.select(threshold=pl.when(pl.col("foo") > 2).then(10).otherwise(0))
shape: (3, 1)
┌───────────┐
│ threshold │
│ ---       │
│ i32       │
╞═══════════╡
│ 0         │
│ 0         │
│ 10        │
└───────────┘

Expressions with multiple outputs can be automatically instantiated as Structs by enabling the setting Config.set_auto_structify(True):

>>> with pl.Config(auto_structify=True):
...     df.select(
...         is_odd=(pl.col(pl.INTEGER_DTYPES) % 2).name.suffix("_is_odd"),
...     )
shape: (3, 1)
┌───────────┐
│ is_odd    │
│ ---       │
│ struct[2] │
╞═══════════╡
│ {1,0}     │
│ {0,1}     │
│ {1,0}     │
└───────────┘
select_seq(
*exprs: IntoExpr | Iterable[IntoExpr],
**named_exprs: IntoExpr,
) DataFrame[source]

Select columns from this DataFrame.

This will run all expression sequentially instead of in parallel. Use this when the work per expression is cheap.

Parameters:
*exprs

Column(s) to select, specified as positional arguments. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.

**named_exprs

Additional columns to select, specified as keyword arguments. The columns will be renamed to the keyword used.

See also

select
set_sorted(
column: str | Iterable[str],
*more_columns: str,
descending: bool = False,
) DataFrame[source]

Indicate that one or multiple columns are sorted.

Parameters:
column

Columns that are sorted

more_columns

Additional columns that are sorted, specified as positional arguments.

descending

Whether the columns are sorted in descending order.

property shape: tuple[int, int][source]

Get the shape of the DataFrame.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5]})
>>> df.shape
(5, 1)
shift(n: int = 1, *, fill_value: IntoExpr | None = None) DataFrame[source]

Shift values by the given number of indices.

Parameters:
n

Number of indices to shift forward. If a negative value is passed, values are shifted in the opposite direction instead.

fill_value

Fill the resulting null values with this value. Accepts expression input. Non-expression inputs are parsed as literals.

Notes

This method is similar to the LAG operation in SQL when the value for n is positive. With a negative value for n, it is similar to LEAD.

Examples

By default, values are shifted forward by one index.

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

Pass a negative value to shift in the opposite direction instead.

>>> df.shift(-2)
shape: (4, 2)
┌──────┬──────┐
│ a    ┆ b    │
│ ---  ┆ ---  │
│ i64  ┆ i64  │
╞══════╪══════╡
│ 3    ┆ 7    │
│ 4    ┆ 8    │
│ null ┆ null │
│ null ┆ null │
└──────┴──────┘

Specify fill_value to fill the resulting null values.

>>> df.shift(-2, fill_value=100)
shape: (4, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 3   ┆ 7   │
│ 4   ┆ 8   │
│ 100 ┆ 100 │
│ 100 ┆ 100 │
└─────┴─────┘
shift_and_fill(
fill_value: int | str | float,
*,
n: int = 1,
) DataFrame[source]

Shift values by the given number of places and fill the resulting null values.

Deprecated since version 0.19.12: Use shift() instead.

Parameters:
fill_value

fill None values with this value.

n

Number of places to shift (may be negative).

shrink_to_fit(*, in_place: bool = False) Self[source]

Shrink DataFrame memory usage.

Shrinks to fit the exact capacity needed to hold the data.

slice(offset: int, length: int | None = None) Self[source]

Get a slice of this DataFrame.

Parameters:
offset

Start index. Negative indexing is supported.

length

Length of the slice. If set to None, all rows starting at the offset will be selected.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6.0, 7.0, 8.0],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.slice(1, 2)
shape: (2, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ str │
╞═════╪═════╪═════╡
│ 2   ┆ 7.0 ┆ b   │
│ 3   ┆ 8.0 ┆ c   │
└─────┴─────┴─────┘
sort(
by: IntoExpr | Iterable[IntoExpr],
*more_by: IntoExpr,
descending: bool | Sequence[bool] = False,
nulls_last: bool = False,
) DataFrame[source]

Sort the dataframe by the given columns.

Parameters:
by

Column(s) to sort by. Accepts expression input. Strings are parsed as column names.

*more_by

Additional columns to sort by, specified as positional arguments.

descending

Sort in descending order. When sorting by multiple columns, can be specified per column by passing a sequence of booleans.

nulls_last

Place null values last.

Examples

Pass a single column name to sort by that column.

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, None],
...         "b": [6.0, 5.0, 4.0],
...         "c": ["a", "c", "b"],
...     }
... )
>>> df.sort("a")
shape: (3, 3)
┌──────┬─────┬─────┐
│ a    ┆ b   ┆ c   │
│ ---  ┆ --- ┆ --- │
│ i64  ┆ f64 ┆ str │
╞══════╪═════╪═════╡
│ null ┆ 4.0 ┆ b   │
│ 1    ┆ 6.0 ┆ a   │
│ 2    ┆ 5.0 ┆ c   │
└──────┴─────┴─────┘

Sorting by expressions is also supported.

>>> df.sort(pl.col("a") + pl.col("b") * 2, nulls_last=True)
shape: (3, 3)
┌──────┬─────┬─────┐
│ a    ┆ b   ┆ c   │
│ ---  ┆ --- ┆ --- │
│ i64  ┆ f64 ┆ str │
╞══════╪═════╪═════╡
│ 2    ┆ 5.0 ┆ c   │
│ 1    ┆ 6.0 ┆ a   │
│ null ┆ 4.0 ┆ b   │
└──────┴─────┴─────┘

Sort by multiple columns by passing a list of columns.

>>> df.sort(["c", "a"], descending=True)
shape: (3, 3)
┌──────┬─────┬─────┐
│ a    ┆ b   ┆ c   │
│ ---  ┆ --- ┆ --- │
│ i64  ┆ f64 ┆ str │
╞══════╪═════╪═════╡
│ 2    ┆ 5.0 ┆ c   │
│ null ┆ 4.0 ┆ b   │
│ 1    ┆ 6.0 ┆ a   │
└──────┴─────┴─────┘

Or use positional arguments to sort by multiple columns in the same way.

>>> df.sort("c", "a", descending=[False, True])
shape: (3, 3)
┌──────┬─────┬─────┐
│ a    ┆ b   ┆ c   │
│ ---  ┆ --- ┆ --- │
│ i64  ┆ f64 ┆ str │
╞══════╪═════╪═════╡
│ 1    ┆ 6.0 ┆ a   │
│ null ┆ 4.0 ┆ b   │
│ 2    ┆ 5.0 ┆ c   │
└──────┴─────┴─────┘
std(ddof: int = 1) Self[source]

Aggregate the columns of this DataFrame to their standard deviation value.

Parameters:
ddof

“Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of elements. By default ddof is 1.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.std()
shape: (1, 3)
┌─────┬─────┬──────┐
│ foo ┆ bar ┆ ham  │
│ --- ┆ --- ┆ ---  │
│ f64 ┆ f64 ┆ str  │
╞═════╪═════╪══════╡
│ 1.0 ┆ 1.0 ┆ null │
└─────┴─────┴──────┘
>>> df.std(ddof=0)
shape: (1, 3)
┌──────────┬──────────┬──────┐
│ foo      ┆ bar      ┆ ham  │
│ ---      ┆ ---      ┆ ---  │
│ f64      ┆ f64      ┆ str  │
╞══════════╪══════════╪══════╡
│ 0.816497 ┆ 0.816497 ┆ null │
└──────────┴──────────┴──────┘
sum(
*,
axis: int | None = None,
null_strategy: NullStrategy = 'ignore',
) Self | Series[source]

Aggregate the columns of this DataFrame to their sum value.

Parameters:
axis

Either 0 (vertical) or 1 (horizontal).

Deprecated since version 0.19.14: This argument will be removed in a future version. This method will only support vertical aggregation, as if axis were set to 0. To perform horizontal aggregation, use sum_horizontal().

null_strategy{‘ignore’, ‘propagate’}

This argument is only used if axis == 1.

Deprecated since version 0.19.14: This argument will be removed in a future version.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.sum()
shape: (1, 3)
┌─────┬─────┬──────┐
│ foo ┆ bar ┆ ham  │
│ --- ┆ --- ┆ ---  │
│ i64 ┆ i64 ┆ str  │
╞═════╪═════╪══════╡
│ 6   ┆ 21  ┆ null │
└─────┴─────┴──────┘
sum_horizontal(*, ignore_nulls: bool = True) Series[source]

Sum all values horizontally across columns.

Parameters:
ignore_nulls

Ignore null values (default). If set to False, any null value in the input will lead to a null output.

Returns:
Series

A Series named "sum".

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [4.0, 5.0, 6.0],
...     }
... )
>>> df.sum_horizontal()
shape: (3,)
Series: 'sum' [f64]
[
        5.0
        7.0
        9.0
]
tail(n: int = 5) Self[source]

Get the last n rows.

Parameters:
n

Number of rows to return. If a negative value is passed, return all rows except the first abs(n).

See also

head, slice

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3, 4, 5],
...         "bar": [6, 7, 8, 9, 10],
...         "ham": ["a", "b", "c", "d", "e"],
...     }
... )
>>> df.tail(3)
shape: (3, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 3   ┆ 8   ┆ c   │
│ 4   ┆ 9   ┆ d   │
│ 5   ┆ 10  ┆ e   │
└─────┴─────┴─────┘

Pass a negative value to get all rows except the first abs(n).

>>> df.tail(-3)
shape: (2, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 4   ┆ 9   ┆ d   │
│ 5   ┆ 10  ┆ e   │
└─────┴─────┴─────┘
take_every(n: int, offset: int = 0) DataFrame[source]

Take every nth row in the DataFrame and return as a new DataFrame.

Deprecated since version 0.19.14: This method has been renamed to gather_every().

Parameters:
n

Gather every n-th row.

offset

Starting index.

to_arrow() Table[source]

Collect the underlying arrow arrays in an Arrow Table.

This operation is mostly zero copy.

Data types that do copy:
  • CategoricalType

Examples

>>> df = pl.DataFrame(
...     {"foo": [1, 2, 3, 4, 5, 6], "bar": ["a", "b", "c", "d", "e", "f"]}
... )
>>> df.to_arrow()
pyarrow.Table
foo: int64
bar: large_string
----
foo: [[1,2,3,4,5,6]]
bar: [["a","b","c","d","e","f"]]
to_dict(*, as_series: bool = True) dict[str, Series] | dict[str, list[Any]][source]

Convert DataFrame to a dictionary mapping column name to values.

Parameters:
as_series

True -> Values are Series False -> Values are List[Any]

Examples

>>> df = pl.DataFrame(
...     {
...         "A": [1, 2, 3, 4, 5],
...         "fruits": ["banana", "banana", "apple", "apple", "banana"],
...         "B": [5, 4, 3, 2, 1],
...         "cars": ["beetle", "audi", "beetle", "beetle", "beetle"],
...         "optional": [28, 300, None, 2, -30],
...     }
... )
>>> df
shape: (5, 5)
┌─────┬────────┬─────┬────────┬──────────┐
│ A   ┆ fruits ┆ B   ┆ cars   ┆ optional │
│ --- ┆ ---    ┆ --- ┆ ---    ┆ ---      │
│ i64 ┆ str    ┆ i64 ┆ str    ┆ i64      │
╞═════╪════════╪═════╪════════╪══════════╡
│ 1   ┆ banana ┆ 5   ┆ beetle ┆ 28       │
│ 2   ┆ banana ┆ 4   ┆ audi   ┆ 300      │
│ 3   ┆ apple  ┆ 3   ┆ beetle ┆ null     │
│ 4   ┆ apple  ┆ 2   ┆ beetle ┆ 2        │
│ 5   ┆ banana ┆ 1   ┆ beetle ┆ -30      │
└─────┴────────┴─────┴────────┴──────────┘
>>> df.to_dict(as_series=False)
{'A': [1, 2, 3, 4, 5],
'fruits': ['banana', 'banana', 'apple', 'apple', 'banana'],
'B': [5, 4, 3, 2, 1],
'cars': ['beetle', 'audi', 'beetle', 'beetle', 'beetle'],
'optional': [28, 300, None, 2, -30]}
>>> df.to_dict(as_series=True)
{'A': shape: (5,)
Series: 'A' [i64]
[
    1
    2
    3
    4
    5
], 'fruits': shape: (5,)
Series: 'fruits' [str]
[
    "banana"
    "banana"
    "apple"
    "apple"
    "banana"
], 'B': shape: (5,)
Series: 'B' [i64]
[
    5
    4
    3
    2
    1
], 'cars': shape: (5,)
Series: 'cars' [str]
[
    "beetle"
    "audi"
    "beetle"
    "beetle"
    "beetle"
], 'optional': shape: (5,)
Series: 'optional' [i64]
[
    28
    300
    null
    2
    -30
]}
to_dicts() list[dict[str, Any]][source]

Convert every row to a dictionary of Python-native values.

Notes

If you have ns-precision temporal values you should be aware that Python natively only supports up to μs-precision; ns-precision values will be truncated to microseconds on conversion to Python. If this matters to your use-case you should export to a different format (such as Arrow or NumPy).

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
>>> df.to_dicts()
[{'foo': 1, 'bar': 4}, {'foo': 2, 'bar': 5}, {'foo': 3, 'bar': 6}]
to_dummies(
columns: ColumnNameOrSelector | Sequence[ColumnNameOrSelector] | None = None,
*,
separator: str = '_',
drop_first: bool = False,
) Self[source]

Convert categorical variables into dummy/indicator variables.

Parameters:
columns

Column name(s) or selector(s) that should be converted to dummy variables. If set to None (default), convert all columns.

separator

Separator/delimiter used when generating column names.

drop_first

Remove the first category from the variables being encoded.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2],
...         "bar": [3, 4],
...         "ham": ["a", "b"],
...     }
... )
>>> df.to_dummies()
shape: (2, 6)
┌───────┬───────┬───────┬───────┬───────┬───────┐
│ foo_1 ┆ foo_2 ┆ bar_3 ┆ bar_4 ┆ ham_a ┆ ham_b │
│ ---   ┆ ---   ┆ ---   ┆ ---   ┆ ---   ┆ ---   │
│ u8    ┆ u8    ┆ u8    ┆ u8    ┆ u8    ┆ u8    │
╞═══════╪═══════╪═══════╪═══════╪═══════╪═══════╡
│ 1     ┆ 0     ┆ 1     ┆ 0     ┆ 1     ┆ 0     │
│ 0     ┆ 1     ┆ 0     ┆ 1     ┆ 0     ┆ 1     │
└───────┴───────┴───────┴───────┴───────┴───────┘
>>> df.to_dummies(drop_first=True)
shape: (2, 3)
┌───────┬───────┬───────┐
│ foo_2 ┆ bar_4 ┆ ham_b │
│ ---   ┆ ---   ┆ ---   │
│ u8    ┆ u8    ┆ u8    │
╞═══════╪═══════╪═══════╡
│ 0     ┆ 0     ┆ 0     │
│ 1     ┆ 1     ┆ 1     │
└───────┴───────┴───────┘
>>> import polars.selectors as cs
>>> df.to_dummies(cs.integer(), separator=":")
shape: (2, 5)
┌───────┬───────┬───────┬───────┬─────┐
│ foo:1 ┆ foo:2 ┆ bar:3 ┆ bar:4 ┆ ham │
│ ---   ┆ ---   ┆ ---   ┆ ---   ┆ --- │
│ u8    ┆ u8    ┆ u8    ┆ u8    ┆ str │
╞═══════╪═══════╪═══════╪═══════╪═════╡
│ 1     ┆ 0     ┆ 1     ┆ 0     ┆ a   │
│ 0     ┆ 1     ┆ 0     ┆ 1     ┆ b   │
└───────┴───────┴───────┴───────┴─────┘
>>> df.to_dummies(cs.integer(), drop_first=True, separator=":")
shape: (2, 3)
┌───────┬───────┬─────┐
│ foo:2 ┆ bar:4 ┆ ham │
│ ---   ┆ ---   ┆ --- │
│ u8    ┆ u8    ┆ str │
╞═══════╪═══════╪═════╡
│ 0     ┆ 0     ┆ a   │
│ 1     ┆ 1     ┆ b   │
└───────┴───────┴─────┘
to_init_repr(n: int = 1000) str[source]

Convert DataFrame to instantiatable string representation.

Parameters:
n

Only use first n rows.

Examples

>>> df = pl.DataFrame(
...     [
...         pl.Series("foo", [1, 2, 3], dtype=pl.UInt8),
...         pl.Series("bar", [6.0, 7.0, 8.0], dtype=pl.Float32),
...         pl.Series("ham", ["a", "b", "c"], dtype=pl.String),
...     ]
... )
>>> print(df.to_init_repr())
pl.DataFrame(
    [
        pl.Series("foo", [1, 2, 3], dtype=pl.UInt8),
        pl.Series("bar", [6.0, 7.0, 8.0], dtype=pl.Float32),
        pl.Series("ham", ['a', 'b', 'c'], dtype=pl.String),
    ]
)
>>> df_from_str_repr = eval(df.to_init_repr())
>>> df_from_str_repr
shape: (3, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ u8  ┆ f32 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 6.0 ┆ a   │
│ 2   ┆ 7.0 ┆ b   │
│ 3   ┆ 8.0 ┆ c   │
└─────┴─────┴─────┘
to_numpy(
*,
structured: bool = False,
order: IndexOrder = 'fortran',
allow_copy: bool = True,
writable: bool = False,
use_pyarrow: bool = True,
) np.ndarray[Any, Any][source]

Convert this DataFrame to a NumPy ndarray.

Parameters:
structured

Return a structured array with a data type that corresponds to the DataFrame schema. If set to False (default), a 2D ndarray is returned instead.

order

The index order of the returned NumPy array, either C-like or Fortran-like. In general, using the Fortran-like index order is faster. However, the C-like order might be more appropriate to use for downstream applications to prevent cloning data, e.g. when reshaping into a one-dimensional array. Note that this option only takes effect if structured is set to False and the DataFrame dtypes allow for a global dtype for all columns.

allow_copy

Allow memory to be copied to perform the conversion. If set to False, causes conversions that are not zero-copy to fail.

writable

Ensure the resulting array is writable. This will force a copy of the data if the array was created without copy, as the underlying Arrow data is immutable.

use_pyarrow

Use pyarrow.Array.to_numpy

function for the conversion to numpy if necessary.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6.5, 7.0, 8.5],
...         "ham": ["a", "b", "c"],
...     },
...     schema_overrides={"foo": pl.UInt8, "bar": pl.Float32},
... )

Export to a standard 2D numpy array.

>>> df.to_numpy()
array([[1, 6.5, 'a'],
       [2, 7.0, 'b'],
       [3, 8.5, 'c']], dtype=object)

Export to a structured array, which can better-preserve individual column data, such as name and dtype…

>>> df.to_numpy(structured=True)
array([(1, 6.5, 'a'), (2, 7. , 'b'), (3, 8.5, 'c')],
      dtype=[('foo', 'u1'), ('bar', '<f4'), ('ham', '<U1')])

…optionally going on to view as a record array:

>>> import numpy as np
>>> df.to_numpy(structured=True).view(np.recarray)
rec.array([(1, 6.5, 'a'), (2, 7. , 'b'), (3, 8.5, 'c')],
          dtype=[('foo', 'u1'), ('bar', '<f4'), ('ham', '<U1')])
to_pandas(
*,
use_pyarrow_extension_array: bool = False,
**kwargs: Any,
) DataFrame[source]

Convert this DataFrame to a pandas DataFrame.

This operation copies data if use_pyarrow_extension_array is not enabled.

Parameters:
use_pyarrow_extension_array

Use PyArrow-backed extension arrays instead of NumPy arrays for the columns of the pandas DataFrame. This allows zero copy operations and preservation of null values. Subsequent operations on the resulting pandas DataFrame may trigger conversion to NumPy if those operations are not supported by PyArrow compute functions.

**kwargs

Additional keyword arguments to be passed to pyarrow.Table.to_pandas().

Returns:
pandas.DataFrame

Notes

This operation requires that both pandas and pyarrow are installed.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6.0, 7.0, 8.0],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.to_pandas()
   foo  bar ham
0    1  6.0   a
1    2  7.0   b
2    3  8.0   c

Null values in numeric columns are converted to NaN.

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, None],
...         "bar": [6.0, None, 8.0],
...         "ham": [None, "b", "c"],
...     }
... )
>>> df.to_pandas()
   foo  bar   ham
0  1.0  6.0  None
1  2.0  NaN     b
2  NaN  8.0     c

Pass use_pyarrow_extension_array=True to get a pandas DataFrame with columns backed by PyArrow extension arrays. This will preserve null values.

>>> df.to_pandas(use_pyarrow_extension_array=True)
    foo   bar   ham
0     1   6.0  <NA>
1     2  <NA>     b
2  <NA>   8.0     c
>>> _.dtypes
foo           int64[pyarrow]
bar          double[pyarrow]
ham    large_string[pyarrow]
dtype: object
to_series(index: int = 0) Series[source]

Select column as Series at index location.

Parameters:
index

Location of selection.

See also

get_column

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.to_series(1)
shape: (3,)
Series: 'bar' [i64]
[
        6
        7
        8
]
to_struct(name: str = '') Series[source]

Convert a DataFrame to a Series of type Struct.

Parameters:
name

Name for the struct Series

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3, 4, 5],
...         "b": ["one", "two", "three", "four", "five"],
...     }
... )
>>> df.to_struct("nums")
shape: (5,)
Series: 'nums' [struct[2]]
[
    {1,"one"}
    {2,"two"}
    {3,"three"}
    {4,"four"}
    {5,"five"}
]
top_k(
k: int,
*,
by: IntoExpr | Iterable[IntoExpr],
descending: bool | Sequence[bool] = False,
nulls_last: bool = False,
maintain_order: bool = False,
) DataFrame[source]

Return the k largest elements.

If descending=True the smallest elements will be given.

Parameters:
k

Number of rows to return.

by

Column(s) included in sort order. Accepts expression input. Strings are parsed as column names.

descending

Return the k smallest. Top-k by multiple columns can be specified per column by passing a sequence of booleans.

nulls_last

Place null values last.

maintain_order

Whether the order should be maintained if elements are equal. Note that if true streaming is not possible and performance might be worse since this requires a stable search.

See also

bottom_k

Examples

>>> df = pl.DataFrame(
...     {
...         "a": ["a", "b", "a", "b", "b", "c"],
...         "b": [2, 1, 1, 3, 2, 1],
...     }
... )

Get the rows which contain the 4 largest values in column b.

>>> df.top_k(4, by="b")
shape: (4, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════╪═════╡
│ b   ┆ 3   │
│ a   ┆ 2   │
│ b   ┆ 2   │
│ b   ┆ 1   │
└─────┴─────┘

Get the rows which contain the 4 largest values when sorting on column b and a.

>>> df.top_k(4, by=["b", "a"])
shape: (4, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════╪═════╡
│ b   ┆ 3   │
│ b   ┆ 2   │
│ a   ┆ 2   │
│ c   ┆ 1   │
└─────┴─────┘
transpose(
*,
include_header: bool = False,
header_name: str = 'column',
column_names: str | Iterable[str] | None = None,
) Self[source]

Transpose a DataFrame over the diagonal.

Parameters:
include_header

If set, the column names will be added as first column.

header_name

If include_header is set, this determines the name of the column that will be inserted.

column_names

Optional iterable yielding strings or a string naming an existing column. These will name the value (non-header) columns in the transposed data.

Returns:
DataFrame

Notes

This is a very expensive operation. Perhaps you can do it differently.

Examples

>>> df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df.transpose(include_header=True)
shape: (2, 4)
┌────────┬──────────┬──────────┬──────────┐
│ column ┆ column_0 ┆ column_1 ┆ column_2 │
│ ---    ┆ ---      ┆ ---      ┆ ---      │
│ str    ┆ i64      ┆ i64      ┆ i64      │
╞════════╪══════════╪══════════╪══════════╡
│ a      ┆ 1        ┆ 2        ┆ 3        │
│ b      ┆ 4        ┆ 5        ┆ 6        │
└────────┴──────────┴──────────┴──────────┘

Replace the auto-generated column names with a list

>>> df.transpose(include_header=False, column_names=["x", "y", "z"])
shape: (2, 3)
┌─────┬─────┬─────┐
│ x   ┆ y   ┆ z   │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1   ┆ 2   ┆ 3   │
│ 4   ┆ 5   ┆ 6   │
└─────┴─────┴─────┘

Include the header as a separate column

>>> df.transpose(
...     include_header=True, header_name="foo", column_names=["x", "y", "z"]
... )
shape: (2, 4)
┌─────┬─────┬─────┬─────┐
│ foo ┆ x   ┆ y   ┆ z   │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╪═════╡
│ a   ┆ 1   ┆ 2   ┆ 3   │
│ b   ┆ 4   ┆ 5   ┆ 6   │
└─────┴─────┴─────┴─────┘

Replace the auto-generated column with column names from a generator function

>>> def name_generator():
...     base_name = "my_column_"
...     count = 0
...     while True:
...         yield f"{base_name}{count}"
...         count += 1
>>> df.transpose(include_header=False, column_names=name_generator())
shape: (2, 3)
┌─────────────┬─────────────┬─────────────┐
│ my_column_0 ┆ my_column_1 ┆ my_column_2 │
│ ---         ┆ ---         ┆ ---         │
│ i64         ┆ i64         ┆ i64         │
╞═════════════╪═════════════╪═════════════╡
│ 1           ┆ 2           ┆ 3           │
│ 4           ┆ 5           ┆ 6           │
└─────────────┴─────────────┴─────────────┘

Use an existing column as the new column names

>>> df = pl.DataFrame(dict(id=["i", "j", "k"], a=[1, 2, 3], b=[4, 5, 6]))
>>> df.transpose(column_names="id")
shape: (2, 3)
┌─────┬─────┬─────┐
│ i   ┆ j   ┆ k   │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1   ┆ 2   ┆ 3   │
│ 4   ┆ 5   ┆ 6   │
└─────┴─────┴─────┘
>>> df.transpose(include_header=True, header_name="new_id", column_names="id")
shape: (2, 4)
┌────────┬─────┬─────┬─────┐
│ new_id ┆ i   ┆ j   ┆ k   │
│ ---    ┆ --- ┆ --- ┆ --- │
│ str    ┆ i64 ┆ i64 ┆ i64 │
╞════════╪═════╪═════╪═════╡
│ a      ┆ 1   ┆ 2   ┆ 3   │
│ b      ┆ 4   ┆ 5   ┆ 6   │
└────────┴─────┴─────┴─────┘
unique(
subset: ColumnNameOrSelector | Collection[ColumnNameOrSelector] | None = None,
*,
keep: UniqueKeepStrategy = 'any',
maintain_order: bool = False,
) DataFrame[source]

Drop duplicate rows from this dataframe.

Parameters:
subset

Column name(s) or selector(s), to consider when identifying duplicate rows. If set to None (default), use all columns.

keep{‘first’, ‘last’, ‘any’, ‘none’}

Which of the duplicate rows to keep.

  • ‘any’: Does not give any guarantee of which row is kept.

    This allows more optimizations.

  • ‘none’: Don’t keep duplicate rows.

  • ‘first’: Keep first unique row.

  • ‘last’: Keep last unique row.

maintain_order

Keep the same order as the original DataFrame. This is more expensive to compute. Settings this to True blocks the possibility to run on the streaming engine.

Returns:
DataFrame

DataFrame with unique rows.

Warning

This method will fail if there is a column of type List in the DataFrame or subset.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3, 1],
...         "bar": ["a", "a", "a", "a"],
...         "ham": ["b", "b", "b", "b"],
...     }
... )
>>> df.unique(maintain_order=True)
shape: (3, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ str ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ a   ┆ b   │
│ 2   ┆ a   ┆ b   │
│ 3   ┆ a   ┆ b   │
└─────┴─────┴─────┘
>>> df.unique(subset=["bar", "ham"], maintain_order=True)
shape: (1, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ str ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ a   ┆ b   │
└─────┴─────┴─────┘
>>> df.unique(keep="last", maintain_order=True)
shape: (3, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ str ┆ str │
╞═════╪═════╪═════╡
│ 2   ┆ a   ┆ b   │
│ 3   ┆ a   ┆ b   │
│ 1   ┆ a   ┆ b   │
└─────┴─────┴─────┘
unnest(
columns: ColumnNameOrSelector | Collection[ColumnNameOrSelector],
*more_columns: ColumnNameOrSelector,
) Self[source]

Decompose struct columns into separate columns for each of their fields.

The new columns will be inserted into the dataframe at the location of the struct column.

Parameters:
columns

Name of the struct column(s) that should be unnested.

*more_columns

Additional columns to unnest, specified as positional arguments.

Examples

>>> df = pl.DataFrame(
...     {
...         "before": ["foo", "bar"],
...         "t_a": [1, 2],
...         "t_b": ["a", "b"],
...         "t_c": [True, None],
...         "t_d": [[1, 2], [3]],
...         "after": ["baz", "womp"],
...     }
... ).select("before", pl.struct(pl.col("^t_.$")).alias("t_struct"), "after")
>>> df
shape: (2, 3)
┌────────┬─────────────────────┬───────┐
│ before ┆ t_struct            ┆ after │
│ ---    ┆ ---                 ┆ ---   │
│ str    ┆ struct[4]           ┆ str   │
╞════════╪═════════════════════╪═══════╡
│ foo    ┆ {1,"a",true,[1, 2]} ┆ baz   │
│ bar    ┆ {2,"b",null,[3]}    ┆ womp  │
└────────┴─────────────────────┴───────┘
>>> df.unnest("t_struct")
shape: (2, 6)
┌────────┬─────┬─────┬──────┬───────────┬───────┐
│ before ┆ t_a ┆ t_b ┆ t_c  ┆ t_d       ┆ after │
│ ---    ┆ --- ┆ --- ┆ ---  ┆ ---       ┆ ---   │
│ str    ┆ i64 ┆ str ┆ bool ┆ list[i64] ┆ str   │
╞════════╪═════╪═════╪══════╪═══════════╪═══════╡
│ foo    ┆ 1   ┆ a   ┆ true ┆ [1, 2]    ┆ baz   │
│ bar    ┆ 2   ┆ b   ┆ null ┆ [3]       ┆ womp  │
└────────┴─────┴─────┴──────┴───────────┴───────┘
unstack(
step: int,
how: UnstackDirection = 'vertical',
columns: ColumnNameOrSelector | Sequence[ColumnNameOrSelector] | None = None,
fill_values: list[Any] | None = None,
) DataFrame[source]

Unstack a long table to a wide form without doing an aggregation.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

This can be much faster than a pivot, because it can skip the grouping phase.

Parameters:
step

Number of rows in the unstacked frame.

how{ ‘vertical’, ‘horizontal’ }

Direction of the unstack.

columns

Column name(s) or selector(s) to include in the operation. If set to None (default), use all columns.

fill_values

Fill values that don’t fit the new size with this value.

Examples

>>> from string import ascii_uppercase
>>> df = pl.DataFrame(
...     {
...         "x": list(ascii_uppercase[0:8]),
...         "y": pl.int_range(1, 9, eager=True),
...     }
... ).with_columns(
...     z=pl.int_ranges(pl.col("y"), pl.col("y") + 2, dtype=pl.UInt8),
... )
>>> df
shape: (8, 3)
┌─────┬─────┬──────────┐
│ x   ┆ y   ┆ z        │
│ --- ┆ --- ┆ ---      │
│ str ┆ i64 ┆ list[u8] │
╞═════╪═════╪══════════╡
│ A   ┆ 1   ┆ [1, 2]   │
│ B   ┆ 2   ┆ [2, 3]   │
│ C   ┆ 3   ┆ [3, 4]   │
│ D   ┆ 4   ┆ [4, 5]   │
│ E   ┆ 5   ┆ [5, 6]   │
│ F   ┆ 6   ┆ [6, 7]   │
│ G   ┆ 7   ┆ [7, 8]   │
│ H   ┆ 8   ┆ [8, 9]   │
└─────┴─────┴──────────┘
>>> df.unstack(step=4, how="vertical")
shape: (4, 6)
┌─────┬─────┬─────┬─────┬──────────┬──────────┐
│ x_0 ┆ x_1 ┆ y_0 ┆ y_1 ┆ z_0      ┆ z_1      │
│ --- ┆ --- ┆ --- ┆ --- ┆ ---      ┆ ---      │
│ str ┆ str ┆ i64 ┆ i64 ┆ list[u8] ┆ list[u8] │
╞═════╪═════╪═════╪═════╪══════════╪══════════╡
│ A   ┆ E   ┆ 1   ┆ 5   ┆ [1, 2]   ┆ [5, 6]   │
│ B   ┆ F   ┆ 2   ┆ 6   ┆ [2, 3]   ┆ [6, 7]   │
│ C   ┆ G   ┆ 3   ┆ 7   ┆ [3, 4]   ┆ [7, 8]   │
│ D   ┆ H   ┆ 4   ┆ 8   ┆ [4, 5]   ┆ [8, 9]   │
└─────┴─────┴─────┴─────┴──────────┴──────────┘
>>> df.unstack(step=2, how="horizontal")
shape: (4, 6)
┌─────┬─────┬─────┬─────┬──────────┬──────────┐
│ x_0 ┆ x_1 ┆ y_0 ┆ y_1 ┆ z_0      ┆ z_1      │
│ --- ┆ --- ┆ --- ┆ --- ┆ ---      ┆ ---      │
│ str ┆ str ┆ i64 ┆ i64 ┆ list[u8] ┆ list[u8] │
╞═════╪═════╪═════╪═════╪══════════╪══════════╡
│ A   ┆ B   ┆ 1   ┆ 2   ┆ [1, 2]   ┆ [2, 3]   │
│ C   ┆ D   ┆ 3   ┆ 4   ┆ [3, 4]   ┆ [4, 5]   │
│ E   ┆ F   ┆ 5   ┆ 6   ┆ [5, 6]   ┆ [6, 7]   │
│ G   ┆ H   ┆ 7   ┆ 8   ┆ [7, 8]   ┆ [8, 9]   │
└─────┴─────┴─────┴─────┴──────────┴──────────┘
>>> import polars.selectors as cs
>>> df.unstack(step=5, columns=cs.numeric(), fill_values=0)
shape: (5, 2)
┌─────┬─────┐
│ y_0 ┆ y_1 │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 6   │
│ 2   ┆ 7   │
│ 3   ┆ 8   │
│ 4   ┆ 0   │
│ 5   ┆ 0   │
└─────┴─────┘
update(
other: DataFrame,
on: str | Sequence[str] | None = None,
how: Literal['left', 'inner', 'outer'] = 'left',
*,
left_on: str | Sequence[str] | None = None,
right_on: str | Sequence[str] | None = None,
include_nulls: bool = False,
) DataFrame[source]

Update the values in this DataFrame with the values in other.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

By default, null values in the right frame are ignored. Use include_nulls=False to overwrite values in this frame with null values in the other frame.

Parameters:
other

DataFrame that will be used to update the values

on

Column names that will be joined on. If set to None (default), the implicit row index of each frame is used as a join key.

how{‘left’, ‘inner’, ‘outer’}
  • ‘left’ will keep all rows from the left table; rows may be duplicated if multiple rows in the right frame match the left row’s key.

  • ‘inner’ keeps only those rows where the key exists in both frames.

  • ‘outer’ will update existing rows where the key matches while also adding any new rows contained in the given frame.

left_on

Join column(s) of the left DataFrame.

right_on

Join column(s) of the right DataFrame.

include_nulls

If True, null values from the right dataframe will be used to update the left dataframe.

Notes

This is syntactic sugar for a left/inner join, with an optional coalesce when include_nulls = False

Examples

>>> df = pl.DataFrame(
...     {
...         "A": [1, 2, 3, 4],
...         "B": [400, 500, 600, 700],
...     }
... )
>>> df
shape: (4, 2)
┌─────┬─────┐
│ A   ┆ B   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 400 │
│ 2   ┆ 500 │
│ 3   ┆ 600 │
│ 4   ┆ 700 │
└─────┴─────┘
>>> new_df = pl.DataFrame(
...     {
...         "B": [-66, None, -99],
...         "C": [5, 3, 1],
...     }
... )

Update df values with the non-null values in new_df, by row index:

>>> df.update(new_df)
shape: (4, 2)
┌─────┬─────┐
│ A   ┆ B   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ -66 │
│ 2   ┆ 500 │
│ 3   ┆ -99 │
│ 4   ┆ 700 │
└─────┴─────┘

Update df values with the non-null values in new_df, by row index, but only keeping those rows that are common to both frames:

>>> df.update(new_df, how="inner")
shape: (3, 2)
┌─────┬─────┐
│ A   ┆ B   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ -66 │
│ 2   ┆ 500 │
│ 3   ┆ -99 │
└─────┴─────┘

Update df values with the non-null values in new_df, using an outer join strategy that defines explicit join columns in each frame:

>>> df.update(new_df, left_on=["A"], right_on=["C"], how="outer")
shape: (5, 2)
┌─────┬─────┐
│ A   ┆ B   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ -99 │
│ 2   ┆ 500 │
│ 3   ┆ 600 │
│ 4   ┆ 700 │
│ 5   ┆ -66 │
└─────┴─────┘

Update df values including null values in new_df, using an outer join strategy that defines explicit join columns in each frame:

>>> df.update(
...     new_df, left_on="A", right_on="C", how="outer", include_nulls=True
... )
shape: (5, 2)
┌─────┬──────┐
│ A   ┆ B    │
│ --- ┆ ---  │
│ i64 ┆ i64  │
╞═════╪══════╡
│ 1   ┆ -99  │
│ 2   ┆ 500  │
│ 3   ┆ null │
│ 4   ┆ 700  │
│ 5   ┆ -66  │
└─────┴──────┘
upsample(
time_column: str,
*,
every: str | timedelta,
offset: str | timedelta | None = None,
by: str | Sequence[str] | None = None,
maintain_order: bool = False,
) Self[source]

Upsample a DataFrame at a regular frequency.

The every and offset arguments are created with the following string language:

  • 1ns (1 nanosecond)

  • 1us (1 microsecond)

  • 1ms (1 millisecond)

  • 1s (1 second)

  • 1m (1 minute)

  • 1h (1 hour)

  • 1d (1 calendar day)

  • 1w (1 calendar week)

  • 1mo (1 calendar month)

  • 1q (1 calendar quarter)

  • 1y (1 calendar year)

  • 1i (1 index count)

Or combine them:

  • “3d12h4m25s” # 3 days, 12 hours, 4 minutes, and 25 seconds

By “calendar day”, we mean the corresponding time on the next day (which may not be 24 hours, due to daylight savings). Similarly for “calendar week”, “calendar month”, “calendar quarter”, and “calendar year”.

Parameters:
time_column

Time column will be used to determine a date_range. Note that this column has to be sorted for the output to make sense.

every

Interval will start ‘every’ duration.

offset

Change the start of the date_range by this offset.

by

First group by these columns and then upsample for every group.

maintain_order

Keep the ordering predictable. This is slower.

Returns:
DataFrame

Result will be sorted by time_column (but note that if by columns are passed, it will only be sorted within each by group).

Examples

Upsample a DataFrame by a certain interval.

>>> from datetime import datetime
>>> df = pl.DataFrame(
...     {
...         "time": [
...             datetime(2021, 2, 1),
...             datetime(2021, 4, 1),
...             datetime(2021, 5, 1),
...             datetime(2021, 6, 1),
...         ],
...         "groups": ["A", "B", "A", "B"],
...         "values": [0, 1, 2, 3],
...     }
... ).set_sorted("time")
>>> df.upsample(
...     time_column="time", every="1mo", by="groups", maintain_order=True
... ).select(pl.all().forward_fill())
shape: (7, 3)
┌─────────────────────┬────────┬────────┐
│ time                ┆ groups ┆ values │
│ ---                 ┆ ---    ┆ ---    │
│ datetime[μs]        ┆ str    ┆ i64    │
╞═════════════════════╪════════╪════════╡
│ 2021-02-01 00:00:00 ┆ A      ┆ 0      │
│ 2021-03-01 00:00:00 ┆ A      ┆ 0      │
│ 2021-04-01 00:00:00 ┆ A      ┆ 0      │
│ 2021-05-01 00:00:00 ┆ A      ┆ 2      │
│ 2021-04-01 00:00:00 ┆ B      ┆ 1      │
│ 2021-05-01 00:00:00 ┆ B      ┆ 1      │
│ 2021-06-01 00:00:00 ┆ B      ┆ 3      │
└─────────────────────┴────────┴────────┘
var(ddof: int = 1) Self[source]

Aggregate the columns of this DataFrame to their variance value.

Parameters:
ddof

“Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of elements. By default ddof is 1.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.var()
shape: (1, 3)
┌─────┬─────┬──────┐
│ foo ┆ bar ┆ ham  │
│ --- ┆ --- ┆ ---  │
│ f64 ┆ f64 ┆ str  │
╞═════╪═════╪══════╡
│ 1.0 ┆ 1.0 ┆ null │
└─────┴─────┴──────┘
>>> df.var(ddof=0)
shape: (1, 3)
┌──────────┬──────────┬──────┐
│ foo      ┆ bar      ┆ ham  │
│ ---      ┆ ---      ┆ ---  │
│ f64      ┆ f64      ┆ str  │
╞══════════╪══════════╪══════╡
│ 0.666667 ┆ 0.666667 ┆ null │
└──────────┴──────────┴──────┘
vstack(other: DataFrame, *, in_place: bool = False) Self[source]

Grow this DataFrame vertically by stacking a DataFrame to it.

Parameters:
other

DataFrame to stack.

in_place

Modify in place.

See also

extend

Examples

>>> df1 = pl.DataFrame(
...     {
...         "foo": [1, 2],
...         "bar": [6, 7],
...         "ham": ["a", "b"],
...     }
... )
>>> df2 = pl.DataFrame(
...     {
...         "foo": [3, 4],
...         "bar": [8, 9],
...         "ham": ["c", "d"],
...     }
... )
>>> df1.vstack(df2)
shape: (4, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 6   ┆ a   │
│ 2   ┆ 7   ┆ b   │
│ 3   ┆ 8   ┆ c   │
│ 4   ┆ 9   ┆ d   │
└─────┴─────┴─────┘
property width: int[source]

Get the width of the DataFrame.

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5]})
>>> df.width
1
with_columns(
*exprs: IntoExpr | Iterable[IntoExpr],
**named_exprs: IntoExpr,
) DataFrame[source]

Add columns to this DataFrame.

Added columns will replace existing columns with the same name.

Parameters:
*exprs

Column(s) to add, specified as positional arguments. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.

**named_exprs

Additional columns to add, specified as keyword arguments. The columns will be renamed to the keyword used.

Returns:
DataFrame

A new DataFrame with the columns added.

Notes

Creating a new DataFrame using this method does not create a new copy of existing data.

Examples

Pass an expression to add it as a new column.

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3, 4],
...         "b": [0.5, 4, 10, 13],
...         "c": [True, True, False, True],
...     }
... )
>>> df.with_columns((pl.col("a") ** 2).alias("a^2"))
shape: (4, 4)
┌─────┬──────┬───────┬──────┐
│ a   ┆ b    ┆ c     ┆ a^2  │
│ --- ┆ ---  ┆ ---   ┆ ---  │
│ i64 ┆ f64  ┆ bool  ┆ f64  │
╞═════╪══════╪═══════╪══════╡
│ 1   ┆ 0.5  ┆ true  ┆ 1.0  │
│ 2   ┆ 4.0  ┆ true  ┆ 4.0  │
│ 3   ┆ 10.0 ┆ false ┆ 9.0  │
│ 4   ┆ 13.0 ┆ true  ┆ 16.0 │
└─────┴──────┴───────┴──────┘

Added columns will replace existing columns with the same name.

>>> df.with_columns(pl.col("a").cast(pl.Float64))
shape: (4, 3)
┌─────┬──────┬───────┐
│ a   ┆ b    ┆ c     │
│ --- ┆ ---  ┆ ---   │
│ f64 ┆ f64  ┆ bool  │
╞═════╪══════╪═══════╡
│ 1.0 ┆ 0.5  ┆ true  │
│ 2.0 ┆ 4.0  ┆ true  │
│ 3.0 ┆ 10.0 ┆ false │
│ 4.0 ┆ 13.0 ┆ true  │
└─────┴──────┴───────┘

Multiple columns can be added by passing a list of expressions.

>>> df.with_columns(
...     [
...         (pl.col("a") ** 2).alias("a^2"),
...         (pl.col("b") / 2).alias("b/2"),
...         (pl.col("c").not_()).alias("not c"),
...     ]
... )
shape: (4, 6)
┌─────┬──────┬───────┬──────┬──────┬───────┐
│ a   ┆ b    ┆ c     ┆ a^2  ┆ b/2  ┆ not c │
│ --- ┆ ---  ┆ ---   ┆ ---  ┆ ---  ┆ ---   │
│ i64 ┆ f64  ┆ bool  ┆ f64  ┆ f64  ┆ bool  │
╞═════╪══════╪═══════╪══════╪══════╪═══════╡
│ 1   ┆ 0.5  ┆ true  ┆ 1.0  ┆ 0.25 ┆ false │
│ 2   ┆ 4.0  ┆ true  ┆ 4.0  ┆ 2.0  ┆ false │
│ 3   ┆ 10.0 ┆ false ┆ 9.0  ┆ 5.0  ┆ true  │
│ 4   ┆ 13.0 ┆ true  ┆ 16.0 ┆ 6.5  ┆ false │
└─────┴──────┴───────┴──────┴──────┴───────┘

Multiple columns also can be added using positional arguments instead of a list.

>>> df.with_columns(
...     (pl.col("a") ** 2).alias("a^2"),
...     (pl.col("b") / 2).alias("b/2"),
...     (pl.col("c").not_()).alias("not c"),
... )
shape: (4, 6)
┌─────┬──────┬───────┬──────┬──────┬───────┐
│ a   ┆ b    ┆ c     ┆ a^2  ┆ b/2  ┆ not c │
│ --- ┆ ---  ┆ ---   ┆ ---  ┆ ---  ┆ ---   │
│ i64 ┆ f64  ┆ bool  ┆ f64  ┆ f64  ┆ bool  │
╞═════╪══════╪═══════╪══════╪══════╪═══════╡
│ 1   ┆ 0.5  ┆ true  ┆ 1.0  ┆ 0.25 ┆ false │
│ 2   ┆ 4.0  ┆ true  ┆ 4.0  ┆ 2.0  ┆ false │
│ 3   ┆ 10.0 ┆ false ┆ 9.0  ┆ 5.0  ┆ true  │
│ 4   ┆ 13.0 ┆ true  ┆ 16.0 ┆ 6.5  ┆ false │
└─────┴──────┴───────┴──────┴──────┴───────┘

Use keyword arguments to easily name your expression inputs.

>>> df.with_columns(
...     ab=pl.col("a") * pl.col("b"),
...     not_c=pl.col("c").not_(),
... )
shape: (4, 5)
┌─────┬──────┬───────┬──────┬───────┐
│ a   ┆ b    ┆ c     ┆ ab   ┆ not_c │
│ --- ┆ ---  ┆ ---   ┆ ---  ┆ ---   │
│ i64 ┆ f64  ┆ bool  ┆ f64  ┆ bool  │
╞═════╪══════╪═══════╪══════╪═══════╡
│ 1   ┆ 0.5  ┆ true  ┆ 0.5  ┆ false │
│ 2   ┆ 4.0  ┆ true  ┆ 8.0  ┆ false │
│ 3   ┆ 10.0 ┆ false ┆ 30.0 ┆ true  │
│ 4   ┆ 13.0 ┆ true  ┆ 52.0 ┆ false │
└─────┴──────┴───────┴──────┴───────┘

Expressions with multiple outputs can be automatically instantiated as Structs by enabling the setting Config.set_auto_structify(True):

>>> with pl.Config(auto_structify=True):
...     df.drop("c").with_columns(
...         diffs=pl.col(["a", "b"]).diff().name.suffix("_diff"),
...     )
shape: (4, 3)
┌─────┬──────┬─────────────┐
│ a   ┆ b    ┆ diffs       │
│ --- ┆ ---  ┆ ---         │
│ i64 ┆ f64  ┆ struct[2]   │
╞═════╪══════╪═════════════╡
│ 1   ┆ 0.5  ┆ {null,null} │
│ 2   ┆ 4.0  ┆ {1,3.5}     │
│ 3   ┆ 10.0 ┆ {1,6.0}     │
│ 4   ┆ 13.0 ┆ {1,3.0}     │
└─────┴──────┴─────────────┘
with_columns_seq(
*exprs: IntoExpr | Iterable[IntoExpr],
**named_exprs: IntoExpr,
) DataFrame[source]

Add columns to this DataFrame.

Added columns will replace existing columns with the same name.

This will run all expression sequentially instead of in parallel. Use this when the work per expression is cheap.

Parameters:
*exprs

Column(s) to add, specified as positional arguments. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.

**named_exprs

Additional columns to add, specified as keyword arguments. The columns will be renamed to the keyword used.

Returns:
DataFrame

A new DataFrame with the columns added.

See also

with_columns
with_row_count(name: str = 'row_nr', offset: int = 0) Self[source]

Add a column at index 0 that counts the rows.

Deprecated since version Use: with_row_index() instead. Note that the default column name has changed from ‘row_nr’ to ‘index’.

Parameters:
name

Name of the column to add.

offset

Start the row count at this offset. Default = 0

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 3, 5],
...         "b": [2, 4, 6],
...     }
... )
>>> df.with_row_count()  
shape: (3, 3)
┌────────┬─────┬─────┐
│ row_nr ┆ a   ┆ b   │
│ ---    ┆ --- ┆ --- │
│ u32    ┆ i64 ┆ i64 │
╞════════╪═════╪═════╡
│ 0      ┆ 1   ┆ 2   │
│ 1      ┆ 3   ┆ 4   │
│ 2      ┆ 5   ┆ 6   │
└────────┴─────┴─────┘
with_row_index(name: str = 'index', offset: int = 0) Self[source]

Add a row index as the first column in the DataFrame.

Parameters:
name

Name of the index column.

offset

Start the index at this offset. Cannot be negative.

Notes

The resulting column does not have any special properties. It is a regular column of type UInt32 (or UInt64 in polars-u64-idx).

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 3, 5],
...         "b": [2, 4, 6],
...     }
... )
>>> df.with_row_index()
shape: (3, 3)
┌───────┬─────┬─────┐
│ index ┆ a   ┆ b   │
│ ---   ┆ --- ┆ --- │
│ u32   ┆ i64 ┆ i64 │
╞═══════╪═════╪═════╡
│ 0     ┆ 1   ┆ 2   │
│ 1     ┆ 3   ┆ 4   │
│ 2     ┆ 5   ┆ 6   │
└───────┴─────┴─────┘
>>> df.with_row_index("id", offset=1000)
shape: (3, 3)
┌──────┬─────┬─────┐
│ id   ┆ a   ┆ b   │
│ ---  ┆ --- ┆ --- │
│ u32  ┆ i64 ┆ i64 │
╞══════╪═════╪═════╡
│ 1000 ┆ 1   ┆ 2   │
│ 1001 ┆ 3   ┆ 4   │
│ 1002 ┆ 5   ┆ 6   │
└──────┴─────┴─────┘

An index column can also be created using the expressions int_range() and len().

>>> df.select(
...     pl.int_range(pl.len(), dtype=pl.UInt32).alias("index"),
...     pl.all(),
... )
shape: (3, 3)
┌───────┬─────┬─────┐
│ index ┆ a   ┆ b   │
│ ---   ┆ --- ┆ --- │
│ u32   ┆ i64 ┆ i64 │
╞═══════╪═════╪═════╡
│ 0     ┆ 1   ┆ 2   │
│ 1     ┆ 3   ┆ 4   │
│ 2     ┆ 5   ┆ 6   │
└───────┴─────┴─────┘
write_avro(
file: str | Path | IO[bytes],
compression: AvroCompression = 'uncompressed',
name: str = '',
) None[source]

Write to Apache Avro file.

Parameters:
file

File path or writable file-like object to which the data will be written.

compression{‘uncompressed’, ‘snappy’, ‘deflate’}

Compression method. Defaults to “uncompressed”.

name

Schema name. Defaults to empty string.

Examples

>>> import pathlib
>>>
>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3, 4, 5],
...         "bar": [6, 7, 8, 9, 10],
...         "ham": ["a", "b", "c", "d", "e"],
...     }
... )
>>> path: pathlib.Path = dirpath / "new_file.avro"
>>> df.write_avro(path)
write_csv(
file: str | Path | IO[str] | IO[bytes] | None = None,
*,
include_bom: bool = False,
include_header: bool = True,
separator: str = ',',
line_terminator: str = '\n',
quote_char: str = '"',
batch_size: int = 1024,
datetime_format: str | None = None,
date_format: str | None = None,
time_format: str | None = None,
float_precision: int | None = None,
null_value: str | None = None,
quote_style: CsvQuoteStyle | None = None,
) str | None[source]

Write to comma-separated values (CSV) file.

Parameters:
file

File path or writable file-like object to which the result will be written. If set to None (default), the output is returned as a string instead.

include_bom

Whether to include UTF-8 BOM in the CSV output.

include_header

Whether to include header in the CSV output.

separator

Separate CSV fields with this symbol.

line_terminator

String used to end each row.

quote_char

Byte to use as quoting character.

batch_size

Number of rows that will be processed per thread.

datetime_format

A format string, with the specifiers defined by the chrono Rust crate. If no format specified, the default fractional-second precision is inferred from the maximum timeunit found in the frame’s Datetime cols (if any).

date_format

A format string, with the specifiers defined by the chrono Rust crate.

time_format

A format string, with the specifiers defined by the chrono Rust crate.

float_precision

Number of decimal places to write, applied to both Float32 and Float64 datatypes.

null_value

A string representing null values (defaulting to the empty string).

quote_style{‘necessary’, ‘always’, ‘non_numeric’, ‘never’}

Determines the quoting strategy used.

  • necessary (default): This puts quotes around fields only when necessary. They are necessary when fields contain a quote, separator or record terminator. Quotes are also necessary when writing an empty record (which is indistinguishable from a record with one empty field). This is the default.

  • always: This puts quotes around every field. Always.

  • never: This never puts quotes around fields, even if that results in invalid CSV data (e.g.: by not quoting strings containing the separator).

  • non_numeric: This puts quotes around all fields that are non-numeric. Namely, when writing a field that does not parse as a valid float or integer, then quotes will be used even if they aren`t strictly necessary.

Examples

>>> import pathlib
>>>
>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3, 4, 5],
...         "bar": [6, 7, 8, 9, 10],
...         "ham": ["a", "b", "c", "d", "e"],
...     }
... )
>>> path: pathlib.Path = dirpath / "new_file.csv"
>>> df.write_csv(path, separator=",")
write_database(
table_name: str,
connection: str,
*,
if_table_exists: DbWriteMode = 'fail',
engine: DbWriteEngine = 'sqlalchemy',
) int[source]

Write a polars frame to a database.

Parameters:
table_name

Schema-qualified name of the table to create or append to in the target SQL database. If your table name contains special characters, it should be quoted.

connection

Connection URI string, for example:

  • “postgresql://user:pass@server:port/database”

  • “sqlite:////path/to/database.db”

if_table_exists{‘append’, ‘replace’, ‘fail’}

The insert mode:

  • ‘replace’ will create a new database table, overwriting an existing one.

  • ‘append’ will append to an existing table.

  • ‘fail’ will fail if table already exists.

engine{‘sqlalchemy’, ‘adbc’}

Select the engine to use for writing frame data.

Returns:
int

The number of rows affected, if the driver provides this information. Otherwise, returns -1.

write_delta(
target: str | Path | deltalake.DeltaTable,
*,
mode: Literal['error', 'append', 'overwrite', 'ignore', 'merge'] = 'error',
overwrite_schema: bool | None = None,
storage_options: dict[str, str] | None = None,
delta_write_options: dict[str, Any] | None = None,
delta_merge_options: dict[str, Any] | None = None,
) deltalake.table.TableMerger | None[source]

Write DataFrame as delta table.

Parameters:
target

URI of a table or a DeltaTable object.

mode{‘error’, ‘append’, ‘overwrite’, ‘ignore’, ‘merge’}

How to handle existing data.

  • If ‘error’, throw an error if the table already exists (default).

  • If ‘append’, will add new data.

  • If ‘overwrite’, will replace table with new data.

  • If ‘ignore’, will not write anything if table already exists.

  • If ‘merge’, return a TableMerger object to merge data from the DataFrame with the existing data.

overwrite_schema

If True, allows updating the schema of the table.

Deprecated since version 0.20.14: Use the parameter delta_write_options instead and pass {"schema_mode": "overwrite"}.

storage_options

Extra options for the storage backends supported by deltalake. For cloud storages, this may include configurations for authentication etc.

  • See a list of supported storage options for S3 here.

  • See a list of supported storage options for GCS here.

  • See a list of supported storage options for Azure here.

delta_write_options

Additional keyword arguments while writing a Delta lake Table. See a list of supported write options here.

delta_merge_options

Keyword arguments which are required to MERGE a Delta lake Table. See a list of supported merge options here.

Raises:
TypeError

If the DataFrame contains unsupported data types.

ArrowInvalidError

If the DataFrame contains data types that could not be cast to their primitive type.

TableNotFoundError

If the delta table doesn’t exist and MERGE action is triggered

Notes

The Polars data types Null, Categorical and Time are not supported by the delta protocol specification and will raise a TypeError.

Polars columns are always nullable. To write data to a delta table with non-nullable columns, a custom pyarrow schema has to be passed to the delta_write_options. See the last example below.

Examples

Write a dataframe to the local filesystem as a Delta Lake table.

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3, 4, 5],
...         "bar": [6, 7, 8, 9, 10],
...         "ham": ["a", "b", "c", "d", "e"],
...     }
... )
>>> table_path = "/path/to/delta-table/"
>>> df.write_delta(table_path)  

Append data to an existing Delta Lake table on the local filesystem. Note that this will fail if the schema of the new data does not match the schema of the existing table.

>>> df.write_delta(table_path, mode="append")  

Overwrite a Delta Lake table as a new version. If the schemas of the new and old data are the same, specifying the schema_mode is not required.

>>> existing_table_path = "/path/to/delta-table/"
>>> df.write_delta(
...     existing_table_path,
...     mode="overwrite",
...     delta_write_options={"schema_mode": "overwrite"},
... )  

Write a DataFrame as a Delta Lake table to a cloud object store like S3.

>>> table_path = "s3://bucket/prefix/to/delta-table/"
>>> df.write_delta(
...     table_path,
...     storage_options={
...         "AWS_REGION": "THE_AWS_REGION",
...         "AWS_ACCESS_KEY_ID": "THE_AWS_ACCESS_KEY_ID",
...         "AWS_SECRET_ACCESS_KEY": "THE_AWS_SECRET_ACCESS_KEY",
...     },
... )  

Write DataFrame as a Delta Lake table with non-nullable columns.

>>> import pyarrow as pa
>>> existing_table_path = "/path/to/delta-table/"
>>> df.write_delta(
...     existing_table_path,
...     delta_write_options={
...         "schema": pa.schema([pa.field("foo", pa.int64(), nullable=False)])
...     },
... )  

Merge the DataFrame with an existing Delta Lake table. For all TableMerger methods, check the deltalake docs here.

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3, 4, 5],
...         "bar": [6, 7, 8, 9, 10],
...         "ham": ["a", "b", "c", "d", "e"],
...     }
... )
>>> table_path = "/path/to/delta-table/"
>>> (
...     df.write_delta(
...         "table_path",
...         mode="merge",
...         delta_merge_options={
...             "predicate": "s.foo = t.foo",
...             "source_alias": "s",
...             "target_alias": "t",
...         },
...     )
...     .when_matched_update_all()
...     .when_not_matched_insert_all()
...     .execute()
... )  
write_excel(
workbook: Workbook | IO[bytes] | Path | str | None = None,
worksheet: str | None = None,
*,
position: tuple[int, int] | str = 'A1',
table_style: str | dict[str, Any] | None = None,
table_name: str | None = None,
column_formats: ColumnFormatDict | None = None,
dtype_formats: dict[OneOrMoreDataTypes, str] | None = None,
conditional_formats: ConditionalFormatDict | None = None,
header_format: dict[str, Any] | None = None,
column_totals: ColumnTotalsDefinition | None = None,
column_widths: ColumnWidthsDefinition | None = None,
row_totals: RowTotalsDefinition | None = None,
row_heights: dict[int | tuple[int, ...], int] | int | None = None,
sparklines: dict[str, Sequence[str] | dict[str, Any]] | None = None,
formulas: dict[str, str | dict[str, str]] | None = None,
float_precision: int = 3,
include_header: bool = True,
autofilter: bool = True,
autofit: bool = False,
hidden_columns: Sequence[str] | SelectorType | None = None,
hide_gridlines: bool = False,
sheet_zoom: int | None = None,
freeze_panes: str | tuple[int, int] | tuple[str, int, int] | tuple[int, int, int, int] | None = None,
) Workbook[source]

Write frame data to a table in an Excel workbook/worksheet.

Parameters:
workbookWorkbook

String name or path of the workbook to create, BytesIO object to write into, or an open xlsxwriter.Workbook object that has not been closed. If None, writes to a dataframe.xlsx workbook in the working directory.

worksheetstr

Name of target worksheet; if None, writes to “Sheet1” when creating a new workbook (note that writing to an existing workbook requires a valid existing -or new- worksheet name).

position{str, tuple}

Table position in Excel notation (eg: “A1”), or a (row,col) integer tuple.

table_style{str, dict}

A named Excel table style, such as “Table Style Medium 4”, or a dictionary of {"key":value,} options containing one or more of the following keys: “style”, “first_column”, “last_column”, “banded_columns, “banded_rows”.

table_namestr

Name of the output table object in the worksheet; can then be referred to in the sheet by formulae/charts, or by subsequent xlsxwriter operations.

column_formatsdict

A {colname(s):str,} or {selector:str,} dictionary for applying an Excel format string to the given columns. Formats defined here (such as “dd/mm/yyyy”, “0.00%”, etc) will override any defined in dtype_formats.

dtype_formatsdict

A {dtype:str,} dictionary that sets the default Excel format for the given dtype. (This can be overridden on a per-column basis by the column_formats param). It is also valid to use dtype groups such as pl.FLOAT_DTYPES as the dtype/format key, to simplify setting uniform integer and float formats.

conditional_formatsdict

A dictionary of colname (or selector) keys to a format str, dict, or list that defines conditional formatting options for the specified columns.

  • If supplying a string typename, should be one of the valid xlsxwriter types such as “3_color_scale”, “data_bar”, etc.

  • If supplying a dictionary you can make use of any/all xlsxwriter supported options, including icon sets, formulae, etc.

  • Supplying multiple columns as a tuple/key will apply a single format across all columns - this is effective in creating a heatmap, as the min/max values will be determined across the entire range, not per-column.

  • Finally, you can also supply a list made up from the above options in order to apply more than one conditional format to the same range.

header_formatdict

A {key:value,} dictionary of xlsxwriter format options to apply to the table header row, such as {"bold":True, "font_color":"#702963"}.

column_totals{bool, list, dict}

Add a column-total row to the exported table.

  • If True, all numeric columns will have an associated total using “sum”.

  • If passing a string, it must be one of the valid total function names and all numeric columns will have an associated total using that function.

  • If passing a list of colnames, only those given will have a total.

  • For more control, pass a {colname:funcname,} dict.

Valid total function names are “average”, “count_nums”, “count”, “max”, “min”, “std_dev”, “sum”, and “var”.

column_widths{dict, int}

A {colname:int,} or {selector:int,} dict or a single integer that sets (or overrides if autofitting) table column widths, in integer pixel units. If given as an integer the same value is used for all table columns.

row_totals{dict, bool}

Add a row-total column to the right-hand side of the exported table.

  • If True, a column called “total” will be added at the end of the table that applies a “sum” function row-wise across all numeric columns.

  • If passing a list/sequence of column names, only the matching columns will participate in the sum.

  • Can also pass a {colname:columns,} dictionary to create one or more total columns with distinct names, referencing different columns.

row_heights{dict, int}

An int or {row_index:int,} dictionary that sets the height of the given rows (if providing a dictionary) or all rows (if providing an integer) that intersect with the table body (including any header and total row) in integer pixel units. Note that row_index starts at zero and will be the header row (unless include_header is False).

sparklinesdict

A {colname:list,} or {colname:dict,} dictionary defining one or more sparklines to be written into a new column in the table.

  • If passing a list of colnames (used as the source of the sparkline data) the default sparkline settings are used (eg: line chart with no markers).

  • For more control an xlsxwriter-compliant options dict can be supplied, in which case three additional polars-specific keys are available: “columns”, “insert_before”, and “insert_after”. These allow you to define the source columns and position the sparkline(s) with respect to other table columns. If no position directive is given, sparklines are added to the end of the table (eg: to the far right) in the order they are given.

formulasdict

A {colname:formula,} or {colname:dict,} dictionary defining one or more formulas to be written into a new column in the table. Note that you are strongly advised to use structured references in your formulae wherever possible to make it simple to reference columns by name.

  • If providing a string formula (such as “=[@colx]*[@coly]”) the column will be added to the end of the table (eg: to the far right), after any default sparklines and before any row_totals.

  • For the most control supply an options dictionary with the following keys: “formula” (mandatory), one of “insert_before” or “insert_after”, and optionally “return_dtype”. The latter is used to appropriately format the output of the formula and allow it to participate in row/column totals.

float_precisionint

Default number of decimals displayed for floating point columns (note that this is purely a formatting directive; the actual values are not rounded).

include_headerbool

Indicate if the table should be created with a header row.

autofilterbool

If the table has headers, provide autofilter capability.

autofitbool

Calculate individual column widths from the data.

hidden_columnslist

A list or selector representing table columns to hide in the worksheet.

hide_gridlinesbool

Do not display any gridlines on the output worksheet.

sheet_zoomint

Set the default zoom level of the output worksheet.

freeze_panesstr | (str, int, int) | (int, int) | (int, int, int, int)

Freeze workbook panes.

  • If (row, col) is supplied, panes are split at the top-left corner of the specified cell, which are 0-indexed. Thus, to freeze only the top row, supply (1, 0).

  • Alternatively, cell notation can be used to supply the cell. For example, “A2” indicates the split occurs at the top-left of cell A2, which is the equivalent of (1, 0).

  • If (row, col, top_row, top_col) are supplied, the panes are split based on the row and col, and the scrolling region is initialized to begin at the top_row and top_col. Thus, to freeze only the top row and have the scrolling region begin at row 10, column D (5th col), supply (1, 0, 9, 4). Using cell notation for (row, col), supplying (“A2”, 9, 4) is equivalent.

Notes

Examples

Instantiate a basic DataFrame:

>>> from random import uniform
>>> from datetime import date
>>>
>>> df = pl.DataFrame(
...     {
...         "dtm": [date(2023, 1, 1), date(2023, 1, 2), date(2023, 1, 3)],
...         "num": [uniform(-500, 500), uniform(-500, 500), uniform(-500, 500)],
...         "val": [10_000, 20_000, 30_000],
...     }
... )

Export to “dataframe.xlsx” (the default workbook name, if not specified) in the working directory, add column totals (“sum” by default) on all numeric columns, then autofit:

>>> df.write_excel(column_totals=True, autofit=True)  

Write frame to a specific location on the sheet, set a named table style, apply US-style date formatting, increase default float precision, apply a non-default total function to a single column, autofit:

>>> df.write_excel(  
...     position="B4",
...     table_style="Table Style Light 16",
...     dtype_formats={pl.Date: "mm/dd/yyyy"},
...     column_totals={"num": "average"},
...     float_precision=6,
...     autofit=True,
... )

Write the same frame to a named worksheet twice, applying different styles and conditional formatting to each table, adding table titles using explicit xlsxwriter integration:

>>> from xlsxwriter import Workbook
>>> with Workbook("multi_frame.xlsx") as wb:  
...     # basic/default conditional formatting
...     df.write_excel(
...         workbook=wb,
...         worksheet="data",
...         position=(3, 1),  # specify position as (row,col) coordinates
...         conditional_formats={"num": "3_color_scale", "val": "data_bar"},
...         table_style="Table Style Medium 4",
...     )
...
...     # advanced conditional formatting, custom styles
...     df.write_excel(
...         workbook=wb,
...         worksheet="data",
...         position=(len(df) + 7, 1),
...         table_style={
...             "style": "Table Style Light 4",
...             "first_column": True,
...         },
...         conditional_formats={
...             "num": {
...                 "type": "3_color_scale",
...                 "min_color": "#76933c",
...                 "mid_color": "#c4d79b",
...                 "max_color": "#ebf1de",
...             },
...             "val": {
...                 "type": "data_bar",
...                 "data_bar_2010": True,
...                 "bar_color": "#9bbb59",
...                 "bar_negative_color_same": True,
...                 "bar_negative_border_color_same": True,
...             },
...         },
...         column_formats={"num": "#,##0.000;[White]-#,##0.000"},
...         column_widths={"val": 125},
...         autofit=True,
...     )
...
...     # add some table titles (with a custom format)
...     ws = wb.get_worksheet_by_name("data")
...     fmt_title = wb.add_format(
...         {
...             "font_color": "#4f6228",
...             "font_size": 12,
...             "italic": True,
...             "bold": True,
...         }
...     )
...     ws.write(2, 1, "Basic/default conditional formatting", fmt_title)
...     ws.write(len(df) + 6, 1, "Customised conditional formatting", fmt_title)

Export a table containing two different types of sparklines. Use default options for the “trend” sparkline and customised options (and positioning) for the “+/-” win_loss sparkline, with non-default integer dtype formatting, column totals, a subtle two-tone heatmap and hidden worksheet gridlines:

>>> df = pl.DataFrame(
...     {
...         "id": ["aaa", "bbb", "ccc", "ddd", "eee"],
...         "q1": [100, 55, -20, 0, 35],
...         "q2": [30, -10, 15, 60, 20],
...         "q3": [-50, 0, 40, 80, 80],
...         "q4": [75, 55, 25, -10, -55],
...     }
... )
>>> df.write_excel(  
...     table_style="Table Style Light 2",
...     # apply accounting format to all flavours of integer
...     dtype_formats={pl.INTEGER_DTYPES: "#,##0_);(#,##0)"},
...     sparklines={
...         # default options; just provide source cols
...         "trend": ["q1", "q2", "q3", "q4"],
...         # customised sparkline type, with positioning directive
...         "+/-": {
...             "columns": ["q1", "q2", "q3", "q4"],
...             "insert_after": "id",
...             "type": "win_loss",
...         },
...     },
...     conditional_formats={
...         # create a unified multi-column heatmap
...         ("q1", "q2", "q3", "q4"): {
...             "type": "2_color_scale",
...             "min_color": "#95b3d7",
...             "max_color": "#ffffff",
...         },
...     },
...     column_totals=["q1", "q2", "q3", "q4"],
...     row_totals=True,
...     hide_gridlines=True,
... )

Export a table containing an Excel formula-based column that calculates a standardised Z-score, showing use of structured references in conjunction with positioning directives, column totals, and custom formatting.

>>> df = pl.DataFrame(
...     {
...         "id": ["a123", "b345", "c567", "d789", "e101"],
...         "points": [99, 45, 50, 85, 35],
...     }
... )
>>> df.write_excel(  
...     table_style={
...         "style": "Table Style Medium 15",
...         "first_column": True,
...     },
...     column_formats={
...         "id": {"font": "Consolas"},
...         "points": {"align": "center"},
...         "z-score": {"align": "center"},
...     },
...     column_totals="average",
...     formulas={
...         "z-score": {
...             # use structured references to refer to the table columns and 'totals' row
...             "formula": "=STANDARDIZE([@points], [[#Totals],[points]], STDEV([points]))",
...             "insert_after": "points",
...             "return_dtype": pl.Float64,
...         }
...     },
...     hide_gridlines=True,
...     sheet_zoom=125,
... )
write_ipc(
file: str | Path | IO[bytes] | None,
compression: IpcCompression = 'uncompressed',
*,
future: bool = False,
) BytesIO | None[source]

Write to Arrow IPC binary stream or Feather file.

See “File or Random Access format” in https://arrow.apache.org/docs/python/ipc.html.

Parameters:
file

Path or writable file-like object to which the IPC data will be written. If set to None, the output is returned as a BytesIO object.

compression{‘uncompressed’, ‘lz4’, ‘zstd’}

Compression method. Defaults to “uncompressed”.

future

Setting this to True will write Polars’ internal data structures that might not be available by other Arrow implementations.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

Examples

>>> import pathlib
>>>
>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3, 4, 5],
...         "bar": [6, 7, 8, 9, 10],
...         "ham": ["a", "b", "c", "d", "e"],
...     }
... )
>>> path: pathlib.Path = dirpath / "new_file.arrow"
>>> df.write_ipc(path)
write_ipc_stream(
file: str | Path | IO[bytes] | None,
compression: IpcCompression = 'uncompressed',
) BytesIO | None[source]

Write to Arrow IPC record batch stream.

See “Streaming format” in https://arrow.apache.org/docs/python/ipc.html.

Parameters:
file

Path or writable file-like object to which the IPC record batch data will be written. If set to None, the output is returned as a BytesIO object.

compression{‘uncompressed’, ‘lz4’, ‘zstd’}

Compression method. Defaults to “uncompressed”.

Examples

>>> import pathlib
>>>
>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3, 4, 5],
...         "bar": [6, 7, 8, 9, 10],
...         "ham": ["a", "b", "c", "d", "e"],
...     }
... )
>>> path: pathlib.Path = dirpath / "new_file.arrow"
>>> df.write_ipc_stream(path)
write_json(
file: IOBase | str | Path | None = None,
*,
pretty: bool = False,
row_oriented: bool = False,
) str | None[source]

Serialize to JSON representation.

Parameters:
file

File path or writable file-like object to which the result will be written. If set to None (default), the output is returned as a string instead.

pretty

Pretty serialize json.

row_oriented

Write to row oriented json. This is slower, but more common.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...     }
... )
>>> df.write_json()
'{"columns":[{"name":"foo","datatype":"Int64","bit_settings":"","values":[1,2,3]},{"name":"bar","datatype":"Int64","bit_settings":"","values":[6,7,8]}]}'
>>> df.write_json(row_oriented=True)
'[{"foo":1,"bar":6},{"foo":2,"bar":7},{"foo":3,"bar":8}]'
write_ndjson(file: IOBase | str | Path | None = None) str | None[source]

Serialize to newline delimited JSON representation.

Parameters:
file

File path or writable file-like object to which the result will be written. If set to None (default), the output is returned as a string instead.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...     }
... )
>>> df.write_ndjson()
'{"foo":1,"bar":6}\n{"foo":2,"bar":7}\n{"foo":3,"bar":8}\n'
write_parquet(
file: str | Path | BytesIO,
*,
compression: ParquetCompression = 'zstd',
compression_level: int | None = None,
statistics: bool = False,
row_group_size: int | None = None,
data_page_size: int | None = None,
use_pyarrow: bool = False,
pyarrow_options: dict[str, Any] | None = None,
) None[source]

Write to Apache Parquet file.

Parameters:
file

File path or writable file-like object to which the result will be written.

compression{‘lz4’, ‘uncompressed’, ‘snappy’, ‘gzip’, ‘lzo’, ‘brotli’, ‘zstd’}

Choose “zstd” for good compression performance. Choose “lz4” for fast compression/decompression. Choose “snappy” for more backwards compatibility guarantees when you deal with older parquet readers.

compression_level

The level of compression to use. Higher compression means smaller files on disk.

  • “gzip” : min-level: 0, max-level: 10.

  • “brotli” : min-level: 0, max-level: 11.

  • “zstd” : min-level: 1, max-level: 22.

statistics

Write statistics to the parquet headers. This requires extra compute.

row_group_size

Size of the row groups in number of rows. Defaults to 512^2 rows.

data_page_size

Size of the data page in bytes. Defaults to 1024^2 bytes.

use_pyarrow

Use C++ parquet implementation vs Rust parquet implementation. At the moment C++ supports more features.

pyarrow_options

Arguments passed to pyarrow.parquet.write_table.

If you pass partition_cols here, the dataset will be written using pyarrow.parquet.write_to_dataset. The partition_cols parameter leads to write the dataset to a directory. Similar to Spark’s partitioned datasets.

Examples

>>> import pathlib
>>>
>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3, 4, 5],
...         "bar": [6, 7, 8, 9, 10],
...         "ham": ["a", "b", "c", "d", "e"],
...     }
... )
>>> path: pathlib.Path = dirpath / "new_file.parquet"
>>> df.write_parquet(path)

We can use pyarrow with use_pyarrow_write_to_dataset=True to write partitioned datasets. The following example will write the first row to ../watermark=1/.parquet and the other rows to ../watermark=2/.parquet.

>>> df = pl.DataFrame({"a": [1, 2, 3], "watermark": [1, 2, 2]})
>>> path: pathlib.Path = dirpath / "partitioned_object"
>>> df.write_parquet(
...     path,
...     use_pyarrow=True,
...     pyarrow_options={"partition_cols": ["watermark"]},
... )