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,

)

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 specified:

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

Methods:

approx_n_unique	Approximate count of unique values.
bottom_k	Return the k smallest rows.
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.
collect_schema	Get an ordered mapping of column names to their data type.
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.
deserialize	Read a serialized DataFrame from a file.
drop	Remove columns from the dataframe.
drop_in_place	Drop a single column in-place and return the dropped column.
drop_nans	Drop all rows that contain one or more NaN values.
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.
fold	Apply a horizontal reduction on a 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).
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_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	Returns True if the DataFrame contains no rows.
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.
join_where	Perform a join based on one or multiple (in)equality predicates.
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_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.
serialize	Serialize this DataFrame to a file or string in JSON format.
set_sorted	Indicate that one or multiple columns are sorted.
shift	Shift values by the given number of indices.
shrink_to_fit	Shrink DataFrame memory usage.
slice	Get a slice of this DataFrame.
sort	Sort the dataframe by the given columns.
sql	Execute a SQL query against the DataFrame.
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.
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 instantiable string representation.
to_jax	Convert DataFrame to a Jax Array, or dict of Jax Arrays.
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.
to_torch	Convert DataFrame to a PyTorch Tensor, Dataset, or dict of Tensors.
top_k	Return the k largest rows.
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.
unpivot	Unpivot a DataFrame from wide to long format.
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_clipboard	Copy DataFrame in csv format to the system clipboard with write_csv.
write_csv	Write to comma-separated values (CSV) file.
write_database	Write the data in a Polars DataFrame 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 column data types.
flags	Get flags that are set on the columns of this DataFrame.
height	Get the number of rows.
plot	Create a plot namespace.
schema	Get an ordered mapping of column names to their data type.
shape	Get the shape of the DataFrame.
style	Create a Great Table for styling.
width	Get the number of columns.

approx_n_unique() → DataFrame

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],

reverse: bool | Sequence[bool] = False,

) → DataFrame

Return the k smallest rows.

Non-null elements are always preferred over null elements, regardless of the value of reverse. The output is not guaranteed to be in any particular order, call sort() after this function if you wish the output to be sorted.

Parameters:

k

Number of rows to return.

by

Column(s) used to determine the bottom rows. Accepts expression input. Strings are parsed as column names.

reverse

Consider the k largest elements of the by column(s) (instead of the k smallest). This can be specified per column by passing a sequence of booleans.

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 | PythonDataType] | PolarsDataType,

*,

strict: bool = True,

) → DataFrame

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

Raise if cast is invalid on rows after predicates are pusded down. If False, invalid casts will produce null values.

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) → DataFrame

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() → DataFrame

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  │
└─────┴──────┴───────┘

collect_schema() → Schema

Get an ordered mapping of column names to their data type.

This is an alias for the schema property.

See also

schema

Notes

This method is included to facilitate writing code that is generic for both DataFrame and LazyFrame.

Examples

Determine the schema.

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6.0, 7.0, 8.0],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.collect_schema()
Schema({'foo': Int64, 'bar': Float64, 'ham': String})

Access various properties of the schema using the Schema object.

>>> schema = df.collect_schema()
>>> schema["bar"]
Float64
>>> schema.names()
['foo', 'bar', 'ham']
>>> schema.dtypes()
[Int64, Float64, String]
>>> schema.len()
3

property columns: list[str]

Get or set column names.

Returns:

list of str

A list containing the name of each column in order.

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

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

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

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 16:00:00 ┆ 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 16:00:00 ┆ 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 │
└────────────┴──────────┴──────────┴──────────┴──────┴─────────────────────┴──────────┘

classmethod deserialize(

source: str | Path | IOBase,

*,

format: SerializationFormat = 'binary',

) → DataFrame

Read a serialized DataFrame from a file.

Parameters:

source

Path to a file or a file-like object (by file-like object, we refer to objects that have a read() method, such as a file handler (e.g. via builtin open function) or BytesIO).

format

The format with which the DataFrame was serialized. Options:

• "binary": Deserialize from binary format (bytes). This is the default.

• "json": Deserialize from JSON format (string).

See also

DataFrame.serialize

Notes

Serialization is not stable across Polars versions: a LazyFrame serialized in one Polars version may not be deserializable in another Polars version.

Examples

>>> import io
>>> df = pl.DataFrame({"a": [1, 2, 3], "b": [4.0, 5.0, 6.0]})
>>> bytes = df.serialize()
>>> pl.DataFrame.deserialize(io.BytesIO(bytes))
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ f64 │
╞═════╪═════╡
│ 1   ┆ 4.0 │
│ 2   ┆ 5.0 │
│ 3   ┆ 6.0 │
└─────┴─────┘

drop(

*columns: ColumnNameOrSelector | Iterable[ColumnNameOrSelector],

strict: bool = True,

) → DataFrame

Remove columns from the dataframe.

Parameters:

*columns

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

strict

Validate that all column names exist in the current schema, and throw an exception if any do not.

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

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_nans(

subset: ColumnNameOrSelector | Collection[ColumnNameOrSelector] | None = None,

) → DataFrame

Drop all rows that contain one or more NaN values.

The original order of the remaining rows is preserved.

Parameters:

subset

Column name(s) for which NaN values are considered; if set to None (default), use all columns (note that only floating-point columns can contain NaNs).

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [-20.5, float("nan"), 80.0],
...         "bar": [float("nan"), 110.0, 25.5],
...         "ham": ["xxx", "yyy", None],
...     }
... )

The default behavior of this method is to drop rows where any single value in the row is NaN:

>>> df.drop_nans()
shape: (1, 3)
┌──────┬──────┬──────┐
│ foo  ┆ bar  ┆ ham  │
│ ---  ┆ ---  ┆ ---  │
│ f64  ┆ f64  ┆ str  │
╞══════╪══════╪══════╡
│ 80.0 ┆ 25.5 ┆ null │
└──────┴──────┴──────┘

This behaviour can be constrained to consider only a subset of columns, as defined by name, or with a selector. For example, dropping rows only if there is a NaN in the “bar” column:

>>> df.drop_nans(subset=["bar"])
shape: (2, 3)
┌──────┬───────┬──────┐
│ foo  ┆ bar   ┆ ham  │
│ ---  ┆ ---   ┆ ---  │
│ f64  ┆ f64   ┆ str  │
╞══════╪═══════╪══════╡
│ NaN  ┆ 110.0 ┆ yyy  │
│ 80.0 ┆ 25.5  ┆ null │
└──────┴───────┴──────┘

Dropping a row only if all values are NaN requires a different formulation:

>>> df = pl.DataFrame(
...     {
...         "a": [float("nan"), float("nan"), float("nan"), float("nan")],
...         "b": [10.0, 2.5, float("nan"), 5.25],
...         "c": [65.75, float("nan"), float("nan"), 10.5],
...     }
... )
>>> df.filter(~pl.all_horizontal(pl.all().is_nan()))
shape: (3, 3)
┌─────┬──────┬───────┐
│ a   ┆ b    ┆ c     │
│ --- ┆ ---  ┆ ---   │
│ f64 ┆ f64  ┆ f64   │
╞═════╪══════╪═══════╡
│ NaN ┆ 10.0 ┆ 65.75 │
│ NaN ┆ 2.5  ┆ NaN   │
│ NaN ┆ 5.25 ┆ 10.5  │
└─────┴──────┴───────┘

drop_nulls(

subset: ColumnNameOrSelector | Collection[ColumnNameOrSelector] | None = None,

) → DataFrame

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]

Get the column data types.

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

Returns:

list of DataType

A list containing the data type of each column in order.

See also

schema

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

Check whether the DataFrame is equal to another DataFrame.

Parameters:

other

DataFrame to compare with.

null_equal

Consider null values as equal.

See also

polars.testing.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

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

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,

) → DataFrame

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

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 | Expr | None = None,

strategy: FillNullStrategy | None = None,

limit: int | None = None,

*,

matches_supertype: bool = True,

) → DataFrame

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

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

The original order of the remaining rows is preserved.

Rows where the filter does not evaluate to True are discarded, including nulls.

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 &.

Notes

If you are transitioning from pandas and performing filter operations based on the comparison of two or more columns, please note that in Polars, any comparison involving null values will always result in null. As a result, these rows will be filtered out. Ensure to handle null values appropriately to avoid unintended filtering (See examples below).

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3, None, 4, None, 0],
...         "bar": [6, 7, 8, None, None, 9, 0],
...         "ham": ["a", "b", "c", None, "d", "e", "f"],
...     }
... )

Filter on one condition:

>>> df.filter(pl.col("foo") > 1)
shape: (3, 3)
┌─────┬──────┬─────┐
│ foo ┆ bar  ┆ ham │
│ --- ┆ ---  ┆ --- │
│ i64 ┆ i64  ┆ str │
╞═════╪══════╪═════╡
│ 2   ┆ 7    ┆ b   │
│ 3   ┆ 8    ┆ c   │
│ 4   ┆ null ┆ d   │
└─────┴──────┴─────┘

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: (2, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 6   ┆ a   │
│ 0   ┆ 0   ┆ f   │
└─────┴─────┴─────┘

Provide multiple filters using **kwargs syntax:

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

Filter by comparing two columns against each other

>>> df.filter(pl.col("foo") == pl.col("bar"))
shape: (1, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 0   ┆ 0   ┆ f   │
└─────┴─────┴─────┘

>>> df.filter(pl.col("foo") != pl.col("bar"))
shape: (3, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ 6   ┆ a   │
│ 2   ┆ 7   ┆ b   │
│ 3   ┆ 8   ┆ c   │
└─────┴─────┴─────┘

Notice how the row with None values is filtered out. In order to keep the same behavior as pandas, use:

>>> df.filter(pl.col("foo").ne_missing(pl.col("bar")))
shape: (5, 3)
┌──────┬──────┬─────┐
│ foo  ┆ bar  ┆ ham │
│ ---  ┆ ---  ┆ --- │
│ i64  ┆ i64  ┆ str │
╞══════╪══════╪═════╡
│ 1    ┆ 6    ┆ a   │
│ 2    ┆ 7    ┆ b   │
│ 3    ┆ 8    ┆ c   │
│ 4    ┆ null ┆ d   │
│ null ┆ 9    ┆ e   │
└──────┴──────┴─────┘

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

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

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", None],
...         "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
]

gather_every(n: int, offset: int = 0) → DataFrame

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,

*,

default: Any | NoDefault = <no_default>,

) → Series | Any

Get a single column by name.

Parameters:

name

String name of the column to retrieve.

default

Value to return if the column does not exist; if not explicitly set and the column is not present a ColumnNotFoundError exception is raised.

Returns:

Series (or arbitrary default value, if specified).

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
]

Missing column handling; can optionally provide an arbitrary default value to the method (otherwise a ColumnNotFoundError exception is raised).

>>> df.get_column("baz", default=pl.Series("baz", ["?", "?", "?"]))
shape: (3,)
Series: 'baz' [str]
[
    "?"
    "?"
    "?"
]
>>> res = df.get_column("baz", default=None)
>>> res is None
True

get_column_index(name: str) → int

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
>>> df.get_column_index("sandwich")  
ColumnNotFoundError: sandwich

get_columns() → list[Series]

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

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

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,

include_boundaries: bool = False,

closed: ClosedInterval = 'left',

label: Label = 'left',

group_by: IntoExpr | Iterable[IntoExpr] | None = None,

start_by: StartBy = 'window',

) → DynamicGroupBy

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, every, and the earliest datapoint. See the start_by argument description for details.

Warning

The index column must be sorted in ascending order. If group_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 group_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, does not take effect if start_by is ‘datapoint’. Defaults to zero.

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

group_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.

  The resulting window is then shifted back until the earliest datapoint is in or in front of it.

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 group_by columns are passed, it will only be sorted within each 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.

>>> 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: (4, 2)
┌─────────────────────┬───────────┐
│ time                ┆ n         │
│ ---                 ┆ ---       │
│ datetime[μs]        ┆ list[i64] │
╞═════════════════════╪═══════════╡
│ 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",
...     group_by="groups",
...     include_boundaries=True,
... ).agg(pl.col("n"))
shape: (6, 5)
┌────────┬─────────────────────┬─────────────────────┬─────────────────────┬───────────┐
│ groups ┆ _lower_boundary     ┆ _upper_boundary     ┆ time                ┆ n         │
│ ---    ┆ ---                 ┆ ---                 ┆ ---                 ┆ ---       │
│ str    ┆ datetime[μs]        ┆ datetime[μs]        ┆ datetime[μs]        ┆ list[i64] │
╞════════╪═════════════════════╪═════════════════════╪═════════════════════╪═══════════╡
│ 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"]           │
└─────────────────┴─────────────────┴─────┴─────────────────┘

hash_rows(

seed: int = 0,

seed_1: int | None = None,

seed_2: int | None = None,

seed_3: int | None = None,

) → Series

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) → DataFrame

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

Get the number of rows.

Returns:

int

Examples

>>> df = pl.DataFrame({"foo": [1, 2, 3, 4, 5]})
>>> df.height
5

hstack(

columns: list[Series] | DataFrame,

*,

in_place: bool = False,

) → DataFrame

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_column(index: int, column: IntoExprColumn) → DataFrame

Insert a Series at a certain column index.

This operation is in place.

Parameters:

index

Index at which to insert the new column.

column

Series or expression 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

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

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

Returns True if the DataFrame contains no rows.

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

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

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]

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]]

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]

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,

coalesce: bool | None = None,

maintain_order: MaintainOrderJoin | None = None,

) → DataFrame

Join in SQL-like fashion.

Parameters:

other

DataFrame to join with.

on

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

how{‘inner’, ‘left’, ‘right’, ‘full’, ‘semi’, ‘anti’, ‘cross’}

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

• right

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

• full

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

• cross

  Returns the Cartesian product of rows from both tables

• semi

  Returns rows from the left table that have a match in the right table.

• anti

  Returns rows from the left table that have no match in the right table.

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 by the streaming engine.

join_nulls

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

coalesce

Coalescing behavior (merging of join columns).

• None: -> join specific.

• True: -> Always coalesce join columns.

• False: -> Never coalesce join columns.

Note

Joining on any other expressions than col will turn off coalescing.

maintain_order{‘none’, ‘left’, ‘right’, ‘left_right’, ‘right_left’}

Which DataFrame row order to preserve, if any. Do not rely on any observed ordering without explicitly setting this parameter, as your code may break in a future release. Not specifying any ordering can improve performance Supported for inner, left, right and full joins

• none

  No specific ordering is desired. The ordering might differ across Polars versions or even between different runs.

• left

  Preserves the order of the left DataFrame.

• right

  Preserves the order of the right DataFrame.

• left_right

  First preserves the order of the left DataFrame, then the right.

• right_left

  First preserves the order of the right DataFrame, then the left.

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="full")
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", coalesce=True)
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,

coalesce: bool = True,

) → DataFrame

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)

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.

coalesce

Coalescing behavior (merging of on / left_on / right_on columns):

• True: -> Always coalesce join columns.

• False: -> Never coalesce join columns.

Note that joining on any other expressions than col will turn off coalescing.

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.

You can verify this by passing coalesce=False:

>>> population.join_asof(gdp, on="date", strategy="backward", coalesce=False)
shape: (3, 4)
┌────────────┬────────────┬────────────┬──────┐
│ date       ┆ population ┆ date_right ┆ gdp  │
│ ---        ┆ ---        ┆ ---        ┆ ---  │
│ date       ┆ f64        ┆ date       ┆ i64  │
╞════════════╪════════════╪════════════╪══════╡
│ 2016-03-01 ┆ 82.19      ┆ 2016-01-01 ┆ 4164 │
│ 2018-08-01 ┆ 82.66      ┆ 2018-01-01 ┆ 4566 │
│ 2019-01-01 ┆ 83.12      ┆ 2019-01-01 ┆ 4696 │
└────────────┴────────────┴────────────┴──────┘

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.

They by argument allows joining on another column first, before the asof join. In this example we join by country first, then asof join by date, as above.

>>> gdp_dates = pl.date_range(  # fmt: skip
...     date(2016, 1, 1), date(2020, 1, 1), "1y", eager=True
... )
>>> gdp2 = pl.DataFrame(
...     {
...         "country": ["Germany"] * 5 + ["Netherlands"] * 5,
...         "date": pl.concat([gdp_dates, gdp_dates]),
...         "gdp": [4164, 4411, 4566, 4696, 4827, 784, 833, 914, 910, 909],
...     }
... ).sort("country", "date")
>>>
>>> gdp2
shape: (10, 3)
┌─────────────┬────────────┬──────┐
│ country     ┆ date       ┆ gdp  │
│ ---         ┆ ---        ┆ ---  │
│ str         ┆ date       ┆ i64  │
╞═════════════╪════════════╪══════╡
│ Germany     ┆ 2016-01-01 ┆ 4164 │
│ Germany     ┆ 2017-01-01 ┆ 4411 │
│ Germany     ┆ 2018-01-01 ┆ 4566 │
│ Germany     ┆ 2019-01-01 ┆ 4696 │
│ Germany     ┆ 2020-01-01 ┆ 4827 │
│ Netherlands ┆ 2016-01-01 ┆ 784  │
│ Netherlands ┆ 2017-01-01 ┆ 833  │
│ Netherlands ┆ 2018-01-01 ┆ 914  │
│ Netherlands ┆ 2019-01-01 ┆ 910  │
│ Netherlands ┆ 2020-01-01 ┆ 909  │
└─────────────┴────────────┴──────┘
>>> pop2 = pl.DataFrame(
...     {
...         "country": ["Germany"] * 3 + ["Netherlands"] * 3,
...         "date": [
...             date(2016, 3, 1),
...             date(2018, 8, 1),
...             date(2019, 1, 1),
...             date(2016, 3, 1),
...             date(2018, 8, 1),
...             date(2019, 1, 1),
...         ],
...         "population": [82.19, 82.66, 83.12, 17.11, 17.32, 17.40],
...     }
... ).sort("country", "date")
>>>
>>> pop2
shape: (6, 3)
┌─────────────┬────────────┬────────────┐
│ country     ┆ date       ┆ population │
│ ---         ┆ ---        ┆ ---        │
│ str         ┆ date       ┆ f64        │
╞═════════════╪════════════╪════════════╡
│ Germany     ┆ 2016-03-01 ┆ 82.19      │
│ Germany     ┆ 2018-08-01 ┆ 82.66      │
│ Germany     ┆ 2019-01-01 ┆ 83.12      │
│ Netherlands ┆ 2016-03-01 ┆ 17.11      │
│ Netherlands ┆ 2018-08-01 ┆ 17.32      │
│ Netherlands ┆ 2019-01-01 ┆ 17.4       │
└─────────────┴────────────┴────────────┘
>>> pop2.join_asof(gdp2, by="country", on="date", strategy="nearest")
shape: (6, 4)
┌─────────────┬────────────┬────────────┬──────┐
│ country     ┆ date       ┆ population ┆ gdp  │
│ ---         ┆ ---        ┆ ---        ┆ ---  │
│ str         ┆ date       ┆ f64        ┆ i64  │
╞═════════════╪════════════╪════════════╪══════╡
│ Germany     ┆ 2016-03-01 ┆ 82.19      ┆ 4164 │
│ Germany     ┆ 2018-08-01 ┆ 82.66      ┆ 4696 │
│ Germany     ┆ 2019-01-01 ┆ 83.12      ┆ 4696 │
│ Netherlands ┆ 2016-03-01 ┆ 17.11      ┆ 784  │
│ Netherlands ┆ 2018-08-01 ┆ 17.32      ┆ 910  │
│ Netherlands ┆ 2019-01-01 ┆ 17.4       ┆ 910  │
└─────────────┴────────────┴────────────┴──────┘

join_where(

other: DataFrame,

*predicates: Expr | Iterable[Expr],

suffix: str = '_right',

) → DataFrame

Perform a join based on one or multiple (in)equality predicates.

This performs an inner join, so only rows where all predicates are true are included in the result, and a row from either DataFrame may be included multiple times in the result.

Note

The row order of the input DataFrames is not preserved.

Warning

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

Parameters:

other

DataFrame to join with.

*predicates

(In)Equality condition to join the two tables on. When a column name occurs in both tables, the proper suffix must be applied in the predicate.

suffix

Suffix to append to columns with a duplicate name.

Examples

>>> east = pl.DataFrame(
...     {
...         "id": [100, 101, 102],
...         "dur": [120, 140, 160],
...         "rev": [12, 14, 16],
...         "cores": [2, 8, 4],
...     }
... )
>>> west = pl.DataFrame(
...     {
...         "t_id": [404, 498, 676, 742],
...         "time": [90, 130, 150, 170],
...         "cost": [9, 13, 15, 16],
...         "cores": [4, 2, 1, 4],
...     }
... )
>>> east.join_where(
...     west,
...     pl.col("dur") < pl.col("time"),
...     pl.col("rev") < pl.col("cost"),
... )
shape: (5, 8)
┌─────┬─────┬─────┬───────┬──────┬──────┬──────┬─────────────┐
│ id  ┆ dur ┆ rev ┆ cores ┆ t_id ┆ time ┆ cost ┆ cores_right │
│ --- ┆ --- ┆ --- ┆ ---   ┆ ---  ┆ ---  ┆ ---  ┆ ---         │
│ i64 ┆ i64 ┆ i64 ┆ i64   ┆ i64  ┆ i64  ┆ i64  ┆ i64         │
╞═════╪═════╪═════╪═══════╪══════╪══════╪══════╪═════════════╡
│ 100 ┆ 120 ┆ 12  ┆ 2     ┆ 498  ┆ 130  ┆ 13   ┆ 2           │
│ 100 ┆ 120 ┆ 12  ┆ 2     ┆ 676  ┆ 150  ┆ 15   ┆ 1           │
│ 100 ┆ 120 ┆ 12  ┆ 2     ┆ 742  ┆ 170  ┆ 16   ┆ 4           │
│ 101 ┆ 140 ┆ 14  ┆ 8     ┆ 676  ┆ 150  ┆ 15   ┆ 1           │
│ 101 ┆ 140 ┆ 14  ┆ 8     ┆ 742  ┆ 170  ┆ 16   ┆ 4           │
└─────┴─────┴─────┴───────┴──────┴──────┴──────┴─────────────┘

lazy() → LazyFrame

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

Operations on a LazyFrame are not executed until this is triggered by calling one of:

• .collect()

  (run on all data)

• .explain()

  (print the query plan)

• .show_graph()

  (show the query plan as graphviz graph)

• .collect_schema()

  (return the final frame schema)

Lazy operations are recommended because they allow for query optimization and additional parallelism.

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 at ...>

limit(n: int = 5) → DataFrame

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

Examples

Get the first 3 rows of a DataFrame.

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

map_rows(

function: Callable[[tuple[Any, ...]], Any],

return_dtype: PolarsDataType | None = None,

*,

inference_size: int = 256,

) → DataFrame

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 map_rows 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 map_elements 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)
┌─────┐
│ map │
│ --- │
│ 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() → DataFrame

Aggregate the columns of this DataFrame to their maximum value.

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

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() → DataFrame

Aggregate the columns of this DataFrame to their mean value.

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

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() → DataFrame

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,

) → DataFrame

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’.

Deprecated since version 1.0.0: Please use unpivot() instead.

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”

merge_sorted(

other: DataFrame,

key: str,

) → DataFrame

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() → DataFrame

Aggregate the columns of this DataFrame to their minimum value.

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

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: Literal['first', 'all'] = 'first') → int | list[int]

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

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"], orient="row"
... )
>>> 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() → DataFrame

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[DataFrame] | dict[tuple[object, ...], DataFrame]

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.

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

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(

on: ColumnNameOrSelector | Sequence[ColumnNameOrSelector],

*,

index: ColumnNameOrSelector | Sequence[ColumnNameOrSelector] | None = None,

values: ColumnNameOrSelector | Sequence[ColumnNameOrSelector] | None = None,

aggregate_function: PivotAgg | Expr | None = None,

maintain_order: bool = True,

sort_columns: bool = False,

separator: str = '_',

) → DataFrame

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:

on

The column(s) whose values will be used as the new columns of the output DataFrame.

index

The column(s) that remain from the input to the output. The output DataFrame will have one row for each unique combination of the index’s values. If None, all remaining columns not specified on on and values will be used. At least one of index and values must be specified.

values

The existing column(s) of values which will be moved under the new columns from index. If an aggregation is specified, these are the values on which the aggregation will be computed. If None, all remaining columns not specified on on and index will be used. At least one of index and values must be specified.

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 in case of multiple values columns.

Returns:

DataFrame

Notes

In some other frameworks, you might know this operation as pivot_wider.

Examples

You can use pivot to reshape a dataframe from “long” to “wide” format.

For example, suppose we have a dataframe of test scores achieved by some students, where each row represents a distinct test.

>>> df = pl.DataFrame(
...     {
...         "name": ["Cady", "Cady", "Karen", "Karen"],
...         "subject": ["maths", "physics", "maths", "physics"],
...         "test_1": [98, 99, 61, 58],
...         "test_2": [100, 100, 60, 60],
...     }
... )
>>> df
shape: (4, 4)
┌───────┬─────────┬────────┬────────┐
│ name  ┆ subject ┆ test_1 ┆ test_2 │
│ ---   ┆ ---     ┆ ---    ┆ ---    │
│ str   ┆ str     ┆ i64    ┆ i64    │
╞═══════╪═════════╪════════╪════════╡
│ Cady  ┆ maths   ┆ 98     ┆ 100    │
│ Cady  ┆ physics ┆ 99     ┆ 100    │
│ Karen ┆ maths   ┆ 61     ┆ 60     │
│ Karen ┆ physics ┆ 58     ┆ 60     │
└───────┴─────────┴────────┴────────┘

Using pivot, we can reshape so we have one row per student, with different subjects as columns, and their test_1 scores as values:

>>> df.pivot("subject", index="name", values="test_1")
shape: (2, 3)
┌───────┬───────┬─────────┐
│ name  ┆ maths ┆ physics │
│ ---   ┆ ---   ┆ ---     │
│ str   ┆ i64   ┆ i64     │
╞═══════╪═══════╪═════════╡
│ Cady  ┆ 98    ┆ 99      │
│ Karen ┆ 61    ┆ 58      │
└───────┴───────┴─────────┘

You can use selectors too - here we include all test scores in the pivoted table:

>>> import polars.selectors as cs
>>> df.pivot("subject", values=cs.starts_with("test"))
shape: (2, 5)
┌───────┬──────────────┬────────────────┬──────────────┬────────────────┐
│ name  ┆ test_1_maths ┆ test_1_physics ┆ test_2_maths ┆ test_2_physics │
│ ---   ┆ ---          ┆ ---            ┆ ---          ┆ ---            │
│ str   ┆ i64          ┆ i64            ┆ i64          ┆ i64            │
╞═══════╪══════════════╪════════════════╪══════════════╪════════════════╡
│ Cady  ┆ 98           ┆ 99             ┆ 100          ┆ 100            │
│ Karen ┆ 61           ┆ 58             ┆ 60           ┆ 60             │
└───────┴──────────────┴────────────────┴──────────────┴────────────────┘

If you end up with multiple values per cell, you can specify how to aggregate them with aggregate_function:

>>> df = pl.DataFrame(
...     {
...         "ix": [1, 1, 2, 2, 1, 2],
...         "col": ["a", "a", "a", "a", "b", "b"],
...         "foo": [0, 1, 2, 2, 7, 1],
...         "bar": [0, 2, 0, 0, 9, 4],
...     }
... )
>>> df.pivot("col", index="ix", aggregate_function="sum")
shape: (2, 5)
┌─────┬───────┬───────┬───────┬───────┐
│ ix  ┆ foo_a ┆ foo_b ┆ bar_a ┆ bar_b │
│ --- ┆ ---   ┆ ---   ┆ ---   ┆ ---   │
│ i64 ┆ i64   ┆ i64   ┆ i64   ┆ i64   │
╞═════╪═══════╪═══════╪═══════╪═══════╡
│ 1   ┆ 1     ┆ 7     ┆ 2     ┆ 9     │
│ 2   ┆ 4     ┆ 1     ┆ 0     ┆ 4     │
└─────┴───────┴───────┴───────┴───────┘

You can also pass a custom aggregation function using polars.element():

>>> 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(
...     "col2",
...     index="col1",
...     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.group_by() to get the same result as above in lazy mode:

>>> index = pl.col("col1")
>>> on = 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(on == 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: DataFramePlot

Create a plot namespace.

Warning

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

Changed in version 1.6.0: In prior versions of Polars, HvPlot was the plotting backend. If you would like to restore the previous plotting functionality, all you need to do is add import hvplot.polars at the top of your script and replace df.plot with df.hvplot.

Polars does not implement plotting logic itself, but instead defers to Altair:

• df.plot.line(**kwargs) is shorthand for alt.Chart(df).mark_line(tooltip=True).encode(**kwargs).interactive()

• df.plot.point(**kwargs) is shorthand for alt.Chart(df).mark_point(tooltip=True).encode(**kwargs).interactive() (and plot.scatter is provided as an alias)

• df.plot.bar(**kwargs) is shorthand for alt.Chart(df).mark_bar(tooltip=True).encode(**kwargs).interactive()

• for any other attribute attr, df.plot.attr(**kwargs) is shorthand for alt.Chart(df).mark_attr(tooltip=True).encode(**kwargs).interactive()

For configuration, we suggest reading Chart Configuration. For example, you can:

• Change the width/height/title with .properties(width=500, height=350, title="My amazing plot").

• Change the x-axis label rotation with .configure_axisX(labelAngle=30).

• Change the opacity of the points in your scatter plot with .configure_point(opacity=.5).

Examples

Scatter plot:

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

Set the x-axis title by using altair.X:

>>> import altair as alt
>>> df.plot.point(
...     x=alt.X("length", title="Length"), y="width", color="species"
... )  

Line plot:

>>> from datetime import date
>>> df = pl.DataFrame(
...     {
...         "date": [date(2020, 1, 2), date(2020, 1, 3), date(2020, 1, 4)] * 2,
...         "price": [1, 4, 6, 1, 5, 2],
...         "stock": ["a", "a", "a", "b", "b", "b"],
...     }
... )
>>> df.plot.line(x="date", y="price", color="stock")  

Bar plot:

>>> df = pl.DataFrame(
...     {
...         "day": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"] * 2,
...         "group": ["a"] * 7 + ["b"] * 7,
...         "value": [1, 3, 2, 4, 5, 6, 1, 1, 3, 2, 4, 5, 1, 2],
...     }
... )
>>> df.plot.bar(
...     x="day", y="value", color="day", column="group"
... )  

Or, to make a stacked version of the plot above:

>>> df.plot.bar(x="day", y="value", color="group")  

product() → DataFrame

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',

) → DataFrame

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() → DataFrame

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],

*,

strict: bool = True,

) → DataFrame

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.

strict

Validate that all column names exist in the current schema, and throw an exception if any do not. (Note that this parameter is a no-op when passing a function to mapping).

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(index: int, column: Series) → DataFrame

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

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',

group_by: IntoExpr | Iterable[IntoExpr] | None = None,

) → RollingGroupBy

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 group_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).

group_by

Also group by this column/these columns

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 group_by columns are passed, it will only be sorted within each 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     │
└─────────────────────┴───────┴───────┴───────┘

If you use an index count in period or offset, then it’s based on the values in index_column:

>>> df = pl.DataFrame({"int": [0, 4, 5, 6, 8], "value": [1, 4, 2, 4, 1]})
>>> df.rolling("int", period="3i").agg(pl.col("int").alias("aggregated"))
shape: (5, 2)
┌─────┬────────────┐
│ int ┆ aggregated │
│ --- ┆ ---        │
│ i64 ┆ list[i64]  │
╞═════╪════════════╡
│ 0   ┆ [0]        │
│ 4   ┆ [4]        │
│ 5   ┆ [4, 5]     │
│ 6   ┆ [4, 5, 6]  │
│ 8   ┆ [6, 8]     │
└─────┴────────────┘

If you want the index count to be based on row number, then you may want to combine rolling with with_row_index().

row(

index: int | None = None,

*,

by_predicate: Expr | None = None,

named: bool = False,

) → tuple[Any, ...] | dict[str, Any]

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]]

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, Any]

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).

to_dict

Convert DataFrame to a dictionary mapping column name to 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(
...     {
...         "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,

) → DataFrame

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: Schema

Get an ordered mapping of column names to their data type.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6.0, 7.0, 8.0],
...         "ham": ["a", "b", "c"],
...     }
... )
>>> df.schema
Schema({'foo': Int64, 'bar': Float64, 'ham': String})

select(

*exprs: IntoExpr | Iterable[IntoExpr],

**named_exprs: IntoExpr,

) → DataFrame

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.Int64) % 2 == 1).name.suffix("_is_odd"),
...     )
shape: (3, 1)
┌──────────────┐
│ is_odd       │
│ ---          │
│ struct[2]    │
╞══════════════╡
│ {true,false} │
│ {false,true} │
│ {true,false} │
└──────────────┘

select_seq(

*exprs: IntoExpr | Iterable[IntoExpr],

**named_exprs: IntoExpr,

) → DataFrame

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

serialize(

file: IOBase | str | Path | None = None,

*,

format: SerializationFormat = 'binary',

) → bytes | str | None

Serialize this DataFrame to a file or string in JSON format.

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.

format

The format in which to serialize. Options:

• "binary": Serialize to binary format (bytes). This is the default.

• "json": Serialize to JSON format (string).

Notes

Serialization is not stable across Polars versions: a LazyFrame serialized in one Polars version may not be deserializable in another Polars version.

Examples

Serialize the DataFrame into a binary representation.

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3],
...         "bar": [6, 7, 8],
...     }
... )
>>> bytes = df.serialize()
>>> bytes  
b'\xa1gcolumns\x82\xa4dnamecfoohdatatypeeInt64lbit_settings\x00fvalues\x83...'

The bytes can later be deserialized back into a DataFrame.

>>> import io
>>> pl.DataFrame.deserialize(io.BytesIO(bytes))
shape: (3, 2)
┌─────┬─────┐
│ foo ┆ bar │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 6   │
│ 2   ┆ 7   │
│ 3   ┆ 8   │
└─────┴─────┘

set_sorted(

column: str,

*,

descending: bool = False,

) → DataFrame

Indicate that one or multiple columns are sorted.

This can speed up future operations.

Parameters:

column

Column that are sorted

descending

Whether the columns are sorted in descending order.

Warning

This can lead to incorrect results if the data is NOT sorted!! Use with care!

property shape: tuple[int, int]

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

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 │
└─────┴─────┘

shrink_to_fit(*, in_place: bool = False) → DataFrame

Shrink DataFrame memory usage.

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

slice(

offset: int,

length: int | None = None,

) → DataFrame

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 | Sequence[bool] = False,

multithreaded: bool = True,

maintain_order: bool = False,

) → DataFrame

Sort the dataframe by the given columns.

Parameters:

by

Column(s) to sort by. Accepts expression input, including selectors. 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; can specify a single boolean applying to all columns or a sequence of booleans for per-column control.

multithreaded

Sort using multiple threads.

maintain_order

Whether the order should be maintained if elements are equal.

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   │
└──────┴─────┴─────┘

sql(

query: str,

*,

table_name: str = 'self',

) → DataFrame

Execute a SQL query against the DataFrame.

Added in version 0.20.24.

Warning

This functionality is considered unstable, although it is close to being considered stable. It may be changed at any point without it being considered a breaking change.

Parameters:

query

SQL query to execute.

table_name

Optionally provide an explicit name for the table that represents the calling frame (defaults to “self”).

See also

SQLContext

Notes

• The calling frame is automatically registered as a table in the SQL context under the name “self”. If you want access to the DataFrames and LazyFrames found in the current globals, use the top-level pl.sql.

• More control over registration and execution behaviour is available by using the SQLContext object.

• The SQL query executes in lazy mode before being collected and returned as a DataFrame.

Examples

>>> from datetime import date
>>> df1 = pl.DataFrame(
...     {
...         "a": [1, 2, 3],
...         "b": ["zz", "yy", "xx"],
...         "c": [date(1999, 12, 31), date(2010, 10, 10), date(2077, 8, 8)],
...     }
... )

Query the DataFrame using SQL:

>>> df1.sql("SELECT c, b FROM self WHERE a > 1")
shape: (2, 2)
┌────────────┬─────┐
│ c          ┆ b   │
│ ---        ┆ --- │
│ date       ┆ str │
╞════════════╪═════╡
│ 2010-10-10 ┆ yy  │
│ 2077-08-08 ┆ xx  │
└────────────┴─────┘

Apply transformations to a DataFrame using SQL, aliasing “self” to “frame”.

>>> df1.sql(
...     query='''
...         SELECT
...             a,
...             (a % 2 == 0) AS a_is_even,
...             CONCAT_WS(':', b, b) AS b_b,
...             EXTRACT(year FROM c) AS year,
...             0::float4 AS "zero",
...         FROM frame
...     ''',
...     table_name="frame",
... )
shape: (3, 5)
┌─────┬───────────┬───────┬──────┬──────┐
│ a   ┆ a_is_even ┆ b_b   ┆ year ┆ zero │
│ --- ┆ ---       ┆ ---   ┆ ---  ┆ ---  │
│ i64 ┆ bool      ┆ str   ┆ i32  ┆ f32  │
╞═════╪═══════════╪═══════╪══════╪══════╡
│ 1   ┆ false     ┆ zz:zz ┆ 1999 ┆ 0.0  │
│ 2   ┆ true      ┆ yy:yy ┆ 2010 ┆ 0.0  │
│ 3   ┆ false     ┆ xx:xx ┆ 2077 ┆ 0.0  │
└─────┴───────────┴───────┴──────┴──────┘

std(ddof: int = 1) → DataFrame

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 │
└──────────┴──────────┴──────┘

property style: GT

Create a Great Table for styling.

Warning

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

Polars does not implement styling logic itself, but instead defers to the Great Tables package. Please see the Great Tables reference for more information and documentation.

Examples

Import some styling helpers, and create example data:

>>> import polars.selectors as cs
>>> from great_tables import loc, style
>>> df = pl.DataFrame(
...     {
...         "site_id": [0, 1, 2],
...         "measure_a": [5, 4, 6],
...         "measure_b": [7, 3, 3],
...     }
... )

Emphasize the site_id as row names:

>>> df.style.tab_stub(rowname_col="site_id")  

Fill the background for the highest measure_a value row:

>>> df.style.tab_style(
...     style.fill("yellow"),
...     loc.body(rows=pl.col("measure_a") == pl.col("measure_a").max()),
... )  

Put a spanner (high-level label) over measure columns:

>>> df.style.tab_spanner(
...     "Measures", cs.starts_with("measure")
... )  

Format measure_b values to two decimal places:

>>> df.style.fmt_number("measure_b", decimals=2)  

sum() → DataFrame

Aggregate the columns of this DataFrame to their sum value.

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

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) → DataFrame

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   │
└─────┴─────┴─────┘

to_arrow(

*,

compat_level: CompatLevel | None = None,

) → Table

Collect the underlying arrow arrays in an Arrow Table.

This operation is mostly zero copy.

Data types that do copy:

• CategoricalType

Parameters:

compat_level

Use a specific compatibility level when exporting Polars’ internal data structures.

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]]

Convert DataFrame to a dictionary mapping column name to values.

Parameters:

as_series

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

See also

rows_by_key

to_dicts

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]]

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,

) → DataFrame

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

Convert DataFrame to instantiable string representation.

Parameters:

n

Only use first n rows.

See also

polars.Series.to_init_repr

polars.from_repr

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_jax(

return_type: JaxExportType = 'array',

*,

device: jax.Device | str | None = None,

label: str | Expr | Sequence[str | Expr] | None = None,

features: str | Expr | Sequence[str | Expr] | None = None,

dtype: PolarsDataType | None = None,

order: IndexOrder = 'fortran',

) → jax.Array | dict[str, jax.Array]

Convert DataFrame to a Jax Array, or dict of Jax Arrays.

Added in version 0.20.27.

Warning

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

Parameters:

return_type{“array”, “dict”}

Set return type; a Jax Array, or dict of Jax Arrays.

device

Specify the jax Device on which the array will be created; can provide a string (such as “cpu”, “gpu”, or “tpu”) in which case the device is retrieved as jax.devices(string)[0]. For more specific control you can supply the instantiated Device directly. If None, arrays are created on the default device.

label

One or more column names, expressions, or selectors that label the feature data; results in a {"label": ..., "features": ...} dict being returned when return_type is “dict” instead of a {"col": array, } dict.

features

One or more column names, expressions, or selectors that contain the feature data; if omitted, all columns that are not designated as part of the label are used. Only applies when return_type is “dict”.

dtype

Unify the dtype of all returned arrays; this casts any column that is not already of the required dtype before converting to Array. Note that export will be single-precision (32bit) unless the Jax config/environment directs otherwise (eg: “jax_enable_x64” was set True in the config object at startup, or “JAX_ENABLE_X64” is set to “1” in the environment).

order{“c”, “fortran”}

The index order of the returned Jax array, either C-like (row-major) or Fortran-like (column-major).

See also

to_dummies

to_numpy

to_torch

Examples

>>> df = pl.DataFrame(
...     {
...         "lbl": [0, 1, 2, 3],
...         "feat1": [1, 0, 0, 1],
...         "feat2": [1.5, -0.5, 0.0, -2.25],
...     }
... )

Standard return type (2D Array), on the standard device:

>>> df.to_jax()
Array([[ 0.  ,  1.  ,  1.5 ],
       [ 1.  ,  0.  , -0.5 ],
       [ 2.  ,  0.  ,  0.  ],
       [ 3.  ,  1.  , -2.25]], dtype=float32)

Create the Array on the default GPU device:

>>> a = df.to_jax(device="gpu")  
>>> a.device()  
GpuDevice(id=0, process_index=0)

Create the Array on a specific GPU device:

>>> gpu_device = jax.devices("gpu")[1]  
>>> a = df.to_jax(device=gpu_device)  
>>> a.device()  
GpuDevice(id=1, process_index=0)

As a dictionary of individual Arrays:

>>> df.to_jax("dict")
{'lbl': Array([0, 1, 2, 3], dtype=int32),
 'feat1': Array([1, 0, 0, 1], dtype=int32),
 'feat2': Array([ 1.5 , -0.5 ,  0.  , -2.25], dtype=float32)}

As a “label” and “features” dictionary; note that as “features” is not declared, it defaults to all the columns that are not in “label”:

>>> df.to_jax("dict", label="lbl")
{'label': Array([[0],
        [1],
        [2],
        [3]], dtype=int32),
 'features': Array([[ 1.  ,  1.5 ],
        [ 0.  , -0.5 ],
        [ 0.  ,  0.  ],
        [ 1.  , -2.25]], dtype=float32)}

As a “label” and “features” dictionary where each is designated using a col or selector expression (which can also be used to cast the data if the label and features are better-represented with different dtypes):

>>> import polars.selectors as cs
>>> df.to_jax(
...     return_type="dict",
...     features=cs.float(),
...     label=pl.col("lbl").cast(pl.UInt8),
... )
{'label': Array([[0],
        [1],
        [2],
        [3]], dtype=uint8),
 'features': Array([[ 1.5 ],
        [-0.5 ],
        [ 0.  ],
        [-2.25]], dtype=float32)}

to_numpy(

*,

order: IndexOrder = 'fortran',

writable: bool = False,

allow_copy: bool = True,

structured: bool = False,

use_pyarrow: bool | None = None,

) → np.ndarray[Any, Any]

Convert this DataFrame to a NumPy ndarray.

This operation copies data only when necessary. The conversion is zero copy when all of the following hold:

• The DataFrame is fully contiguous in memory, with all Series back-to-back and all Series consisting of a single chunk.

• The data type is an integer or float.

• The DataFrame contains no null values.

• The order parameter is set to fortran (default).

• The writable parameter is set to False (default).

Parameters:

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.

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.

allow_copy

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

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.

use_pyarrow

Use pyarrow.Array.to_numpy

function for the conversion to NumPy if necessary.

Deprecated since version 0.20.28: Polars now uses its native engine by default for conversion to NumPy.

Examples

Numeric data without nulls can be converted without copying data in some cases. The resulting array will not be writable.

>>> df = pl.DataFrame({"a": [1, 2, 3]})
>>> arr = df.to_numpy()
>>> arr
array([[1],
       [2],
       [3]])
>>> arr.flags.writeable
False

Set writable=True to force data copy to make the array writable.

>>> df.to_numpy(writable=True).flags.writeable
True

If the DataFrame contains different numeric data types, the resulting data type will be the supertype. This requires data to be copied. Integer types with nulls are cast to a float type with nan representing a null value.

>>> df = pl.DataFrame({"a": [1, 2, None], "b": [4.0, 5.0, 6.0]})
>>> df.to_numpy()
array([[ 1.,  4.],
       [ 2.,  5.],
       [nan,  6.]])

Set allow_copy=False to raise an error if data would be copied.

>>> s.to_numpy(allow_copy=False)  
Traceback (most recent call last):
...
RuntimeError: copy not allowed: cannot convert to a NumPy array without copying data

Polars defaults to F-contiguous order. Use order="c" to force the resulting array to be C-contiguous.

>>> df.to_numpy(order="c").flags.c_contiguous
True

DataFrames with mixed types will result in an array with an object dtype.

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

Set structured=True to convert to a structured array, which can better preserve individual column data such as name and data type.

>>> df.to_numpy(structured=True)
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

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

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

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"}
]

to_torch(

return_type: TorchExportType = 'tensor',

*,

label: str | Expr | Sequence[str | Expr] | None = None,

features: str | Expr | Sequence[str | Expr] | None = None,

dtype: PolarsDataType | None = None,

) → torch.Tensor | dict[str, torch.Tensor] | PolarsDataset

Convert DataFrame to a PyTorch Tensor, Dataset, or dict of Tensors.

Added in version 0.20.23.

Warning

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

Parameters:

return_type{“tensor”, “dataset”, “dict”}

Set return type; a PyTorch Tensor, PolarsDataset (a frame-specialized TensorDataset), or dict of Tensors.

label

One or more column names, expressions, or selectors that label the feature data; when return_type is “dataset”, the PolarsDataset will return (features, label) tensor tuples for each row. Otherwise, it returns (features,) tensor tuples where the feature contains all the row data.

features

One or more column names, expressions, or selectors that contain the feature data; if omitted, all columns that are not designated as part of the label are used.

dtype

Unify the dtype of all returned tensors; this casts any column that is not of the required dtype before converting to Tensor. This includes the label column unless the label is an expression (such as pl.col("label_column").cast(pl.Int16)).

See also

to_dummies

to_jax

to_numpy

Examples

>>> df = pl.DataFrame(
...     {
...         "lbl": [0, 1, 2, 3],
...         "feat1": [1, 0, 0, 1],
...         "feat2": [1.5, -0.5, 0.0, -2.25],
...     }
... )

Standard return type (Tensor), with f32 supertype:

>>> df.to_torch(dtype=pl.Float32)
tensor([[ 0.0000,  1.0000,  1.5000],
        [ 1.0000,  0.0000, -0.5000],
        [ 2.0000,  0.0000,  0.0000],
        [ 3.0000,  1.0000, -2.2500]])

As a dictionary of individual Tensors:

>>> df.to_torch("dict")
{'lbl': tensor([0, 1, 2, 3]),
 'feat1': tensor([1, 0, 0, 1]),
 'feat2': tensor([ 1.5000, -0.5000,  0.0000, -2.2500], dtype=torch.float64)}

As a “label” and “features” dictionary; note that as “features” is not declared, it defaults to all the columns that are not in “label”:

>>> df.to_torch("dict", label="lbl", dtype=pl.Float32)
{'label': tensor([[0.],
         [1.],
         [2.],
         [3.]]),
 'features': tensor([[ 1.0000,  1.5000],
         [ 0.0000, -0.5000],
         [ 0.0000,  0.0000],
         [ 1.0000, -2.2500]])}

As a PolarsDataset, with f64 supertype:

>>> ds = df.to_torch("dataset", dtype=pl.Float64)
>>> ds[3]
(tensor([ 3.0000,  1.0000, -2.2500], dtype=torch.float64),)
>>> ds[:2]
(tensor([[ 0.0000,  1.0000,  1.5000],
         [ 1.0000,  0.0000, -0.5000]], dtype=torch.float64),)
>>> ds[[0, 3]]
(tensor([[ 0.0000,  1.0000,  1.5000],
         [ 3.0000,  1.0000, -2.2500]], dtype=torch.float64),)

As a convenience the PolarsDataset can opt in to half-precision data for experimentation (usually this would be set on the model/pipeline):

>>> list(ds.half())
[(tensor([0.0000, 1.0000, 1.5000], dtype=torch.float16),),
 (tensor([ 1.0000,  0.0000, -0.5000], dtype=torch.float16),),
 (tensor([2., 0., 0.], dtype=torch.float16),),
 (tensor([ 3.0000,  1.0000, -2.2500], dtype=torch.float16),)]

Pass PolarsDataset to a DataLoader, designating the label:

>>> from torch.utils.data import DataLoader
>>> ds = df.to_torch("dataset", label="lbl")
>>> dl = DataLoader(ds, batch_size=2)
>>> batches = list(dl)
>>> batches[0]
[tensor([[ 1.0000,  1.5000],
         [ 0.0000, -0.5000]], dtype=torch.float64), tensor([0, 1])]

Note that labels can be given as expressions, allowing them to have a dtype independent of the feature columns (multi-column labels are supported).

>>> ds = df.to_torch(
...     return_type="dataset",
...     dtype=pl.Float32,
...     label=pl.col("lbl").cast(pl.Int16),
... )
>>> ds[:2]
(tensor([[ 1.0000,  1.5000],
         [ 0.0000, -0.5000]]), tensor([0, 1], dtype=torch.int16))

Easily integrate with (for example) scikit-learn and other datasets:

>>> from sklearn.datasets import fetch_california_housing  
>>> housing = fetch_california_housing()  
>>> df = pl.DataFrame(
...     data=housing.data,
...     schema=housing.feature_names,
... ).with_columns(
...     Target=housing.target,
... )  
>>> train = df.to_torch("dataset", label="Target")  
>>> loader = DataLoader(
...     train,
...     shuffle=True,
...     batch_size=64,
... )  

top_k(

k: int,

*,

by: IntoExpr | Iterable[IntoExpr],

reverse: bool | Sequence[bool] = False,

) → DataFrame

Return the k largest rows.

Non-null elements are always preferred over null elements, regardless of the value of reverse. The output is not guaranteed to be in any particular order, call sort() after this function if you wish the output to be sorted.

Parameters:

k

Number of rows to return.

by

Column(s) used to determine the top rows. Accepts expression input. Strings are parsed as column names.

reverse

Consider the k smallest elements of the by column(s) (instead of the k largest). This can be specified per column by passing a sequence of booleans.

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,

) → DataFrame

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   │
│ ---    ┆ --- ┆ --- ┆ --- │<