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test_df.py
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test_df.py
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from __future__ import annotations
import contextlib
import sys
import typing
from collections import OrderedDict
from datetime import date, datetime, time, timedelta, timezone
from decimal import Decimal
from io import BytesIO
from operator import floordiv, truediv
from typing import TYPE_CHECKING, Any, Callable, Iterator, Sequence, cast
import numpy as np
import pyarrow as pa
import pytest
import polars as pl
import polars.selectors as cs
from polars._utils.construction import iterable_to_pydf
from polars.datatypes import DTYPE_TEMPORAL_UNITS, INTEGER_DTYPES
from polars.exceptions import ComputeError, TimeZoneAwareConstructorWarning
from polars.testing import (
assert_frame_equal,
assert_frame_not_equal,
assert_series_equal,
)
from polars.testing.parametric import columns
if TYPE_CHECKING:
from zoneinfo import ZoneInfo
from polars.type_aliases import JoinStrategy, UniqueKeepStrategy
else:
from polars._utils.convert import string_to_zoneinfo as ZoneInfo
def test_version() -> None:
pl.__version__
def test_null_count() -> None:
df = pl.DataFrame({"a": [2, 1, 3], "b": ["a", "b", None]})
assert df.null_count().shape == (1, 2)
assert df.null_count().row(0) == (0, 1)
assert df.null_count().row(np.int64(0)) == (0, 1) # type: ignore[call-overload]
def test_init_empty() -> None:
# test various flavours of empty init
for empty in (None, (), [], {}, pa.Table.from_arrays([])):
df = pl.DataFrame(empty)
assert df.shape == (0, 0)
assert df.is_empty()
# note: cannot use df (empty or otherwise) in boolean context
empty_df = pl.DataFrame()
with pytest.raises(TypeError, match="ambiguous"):
not empty_df
def test_special_char_colname_init() -> None:
from string import punctuation
with pl.StringCache():
cols = [(c.name, c.dtype) for c in columns(punctuation)]
df = pl.DataFrame(schema=cols)
assert len(cols) == len(df.columns)
assert len(df.rows()) == 0
assert df.is_empty()
def test_comparisons() -> None:
df = pl.DataFrame({"a": [1, 2], "b": [3, 4]})
# Constants
assert_frame_equal(df == 2, pl.DataFrame({"a": [False, True], "b": [False, False]}))
assert_frame_equal(df != 2, pl.DataFrame({"a": [True, False], "b": [True, True]}))
assert_frame_equal(df < 3.0, pl.DataFrame({"a": [True, True], "b": [False, False]}))
assert_frame_equal(df >= 2, pl.DataFrame({"a": [False, True], "b": [True, True]}))
assert_frame_equal(df <= 2, pl.DataFrame({"a": [True, True], "b": [False, False]}))
with pytest.raises(pl.ComputeError):
df > "2" # noqa: B015
# Series
s = pl.Series([3, 1])
assert_frame_equal(df >= s, pl.DataFrame({"a": [False, True], "b": [True, True]}))
# DataFrame
other = pl.DataFrame({"a": [1, 2], "b": [2, 3]})
assert_frame_equal(
df == other, pl.DataFrame({"a": [True, True], "b": [False, False]})
)
assert_frame_equal(
df != other, pl.DataFrame({"a": [False, False], "b": [True, True]})
)
assert_frame_equal(
df > other, pl.DataFrame({"a": [False, False], "b": [True, True]})
)
assert_frame_equal(
df < other, pl.DataFrame({"a": [False, False], "b": [False, False]})
)
assert_frame_equal(
df >= other, pl.DataFrame({"a": [True, True], "b": [True, True]})
)
assert_frame_equal(
df <= other, pl.DataFrame({"a": [True, True], "b": [False, False]})
)
# DataFrame columns mismatch
with pytest.raises(ValueError):
df == pl.DataFrame({"a": [1, 2], "c": [3, 4]}) # noqa: B015
with pytest.raises(ValueError):
df == pl.DataFrame({"b": [3, 4], "a": [1, 2]}) # noqa: B015
# DataFrame shape mismatch
with pytest.raises(ValueError):
df == pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) # noqa: B015
# Type mismatch
with pytest.raises(pl.ComputeError):
df == pl.DataFrame({"a": [1, 2], "b": ["x", "y"]}) # noqa: B015
def test_selection() -> None:
df = pl.DataFrame({"a": [1, 2, 3], "b": [1.0, 2.0, 3.0], "c": ["a", "b", "c"]})
# get column by name
assert_series_equal(df.get_column("b"), pl.Series("b", [1.0, 2.0, 3.0]))
# get column by index
assert_series_equal(df.to_series(1), pl.Series("b", [1.0, 2.0, 3.0]))
assert_series_equal(df.to_series(-1), pl.Series("c", ["a", "b", "c"]))
# select columns by mask
assert df[:2, :1].rows() == [(1,), (2,)]
assert df[:2, ["a"]].rows() == [(1,), (2,)]
# column selection by string(s) in first dimension
assert df["a"].to_list() == [1, 2, 3]
assert df["b"].to_list() == [1.0, 2.0, 3.0]
assert df["c"].to_list() == ["a", "b", "c"]
# row selection by integers(s) in first dimension
assert_frame_equal(df[0], pl.DataFrame({"a": [1], "b": [1.0], "c": ["a"]}))
assert_frame_equal(df[-1], pl.DataFrame({"a": [3], "b": [3.0], "c": ["c"]}))
# row, column selection when using two dimensions
assert df[:, "a"].to_list() == [1, 2, 3]
assert df[:, 1].to_list() == [1.0, 2.0, 3.0]
assert df[:2, 2].to_list() == ["a", "b"]
assert_frame_equal(
df[[1, 2]], pl.DataFrame({"a": [2, 3], "b": [2.0, 3.0], "c": ["b", "c"]})
)
assert_frame_equal(
df[[-1, -2]], pl.DataFrame({"a": [3, 2], "b": [3.0, 2.0], "c": ["c", "b"]})
)
assert df[["a", "b"]].columns == ["a", "b"]
assert_frame_equal(
df[[1, 2], [1, 2]], pl.DataFrame({"b": [2.0, 3.0], "c": ["b", "c"]})
)
assert typing.cast(str, df[1, 2]) == "b"
assert typing.cast(float, df[1, 1]) == 2.0
assert typing.cast(int, df[2, 0]) == 3
assert df[[2], ["a", "b"]].rows() == [(3, 3.0)]
assert df.to_series(0).name == "a"
assert (df["a"] == df["a"]).sum() == 3
assert (df["c"] == df["a"].cast(str)).sum() == 0
assert df[:, "a":"b"].rows() == [(1, 1.0), (2, 2.0), (3, 3.0)] # type: ignore[misc]
assert df[:, "a":"c"].columns == ["a", "b", "c"] # type: ignore[misc]
expect = pl.DataFrame({"c": ["b"]})
assert_frame_equal(df[1, [2]], expect)
expect = pl.DataFrame({"b": [1.0, 3.0]})
assert_frame_equal(df[[0, 2], [1]], expect)
assert typing.cast(str, df[0, "c"]) == "a"
assert typing.cast(str, df[1, "c"]) == "b"
assert typing.cast(str, df[2, "c"]) == "c"
assert typing.cast(int, df[0, "a"]) == 1
# more slicing
expect = pl.DataFrame({"a": [3, 2, 1], "b": [3.0, 2.0, 1.0], "c": ["c", "b", "a"]})
assert_frame_equal(df[::-1], expect)
expect = pl.DataFrame({"a": [1, 2], "b": [1.0, 2.0], "c": ["a", "b"]})
assert_frame_equal(df[:-1], expect)
expect = pl.DataFrame({"a": [1, 3], "b": [1.0, 3.0], "c": ["a", "c"]})
assert_frame_equal(df[::2], expect)
# only allow boolean values in column position
df = pl.DataFrame(
{
"a": [1, 2],
"b": [2, 3],
"c": [3, 4],
}
)
assert df[:, [False, True, True]].columns == ["b", "c"]
assert df[:, pl.Series([False, True, True])].columns == ["b", "c"]
assert df[:, pl.Series([False, False, False])].columns == []
def test_mixed_sequence_selection() -> None:
df = pl.DataFrame({"a": [1, 2], "b": [3, 4]})
result = df.select(["a", pl.col("b"), pl.lit("c")])
expected = pl.DataFrame({"a": [1, 2], "b": [3, 4], "literal": ["c", "c"]})
assert_frame_equal(result, expected)
def test_from_arrow(monkeypatch: Any) -> None:
monkeypatch.setenv("POLARS_ACTIVATE_DECIMAL", "1")
tbl = pa.table(
{
"a": pa.array([1, 2], pa.timestamp("s")),
"b": pa.array([1, 2], pa.timestamp("ms")),
"c": pa.array([1, 2], pa.timestamp("us")),
"d": pa.array([1, 2], pa.timestamp("ns")),
"e": pa.array([1, 2], pa.int32()),
"decimal1": pa.array([1, 2], pa.decimal128(2, 1)),
}
)
record_batches = tbl.to_batches(max_chunksize=1)
expected_schema = {
"a": pl.Datetime("ms"),
"b": pl.Datetime("ms"),
"c": pl.Datetime("us"),
"d": pl.Datetime("ns"),
"e": pl.Int32,
"decimal1": pl.Decimal(2, 1),
}
expected_data = [
(
datetime(1970, 1, 1, 0, 0, 1),
datetime(1970, 1, 1, 0, 0, 0, 1000),
datetime(1970, 1, 1, 0, 0, 0, 1),
datetime(1970, 1, 1, 0, 0),
1,
Decimal("1.0"),
),
(
datetime(1970, 1, 1, 0, 0, 2),
datetime(1970, 1, 1, 0, 0, 0, 2000),
datetime(1970, 1, 1, 0, 0, 0, 2),
datetime(1970, 1, 1, 0, 0),
2,
Decimal("2.0"),
),
]
for arrow_data in (tbl, record_batches, (rb for rb in record_batches)):
df = cast(pl.DataFrame, pl.from_arrow(arrow_data))
assert df.schema == expected_schema
assert df.rows() == expected_data
# record batches (inc. empty)
for b, n_expected in (
(record_batches[0], 1),
(record_batches[0][:0], 0),
):
df = cast(pl.DataFrame, pl.from_arrow(b))
assert df.schema == expected_schema
assert df.rows() == expected_data[:n_expected]
empty_tbl = tbl[:0] # no rows
df = cast(pl.DataFrame, pl.from_arrow(empty_tbl))
assert df.schema == expected_schema
assert df.rows() == []
# try a single column dtype override
for t in (tbl, empty_tbl):
df = pl.DataFrame(t, schema_overrides={"e": pl.Int8})
override_schema = expected_schema.copy()
override_schema["e"] = pl.Int8
assert df.schema == override_schema
assert df.rows() == expected_data[: (len(df))]
# init from record batches with overrides
df = pl.DataFrame(
{
"id": ["a123", "b345", "c567", "d789", "e101"],
"points": [99, 45, 50, 85, 35],
}
)
tbl = df.to_arrow()
batches = tbl.to_batches(max_chunksize=3)
df0: pl.DataFrame = pl.from_arrow(batches) # type: ignore[assignment]
df1: pl.DataFrame = pl.from_arrow( # type: ignore[assignment]
data=batches,
schema=["x", "y"],
schema_overrides={"y": pl.Int32},
)
df2: pl.DataFrame = pl.from_arrow( # type: ignore[assignment]
data=batches[0],
schema=["x", "y"],
schema_overrides={"y": pl.Int32},
)
assert df0.rows() == df.rows()
assert df1.rows() == df.rows()
assert df2.rows() == df.rows()[:3]
assert df0.schema == {"id": pl.String, "points": pl.Int64}
assert df1.schema == {"x": pl.String, "y": pl.Int32}
assert df2.schema == {"x": pl.String, "y": pl.Int32}
with pytest.raises(TypeError, match="Cannot convert str"):
pl.from_arrow(data="xyz")
with pytest.raises(TypeError, match="Cannot convert int"):
pl.from_arrow(data=(x for x in (1, 2, 3)))
def test_dataframe_membership_operator() -> None:
# cf. issue #4032
df = pl.DataFrame({"name": ["Jane", "John"], "age": [20, 30]})
assert "name" in df
assert "phone" not in df
assert df._ipython_key_completions_() == ["name", "age"]
def test_sort() -> None:
df = pl.DataFrame({"a": [2, 1, 3], "b": [1, 2, 3]})
assert_frame_equal(df.sort("a"), pl.DataFrame({"a": [1, 2, 3], "b": [2, 1, 3]}))
assert_frame_equal(
df.sort(["a", "b"]), pl.DataFrame({"a": [1, 2, 3], "b": [2, 1, 3]})
)
def test_sort_maintain_order() -> None:
l1 = (
pl.LazyFrame({"A": [1] * 4, "B": ["A", "B", "C", "D"]})
.sort("A", maintain_order=True)
.slice(0, 3)
.collect()["B"]
.to_list()
)
l2 = (
pl.LazyFrame({"A": [1] * 4, "B": ["A", "B", "C", "D"]})
.sort("A")
.collect()
.slice(0, 3)["B"]
.to_list()
)
assert l1 == l2 == ["A", "B", "C"]
def test_replace() -> None:
df = pl.DataFrame({"a": [2, 1, 3], "b": [1, 2, 3]})
s = pl.Series("c", [True, False, True])
with pytest.deprecated_call():
df.replace("a", s)
assert_frame_equal(df, pl.DataFrame({"a": [True, False, True], "b": [1, 2, 3]}))
def test_assignment() -> None:
df = pl.DataFrame({"foo": [1, 2, 3], "bar": [2, 3, 4]})
df = df.with_columns(pl.col("foo").alias("foo"))
# make sure that assignment does not change column order
assert df.columns == ["foo", "bar"]
df = df.with_columns(
pl.when(pl.col("foo") > 1).then(9).otherwise(pl.col("foo")).alias("foo")
)
assert df["foo"].to_list() == [1, 9, 9]
def test_insert_column() -> None:
df = (
pl.DataFrame({"z": [3, 4, 5]})
.insert_column(0, pl.Series("x", [1, 2, 3]))
.insert_column(-1, pl.Series("y", [2, 3, 4]))
)
expected_df = pl.DataFrame({"x": [1, 2, 3], "y": [2, 3, 4], "z": [3, 4, 5]})
assert_frame_equal(expected_df, df)
def test_replace_column() -> None:
df = (
pl.DataFrame({"x": [1, 2, 3], "y": [2, 3, 4], "z": [3, 4, 5]})
.replace_column(0, pl.Series("a", [4, 5, 6]))
.replace_column(-2, pl.Series("b", [5, 6, 7]))
.replace_column(-1, pl.Series("c", [6, 7, 8]))
)
expected_df = pl.DataFrame({"a": [4, 5, 6], "b": [5, 6, 7], "c": [6, 7, 8]})
assert_frame_equal(expected_df, df)
def test_to_series() -> None:
df = pl.DataFrame({"x": [1, 2, 3], "y": [2, 3, 4], "z": [3, 4, 5]})
assert_series_equal(df.to_series(), df["x"])
assert_series_equal(df.to_series(0), df["x"])
assert_series_equal(df.to_series(-3), df["x"])
assert_series_equal(df.to_series(1), df["y"])
assert_series_equal(df.to_series(-2), df["y"])
assert_series_equal(df.to_series(2), df["z"])
assert_series_equal(df.to_series(-1), df["z"])
with pytest.raises(TypeError, match="should be an int"):
df.to_series("x") # type: ignore[arg-type]
def test_gather_every() -> None:
df = pl.DataFrame({"a": [1, 2, 3, 4], "b": ["w", "x", "y", "z"]})
expected_df = pl.DataFrame({"a": [1, 3], "b": ["w", "y"]})
assert_frame_equal(expected_df, df.gather_every(2))
expected_df = pl.DataFrame({"a": [2, 4], "b": ["x", "z"]})
assert_frame_equal(expected_df, df.gather_every(2, offset=1))
def test_gather_every_agg() -> None:
df = pl.DataFrame(
{
"g": [1, 1, 1, 2, 2, 2],
"a": ["a", "b", "c", "d", "e", "f"],
}
)
out = df.group_by(pl.col("g")).agg(pl.col("a").gather_every(2)).sort("g")
expected = pl.DataFrame(
{
"g": [1, 2],
"a": [["a", "c"], ["d", "f"]],
}
)
assert_frame_equal(out, expected)
def test_take_misc(fruits_cars: pl.DataFrame) -> None:
df = fruits_cars
# Out of bounds error.
with pytest.raises(pl.OutOfBoundsError):
df.sort("fruits").select(
pl.col("B").reverse().gather([1, 2]).implode().over("fruits"),
"fruits",
)
# Null indices.
assert_frame_equal(
df.select(pl.col("fruits").gather(pl.Series([0, None]))),
pl.DataFrame({"fruits": ["banana", None]}),
)
for index in [[0, 1], pl.Series([0, 1]), np.array([0, 1])]:
out = df.sort("fruits").select(
[
pl.col("B")
.reverse()
.gather(index) # type: ignore[arg-type]
.over("fruits", mapping_strategy="join"),
"fruits",
]
)
assert out[0, "B"].to_list() == [2, 3]
assert out[4, "B"].to_list() == [1, 4]
out = df.sort("fruits").select(
[pl.col("B").reverse().get(pl.lit(1)).over("fruits"), "fruits"]
)
assert out[0, "B"] == 3
assert out[4, "B"] == 4
def test_pipe() -> None:
df = pl.DataFrame({"foo": [1, 2, 3], "bar": [6, None, 8]})
def _multiply(data: pl.DataFrame, mul: int) -> pl.DataFrame:
return data * mul
result = df.pipe(_multiply, mul=3)
assert_frame_equal(result, df * 3)
def test_explode() -> None:
df = pl.DataFrame({"letters": ["c", "a"], "nrs": [[1, 2], [1, 3]]})
out = df.explode("nrs")
assert out["letters"].to_list() == ["c", "c", "a", "a"]
assert out["nrs"].to_list() == [1, 2, 1, 3]
@pytest.mark.parametrize(
("stack", "exp_shape", "exp_columns"),
[
([pl.Series("stacked", [-1, -1, -1])], (3, 3), ["a", "b", "stacked"]),
(
[pl.Series("stacked2", [-1, -1, -1]), pl.Series("stacked3", [-1, -1, -1])],
(3, 4),
["a", "b", "stacked2", "stacked3"],
),
],
)
@pytest.mark.parametrize("in_place", [True, False])
def test_hstack_list_of_series(
stack: list[pl.Series],
exp_shape: tuple[int, int],
exp_columns: list[str],
in_place: bool,
) -> None:
df = pl.DataFrame({"a": [2, 1, 3], "b": ["a", "b", "c"]})
if in_place:
df.hstack(stack, in_place=True)
assert df.shape == exp_shape
assert df.columns == exp_columns
else:
df_out = df.hstack(stack, in_place=False)
assert df_out.shape == exp_shape
assert df_out.columns == exp_columns
@pytest.mark.parametrize("in_place", [True, False])
def test_hstack_dataframe(in_place: bool) -> None:
df = pl.DataFrame({"a": [2, 1, 3], "b": ["a", "b", "c"]})
df2 = pl.DataFrame({"c": [2, 1, 3], "d": ["a", "b", "c"]})
expected = pl.DataFrame(
{"a": [2, 1, 3], "b": ["a", "b", "c"], "c": [2, 1, 3], "d": ["a", "b", "c"]}
)
if in_place:
df.hstack(df2, in_place=True)
assert_frame_equal(df, expected)
else:
df_out = df.hstack(df2, in_place=False)
assert_frame_equal(df_out, expected)
def test_file_buffer() -> None:
f = BytesIO()
f.write(b"1,2,3,4,5,6\n7,8,9,10,11,12")
f.seek(0)
df = pl.read_csv(f, has_header=False)
assert df.shape == (2, 6)
f = BytesIO()
f.write(b"1,2,3,4,5,6\n7,8,9,10,11,12")
f.seek(0)
# check if not fails on TryClone and Length impl in file.rs
with pytest.raises(pl.ComputeError):
pl.read_parquet(f)
def test_shift() -> None:
df = pl.DataFrame({"A": ["a", "b", "c"], "B": [1, 3, 5]})
a = df.shift(1)
b = pl.DataFrame(
{"A": [None, "a", "b"], "B": [None, 1, 3]},
)
assert_frame_equal(a, b)
def test_custom_group_by() -> None:
df = pl.DataFrame({"a": [1, 2, 1, 1], "b": ["a", "b", "c", "c"]})
out = df.group_by("b", maintain_order=True).agg(
[pl.col("a").map_elements(lambda x: x.sum(), return_dtype=pl.Int64)]
)
assert out.rows() == [("a", 1), ("b", 2), ("c", 2)]
def test_multiple_columns_drop() -> None:
df = pl.DataFrame({"a": [2, 1, 3], "b": [1, 2, 3], "c": [1, 2, 3]})
# List input
out = df.drop(["a", "b"])
assert out.columns == ["c"]
# Positional input
out = df.drop("b", "c")
assert out.columns == ["a"]
def test_concat() -> None:
df1 = pl.DataFrame({"a": [2, 1, 3], "b": [1, 2, 3], "c": [1, 2, 3]})
df2 = pl.concat([df1, df1])
assert df2.shape == (6, 3)
assert df2.n_chunks() == 1 # the default is to rechunk
assert df2.rows() == df1.rows() + df1.rows()
assert pl.concat([df1, df1], rechunk=False).n_chunks() == 2
# concat from generator of frames
df3 = pl.concat(items=(df1 for _ in range(2)))
assert_frame_equal(df2, df3)
# check that df4 is not modified following concat of itself
df4 = pl.from_records(((1, 2), (1, 2)))
_ = pl.concat([df4, df4, df4])
assert df4.shape == (2, 2)
assert df4.rows() == [(1, 1), (2, 2)]
# misc error conditions
with pytest.raises(ValueError):
_ = pl.concat([])
with pytest.raises(ValueError):
pl.concat([df1, df1], how="rubbish") # type: ignore[arg-type]
def test_arg_where() -> None:
s = pl.Series([True, False, True, False])
assert_series_equal(pl.arg_where(s, eager=True).cast(int), pl.Series([0, 2]))
def test_to_dummies() -> None:
df = pl.DataFrame({"A": ["a", "b", "c"], "B": [1, 3, 5]})
dummies = df.to_dummies()
assert dummies["A_a"].to_list() == [1, 0, 0]
assert dummies["A_b"].to_list() == [0, 1, 0]
assert dummies["A_c"].to_list() == [0, 0, 1]
df = pl.DataFrame({"a": [1, 2, 3]})
res = df.to_dummies()
expected = pl.DataFrame(
{"a_1": [1, 0, 0], "a_2": [0, 1, 0], "a_3": [0, 0, 1]}
).with_columns(pl.all().cast(pl.UInt8))
assert_frame_equal(res, expected)
df = pl.DataFrame(
{
"i": [1, 2, 3],
"category": ["dog", "cat", "cat"],
},
schema={"i": pl.Int32, "category": pl.Categorical},
)
expected = pl.DataFrame(
{
"i": [1, 2, 3],
"category|cat": [0, 1, 1],
"category|dog": [1, 0, 0],
},
schema={"i": pl.Int32, "category|cat": pl.UInt8, "category|dog": pl.UInt8},
)
for _cols in ("category", cs.string()):
result = df.to_dummies(columns=["category"], separator="|")
assert_frame_equal(result, expected)
# test sorted fast path
result = pl.DataFrame({"x": pl.arange(0, 3, eager=True)}).to_dummies("x")
expected = pl.DataFrame(
{"x_0": [1, 0, 0], "x_1": [0, 1, 0], "x_2": [0, 0, 1]}
).with_columns(pl.all().cast(pl.UInt8))
assert_frame_equal(result, expected)
def test_to_dummies_drop_first() -> None:
df = pl.DataFrame(
{
"foo": [0, 1, 2],
"bar": [3, 4, 5],
"baz": ["x", "y", "z"],
}
)
dm = df.to_dummies()
dd = df.to_dummies(drop_first=True)
assert dd.columns == ["foo_1", "foo_2", "bar_4", "bar_5", "baz_y", "baz_z"]
assert set(dm.columns) - set(dd.columns) == {"foo_0", "bar_3", "baz_x"}
assert_frame_equal(dm.select(dd.columns), dd)
assert dd.rows() == [
(0, 0, 0, 0, 0, 0),
(1, 0, 1, 0, 1, 0),
(0, 1, 0, 1, 0, 1),
]
def test_to_pandas(df: pl.DataFrame) -> None:
# pyarrow cannot deal with unsigned dictionary integer yet.
# pyarrow cannot convert a time64 w/ non-zero nanoseconds
df = df.drop(["cat", "time", "enum"])
df.to_arrow()
df.to_pandas()
# test shifted df
df.shift(2).to_pandas()
df = pl.DataFrame({"col": pl.Series([True, False, True])})
df.shift(2).to_pandas()
def test_from_arrow_table() -> None:
data = {"a": [1, 2], "b": [1, 2]}
tbl = pa.table(data)
df = cast(pl.DataFrame, pl.from_arrow(tbl))
assert_frame_equal(df, pl.DataFrame(data))
def test_df_stats(df: pl.DataFrame) -> None:
df.var()
df.std()
df.min()
df.max()
df.sum()
df.mean()
df.median()
df.quantile(0.4, "nearest")
def test_df_fold() -> None:
df = pl.DataFrame({"a": [2, 1, 3], "b": [1, 2, 3], "c": [1.0, 2.0, 3.0]})
assert_series_equal(
df.fold(lambda s1, s2: s1 + s2), pl.Series("a", [4.0, 5.0, 9.0])
)
assert_series_equal(
df.fold(lambda s1, s2: s1.zip_with(s1 < s2, s2)),
pl.Series("a", [1.0, 1.0, 3.0]),
)
df = pl.DataFrame({"a": ["foo", "bar", "2"], "b": [1, 2, 3], "c": [1.0, 2.0, 3.0]})
out = df.fold(lambda s1, s2: s1 + s2)
assert_series_equal(out, pl.Series("a", ["foo11.0", "bar22.0", "233.0"]))
df = pl.DataFrame({"a": [3, 2, 1], "b": [1, 2, 3], "c": [1.0, 2.0, 3.0]})
# just check dispatch. values are tested on rust side.
assert len(df.sum_horizontal()) == 3
assert len(df.mean_horizontal()) == 3
assert len(df.min_horizontal()) == 3
assert len(df.max_horizontal()) == 3
df_width_one = df[["a"]]
assert_series_equal(df_width_one.fold(lambda s1, s2: s1), df["a"])
def test_fold_filter() -> None:
df = pl.DataFrame({"a": [1, 2, 3], "b": [0, 1, 2]})
out = df.filter(
pl.fold(
acc=pl.lit(True),
function=lambda a, b: a & b,
exprs=[pl.col(c) > 1 for c in df.columns],
)
)
assert out.shape == (1, 2)
assert out.rows() == [(3, 2)]
out = df.filter(
pl.fold(
acc=pl.lit(True),
function=lambda a, b: a | b,
exprs=[pl.col(c) > 1 for c in df.columns],
)
)
assert out.shape == (3, 2)
assert out.rows() == [(1, 0), (2, 1), (3, 2)]
def test_column_names() -> None:
tbl = pa.table(
{
"a": pa.array([1, 2, 3, 4, 5], pa.decimal128(38, 2)),
"b": pa.array([1, 2, 3, 4, 5], pa.int64()),
}
)
for a in (tbl, tbl[:0]):
df = cast(pl.DataFrame, pl.from_arrow(a))
assert df.columns == ["a", "b"]
def test_init_series_edge_cases() -> None:
# confirm that we don't modify the name of the input series in-place
s1 = pl.Series("X", [1, 2, 3])
df1 = pl.DataFrame({"A": s1}, schema_overrides={"A": pl.UInt8})
assert s1.name == "X"
assert df1["A"].name == "A"
# init same series object under different names
df2 = pl.DataFrame({"A": s1, "B": s1})
assert df2.rows(named=True) == [
{"A": 1, "B": 1},
{"A": 2, "B": 2},
{"A": 3, "B": 3},
]
# empty series names should not be overwritten
s2 = pl.Series([1, 2, 3])
s3 = pl.Series([2, 3, 4])
df3 = pl.DataFrame([s2, s3])
assert s2.name == s3.name == ""
assert df3.columns == ["column_0", "column_1"]
def test_head_group_by() -> None:
commodity_prices = {
"commodity": [
"Wheat",
"Wheat",
"Wheat",
"Wheat",
"Corn",
"Corn",
"Corn",
"Corn",
"Corn",
],
"location": [
"StPaul",
"StPaul",
"StPaul",
"Chicago",
"Chicago",
"Chicago",
"Chicago",
"Chicago",
"Chicago",
],
"seller": [
"Bob",
"Charlie",
"Susan",
"Paul",
"Ed",
"Mary",
"Paul",
"Charlie",
"Norman",
],
"price": [1.0, 0.7, 0.8, 0.55, 2.0, 3.0, 2.4, 1.8, 2.1],
}
df = pl.DataFrame(commodity_prices)
# this query flexes the wildcard exclusion quite a bit.
keys = ["commodity", "location"]
out = (
df.sort(by="price", descending=True)
.group_by(keys, maintain_order=True)
.agg([pl.col("*").exclude(keys).head(2).name.keep()])
.explode(pl.col("*").exclude(keys))
)
assert out.shape == (5, 4)
assert out.rows() == [
("Corn", "Chicago", "Mary", 3.0),
("Corn", "Chicago", "Paul", 2.4),
("Wheat", "StPaul", "Bob", 1.0),
("Wheat", "StPaul", "Susan", 0.8),
("Wheat", "Chicago", "Paul", 0.55),
]
df = pl.DataFrame(
{"letters": ["c", "c", "a", "c", "a", "b"], "nrs": [1, 2, 3, 4, 5, 6]}
)
out = df.group_by("letters").tail(2).sort("letters")
assert_frame_equal(
out,
pl.DataFrame({"letters": ["a", "a", "b", "c", "c"], "nrs": [3, 5, 6, 2, 4]}),
)
out = df.group_by("letters").head(2).sort("letters")
assert_frame_equal(
out,
pl.DataFrame({"letters": ["a", "a", "b", "c", "c"], "nrs": [3, 5, 6, 1, 2]}),
)
def test_is_null_is_not_null() -> None:
df = pl.DataFrame({"nrs": [1, 2, None]})
assert df.select(pl.col("nrs").is_null())["nrs"].to_list() == [False, False, True]
assert df.select(pl.col("nrs").is_not_null())["nrs"].to_list() == [
True,
True,
False,
]
def test_is_nan_is_not_nan() -> None:
df = pl.DataFrame({"nrs": np.array([1, 2, np.nan])})
assert df.select(pl.col("nrs").is_nan())["nrs"].to_list() == [False, False, True]
assert df.select(pl.col("nrs").is_not_nan())["nrs"].to_list() == [True, True, False]
def test_is_finite_is_infinite() -> None:
df = pl.DataFrame({"nrs": np.array([1, 2, np.inf])})
assert df.select(pl.col("nrs").is_infinite())["nrs"].to_list() == [
False,
False,
True,
]
assert df.select(pl.col("nrs").is_finite())["nrs"].to_list() == [True, True, False]
def test_len() -> None:
df = pl.DataFrame({"nrs": [1, 2, 3]})
assert cast(int, df.select(pl.col("nrs").len()).item()) == 3
assert len(pl.DataFrame()) == 0
def test_multiple_column_sort() -> None:
df = pl.DataFrame({"a": ["foo", "bar", "2"], "b": [2, 2, 3], "c": [1.0, 2.0, 3.0]})
out = df.sort([pl.col("b"), pl.col("c").reverse()])
assert list(out["c"]) == [2.0, 1.0, 3.0]
assert list(out["b"]) == [2, 2, 3]
# Explicitly specify numpy dtype because of different defaults on Windows
df = pl.DataFrame({"a": np.arange(1, 4, dtype=np.int64), "b": ["a", "a", "b"]})
assert_frame_equal(
df.sort("a", descending=True),
pl.DataFrame({"a": [3, 2, 1], "b": ["b", "a", "a"]}),
)
assert_frame_equal(
df.sort("b", descending=True),
pl.DataFrame({"a": [3, 1, 2], "b": ["b", "a", "a"]}),
)
assert_frame_equal(
df.sort(["b", "a"], descending=[False, True]),
pl.DataFrame({"a": [2, 1, 3], "b": ["a", "a", "b"]}),
)
def test_cast_frame() -> None:
df = pl.DataFrame(
{
"a": [1.0, 2.5, 3.0],
"b": [4, 5, None],
"c": [True, False, True],
"d": [date(2020, 1, 2), date(2021, 3, 4), date(2022, 5, 6)],
}
)
# cast via col:dtype map
assert df.cast(
dtypes={"b": pl.Float32, "c": pl.String, "d": pl.Datetime("ms")}
).schema == {
"a": pl.Float64,
"b": pl.Float32,
"c": pl.String,
"d": pl.Datetime("ms"),
}
# cast via selector:dtype map
assert df.cast(
{
cs.numeric(): pl.UInt8,
cs.temporal(): pl.String,
}
).rows() == [
(1, 4, True, "2020-01-02"),
(2, 5, False, "2021-03-04"),
(3, None, True, "2022-05-06"),
]
# cast all fields to a single type
assert df.cast(pl.String).to_dict(as_series=False) == {
"a": ["1.0", "2.5", "3.0"],
"b": ["4", "5", None],
"c": ["true", "false", "true"],
"d": ["2020-01-02", "2021-03-04", "2022-05-06"],
}
def test_duration_arithmetic() -> None:
df = pl.DataFrame(
{"a": [datetime(2022, 1, 1, 0, 0, 0), datetime(2022, 1, 2, 0, 0, 0)]}
)
d1 = pl.duration(days=3, microseconds=987000)
d2 = pl.duration(days=6, milliseconds=987)
assert_frame_equal(
df.with_columns(
b=(df["a"] + d1),
c=(pl.col("a") + d2),
),
pl.DataFrame(
{
"a": [
datetime(2022, 1, 1, 0, 0, 0),
datetime(2022, 1, 2, 0, 0, 0),
],
"b": [
datetime(2022, 1, 4, 0, 0, 0, 987000),
datetime(2022, 1, 5, 0, 0, 0, 987000),
],
"c": [
datetime(2022, 1, 7, 0, 0, 0, 987000),
datetime(2022, 1, 8, 0, 0, 0, 987000),
],
}
),
)
def test_assign() -> None:
# check if can assign in case of a single column
df = pl.DataFrame({"a": [1, 2, 3]})
# test if we can assign in case of single column
df = df.with_columns(pl.col("a") * 2)
assert list(df["a"]) == [2, 4, 6]
def test_arg_sort_by(df: pl.DataFrame) -> None:
idx_df = df.select(
pl.arg_sort_by(["int_nulls", "floats"], descending=[False, True]).alias("idx")
)