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test_df.py
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test_df.py
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from pypolars import DataFrame, Series
from pypolars.datatypes import *
from pypolars.lazy import *
from pypolars import functions
import pytest
from io import BytesIO
import numpy as np
def test_init():
df = DataFrame({"a": [1, 2, 3], "b": [1.0, 2.0, 3.0]})
# length mismatch
with pytest.raises(RuntimeError):
df = DataFrame({"a": [1, 2, 3], "b": [1.0, 2.0, 3.0, 4.0]})
def test_selection():
df = DataFrame({"a": [1, 2, 3], "b": [1.0, 2.0, 3.0], "c": ["a", "b", "c"]})
assert df["a"].dtype == Int64
assert df["b"].dtype == Float64
assert df["c"].dtype == Utf8
assert df[["a", "b"]].columns == ["a", "b"]
assert df[[True, False, True]].height == 2
assert df[[True, False, True], "b"].shape == (2, 1)
assert df[[True, False, False], ["a", "b"]].shape == (1, 2)
assert df[[0, 1], "b"].shape == (2, 1)
assert df[[2], ["a", "b"]].shape == (1, 2)
assert df.select_at_idx(0).name == "a"
assert (df.a == df["a"]).sum() == 3
assert (df.c == df["a"]).sum() == 0
def test_downsample():
s = Series(
"datetime",
[
946684800000,
946684860000,
946684920000,
946684980000,
946685040000,
946685100000,
946685160000,
946685220000,
946685280000,
946685340000,
946685400000,
946685460000,
946685520000,
946685580000,
946685640000,
946685700000,
946685760000,
946685820000,
946685880000,
946685940000,
],
).cast(Date64)
s2 = s.clone()
df = DataFrame({"a": s, "b": s2})
out = df.downsample("a", rule="minute", n=5).first()
assert out.shape == (4, 2)
# test to_pandas as well.
out = df.to_pandas()
assert out["a"].dtype == "datetime64[ns]"
def test_sort():
df = DataFrame({"a": [2, 1, 3], "b": [1, 2, 3]})
df.sort("a", in_place=True)
assert df.frame_equal(DataFrame({"a": [1, 2, 3], "b": [2, 1, 3]}))
def test_replace():
df = DataFrame({"a": [2, 1, 3], "b": [1, 2, 3]})
s = Series("c", [True, False, True])
df.replace("a", s)
assert df.frame_equal(DataFrame({"c": [True, False, True], "b": [1, 2, 3]}))
def test_slice():
df = DataFrame({"a": [2, 1, 3], "b": ["a", "b", "c"]})
df = df.slice(1, 2)
assert df.frame_equal(DataFrame({"a": [1, 3], "b": ["b", "c"]}))
def test_head_tail():
df = DataFrame({"a": range(10), "b": range(10)})
assert df.head(5).height == 5
assert df.tail(5).height == 5
assert not df.head(5).frame_equal(df.tail(5))
# check if it doesn't fail when out of bounds
assert df.head(100).height == 10
assert df.tail(100).height == 10
def test_groupby():
df = DataFrame(
{
"a": ["a", "b", "a", "b", "b", "c"],
"b": [1, 2, 3, 4, 5, 6],
"c": [6, 5, 4, 3, 2, 1],
}
)
assert (
df.groupby("a")
.select("b")
.sum()
.sort(by_column="a")
.frame_equal(DataFrame({"a": ["a", "b", "c"], "": [4, 11, 6]}))
)
assert (
df.groupby("a")
.select("c")
.sum()
.sort(by_column="a")
.frame_equal(DataFrame({"a": ["a", "b", "c"], "": [10, 10, 1]}))
)
assert (
df.groupby("a")
.select("b")
.min()
.sort(by_column="a")
.frame_equal(DataFrame({"a": ["a", "b", "c"], "": [1, 2, 6]}))
)
assert (
df.groupby("a")
.select("b")
.max()
.sort(by_column="a")
.frame_equal(DataFrame({"a": ["a", "b", "c"], "": [3, 5, 6]}))
)
assert (
df.groupby("a")
.select("b")
.mean()
.sort(by_column="a")
.frame_equal(DataFrame({"a": ["a", "b", "c"], "": [2.0, (2 + 4 + 5) / 3, 6.0]}))
)
assert (
df.groupby("a")
.select("b")
.last()
.sort(by_column="a")
.frame_equal(DataFrame({"a": ["a", "b", "c"], "": [3, 5, 6]}))
)
# check if it runs
(df.groupby("a").select("b").n_unique())
(df.groupby("a").select("b").quantile(0.3))
(df.groupby("a").select("b").agg_list())
gb_df = df.groupby("a").agg({"b": ["sum", "min"], "c": "count"})
assert "b_sum" in gb_df.columns
assert "b_min" in gb_df.columns
#
# # TODO: is false because count is u32
# df.groupby(by="a", select="b", agg="count").frame_equal(
# DataFrame({"a": ["a", "b", "c"], "": [2, 3, 1]})
# )
assert df.groupby("a").apply(lambda df: df[["c"]].sum()).sort("c")["c"][0] == 1
def test_join():
df_left = DataFrame(
{
"a": ["a", "b", "a", "z"],
"b": [1, 2, 3, 4],
"c": [6, 5, 4, 3],
}
)
df_right = DataFrame(
{
"a": ["b", "c", "b", "a"],
"k": [0, 3, 9, 6],
"c": [1, 0, 2, 1],
}
)
joined = df_left.join(df_right, left_on="a", right_on="a").sort("a")
assert joined["b"].series_equal(Series("", [1, 3, 2, 2]))
joined = df_left.join(df_right, left_on="a", right_on="a", how="left").sort("a")
assert joined["c_right"].is_null().sum() == 1
assert joined["b"].series_equal(Series("", [1, 3, 2, 2, 4]))
joined = df_left.join(df_right, left_on="a", right_on="a", how="outer").sort("a")
assert joined["c_right"].null_count() == 1
assert joined["c"].null_count() == 2
assert joined["b"].null_count() == 2
df_a = DataFrame({"a": [1, 2, 1, 1], "b": ["a", "b", "c", "c"]})
df_b = DataFrame(
{"foo": [1, 1, 1], "bar": ["a", "c", "c"], "ham": ["let", "var", "const"]}
)
# just check if join on multiple columns runs
df_a.join(df_b, left_on=["a", "b"], right_on=["foo", "bar"])
eager_join = df_a.join(df_b, left_on="a", right_on="foo")
lazy_join = df_a.lazy().join(df_b.lazy(), left_on="a", right_on="foo").collect()
assert lazy_join.shape == eager_join.shape
def test_hstack():
df = DataFrame({"a": [2, 1, 3], "b": ["a", "b", "c"]})
df.hstack([Series("stacked", [-1, -1, -1])], in_place=True)
assert df.shape == (3, 3)
assert df.columns == ["a", "b", "stacked"]
def test_drop():
df = DataFrame({"a": [2, 1, 3], "b": ["a", "b", "c"], "c": [1, 2, 3]})
df = df.drop("a")
assert df.shape == (3, 2)
df = DataFrame({"a": [2, 1, 3], "b": ["a", "b", "c"], "c": [1, 2, 3]})
s = df.drop_in_place("a")
assert s.name == "a"
def test_file_buffer():
f = BytesIO()
f.write(b"1,2,3,4,5,6\n7,8,9,10,11,12")
f.seek(0)
df = DataFrame.read_csv(f, has_headers=False)
print(df)
assert df.shape == (2, 6)
f.seek(0)
# check if not fails on TryClone and Length impl in file.rs
with pytest.raises(RuntimeError) as e:
df.read_parquet(f)
assert "Invalid Parquet file" in str(e.value)
def test_set():
np.random.seed(1)
df = DataFrame({"foo": np.random.rand(10), "bar": np.arange(10), "ham": ["h"] * 10})
df["new"] = np.random.rand(10)
df[df["new"] > 0.5, "new"] = 1
def test_melt():
df = DataFrame({"A": ["a", "b", "c"], "B": [1, 3, 5], "C": [2, 4, 6]})
melted = df.melt(id_vars="A", value_vars=["B", "C"])
assert melted["value"] == [1, 3, 4, 2, 4, 6]
def test_shift():
df = DataFrame({"A": ["a", "b", "c"], "B": [1, 3, 5]})
a = df.shift(1)
b = DataFrame({"A": [None, "a", "b"], "B": [None, 1, 3]}, nullable=True)
assert a.frame_equal(b, null_equal=True)
def test_to_dummies():
df = 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]
def test_from_pandas():
import pandas as pd
df = pd.DataFrame({"A": ["a", "b", "c"], "B": [1, 3, 5]})
DataFrame(df)
def test_custom_groupby():
df = DataFrame({"A": ["a", "a", "c", "c"], "B": [1, 3, 5, 2]})
assert df.groupby("A").select("B").apply(lambda x: x.sum()).shape == (2, 2)
assert df.groupby("A").select("B").apply(
lambda x: Series("", np.array(x))
).shape == (
2,
2,
)
df = DataFrame({"a": [1, 2, 1, 1], "b": ["a", "b", "c", "c"]})
out = (
df.lazy()
.groupby("b")
.agg([col("a").apply(lambda x: x.sum(), dtype_out=int)])
.collect()
)
assert out.shape == (3, 2)
def test_multiple_columns_drop():
df = DataFrame({"a": [2, 1, 3], "b": [1, 2, 3], "c": [1, 2, 3]})
out = df.drop(["a", "b"])
assert out.columns == ["c"]
def test_concat():
df = DataFrame({"a": [2, 1, 3], "b": [1, 2, 3], "c": [1, 2, 3]})
assert functions.concat([df, df]).shape == (6, 3)