167 lines
6.1 KiB
Python
167 lines
6.1 KiB
Python
|
import numpy as np
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from pandas import (
|
||
|
|
DataFrame,
|
||
|
|
Series,
|
||
|
|
)
|
||
|
|
import pandas._testing as tm
|
||
|
|
|
||
|
|
|
||
|
|
class TestDataFrameClip:
|
||
|
|
def test_clip(self, float_frame):
|
||
|
|
median = float_frame.median().median()
|
||
|
|
original = float_frame.copy()
|
||
|
|
|
||
|
|
double = float_frame.clip(upper=median, lower=median)
|
||
|
|
assert not (double.values != median).any()
|
||
|
|
|
||
|
|
# Verify that float_frame was not changed inplace
|
||
|
|
assert (float_frame.values == original.values).all()
|
||
|
|
|
||
|
|
def test_inplace_clip(self, float_frame):
|
||
|
|
# GH#15388
|
||
|
|
median = float_frame.median().median()
|
||
|
|
frame_copy = float_frame.copy()
|
||
|
|
|
||
|
|
return_value = frame_copy.clip(upper=median, lower=median, inplace=True)
|
||
|
|
assert return_value is None
|
||
|
|
assert not (frame_copy.values != median).any()
|
||
|
|
|
||
|
|
def test_dataframe_clip(self):
|
||
|
|
# GH#2747
|
||
|
|
df = DataFrame(np.random.randn(1000, 2))
|
||
|
|
|
||
|
|
for lb, ub in [(-1, 1), (1, -1)]:
|
||
|
|
clipped_df = df.clip(lb, ub)
|
||
|
|
|
||
|
|
lb, ub = min(lb, ub), max(ub, lb)
|
||
|
|
lb_mask = df.values <= lb
|
||
|
|
ub_mask = df.values >= ub
|
||
|
|
mask = ~lb_mask & ~ub_mask
|
||
|
|
assert (clipped_df.values[lb_mask] == lb).all()
|
||
|
|
assert (clipped_df.values[ub_mask] == ub).all()
|
||
|
|
assert (clipped_df.values[mask] == df.values[mask]).all()
|
||
|
|
|
||
|
|
def test_clip_mixed_numeric(self):
|
||
|
|
# clip on mixed integer or floats
|
||
|
|
# GH#24162, clipping now preserves numeric types per column
|
||
|
|
df = DataFrame({"A": [1, 2, 3], "B": [1.0, np.nan, 3.0]})
|
||
|
|
result = df.clip(1, 2)
|
||
|
|
expected = DataFrame({"A": [1, 2, 2], "B": [1.0, np.nan, 2.0]})
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
df = DataFrame([[1, 2, 3.4], [3, 4, 5.6]], columns=["foo", "bar", "baz"])
|
||
|
|
expected = df.dtypes
|
||
|
|
result = df.clip(upper=3).dtypes
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("inplace", [True, False])
|
||
|
|
def test_clip_against_series(self, inplace):
|
||
|
|
# GH#6966
|
||
|
|
|
||
|
|
df = DataFrame(np.random.randn(1000, 2))
|
||
|
|
lb = Series(np.random.randn(1000))
|
||
|
|
ub = lb + 1
|
||
|
|
|
||
|
|
original = df.copy()
|
||
|
|
clipped_df = df.clip(lb, ub, axis=0, inplace=inplace)
|
||
|
|
|
||
|
|
if inplace:
|
||
|
|
clipped_df = df
|
||
|
|
|
||
|
|
for i in range(2):
|
||
|
|
lb_mask = original.iloc[:, i] <= lb
|
||
|
|
ub_mask = original.iloc[:, i] >= ub
|
||
|
|
mask = ~lb_mask & ~ub_mask
|
||
|
|
|
||
|
|
result = clipped_df.loc[lb_mask, i]
|
||
|
|
tm.assert_series_equal(result, lb[lb_mask], check_names=False)
|
||
|
|
assert result.name == i
|
||
|
|
|
||
|
|
result = clipped_df.loc[ub_mask, i]
|
||
|
|
tm.assert_series_equal(result, ub[ub_mask], check_names=False)
|
||
|
|
assert result.name == i
|
||
|
|
|
||
|
|
tm.assert_series_equal(clipped_df.loc[mask, i], df.loc[mask, i])
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("inplace", [True, False])
|
||
|
|
@pytest.mark.parametrize("lower", [[2, 3, 4], np.asarray([2, 3, 4])])
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"axis,res",
|
||
|
|
[
|
||
|
|
(0, [[2.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 7.0, 7.0]]),
|
||
|
|
(1, [[2.0, 3.0, 4.0], [4.0, 5.0, 6.0], [5.0, 6.0, 7.0]]),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_clip_against_list_like(self, simple_frame, inplace, lower, axis, res):
|
||
|
|
# GH#15390
|
||
|
|
original = simple_frame.copy(deep=True)
|
||
|
|
|
||
|
|
result = original.clip(lower=lower, upper=[5, 6, 7], axis=axis, inplace=inplace)
|
||
|
|
|
||
|
|
expected = DataFrame(res, columns=original.columns, index=original.index)
|
||
|
|
if inplace:
|
||
|
|
result = original
|
||
|
|
tm.assert_frame_equal(result, expected, check_exact=True)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("axis", [0, 1, None])
|
||
|
|
def test_clip_against_frame(self, axis):
|
||
|
|
df = DataFrame(np.random.randn(1000, 2))
|
||
|
|
lb = DataFrame(np.random.randn(1000, 2))
|
||
|
|
ub = lb + 1
|
||
|
|
|
||
|
|
clipped_df = df.clip(lb, ub, axis=axis)
|
||
|
|
|
||
|
|
lb_mask = df <= lb
|
||
|
|
ub_mask = df >= ub
|
||
|
|
mask = ~lb_mask & ~ub_mask
|
||
|
|
|
||
|
|
tm.assert_frame_equal(clipped_df[lb_mask], lb[lb_mask])
|
||
|
|
tm.assert_frame_equal(clipped_df[ub_mask], ub[ub_mask])
|
||
|
|
tm.assert_frame_equal(clipped_df[mask], df[mask])
|
||
|
|
|
||
|
|
def test_clip_against_unordered_columns(self):
|
||
|
|
# GH#20911
|
||
|
|
df1 = DataFrame(np.random.randn(1000, 4), columns=["A", "B", "C", "D"])
|
||
|
|
df2 = DataFrame(np.random.randn(1000, 4), columns=["D", "A", "B", "C"])
|
||
|
|
df3 = DataFrame(df2.values - 1, columns=["B", "D", "C", "A"])
|
||
|
|
result_upper = df1.clip(lower=0, upper=df2)
|
||
|
|
expected_upper = df1.clip(lower=0, upper=df2[df1.columns])
|
||
|
|
result_lower = df1.clip(lower=df3, upper=3)
|
||
|
|
expected_lower = df1.clip(lower=df3[df1.columns], upper=3)
|
||
|
|
result_lower_upper = df1.clip(lower=df3, upper=df2)
|
||
|
|
expected_lower_upper = df1.clip(lower=df3[df1.columns], upper=df2[df1.columns])
|
||
|
|
tm.assert_frame_equal(result_upper, expected_upper)
|
||
|
|
tm.assert_frame_equal(result_lower, expected_lower)
|
||
|
|
tm.assert_frame_equal(result_lower_upper, expected_lower_upper)
|
||
|
|
|
||
|
|
def test_clip_with_na_args(self, float_frame):
|
||
|
|
"""Should process np.nan argument as None"""
|
||
|
|
# GH#17276
|
||
|
|
tm.assert_frame_equal(float_frame.clip(np.nan), float_frame)
|
||
|
|
tm.assert_frame_equal(float_frame.clip(upper=np.nan, lower=np.nan), float_frame)
|
||
|
|
|
||
|
|
# GH#19992 and adjusted in GH#40420
|
||
|
|
df = DataFrame({"col_0": [1, 2, 3], "col_1": [4, 5, 6], "col_2": [7, 8, 9]})
|
||
|
|
|
||
|
|
result = df.clip(lower=[4, 5, np.nan], axis=0)
|
||
|
|
expected = DataFrame(
|
||
|
|
{"col_0": [4, 5, 3], "col_1": [4, 5, 6], "col_2": [7, 8, 9]}
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.clip(lower=[4, 5, np.nan], axis=1)
|
||
|
|
expected = DataFrame(
|
||
|
|
{"col_0": [4, 4, 4], "col_1": [5, 5, 6], "col_2": [7, 8, 9]}
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
# GH#40420
|
||
|
|
data = {"col_0": [9, -3, 0, -1, 5], "col_1": [-2, -7, 6, 8, -5]}
|
||
|
|
df = DataFrame(data)
|
||
|
|
t = Series([2, -4, np.NaN, 6, 3])
|
||
|
|
result = df.clip(lower=t, axis=0)
|
||
|
|
expected = DataFrame({"col_0": [9, -3, 0, 6, 5], "col_1": [2, -4, 6, 8, 3]})
|
||
|
|
tm.assert_frame_equal(result, expected)
|