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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.default_rng(2).standard_normal((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.default_rng(2).standard_normal((1000, 2)))
lb = Series(np.random.default_rng(2).standard_normal(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, inplace, lower, axis, res):
# GH#15390
arr = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])
original = DataFrame(
arr, columns=["one", "two", "three"], index=["a", "b", "c"]
)
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.default_rng(2).standard_normal((1000, 2)))
lb = DataFrame(np.random.default_rng(2).standard_normal((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.default_rng(2).standard_normal((1000, 4)),
columns=["A", "B", "C", "D"],
)
df2 = DataFrame(
np.random.default_rng(2).standard_normal((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]})
msg = "Downcasting behavior in Series and DataFrame methods 'where'"
# TODO: avoid this warning here? seems like we should never be upcasting
# in the first place?
with tm.assert_produces_warning(FutureWarning, match=msg):
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])
with tm.assert_produces_warning(FutureWarning, match=msg):
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)
def test_clip_int_data_with_float_bound(self):
# GH51472
df = DataFrame({"a": [1, 2, 3]})
result = df.clip(lower=1.5)
expected = DataFrame({"a": [1.5, 2.0, 3.0]})
tm.assert_frame_equal(result, expected)
def test_clip_with_list_bound(self):
# GH#54817
df = DataFrame([1, 5])
expected = DataFrame([3, 5])
result = df.clip([3])
tm.assert_frame_equal(result, expected)
expected = DataFrame([1, 3])
result = df.clip(upper=[3])
tm.assert_frame_equal(result, expected)
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