import datetime
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import dateutil
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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DataFrame,
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Series,
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)
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import pandas._testing as tm
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class TestDataFrameMissingData:
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def test_dropEmptyRows(self, float_frame):
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N = len(float_frame.index)
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mat = np.random.randn(N)
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mat[:5] = np.nan
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frame = DataFrame({"foo": mat}, index=float_frame.index)
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original = Series(mat, index=float_frame.index, name="foo")
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expected = original.dropna()
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inplace_frame1, inplace_frame2 = frame.copy(), frame.copy()
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smaller_frame = frame.dropna(how="all")
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# check that original was preserved
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tm.assert_series_equal(frame["foo"], original)
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return_value = inplace_frame1.dropna(how="all", inplace=True)
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tm.assert_series_equal(smaller_frame["foo"], expected)
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tm.assert_series_equal(inplace_frame1["foo"], expected)
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assert return_value is None
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smaller_frame = frame.dropna(how="all", subset=["foo"])
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return_value = inplace_frame2.dropna(how="all", subset=["foo"], inplace=True)
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tm.assert_series_equal(smaller_frame["foo"], expected)
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tm.assert_series_equal(inplace_frame2["foo"], expected)
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assert return_value is None
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def test_dropIncompleteRows(self, float_frame):
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N = len(float_frame.index)
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mat = np.random.randn(N)
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mat[:5] = np.nan
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frame = DataFrame({"foo": mat}, index=float_frame.index)
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frame["bar"] = 5
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original = Series(mat, index=float_frame.index, name="foo")
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inp_frame1, inp_frame2 = frame.copy(), frame.copy()
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smaller_frame = frame.dropna()
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tm.assert_series_equal(frame["foo"], original)
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return_value = inp_frame1.dropna(inplace=True)
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exp = Series(mat[5:], index=float_frame.index[5:], name="foo")
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tm.assert_series_equal(smaller_frame["foo"], exp)
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tm.assert_series_equal(inp_frame1["foo"], exp)
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assert return_value is None
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samesize_frame = frame.dropna(subset=["bar"])
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tm.assert_series_equal(frame["foo"], original)
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assert (frame["bar"] == 5).all()
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return_value = inp_frame2.dropna(subset=["bar"], inplace=True)
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tm.assert_index_equal(samesize_frame.index, float_frame.index)
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tm.assert_index_equal(inp_frame2.index, float_frame.index)
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assert return_value is None
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def test_dropna(self):
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df = DataFrame(np.random.randn(6, 4))
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df.iloc[:2, 2] = np.nan
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dropped = df.dropna(axis=1)
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expected = df.loc[:, [0, 1, 3]]
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inp = df.copy()
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return_value = inp.dropna(axis=1, inplace=True)
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tm.assert_frame_equal(dropped, expected)
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tm.assert_frame_equal(inp, expected)
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assert return_value is None
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dropped = df.dropna(axis=0)
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expected = df.loc[list(range(2, 6))]
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inp = df.copy()
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return_value = inp.dropna(axis=0, inplace=True)
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tm.assert_frame_equal(dropped, expected)
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tm.assert_frame_equal(inp, expected)
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assert return_value is None
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# threshold
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dropped = df.dropna(axis=1, thresh=5)
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expected = df.loc[:, [0, 1, 3]]
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inp = df.copy()
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return_value = inp.dropna(axis=1, thresh=5, inplace=True)
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tm.assert_frame_equal(dropped, expected)
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tm.assert_frame_equal(inp, expected)
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assert return_value is None
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dropped = df.dropna(axis=0, thresh=4)
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expected = df.loc[range(2, 6)]
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inp = df.copy()
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return_value = inp.dropna(axis=0, thresh=4, inplace=True)
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tm.assert_frame_equal(dropped, expected)
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tm.assert_frame_equal(inp, expected)
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assert return_value is None
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dropped = df.dropna(axis=1, thresh=4)
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tm.assert_frame_equal(dropped, df)
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dropped = df.dropna(axis=1, thresh=3)
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tm.assert_frame_equal(dropped, df)
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# subset
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dropped = df.dropna(axis=0, subset=[0, 1, 3])
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inp = df.copy()
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return_value = inp.dropna(axis=0, subset=[0, 1, 3], inplace=True)
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tm.assert_frame_equal(dropped, df)
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tm.assert_frame_equal(inp, df)
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assert return_value is None
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# all
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dropped = df.dropna(axis=1, how="all")
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tm.assert_frame_equal(dropped, df)
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df[2] = np.nan
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dropped = df.dropna(axis=1, how="all")
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expected = df.loc[:, [0, 1, 3]]
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tm.assert_frame_equal(dropped, expected)
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# bad input
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msg = "No axis named 3 for object type DataFrame"
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with pytest.raises(ValueError, match=msg):
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df.dropna(axis=3)
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def test_drop_and_dropna_caching(self):
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# tst that cacher updates
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original = Series([1, 2, np.nan], name="A")
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expected = Series([1, 2], dtype=original.dtype, name="A")
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df = DataFrame({"A": original.values.copy()})
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df2 = df.copy()
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df["A"].dropna()
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tm.assert_series_equal(df["A"], original)
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ser = df["A"]
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return_value = ser.dropna(inplace=True)
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tm.assert_series_equal(ser, expected)
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tm.assert_series_equal(df["A"], original)
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assert return_value is None
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df2["A"].drop([1])
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tm.assert_series_equal(df2["A"], original)
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ser = df2["A"]
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return_value = ser.drop([1], inplace=True)
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tm.assert_series_equal(ser, original.drop([1]))
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tm.assert_series_equal(df2["A"], original)
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assert return_value is None
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def test_dropna_corner(self, float_frame):
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# bad input
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msg = "invalid how option: foo"
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with pytest.raises(ValueError, match=msg):
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float_frame.dropna(how="foo")
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# non-existent column - 8303
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with pytest.raises(KeyError, match=r"^\['X'\]$"):
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float_frame.dropna(subset=["A", "X"])
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def test_dropna_multiple_axes(self):
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df = DataFrame(
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[
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[1, np.nan, 2, 3],
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[4, np.nan, 5, 6],
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[np.nan, np.nan, np.nan, np.nan],
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[7, np.nan, 8, 9],
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]
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)
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# GH20987
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with pytest.raises(TypeError, match="supplying multiple axes"):
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df.dropna(how="all", axis=[0, 1])
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with pytest.raises(TypeError, match="supplying multiple axes"):
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df.dropna(how="all", axis=(0, 1))
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inp = df.copy()
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with pytest.raises(TypeError, match="supplying multiple axes"):
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inp.dropna(how="all", axis=(0, 1), inplace=True)
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def test_dropna_tz_aware_datetime(self):
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# GH13407
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df = DataFrame()
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dt1 = datetime.datetime(2015, 1, 1, tzinfo=dateutil.tz.tzutc())
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dt2 = datetime.datetime(2015, 2, 2, tzinfo=dateutil.tz.tzutc())
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df["Time"] = [dt1]
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result = df.dropna(axis=0)
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expected = DataFrame({"Time": [dt1]})
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tm.assert_frame_equal(result, expected)
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# Ex2
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df = DataFrame({"Time": [dt1, None, np.nan, dt2]})
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result = df.dropna(axis=0)
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expected = DataFrame([dt1, dt2], columns=["Time"], index=[0, 3])
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tm.assert_frame_equal(result, expected)
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def test_dropna_categorical_interval_index(self):
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# GH 25087
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ii = pd.IntervalIndex.from_breaks([0, 2.78, 3.14, 6.28])
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ci = pd.CategoricalIndex(ii)
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df = DataFrame({"A": list("abc")}, index=ci)
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expected = df
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result = df.dropna()
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tm.assert_frame_equal(result, expected)
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def test_dropna_with_duplicate_columns(self):
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df = DataFrame(
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{
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"A": np.random.randn(5),
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"B": np.random.randn(5),
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"C": np.random.randn(5),
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"D": ["a", "b", "c", "d", "e"],
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}
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)
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df.iloc[2, [0, 1, 2]] = np.nan
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df.iloc[0, 0] = np.nan
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df.iloc[1, 1] = np.nan
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df.iloc[:, 3] = np.nan
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expected = df.dropna(subset=["A", "B", "C"], how="all")
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expected.columns = ["A", "A", "B", "C"]
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df.columns = ["A", "A", "B", "C"]
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result = df.dropna(subset=["A", "C"], how="all")
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tm.assert_frame_equal(result, expected)
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def test_set_single_column_subset(self):
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# GH 41021
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df = DataFrame({"A": [1, 2, 3], "B": list("abc"), "C": [4, np.NaN, 5]})
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expected = DataFrame(
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{"A": [1, 3], "B": list("ac"), "C": [4.0, 5.0]}, index=[0, 2]
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)
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result = df.dropna(subset="C")
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tm.assert_frame_equal(result, expected)
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def test_single_column_not_present_in_axis(self):
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# GH 41021
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df = DataFrame({"A": [1, 2, 3]})
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# Column not present
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with pytest.raises(KeyError, match="['D']"):
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df.dropna(subset="D", axis=0)
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def test_subset_is_nparray(self):
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# GH 41021
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df = DataFrame({"A": [1, 2, np.NaN], "B": list("abc"), "C": [4, np.NaN, 5]})
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expected = DataFrame({"A": [1.0], "B": ["a"], "C": [4.0]})
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result = df.dropna(subset=np.array(["A", "C"]))
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tm.assert_frame_equal(result, expected)
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def test_no_nans_in_frame(self, axis):
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# GH#41965
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df = DataFrame([[1, 2], [3, 4]], columns=pd.RangeIndex(0, 2))
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expected = df.copy()
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result = df.dropna(axis=axis)
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tm.assert_frame_equal(result, expected, check_index_type=True)
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def test_how_thresh_param_incompatible(self):
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# GH46575
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df = DataFrame([1, 2, pd.NA])
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msg = "You cannot set both the how and thresh arguments at the same time"
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with pytest.raises(TypeError, match=msg):
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df.dropna(how="all", thresh=2)
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with pytest.raises(TypeError, match=msg):
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df.dropna(how="any", thresh=2)
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with pytest.raises(TypeError, match=msg):
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df.dropna(how=None, thresh=None)
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@pytest.mark.parametrize("val", [1, 1.5])
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def test_dropna_ignore_index(self, val):
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# GH#31725
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df = DataFrame({"a": [1, 2, val]}, index=[3, 2, 1])
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result = df.dropna(ignore_index=True)
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expected = DataFrame({"a": [1, 2, val]})
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tm.assert_frame_equal(result, expected)
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df.dropna(ignore_index=True, inplace=True)
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tm.assert_frame_equal(df, expected)
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