mirror of
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730 lines
25 KiB
Python
730 lines
25 KiB
Python
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 Categorical, DataFrame, Series, Timestamp, date_range
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import pandas._testing as tm
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from pandas.tests.frame.common import _check_mixed_float
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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[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 = pd.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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msg = "must specify how or thresh"
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with pytest.raises(TypeError, match=msg):
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float_frame.dropna(how=None)
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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 = pd.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_fillna_datetime(self, datetime_frame):
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tf = datetime_frame
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tf.loc[tf.index[:5], "A"] = np.nan
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tf.loc[tf.index[-5:], "A"] = np.nan
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zero_filled = datetime_frame.fillna(0)
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assert (zero_filled.loc[zero_filled.index[:5], "A"] == 0).all()
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padded = datetime_frame.fillna(method="pad")
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assert np.isnan(padded.loc[padded.index[:5], "A"]).all()
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assert (
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padded.loc[padded.index[-5:], "A"] == padded.loc[padded.index[-5], "A"]
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).all()
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msg = "Must specify a fill 'value' or 'method'"
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with pytest.raises(ValueError, match=msg):
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datetime_frame.fillna()
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msg = "Cannot specify both 'value' and 'method'"
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with pytest.raises(ValueError, match=msg):
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datetime_frame.fillna(5, method="ffill")
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def test_fillna_mixed_type(self, float_string_frame):
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mf = float_string_frame
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mf.loc[mf.index[5:20], "foo"] = np.nan
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mf.loc[mf.index[-10:], "A"] = np.nan
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# TODO: make stronger assertion here, GH 25640
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mf.fillna(value=0)
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mf.fillna(method="pad")
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def test_fillna_mixed_float(self, mixed_float_frame):
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# mixed numeric (but no float16)
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mf = mixed_float_frame.reindex(columns=["A", "B", "D"])
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mf.loc[mf.index[-10:], "A"] = np.nan
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result = mf.fillna(value=0)
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_check_mixed_float(result, dtype=dict(C=None))
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result = mf.fillna(method="pad")
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_check_mixed_float(result, dtype=dict(C=None))
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def test_fillna_empty(self):
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# empty frame (GH #2778)
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df = DataFrame(columns=["x"])
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for m in ["pad", "backfill"]:
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df.x.fillna(method=m, inplace=True)
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df.x.fillna(method=m)
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def test_fillna_different_dtype(self):
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# with different dtype (GH#3386)
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df = DataFrame(
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[["a", "a", np.nan, "a"], ["b", "b", np.nan, "b"], ["c", "c", np.nan, "c"]]
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)
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result = df.fillna({2: "foo"})
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expected = DataFrame(
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[["a", "a", "foo", "a"], ["b", "b", "foo", "b"], ["c", "c", "foo", "c"]]
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)
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tm.assert_frame_equal(result, expected)
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return_value = df.fillna({2: "foo"}, inplace=True)
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tm.assert_frame_equal(df, expected)
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assert return_value is None
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def test_fillna_limit_and_value(self):
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# limit and value
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df = DataFrame(np.random.randn(10, 3))
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df.iloc[2:7, 0] = np.nan
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df.iloc[3:5, 2] = np.nan
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expected = df.copy()
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expected.iloc[2, 0] = 999
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expected.iloc[3, 2] = 999
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result = df.fillna(999, limit=1)
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tm.assert_frame_equal(result, expected)
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def test_fillna_datelike(self):
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# with datelike
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# GH#6344
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df = DataFrame(
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{
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"Date": [pd.NaT, Timestamp("2014-1-1")],
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"Date2": [Timestamp("2013-1-1"), pd.NaT],
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}
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)
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expected = df.copy()
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expected["Date"] = expected["Date"].fillna(df.loc[df.index[0], "Date2"])
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result = df.fillna(value={"Date": df["Date2"]})
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tm.assert_frame_equal(result, expected)
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def test_fillna_tzaware(self):
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# with timezone
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# GH#15855
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df = pd.DataFrame({"A": [pd.Timestamp("2012-11-11 00:00:00+01:00"), pd.NaT]})
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exp = pd.DataFrame(
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{
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"A": [
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pd.Timestamp("2012-11-11 00:00:00+01:00"),
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pd.Timestamp("2012-11-11 00:00:00+01:00"),
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]
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}
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)
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tm.assert_frame_equal(df.fillna(method="pad"), exp)
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df = pd.DataFrame({"A": [pd.NaT, pd.Timestamp("2012-11-11 00:00:00+01:00")]})
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exp = pd.DataFrame(
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{
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"A": [
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pd.Timestamp("2012-11-11 00:00:00+01:00"),
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pd.Timestamp("2012-11-11 00:00:00+01:00"),
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]
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}
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)
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tm.assert_frame_equal(df.fillna(method="bfill"), exp)
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def test_fillna_tzaware_different_column(self):
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# with timezone in another column
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# GH#15522
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df = pd.DataFrame(
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{
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"A": pd.date_range("20130101", periods=4, tz="US/Eastern"),
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"B": [1, 2, np.nan, np.nan],
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}
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)
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result = df.fillna(method="pad")
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expected = pd.DataFrame(
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{
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"A": pd.date_range("20130101", periods=4, tz="US/Eastern"),
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"B": [1.0, 2.0, 2.0, 2.0],
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}
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)
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tm.assert_frame_equal(result, expected)
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def test_na_actions_categorical(self):
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cat = Categorical([1, 2, 3, np.nan], categories=[1, 2, 3])
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vals = ["a", "b", np.nan, "d"]
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df = DataFrame({"cats": cat, "vals": vals})
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cat2 = Categorical([1, 2, 3, 3], categories=[1, 2, 3])
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vals2 = ["a", "b", "b", "d"]
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df_exp_fill = DataFrame({"cats": cat2, "vals": vals2})
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cat3 = Categorical([1, 2, 3], categories=[1, 2, 3])
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vals3 = ["a", "b", np.nan]
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df_exp_drop_cats = DataFrame({"cats": cat3, "vals": vals3})
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cat4 = Categorical([1, 2], categories=[1, 2, 3])
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vals4 = ["a", "b"]
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df_exp_drop_all = DataFrame({"cats": cat4, "vals": vals4})
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# fillna
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res = df.fillna(value={"cats": 3, "vals": "b"})
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tm.assert_frame_equal(res, df_exp_fill)
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with pytest.raises(ValueError, match=("fill value must be in categories")):
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df.fillna(value={"cats": 4, "vals": "c"})
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res = df.fillna(method="pad")
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tm.assert_frame_equal(res, df_exp_fill)
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# dropna
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res = df.dropna(subset=["cats"])
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tm.assert_frame_equal(res, df_exp_drop_cats)
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res = df.dropna()
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tm.assert_frame_equal(res, df_exp_drop_all)
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# make sure that fillna takes missing values into account
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c = Categorical([np.nan, "b", np.nan], categories=["a", "b"])
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df = pd.DataFrame({"cats": c, "vals": [1, 2, 3]})
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cat_exp = Categorical(["a", "b", "a"], categories=["a", "b"])
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df_exp = DataFrame({"cats": cat_exp, "vals": [1, 2, 3]})
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res = df.fillna("a")
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tm.assert_frame_equal(res, df_exp)
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def test_fillna_categorical_nan(self):
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# GH 14021
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# np.nan should always be a valid filler
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cat = Categorical([np.nan, 2, np.nan])
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val = Categorical([np.nan, np.nan, np.nan])
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df = DataFrame({"cats": cat, "vals": val})
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# GH#32950 df.median() is poorly behaved because there is no
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# Categorical.median
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median = Series({"cats": 2.0, "vals": np.nan})
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res = df.fillna(median)
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v_exp = [np.nan, np.nan, np.nan]
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df_exp = DataFrame({"cats": [2, 2, 2], "vals": v_exp}, dtype="category")
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tm.assert_frame_equal(res, df_exp)
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result = df.cats.fillna(np.nan)
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tm.assert_series_equal(result, df.cats)
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result = df.vals.fillna(np.nan)
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tm.assert_series_equal(result, df.vals)
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idx = pd.DatetimeIndex(
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["2011-01-01 09:00", "2016-01-01 23:45", "2011-01-01 09:00", pd.NaT, pd.NaT]
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)
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df = DataFrame({"a": Categorical(idx)})
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tm.assert_frame_equal(df.fillna(value=pd.NaT), df)
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idx = pd.PeriodIndex(
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["2011-01", "2011-01", "2011-01", pd.NaT, pd.NaT], freq="M"
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)
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df = DataFrame({"a": Categorical(idx)})
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tm.assert_frame_equal(df.fillna(value=pd.NaT), df)
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idx = pd.TimedeltaIndex(["1 days", "2 days", "1 days", pd.NaT, pd.NaT])
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df = DataFrame({"a": Categorical(idx)})
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tm.assert_frame_equal(df.fillna(value=pd.NaT), df)
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def test_fillna_downcast(self):
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# GH 15277
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# infer int64 from float64
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df = pd.DataFrame({"a": [1.0, np.nan]})
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result = df.fillna(0, downcast="infer")
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expected = pd.DataFrame({"a": [1, 0]})
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tm.assert_frame_equal(result, expected)
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# infer int64 from float64 when fillna value is a dict
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df = pd.DataFrame({"a": [1.0, np.nan]})
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result = df.fillna({"a": 0}, downcast="infer")
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expected = pd.DataFrame({"a": [1, 0]})
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tm.assert_frame_equal(result, expected)
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def test_fillna_dtype_conversion(self):
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# make sure that fillna on an empty frame works
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df = DataFrame(index=["A", "B", "C"], columns=[1, 2, 3, 4, 5])
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result = df.dtypes
|
|
expected = Series([np.dtype("object")] * 5, index=[1, 2, 3, 4, 5])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = df.fillna(1)
|
|
expected = DataFrame(1, index=["A", "B", "C"], columns=[1, 2, 3, 4, 5])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# empty block
|
|
df = DataFrame(index=range(3), columns=["A", "B"], dtype="float64")
|
|
result = df.fillna("nan")
|
|
expected = DataFrame("nan", index=range(3), columns=["A", "B"])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# equiv of replace
|
|
df = DataFrame(dict(A=[1, np.nan], B=[1.0, 2.0]))
|
|
for v in ["", 1, np.nan, 1.0]:
|
|
expected = df.replace(np.nan, v)
|
|
result = df.fillna(v)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_fillna_datetime_columns(self):
|
|
# GH 7095
|
|
df = pd.DataFrame(
|
|
{
|
|
"A": [-1, -2, np.nan],
|
|
"B": date_range("20130101", periods=3),
|
|
"C": ["foo", "bar", None],
|
|
"D": ["foo2", "bar2", None],
|
|
},
|
|
index=date_range("20130110", periods=3),
|
|
)
|
|
result = df.fillna("?")
|
|
expected = pd.DataFrame(
|
|
{
|
|
"A": [-1, -2, "?"],
|
|
"B": date_range("20130101", periods=3),
|
|
"C": ["foo", "bar", "?"],
|
|
"D": ["foo2", "bar2", "?"],
|
|
},
|
|
index=date_range("20130110", periods=3),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
df = pd.DataFrame(
|
|
{
|
|
"A": [-1, -2, np.nan],
|
|
"B": [pd.Timestamp("2013-01-01"), pd.Timestamp("2013-01-02"), pd.NaT],
|
|
"C": ["foo", "bar", None],
|
|
"D": ["foo2", "bar2", None],
|
|
},
|
|
index=date_range("20130110", periods=3),
|
|
)
|
|
result = df.fillna("?")
|
|
expected = pd.DataFrame(
|
|
{
|
|
"A": [-1, -2, "?"],
|
|
"B": [pd.Timestamp("2013-01-01"), pd.Timestamp("2013-01-02"), "?"],
|
|
"C": ["foo", "bar", "?"],
|
|
"D": ["foo2", "bar2", "?"],
|
|
},
|
|
index=pd.date_range("20130110", periods=3),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_ffill(self, datetime_frame):
|
|
datetime_frame["A"][:5] = np.nan
|
|
datetime_frame["A"][-5:] = np.nan
|
|
|
|
tm.assert_frame_equal(
|
|
datetime_frame.ffill(), datetime_frame.fillna(method="ffill")
|
|
)
|
|
|
|
def test_bfill(self, datetime_frame):
|
|
datetime_frame["A"][:5] = np.nan
|
|
datetime_frame["A"][-5:] = np.nan
|
|
|
|
tm.assert_frame_equal(
|
|
datetime_frame.bfill(), datetime_frame.fillna(method="bfill")
|
|
)
|
|
|
|
def test_frame_pad_backfill_limit(self):
|
|
index = np.arange(10)
|
|
df = DataFrame(np.random.randn(10, 4), index=index)
|
|
|
|
result = df[:2].reindex(index, method="pad", limit=5)
|
|
|
|
expected = df[:2].reindex(index).fillna(method="pad")
|
|
expected.values[-3:] = np.nan
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df[-2:].reindex(index, method="backfill", limit=5)
|
|
|
|
expected = df[-2:].reindex(index).fillna(method="backfill")
|
|
expected.values[:3] = np.nan
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_frame_fillna_limit(self):
|
|
index = np.arange(10)
|
|
df = DataFrame(np.random.randn(10, 4), index=index)
|
|
|
|
result = df[:2].reindex(index)
|
|
result = result.fillna(method="pad", limit=5)
|
|
|
|
expected = df[:2].reindex(index).fillna(method="pad")
|
|
expected.values[-3:] = np.nan
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df[-2:].reindex(index)
|
|
result = result.fillna(method="backfill", limit=5)
|
|
|
|
expected = df[-2:].reindex(index).fillna(method="backfill")
|
|
expected.values[:3] = np.nan
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_fillna_skip_certain_blocks(self):
|
|
# don't try to fill boolean, int blocks
|
|
|
|
df = DataFrame(np.random.randn(10, 4).astype(int))
|
|
|
|
# it works!
|
|
df.fillna(np.nan)
|
|
|
|
@pytest.mark.parametrize("type", [int, float])
|
|
def test_fillna_positive_limit(self, type):
|
|
df = DataFrame(np.random.randn(10, 4)).astype(type)
|
|
|
|
msg = "Limit must be greater than 0"
|
|
with pytest.raises(ValueError, match=msg):
|
|
df.fillna(0, limit=-5)
|
|
|
|
@pytest.mark.parametrize("type", [int, float])
|
|
def test_fillna_integer_limit(self, type):
|
|
df = DataFrame(np.random.randn(10, 4)).astype(type)
|
|
|
|
msg = "Limit must be an integer"
|
|
with pytest.raises(ValueError, match=msg):
|
|
df.fillna(0, limit=0.5)
|
|
|
|
def test_fillna_inplace(self):
|
|
df = DataFrame(np.random.randn(10, 4))
|
|
df[1][:4] = np.nan
|
|
df[3][-4:] = np.nan
|
|
|
|
expected = df.fillna(value=0)
|
|
assert expected is not df
|
|
|
|
df.fillna(value=0, inplace=True)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
expected = df.fillna(value={0: 0}, inplace=True)
|
|
assert expected is None
|
|
|
|
df[1][:4] = np.nan
|
|
df[3][-4:] = np.nan
|
|
expected = df.fillna(method="ffill")
|
|
assert expected is not df
|
|
|
|
df.fillna(method="ffill", inplace=True)
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
def test_fillna_dict_series(self):
|
|
df = DataFrame(
|
|
{
|
|
"a": [np.nan, 1, 2, np.nan, np.nan],
|
|
"b": [1, 2, 3, np.nan, np.nan],
|
|
"c": [np.nan, 1, 2, 3, 4],
|
|
}
|
|
)
|
|
|
|
result = df.fillna({"a": 0, "b": 5})
|
|
|
|
expected = df.copy()
|
|
expected["a"] = expected["a"].fillna(0)
|
|
expected["b"] = expected["b"].fillna(5)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# it works
|
|
result = df.fillna({"a": 0, "b": 5, "d": 7})
|
|
|
|
# Series treated same as dict
|
|
result = df.fillna(df.max())
|
|
expected = df.fillna(df.max().to_dict())
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# disable this for now
|
|
with pytest.raises(NotImplementedError, match="column by column"):
|
|
df.fillna(df.max(1), axis=1)
|
|
|
|
def test_fillna_dataframe(self):
|
|
# GH 8377
|
|
df = DataFrame(
|
|
{
|
|
"a": [np.nan, 1, 2, np.nan, np.nan],
|
|
"b": [1, 2, 3, np.nan, np.nan],
|
|
"c": [np.nan, 1, 2, 3, 4],
|
|
},
|
|
index=list("VWXYZ"),
|
|
)
|
|
|
|
# df2 may have different index and columns
|
|
df2 = DataFrame(
|
|
{
|
|
"a": [np.nan, 10, 20, 30, 40],
|
|
"b": [50, 60, 70, 80, 90],
|
|
"foo": ["bar"] * 5,
|
|
},
|
|
index=list("VWXuZ"),
|
|
)
|
|
|
|
result = df.fillna(df2)
|
|
|
|
# only those columns and indices which are shared get filled
|
|
expected = DataFrame(
|
|
{
|
|
"a": [np.nan, 1, 2, np.nan, 40],
|
|
"b": [1, 2, 3, np.nan, 90],
|
|
"c": [np.nan, 1, 2, 3, 4],
|
|
},
|
|
index=list("VWXYZ"),
|
|
)
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_fillna_columns(self):
|
|
df = DataFrame(np.random.randn(10, 10))
|
|
df.values[:, ::2] = np.nan
|
|
|
|
result = df.fillna(method="ffill", axis=1)
|
|
expected = df.T.fillna(method="pad").T
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
df.insert(6, "foo", 5)
|
|
result = df.fillna(method="ffill", axis=1)
|
|
expected = df.astype(float).fillna(method="ffill", axis=1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_fillna_invalid_method(self, float_frame):
|
|
with pytest.raises(ValueError, match="ffil"):
|
|
float_frame.fillna(method="ffil")
|
|
|
|
def test_fillna_invalid_value(self, float_frame):
|
|
# list
|
|
msg = '"value" parameter must be a scalar or dict, but you passed a "{}"'
|
|
with pytest.raises(TypeError, match=msg.format("list")):
|
|
float_frame.fillna([1, 2])
|
|
# tuple
|
|
with pytest.raises(TypeError, match=msg.format("tuple")):
|
|
float_frame.fillna((1, 2))
|
|
# frame with series
|
|
msg = (
|
|
'"value" parameter must be a scalar, dict or Series, but you '
|
|
'passed a "DataFrame"'
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
float_frame.iloc[:, 0].fillna(float_frame)
|
|
|
|
def test_fillna_col_reordering(self):
|
|
cols = ["COL." + str(i) for i in range(5, 0, -1)]
|
|
data = np.random.rand(20, 5)
|
|
df = DataFrame(index=range(20), columns=cols, data=data)
|
|
filled = df.fillna(method="ffill")
|
|
assert df.columns.tolist() == filled.columns.tolist()
|
|
|
|
def test_fill_corner(self, float_frame, float_string_frame):
|
|
mf = float_string_frame
|
|
mf.loc[mf.index[5:20], "foo"] = np.nan
|
|
mf.loc[mf.index[-10:], "A"] = np.nan
|
|
|
|
filled = float_string_frame.fillna(value=0)
|
|
assert (filled.loc[filled.index[5:20], "foo"] == 0).all()
|
|
del float_string_frame["foo"]
|
|
|
|
empty_float = float_frame.reindex(columns=[])
|
|
|
|
# TODO(wesm): unused?
|
|
result = empty_float.fillna(value=0) # noqa
|
|
|
|
|
|
def test_fillna_nonconsolidated_frame():
|
|
# https://github.com/pandas-dev/pandas/issues/36495
|
|
df = DataFrame(
|
|
[[1, 1, 1, 1.0], [2, 2, 2, 2.0], [3, 3, 3, 3.0]],
|
|
columns=["i1", "i2", "i3", "f1"],
|
|
)
|
|
df_nonconsol = df.pivot("i1", "i2")
|
|
result = df_nonconsol.fillna(0)
|
|
assert result.isna().sum().sum() == 0
|