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jbrockmendeljreback
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REF/TST: method-specific files for test_append (#30503)
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‎pandas/tests/frame/methods/test_append.py

+179
Original file line numberDiff line numberDiff line change
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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 DataFrame, Series, Timestamp
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import pandas.util.testing as tm
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class TestDataFrameAppend:
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def test_append_empty_list(self):
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# GH 28769
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df = DataFrame()
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result = df.append([])
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expected = df
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tm.assert_frame_equal(result, expected)
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assert result is not df
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df = DataFrame(np.random.randn(5, 4), columns=["foo", "bar", "baz", "qux"])
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result = df.append([])
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expected = df
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tm.assert_frame_equal(result, expected)
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assert result is not df # .append() should return a new object
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def test_append_series_dict(self):
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df = DataFrame(np.random.randn(5, 4), columns=["foo", "bar", "baz", "qux"])
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series = df.loc[4]
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msg = "Indexes have overlapping values"
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with pytest.raises(ValueError, match=msg):
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df.append(series, verify_integrity=True)
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series.name = None
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msg = "Can only append a Series if ignore_index=True"
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with pytest.raises(TypeError, match=msg):
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df.append(series, verify_integrity=True)
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result = df.append(series[::-1], ignore_index=True)
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expected = df.append(
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DataFrame({0: series[::-1]}, index=df.columns).T, ignore_index=True
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)
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tm.assert_frame_equal(result, expected)
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# dict
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result = df.append(series.to_dict(), ignore_index=True)
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tm.assert_frame_equal(result, expected)
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result = df.append(series[::-1][:3], ignore_index=True)
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expected = df.append(
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DataFrame({0: series[::-1][:3]}).T, ignore_index=True, sort=True
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)
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tm.assert_frame_equal(result, expected.loc[:, result.columns])
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# can append when name set
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row = df.loc[4]
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row.name = 5
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result = df.append(row)
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expected = df.append(df[-1:], ignore_index=True)
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tm.assert_frame_equal(result, expected)
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def test_append_list_of_series_dicts(self):
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df = DataFrame(np.random.randn(5, 4), columns=["foo", "bar", "baz", "qux"])
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dicts = [x.to_dict() for idx, x in df.iterrows()]
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result = df.append(dicts, ignore_index=True)
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expected = df.append(df, ignore_index=True)
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tm.assert_frame_equal(result, expected)
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# different columns
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dicts = [
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{"foo": 1, "bar": 2, "baz": 3, "peekaboo": 4},
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{"foo": 5, "bar": 6, "baz": 7, "peekaboo": 8},
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]
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result = df.append(dicts, ignore_index=True, sort=True)
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expected = df.append(DataFrame(dicts), ignore_index=True, sort=True)
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tm.assert_frame_equal(result, expected)
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def test_append_missing_cols(self):
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# GH22252
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# exercise the conditional branch in append method where the data
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# to be appended is a list and does not contain all columns that are in
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# the target DataFrame
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df = DataFrame(np.random.randn(5, 4), columns=["foo", "bar", "baz", "qux"])
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dicts = [{"foo": 9}, {"bar": 10}]
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with tm.assert_produces_warning(None):
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result = df.append(dicts, ignore_index=True, sort=True)
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expected = df.append(DataFrame(dicts), ignore_index=True, sort=True)
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tm.assert_frame_equal(result, expected)
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def test_append_empty_dataframe(self):
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# Empty df append empty df
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df1 = DataFrame()
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df2 = DataFrame()
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result = df1.append(df2)
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expected = df1.copy()
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tm.assert_frame_equal(result, expected)
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# Non-empty df append empty df
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df1 = DataFrame(np.random.randn(5, 2))
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df2 = DataFrame()
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result = df1.append(df2)
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expected = df1.copy()
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tm.assert_frame_equal(result, expected)
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# Empty df with columns append empty df
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df1 = DataFrame(columns=["bar", "foo"])
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df2 = DataFrame()
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result = df1.append(df2)
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expected = df1.copy()
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tm.assert_frame_equal(result, expected)
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# Non-Empty df with columns append empty df
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df1 = DataFrame(np.random.randn(5, 2), columns=["bar", "foo"])
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df2 = DataFrame()
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result = df1.append(df2)
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expected = df1.copy()
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tm.assert_frame_equal(result, expected)
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def test_append_dtypes(self):
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# GH 5754
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# row appends of different dtypes (so need to do by-item)
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# can sometimes infer the correct type
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df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(5))
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df2 = DataFrame()
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result = df1.append(df2)
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expected = df1.copy()
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tm.assert_frame_equal(result, expected)
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df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1))
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df2 = DataFrame({"bar": "foo"}, index=range(1, 2))
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result = df1.append(df2)
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expected = DataFrame({"bar": [Timestamp("20130101"), "foo"]})
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tm.assert_frame_equal(result, expected)
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df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1))
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df2 = DataFrame({"bar": np.nan}, index=range(1, 2))
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result = df1.append(df2)
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expected = DataFrame(
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{"bar": Series([Timestamp("20130101"), np.nan], dtype="M8[ns]")}
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)
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tm.assert_frame_equal(result, expected)
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df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1))
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df2 = DataFrame({"bar": np.nan}, index=range(1, 2), dtype=object)
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result = df1.append(df2)
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expected = DataFrame(
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{"bar": Series([Timestamp("20130101"), np.nan], dtype="M8[ns]")}
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)
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tm.assert_frame_equal(result, expected)
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df1 = DataFrame({"bar": np.nan}, index=range(1))
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df2 = DataFrame({"bar": Timestamp("20130101")}, index=range(1, 2))
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result = df1.append(df2)
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expected = DataFrame(
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{"bar": Series([np.nan, Timestamp("20130101")], dtype="M8[ns]")}
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)
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tm.assert_frame_equal(result, expected)
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df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1))
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df2 = DataFrame({"bar": 1}, index=range(1, 2), dtype=object)
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result = df1.append(df2)
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expected = DataFrame({"bar": Series([Timestamp("20130101"), 1])})
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"timestamp", ["2019-07-19 07:04:57+0100", "2019-07-19 07:04:57"]
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)
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def test_append_timestamps_aware_or_naive(self, tz_naive_fixture, timestamp):
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# GH 30238
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tz = tz_naive_fixture
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df = pd.DataFrame([pd.Timestamp(timestamp, tz=tz)])
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result = df.append(df.iloc[0]).iloc[-1]
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expected = pd.Series(pd.Timestamp(timestamp, tz=tz), name=0)
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tm.assert_series_equal(result, expected)

‎pandas/tests/frame/methods/test_cov_corr.py

+5-22
Original file line numberDiff line numberDiff line change
@@ -62,32 +62,15 @@ def test_cov(self, float_frame, float_string_frame):
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class TestDataFrameCorr:
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# DataFrame.corr(), as opposed to DataFrame.corrwith
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@staticmethod
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def _check_method(frame, method="pearson"):
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correls = frame.corr(method=method)
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expected = frame["A"].corr(frame["C"], method=method)
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tm.assert_almost_equal(correls["A"]["C"], expected)
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@td.skip_if_no_scipy
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def test_corr_pearson(self, float_frame):
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float_frame["A"][:5] = np.nan
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float_frame["B"][5:10] = np.nan
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self._check_method(float_frame, "pearson")
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@pytest.mark.parametrize("method", ["pearson", "kendall", "spearman"])
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@td.skip_if_no_scipy
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def test_corr_kendall(self, float_frame):
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def test_corr_scipy_method(self, float_frame, method):
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float_frame["A"][:5] = np.nan
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float_frame["B"][5:10] = np.nan
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self._check_method(float_frame, "kendall")
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@td.skip_if_no_scipy
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def test_corr_spearman(self, float_frame):
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float_frame["A"][:5] = np.nan
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float_frame["B"][5:10] = np.nan
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self._check_method(float_frame, "spearman")
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correls = float_frame.corr(method=method)
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expected = float_frame["A"].corr(float_frame["C"], method=method)
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tm.assert_almost_equal(correls["A"]["C"], expected)
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# ---------------------------------------------------------------------
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‎pandas/tests/frame/methods/test_rank.py

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@@ -113,6 +113,15 @@ def test_rank2(self):
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exp = DataFrame({"a": [3.5, 1.0, 3.5, 5.0, 6.0, 7.0, 2.0]})
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tm.assert_frame_equal(df.rank(), exp)
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def test_rank_does_not_mutate(self):
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# GH#18521
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# Check rank does not mutate DataFrame
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df = DataFrame(np.random.randn(10, 3), dtype="float64")
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expected = df.copy()
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df.rank()
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result = df
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tm.assert_frame_equal(result, expected)
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def test_rank_mixed_frame(self, float_string_frame):
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float_string_frame["datetime"] = datetime.now()
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float_string_frame["timedelta"] = timedelta(days=1, seconds=1)

‎pandas/tests/frame/test_analytics.py

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Original file line numberDiff line numberDiff line change
@@ -1248,15 +1248,6 @@ def test_matmul(self):
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# ---------------------------------------------------------------------
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# Unsorted
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def test_series_nat_conversion(self):
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# GH 18521
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# Check rank does not mutate DataFrame
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df = DataFrame(np.random.randn(10, 3), dtype="float64")
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expected = df.copy()
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df.rank()
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result = df
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tm.assert_frame_equal(result, expected)
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def test_series_broadcasting(self):
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# smoke test for numpy warnings
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# GH 16378, GH 16306

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