212 lines
7.5 KiB
Python
212 lines
7.5 KiB
Python
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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._testing as tm
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class TestDataFrameAppend:
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@pytest.mark.parametrize("klass", [Series, DataFrame])
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def test_append_multiindex(self, multiindex_dataframe_random_data, klass):
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obj = multiindex_dataframe_random_data
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if klass is Series:
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obj = obj["A"]
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a = obj[:5]
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b = obj[5:]
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result = a.append(b)
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tm.assert_equal(result, obj)
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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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msg = "Can only append a dict if ignore_index=True"
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with pytest.raises(TypeError, match=msg):
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df.append(series.to_dict())
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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 = DataFrame([Timestamp(timestamp, tz=tz)])
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result = df.append(df.iloc[0]).iloc[-1]
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expected = Series(Timestamp(timestamp, tz=tz), name=0)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"data, dtype",
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[
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([1], pd.Int64Dtype()),
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([1], pd.CategoricalDtype()),
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([pd.Interval(left=0, right=5)], pd.IntervalDtype()),
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([pd.Period("2000-03", freq="M")], pd.PeriodDtype("M")),
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([1], pd.SparseDtype()),
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],
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)
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def test_other_dtypes(self, data, dtype):
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df = DataFrame(data, dtype=dtype)
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result = df.append(df.iloc[0]).iloc[-1]
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expected = Series(data, name=0, dtype=dtype)
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tm.assert_series_equal(result, expected)
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