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usr
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local
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lib64
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python3.6
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site-packages
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pandas
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tests
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frame
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methods
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/usr/local/lib64/python3.6/site-packages/pandas/tests/frame/methods
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__pycache__/
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test_align.py
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test_append.py
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test_asfreq.py
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test_asof.py
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test_assign.py
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test_astype.py
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test_at_time.py
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test_between_time.py
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test_clip.py
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test_combine.py
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test_combine_first.py
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test_compare.py
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test_count.py
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test_cov_corr.py
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test_describe.py
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test_diff.py
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test_drop.py
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test_droplevel.py
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test_drop_duplicates.py
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test_duplicated.py
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test_explode.py
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test_filter.py
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test_first_and_last.py
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test_head_tail.py
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test_interpolate.py
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test_isin.py
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test_nlargest.py
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test_pct_change.py
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test_pop.py
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test_quantile.py
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test_rank.py
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test_reindex_like.py
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test_rename.py
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test_rename_axis.py
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test_replace.py
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test_reset_index.py
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test_round.py
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test_select_dtypes.py
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test_set_index.py
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test_shift.py
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test_sort_index.py
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test_sort_values.py
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test_to_dict.py
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test_to_period.py
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test_to_records.py
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test_to_timestamp.py
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test_transpose.py
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test_truncate.py
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test_tz_convert.py
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test_tz_localize.py
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test_update.py
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test_value_counts.py
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__init__.py
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Edit:
/usr/local/lib64/python3.6/site-packages/pandas/tests/frame/methods/test_append.py
(7399B)
import numpy as np import pytest import pandas as pd from pandas import DataFrame, Series, Timestamp import pandas._testing as tm class TestDataFrameAppend: def test_append_empty_list(self): # GH 28769 df = DataFrame() result = df.append([]) expected = df tm.assert_frame_equal(result, expected) assert result is not df df = DataFrame(np.random.randn(5, 4), columns=["foo", "bar", "baz", "qux"]) result = df.append([]) expected = df tm.assert_frame_equal(result, expected) assert result is not df # .append() should return a new object def test_append_series_dict(self): df = DataFrame(np.random.randn(5, 4), columns=["foo", "bar", "baz", "qux"]) series = df.loc[4] msg = "Indexes have overlapping values" with pytest.raises(ValueError, match=msg): df.append(series, verify_integrity=True) series.name = None msg = "Can only append a Series if ignore_index=True" with pytest.raises(TypeError, match=msg): df.append(series, verify_integrity=True) result = df.append(series[::-1], ignore_index=True) expected = df.append( DataFrame({0: series[::-1]}, index=df.columns).T, ignore_index=True ) tm.assert_frame_equal(result, expected) # dict result = df.append(series.to_dict(), ignore_index=True) tm.assert_frame_equal(result, expected) result = df.append(series[::-1][:3], ignore_index=True) expected = df.append( DataFrame({0: series[::-1][:3]}).T, ignore_index=True, sort=True ) tm.assert_frame_equal(result, expected.loc[:, result.columns]) msg = "Can only append a dict if ignore_index=True" with pytest.raises(TypeError, match=msg): df.append(series.to_dict()) # can append when name set row = df.loc[4] row.name = 5 result = df.append(row) expected = df.append(df[-1:], ignore_index=True) tm.assert_frame_equal(result, expected) def test_append_list_of_series_dicts(self): df = DataFrame(np.random.randn(5, 4), columns=["foo", "bar", "baz", "qux"]) dicts = [x.to_dict() for idx, x in df.iterrows()] result = df.append(dicts, ignore_index=True) expected = df.append(df, ignore_index=True) tm.assert_frame_equal(result, expected) # different columns dicts = [ {"foo": 1, "bar": 2, "baz": 3, "peekaboo": 4}, {"foo": 5, "bar": 6, "baz": 7, "peekaboo": 8}, ] result = df.append(dicts, ignore_index=True, sort=True) expected = df.append(DataFrame(dicts), ignore_index=True, sort=True) tm.assert_frame_equal(result, expected) def test_append_missing_cols(self): # GH22252 # exercise the conditional branch in append method where the data # to be appended is a list and does not contain all columns that are in # the target DataFrame df = DataFrame(np.random.randn(5, 4), columns=["foo", "bar", "baz", "qux"]) dicts = [{"foo": 9}, {"bar": 10}] with tm.assert_produces_warning(None): result = df.append(dicts, ignore_index=True, sort=True) expected = df.append(DataFrame(dicts), ignore_index=True, sort=True) tm.assert_frame_equal(result, expected) def test_append_empty_dataframe(self): # Empty df append empty df df1 = DataFrame() df2 = DataFrame() result = df1.append(df2) expected = df1.copy() tm.assert_frame_equal(result, expected) # Non-empty df append empty df df1 = DataFrame(np.random.randn(5, 2)) df2 = DataFrame() result = df1.append(df2) expected = df1.copy() tm.assert_frame_equal(result, expected) # Empty df with columns append empty df df1 = DataFrame(columns=["bar", "foo"]) df2 = DataFrame() result = df1.append(df2) expected = df1.copy() tm.assert_frame_equal(result, expected) # Non-Empty df with columns append empty df df1 = DataFrame(np.random.randn(5, 2), columns=["bar", "foo"]) df2 = DataFrame() result = df1.append(df2) expected = df1.copy() tm.assert_frame_equal(result, expected) def test_append_dtypes(self): # GH 5754 # row appends of different dtypes (so need to do by-item) # can sometimes infer the correct type df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(5)) df2 = DataFrame() result = df1.append(df2) expected = df1.copy() tm.assert_frame_equal(result, expected) df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1)) df2 = DataFrame({"bar": "foo"}, index=range(1, 2)) result = df1.append(df2) expected = DataFrame({"bar": [Timestamp("20130101"), "foo"]}) tm.assert_frame_equal(result, expected) df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1)) df2 = DataFrame({"bar": np.nan}, index=range(1, 2)) result = df1.append(df2) expected = DataFrame( {"bar": Series([Timestamp("20130101"), np.nan], dtype="M8[ns]")} ) tm.assert_frame_equal(result, expected) df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1)) df2 = DataFrame({"bar": np.nan}, index=range(1, 2), dtype=object) result = df1.append(df2) expected = DataFrame( {"bar": Series([Timestamp("20130101"), np.nan], dtype="M8[ns]")} ) tm.assert_frame_equal(result, expected) df1 = DataFrame({"bar": np.nan}, index=range(1)) df2 = DataFrame({"bar": Timestamp("20130101")}, index=range(1, 2)) result = df1.append(df2) expected = DataFrame( {"bar": Series([np.nan, Timestamp("20130101")], dtype="M8[ns]")} ) tm.assert_frame_equal(result, expected) df1 = DataFrame({"bar": Timestamp("20130101")}, index=range(1)) df2 = DataFrame({"bar": 1}, index=range(1, 2), dtype=object) result = df1.append(df2) expected = DataFrame({"bar": Series([Timestamp("20130101"), 1])}) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "timestamp", ["2019-07-19 07:04:57+0100", "2019-07-19 07:04:57"] ) def test_append_timestamps_aware_or_naive(self, tz_naive_fixture, timestamp): # GH 30238 tz = tz_naive_fixture df = pd.DataFrame([pd.Timestamp(timestamp, tz=tz)]) result = df.append(df.iloc[0]).iloc[-1] expected = pd.Series(pd.Timestamp(timestamp, tz=tz), name=0) tm.assert_series_equal(result, expected) @pytest.mark.parametrize( "data, dtype", [ ([1], pd.Int64Dtype()), ([1], pd.CategoricalDtype()), ([pd.Interval(left=0, right=5)], pd.IntervalDtype()), ([pd.Period("2000-03", freq="M")], pd.PeriodDtype("M")), ([1], pd.SparseDtype()), ], ) def test_other_dtypes(self, data, dtype): df = pd.DataFrame(data, dtype=dtype) result = df.append(df.iloc[0]).iloc[-1] expected = pd.Series(data, name=0, dtype=dtype) tm.assert_series_equal(result, expected)
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