FM / methods

/usr/local/lib64/python3.6/site-packages/pandas/tests/frame/methods/

/usr/local/lib64/python3.6/site-packages/pandas/tests/frame/methods

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
test_align.py	10339	0644	edit dl rm
test_append.py	7399	0644	edit dl rm
test_asfreq.py	1970	0644	edit dl rm
test_asof.py	5767	0644	edit dl rm
test_assign.py	2982	0644	edit dl rm
test_astype.py	21027	0644	edit dl rm
test_at_time.py	3205	0644	edit dl rm
test_between_time.py	3675	0644	edit dl rm
test_clip.py	6028	0644	edit dl rm
test_combine.py	1359	0644	edit dl rm
test_combine_first.py	12748	0644	edit dl rm
test_compare.py	6158	0644	edit dl rm
test_count.py	1068	0644	edit dl rm
test_cov_corr.py	11888	0644	edit dl rm
test_describe.py	12492	0644	edit dl rm
test_diff.py	7374	0644	edit dl rm
test_drop.py	15662	0644	edit dl rm
test_droplevel.py	855	0644	edit dl rm
test_drop_duplicates.py	13387	0644	edit dl rm
test_duplicated.py	3191	0644	edit dl rm
test_explode.py	5294	0644	edit dl rm
test_filter.py	4933	0644	edit dl rm
test_first_and_last.py	1803	0644	edit dl rm
test_head_tail.py	1194	0644	edit dl rm
test_interpolate.py	11816	0644	edit dl rm
test_isin.py	7324	0644	edit dl rm
test_nlargest.py	6731	0644	edit dl rm
test_pct_change.py	3436	0644	edit dl rm
test_pop.py	1226	0644	edit dl rm
test_quantile.py	18028	0644	edit dl rm
test_rank.py	11377	0644	edit dl rm
test_reindex_like.py	1187	0644	edit dl rm
test_rename.py	12543	0644	edit dl rm
test_rename_axis.py	4074	0644	edit dl rm
test_replace.py	59230	0644	edit dl rm
test_reset_index.py	12506	0644	edit dl rm
test_round.py	7916	0644	edit dl rm
test_select_dtypes.py	12104	0644	edit dl rm
test_set_index.py	19571	0644	edit dl rm
test_shift.py	10324	0644	edit dl rm
test_sort_index.py	25678	0644	edit dl rm
test_sort_values.py	25358	0644	edit dl rm
test_to_dict.py	9989	0644	edit dl rm
test_to_period.py	984	0644	edit dl rm
test_to_records.py	13981	0644	edit dl rm
test_to_timestamp.py	4020	0644	edit dl rm
test_transpose.py	2426	0644	edit dl rm
test_truncate.py	4180	0644	edit dl rm
test_tz_convert.py	3415	0644	edit dl rm
test_tz_localize.py	666	0644	edit dl rm
test_update.py	4259	0644	edit dl rm
test_value_counts.py	2631	0644	edit dl rm
__init__.py	229	0644	edit dl rm

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)

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)