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_diff.py (7374B)

import numpy as np
import pytest

import pandas as pd
from pandas import DataFrame, Series, Timestamp, date_range
import pandas._testing as tm

class TestDataFrameDiff:
    def test_diff(self, datetime_frame):
        the_diff = datetime_frame.diff(1)

tm.assert_series_equal(
            the_diff["A"], datetime_frame["A"] - datetime_frame["A"].shift(1)
        )

# int dtype
        a = 10_000_000_000_000_000
        b = a + 1
        s = Series([a, b])

rs = DataFrame({"s": s}).diff()
        assert rs.s[1] == 1

# mixed numeric
        tf = datetime_frame.astype("float32")
        the_diff = tf.diff(1)
        tm.assert_series_equal(the_diff["A"], tf["A"] - tf["A"].shift(1))

# GH#10907
        df = pd.DataFrame({"y": pd.Series([2]), "z": pd.Series([3])})
        df.insert(0, "x", 1)
        result = df.diff(axis=1)
        expected = pd.DataFrame(
            {"x": np.nan, "y": pd.Series(1), "z": pd.Series(1)}
        ).astype("float64")
        tm.assert_frame_equal(result, expected)

@pytest.mark.parametrize("tz", [None, "UTC"])
    def test_diff_datetime_axis0(self, tz):
        # GH#18578
        df = DataFrame(
            {
                0: date_range("2010", freq="D", periods=2, tz=tz),
                1: date_range("2010", freq="D", periods=2, tz=tz),
            }
        )

result = df.diff(axis=0)
        expected = DataFrame(
            {
                0: pd.TimedeltaIndex(["NaT", "1 days"]),
                1: pd.TimedeltaIndex(["NaT", "1 days"]),
            }
        )
        tm.assert_frame_equal(result, expected)

@pytest.mark.parametrize("tz", [None, "UTC"])
    def test_diff_datetime_axis1(self, tz):
        # GH#18578
        df = DataFrame(
            {
                0: date_range("2010", freq="D", periods=2, tz=tz),
                1: date_range("2010", freq="D", periods=2, tz=tz),
            }
        )

result = df.diff(axis=1)
        expected = DataFrame(
            {
                0: pd.TimedeltaIndex(["NaT", "NaT"]),
                1: pd.TimedeltaIndex(["0 days", "0 days"]),
            }
        )
        tm.assert_frame_equal(result, expected)

def test_diff_timedelta(self):
        # GH#4533
        df = DataFrame(
            dict(
                time=[Timestamp("20130101 9:01"), Timestamp("20130101 9:02")],
                value=[1.0, 2.0],
            )
        )

res = df.diff()
        exp = DataFrame(
            [[pd.NaT, np.nan], [pd.Timedelta("00:01:00"), 1]], columns=["time", "value"]
        )
        tm.assert_frame_equal(res, exp)

def test_diff_mixed_dtype(self):
        df = DataFrame(np.random.randn(5, 3))
        df["A"] = np.array([1, 2, 3, 4, 5], dtype=object)

result = df.diff()
        assert result[0].dtype == np.float64

def test_diff_neg_n(self, datetime_frame):
        rs = datetime_frame.diff(-1)
        xp = datetime_frame - datetime_frame.shift(-1)
        tm.assert_frame_equal(rs, xp)

def test_diff_float_n(self, datetime_frame):
        rs = datetime_frame.diff(1.0)
        xp = datetime_frame.diff(1)
        tm.assert_frame_equal(rs, xp)

def test_diff_axis(self):
        # GH#9727
        df = DataFrame([[1.0, 2.0], [3.0, 4.0]])
        tm.assert_frame_equal(
            df.diff(axis=1), DataFrame([[np.nan, 1.0], [np.nan, 1.0]])
        )
        tm.assert_frame_equal(
            df.diff(axis=0), DataFrame([[np.nan, np.nan], [2.0, 2.0]])
        )

@pytest.mark.xfail(
        reason="GH#32995 needs to operate column-wise or do inference",
        raises=AssertionError,
    )
    def test_diff_period(self):
        # GH#32995 Don't pass an incorrect axis
        #  TODO(EA2D): this bug wouldn't have happened with 2D EA
        pi = pd.date_range("2016-01-01", periods=3).to_period("D")
        df = pd.DataFrame({"A": pi})

result = df.diff(1, axis=1)

# TODO: should we make Block.diff do type inference?  or maybe algos.diff?
        expected = (df - pd.NaT).astype(object)
        tm.assert_frame_equal(result, expected)

def test_diff_axis1_mixed_dtypes(self):
        # GH#32995 operate column-wise when we have mixed dtypes and axis=1
        df = pd.DataFrame({"A": range(3), "B": 2 * np.arange(3, dtype=np.float64)})

expected = pd.DataFrame({"A": [np.nan, np.nan, np.nan], "B": df["B"] / 2})

result = df.diff(axis=1)
        tm.assert_frame_equal(result, expected)

def test_diff_axis1_mixed_dtypes_large_periods(self):
        # GH#32995 operate column-wise when we have mixed dtypes and axis=1
        df = pd.DataFrame({"A": range(3), "B": 2 * np.arange(3, dtype=np.float64)})

expected = df * np.nan

result = df.diff(axis=1, periods=3)
        tm.assert_frame_equal(result, expected)

def test_diff_axis1_mixed_dtypes_negative_periods(self):
        # GH#32995 operate column-wise when we have mixed dtypes and axis=1
        df = pd.DataFrame({"A": range(3), "B": 2 * np.arange(3, dtype=np.float64)})

expected = pd.DataFrame({"A": -1.0 * df["A"], "B": df["B"] * np.nan})

result = df.diff(axis=1, periods=-1)
        tm.assert_frame_equal(result, expected)

def test_diff_sparse(self):
        # GH#28813 .diff() should work for sparse dataframes as well
        sparse_df = pd.DataFrame([[0, 1], [1, 0]], dtype="Sparse[int]")

result = sparse_df.diff()
        expected = pd.DataFrame(
            [[np.nan, np.nan], [1.0, -1.0]], dtype=pd.SparseDtype("float", 0.0)
        )

tm.assert_frame_equal(result, expected)

@pytest.mark.parametrize(
        "axis,expected",
        [
            (
                0,
                pd.DataFrame(
                    {
                        "a": [np.nan, 0, 1, 0, np.nan, np.nan, np.nan, 0],
                        "b": [np.nan, 1, np.nan, np.nan, -2, 1, np.nan, np.nan],
                        "c": np.repeat(np.nan, 8),
                        "d": [np.nan, 3, 5, 7, 9, 11, 13, 15],
                    },
                    dtype="Int64",
                ),
            ),
            (
                1,
                pd.DataFrame(
                    {
                        "a": np.repeat(np.nan, 8),
                        "b": [0, 1, np.nan, 1, np.nan, np.nan, np.nan, 0],
                        "c": np.repeat(np.nan, 8),
                        "d": np.repeat(np.nan, 8),
                    },
                    dtype="Int64",
                ),
            ),
        ],
    )
    def test_diff_integer_na(self, axis, expected):
        # GH#24171 IntegerNA Support for DataFrame.diff()
        df = pd.DataFrame(
            {
                "a": np.repeat([0, 1, np.nan, 2], 2),
                "b": np.tile([0, 1, np.nan, 2], 2),
                "c": np.repeat(np.nan, 8),
                "d": np.arange(1, 9) ** 2,
            },
            dtype="Int64",
        )

# Test case for default behaviour of diff
        result = df.diff(axis=axis)
        tm.assert_frame_equal(result, expected)

def test_diff_readonly(self):
        # https://github.com/pandas-dev/pandas/issues/35559
        arr = np.random.randn(5, 2)
        arr.flags.writeable = False
        df = pd.DataFrame(arr)
        result = df.diff()
        expected = pd.DataFrame(np.array(df)).diff()
        tm.assert_frame_equal(result, expected)

import numpy as np import pytest import pandas as pd from pandas import DataFrame, Series, Timestamp, date_range import pandas._testing as tm class TestDataFrameDiff: def test_diff(self, datetime_frame): the_diff = datetime_frame.diff(1) tm.assert_series_equal( the_diff["A"], datetime_frame["A"] - datetime_frame["A"].shift(1) ) # int dtype a = 10_000_000_000_000_000 b = a + 1 s = Series([a, b]) rs = DataFrame({"s": s}).diff() assert rs.s[1] == 1 # mixed numeric tf = datetime_frame.astype("float32") the_diff = tf.diff(1) tm.assert_series_equal(the_diff["A"], tf["A"] - tf["A"].shift(1)) # GH#10907 df = pd.DataFrame({"y": pd.Series([2]), "z": pd.Series([3])}) df.insert(0, "x", 1) result = df.diff(axis=1) expected = pd.DataFrame( {"x": np.nan, "y": pd.Series(1), "z": pd.Series(1)} ).astype("float64") tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("tz", [None, "UTC"]) def test_diff_datetime_axis0(self, tz): # GH#18578 df = DataFrame( { 0: date_range("2010", freq="D", periods=2, tz=tz), 1: date_range("2010", freq="D", periods=2, tz=tz), } ) result = df.diff(axis=0) expected = DataFrame( { 0: pd.TimedeltaIndex(["NaT", "1 days"]), 1: pd.TimedeltaIndex(["NaT", "1 days"]), } ) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("tz", [None, "UTC"]) def test_diff_datetime_axis1(self, tz): # GH#18578 df = DataFrame( { 0: date_range("2010", freq="D", periods=2, tz=tz), 1: date_range("2010", freq="D", periods=2, tz=tz), } ) result = df.diff(axis=1) expected = DataFrame( { 0: pd.TimedeltaIndex(["NaT", "NaT"]), 1: pd.TimedeltaIndex(["0 days", "0 days"]), } ) tm.assert_frame_equal(result, expected) def test_diff_timedelta(self): # GH#4533 df = DataFrame( dict( time=[Timestamp("20130101 9:01"), Timestamp("20130101 9:02")], value=[1.0, 2.0], ) ) res = df.diff() exp = DataFrame( [[pd.NaT, np.nan], [pd.Timedelta("00:01:00"), 1]], columns=["time", "value"] ) tm.assert_frame_equal(res, exp) def test_diff_mixed_dtype(self): df = DataFrame(np.random.randn(5, 3)) df["A"] = np.array([1, 2, 3, 4, 5], dtype=object) result = df.diff() assert result[0].dtype == np.float64 def test_diff_neg_n(self, datetime_frame): rs = datetime_frame.diff(-1) xp = datetime_frame - datetime_frame.shift(-1) tm.assert_frame_equal(rs, xp) def test_diff_float_n(self, datetime_frame): rs = datetime_frame.diff(1.0) xp = datetime_frame.diff(1) tm.assert_frame_equal(rs, xp) def test_diff_axis(self): # GH#9727 df = DataFrame([[1.0, 2.0], [3.0, 4.0]]) tm.assert_frame_equal( df.diff(axis=1), DataFrame([[np.nan, 1.0], [np.nan, 1.0]]) ) tm.assert_frame_equal( df.diff(axis=0), DataFrame([[np.nan, np.nan], [2.0, 2.0]]) ) @pytest.mark.xfail( reason="GH#32995 needs to operate column-wise or do inference", raises=AssertionError, ) def test_diff_period(self): # GH#32995 Don't pass an incorrect axis # TODO(EA2D): this bug wouldn't have happened with 2D EA pi = pd.date_range("2016-01-01", periods=3).to_period("D") df = pd.DataFrame({"A": pi}) result = df.diff(1, axis=1) # TODO: should we make Block.diff do type inference? or maybe algos.diff? expected = (df - pd.NaT).astype(object) tm.assert_frame_equal(result, expected) def test_diff_axis1_mixed_dtypes(self): # GH#32995 operate column-wise when we have mixed dtypes and axis=1 df = pd.DataFrame({"A": range(3), "B": 2 * np.arange(3, dtype=np.float64)}) expected = pd.DataFrame({"A": [np.nan, np.nan, np.nan], "B": df["B"] / 2}) result = df.diff(axis=1) tm.assert_frame_equal(result, expected) def test_diff_axis1_mixed_dtypes_large_periods(self): # GH#32995 operate column-wise when we have mixed dtypes and axis=1 df = pd.DataFrame({"A": range(3), "B": 2 * np.arange(3, dtype=np.float64)}) expected = df * np.nan result = df.diff(axis=1, periods=3) tm.assert_frame_equal(result, expected) def test_diff_axis1_mixed_dtypes_negative_periods(self): # GH#32995 operate column-wise when we have mixed dtypes and axis=1 df = pd.DataFrame({"A": range(3), "B": 2 * np.arange(3, dtype=np.float64)}) expected = pd.DataFrame({"A": -1.0 * df["A"], "B": df["B"] * np.nan}) result = df.diff(axis=1, periods=-1) tm.assert_frame_equal(result, expected) def test_diff_sparse(self): # GH#28813 .diff() should work for sparse dataframes as well sparse_df = pd.DataFrame([[0, 1], [1, 0]], dtype="Sparse[int]") result = sparse_df.diff() expected = pd.DataFrame( [[np.nan, np.nan], [1.0, -1.0]], dtype=pd.SparseDtype("float", 0.0) ) tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "axis,expected", [ ( 0, pd.DataFrame( { "a": [np.nan, 0, 1, 0, np.nan, np.nan, np.nan, 0], "b": [np.nan, 1, np.nan, np.nan, -2, 1, np.nan, np.nan], "c": np.repeat(np.nan, 8), "d": [np.nan, 3, 5, 7, 9, 11, 13, 15], }, dtype="Int64", ), ), ( 1, pd.DataFrame( { "a": np.repeat(np.nan, 8), "b": [0, 1, np.nan, 1, np.nan, np.nan, np.nan, 0], "c": np.repeat(np.nan, 8), "d": np.repeat(np.nan, 8), }, dtype="Int64", ), ), ], ) def test_diff_integer_na(self, axis, expected): # GH#24171 IntegerNA Support for DataFrame.diff() df = pd.DataFrame( { "a": np.repeat([0, 1, np.nan, 2], 2), "b": np.tile([0, 1, np.nan, 2], 2), "c": np.repeat(np.nan, 8), "d": np.arange(1, 9) ** 2, }, dtype="Int64", ) # Test case for default behaviour of diff result = df.diff(axis=axis) tm.assert_frame_equal(result, expected) def test_diff_readonly(self): # https://github.com/pandas-dev/pandas/issues/35559 arr = np.random.randn(5, 2) arr.flags.writeable = False df = pd.DataFrame(arr) result = df.diff() expected = pd.DataFrame(np.array(df)).diff() tm.assert_frame_equal(result, expected)