121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
from pandas import (
|
|
DataFrame,
|
|
Series,
|
|
)
|
|
import pandas._testing as tm
|
|
|
|
|
|
class TestDataFramePctChange:
|
|
@pytest.mark.parametrize(
|
|
"periods,fill_method,limit,exp",
|
|
[
|
|
(1, "ffill", None, [np.nan, np.nan, np.nan, 1, 1, 1.5, 0, 0]),
|
|
(1, "ffill", 1, [np.nan, np.nan, np.nan, 1, 1, 1.5, 0, np.nan]),
|
|
(1, "bfill", None, [np.nan, 0, 0, 1, 1, 1.5, np.nan, np.nan]),
|
|
(1, "bfill", 1, [np.nan, np.nan, 0, 1, 1, 1.5, np.nan, np.nan]),
|
|
(-1, "ffill", None, [np.nan, np.nan, -0.5, -0.5, -0.6, 0, 0, np.nan]),
|
|
(-1, "ffill", 1, [np.nan, np.nan, -0.5, -0.5, -0.6, 0, np.nan, np.nan]),
|
|
(-1, "bfill", None, [0, 0, -0.5, -0.5, -0.6, np.nan, np.nan, np.nan]),
|
|
(-1, "bfill", 1, [np.nan, 0, -0.5, -0.5, -0.6, np.nan, np.nan, np.nan]),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("klass", [DataFrame, Series])
|
|
def test_pct_change_with_nas(self, periods, fill_method, limit, exp, klass):
|
|
vals = [np.nan, np.nan, 1, 2, 4, 10, np.nan, np.nan]
|
|
obj = klass(vals)
|
|
|
|
res = obj.pct_change(periods=periods, fill_method=fill_method, limit=limit)
|
|
tm.assert_equal(res, klass(exp))
|
|
|
|
def test_pct_change_numeric(self):
|
|
# GH#11150
|
|
pnl = DataFrame(
|
|
[np.arange(0, 40, 10), np.arange(0, 40, 10), np.arange(0, 40, 10)]
|
|
).astype(np.float64)
|
|
pnl.iat[1, 0] = np.nan
|
|
pnl.iat[1, 1] = np.nan
|
|
pnl.iat[2, 3] = 60
|
|
|
|
for axis in range(2):
|
|
expected = pnl.ffill(axis=axis) / pnl.ffill(axis=axis).shift(axis=axis) - 1
|
|
result = pnl.pct_change(axis=axis, fill_method="pad")
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_pct_change(self, datetime_frame):
|
|
rs = datetime_frame.pct_change(fill_method=None)
|
|
tm.assert_frame_equal(rs, datetime_frame / datetime_frame.shift(1) - 1)
|
|
|
|
rs = datetime_frame.pct_change(2)
|
|
filled = datetime_frame.fillna(method="pad")
|
|
tm.assert_frame_equal(rs, filled / filled.shift(2) - 1)
|
|
|
|
rs = datetime_frame.pct_change(fill_method="bfill", limit=1)
|
|
filled = datetime_frame.fillna(method="bfill", limit=1)
|
|
tm.assert_frame_equal(rs, filled / filled.shift(1) - 1)
|
|
|
|
rs = datetime_frame.pct_change(freq="5D")
|
|
filled = datetime_frame.fillna(method="pad")
|
|
tm.assert_frame_equal(
|
|
rs, (filled / filled.shift(freq="5D") - 1).reindex_like(filled)
|
|
)
|
|
|
|
def test_pct_change_shift_over_nas(self):
|
|
s = Series([1.0, 1.5, np.nan, 2.5, 3.0])
|
|
|
|
df = DataFrame({"a": s, "b": s})
|
|
|
|
chg = df.pct_change()
|
|
expected = Series([np.nan, 0.5, 0.0, 2.5 / 1.5 - 1, 0.2])
|
|
edf = DataFrame({"a": expected, "b": expected})
|
|
tm.assert_frame_equal(chg, edf)
|
|
|
|
@pytest.mark.parametrize(
|
|
"freq, periods, fill_method, limit",
|
|
[
|
|
("5B", 5, None, None),
|
|
("3B", 3, None, None),
|
|
("3B", 3, "bfill", None),
|
|
("7B", 7, "pad", 1),
|
|
("7B", 7, "bfill", 3),
|
|
("14B", 14, None, None),
|
|
],
|
|
)
|
|
def test_pct_change_periods_freq(
|
|
self, datetime_frame, freq, periods, fill_method, limit
|
|
):
|
|
# GH#7292
|
|
rs_freq = datetime_frame.pct_change(
|
|
freq=freq, fill_method=fill_method, limit=limit
|
|
)
|
|
rs_periods = datetime_frame.pct_change(
|
|
periods, fill_method=fill_method, limit=limit
|
|
)
|
|
tm.assert_frame_equal(rs_freq, rs_periods)
|
|
|
|
empty_ts = DataFrame(index=datetime_frame.index, columns=datetime_frame.columns)
|
|
rs_freq = empty_ts.pct_change(freq=freq, fill_method=fill_method, limit=limit)
|
|
rs_periods = empty_ts.pct_change(periods, fill_method=fill_method, limit=limit)
|
|
tm.assert_frame_equal(rs_freq, rs_periods)
|
|
|
|
|
|
@pytest.mark.parametrize("fill_method", ["pad", "ffill", None])
|
|
def test_pct_change_with_duplicated_indices(fill_method):
|
|
# GH30463
|
|
data = DataFrame(
|
|
{0: [np.nan, 1, 2, 3, 9, 18], 1: [0, 1, np.nan, 3, 9, 18]}, index=["a", "b"] * 3
|
|
)
|
|
result = data.pct_change(fill_method=fill_method)
|
|
if fill_method is None:
|
|
second_column = [np.nan, np.inf, np.nan, np.nan, 2.0, 1.0]
|
|
else:
|
|
second_column = [np.nan, np.inf, 0.0, 2.0, 2.0, 1.0]
|
|
expected = DataFrame(
|
|
{0: [np.nan, np.nan, 1.0, 0.5, 2.0, 1.0], 1: second_column},
|
|
index=["a", "b"] * 3,
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|