python-3.x 使用NaN计算pct_change时如何避免bfill或fill

jvidinwx 于 2023-02-10 发布在 Python

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对于如下所示的df，我使用pct_change()来计算滚动百分比变化：

price = [np.NaN, 10, 13, np.NaN, np.NaN, 9]
df = pd. DataFrame(price, columns = ['price'])
df
Out[75]:
    price
0 NaN
1 10.0
2 13.0
3 NaN
4 NaN
5 9.0

但我得到了这些意想不到的结果：

df.price.pct_change(periods = 1, fill_method='bfill')
Out[76]:
0 NaN
1 0.000000
2 0.300000
3 -0.307692
4 0.000000
5 0.000000
Name: price, dtype: float64

df.price.pct_change(periods = 1, fill_method='pad')
Out[77]:
0 NaN
1 NaN
2 0.300000
3 0.000000
4 0.000000
5 -0.307692
Name: price, dtype: float64

df.price.pct_change(periods = 1, fill_method='ffill')
Out[78]:
0 NaN
1 NaN
2 0.300000
3 0.000000
4 0.000000
5 -0.307692
Name: price, dtype: float64

我希望在使用NaN s进行计算时，结果将是NaN s，而不是向前或向后填充，然后再进行计算。
我能问一下如何实现吗？谢谢。
预期结果：

0 NaN
1 NaN
2 0.300000
3 NaN
4 NaN
5 NaN
Name: price, dtype: float64

参考：
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pct_change.html

python-3.x

来源：https://stackoverflow.com/questions/75395345/how-to-avoid-bfill-or-ffill-when-calculating-pct-change-with-nans

1条答案

按热度按时间

uoifb46i1#

也许您可以使用diff和shift手动计算pct：

period = 1
pct = df.price.diff().div(df.price.shift(period))
print(pct)

# Output
0    NaN
1    NaN
2    0.3
3    NaN
4    NaN
5    NaN
Name: price, dtype: float64

更新：您可以传递fill_method=None

period = 1
pct = df.price.pct_change(periods=period, fill_method=None)
print(pct)

# Output
0    NaN
1    NaN
2    0.3
3    NaN
4    NaN
5    NaN
Name: price, dtype: float64

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python-3.x 使用NaN计算pct_change时如何避免bfill或fill

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