Pandas DataFrame:滚动和扩展功能之间的区别 [英] Pandas DataFrame: difference between rolling and expanding function

问题描述

谁能帮助我从pandas文档中提供的示例中了解滚动功能和扩展功能之间的区别.

Can anyone help me understand the difference between rolling and expanding function from the example given in the pandas docs.

df = DataFrame({'B': [0, 1, 2, np.nan, 4]})
df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0


df.expanding(2).sum()
     B
0  NaN  # 0 + NaN
1  1.0  # 1 + 0
2  3.0  # 2 + 1
3  3.0  # ??
4  7.0  # ?? 

df.rolling(2).sum()
     B
0  NaN  # 0 + NaN
1  1.0  # 1 + 0
2  3.0  # 2 + 1
3  NaN  # NaN + 2
4  NaN  # 4 + NaN

我在每一行中都加注以表明我对计算的理解. rolling函数是否正确? expanding呢?第三和第四行中的3和7来自哪里?

I give comment to each row to show my understanding of the calculation. Is that true for rolling function? What about expanding? Where are 3 and 7 in 3rd and 4th rows coming from?

推荐答案

expanding中的2是min_periods而不是window

df.expanding(min_periods=1).sum()
Out[117]: 
     B
0  0.0
1  1.0
2  3.0
3  3.0
4  7.0

如果希望与rolling window相同的结果将等于数据帧的长度

If you want the same result with rolling window will be equal to the length of dataframe

df.rolling(window=len(df),min_periods=1).sum()
Out[116]: 
     B
0  0.0
1  1.0
2  3.0
3  3.0
4  7.0

这篇关于Pandas DataFrame:滚动和扩展功能之间的区别的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！