按列/年的移动平均数 - python、pandas

Question

我需要按国家 [noc] 在列 "total_medals" 上建立一个移动平均线，用于所有前几年 - 我的数据如下：

 medal     Bronze  Gold  Medal  Silver  **total_medals**
    noc year                                           
    ALG 1984     2.0   NaN    NaN     NaN           2.0
        1992     4.0   2.0    NaN     NaN           6.0
        1996     2.0   1.0            4.0           7.0
    ANZ 1984     2.0  15.0    NaN     2.0          19.0
        1992     3.0   5.0    NaN     2.0          10.0
        1996     1.0   2.0            2.0           5.0
    ARG 1984     2.0   6.0    NaN     3.0          11.0
        1992     5.0   3.0    NaN    24.0          32.0
        1992     3.0   7.0    NaN     5.0          15.0

我想要每个国家/地区和年份的移动平均值（即对于 ALG：1984 年平均 (total_medals)=2.0；1992 年平均(total_medals) = (2.0+6.0)/2 = 4.0; 1996 Acg(total_medals) = (2.0+6.0+7.0)/3 = 5.0) - 移动平均线应出现在新列中（total_medals 旁边）。

此外，对于每个国家和年份的组合，名为 "performance" 的新列应该是 "total_medals" 除以 "moving average"

的分数

Answer 1

示例数据帧:

print(df)

          medal  Bronze  Gold  Medal  Silver 
noc year                                     
ALG 1984    2.0     NaN   NaN    NaN     2.0 
    1992    4.0     2.0   NaN    NaN     6.0 
    1996    2.0     1.0   NaN    4.0     7.0 
ANZ 1984    2.0    15.0   NaN    2.0    19.0 
    1992    3.0     5.0   NaN    2.0    10.0 
    1996    1.0     2.0   NaN    2.0     5.0 
ARG 1984    2.0     6.0   NaN    3.0    11.0 
    1992    5.0     3.0   NaN   24.0    32.0 
    1992    3.0     7.0   NaN    5.0    15.0

使用DataFrame.groupby + expanding:

df['total_mean']=df.groupby(level=0,sort=False).Silver.apply(lambda x: x.expanding(1).mean())
print(df)

          medal  Bronze  Gold  Medal  Silver  total_medals 
noc year                                                 
ALG 1984    2.0     NaN   NaN    NaN     2.0    2.000000 
    1992    4.0     2.0   NaN    NaN     6.0    4.000000 
    1996    2.0     1.0   NaN    4.0     7.0    5.000000 
ANZ 1984    2.0    15.0   NaN    2.0    19.0   19.000000 
    1992    3.0     5.0   NaN    2.0    10.0   14.500000 
    1996    1.0     2.0   NaN    2.0     5.0   11.333333 
ARG 1984    2.0     6.0   NaN    3.0    11.0   11.000000 
    1992    5.0     3.0   NaN   24.0    32.0   21.500000 
    1992    3.0     7.0   NaN    5.0    15.0   19.333333

bonze 滞后

s=df.groupby('noc').apply(lambda x: x['Bronze']/x['total_medals'].shift())
s.index=s.index.droplevel()
df['bronze_lagged']=s

您可以为此创建一个函数...

def lagged_medals(type_of_medal):
    s=df.groupby('noc').apply(lambda x: x[type_of_medal]/x['total_medals'].shift())
    s.index=s.index.droplevel()
    df[f'{type_of_medal}_lagged']=s

lagged_medals('Silver')
#print(df)

按列/年的移动平均数 - python、pandas

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