零值的 Pandas 汇总表答案

【问题标题】：Pandas summary table with zero values零值的 Pandas 汇总表
【发布时间】：2020-09-06 14:02:32
【问题描述】：

我正在尝试使用来自 python 的 pandas 的 .describe() 创建一个汇总表。

我有以下数据框：

df = pd.DataFrame({'Group':['Group1', 'Group1', 'Group1', 'Group2', 'Group2', 'Group2', 'Group3', 'Group3', 'Group4'],
'Cat':['Cat1', 'Cat2', 'Cat3', 'Cat4', 'Cat5', 'Cat', 'Cat7', 'Cat8', 'Cat9'],
'Value':[1230,4019,9491,9588,6402,1923,492,8589,8582]})
df

    Group   Cat Value
0   Group1  Cat1    1230
1   Group1  Cat2    4019
2   Group1  Cat3    9491
3   Group2  Cat4    9588
4   Group2  Cat5    6402
5   Group2  Cat     1923
6   Group3  Cat7    492
7   Group3  Cat8    8589
8   Group4  Cat9    8582

我想生成一个按 Group 和 Cat 分组的汇总表，所有不在 Group 中的 Cat 都以相同的方式出现，所有值 = 0。

我正在尝试：

        df.groupby(['Group', 'Cat']).describe()

# That has the following output:
            Value
    count   mean    std min 25% 50% 75% max
    Group   Cat                             
    Group1  Cat1    1.0 1230.0  NaN 1230.0  1230.0  1230.0  1230.0  1230.0
            Cat2    1.0 4019.0  NaN 4019.0  4019.0  4019.0  4019.0  4019.0
            Cat3    1.0 9491.0  NaN 9491.0  9491.0  9491.0  9491.0  9491.0
    Group2  Cat     1.0 1923.0  NaN 1923.0  1923.0  1923.0  1923.0  1923.0
            Cat4    1.0 9588.0  NaN 9588.0  9588.0  9588.0  9588.0  9588.0
            Cat5    1.0 6402.0  NaN 6402.0  6402.0  6402.0  6402.0  6402.0
    Group3  Cat7    1.0 492.0   NaN 492.0   492.0   492.0   492.0   492.0
            Cat8    1.0 8589.0  NaN 8589.0  8589.0  8589.0  8589.0  8589.0
    Group4  Cat9    1.0 8582.0  NaN 8582.0  8582.0  8582.0  8582.0  8582.0

但我想要的输出是：

                Value
    count   mean    std min 25% 50% 75% max
    Group   Cat                             
    Group1  Cat1    1.0 1230.0  NaN 1230.0  1230.0  1230.0  1230.0  1230.0
            Cat2    1.0 4019.0  NaN 4019.0  4019.0  4019.0  4019.0  4019.0
            Cat3    1.0 9491.0  NaN 9491.0  9491.0  9491.0  9491.0  9491.0
            Cat4    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat5    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat6    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat7    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat8    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat9    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
    Group2  Cat     1.0 1923.0  NaN 1923.0  1923.0  1923.0  1923.0  1923.0
            Cat1    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat2    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat3    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat4    1.0 9588.0  NaN 9588.0  9588.0  9588.0  9588.0  9588.0
            Cat5    1.0 6402.0  NaN 6402.0  6402.0  6402.0  6402.0  6402.0
            Cat6    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat7    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat8    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat9    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
    Group3  Cat1    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat2    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat3    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat4    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat5    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat6    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat7    1.0 492.0   NaN 492.0   492.0   492.0   492.0   492.0
            Cat8    1.0 8589.0  NaN 8589.0  8589.0  8589.0  8589.0  8589.0
            Cat9    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
    Group4  Cat1    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat2    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat3    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat4    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat5    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat6    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat7    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat8    0.0 0.0     NaN 0.0     0.0     0.0     0.0     0.0
            Cat9    1.0 8582.0  NaN 8582.0  8582.0  8582.0  8582.0  8582.0

我想知道如何得到这个输出。

【问题讨论】：

标签： python pandas dataframe pandas-groupby

【解决方案1】：

你也可以从你得到的索引和reindex创建一个笛卡尔积索引列表：

out = df.groupby(['Group', 'Cat']).describe()
idx = pd.MultiIndex.from_product((out.index.levels[0],out.index.levels[1]))
out = out.reindex(idx,fill_value=0)

            Value                                                     
            count    mean  std     min     25%     50%     75%     max
Group1 Cat    0.0     0.0  0.0     0.0     0.0     0.0     0.0     0.0
       Cat1   1.0  1230.0  NaN  1230.0  1230.0  1230.0  1230.0  1230.0
       Cat2   1.0  4019.0  NaN  4019.0  4019.0  4019.0  4019.0  4019.0
       Cat3   1.0  9491.0  NaN  9491.0  9491.0  9491.0  9491.0  9491.0
       Cat4   0.0     0.0  0.0     0.0     0.0     0.0     0.0     0.0
       Cat5   0.0     0.0  0.0     0.0     0.0     0.0     0.0     0.0
       Cat7   0.0     0.0  0.0     0.0     0.0     0.0     0.0     0.0
       Cat8   0.0     0.0  0.0     0.0     0.0     0.0     0.0     0.0
       Cat9   0.0     0.0  0.0     0.0     0.0     0.0     0.0     0.0
Group2 Cat    1.0  1923.0  NaN  1923.0  1923.0  1923.0  1923.0  1923.0
       Cat1   0.0     0.0  0.0     0.0     0.0     0.0     0.0     0.0
       Cat2   0.0     0.0  0.0     0.0     0.0     0.0     0.0     0.0
       Cat3   0.0     0.0  0.0     0.0     0.0     0.0     0.0     0.0
       Cat4   1.0  9588.0  NaN  9588.0  9588.0  9588.0  9588.0  9588.0
       Cat5   1.0  6402.0  NaN  6402.0  6402.0  6402.0  6402.0  6402.0
       Cat7   0.0     0.0  0.0     0.0     0.0     0.0     0.0     0.0
       Cat8   0.0     0.0  0.0     0.0     0.0     0.0     0.0     0.0
       Cat9   0.0     0.0  0.0     0.0     0.0     0.0     0.0     0.0
Group3 Cat    0.0     0.0  0.0     0.0     0.0     0.0     0.0     0.0
       Cat1   0.0     0.0  0.0     0.0     0.0     0.0     0.0     0.0
       ....................................
       ...............................

【讨论】：

【解决方案2】：

检查unstack + stack，注意我还建议将行值保持为NaN，不要填0

out = df.groupby(['Group', 'Cat']).describe().unstack().stack(dropna=False)

【讨论】：