为每行值生成描述性统计数据并动态转置

Question

我有一个如下所示的数据框

df = pd.DataFrame({
'subject_id':[1,1,1,1,2,2,2,2,3,3,4,4,4,4,4],
'readings' : ['READ_1','READ_2','READ_1','READ_3','READ_1','READ_5','READ_6','READ_8','READ_10','READ_12','READ_11','READ_14','READ_09','READ_08','READ_07'],
'val' :[5,6,7,11,5,7,16,12,13,56,32,13,45,43,46],
})

我想做的是获取现有列的描述性统计/汇总形式，而不是拥有原始列。我希望看到 (min,max,25%,75%,std,var) 作为每个主题的新列

我尝试了以下，但输出不准确

df.groupby(['subject_id','readings']).describe().reset_index()   #this gives some output but it isn't exact
df.groupby(['subject_id','readings']).pivot_table(values='val', index='subject_id', columns='readings').describe()  # this throws error

我希望我的输出如下所示。基本上它将是一个宽而稀疏的矩阵。由于屏幕截图很宽，我无法进一步放大。如果你点击图片，你会更好的显示预期的输出

Answer 1

在describe 之后使用Series.unstack 进行整形，然后使用DataFrame.swaplevel 并按原始添加DataFrame.reindex 进行排序：

df = (df.groupby(['subject_id','readings'])['val']
        .describe()
        .unstack()
        .swaplevel(0,1,axis=1)
        .reindex(df['readings'].unique(), axis=1, level=0))
df.columns = df.columns.map('_'.join)
df = df.reset_index()
print (df)

   subject_id  READ_1_count  READ_1_mean  READ_1_std  READ_1_min  READ_1_25%  \
0           1           2.0          6.0    1.414214         5.0         5.5   
1           2           1.0          5.0         NaN         5.0         5.0   
2           3           NaN          NaN         NaN         NaN         NaN   
3           4           NaN          NaN         NaN         NaN         NaN   

   READ_1_50%  READ_1_75%  READ_1_max  READ_2_count  ...  READ_08_75%  \
0         6.0         6.5         7.0           1.0  ...          NaN   
1         5.0         5.0         5.0           NaN  ...          NaN   
2         NaN         NaN         NaN           NaN  ...          NaN   
3         NaN         NaN         NaN           NaN  ...         43.0   

   READ_08_max  READ_07_count  READ_07_mean  READ_07_std  READ_07_min  \
0          NaN            NaN           NaN          NaN          NaN   
1          NaN            NaN           NaN          NaN          NaN   
2          NaN            NaN           NaN          NaN          NaN   
3         43.0            1.0          46.0          NaN         46.0   

   READ_07_25%  READ_07_50%  READ_07_75%  READ_07_max  
0          NaN          NaN          NaN          NaN  
1          NaN          NaN          NaN          NaN  
2          NaN          NaN          NaN          NaN  
3         46.0         46.0         46.0         46.0  

[4 rows x 105 columns]