我有一個如下所示的數據框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],})我想做的是獲取現有列的描述性統計/匯總形式，而不是擁有原始列。我希望看到 ( min, max, 25%, 75%, std, var) 作為每個主題的新列我嘗試了以下但輸出不準確df.groupby(['subject_id','readings']).describe().reset_index() #this gives some output but it isn't exactdf.groupby(['subject_id','readings']).pivot_table(values='val', index='subject_id', columns='readings').describe() # this throws error我希望我的輸出如下所示?；旧纤鼘⑹且粋€寬而稀疏的矩陣。由于屏幕截圖很寬，我無法進一步放大。如果單擊圖像，您將更好地顯示預期輸出
1 回答
jeck貓
TA貢獻1909條經驗 獲得超7個贊
用于Series.unstackreshape after describe, thenDataFrame.swaplevel和 for order like in original add DataFrame.reindex：
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]
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