在Python中取消堆栈行标签(透视表)

MarthaF • 4 年前 • 649 次点击

我想使用Python取消对行标签名称的备份。

已清除数据以删除总计行和na行。

代码的外观:

Description   | Table    |  Chair
***Manila***  |          |   
Apple         |     1    |    3
Pair          |     0    |    1
Orange        |     1    |    0
Watermelon    |     0    |    5
Banana        |     0    |    7
***Quezon***  |          |  
DragonFruit   |     0    |    0
StarApple     |     0    |    0
Longan        |     0    |    1
Cherries      |     1    |    2
Mango         |     0    |    5

表格图像:

how raw data looks like

我希望代码看起来像:

Description  |   Day   |    Table  |  Chair
Manila    |    1     |     1    |   3
Manila    |    2     |     0    |   1
Manila    |    3     |     1    |   0
Manila    |    4     |     0    |   5
Manila    |    5     |     0    |   7
Quezon    |    1     |     0    |   0
Quezon    |    2     |     0    |   0
Quezon    |    3     |     0    |   1
Quezon    |    4     |     1    |   2
Quezon    |    5     |     0    |   5

表格图像:

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649 次点击

文章 [ 2 ] | 最新文章 4 年前

• 1 楼

Gustavo Gradvohl 4 年前

如果您的表是pandas数据框,请按下图所示重置索引。

• 2 楼

Scott Boston 4 年前

IIUC,试试这个:

df = pd.DataFrame({'Description':['Manila',1,2,3,4,5,'Quezon',1,2,3,4,5],
                  'Table':['',1,0,1,0,0,'',0,0,0,1,0],
                  'Chair':['',3,1,0,5,7,'',0,0,1,2,5]})

print(df)

输出:

   Description Table Chair
0       Manila            
1            1     1     3
2            2     0     1
3            3     1     0
4            4     0     5
5            5     0     7
6       Quezon            
7            1     0     0
8            2     0     0
9            3     0     1
10           4     1     2
11           5     0     5

仅使用正则表达式从单词创建新列并向前填充:

df['Group'] = df['Description'].str.extract('(\w+)').ffill()

#Drop those "header records"  
df_out = df[df['Description'].str.contains('\w+').isna()]\
           .reindex(['Group','Description','Table','Chair'], axis=1)

print(df_out)

输出:

     Group Description Table Chair
1   Manila           1     1     3
2   Manila           2     0     1
3   Manila           3     1     0
4   Manila           4     0     5
5   Manila           5     0     7
7   Quezon           1     0     0
8   Quezon           2     0     0
9   Quezon           3     0     1
10  Quezon           4     1     2
11  Quezon           5     0     5

#Another way, look for blanks in table or chairs:

 df = pd.DataFrame({'Description':['Manila',1,2,3,4,5,'Quezon',1,2,3,4,5],
                  'Table':[np.nan,1,0,1,0,0,np.nan,0,0,0,1,0],
                  'Chair':[np.nan,3,1,0,5,7,np.nan,0,0,1,2,5]})


m = df['Table'].isna()

df['Group'] = df.loc[m, 'Description']

df['Group'] = df['Group'].ffill()

df_out = df.loc[~m].reindex(['Group','Description','Table','Chair'], axis=1)

输出:

    Group Description  Table  Chair
1   Manila           1    1.0    3.0
2   Manila           2    0.0    1.0
3   Manila           3    1.0    0.0
4   Manila           4    0.0    5.0
5   Manila           5    0.0    7.0
7   Quezon           1    0.0    0.0
8   Quezon           2    0.0    0.0
9   Quezon           3    0.0    1.0
10  Quezon           4    1.0    2.0
11  Quezon           5    0.0    5.0

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