如何将数据框转换为宽格式？

如何解决如何将数据框转换为宽格式？

假设我有一个像这样的pandas DataFrame：

import pandas as pd

data = pd.DataFrame({'header': ['age','height','weight','country','age','bank_id','country'],'values': ['1','6 ft','10 kg','India','2','5 ft','20 kg','A123','3','5.5 ft','30 kg','Japan']})

# display(data)
     header  values
0       age       1
1    height    6 ft
2    weight   10 kg
3   country   India
4       age       2
5    height    5 ft
6    weight   20 kg
7   bank_id    A123
8       age       3
9    height  5.5 ft
10   weight   30 kg
11  country   Japan

现在，我想使用Python转置它们，使它们看起来像这样：

有些行没有数据，它们将保持空白。

我正在尝试使用代码：

data.pivot_table(columns="header",values="values",aggfunc="max")

[out]:
header age bank_id country height weight
values   3    A123   Japan   6 ft  30 kg

但是它没有给出正确的结果。它只显示一行。

解决方法

• 数据透视表无法按预期工作，因为data中的值具有唯一索引。
• 为了使数据透视表正确关联值，组必须共享一个索引。
• 在这种情况下，行可以按4进行分组并进行排序，因此我们可以创建新索引并正确旋转data。
• 这使用assignment expression，:=，并且仅在python 3.8中有效。

import pandas as pd

# set up test dataframe
data = pd.DataFrame({'header': ['age','height','weight','country','age','bank_id','country'],'values': ['1','6 ft','10 kg','India','2','5 ft','20 kg','A123','3','5.5 ft','30 kg','Japan']})

# create a unique index; replace 4 with the real group size
# the associated groups in data,must be consecutive
x = 0
data.index = [x := x+1 if i%4 == 0 else x for i,_ in enumerate(data.index)]

# see the indices are matched for each group compared to the OP
    header  values
1      age       1
1   height    6 ft
1   weight   10 kg
1  country   India
2      age       2
2   height    5 ft
2   weight   20 kg
2  bank_id    A123
3      age       3
3   height  5.5 ft
3   weight   30 kg
3  country   Japan

# create a wide dataframe
wide = data.pivot(columns='header',values='values').reset_index(drop=True)

# header is the .name of columns,to make it nothing
wide.columns.name = None

# display(wide)
age bank_id country  height weight
  1     NaN   India    6 ft  10 kg
  2    A123     NaN    5 ft  20 kg
  3     NaN   Japan  5.5 ft  30 kg

,

一个选项是旋转列，以获取新数据帧的不同列，然后消除每列的NaN值，最后使用pandas.concat函数将它们组合起来：

import pandas as pd

data = pd.DataFrame({'header': ["age","height","weight","bank_id","country","age","country" ],'values': [   "1","6 ft","10 kg","","India","2","5 ft","20 kg","A123","3","5.5 ft","30 kg","Japan" ]})

pvt_data = data.pivot( columns='header',values='values' )
ls_col = list(pvt_data.columns)

ls_cols = []
for col in ls_col:     
    ls_cols.append(pvt_data[col].dropna().reset_index(drop=True,inplace=False))

print( pd.concat([ls_cols[0],ls_cols[1],ls_cols[2],ls_cols[3],ls_cols[4]],axis=1) )

      age bank_id country  height weight
    0   1           India    6 ft  10 kg
    1   2    A123            5 ft  20 kg
    2   3     NaN   Japan  5.5 ft  30 kg