将 Pandas 数据框的行转换为列

Question

I have the following pandas dataframe with 4000 rows

A   B   C
xq  34  1
xy  23  1
xt  13  1
xq  55  2
xy  66  2
xt  77  2
xq  12  3
xt  89  3
xq  90  4
xy  45  4
xt  12  4

What would be an efficient method to convert it to a pandas dataframe as shown below?

id  xq  xy  xt
1   34  23  13
2   55  66  77
3   12      89
4   90  45  12

Answer 1

You already have the answer from @sammywemmy.

If you want to remove the multi_level, you can reset index and rename column C to id.

import pandas as pd
x = [['xq',  34,  1],
['xy',  23,  1],
['xt',  13,  1],
['xq',  55,  2],
['xy',  66,  2],
['xt',  77,  2],
['xq',  12,  3],
['xt',  89,  3],
['xq',  90,  4],
['xy',  45,  4],
['xt',  12,  4]]

df = pd.DataFrame(x, columns = ['A','B','C'])

df1 = (df.pivot(index='C', columns='A', values='B')
         .fillna('')
         .reset_index()
         .rename(columns = {'C':'id'}))

print (df1)

The output will be:

A  id    xq    xt    xy
0   1  34.0  13.0  23.0
1   2  55.0  77.0  66.0
2   3  12.0  89.0     
3   4  90.0  12.0  45.0

Note that you didn't have a xy value for 3, so it is a NaN. By adding .fillna(''), the NaN value is set to empty string.