合并和拆分熊猫数据框中的列

Question

I want to know how to merge multiple columns, and split them again.

Input data

A B C
1 3 5
2 4 6

Merge A, B, C to one column X

X
1
2
3
4
5
6

Process something with X, then split X into A, B, C again. The number of rows for A, B, C is same(2).

A B C
1 3 5
2 4 6

Is there any simple way for this work?

Answer 1

Start with df:

   A  B  C
0  1  3  5
1  2  4  6

Next, get all values in one column:

df2 = df.unstack().reset_index(drop=True).rename('X').to_frame()

print(df2)

   X
0  1
1  2
2  3
3  4
4  5
5  6

And, convert back to original shape:

df3  = pd.DataFrame(df2.values.reshape(2,-1, order='F'), columns=list('ABC'))
print(df3)

   A  B  C
0  1  3  5
1  2  4  6

Answer 2

Setup

df=pd.DataFrame({'A': {0: 1, 1: 2}, 'B': {0: 3, 1: 4}, 'C': {0: 5, 1: 6}})

df
Out[684]: 
   A  B  C
0  1  3  5
1  2  4  6

Solution

Merge df to 1 column:

df2 = pd.DataFrame(df.values.flatten('F'),columns=['X'])
Out[686]: 
   X
0  1
1  2
2  3
3  4
4  5
5  6

Split it back to 3 columns:

pd.DataFrame(df2.values.reshape(-1,3,order='F'),columns=['A','B','C'])
Out[701]: 
   A  B  C
0  1  3  5
1  2  4  6

Answer 3

un unwind in the way you'd like, you need to either unstack or ravel with order='F'

Option 1

def proc1(df):
    v = df.values
    s = v.ravel('F')
    s = s * 2
    return pd.DataFrame(s.reshape(v.shape, order='F'), df.index, df.columns)

proc1(df)

   A  B   C
0  2  6  10
1  4  8  12

Option 2

def proc2(df):
    return df.unstack().mul(2).unstack(0)


proc2(df)

   A  B   C
0  2  6  10
1  4  8  12