merging multiple columns into one columns in pandas

问题

I have a dataframe called ref(first dataframe) with columns c1, c2 ,c3 and c4.

ref= pd.DataFrame([[1,3,.3,7],[0,4,.5,4.5],[2,5,.6,3]], columns=['c1','c2','c3','c4'])
print(ref)
   c1  c2   c3   c4
0   1   3  0.3  7.0
1   0   4  0.5  4.5
2   2   5  0.6  3.0

I wanted to create a new column i.e, c5 ( second dataframe) that has all the values from columns c1,c2,c3 and c4.

I tried concat, merge columns but i cannot get it work.

Please let me know if you have a solutions?

回答1:

You can use unstack for creating Series from DataFrame and then concat to original:

print (pd.concat([ref, ref.unstack().reset_index(drop=True).rename('c5')], axis=1))
     c1   c2   c3   c4   c5
0   1.0  3.0  0.3  7.0  1.0
1   0.0  4.0  0.5  4.5  0.0
2   2.0  5.0  0.6  3.0  2.0
3   NaN  NaN  NaN  NaN  3.0
4   NaN  NaN  NaN  NaN  4.0
5   NaN  NaN  NaN  NaN  5.0
6   NaN  NaN  NaN  NaN  0.3
7   NaN  NaN  NaN  NaN  0.5
8   NaN  NaN  NaN  NaN  0.6
9   NaN  NaN  NaN  NaN  7.0
10  NaN  NaN  NaN  NaN  4.5
11  NaN  NaN  NaN  NaN  3.0

Alternative solution for creating Series is convert df to numpy array by values and then reshape by ravel:

    print (pd.concat([ref, pd.Series(ref.values.ravel('F'), name='c5')], axis=1))
         c1   c2   c3   c4   c5
    0   1.0  3.0  0.3  7.0  1.0
    1   0.0  4.0  0.5  4.5  0.0
    2   2.0  5.0  0.6  3.0  2.0
    3   NaN  NaN  NaN  NaN  3.0
    4   NaN  NaN  NaN  NaN  4.0
    5   NaN  NaN  NaN  NaN  5.0
    6   NaN  NaN  NaN  NaN  0.3
    7   NaN  NaN  NaN  NaN  0.5
    8   NaN  NaN  NaN  NaN  0.6
    9   NaN  NaN  NaN  NaN  7.0
    10  NaN  NaN  NaN  NaN  4.5
    11  NaN  NaN  NaN  NaN  3.0

回答2:

using join + ravel('F')

ref.join(pd.Series(ref.values.ravel('F')).to_frame('c5'), how='right')

using join + T.ravel()

ref.join(pd.Series(ref.values.T.ravel()).to_frame('c5'), how='right')

pd.concat + T.stack() + rename

pd.concat([ref, ref.T.stack().reset_index(drop=True).rename('c5')], axis=1)

way too many transposes + append

ref.T.append(ref.T.stack().reset_index(drop=True).rename('c5')).T

combine_first + ravel('F') <--- my favorite

ref.combine_first(pd.Series(ref.values.ravel('F')).to_frame('c5'))

All yield

     c1   c2   c3   c4   c5
0   1.0  3.0  0.3  7.0  1.0
1   0.0  4.0  0.5  4.5  0.0
2   2.0  5.0  0.6  3.0  2.0
3   NaN  NaN  NaN  NaN  3.0
4   NaN  NaN  NaN  NaN  4.0
5   NaN  NaN  NaN  NaN  5.0
6   NaN  NaN  NaN  NaN  0.3
7   NaN  NaN  NaN  NaN  0.5
8   NaN  NaN  NaN  NaN  0.6
9   NaN  NaN  NaN  NaN  7.0
10  NaN  NaN  NaN  NaN  4.5
11  NaN  NaN  NaN  NaN  3.0

回答3:

use the list(zip()) as follows:

d=list(zip(df1.c1,df1.c2,df1.c3,df1.c4))
df2['c5']=pd.Series(d)

回答4:

try this one, works as you expected import numpy as np import pandas as pd

df = pd.DataFrame([[1,2,3,4],[2,3,4,5],[3,4,5,6]], columns=['c1','c2','c3','c4'])
print(df)
r = len(df['c1'])
c = len(list(df))

ndata = list(df.c1) + list(df.c2) + list(df.c3) + list(df.c4)
r = len(ndata) - r
t = r*c
dfnan = pd.DataFrame(np.reshape([np.nan]*t, (r,c)), columns=list(df))
df = df.append(dfnan)
df['c5'] = ndata
print(df)

output is below

回答5:

This could be a fast option and maybe you can use it inside a loop.

import numpy as np

import pandas as pd

df = pd.DataFrame([[1,2,3,4],[2,3,4,5],[3,4,5,6]], columns=['c1','c2','c3','c4'])

df['c5'] = df.iloc[:,0].astype(str) + df.iloc[:,1].astype(str) + df.iloc[:,2].astype(str) + df.iloc[:,3].astype(str)

Greetings

来源：https://stackoverflow.com/questions/41627678/merging-multiple-columns-into-one-columns-in-pandas

标签

python-3.x

pandas

stack

concat