Changing structure of pandas dataframe

Question

Is there a function that can swap between the following dataframes(df1,df2):

import random
import pandas as pd
numbers = random.sample(range(1,50), 10)
d = {'num': list(range(1,6)) + list(range(1,6)),'values':numbers,'type':['a']*5 + ['b']*5}
df = pd.DataFrame(d)

e = {'num': list(range(1,6)) ,'a':numbers[:5],'b':numbers[5:]}
df2 = pd.DataFrame(e)

Dataframe df1:

   #df1
   num type  values
0    1    a      18
1    2    a      26
2    3    a      34
3    4    a      21
4    5    a      48
5    1    b       1
6    2    b      19
7    3    b      36
8    4    b      42
9    5    b      30

Dataframe df2:

    a   b  num
0  18   1    1
1  26  19    2
2  34  36    3
3  21  42    4
4  48  30    5

I take the first df and the type column becomes a type name with the variables.Is there a function that can do this(from df1 to df2) and the vice-verca action(from df2 to df1)

jezrael · Accepted Answer · 2016-02-25 10:15:36Z

You can use stack and pivot:

print df
   num type  values
0    1    a      20
1    2    a      25
2    3    a       2
3    4    a      27
4    5    a      29
5    1    b      39
6    2    b      40
7    3    b       6
8    4    b      17
9    5    b      47
print df2
    a   b  num
0  20  39    1
1  25  40    2
2   2   6    3
3  27  17    4
4  29  47    5

df1 = df2.set_index('num').stack().reset_index()
df1.columns = ['num','type','values']
df1 = df1.sort_values('type')
print df1
   num type  values
0    1    a      20
2    2    a      46
4    3    a      21
6    4    a      33
8    5    a      10
1    1    b      45
3    2    b      39
5    3    b      38
7    4    b      37
9    5    b      34

df3 = df.pivot(index='num', columns='type', values='values').reset_index()
df3.columns.name = None
df3 = df3[['a','b','num']]
print df3
    a   b  num
0  46  23    1
1  38   6    2
2  36  47    3
3  33  34    4
4  15   1    5

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