Recall individual datrames from a concatentaed dataframe in Python

Question

I have two dataframes as such:

#Load the required libraries
import pandas as pd
import numpy as np

#Create dataset-1
A_1 = {'id': [1, 1, 1, 1,
               2, 2,
               3, 3, 3, 3, 3, 3,
               4, 4, 4, ],
        'cycle': [1,2, 3, 4,
                  1,2, 
                  1,2, 3, 4, 5,6,
                  1,2, 3, ],
        'Salary': [7,9,10,11,
                   4, 5,
                   8,9,10,11,12,13,
                   7, 7,9,],
        'Days': [123, 128, 141, 52,
                 96, 120,
                 15,123, 128, 66, 120, 141,
                 123, 128, 66, ],
        }

#Convert to dataframe-1
A_1 = pd.DataFrame(A_1)
print("\n A_1 = \n", A_1)


#Create dataset-2
A_2 = {'id': [1, 1, 1, 1,
               2, 2,
               3, 3, 3, 
               4, 4, 4, ],
        'cycle': [1,2, 3, 4,
                  1,2, 
                  1,2, 3, 
                  1,2, 3, ],
        'Salary': [8,9,10,12,
                   4, 5,
                   8,9,14,
                   4, 8,9,],
        'Days': [98, 128, 141, 52,
                 1, 5,
                 15,99, 7, 
                 6, 128, 66, ],
        }

#Convert to dataframe-2
A_2 = pd.DataFrame(A_2)
print("\n A_2 = \n", A_2)

The above two dataframes apear as such:

I have concatenated the above dataframes as such:

#Concatenate A_1 and A_2
A_concat = pd.concat([A_1, A_2], axis=0)
print("\n A_concat = \n", A_concat)

The concatenated dataframe appears as such:

Now, I need to Recall A_1 and A_2 from A_concat (Refer above figure). I tried something as such:

#Recall A_1 
A_1_recall = A_concat[1]
print("\n A_1_recall = \n", A_1_recall)


#Recall A__2
A_2_recall = A_concat[2]
print("\n A_2_recall = \n", A_2_recall)

However, I don't get the desired output.

I expect that A_1_recall should give me the elements as that in A_1 and A_2_recall should give me the elements as that in A_2, as such:

Can somebody please let me know how do I achieve this task in Python?

Answer 1

You can provide a set of keys when doing pd.concat():

A_concat = pd.concat([A_1, A_2], axis=0, keys=[1,2])

A_concat now looks like this:

      id  cycle  Salary  Days
1 0    1      1       7   123
  1    1      2       9   128
  2    1      3      10   141
  3    1      4      11    52
  4    2      1       4    96
  5    2      2       5   120
  6    3      1       8    15
  7    3      2       9   123
  8    3      3      10   128
  9    3      4      11    66
  10   3      5      12   120
  11   3      6      13   141
  12   4      1       7   123
  13   4      2       7   128
  14   4      3       9    66
2 0    1      1       8    98
  1    1      2       9   128
  2    1      3      10   141
  3    1      4      12    52
  4    2      1       4     1
  5    2      2       5     5
  6    3      1       8    15
  7    3      2       9    99
  8    3      3      14     7
  9    4      1       4     6
  10   4      2       8   128
  11   4      3       9    66

Now your A_1_recall and A_2_recall can be initiated like so:

A_1_recall = A_concat.loc[1,:]
A_2_recall = A_concat.loc[2,:]

A_1_recall:

     id  cycle  Salary  Days
0    1      1       7   123
1    1      2       9   128
2    1      3      10   141
3    1      4      11    52
4    2      1       4    96
5    2      2       5   120
6    3      1       8    15
7    3      2       9   123
8    3      3      10   128
9    3      4      11    66
10   3      5      12   120
11   3      6      13   141
12   4      1       7   123
13   4      2       7   128
14   4      3       9    66

A_2_recall:

     id  cycle  Salary  Days
0    1      1       8    98
1    1      2       9   128
2    1      3      10   141
3    1      4      12    52
4    2      1       4     1
5    2      2       5     5
6    3      1       8    15
7    3      2       9    99
8    3      3      14     7
9    4      1       4     6
10   4      2       8   128
11   4      3       9    66