CODEWITHSUNDEEP
pandasdataframesplitgroup-by
devu1729
devu1729

Reputation: 13

How to divide the dataframe in pandas into multiple dataframes based on the group by results?

      Date_A                Date_B        Date_C                Amount
0   09/01/2016            09/01/2016    01/01/1800           2405814.36   
1   09/01/2016            09/01/2016    09/01/2016          11347445.71   
2   09/01/2016            10/01/2016    01/01/1800           5005106.94   
3   09/01/2016            10/01/2016    09/02/2016            391679.95   
4   09/01/2016            10/01/2016    10/01/2016          15982401.76   
5   09/01/2016            11/01/2016    01/01/1800           3625649.29   
6   09/01/2016            11/01/2016    10/14/2016            200730.30   
7   09/01/2016            11/01/2016    11/01/2016           3801867.77   
8   09/01/2016            01/01/2017    01/01/2017            398961.22   
9   10/01/2016            10/01/2016    01/01/1800           1357447.92   
10  10/01/2016            10/01/2016    10/01/2016          12365703.34   
11  10/01/2016            11/01/2016    01/01/1800           2783979.14   
12  10/01/2016            11/01/2016    10/21/2016             67891.65   
13  10/01/2016            11/01/2016    11/01/2016          10863491.98   
14  10/01/2016            12/01/2016    01/01/1800           1005897.36   
15  10/01/2016            12/01/2016    12/01/2016           1757775.53   
16  10/01/2016            01/01/2017    01/01/1800            394251.89   
17  10/01/2016            01/01/2017    12/09/2016            518831.09   
18  10/01/2016            02/01/2017    02/01/2017             29021.55   
19  11/01/2016            11/01/2016    01/01/1800          16732269.88   
20  11/01/2016            11/01/2016    11/01/2016          24903566.09   
21  11/01/2016            12/01/2016    01/01/1800          19229021.72   
22  11/01/2016            12/01/2016    12/01/2016          16198633.56   
23  11/01/2016            12/01/2016    12/20/2016            119524.82   
24  11/01/2016            01/01/2017    01/01/1800           8032009.03   
25  11/01/2016            01/01/2017    01/01/2017           1407168.25   
26  11/01/2016            02/01/2017    02/01/2017            350913.09   
27  12/01/2016            12/01/2016    01/01/1800          11399137.45   
28  12/01/2016            12/01/2016    12/01/2016          10973693.60   
29  12/01/2016            12/01/2016    12/06/2016            144625.55   
30  12/01/2016            12/09/2016    12/09/2016            134749.29   
31  12/01/2016            12/16/2016    12/16/2016            325738.94   
32  12/01/2016            01/01/2017    01/01/1800          12583630.38   
33  12/01/2016            01/01/2017    01/01/2017          16438291.27   
34  12/01/2016            02/01/2017    01/01/1800           6681684.58   
35  12/01/2016            02/01/2017    02/01/2017           1367619.74   
36  01/01/2017            01/01/2017    01/01/1800           5220981.26   
37  01/01/2017            01/01/2017    01/01/2017          13408941.99   
38  01/01/2017            02/01/2017    01/01/1800           4085550.81   
39  01/01/2017            02/01/2017    02/01/2017          11957184.41   
40  01/01/2017            03/01/2017    01/01/1800            203253.80   
41  02/01/2017            02/01/2017    01/01/1800           6027172.13   
42  02/01/2017            02/01/2017    02/01/2017           6795843.54   
43  02/01/2017            03/01/2017    01/01/1800            197842.86

This is the original dataframe which has to be divided into 6 dataframes by performing group by on Date_A. The result should output 6 dataframes with Date_A values being 09/01/2016,10/01/2016,11/01/2016,12/01/2016,01/01/2017 and 02/01/2017.

Upvotes: 1

Views: 1085

Answers (1)

jezrael
jezrael

Reputation: 862591

You can use groupby for create dictionary of DataFrames:

d = dict(tuple(df.groupby('Date_A')))
print (d['09/01/2016'])
       Date_A      Date_B      Date_C       Amount
0  09/01/2016  09/01/2016  01/01/1800   2405814.36
1  09/01/2016  09/01/2016  09/01/2016  11347445.71
2  09/01/2016  10/01/2016  01/01/1800   5005106.94
3  09/01/2016  10/01/2016  09/02/2016    391679.95
4  09/01/2016  10/01/2016  10/01/2016  15982401.76
5  09/01/2016  11/01/2016  01/01/1800   3625649.29
6  09/01/2016  11/01/2016  10/14/2016    200730.30
7  09/01/2016  11/01/2016  11/01/2016   3801867.77
8  09/01/2016  01/01/2017  01/01/2017    398961.22

EDIT:

For looping use items():

for i, df in d.items():
    print (i)
    print (df)

Upvotes: 1

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