CODEWITHSUNDEEP
pythonpandasdatedatetimemerge
ash90
ash90

Reputation: 123

Merge two dataframes (different sizes) based on date time index

I have two data frames, let's say, df_1 with shape (2000*4) and df_2 with shape (69*4). The data for df_1 are available per minute for 2000 minutes, however, data for df_2 are available only on certain minutes (69 data points spread over 2000 minutes). I want to merge them based on the DateTime index such that I get a final data frame of shape (2000*8).

df_1

Datetime                      X1  X2  X3  X4

15/1/2020 08:01:00            1   2   3  4
15/1/2020 08:02:00            5   6   7  8 
15/1/2020 08:03:00            9   10  11 12
15/1/2020 08:04:00            13  14  15 16
.
.
15/1/2020 23:59:00            17  18  19 20

df_2 

Datetime                        Y1  Y2  Y3  Y4

15/1/2020 08:01:00               A  B   C   D
15/1/2020 09:30:00               E  F   G   H
15/1/2020 15:03:00               I  J   K   L
15/1/2020 18:04:00
.
.
15/1/2020 23:59:00               M  N   O   p

output 

Datetime                        X1  X2  X3  X4 Y1  Y2  Y3  Y4

15/1/2020 08:01:00              1   2   3  4  A  B   C   D
15/1/2020 08:02:00              5   6   7  8  Nan Nan Nan NAn
15/1/2020 08:03:00              9  10   11 12 Nan Nan Nan nan
15/1/2020 08:04:00
15/1/2020 09:30:00
15/1/2020 15:03:00
15/1/2020 18:04:00              
.
.
15/1/2020 23:59:00              17  18  19 20  M  N   O   p

Upvotes: 0

Views: 1252

Answers (1)

Celius Stingher
Celius Stingher

Reputation: 18367

You can perform a join or concat. Since join is in the comments, I'll use pd.concat():

final_df = pd.concat([df_1,df_2],axis=1,join='outer')

Here's an example:

import pandas as pd
df1 = pd.DataFrame({'index':['A','B','C','D','E','F'],"A":[1,2,3,4,5,6]}).set_index('index')
df2 = pd.DataFrame({'index':['B','D','F'],"B":[20,30,40]}).set_index('index')

df_output = pd.concat([df1,df2],axis=1,join='outer')

Output:

    A   B
A   1   NaN
B   2   20.0
C   3   NaN
D   4   30.0
E   5   NaN
F   6   40.0

Upvotes: 2

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