CODEWITHSUNDEEP
pythonpandasdataframe
Sahil
Sahil

Reputation: 439

Comparing/Mapping different series in different Dataframes

I have two data frames. Dataframe "A" which is the main dataframe has 3 columns "Number", "donation" and "Var1" . Dataframe B has 2 columns "Number" and "location". The "Number" column in DataFrame B is a subset of "Number" in A. What I would like to do is form a new column in DataFrame A - "NEW" which would map the values of numbers in both the column and if its present in DataFrame B would add value as 1 else all other values will be 0.

>>>DFA
Number donation Var1
243     4        45
677     56       34
909     34       22
565     78       24 
568     90       21
784     33       88
787     22       66
>>>DFB
Number location
909     PB
565     WB
784     AU

These are the two dataframes, I want the DFA with a new column which looks something like this.

>>>DFA
Number donation Var1 NEW
243     4        45   0 
677     56       34   0
909     34       22   1
565     78       24   1
568     90       21   0
784     33       88   1
787     22       66   0

This has a new column which has value as 1 if the Number was present in DFB if absent it gives 0.

Upvotes: 1

Views: 42

Answers (1)

unutbu
unutbu

Reputation: 880807

You could use the isin method:

DFA['NEW'] = (DFA['Number'].isin(DFB['Number'])).astype(int)

For example,

import pandas as pd

DFA = pd.DataFrame({'Number': [243, 677, 909, 565, 568, 784, 787],
                    'Var1': [45, 34, 22, 24, 21, 88, 66],
                    'donation': [4, 56, 34, 78, 90, 33, 22]})
DFB = pd.DataFrame({'Number': [909, 565, 784], 'location': ['PB', 'WB', 'AU']})

DFA['NEW'] = (DFA['Number'].isin(DFB['Number'])).astype(int)
print(DFA)

yields

   Number  Var1  donation  NEW
0     243    45         4    0
1     677    34        56    0
2     909    22        34    1
3     565    24        78    1
4     568    21        90    0
5     784    88        33    1
6     787    66        22    0

Upvotes: 2

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