Get the differences from two dataframes
Question:
I have the following dataframe:
country
coin
USA
coin1
USA
coin2
Mexico
coin3
Each coin is unique, and it can change the country. For example:
country
coin
USA
coin1
Mexico
coin2
Mexico
coin3
What I’m trying to find is a way to see which lines have changed. My desired output:
country
coin
Mexico
Coin2
Answers:
You could use concat to combine them, and then use drop_duplicates to get the difference. For example:
concat([df1,df2]).drop_duplicates(keep=False)
EDIT:
To get just the one row, you can get the negation of everything common between the two dataframes by turning applying list to them and using .isin to find commonalities.
df1[~df1.apply(list,1).isin(df2.apply(list,1))]
I have the following dataframe:
| country | coin |
|---|---|
| USA | coin1 |
| USA | coin2 |
| Mexico | coin3 |
Each coin is unique, and it can change the country. For example:
| country | coin |
|---|---|
| USA | coin1 |
| Mexico | coin2 |
| Mexico | coin3 |
What I’m trying to find is a way to see which lines have changed. My desired output:
| country | coin |
|---|---|
| Mexico | Coin2 |
You could use concat to combine them, and then use drop_duplicates to get the difference. For example:
concat([df1,df2]).drop_duplicates(keep=False)
EDIT:
To get just the one row, you can get the negation of everything common between the two dataframes by turning applying list to them and using .isin to find commonalities.
df1[~df1.apply(list,1).isin(df2.apply(list,1))]