Difference of two columns if condition is met
To get the difference between two columns of a pandas dataframe when specifying a condition I'm currently using the following code. For example, to get difference between column A and B for the rows where A > B:
import pandas as pd
df = pd.DataFrame({'A' : [4,5,6,7], 'B' : [1,2,10,11]}); df
A B
0 4 1
1 5 2
2 6 10
3 7 11
df2 = df.loc[df.A > df.B]
df2.A - df2.B
0 3
1 3
Is there a way to "pipe" this instead of creating the temporary dataframe df2 above?
pandas dataframe
asked Nov 16 '18 at 10:43
PedroAPedroA
635823
add a comment |
To get the difference between two columns of a pandas dataframe when specifying a condition I'm currently using the following code. For example, to get difference between column A and B for the rows where A > B:
import pandas as pd
df = pd.DataFrame({'A' : [4,5,6,7], 'B' : [1,2,10,11]}); df
A B
0 4 1
1 5 2
2 6 10
3 7 11
df2 = df.loc[df.A > df.B]
df2.A - df2.B
0 3
1 3
Is there a way to "pipe" this instead of creating the temporary dataframe df2 above?
pandas dataframe
asked Nov 16 '18 at 10:43
PedroAPedroA
635823
add a comment |
To get the difference between two columns of a pandas dataframe when specifying a condition I'm currently using the following code. For example, to get difference between column A and B for the rows where A > B:
import pandas as pd
df = pd.DataFrame({'A' : [4,5,6,7], 'B' : [1,2,10,11]}); df
A B
0 4 1
1 5 2
2 6 10
3 7 11
df2 = df.loc[df.A > df.B]
df2.A - df2.B
0 3
1 3
Is there a way to "pipe" this instead of creating the temporary dataframe df2 above?
pandas dataframe
asked Nov 16 '18 at 10:43
PedroAPedroA
635823
To get the difference between two columns of a pandas dataframe when specifying a condition I'm currently using the following code. For example, to get difference between column A and B for the rows where A > B:
import pandas as pd
df = pd.DataFrame({'A' : [4,5,6,7], 'B' : [1,2,10,11]}); df
A B
0 4 1
1 5 2
2 6 10
3 7 11
df2 = df.loc[df.A > df.B]
df2.A - df2.B
0 3
1 3
Is there a way to "pipe" this instead of creating the temporary dataframe df2 above?
pandas dataframe
pandas dataframe
asked Nov 16 '18 at 10:43
PedroAPedroA
635823
asked Nov 16 '18 at 10:43
PedroAPedroA
635823
asked Nov 16 '18 at 10:43
PedroAPedroA
635823
asked Nov 16 '18 at 10:43
PedroAPedroA
635823
asked Nov 16 '18 at 10:43
PedroAPedroA
635823
635823
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
One method using eval and has high performance while working with huge data:
df.loc[df.A > df.B].eval('A - B')
0 3
1 3
dtype: int64
answered Nov 16 '18 at 10:46
Sandeep KadapaSandeep Kadapa
7,398831
add a comment |
Simply join the two commands and select the columns you want to subtract:
In [2337]: df.loc[df.A > df.B]['A'] - df.loc[df.A > df.B]['B']
Out[2337]:
0 3
1 3
dtype: int64
answered Nov 16 '18 at 10:47
Mayank PorwalMayank Porwal
5,0182725
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
One method using eval and has high performance while working with huge data:
df.loc[df.A > df.B].eval('A - B')
0 3
1 3
dtype: int64
answered Nov 16 '18 at 10:46
Sandeep KadapaSandeep Kadapa
7,398831
add a comment |
One method using eval and has high performance while working with huge data:
df.loc[df.A > df.B].eval('A - B')
0 3
1 3
dtype: int64
answered Nov 16 '18 at 10:46
Sandeep KadapaSandeep Kadapa
7,398831
add a comment |
One method using eval and has high performance while working with huge data:
df.loc[df.A > df.B].eval('A - B')
0 3
1 3
dtype: int64
answered Nov 16 '18 at 10:46
Sandeep KadapaSandeep Kadapa
7,398831
One method using eval and has high performance while working with huge data:
df.loc[df.A > df.B].eval('A - B')
0 3
1 3
dtype: int64
answered Nov 16 '18 at 10:46
Sandeep KadapaSandeep Kadapa
7,398831
edited Nov 16 '18 at 11:15
answered Nov 16 '18 at 10:46
Sandeep KadapaSandeep Kadapa
7,398831
answered Nov 16 '18 at 10:46
Sandeep KadapaSandeep Kadapa
7,398831
answered Nov 16 '18 at 10:46
Sandeep KadapaSandeep Kadapa
7,398831
7,398831
add a comment |
add a comment |
Simply join the two commands and select the columns you want to subtract:
In [2337]: df.loc[df.A > df.B]['A'] - df.loc[df.A > df.B]['B']
Out[2337]:
0 3
1 3
dtype: int64
answered Nov 16 '18 at 10:47
Mayank PorwalMayank Porwal
5,0182725
add a comment |
Simply join the two commands and select the columns you want to subtract:
In [2337]: df.loc[df.A > df.B]['A'] - df.loc[df.A > df.B]['B']
Out[2337]:
0 3
1 3
dtype: int64
answered Nov 16 '18 at 10:47
Mayank PorwalMayank Porwal
5,0182725
add a comment |
Simply join the two commands and select the columns you want to subtract:
In [2337]: df.loc[df.A > df.B]['A'] - df.loc[df.A > df.B]['B']
Out[2337]:
0 3
1 3
dtype: int64
answered Nov 16 '18 at 10:47
Mayank PorwalMayank Porwal
5,0182725
Simply join the two commands and select the columns you want to subtract:
In [2337]: df.loc[df.A > df.B]['A'] - df.loc[df.A > df.B]['B']
Out[2337]:
0 3
1 3
dtype: int64
answered Nov 16 '18 at 10:47
Mayank PorwalMayank Porwal
5,0182725
answered Nov 16 '18 at 10:47
Mayank PorwalMayank Porwal
5,0182725
answered Nov 16 '18 at 10:47
Mayank PorwalMayank Porwal
5,0182725
answered Nov 16 '18 at 10:47
Mayank PorwalMayank Porwal
5,0182725
5,0182725
add a comment |
add a comment |
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