Lets say i have a dataframe df with columns a, b like below: a b 1 4 2 5 3 6
Lets assume we have similar function which returns 2 values
fun calc(a, b, type):
if type=='both':
c=a+b
d=a-b
return c, d
how to store the returned values to a new column in dataframe df i tried below syntax, but getting error mentioning
df[['c', 'd']] = df['a', 'b'].apply(calc, type='both', axis=1)
but getting error as calc got an unexpected keyword argument 'axis'
You use the following code
df[['c', 'd']] = df['a', 'b'].apply(calc, type='both', axis=1)
There are several issues:
df[['a', 'b']].pandas.DataFrame.apply() doesn't have type argument.axis=1, calc should only take one argument which is the row of dataframe.If you want to pass extra arguments to calc, you can do in two ways
args argument of apply()def calc(row, type):
a = row['a']
b = row['b']
if type=='both':
c = a+b
d = a-b
return c, d
df[['c', 'd']] = df[['a', 'b']].apply(calc, args=('both',), axis=1, result_type='expand')
def calc(row, type):
a = row['a']
b = row['b']
if type=='both':
c = a+b
d = a-b
return c, d
df[['c', 'd']] = df[['a', 'b']].apply(lambda row: calc(row, 'both'), axis=1, result_type='expand')
At last, since type is a built-in function of Python. You'd better not use it as variable name.
A MRE
import pandas as pd
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
def calc(row, type):
a = row['a']
b = row['b']
if type=='both':
c = a+b
d = a-b
return c, d
df[['c', 'd']] = df[['a', 'b']].apply(lambda row: calc(row, 'both'), axis=1, result_type='expand')
df[['e', 'f']] = df[['a', 'b']].apply(calc, args=('both',), axis=1, result_type='expand')
print(df)
a b c d e f
0 1 4 5 -3 5 -3
1 2 5 7 -3 7 -3
2 3 6 9 -3 9 -3
defnotfunin python. I think you also meantdf[['a', 'b']].apply(calc...to select those two columns.typeas a keyword argument for your function astypeis a built-in function in Python.