Is there a way to reassign values in a pandas dataframe using the .apply() method?

I have this code:

import pandas as pd
df = pd.DataFrame({'switch': ['ON', 'OFF', 'ON'], 
                   'value': [10, 15, 20]})
print (df, '\n')


def myfunc(row):
    if row['switch']  == 'ON':
        row['value'] = 500
    elif row['switch']  == 'OFF':
        row['value'] = 0

df = df.apply(myfunc, axis=1)
print (df)

The code is not working. I am trying to achieve the following output after running the .apply() method:

  switch  value
0     ON     500
1    OFF     0
2     ON     500 

Why is the "row['value'] = 500" assignment not working and how can I rewrite it to make it work?

CodePudding user response：

its not working because your function needs to return the value. also, you need to assign it back to the dataframe column for it to be present.

def f(row):
  if row['switch'] == 'ON':
    return 500
  elif row['switch'] == 'OFF':
    return 0
df['value'] = df.apply(f, axis=1)

df now has the values:

  switch  value
0     ON    500
1    OFF      0
2     ON    500

one thing to note here is whether switch can have any other values other than ON and OFF.

• if those are the only permitted values, then you may replace the named function with a lambda expression.
• if other values are present, then they will currently be set to None since your if condition block does not handle them. You would need to set a value for every type of switch or a default value to end up with a data frame without None in value

CodePudding user response：

In addition to you not returning the value which is causing the error, I would suggest that you do not use apply() instead use a vectorized version using np.where() which is much faster.

import numpy as np

df['value'] = np.where(df['switch'] == "ON", 500, 0)

CodePudding user response：

you can write if..else in lambda like below:

>>> df['value'] = df['switch'].apply(lambda x : 500 if x == 'ON' else 0)
>>> df
    switch  value
0   ON      500
1   OFF     0
2   ON      500

If you want to write function try this:

def myfunc(x):
    if x  == 'ON':
        return 500
    elif x  == 'OFF':
        return 0
        
df['value'] = df['switch'].apply(myfunc)

You can use np.select and write multi condition like below:

import numpy as np
df['value'] = np.select([df['switch']=='ON',df['switch']=='OFF'], [500,0])