CODEWITHSUNDEEP
pythonpython-3.xpandas
goidelg
goidelg

Reputation: 326

Changing some values in df column based on another

I have a df with multiple columns and values. Say:

ID Name Cost
123 Jo $10
345 Bella $20
567 IgnoreMe $5000

I also have a defined list of names to ignore. In this example, it contains one value, but it can have more.

names_to_ignore = ['ignoreme']

The goal is to replace all cost values with null when Name is in the ignore list.

I tried:

    #aligning conventions
    df = df.apply(lambda var: var.lower())
    ignore_set = [x.lower() for x in ignore_set]

    #ignoring
    df.loc[df['Name'] in ignore_set, 'Cost'] = ''

But it didn't work. I get:

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Any thoughts?

Upvotes: 0

Views: 43

Answers (2)

pythondumb
pythondumb

Reputation: 1247

You can try np.where().

 df['cost'] = np.where( (n for n in df['Name'] if n in names_to_ignore), None, df['cost'])

Upvotes: 0

Anurag Dabas
Anurag Dabas

Reputation: 24324

Try:

names_to_ignore = ['ignoreme','IgnoreMe']

Finally:

c=df['Name'].isin(names_to_ignore)  #checking if this condition satisfies or not
df.loc[c,'Cost']=float('NaN')

OR

via np.where():

#import numpy as np
df['Cost']=np.where(c,np.nan,c)

OR

via mask():

df['Cost']=df['Cost'].mask(c)

OR

via where():

df['Cost']=df['Cost'].where(~c)

Upvotes: 1

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