How to make a new column with different values using "AND" in pandas?-CodePudding

I have a dataframe,containing values of name and verified columns, i want to see if the conditions meet and it generates a new column with different valuess based on the condition, For eg:

Name	Verified
Mary	Yes
Julie	No
Mary	No

Expected data:

Name	Verified	Identity
Mary	Yes	Bot
Julie	No	Bot
Mary	No	Human

What i have done: I require a field where condition is if name is Mary and verified is No then print Human else Bot,

df['Identity']=df((df['name'] == 'Mary') & (df['Verified'] == 'No)), I am not sure how to print human or bot based on the condition, can anyone please help?Thank you

CodePudding user response：

You were close. Once you correct the structure of your conditional statement, you could do something like map, or use numpy where

df['Identity'] = ((df['Name'].eq('Mary')) & (df['Verified'].eq('No'))).map({True:'Human',False:'Bot'})

Or using numpy where

import numpy as np
df['Identity'] = np.where((df['Name'].eq('Mary')) & (df['Verified'].eq('No')),'Human','Bot')

CodePudding user response：

here is another way to it using np.where

df['Identify']=np.where(df['Name'].eq('Mary') & df['Verified'].eq('No'), 'Human', 'Bot')
df

    Name    Verified    Identify
0   Mary    Yes         Bot
1   Julie   No          Bot
2   Mary    No          Human

CodePudding user response：

You can use masks, it's faster than apply and map methods:

mask = (df['name'] == 'Mary') & (df['Verified'] == 'No')
df.loc[mask,'Identity'] = 'Human'
df.loc[~mask,'Identity'] = 'Bot'

CodePudding user response：

How about this:

df['Identity'] = "Bot"
df.loc[(df['Name'] == "Mary") & (df['Verified'] == "No"), 'Identity'] = "Human"