Add columns to dataframe based on a dictionary-CodePudding

If have a dataframe like this:

df = pd.DataFrame({ 
'ID': ['1', '4', '4', '3', '3', '3'], 
'club': ['arts', 'math', 'theatre', 'poetry', 'dance', 'cricket']
})

and I have a dictionary named tag_dict:

{'1': {'Granted'},
 '3': {'Granted'}}

The keys of the dictionary match with some IDs in the ID column on data frame. Now, I want to create a new column "Tag" in Dataframe such that

If a value in the ID column matches with the keys of a dictionary, then we have to place the value of that key in the dictionary else place '-' in that field

The output should look like this:

df = PD.DataFrame({ 
'ID': ['1', '4', '4', '3', '3', '3'], 
'club': ['arts', 'math', 'theatre', 'poetry', 'dance', 'cricket'],
'tag':['Granted','-','-','Granted','Granted','Granted']
})

CodePudding user response：

I'm not sure what the purpose of the curly brackets arount Granted is but you could use apply:

df = pd.DataFrame({ 
'ID': ['1', '4', '4', '3', '3', '3'], 
'club': ['arts', 'math', 'theatre', 'poetry', 'dance', 'cricket']
})

tag_dict = {'1': 'Granted',
 '3': 'Granted'}

df['tag'] = df['ID'].apply(lambda x: tag_dict.get(x, '-'))
print(df)

Output:

  ID     club      tag
0  1     arts  Granted
1  4     math        -
2  4  theatre        -
3  3   poetry  Granted
4  3    dance  Granted
5  3  cricket  Granted

CodePudding user response：

Solution with .map:

df["tag"] = df["ID"].map(dct).apply(lambda x: "-" if pd.isna(x) else [*x][0])
print(df)

Prints:

  ID     club      tag
0  1     arts  Granted
1  4     math        -
2  4  theatre        -
3  3   poetry  Granted
4  3    dance  Granted
5  3  cricket  Granted

CodePudding user response：

import pandas as pd
df = pd.DataFrame({ 
'ID': ['1', '4', '4', '3', '3', '3'], 
'club': ['arts', 'math', 'theatre', 'poetry', 'dance', 'cricket']})
        
# I've removed the {} around your items. Feel free to add more key:value pairs
my_dict = {'1': 'Granted', '3': 'Granted'}
        
# use .map() to match your keys to their values
df['Tag'] = df['ID'].map(my_dict)
        
# if required, fill in NaN values with '-'
nan_rows = df['Tag'].isna()
df.loc[nan_rows, 'Tag'] = '-'
df

End result: