Take this table with two colums:

Made using this:

import pandas as pd

df = pd.DataFrame({
    'Fruit': ["Apple", "Orange", "Apple", "Apple", "Kiwi", "Banana", "Apple", "Orange", "Orange", "Banana"],
    'First Occurence?': ["Yes"]*10
})

How can I efficiently change values of the second column where the value in the first column has already appeared at least once? The desired result being this:

• It can't be a loop as this takes too long
• The rows must end up in the same order they started

Thanks in advance!

CodePudding user response：

you can use duplicated to identify the duplicate values and then using mask, assign a NO

# duplicated Fruits will come up as False, and First_occurrence be made to No
df['First Occurence?']= df['First Occurence?'].mask(df.duplicated('Fruit'),'No')

    Fruit   First Occurence?
0   Apple   Yes
1   Orange  Yes
2   Apple   No
3   Apple   No
4   Kiwi    Yes
5   Banana  Yes
6   Apple   No
7   Orange  No
8   Orange  No
9   Banana  No

CodePudding user response：

Use duplicated and numpy.where:

df['First Occurence?'] = np.where(df['Fruit'].duplicated(), 'No', 'Yes')

More generic alternative with groupby.cumcount that enable to select any nth item (0-based):

df['First Occurence?'] = np.where(df.groupby('Fruit').cumcount().eq(0), 'Yes', 'No')

output:

    Fruit First Occurence?
0   Apple              Yes
1  Orange              Yes
2   Apple               No
3   Apple               No
4    Kiwi              Yes
5  Banana              Yes
6   Apple               No
7  Orange               No
8  Orange               No
9  Banana               No