df2 = np.where(df2['color'] != 'blue' | 'red')
I want to create one category for many categorical values, such as:
If the color is not blue or red, call the color "other"
Please and thank you <3
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Progman
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bowen_eldr
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`df['color'] = np.where(df['color'].isin(['blue', 'red']), df['color'], 'Other')` as [jezrael recommends](https://stackoverflow.com/a/51323112/15497888) – Henry Ecker Nov 08 '21 at 20:35
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You are basically halfway there. You just have to provide 2 more parameters to achieve what you want.
df2['color'] = np.where((df2['color'] == 'blue') | (df2['color'] == 'red'), df2['color'], 'other')
Reading the equality is easier because there is less cognitive load. If the condition is True the df2['color'] will be selected. If the condition is false for that row 'other' will be selected
alparslan mimaroğlu
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True. My mind played tricks with me :D Fixed it instantly. Thank you – alparslan mimaroğlu Nov 08 '21 at 20:33
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Thank you! I tried that way because I have WAY more instances that will become "other," and the few rest will remain the original color. – bowen_eldr Nov 08 '21 at 20:36
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Glad I could help. If you can accept the answer I'll appreciate it. – alparslan mimaroğlu Nov 08 '21 at 20:39