How to update an existing column of a dataframe based on values in another column?

Question

In python, I'm dealing with a sales data of about 21,000 houses. Each house (row) has columns 'sqft_living' (int64), and 'grade' (int64) among others. I want to increase the 'grade' of every house by 2 if its sqft_living > 400, or by 1 otherwise. How do I do this?

Barry the Platipus · Accepted Answer · 2022-07-16 14:33:01Z

1

df = pd.DataFrame()
df['sqft_living'] = range(396, 405)
df['grade'] = range(9)
df

So we have:

sqft_living     grade
0   396     0
1   397     1
2   398     2
3   399     3
4   400     4
5   401     5
6   402     6
7   403     7
8   404     8

We apply the condition:

df['grade'] = df.apply( lambda row: row.grade + 1 if row.sqft_living < 400 else row.grade + 2 , axis=1)
df

New dataframe:

sqft_living     grade
0   396     1
1   397     2
2   398     3
3   399     4
4   400     6
5   401     7
6   402     8
7   403     9
8   404     10

edited Jul 16, 2022 at 14:33

answered Jul 16, 2022 at 13:22

Barry the Platipus

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2 Comments

sj95126 Over a year ago

Just a suggestion, but df['grade'] = range(9) is simpler and more efficient. No need to use a list comprehension.

Barry the Platipus Over a year ago

Thanks @sj95126, you are right. I was concentrating on the actual response - the df.apply lambda.

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