I am looking to update the values in a pandas series that satisfy a certain condition and take the corresponding value from another column.
Specifically, I want to look at the subcluster
column and if the value equals 1, I want the record to update to the corresponding value in the cluster
column.
For example:
Cluster | Subcluster |
---|---|
3 | 1 |
3 | 2 |
3 | 1 |
3 | 4 |
4 | 1 |
4 | 2 |
Should result in this
Cluster | Subcluster |
---|---|
3 | 3 |
3 | 2 |
3 | 3 |
3 | 4 |
4 | 4 |
4 | 2 |
I've been trying to use apply and a lambda function, but can't seem to get it to work properly. Any advice would be greatly appreciated. Thanks!
You can use np.where
:
import numpy as np
df['Subcluster'] = np.where(df['Subcluster'].eq(1), df['Cluster'], df['Subcluster'])
Output:
Cluster Subcluster
0 3 3
1 3 2
2 3 3
3 3 4
4 4 4
5 4 2
In your case try mask
df.Subcluster.mask(lambda x : x==1, df.Cluster,inplace=True)
df
Out[12]:
Cluster Subcluster
0 3 3
1 3 2
2 3 3
3 3 4
4 4 4
5 4 2
Or
df.loc[df.Subcluster==1,'Subcluster'] = df['Cluster']
Really all you need here is to use .loc with a mask (you don't actually need to create the mask, you could apply a mask inline)
df = pd.DataFrame({'cluster':np.random.randint(0,10,10)
,'subcluster':np.random.randint(0,3,10)}
)
df.to_clipboard(sep=',')
df
at this point
,cluster,subcluster
0,8,0
1,5,2
2,6,2
3,6,1
4,8,0
5,1,1
6,0,0
7,6,0
8,1,0
9,3,1
create and apply the mask (you could do this all in one line)
mask = df.subcluster == 1
df.loc[mask,'subcluster'] = df.loc[mask,'cluster']
df.to_clipboard(sep=',')
final output:
,cluster,subcluster
0,8,0
1,5,2
2,6,2
3,6,6
4,8,0
5,1,1
6,0,0
7,6,0
8,1,0
9,3,3
Here's the lambda you couldn't write. In lamba, x
corresponds to the index, so you can use that to refer a specific row in a column.
df['Subcluster'] = df.apply(lambda x: x['Cluster'] if x['Subcluster'] == 1 else x['Subcluster'], axis = 1)
And the output:
Cluster Subcluster
0 3 3
1 3 2
2 3 3
3 3 4
4 4 4
5 4 2
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