Python : Scale columns in pandas dataframe

Question

I got a dataframe with several columns. Each of the columns needs to be scaled with individual values and I would like to know if there is any oneliner that will scale the columns appropriate given a dictionary or something else.

Eg. scalingDictionary = {'a': 10, 'b': 5, 'c':0.1} df = pd.Dataframe({'a':[2,4,6,8], 'b':[3,6,9,12], 'c':[1,2,3,4]})

oneliner scaling ... where each column is multiplied with desired value from the dictionary should give the desired output

a    b    c
20   15   0.1
40   30   0.2
60   45   0.3
80   60   0.4

Are keys always same like columns names ?

jezrael
– jezrael

2020-11-30 09:23:28 +00:00
Commented Nov 30, 2020 at 9:23 — jezrael
– jezrael, Commented Nov 30, 2020 at 9:23

jezrael · Accepted Answer · 2020-11-30 09:21:22Z

1

Multiple DataFrame with dictionary, working well if keys are same like columns names:

df = df.mul(scalingDictionary)    
print (df)
      a     b    c
0  20.0  15.0  0.1
1  40.0  30.0  0.2
2  60.0  45.0  0.3
3  80.0  60.0  0.4

If some columns not match:

scalingDictionary = {'a': 10, 'b': 5} 

df = pd.DataFrame({'a':[2,4,6,8], 'b':[3,6,9,12], 'c':[1,2,3,4]})

df = df.mul(pd.Series(scalingDictionary).reindex(df.columns, fill_value=1))
print (df)
    a   b  c
0  20  15  1
1  40  30  2
2  60  45  3
3  80  60  4

Or:

df = df.mul({**dict.fromkeys(df.columns, 1), **scalingDictionary})
print (df)
    a   b  c
0  20  15  1
1  40  30  2
2  60  45  3
3  80  60  4

answered Nov 30, 2020 at 9:21

jezrael

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