Create Dummy variables from multiple variables in python

Question

I am having trouble to create dummy variables from a dataset like this one:

x = pd.DataFrame({'Temp':['Hot','Cold','Warm','Cold'],'Temp_2':[np.nan,'Warm','Cold',np.nan]

Note that the values are the same in both variables (Hot, Cold or Warm).

    Temp    Temp_2
0   Hot     NaN
1   Cold    Warm
2   Warm    Cold
3   Cold    NaN

So my problem is when using pd.get_dummies, the function does not take into consideration this relationship and codifies both variables independently.

    Temp_Cold   Temp_Hot    Temp_Warm   Temp_2_Cold      Temp_2_Warm
0       0           1           0              0               0
1       1           0           0              0               1
2       0           0           1              1               0
3       1           0           0              0               0

Is there a way I can codify it so i can get it like this?

    Cold    Hot Warm
0     0      1    0
1     1      0    1
2     1      0    1
3     1      0    0

Thanks,

One hot encoding means there should be only one 1 and remaining values 0 in a row for that column. Your logic is not suited using get_dummies() — Prakash Dahal
– Prakash Dahal, Commented Jul 1, 2021 at 7:32
I am wondering what will happen if there are 2 same values like Hot and Hot in a same row in that x dataframe? — Prakash Dahal
– Prakash Dahal, Commented Jul 1, 2021 at 7:51
Hi Prakash, actually the database i am using has up to four variables and cannot have a value duplicated in the same row. — Bernat
– Bernat, Commented Jul 1, 2021 at 8:07

aurelien_morel · Accepted Answer · 2021-07-01 07:38:08Z

1

You can do something like this:

x = pd.DataFrame({'Temp':['Hot','Cold','Warm','Cold'],'Temp_2':[np.nan,'Warm','Cold',np.nan]})
print(x)
a = pd.get_dummies(x, prefix=['',''])
b = a.groupby(lambda x:x, axis=1).sum()
print(b)

It is not so clean but works. Using prefix allows to have the same name in the columns generated from temp and temp_2.

   _Cold  _Hot  _Warm
0      0     1      0
1      1     0      1
2      1     0      1
3      1     0      0

answered Jul 1, 2021 at 7:38

aurelien_morel

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1 Comment

Bernat Over a year ago

Hey aurelien, thanks. I think the groupby worked for me!

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