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I must create the dummy variables for the column that could have 16 values (0-15), but not necessary has all 16 values when I create dummy variables based on it:

  my_column
0  3
1  4
2  7
3  1
4  9

I expect my dummy variables have 16 columns, or more - any another value that fixed by me in advance, and the number in the name of column corresponds to the value of my_column, but if my_column have only , let's say, 5 values from 16 possible values, the method pd.get_dummies will create only 5 columns (as expected from this method though) as following :

 my_column  1  3  4  7  9
0  3        0  1  0  0  0
1  4        0  0  1  0  0
2  7        0  0  0  1  0
3  1        1  0  0  0  0
4  9        0  0  0  0  1

How can I achieve one of the following results ?

 my_column   0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
    0  3     0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0
    1  4     0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0
    2  7     0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0
    3  1     0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0
    4  9     0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0
Dimgold
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Andrew_457
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1 Answers1

4

Use get_dummies + reindex on the columns - 

v = pd.get_dummies(df.my_column).reindex(columns=range(0, 16), fill_value=0)

According to the docs, reindex will -

Conform DataFrame to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.

fill_value=0 will fill all missing columns with zeros.

You can add the original column to the result with insert or concat - 

v.insert(0, 'my_column', df.my_column)

v = pd.concat([df, v], 1)   # alternative to insert

v

   my_column  0  1  2  3  4  5  6  7  8  9  10  11  12  13  14  15
0          3  0  0  0  1  0  0  0  0  0  0   0   0   0   0   0   0
1          4  0  0  0  0  1  0  0  0  0  0   0   0   0   0   0   0
2          7  0  0  0  0  0  0  0  1  0  0   0   0   0   0   0   0
3          1  0  1  0  0  0  0  0  0  0  0   0   0   0   0   0   0
4          9  0  0  0  0  0  0  0  0  0  1   0   0   0   0   0   0
cs95
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