I have a dataframe with one column called label
which has the values [0,1,2,3,4,5,6,8,9]
. I would like to make dummy columns out of this, but I would like some labels to be joined together, so for example I want dummy_012
to be 1 if the observation has either label 0, 1 or 2.
If i use the command df2 = pd.get_dummies(df, columns=['label'])
, it would create 9 columns, 1 for each label.
I know I can use df2['dummy_012']=df2['dummy_0']+df2['dummy_1']+df2['dummy_2']
after that to turn it into one joint column, but I want to know if there's a more pythonic way of doing it (or some function where i can just change the parameters to the joins).
Maybe this approach can give a idea:
groups = ['012', '345', '6789']
for gp in groups:
df.loc[df['Label'].isin([int(x) for x in gp]), 'Label_Group'] = f'dummies_{gp}'
Output:
Label Label_Group
0 0 dummies_012
1 1 dummies_012
2 2 dummies_012
3 3 dummies_345
4 4 dummies_345
5 5 dummies_345
6 6 dummies_6789
7 8 dummies_6789
8 9 dummies_6789
And then apply dummy:
df_dummies = pd.get_dummies(df['Label_Group'])
dummies_012 dummies_345 dummies_6789
0 1 0 0
1 1 0 0
2 1 0 0
3 0 1 0
4 0 1 0
5 0 1 0
6 0 0 1
7 0 0 1
8 0 0 1
I don't know that this is pythonic because a more elegant solution might exist, but I does allow you to change parameters and it's vectorized. I've read that get_dummies() can be a bit slow with large amounts of data and vectorizing pandas is good practice in general. So I vectorized this function and had it do its calculations with numpy arrays. It should give you a boost in performance as the dataset increases in size compared to similar functions.
This function will take your dataframe and a list of numbers as strings and will return your dataframe with the column you wanted.
def get_dummy(df,column_nos):
new_col_name = 'dummy_'+''.join([i for i in column_nos])
vector_sum = sum([df[i].values for i in column_nos])
df[new_col_name] = [1 if i>0 else 0 for i in vector_sum]
return df
In case you'd rather the input to be integers rather than strings, you can tweak the above function to look like below.
def get_dummy(df,column_nos):
column_names = ['dummy_'+str(i) for i in column_nos]
new_col_name = 'dummy_'+''.join([str(i) for i in sorted(column_nos)])
vector_sum = sum([df[i].values for i in column_names])
df[new_col_name] = [1 if i>0 else 0 for i in vector_sum]
return df
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