How to transform a column with 3 possible values into 3 columns with 0 if the value is absent and 1 if it is present

Question

This is my dataframe :

	col_1	col_2	col_3
1	A	abc	x
2	A	abc	y
3	A	abc	z
4	B	bcd	x
5	B	bcd	z
6	C	cde	x
7	D	def	y

and this is what I want :

	col_1	col_2	col_x	col_y	col_z
1	A	abc	1	1	1
4	B	bcd	1	0	1
6	C	cde	1	0	0
7	D	def	0	1	0

I try with df.pivot but I don't known how insert 0 and 1 and how to drop unused rows at once.

Answer 1

Use crosstab with DataFrame.add_prefix and then DataFrame.clip for only 0, 1 values if duplicates in data. If no duplicates is possible remove clip:

df = (pd.crosstab([df['col_1'], df['col_2']], df['col_3'])
        .add_prefix('col_')
        .clip(upper=1)
        .reset_index()
        .rename_axis(None, axis=1))
print (df)
  col_1 col_2  col_x  col_y  col_z
0     A   abc      1      1      1
1     B   bcd      1      0      1
2     C   cde      1      0      0
3     D   def      0      1      0

Answer 2

One option is to execute a pd.get_dummies before grouping:

(pd.get_dummies(df, columns=['col_3'], prefix='col')
   .groupby(['col_1', 'col_2'], as_index = False)
   .sum()
)
  col_1 col_2  col_x  col_y  col_z
0     A   abc      1      1      1
1     B   bcd      1      0      1
2     C   cde      1      0      0
3     D   def      0      1      0