pd get dummies

1note:
2dummies = pd.get_dummies(df[['column_1']], drop_first=True)
3df = pd.concat([df.drop(['column_1'],axis=1), dummies],axis=1)
4
5
6note:for more that one coloum keep ading in the list 
7dummies = pd.get_dummies(df[['column_1', 'column_2','column_3']], drop_first=True)
8df = pd.concat([df.drop(['column_1', 'column_1'],axis=1), dummies],axis=1)
9

1note:
2dummies = pd.get_dummies(df[['column_1']], drop_first=True)
3
4note:for more that one coloum keep ading in the list 
5dummies = pd.get_dummies(df[['column_1', 'column_2','column_3']], drop_first=True)

1>>> s = pd.Series(list('abca'))
2
3>>> pd.get_dummies(s)
4   a  b  c
50  1  0  0
61  0  1  0
72  0  0  1
83  1  0  0
9

1df = pd.get_dummies(df, columns=['type'])

1>>> pd.get_dummies(pd.Series(list('abc')), dtype=float)
2     a    b    c
30  1.0  0.0  0.0
41  0.0  1.0  0.0
52  0.0  0.0  1.0
6

1#this do dummie verable do a very intersting thing they turn calegorical 
2#variables in numerical like M and F they can 0 for M and 1 for F  
3
4
5pd.get_dummies(df.Gender)
6
7	F	M
80	0	1
91	1	0
102	1	0
113	0	1
124	1	0
13...	...	...
14202	1	0
15203	0	1
16204	0	1
17205	0	1
18206	1	0
19207 rows × 2 columns
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