Quickest Way to Make a Get_Dummies Type Dataframe from a Column with a Multiple of Strings

Quickest way to make a get_dummies type dataframe from a column with a multiple of strings

You can use:

>>> df['col2'].str.get_dummies(sep=',')
   A  B  C  G
0  1  1  0  0
1  1  0  1  1
2  0  1  0  0

To join the Dataframes:

>>> pd.concat([df, df['col2'].str.get_dummies(sep=',')], axis=1)
   col1   col2  A  B  C  G
0     6    A,B  1  1  0  0
1    15  C,G,A  1  0  1  1
2    25      B  0  1  0  0

Pandas Split String in colum into colums with 0/1 ; get dummies on all characters of a string

You can use str.get_dummies with an empty separator to get all letters:

df['Code'].str.get_dummies(sep='')

joining to original data:

df2 = df.drop('Code', axis=1).join(df['Code'].str.get_dummies(sep=''))

output:

  other columns   A  B  C  R
0           ...   1  1  1  0
1           ...   1  1  1  0
2           ...   0  0  0  1

Apply pd.get_dummies() on string type columns of pandas dataframe?

After some investigation, I have no idea why this might be occurring, especially since it works for single columns. I'm guessing it's a bug, because there seem to be quite a few of them centered around the pd.NA type (which convert_dtypes is in support of.)

I recommend opening a bug report at https://github.com/pandas/pandas-dev.

Running get_dummies on several DataFrame columns?

With pandas 0.19, you can do that in a single line :

pd.get_dummies(data=df, columns=['A', 'B'])

Columns specifies where to do the One Hot Encoding.

>>> df
   A  B  C
0  a  c  1
1  b  c  2
2  a  b  3

>>> pd.get_dummies(data=df, columns=['A', 'B'])
   C  A_a  A_b  B_b  B_c
0  1  1.0  0.0  0.0  1.0
1  2  0.0  1.0  0.0  1.0
2  3  1.0  0.0  1.0  0.0

Create dummies from column with multiple values in pandas

I know it's been a while since this question was asked, but there is (at least now there is) a one-liner that is supported by the documentation:

In [4]: df
Out[4]:
      label
0  (a, c, e)
1     (a, d)
2       (b,)
3     (d, e)

In [5]: df['label'].str.join(sep='*').str.get_dummies(sep='*')
Out[5]:
   a  b  c  d  e
0  1  0  1  0  1
1  1  0  0  1  0
2  0  1  0  0  0
3  0  0  0  1  1

If the name of the column is in string return 1 in Python

How about str.get_dummies?

df = pd.concat([df, df['LanguageHaveWorkedWith'].str.get_dummies(sep=';')], axis=1)

How to create dummies for certain columns with pandas.get_dummies()

It can be done without concatenation, using get_dummies() with required parameters

In [294]: pd.get_dummies(df, prefix=['A', 'D'], columns=['A', 'D'])
Out[294]: 
   B  C  A_x  A_y  D_j  D_l
0  z  1  1.0  0.0  1.0  0.0
1  u  2  0.0  1.0  0.0  1.0
2  z  3  1.0  0.0  1.0  0.0

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