removing redundant columns when using get_dummies

Question

Hi have a pandas dataframe df containing categorical variables.

df=pandas.DataFrame(data=[['male','blue'],['female','brown'],
['male','black']],columns=['gender','eyes'])

df
Out[16]: 
   gender   eyes
0    male   blue
1  female  brown
2    male  black

using the function get_dummies I get the following dataframe

df_dummies = pandas.get_dummies(df)

df_dummies
Out[18]: 
   gender_female  gender_male  eyes_black  eyes_blue  eyes_brown
0              0            1           0          1           0
1              1            0           0          0           1
2              0            1           1          0           0

Owever the columns gender_female and gender_male contain the same information because the original column could assume a binary value. Is there a (smart) way to keep only one of the 2 columns?

UPDATED

The use of

df_dummies = pandas.get_dummies(df,drop_first=True)

Would give me

df_dummies
Out[21]: 
   gender_male  eyes_blue  eyes_brown
0            1          1           0
1            0          0           1
2            1          0           0

but I would like to remove the columns for which originally I had only 2 possibilities

The desired result should be

df_dummies
Out[18]: 
   gender_male  eyes_black  eyes_blue  eyes_brown
0  1           0          1           0
1  0           0          0           1
2  1           1          0           0

Answer 1

Alternatively, you can split the dataframe into parts you want to apply drop_first=True and parts you don't. Then concatenate them together.

df1 = df.iloc[:, 0:2]
df2 = df.iloc[:, 2:]
df1 = pd.get_dummies(df1 ,drop_first=True)

df = pd.concat([df1, df2], axis=1)