In [1]:

    

import pandas as pd
import numpy as np


    

In [2]:

    

df = pd.read_csv('data/src/sample_pandas_normal.csv', index_col=0)


    

In [3]:

    

df['sex'] = ['female', np.nan, 'male', 'male', 'female', 'male']
df['rank'] = [2, 1, 1, 0, 2, 0]


    

In [4]:

    

print(df)


    

    




         age state  point     sex  rank
name                                   
Alice     24    NY     64  female     2
Bob       42    CA     92     NaN     1
Charlie   18    CA     70    male     1
Dave      68    TX     70    male     0
Ellen     24    CA     88  female     2
Frank     30    NY     57    male     0

In [5]:

    

print(pd.get_dummies(df['sex']))


    

    




         female  male
name                 
Alice         1     0
Bob           0     0
Charlie       0     1
Dave          0     1
Ellen         1     0
Frank         0     1

In [6]:

    

print(pd.get_dummies(['male', 1, 1, 2]))


    

    




   1  2  male
0  0  0     1
1  1  0     0
2  1  0     0
3  0  1     0

In [7]:

    

print(pd.get_dummies(np.arange(6)))


    

    




   0  1  2  3  4  5
0  1  0  0  0  0  0
1  0  1  0  0  0  0
2  0  0  1  0  0  0
3  0  0  0  1  0  0
4  0  0  0  0  1  0
5  0  0  0  0  0  1

In [8]:

    

# print(pd.get_dummies(np.arange(6).reshape((2, 3))))
# Exception: Data must be 1-dimensional


    

In [9]:

    

print(pd.get_dummies(df))


    

    




         age  point  rank  state_CA  state_NY  state_TX  sex_female  sex_male
name                                                                         
Alice     24     64     2         0         1         0           1         0
Bob       42     92     1         1         0         0           0         0
Charlie   18     70     1         1         0         0           0         1
Dave      68     70     0         0         0         1           0         1
Ellen     24     88     2         1         0         0           1         0
Frank     30     57     0         0         1         0           0         1

In [10]:

    

print(pd.get_dummies(df, drop_first=True))


    

    




         age  point  rank  state_NY  state_TX  sex_male
name                                                   
Alice     24     64     2         1         0         0
Bob       42     92     1         0         0         0
Charlie   18     70     1         0         0         1
Dave      68     70     0         0         1         1
Ellen     24     88     2         0         0         0
Frank     30     57     0         1         0         1

In [11]:

    

print(pd.get_dummies(df, drop_first=True, dummy_na=True))


    

    




         age  point  rank  state_NY  state_TX  state_nan  sex_male  sex_nan
name                                                                       
Alice     24     64     2         1         0          0         0        0
Bob       42     92     1         0         0          0         0        1
Charlie   18     70     1         0         0          0         1        0
Dave      68     70     0         0         1          0         1        0
Ellen     24     88     2         0         0          0         0        0
Frank     30     57     0         1         0          0         1        0

In [12]:

    

print(pd.get_dummies(df, drop_first=True, prefix='', prefix_sep=''))


    

    




         age  point  rank  NY  TX  male
name                                   
Alice     24     64     2   1   0     0
Bob       42     92     1   0   0     0
Charlie   18     70     1   0   0     1
Dave      68     70     0   0   1     1
Ellen     24     88     2   0   0     0
Frank     30     57     0   1   0     1

In [13]:

    

print(pd.get_dummies(df, drop_first=True, prefix=['ST', 'sex'], prefix_sep='-'))


    

    




         age  point  rank  ST-NY  ST-TX  sex-male
name                                             
Alice     24     64     2      1      0         0
Bob       42     92     1      0      0         0
Charlie   18     70     1      0      0         1
Dave      68     70     0      0      1         1
Ellen     24     88     2      0      0         0
Frank     30     57     0      1      0         1

In [14]:

    

print(pd.get_dummies(df, drop_first=True, prefix={'state': 'ST', 'sex': 'sex'}, prefix_sep='-'))


    

    




         age  point  rank  ST-NY  ST-TX  sex-male
name                                             
Alice     24     64     2      1      0         0
Bob       42     92     1      0      0         0
Charlie   18     70     1      0      0         1
Dave      68     70     0      0      1         1
Ellen     24     88     2      0      0         0
Frank     30     57     0      1      0         1

In [15]:

    

print(pd.get_dummies(df, drop_first=True, columns=['sex', 'rank']))


    

    




         age state  point  sex_male  rank_1  rank_2
name                                               
Alice     24    NY     64         0       0       1
Bob       42    CA     92         0       1       0
Charlie   18    CA     70         1       1       0
Dave      68    TX     70         1       0       0
Ellen     24    CA     88         0       0       1
Frank     30    NY     57         1       0       0

In [16]:

    

df['rank'] = df['rank'].astype(object)
print(pd.get_dummies(df, drop_first=True))


    

    




         age  point  state_NY  state_TX  sex_male  rank_1  rank_2
name                                                             
Alice     24     64         1         0         0       0       1
Bob       42     92         0         0         0       1       0
Charlie   18     70         0         0         1       1       0
Dave      68     70         0         1         1       0       0
Ellen     24     88         0         0         0       0       1
Frank     30     57         1         0         1       0       0

In [17]:

    

print(df['state'].map({'CA': 0, 'NY': 1, 'TX': 2}))


    

    




name
Alice      1
Bob        0
Charlie    0
Dave       2
Ellen      0
Frank      1
Name: state, dtype: int64

In [18]:

    

df['state'] = df['state'].map({'CA': 0, 'NY': 1, 'TX': 2})
print(df)


    

    




         age  state  point     sex rank
name                                   
Alice     24      1     64  female    2
Bob       42      0     92     NaN    1
Charlie   18      0     70    male    1
Dave      68      2     70    male    0
Ellen     24      0     88  female    2
Frank     30      1     57    male    0