In [1]:

    

import pandas as pd


    

In [2]:

    

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


    

In [3]:

    

print(df)


    

    




      name  age state  point
0    Alice   24    NY     64
1      Bob   42    CA     92
2  Charlie   18    CA     70
3     Dave   68    TX     70
4    Ellen   24    CA     88
5    Frank   30    NY     57

In [4]:

    

s = df['state']
print(s)


    

    




0    NY
1    CA
2    CA
3    TX
4    CA
5    NY
Name: state, dtype: object

In [5]:

    

s_map_all = s.map({'NY': 'NewYork', 'CA': 'California', 'TX': 'Texas'})
print(s_map_all)


    

    




0       NewYork
1    California
2    California
3         Texas
4    California
5       NewYork
Name: state, dtype: object

In [6]:

    

s_replace_all = s.replace({'NY': 'NewYork', 'CA': 'California', 'TX': 'Texas'})
print(s_replace_all)


    

    




0       NewYork
1    California
2    California
3         Texas
4    California
5       NewYork
Name: state, dtype: object

In [7]:

    

s_map = s.map({'NY': 'NewYork'})
print(s_map)


    

    




0    NewYork
1        NaN
2        NaN
3        NaN
4        NaN
5    NewYork
Name: state, dtype: object

In [8]:

    

s_replace = s.replace({'NY': 'NewYork'})
print(s_replace)


    

    




0    NewYork
1         CA
2         CA
3         TX
4         CA
5    NewYork
Name: state, dtype: object

In [9]:

    

s_copy = s.copy()
s_copy.update(s_copy.map({'NY': 'NewYork'}))
print(s_copy)


    

    




0    NewYork
1         CA
2         CA
3         TX
4         CA
5    NewYork
Name: state, dtype: object

In [10]:

    

s_copy = s.copy()
s_copy.replace({'NY': 'NewYork'}, inplace=True)
print(s_copy)


    

    




0    NewYork
1         CA
2         CA
3         TX
4         CA
5    NewYork
Name: state, dtype: object

In [11]:

    

s_map_num = s.map({'NY': 0, 'CA': 1, 'TX': 2})
print(s_map_num)


    

    




0    0
1    1
2    1
3    2
4    1
5    0
Name: state, dtype: int64

In [12]:

    

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


    

    




      name  age  state  point
0    Alice   24      0     64
1      Bob   42      1     92
2  Charlie   18      1     70
3     Dave   68      2     70
4    Ellen   24      1     88
5    Frank   30      0     57

In [13]:

    

print(df['state'].dtype)


    

    




int64