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In [1]:

    
import pandas as pd



In [2]:

    
df = pd.read_csv('data/src/sample_pandas_normal.csv')
df.iloc[1, 3] = 24
print(df)









    



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



In [3]:

    
print(df.replace('CA', 'California'))









    



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



In [4]:

    
print(df.replace(24, 100))









    



      name  age state  point
0    Alice  100    NY     64
1      Bob   42    CA    100
2  Charlie   18    CA     70
3     Dave   68    TX     70
4    Ellen  100    CA     88
5    Frank   30    NY     57



In [5]:

    
print(df.replace({'CA': 'California', 24: 100}))









    



      name  age       state  point
0    Alice  100          NY     64
1      Bob   42  California    100
2  Charlie   18  California     70
3     Dave   68          TX     70
4    Ellen  100  California     88
5    Frank   30          NY     57



In [6]:

    
print(df.replace(['CA', 24], ['California', 100]))









    



      name  age       state  point
0    Alice  100          NY     64
1      Bob   42  California    100
2  Charlie   18  California     70
3     Dave   68          TX     70
4    Ellen  100  California     88
5    Frank   30          NY     57



In [7]:

    
# print(df.replace(['CA', 24, 'NY'], ['California', 100]))
# ValueError: Replacement lists must match in length. Expecting 3 got 2



In [8]:

    
print(df.replace(['CA', 24], 'XXX'))









    



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



In [9]:

    
print(df.replace({'CA': 'NY', 'NY': 'XXX'}))









    



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



In [10]:

    
print(df.replace({'NY': 'XXX', 'CA': 'NY'}))









    



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



In [11]:

    
print(df.replace({'NY': 'XXX'}).replace({'CA': 'NY'}))









    



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



In [12]:

    
print(df.replace(['CA', 'NY'], ['NY', 'XXX']))









    



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



In [13]:

    
print(df.replace(['NY', 'CA'], ['XXX', 'NY']))









    



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



In [14]:

    
print(df.replace({'age': {24: 100}}))









    



      name  age state  point
0    Alice  100    NY     64
1      Bob   42    CA     24
2  Charlie   18    CA     70
3     Dave   68    TX     70
4    Ellen  100    CA     88
5    Frank   30    NY     57



In [15]:

    
print(df.replace({'age': {24: 100, 18: 0}, 'point': {24: 50}}))









    



      name  age state  point
0    Alice  100    NY     64
1      Bob   42    CA     50
2  Charlie    0    CA     70
3     Dave   68    TX     70
4    Ellen  100    CA     88
5    Frank   30    NY     57



In [16]:

    
# print(df.replace({'age': [[24, 18], [100, 0]], 'point': {24: 50}}))
# TypeError: If a nested mapping is passed, all values of the top level mapping must be mappings



In [17]:

    
print(df.replace({'age': 24, 'point': 70}, 100))









    



      name  age state  point
0    Alice  100    NY     64
1      Bob   42    CA     24
2  Charlie   18    CA    100
3     Dave   68    TX    100
4    Ellen  100    CA     88
5    Frank   30    NY     57



In [18]:

    
print(df.replace({'age': [24, 18], 'point': 70}, 100))









    



      name  age state  point
0    Alice  100    NY     64
1      Bob   42    CA     24
2  Charlie  100    CA    100
3     Dave   68    TX    100
4    Ellen  100    CA     88
5    Frank   30    NY     57



In [19]:

    
print(df.replace('li', 'LI'))









    



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



In [20]:

    
print(df.replace('(.*)li(.*)', r'\1LI\2', regex=True))









    



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



In [21]:

    
df['name'] = df['name'].str.replace('li', 'LI')
print(df)









    



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



In [22]:

    
df = pd.read_csv('data/src/sample_pandas_normal.csv')
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 [23]:

    
df.replace('CA', 'California', inplace=True)
print(df)









    



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