

In [1]:

    
import pandas as pd



In [4]:

    
df = pd.DataFrame({
    'name':['john','mary','paul'],
    'age':[30,25,40],
    'city':['new york','los angeles','london']
})
df









    Out[4]:







  
    
      
      age
      city
      name
    
  
  
    
      0
      30
      new york
      john
    
    
      1
      25
      los angeles
      mary
    
    
      2
      40
      london
      paul

simplest possible example: replace one value with another



In [6]:

    
df.replace([25],40)









    Out[6]:







  
    
      
      age
      city
      name
    
  
  
    
      0
      30
      new york
      john
    
    
      1
      40
      los angeles
      mary
    
    
      2
      40
      london
      paul

replace with dict



In [8]:

    
df.replace({
    25:26,
    'john':'johnny'
})









    Out[8]:







  
    
      
      age
      city
      name
    
  
  
    
      0
      30
      new york
      johnny
    
    
      1
      26
      los angeles
      mary
    
    
      2
      40
      london
      paul

with regex



In [11]:

    
df.replace('jo.+','FOO',regex=True)









    Out[11]:







  
    
      
      age
      city
      name
    
  
  
    
      0
      30
      new york
      FOO
    
    
      1
      25
      los angeles
      mary
    
    
      2
      40
      london
      paul

replace in column



In [18]:

    
df = pd.DataFrame({
    'name':['john','mary','paul'],
    'num_children':[0,4,5],
    'num_pets':[0,1,2]
})
df









    Out[18]:







  
    
      
      name
      num_children
      num_pets
    
  
  
    
      0
      john
      0
      0
    
    
      1
      mary
      4
      1
    
    
      2
      paul
      5
      2



In [19]:

    
df.replace({'num_pets':{0:1}})









    Out[19]:







  
    
      
      name
      num_children
      num_pets
    
  
  
    
      0
      john
      0
      1
    
    
      1
      mary
      4
      1
    
    
      2
      paul
      5
      2



In [ ]: