Title: Replacing Values In Pandas
Slug: pandas_replace_values
Summary: Replacing Values In Pandas
Date: 2016-05-01 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon

import modules



In [1]:

    
import pandas as pd
import numpy as np

Create dataframe



In [2]:

    
raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], 
        'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], 
        'age': [42, 52, 36, 24, 73], 
        'preTestScore': [-999, -999, -999, 2, 1],
        'postTestScore': [2, 2, -999, 2, -999]}
df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])
df









    Out[2]:






  
    
      
      first_name
      last_name
      age
      preTestScore
      postTestScore
    
  
  
    
      0
      Jason
      Miller
      42
      -999
      2
    
    
      1
      Molly
      Jacobson
      52
      -999
      2
    
    
      2
      Tina
      Ali
      36
      -999
      -999
    
    
      3
      Jake
      Milner
      24
      2
      2
    
    
      4
      Amy
      Cooze
      73
      1
      -999

Replace all values of -999 with NAN



In [3]:

    
df.replace(-999, np.nan)









    Out[3]:






  
    
      
      first_name
      last_name
      age
      preTestScore
      postTestScore
    
  
  
    
      0
      Jason
      Miller
      42
      NaN
      2.0
    
    
      1
      Molly
      Jacobson
      52
      NaN
      2.0
    
    
      2
      Tina
      Ali
      36
      NaN
      NaN
    
    
      3
      Jake
      Milner
      24
      2.0
      2.0
    
    
      4
      Amy
      Cooze
      73
      1.0
      NaN

	first_name	last_name	age	preTestScore	postTestScore
0	Jason	Miller	42	-999	2
1	Molly	Jacobson	52	-999	2
2	Tina	Ali	36	-999	-999
3	Jake	Milner	24	2	2
4	Amy	Cooze	73	1	-999