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Python | Pandas DataFrame.ix[ ]

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  • Difficulty Level : Basic
  • Last Updated : 28 Dec, 2018
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Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Pandas DataFrame.ix[ ] is both Label and Integer based slicing technique. Besides pure label based and integer based, Pandas provides a hybrid method for selections and subsetting the object using the ix[] operator. ix[] is the most general indexer and will support any of the inputs in loc[] and iloc[].

Syntax: DataFrame.ix[ ]

Parameters:
Index Position: Index position of rows in integer or list of integer.
Index label: String or list of string of index label of rows

Returns: Data frame or Series depending on parameters

Code #1:




# importing pandas package 
import pandas as geek
    
# making data frame from csv file
    
# Integer slicing
print("Slicing only rows(till index 4):")
x1 = data.ix[:4, ]
print(x1, "\n")
   
print("Slicing rows and columns(rows=4, col 1-4, excluding 4):")
x2 = data.ix[:4, 1:4]
print(x2)

Output :


 
Code #2:




# importing pandas package 
import pandas as geek
    
# making data frame from csv file
data = geek.read_csv("nba.csv")  
    
# Index slicing on Height column
print("After index slicing:")
x1 = data.ix[10:20, 'Height']
print(x1, "\n")
  
# Index slicing on Salary column
x2 = data.ix[10:20, 'Salary']
print(x2)

Output:

 

Code #3:




# importing pandas and numpy
import pandas as pd
import numpy as np
   
df = pd.DataFrame(np.random.randn(10, 4),
          columns = ['A', 'B', 'C', 'D'])
  
print("Original DataFrame: \n" , df)
   
# Integer slicing
print("\n Slicing only rows:")
print("--------------------------")
x1 = df.ix[:4, ]
print(x1)
   
print("\n Slicing rows and columns:")
print("----------------------------")
x2 = df.ix[:4, 1:3]
print(x2)

Output :

 
Code #4:




# importing pandas and numpy
import pandas as pd
import numpy as np
   
df = pd.DataFrame(np.random.randn(10, 4),
          columns = ['A', 'B', 'C', 'D'])
  
print("Original DataFrame: \n" , df)
   
# Integer slicing (printing all the rows of column 'A')
print("\n After index slicing (On 'A'):")
print("--------------------------")
x = df.ix[:, 'A']
  
print(x)

Output :


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