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Python | Pandas DataFrame.ix[ ]

Last Updated : 26 Jun, 2025
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Python's Pandas library is a powerful tool for data analysis, it provides DataFrame.ix[] method to select a subset of data using both label-based and integer-based indexing.

Important Note: DataFrame.ix[] method has been deprecated since Pandas version 0.20.0 and is no longer recommended for use in newer versions. Instead, use loc[] for label-based indexing and iloc[] for integer-based indexing.

Syntax of DataFrame.ix[]

DataFrame.ix[ ]

Parameters:

  • Index Position: Integer or list of integers specifying row positions.
  • Index Label: String or list of strings specifying row labels.

Returns: A DataFrame or Series, depending on the parameters.

Code #1:

Python3 1== 
# importing pandas package 
import pandas as geek
  
# making data frame from csv file
data = geek.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv")  
  
# 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:

Python3 1== 
# 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:

Python 
# 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:

Python 
# 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 :


A

ArkadipGhosh
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