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:
# 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:
# 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 :
