Python | Pandas DataFrame.ix[ ] Last Updated : 26 Jun, 2025 Comments Improve Suggest changes Like Article Like Report 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 : Comment More infoAdvertise with us Next Article Python | Pandas Series.str.slice() A ArkadipGhosh Follow Improve Article Tags : Python Python-pandas Python pandas-dataFrame Pandas-DataFrame-Methods Practice Tags : python Similar Reads Pandas Tutorial Pandas is an open-source software library designed for data manipulation and analysis. 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Using loc[] - By Specifying its Index and ValuesThe loc[] method is ideal for directly modifying an existing DataFrame, making it more memory-efficient compar 4 min read Grouping DataPandas GroupByThe groupby() function in Pandas is important for data analysis as it allows us to group data by one or more categories and then apply different functions to those groups. This technique is used for handling large datasets efficiently and performing operations like aggregation, transformation and fi 4 min read Grouping Rows in pandasPandas is the most popular Python library that is used for data analysis. It provides highly optimized performance with back-end source code is purely written in C or Python. Let's see how to group rows in Pandas Dataframe with help of multiple examples. 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The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Pandas Series.append() function is used to 4 min read Pandas Index.append() - PythonIndex.append() method in Pandas is used to concatenate or append one Index object with another Index or a list/tuple of Index objects, returning a new Index object. It does not modify the original Index. Example:Pythonimport pandas as pd idx1 = pd.Index([1, 2, 3]) idx2 = pd.Index([4, 5]) res = idx1. 2 min read Python | Pandas Series.combine()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 Series.combine() is a series mathematical operation method. This is used to com 3 min read Add a row at top in pandas DataFramePandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Let's see how can we can add a row at top in pandas DataFrame.Observe this dataset first. Python3 # importing pandas module import pandas as pd # making data fram 1 min read Python | Pandas str.join() to join string/list elements with passed delimiterPython 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 str.join() method is used to join all elements in list present in a series with 2 min read Join two text columns into a single column in PandasLet's see the different methods to join two text columns into a single column. Method #1: Using cat() function We can also use different separators during join. e.g. -, _, " " etc. Python3 1== # importing pandas import pandas as pd df = pd.DataFrame({'Last': ['Gaitonde', 'Singh', 'Mathur'], 'First': 2 min read How To Compare Two Dataframes with Pandas compare?A DataFrame is a 2D structure composed of rows and columns, and where data is stored into a tubular form. It is mutable in terms of size, and heterogeneous tabular data. Arithmetic operations can also be performed on both row and column labels. To know more about the creation of Pandas DataFrame. He 5 min read How to compare the elements of the two Pandas Series?Sometimes we need to compare pandas series to perform some comparative analysis. It is possible to compare two pandas Series with help of Relational operators, we can easily compare the corresponding elements of two series at a time. The result will be displayed in form of True or False. 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