
- Python Pandas - Home
- Python Pandas - Introduction
- Python Pandas - Environment Setup
- Python Pandas - Basics
- Python Pandas - Introduction to Data Structures
- Python Pandas - Index Objects
- Python Pandas - Panel
- Python Pandas - Basic Functionality
- Python Pandas - Indexing & Selecting Data
- Python Pandas - Series
- Python Pandas - Series
- Python Pandas - Slicing a Series Object
- Python Pandas - Attributes of a Series Object
- Python Pandas - Arithmetic Operations on Series Object
- Python Pandas - Converting Series to Other Objects
- Python Pandas - DataFrame
- Python Pandas - DataFrame
- Python Pandas - Accessing DataFrame
- Python Pandas - Slicing a DataFrame Object
- Python Pandas - Modifying DataFrame
- Python Pandas - Removing Rows from a DataFrame
- Python Pandas - Arithmetic Operations on DataFrame
- Python Pandas - IO Tools
- Python Pandas - IO Tools
- Python Pandas - Working with CSV Format
- Python Pandas - Reading & Writing JSON Files
- Python Pandas - Reading Data from an Excel File
- Python Pandas - Writing Data to Excel Files
- Python Pandas - Working with HTML Data
- Python Pandas - Clipboard
- Python Pandas - Working with HDF5 Format
- Python Pandas - Comparison with SQL
- Python Pandas - Data Handling
- Python Pandas - Sorting
- Python Pandas - Reindexing
- Python Pandas - Iteration
- Python Pandas - Concatenation
- Python Pandas - Statistical Functions
- Python Pandas - Descriptive Statistics
- Python Pandas - Working with Text Data
- Python Pandas - Function Application
- Python Pandas - Options & Customization
- Python Pandas - Window Functions
- Python Pandas - Aggregations
- Python Pandas - Merging/Joining
- Python Pandas - MultiIndex
- Python Pandas - Basics of MultiIndex
- Python Pandas - Indexing with MultiIndex
- Python Pandas - Advanced Reindexing with MultiIndex
- Python Pandas - Renaming MultiIndex Labels
- Python Pandas - Sorting a MultiIndex
- Python Pandas - Binary Operations
- Python Pandas - Binary Comparison Operations
- Python Pandas - Boolean Indexing
- Python Pandas - Boolean Masking
- Python Pandas - Data Reshaping & Pivoting
- Python Pandas - Pivoting
- Python Pandas - Stacking & Unstacking
- Python Pandas - Melting
- Python Pandas - Computing Dummy Variables
- Python Pandas - Categorical Data
- Python Pandas - Categorical Data
- Python Pandas - Ordering & Sorting Categorical Data
- Python Pandas - Comparing Categorical Data
- Python Pandas - Handling Missing Data
- Python Pandas - Missing Data
- Python Pandas - Filling Missing Data
- Python Pandas - Interpolation of Missing Values
- Python Pandas - Dropping Missing Data
- Python Pandas - Calculations with Missing Data
- Python Pandas - Handling Duplicates
- Python Pandas - Duplicated Data
- Python Pandas - Counting & Retrieving Unique Elements
- Python Pandas - Duplicated Labels
- Python Pandas - Grouping & Aggregation
- Python Pandas - GroupBy
- Python Pandas - Time-series Data
- Python Pandas - Date Functionality
- Python Pandas - Timedelta
- Python Pandas - Sparse Data Structures
- Python Pandas - Sparse Data
- Python Pandas - Visualization
- Python Pandas - Visualization
- Python Pandas - Additional Concepts
- Python Pandas - Caveats & Gotchas
Pandas Series.str.center() Method
The Series.str.center() method in Pandas is used to pad both the left and right sides of strings in a Series or Index to a specified width.
This method ensures that the string is centered within the new width, with additional characters filled by a specified fill character. This operation is similar to the string method str.center() in Python.
Syntax
Following is the syntax of the Pandas Series.str.center() method −
Series.str.center(width, fillchar=' ')
Parameters
The Series.str.center() method accepts the following parameters −
- width: An integer specifying the minimum width of the resulting string. Additional characters will be filled with fillchar.
- fillchar: A string specifying the character for filling, default is whitespace.
Return Value
The Series.str.center() method returns a new Series with the strings centered and padded to the specified width using the specified fill character.
Example 1
In this example, we demonstrate the basic usage of the Series.str.center() method by applying it to a Series of strings.
import pandas as pd # Create a Series of strings s = pd.Series(['dog', 'lion', 'panda']) # Display the input Series print("Input Series") print(s) # Center the strings with a width of 8 and fill character '.' print("Series after calling center with width=8 and fillchar='.':") print(s.str.center(8, fillchar='.'))
When we run the above code, it produces the following output −
Input Series 0 dog 1 lion 2 panda dtype: object Series after calling center with width=8 and fillchar='.': 0 ..dog... 1 ..lion.. 2 .panda.. dtype: object
Example 2
This example demonstrates how to use the Series.str.center() method to format the 'Animal' column in a DataFrame, by centering each animal name to a specified width with a custom fill character.
import pandas as pd # Create a DataFrame df = pd.DataFrame({'Animal': ['dog', 'lion', 'panda'], 'Legs': [4, 4, 2]}) print("Input DataFrame:") print(df) # Center the strings in the 'Animal' column with a width of 8 and fill character '-' df['Animal'] = df['Animal'].str.center(8, fillchar='-') print("DataFrame after applying center with width=8 and fillchar='-':") print(df)
Following is the output of the above code −
Input DataFrame: Animal Legs 0 dog 4 1 lion 4 2 panda 2 DataFrame after applying center with width=8 and fillchar='-': Animal Legs 0 --dog--- 4 1 --lion-- 4 2 -panda-- 2
Example 3
In this example, we apply the Series.str.center() method to an Index object. This showcases how you can use it to format the index labels in a DataFrame by centering them with a specified width and fill character.
import pandas as pd # Create a DataFrame with an Index df = pd.DataFrame({'Value': [1, 2, 3]}, index=['first', 'second', 'third']) # Display the Input DataFrame print("Input DataFrame:") print(df) # Center the index labels with a width of 10 and fill character '*' df.index = df.index.str.center(10, fillchar='*') # Display the Modified DataFrame print("Modified DataFrame:") print(df)
Output of the above code is as follows −
Input DataFrame: Value first 1 second 2 third 3 Modified DataFrame: Value **first*** 1 **second** 2 **third*** 3