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Pandas Introduction - GeeksforGeeks

Last Updated : 12 Jul, 2025

Pandas is open-source Python library which is used for data manipulation and analysis. It consist of data structures and functions to perform efficient operations on data. It is well-suited for working with tabular data such as spreadsheets or SQL tables. It is used in data science because it works well with other important libraries. It is built on top of the NumPy library as it makes easier to manipulate and analyze. Pandas is used in other libraries such as:

• Matplotlib for plotting graphs
• SciPy for statistical analysis
• Scikit-learn for machine learning algorithms.
• It uses many functionalities provided by NumPy library.

Here is a various tasks that we can do using Pandas:

• Data Cleaning, Merging and Joining: Clean and combine data from multiple sources, handling inconsistencies and duplicates.
• Handling Missing Data: Manage missing values (NaN) in both floating and non-floating point data.
• Column Insertion and Deletion: Easily add, remove or modify columns in a DataFrame.
• Group By Operations: Use "split-apply-combine" to group and analyze data.
• Data Visualization: Create visualizations with Matplotlib and Seaborn, integrated with Pandas.

To learn Pandas from basic to advanced refer to our page: Pandas tutorial

Let's see how to start working with the Python Pandas library:

First step in working with Pandas is to ensure whether it is installed in the system or not.  If not then we need to install it on our system using the pip command.

pip install pandas

For more reference, take a look at this article on installing pandas.

After the Pandas have been installed in the system we need to import the library. This module is imported using:

import pandas as pd

Note: pd is just an alias for Pandas. It’s not required but using it makes the code shorter when calling methods or properties.

Pandas provide two data structures for manipulating data which are as follows:

A Pandas Series is one-dimensional labeled array capable of holding data of any type (integer, string, float, Python objects etc.). The axis labels are collectively called indexes.

Pandas Series is created by loading the datasets from existing storage which can be a SQL database, a CSV file or an Excel file. It can be created from lists, dictionaries, scalar values, etc.

Example: Creating a series using the Pandas Library.

import pandas as pd 
import numpy as np

ser = pd.Series() 
print("Pandas Series: ", ser) 

data = np.array(['g', 'e', 'e', 'k', 's']) 
  
ser = pd.Series(data) 
print("Pandas Series:\n", ser)

Output:

Pandas DataFrame is a two-dimensional data structure with labeled axes (rows and columns). It is created by loading the datasets from existing storage which can be a SQL database, a CSV file or an Excel file. It can be created from lists, dictionaries, a list of dictionaries etc.

Example: Creating a DataFrame Using the Pandas Library

import pandas as pd 
   
df = pd.DataFrame() 
print(df)

lst = ['Geeks', 'For', 'Geeks', 'is', 'portal', 'for', 'Geeks'] 
  
df = pd.DataFrame(lst) 
print(df)

Output:

With Pandas, we have a flexible tool to handle data efficiently whether we're cleaning, analyzing or visualizing it for our next project.

Getting started with pandas

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