Pandas is a Python library used for data manipulation and analysis. Pandas provides a convenient way to analyze and clean data.
The Pandas library introduces two new data structures to Python - Series and DataFrame, both of which are built on top of NumPy.
What is Pandas Used for?
Pandas is a powerful library generally used for:
- Data Cleaning
- Data Transformation
- Data Analysis
- Machine Learning
- Data Visualization
Why Use Pandas?
Some of the reasons why we should use Pandas are as follows:
1. Handle Large Data Efficiently
Pandas is designed for handling large datasets. It provides powerful tools that simplify tasks like data filtering, transforming, and merging.
It also provides built-in functions to work with formats like CSV, JSON, TXT, Excel, and SQL databases.
2. Tabular Data Representation
Pandas DataFrames, the primary data structure of Pandas, handle data in tabular format. This allows easy indexing, selecting, replacing, and slicing of data.
3. Data Cleaning and Preprocessing
Data cleaning and preprocessing are essential steps in the data analysis pipeline, and Pandas provides powerful tools to facilitate these tasks. It has methods for handling missing values, removing duplicates, handling outliers, data normalization, etc.
4. Time Series Functionality
Pandas contains an extensive set of tools for working with dates, times, and time-indexed data as it was initially developed for financial modeling.
5. Free and Open-Source
Pandas follows the same principles as Python, allowing you to use and distribute Pandas for free, even for commercial use.
Install Pandas
To install pandas, you need Python and PIP installed in your system. If you have Python and PIP installed already, you can install pandas by entering the following command in the terminal:
pip install pandas
If the installation completes without any errors, Pandas is now successfully installed on your system. You can start using it in your Python projects by importing the Pandas library.
Import Pandas in Python
We can import Pandas in Python using the import statement.
import pandas as pd
The code above imports the pandas library into our program with the alias pd.
After this import statement, we can use Pandas functions and objects by calling them with pd.
For example, you can use Pandas dataframe in your program using pd.DataFrame().
Notes:
- If we import pandas without an alias using
import pandas, we can create a DataFrame using thepandas.DataFrame()function. - Using an alias
pdis a common convention among Python programmers, as it makes it easier and quicker to refer to the pandas library in your code.