This module explains how the Pandas library works and how it is used in Python programming.
Pandas is a powerful, open-source library designed for data manipulation and analysis.
It provides fast, flexible, and expressive data structures that make it easy to work with structured data such as tables or time series.
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📊 Series
A one-dimensional labeled array that can store any data type (integers, strings, floats, etc.).
It’s similar to a single column in an Excel sheet or a list with labels. -
🧮 DataFrame
A two-dimensional labeled data structure consisting of rows and columns.
It’s like a table in a database or an Excel spreadsheet, allowing easy manipulation and analysis of tabular data.
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📂 Data Loading and Saving
Pandas can read and write data from multiple file formats such as
CSV, Excel, SQL, JSON, and more. -
🧼 Data Cleaning
Helps handle missing data (NaN values), remove duplicates, and fix inconsistent or invalid entries. -
🔄 Data Transformation
Supports operations like filtering, sorting, merging, joining, grouping, and reshaping datasets efficiently. -
📈 Data Analysis
Includes built-in functions for statistical calculations such as
mean, min, max, median, and aggregate operations.
Pandas also integrates seamlessly with NumPy, Matplotlib, and Scikit-learn for advanced data analysis and visualization.
Pandas simplifies complex data operations, making it a must-have tool for data analysts, scientists, and machine learning developers.
Whether you're cleaning messy data or performing powerful data transformations, Pandas makes it efficient and intuitive.