Working with Pandas

Working with Pandas in Python | Python Data Analysis Tutorial
In this video, we explore how to work with Pandas, one of the most popular libraries for data manipulation and analysis in Python. Pandas provides powerful data structures like Series and DataFrames, allowing you to work with structured data efficiently and perform various data operations with ease.
Topics covered in this tutorial include:
Introduction to Pandas: Setting up and understanding Pandas DataFrames and Series.
Loading Data: Importing data from various sources like CSV, Excel, and SQL databases using read_csv(), read_excel(), and more.
Exploring Data: Basic methods for inspecting and summarizing your data with .head(), .tail(), .info(), and .describe().
Data Selection and Indexing: How to access specific rows, columns, and subsets of data using .loc[], .iloc[], and boolean indexing.
Data Cleaning: Handling missing data, duplicates, and outliers for clean analysis.
Data Transformation: Applying functions and modifying data using .apply(), .map(), and .replace().
Grouping and Aggregation: Grouping data for aggregation and summary statistics with .groupby().
Merging and Joining: Combining multiple DataFrames using merge(), concat(), and join().
With practical examples and detailed explanations, this video will help you harness the power of Pandas for data manipulation, analysis, and visualization.
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