Pandas Tutorial: Read CSV, Filter, Group By & Sort Data in Python
Learn Pandas DataFrame operations from scratch! In this comprehensive Pandas tutorial, we'll walk through reading CSV files, filtering data, using group by aggregations, and sorting results using Python's most popular data analysis library.
This hands-on tutorial covers essential Pandas data processing techniques using a real car sales dataset with 5,000 records. We'll work directly in VS Code notebooks (Jupyter notebooks in VS Code) and explore core Pandas functions that every data analyst and data engineer needs to know.
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What You'll Learn:
✅ Set up VS Code Jupyter notebook kernel and iPyKernel
✅ Read CSV files using pandas.read_csv() with relative paths
✅ Explore DataFrame structure with .shape and .head()
✅ Filter data using Pandas query function with variables
✅ Pass dynamic values to query using @variable syntax
✅ Use group by aggregation to calculate totals and counts
✅ Understand index vs columns in Pandas DataFrames
✅ Apply reset_index() to convert index to column
✅ Sort DataFrame in descending order using sort_values()
✅ Work with 5,000+ record datasets efficiently
Key Pandas Functions Covered:
pd.read_csv() - Reading CSV files
.query() - Filtering data with SQL-like syntax
.groupby() - Grouping and aggregating data
.agg() - Multiple aggregation functions
.reset_index() - Converting index to column
.sort_values() - Sorting DataFrame data
.shape and .head() - DataFrame exploration
💡 Real-World Example: We'll analyze car sales data across 10 different states, filter for specific states like Florida, calculate total revenue and sale counts per state, and identify which state has the highest sales.
This is part of our comprehensive Pandas, Polars, and PySpark comparison series. Perfect for beginners learning Python data analysis or professionals transitioning between data processing libraries!
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