Data Wrangling with Pandas and NumPy.pptx

presentation on data wrangling

1. Data Wrangling with Pandas & NumPy using Python

2. What is Data Wrangling? It's the process of transforming raw data into a clean, organized, and usable format. We're turning chaos into clarity.

3. "Garbage In, Garbage Out" The most common phrase in data. If your data is wrong, your results will be wrong. Inaccurate Analysis You can't trust insights (graphs, stats, reports) that are based on flawed data. Failed Models Machine learning models are very sensitive to data quality, type, and format. Wasted Time Fixing problems *after* analysis is much harder than cleaning the data first. Why Does it Matter?

4. 80% of a data scientist's time is spent on data wrangling. It's the most critical step. Most real-world data is "dirty": • Missing values (null, N/A) • Incorrect data types ('5' as a string) • Typos and errors ('New Yrok') • Irrelevant data The "80/20" Rule of Data Science

5. Pandas The "Excel" of Python. It's our main tool for handling data in tables (called DataFrames). We use it to load, clean, filter, and aggregate data. NumPy The "engine" for fast math. It provides the foundation for Pandas and gives us a special object for missing values: np.nan (Not a Number). Our Tools for the Job

6. Live Demo Let's clean some real data.

7. 1. Inspect Load the data. Use .head() and .info() to find problems. 2. Clean Handle missing values (.fillna()) and fix data types (.astype()). 3. Shape Filter for data we need. Create new columns (feature engineering). 4. Analyze Ask a simple question using .groupby() to get an answer. Our Demo Workflow

8. Live Demo in Progress Follow along in the Jupyter Notebook...

9. Recap What did we learn?

10. Data is (almost) always messy. Expect it. Pandas is your #1 tool for tabular data. .info(), .fillna(), and .astype() are your best friends. Clean data = reliable insights. You can now trust your analysis. Practice is everything. The best way to learn is by wrangling different datasets. Key Takeaways

11. Questions? Thank you for your time!