Pandas Summary & Next Steps

Well done! You can now load data into DataFrames, inspect it, select and filter rows and columns, handle missing values, and group, aggregate, transform, and combine data — the complete data-analysis workflow.

What you have learned

• Series and DataFrames — the core objects
• Reading, inspecting, and describing data
• Selecting columns, rows (loc/iloc), and filtering
• Adding columns, handling NaN, sorting
• groupby, agg, apply, and merge

A quick end-to-end example

import pandas as pd
df = pd.DataFrame({
  "region": ["North", "South", "North", "South"],
  "sales":  [100, 150, 200, 90]
})
summary = df.groupby("region")["sales"].sum()
print(summary)
print("Best region:", summary.idxmax())
print(df["region"].value_counts())

Where to go next

1. Machine Learning — feed your cleaned DataFrames into models
2. Learn pd.read_csv with a real dataset on your computer
3. Explore plotting: df.plot() turns data into charts

💡 Keep going: NumPy + Pandas is the foundation of every data career. Practise on datasets that interest you — sports, money, games — and the skills stick.