Formation 90 min read Data Manipulation with Pandas

📦 Filtering and Selection with Pandas

Python & Data Science Chapter : Data Manipulation with Pandas Sub-chapter : Filtering and selection

Learning objectives

🎯 Objectives:\n
1Select columns\n2. Filter rows with conditions\n3. Use loc and iloc\n4. Sort data

Introduction

📖 Data selection and filtering are essential in data analysis.

Theoretical content

Selection and filtering:\n
PYTHON
\ndf["column"]\ndf[df["age"] > 25]\n

Practical examples

💻 Example: Sales analysis\n
PYTHON
\nsales[sales["sales"] > 200]\n

Best practices

1Use loc for labels\n✅ 2. Use iloc for positions\n✅ 3. Use & for AND, | for OR

Common pitfalls

Modifying a view instead of a copy\n
Use .copy() to create a copy

Summary

df["col"]: select column\n✅ df[condition]: filtering\n✅ df.loc[]: label selection\n✅ df.iloc[]: position selection\n✅ df.sort_values(): sorting

Additional resources

📚 pandas.pydata.org/docs/user_guide/indexing.html