Formation 90 min read Data Manipulation with Pandas

📦 Introduction to Pandas (DataFrames)

Python & Data Science Chapter : Data Manipulation with Pandas Sub-chapter : DataFrames and Series

Learning objectives

🎯 Objectives:\n
1Install and import Pandas\n2. Create DataFrames\n3. Load CSV/Excel data\n4. Explore data

Introduction

📖 Pandas is the essential library for data analysis in Python.

Theoretical content

Pandas - DataFrame:\n
PYTHON
\nimport pandas as pd\ndf = pd.DataFrame({"name": ["Alice"], "age": [25]})\n

Practical examples

💻 Example: Data analysis\n
PYTHON
\nimport pandas as pd\ndf = pd.DataFrame({"month": ["Jan"], "sales": [1000]})\n

Best practices

1Always import pandas as pd\n✅ 2. Use head() for preview\n✅ 3. Use info() for structure\n✅ 4. Use describe() for statistics

Common pitfalls

Forgetting to install pandas\n
pip install pandas

Summary

Pandas: analysis library\n✅ DataFrame: 2D table\n✅ read_csv(): load CSV\n✅ head(): preview\n✅ info(): structure\n✅ describe(): statistics

Additional resources

📚 pandas.pydata.org/docs