Python for Data Analysis

Python for data work: Pandas, analysis, and workflows.

Core libraries

• Pandas — DataFrames, series, and data manipulation
• NumPy — Arrays and numerical operations
• Matplotlib / Seaborn — Plotting and visualization
• Jupyter — Interactive notebooks for exploration

Common patterns

Reading and shaping

import pandas as pd

df = pd.read_csv("data.csv")
df = df.rename(columns=str.lower).dropna(subset=["key_col"])

Aggregation

summary = df.groupby("category").agg(
    count=("id", "count"),
    total=("amount", "sum"),
)

Merging

merged = pd.merge(left, right, on="id", how="left")

Best practices

• Use vectorized operations instead of loops when possible
• Set dtype when loading to control memory
• Prefer method chaining for readability
• Use pd.read_sql for database queries; avoid loading full tables into memory