Overview

Want to quickly master Python data analytics through working examples? This by-example guide teaches 95% of practical data analytics through 85 annotated examples organized by complexity level, targeting pandas 3.0.2, numpy 2.4.4, scikit-learn 1.8.0, and polars 1.40.1.

What Is By-Example Learning?

By-example learning is an example-first approach where you learn through annotated, runnable Python code rather than narrative explanations. Each example is self-contained, immediately executable, and heavily commented to show:

What each operation does - Inline # => comments explain the purpose and mechanism
Expected outputs - Show actual values, shapes, and types after each operation
Breaking changes - Critical version differences for pandas 3.0.2 and numpy 2.4.4
Key takeaways - 1-2 sentence summaries of core concepts

This approach is ideal for experienced developers who know at least one programming language and want to quickly understand Python data analytics syntax, library conventions, and patterns through working code.

Learning Path

The tutorial guides you through 85 examples organized into three progressive levels.

%% Color Palette: Blue #0173B2, Orange #DE8F05, Teal #029E73, Purple #CC78BC, Brown #CA9161
graph TD
    A["Beginner (Examples 1-28)<br/>Loading data, pandas basics,<br/>numpy, basic visualization"]
    B["Intermediate (Examples 29-57)<br/>Advanced pandas, polars, DuckDB,<br/>scikit-learn pipelines, stats"]
    C["Advanced (Examples 58-85)<br/>ML models, time series, financial,<br/>production pipelines, dashboards"]
 
    A -->|Master foundations| B
    B -->|Advanced patterns| C
 
    style A fill:#0173B2,stroke:#000,color:#fff
    style B fill:#DE8F05,stroke:#000,color:#fff
    style C fill:#029E73,stroke:#000,color:#fff

Version Landscape

This tutorial targets the current major versions as of 2026. Understanding breaking changes is critical — several libraries have major API shifts that break older code.

%% Color Palette: Blue #0173B2, Orange #DE8F05, Teal #029E73, Purple #CC78BC, Brown #CA9161
graph LR
    A["pandas 3.0.2<br/>CoW enforced<br/>New string dtype"]
    B["numpy 2.4.4<br/>np.float64 not np.float_<br/>np.nan not np.NaN"]
    C["scikit-learn 1.8.0<br/>set_output pandas<br/>HistGradientBoosting"]
    D["polars 1.40.1<br/>group_by not groupby<br/>Lazy evaluation"]
 
    style A fill:#0173B2,stroke:#000,color:#fff
    style B fill:#DE8F05,stroke:#000,color:#fff
    style C fill:#029E73,stroke:#000,color:#fff
    style D fill:#CC78BC,stroke:#000,color:#fff

What This Tutorial Covers

Core Libraries

pandas 3.0.2 - DataFrame operations, Copy-on-Write, new string dtype, deprecated freq aliases
numpy 2.4.4 - Arrays, broadcasting, new random API, updated type names
scikit-learn 1.8.0 - Preprocessing, pipelines, classification, regression, clustering
polars 1.40.1 - High-performance DataFrames with lazy evaluation and SIMD acceleration

Visualization

matplotlib 3.10.x - Foundation plotting with fig, ax = plt.subplots() pattern
seaborn 0.13.2 - Statistical visualization including the new sns.objects interface
plotly 6.x - Interactive charts with px.scatter(), px.line(), go.Figure()

Data Sources and Storage

DuckDB 1.2.x - In-process SQL analytics on DataFrames and Parquet files
PyArrow 20.0.0 - Apache Arrow format, Parquet reading, Arrow-backed DataFrames
yfinance 0.2.x - Financial data fetching from Yahoo Finance

Statistical Analysis

scipy 1.15.x - Statistical tests, hypothesis testing, optimization
statsmodels 0.14.x - OLS regression, time series ARIMA, seasonal decomposition

Production Patterns

Reproducible pipelines with type hints and docstrings
Data validation with pandera
Streamlit dashboards
Packaging with pyproject.toml and uv

Critical Breaking Changes to Know

Before writing any code, understand these breaking changes in pandas 3.0.2 and numpy 2.4.4:

pandas 3.0.2 Breaking Changes

Copy-on-Write (CoW) is now enforced:

# WRONG in pandas 3.0.2 - raises ChainedAssignmentError
df["subset"]["col"] = value
 
# CORRECT - use df.loc for modifications
df.loc[mask, "col"] = value

New string dtype - strings no longer use object dtype:

# pandas 3.0.2 default string storage
df["name"].dtype  # => StringDtype (not object)

Deprecated frequency aliases changed:

# WRONG (pandas 2.x aliases)
df.resample("M")   # Month-end was "M"
df.resample("Y")   # Year-end was "Y"
df.resample("Q")   # Quarter-end was "Q"
 
# CORRECT (pandas 3.0.2 aliases)
df.resample("ME")  # Month-end is now "ME"
df.resample("YE")  # Year-end is now "YE"
df.resample("QE")  # Quarter-end is now "QE"

applymap() replaced by map():

# WRONG (deprecated in pandas 3.x)
df.applymap(lambda x: x * 2)
 
# CORRECT (pandas 3.0.2)
df.map(lambda x: x * 2)

numpy 2.4.4 Breaking Changes

Type alias names changed:

# WRONG (numpy 1.x aliases, removed in 2.x)
np.float_   # Removed
np.int_     # Removed (means intp now, not int64)
np.complex_ # Removed
np.NaN      # Removed (was alias for float("nan"))
 
# CORRECT (numpy 2.4.4)
np.float64  # Explicit 64-bit float
np.intp     # Platform pointer integer
np.complex128  # 128-bit complex
np.nan      # Lowercase nan (built-in float)

New random API (use this, not deprecated np.random.seed()):

# WRONG (deprecated, non-reproducible across processes)
np.random.seed(42)
np.random.normal(0, 1, 100)
 
# CORRECT (numpy 2.4.4 style)
rng = np.random.default_rng(seed=42)
rng.normal(0, 1, 100)

Prerequisites

This tutorial assumes you:

Know at least one programming language (Python experience helpful but not required)
Understand basic programming concepts (loops, functions, data structures)
Have Python 3.11+ installed with pip or uv

Installation:

pip install pandas==3.0.2 numpy==2.4.4 scikit-learn==1.8.0 polars==1.40.1
pip install matplotlib seaborn plotly scipy statsmodels
pip install pyarrow duckdb yfinance pandera streamlit

Or with uv (faster):

uv pip install pandas numpy scikit-learn polars matplotlib seaborn plotly scipy statsmodels pyarrow duckdb yfinance pandera streamlit

How to Use This Guide

Each example follows a consistent five-part structure:

Brief explanation - What this example demonstrates (1-3 sentences)
Optional diagram - Visual representation for complex concepts
Heavily annotated code - Working Python with # => comments showing values and outputs
Key takeaway - 1-2 sentence lesson summary
Why It Matters - 50-100 words on practical significance

The # => annotation pattern documents what happens at each step:

import pandas as pd                    # => pandas 3.0.2
 
df = pd.read_csv("sales.csv")         # => loads CSV into DataFrame
print(df.shape)                        # => (1000, 8) - rows, columns
print(df.dtypes)                       # => shows column types per column

Work through examples sequentially within each level, or jump directly to the example that covers your immediate need. Each example is self-contained.

Overview

Command Palette