Learning Pandas 2, Second Edition
Language: English
Published by GitforGits, 2026
- Softcover
- New
Condition: New
US$ 65.39
Quantity: 1 available
Add to basketSeller Inventory # KALAMO-9789349174665
- Title
- Learning Pandas 2, Second Edition
- Author
- Rosch, Matthew
- Publisher
- GitforGits
- Publication year
- 2026
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 9349174669
- ISBN 13
- 9789349174665
- Edition
- 2nd Edition
This book has been updated with Pandas 2.3, and it's exactly what ML engineers, data scientists and data engineers have been waiting for. It's a hands-on desk guide that's full of solutions, and it's the most up-to-date, production-ready book to the most widely used data manipulation library in the Python ecosystem.
This book covers all the big changes in Pandas 2.3, like Copy-on-Write semantics, PyArrow-backed types that save over 50% memory, the new default StringDtype, and the deprecated frequency aliases that are messing up time series pipelines everywhere. All the chapters are based on one growing application using a real Customer Churn dataset, so every technique is put into a context where you can trace it and use it in production.Once you've got the hang of pandas, you will be exploring deep into feature engineering with feature_engine and scikit-learn's set_output API, dealing with class imbalance with SMOTE and ADASYN, and doing distributed computing with Dask, as well as JIT-compiled custom functions with Numba and JAX. On top of that, you'll be able to handle full NLP pipelines from TF-IDF to LDA topic modelling, and geospatial analysis with GeoPandas.
It doesn't matter if you're building ML pipelines, scaling data infrastructure, or connecting pandas to TensorFlow, PyTorch, or JAX, this book will give you the practical depth and modern patterns to do it correctly on pandas 2.3 today, and stay forward-compatible with pandas 3.0 tomorrow.
Key Features
- Build memory-efficient pipelines using PyArrow backends and targeted dtype choices.
- Write Copy-on-Write-safe assignment patterns that work on pandas 2.3 and 3.0.
- Engineer rich ML features using ratios, bins, group statistics, and interaction terms.
- Handle class imbalance with SMOTE, ADASYN, and quantified pandas-based profiling.
- Scale datasets beyond RAM using Dask lazy evaluation and distributed cluster computing.
- Accelerate custom scoring functions with Numba JIT and JAX-compiled batch operations.
- Extract sentiment, topics, and clusters from raw text using TF-IDF and LDA pipelines.
- Perform spatial joins, buffer analysis, and geocoding with GeoPandas and geopy.
- Preserve named DataFrames throughout sklearn Pipelines using the set_output API.
- Migrate confidently from legacy pandas patterns to pandas 2.3 production standards.
Table of Content
- Getting Started with Pandas 2.3
- Data Read, Storage, and File Formats
- Indexing and Selecting Data
- Data Manipulation and Transformation
- Time Series and DateTime Operations
- Performance Optimization and Scaling
- Machine Learning with Pandas 2.3
- Text Mining and NLP
- Geospatial Data Analysis
"Synopsis" may belong to another edition of this title.
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| Item | 15 to 30 business days | 15 to 30 business days |
|---|---|---|
| First item | US$ 22.16 | US$ 22.16 |
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España:
Los envíos gratuitos se tramitan por Correo postal. Son envíos sin código de seguimiento. El cartero dejará un aviso de recogida en el buzón. Este tipo de envíos "PostLibris" no tienen entrega a domicilio, la entrega es en su oficina de Correos más cercana. El plazo estimado de entrega es orientativo y no vinculante ya que depende de la gestión de cada oficina de Correos en concreto.
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Cualquier incidencia debe ser comunicada en un plazo de 30 días desde la fecha estimada de entrega.
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