Vendor Profile
Shoeisha Co., Ltd.
| Address | 5 Funamachi Shinjuku-ku Tokyo, JAPAN ZIP:160-0006 |
|---|---|
| Representative Name | Kaoru Usui |
| Annual Revenue | closed |
| No. of Employees | 185 |
| Web Site URL |
SD item code:12973901
| Detail | Price & Quantity | ||
|---|---|---|---|
| S1 |
Tiago Rodrigues Ant?o Originally written by
Supervised by Queep Co.
Translated by Queep Co.
 Original text before translation
Tiago Rodrigues Ant?o 原著
株式会社クイープ 監修
株式会社クイープ 翻訳
(183730)
JAN:9784798183732
|
(183730)
JAN:9784798183732
Wholesale Price: Members Only
1 pc /set
In Stock
|
|
| Shipping Date |
|---|
|
About 1 week
|
| Dimensions |
|---|
|
Format:B5
Number of pages: 352 |
| Specifications |
|---|
|
Country of manufacture: Japan
Material / component: Format:Book (paper)
Year of manufacture: 2024
Product tag: None
|
Description
| [Yes, all for speed.] When dealing with datasets in Python, the last thing that matters is processing speed. The larger the data, the faster the processing speed, and a little ingenuity is the key to overcoming the current situation [of data overflow]. If you understand the characteristics of Python, maximize its performance, and properly use high-performance libraries, you can achieve blazing-fast processing in Python, which is often said to be slow and sluggish. This book presents a multi-faceted approach, starting with built-in features, threading characteristics, CPython's Global Interpreter Lock (GIL), and moving on to Cython and the use of GPUs, to help you write Python applications more efficiently than can be obtained by simply increasing machine performance or number of machines. support for writing Python applications. [This book is the Japanese translation of [Fast Python: High performance techniques for large datasets]. *More info* *Part 1 Basic Approaches *Chapter 1: The Urgent Need for More Efficient Data Processing *Chapter 2 Maximizing the Performance of Embedded Functions *Chapter 3 Concurrency, Parallelism, and Asynchronous Processing *Chapter 4 High Performance NumPy *Part 2 Hardware *Chapter 5 Reimplementing critical code with Cython *Chapter 6 Memory hierarchy, storage, networking *Part 3 Applications and libraries for modern data processing *Chapter 7 High performance pandas and Apache Arrow *Chapter 8 Storing Big Data *Part 4 Advanced topics *Chapter 9 Data analysis using GPU computing *Chapter 10 Analyzing Big Data with Dask *Appendix A Setting Up Your Environment *Appendix B Generating Efficient Low-Level Code with Numba |
More
| Shipping Method | Estimated Arrival |
|---|---|
| Sea Mail | From Dec.26th 2025 to Feb.27th 2026 |
| Air Mail | From Dec.10th 2025 to Dec.12th 2025 |
| EMS | From Dec.9th 2025 to Dec.12th 2025 |
| Pantos Express | From Dec.11th 2025 to Dec.16th 2025 |
| DHL | From Dec.9th 2025 to Dec.11th 2025 |
| UPS | From Dec.9th 2025 to Dec.11th 2025 |
| FedEx | From Dec.9th 2025 to Dec.11th 2025 |
|
Some trading conditions may be applicable only in Japan.
This product (book) is subject to the Resale Price Maintenance Program. The law allows the manufacturer (publisher) to specify the sales price. We ask that your company also adhere to the resale price specified by us. In the unlikely event that you fail to do so, we may terminate the transaction. Thank you very much for your understanding and cooperation.
|
Other items from this category:
When dealing with datasets in Python, the last thing that matters is processing speed. The larger the data, the faster the processing speed, and a little ingenuity is the key to overcoming the current situation [of data overflow].
If you understand the characteristics of Python, maximize its performance, and properly use high-performance libraries, you can achieve blazing-fast processing in Python, which is often said to be slow and sluggish.
This book presents a multi-faceted approach, starting with built-in features, threading characteristics, CPython's Global Interpreter Lock (GIL), and moving on to Cython and the use of GPUs, to help you write Python applications more efficiently than can be obtained by simply increasing machine performance or number of machines. support for writing Python applications.
[This book is the Japanese translation of [Fast Python: High performance techniques for large datasets].
*More info*
*Part 1 Basic Approaches
*Chapter 1: The Urgent Need for More Efficient Data Processing
*Chapter 2 Maximizing the Performance of Embedded Functions
*Chapter 3 Concurrency, Parallelism, and Asynchronous Processing
*Chapter 4 High Performance NumPy
*Part 2 Hardware
*Chapter 5 Reimplementing critical code with Cython
*Chapter 6 Memory hierarchy, storage, networking
*Part 3 Applications and libraries for modern data processing
*Chapter 7 High performance pandas and Apache Arrow
*Chapter 8 Storing Big Data
*Part 4 Advanced topics
*Chapter 9 Data analysis using GPU computing
*Chapter 10 Analyzing Big Data with Dask
*Appendix A Setting Up Your Environment
*Appendix B Generating Efficient Low-Level Code with Numba