这不本教你「如何做出漂亮模型」的书,而是一份给已经会写 Python 的人看的案头工具指南。它把 NumPy、Pandas、Matplotlib、Scikit-Learn 等常用包串成一条完整的工具链,重点不在炫技,而在让你快速查、快速上手。读者普遍反馈:它最大的价值是给了真实的使用场景和可跑的代码示例,让你不必啃完冗长的官方文档,就知道「每个包大概能干什么」;相比 McKinney 那本偏入门的《Python for Data Analysis》,它更像一本可以随时翻查的 handbook。适合想系统梳理数据科学工具、需要一本速查字典的工程师与科研人;但如果你期待书中讲解完整的项目案例或深入算法原理,可能会觉得例子偏「实验性」,数学推导需要你自己补。
AI导读
核心看点
系统讲解IPython、NumPy、Pandas等核心工具
涵盖数据清洗、可视化及机器学习建模全流程
提供丰富代码示例,适合快速查阅与实战参考
读者共识
内容全面且浅显,是极佳的数据科学入门参考书
相比官方文档更友好,提供了常用场景的具体示例
适合有编程基础者,理论深度适中,实用性强
精彩摘录
"Looking through the Python 3.4 source code, we find that the integer (long) type definition effectively looks like this (once the C macros are expanded):"
"Here PyObject_HEAD is the part of the structure containing the reference count, type code, and other pieces mentioned before. Notice the difference here: a C integer is essentially a label for a position in memory whose bytes encode an integer value. A Python integer is a pointer to a position in me"
conceptions in this version are moreorless clearer than the Chinese translation version. suitable for beginners. But better to use together with the documents of numpy and pandas. some operations used in the book are deprecated now.
Nunpy, Pandas, Matplotlib三剑客,讲的挺清楚。
basic python reference for numpy, pandas, matplotlib; introduction to machine learning with sklearn