官网下载特别慢，下载了几个多小时才下载完。适用于python 3.5.4，欢迎各位下载使用。

MYDBSCAN：基于密度的聚类DBSCAN（Density-Based Spatial Clustering of Applications with Noise）算法的底层实现 MYAP：基于划分的聚类AP（Affinity Propagation Clustering Algorithm ）算法的底层实现--近邻传播聚类算法 Adaptive-DBSCAN：自适应的基于密度的空间聚类（Adaptive Density-Based Spatial Clustering of Applications with Noise）算法的底层实现 MYOPTICS：基于密度的聚类OPTICS（Ordering points to identify the clustering structure）算法的底层实现 MYKMeans：基于划分的聚类KMeans算法的底层实现 MYCFSFDP：基于划分和密度的聚类CFSFDP（Clustering by fast search and find of density peaks）算法的底层实现

这是适用于python35的numpy+mkl包，单独先装numpy容易导致后期安装不上mkl，建议一起安装

numpy官方用户手册150页，适合做numpy快速入门，numpyapi手册可以作为参考工具书

由官网没有64位工具包，楼主耗时两天配置python64位下numpy、matplotlib、scipy、dateutil、pyparsing的安装，附带安装包和操作指南。实现傻瓜式安装

NumPy是使用Python进行科学计算的基础软件包。除了明显的科学用途外，NumPy还可以用作通用数据的高效多维容器。可以定义任意数据类型。这使NumPy能够无缝快速地与各种数据库集成。

绘制散点图 假设通过爬虫你获取到了北京2016年3,10月份每天白天的最高气温(分别位于列表a,b),那么此时如何寻找出气温和随时间(天)变化的某种规律? a = [11,17,16,11,12,11,12,6,6,7,8,9,12,15,14,17,18,21,16,17,20,14,15,15,15,19,21,22,22,22,23] b = [26,26,28,19,21,17,16,19,18,20,20,19,22,23,17,20,21,20,22,15,11,15,5,13,17,10,11,13,12,13,6] 数据来源: http://lishi.tianqi.com/beijing/index.html

Python, a multi-paradigm programming language, has become the language of choice for data scientists for visualization, data analysis, and machine learning. Hands-On Data Analysis with NumPy and Pandas starts by guiding you in setting up the right environment for data analysis with Python, along with helping you install the correct Python distribution. In addition to this, you will work with the Jupyter notebook and set up a database. Once you have covered Jupyter, you will dig deep into Python’s NumPy package, a powerful extension with advanced mathematical functions. You will then move on to creating NumPy arrays and employing different array methods and functions. You will explore Python’s pandas extension which will help you get to grips with data mining and learn to subset your data. Last but not the least you will grasp how to manage your datasets by sorting and ranking them. By the end of this book, you will have learned to index and group your data for sophisticated data analysis and manipulation. What You Will Learn • Understand how to install and manage Anaconda • Read, sort, and map data using NumPy and pandas • Find out how to create and slice data arrays using NumPy • Discover how to subset your DataFrames using pandas • Handle missing data in a pandas DataFrame • Explore hierarchical indexing and plotting with pandas

Title: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2nd Edition Author: Wes McKinney Length: 550 pages Edition: 2 Language: English Publisher: O'Reilly Media Publication Date: 2017-09-25 ISBN-10: 1491957662 ISBN-13: 9781491957660 Table of Contents Chapter 1 Preliminaries Chapter 2 Python Language Basics, IPython, and Jupyter Notebooks Chapter 3 Built-in Data Structures, Functions, and Files Chapter 4 NumPy Basics: Arrays and Vectorized Computation Chapter 5 Getting Started with pandas Chapter 6 Data Loading, Storage, and File Formats Chapter 7 Data Cleaning and Preparation Chapter 8 Data Wrangling: Join, Combine, and Reshape Chapter 9 Plotting and Visualization Chapter 10 Data Aggregation and Group Operations Chapter 11 Interlude: Data Analysis Examples Chapter 12 Time Series Chapter 13 Advanced NumPy Chapter 14 Using Modeling Libraries with pandas Chapter 15 Examples Data Sets Appendix Advanced IPython and Jupyter

吴恩达网易云公开课《深度学习》week4--deep_nn_model二分类