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

纯手写卷积神经网络，未使用任何神经网络框架，使用numpy纯手写卷积神经网络，研究此代码可充分搞懂卷积神经网络原理，本人也是通过此代码亲自走过来的。代码简单。 适用人群：适用于有意愿彻底搞懂卷积神经网络底层原理的同学，适合做该领域研究的学者，较易上手。 阅读建议：对于想学习python的同学，可通过此小项目一边研习python代码语法，一边学习卷积神经网络算法，可以很快入门python,并掌握基础的卷积神经网络算法。

numpy-1.13.3+mkl-cp27-cp27m-win_amd64.whl，安装包，可以便捷地在python里配置numpy工具，已经实际操作测试过，可顺利运行！

numpy-1.14.5+mkl-cp36-cp36m-win_amd64.zip科学计算包库，.whl格式，pip安装毫无压力

生成椭圆的matlab代码椭圆拟合python 使用python / numpy查找适合任意数据的椭圆体，对其进行绘制或写入文件。 （应该同时适用于python 2.7和python 3） 用于3轴磁力计校准。 如果您想使椭圆适合任意数据，请查看问题＃11-我试图解释如何做到这一点。 还要浏览其他未解决的问题。 Project是从matlab / octave到python / numpy的端口，并在matplotlib上添加了一些数据正则化和奇特的绘图功能。 它使用最小二乘法。 一些用于绘图的代码取自 输入文件示例是mag_out.txt，由包含3个数字的行组成，这些数字代表点坐标。 使用plot_ellipsoid.py查看输入数据，拟合的椭球以及将数据传输到球体表面上。 使用get_calibration_ellipsoid.py生成magcal_ellipsoid.txt，其中第一行具有拟合的椭球中心坐标，其他三行是椭球轴。 要求是： 麻木 matplotlib进行绘图 更新：有关Matlab的关闭版本，请点击此处 更新2：版本3中的Matplotlib不支持等轴，因此，精图可

步态数据上的预测建模：使用LSTM将预测模型应用于时序步态数据的实验的最终结果和Python代码。 “重采样和时代测试”显示了一次优化模型参数两次的第一次迭代的结果。 “批次大小和神经元测试”显示第二次测试的结果，优化了其余两个参数

Inter MKL帮助文档（参考手册）for C，2019, 2000多页

机器学习预科知识：“numpy+pandas+matplotlib基础”