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AI AI development foundation: Python library

Python has a rich class library, which provides strong support for data science, machine learning, natural language processing and other fields, significantly reducing the work difficulty of AI developers.
This collection collects and collates some important Python class library learning resources, including NumPy SciPy、Pandas、Matplotlib、Seaborn。 I hope it will be helpful to everyone.

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AI AI development foundation: Python library document list

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Python Data Science Manual: label: python; Points: 1 Type: Technical document Uploader: Hyperplatinum Upload time: May 30, 2021; Introduction: This book is a reference book for science, research and calculation and statistical methods focusing on data depth needs. This book has five chapters, each chapter introduces one or two key toolkits in Python data science. First, IPython and Jupyter provide the computing environment required by data scientists; Chapter 2 explains NumPy, which can provide an array object. It can store and operate large arrays efficiently in Python; Chapter 3 mainly deals with Pandas, which provides DataFrame objects. It can use Python to efficiently store and operate tagged/columnar data; The protagonist of Chapter 4 is Matplotlib, which provides Python with many data visualization functions; Chapter 5 focuses on Scikit Lean, which provides an efficient and clean Python implementation for the most important machine learning algorithm. This book is suitable for data science researchers with programming background who intend to use open source Python tools as data analysis, operation, visualization and learning.

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Python Scientific Computing _ Zhang Ruoyu: label: Python Scientific computing; Points: 2 Type: Application Document Uploader: to glance over things hurriedly Upload time: January 29, 2024; Introduction: Author: Zhang Ruoyu Year of publication: 2012 This book introduces how to develop scientific computing applications in Python. In addition to numerical computing, it also focuses on how to make interactive 2D and 3D images, how to design sophisticated program interfaces, how to combine with high-speed computing programs written in C language, and how to write sound and image processing algorithms. The Python extension libraries involved in the book include NumPy, Scipy, SymPy, matplotlib, Traits, TraitsUI, Chaco, TVTK, Mayavi, VPython, OpenCV, etc. The application fields involved include numerical operation, symbol operation, two-dimensional chart, three-dimensional data visualization, three-dimensional animation demonstration, image processing, interface design, etc. In the book, a large number of examples are used to guide the readers to learn deeply step by step. Each example program has a detailed explanation and can run normally in the running environment recommended by the book. In addition, this book is attached with a large number of charts and illustrations to reduce the length of theoretical introduction and formula derivation, so that readers can learn and master theoretical knowledge through examples and data.

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Basic Course of Python Data Analysis: NumPy Learning Guide (Version 2): label: python; Points: 1 Type: Technical document Uploader: Hyperplatinum Upload time: May 30, 2021; Introduction: NumPy is an excellent scientific computing library, which provides many practical mathematical functions, powerful multidimensional array objects and excellent computing performance. It can not only replace many functions of Matlab and Mathematica, but also has become an important part of Python scientific computing ecosystem. But unlike these commercial products, it is free open source software. This book starts with the installation of NumPy, and gradually transitions to array objects, common functions, matrix operations, linear algebra, financial functions, window functions, quality control and other contents. It is dedicated to comprehensively explaining NumPy and its use to beginner and intermediate Python programmers. In addition, through the rich examples in the book, you will also learn Matplotlib drawing, and use other Python scientific computing libraries (such as Scipy and Scikits) in combination to make your work more effective and code more concise and efficient. Main contents: Install NumPy on different platforms; Use simple and efficient NumPy code to achieve high-performance computing; Use powerful general functions; Use NumPy arrays and matrices; The NumPy module can easily perform complex numerical calculations; Matplotlib drawing; NumPy code test.

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Python data visualization (dark horse programmer): label: python; Points: 1 Type: Technical document Uploader: throw away a brick in order to get a gem Upload time: May 28, 2023; Introduction: This book comprehensively introduces the relevant knowledge of Python data visualization in the form of combining theory with examples, using Anaconda 3 as the main development tool. The book is divided into nine chapters. Chapter 1 introduces the introduction to data visualization and matplotlib; Chapters 2 to 8 comprehensively introduce the core knowledge of matplotlib, including the use of matplotlib to draw simple charts, the customization of chart auxiliary elements, the beautification of chart styles, the drawing of sub charts and coordinate axis sharing, the customization of coordinate axes, the drawing of 3D charts and statistical maps, and the use of matplotlib to draw advanced charts; Chapter 9 introduces the basic knowledge of pyecharts. In addition to Chapter 1, other chapters are equipped with rich examples. Readers can practice while learning to consolidate their knowledge and improve their practical development ability in practice. This book can be used not only as a textbook for computer related majors in higher education and colleges, but also as an introductory book for data visualization technology enthusiasts. Chapter 1 Data visualization and matplotlib 001 1.1 Data visualization overview 002 1.1.1 What is data visualization 002 1.1.2 Common data visualization methods 003 1.1.3 Select the correct data visualization chart 010 1.2 Common data visualization library 012 1.3 Getting to know matplotlib 013 1.3.1 Overview of matplotlib 013 1.3.2 Install matplotlib 014 1.4 Use matplotlib to draw diagram017 1.5 Summary of this chapter 019 1.6 Exercise 019 Chapter 2 Use matplotlib to draw simple Chart 021 2.1 Draw line chart 022 2.1.1 Use plot() to draw a line chart 022 2.1.2 Example 1: Mild weather in the next 15 years Temperature 022 2.2 Draw Column Chart or Stacked Column Chart 024 2.2.1 Use bar () to draw a column chart or stack Column Chart 024 2.2.2 Example 2: Ali in 2013-2019 GMV 027 of Baba Taobao and Tmall platforms 2.3 Draw Bar Chart or Stacked Bar Chart 028 2.3.1 Use barh() to draw bar chart or stack Bar Chart 028 2.3.2 Example 3: Online shopping of various commodity types Substitution rate 031 2.4 Draw Stacked Area Chart033 2.4.1 Using stackplot() to Plot Stacks Area Drawing 033 2.4.2 Example 4: Logistics costs of logistics companies Statistics 034 2.5 Draw Histogram 035 2.5.1 Use hist() to draw histograms 035 2.5.2 Example 5: grayscale of face recognition Histogram 036 2.6 Draw pie chart or doughnut chart 037 2.6.1 Use pie () to draw a pie chart or Doughnut 037 2.6.2 Example 6: Paying Baoyue Bill Report 039 2.7 Draw Scatter Chart or Bubble Chart 040 2.7.1 Use scatter() to draw a scatter plot or Bubble 040 2.7.2 Example 7: The relationship between vehicle speed and braking distance Relationship 041 2.8 Draw box diagram042 2.8.1 Use boxplot () to draw boxplot 042 2.8.2 Example 8: 2017 and 2018 National Power Generation Statistics 044 2.9 Draw radar map 045 2.9.1 Draw radar map with polar() 045 2.9.2 Example 9: Holland's professional interest Test 045 2.10 Draw error bar diagram 047 2.10.1 Using errorbar() to plot errors Bar drawing 047 2.10.2 Example 10: Four tree species in different seasons Fine root biomass 048 2.11 Summary of this chapter 049 2.12 Exercise 049 Chapter 3 Customization of Chart Auxiliary Elements 052 3.1 Understand the common auxiliary elements of charts 053 3.2 Set the label, scale range and Scale label 054 3.2.1 Set the label of coordinate axis 054 3.2.2 Setting the scale range and scale label056 3.2.3 Example 1: box office of Chinese films in 2019 Leaderboard 057 3.3 Add title and legend 059 3.3.1 Add title 059 3.3.2 Add key 059 3.3.3 Example 2: Alipay Bill Report (Add title and legend) 061 3.4 Display grid 062 3.4.1 Display grid of specified style 062 3.4.2 Example 3: vehicle speed and braking distance Relationship (Add Grid) 063 3.5 Add reference line and reference area 064 3.5.1 Add reference line 064 3.5.2 Add reference area 065 3.5.3 Example 4: all classes of senior two English Performance Evaluation for Male and Female Students 066 3.6 Add note text 068 3.6.1 Adding Pointing Note Text068 3.6.2 Adding Non directional Annotation Text069 3.6.3 Example 5: 2013-2019 Alibaba Taobao and Tmall GMV (add note text) 071 3.7 Add Form 072 3.7.1 Adding a Custom Style Table 072 3.7.2 Example 6: Ratio of jam and bread ingredients 073 3.8 Summary of this chapter 075 3.9 Exercise 075 Chapter 4 Beautifying Chart Style 077 4.1 Chart Style Overview 078 4.1.1 Default Chart Style 078 4.1.2 Modifying Chart Style 079 4.2 Use color 080 4.2.1 Use basic color 081 4.2.2 Using the Color Mapping Table 082 4.2.3 Example 1: Different species in two regions Book purchase 083 4.3 Select linetype 084 4.3.1 Select the type of line 084 4.3.2 Example 2: July 2017 vs July 2019 International foreign exchange market Trend of USD/RMB exchange rate 085 4.4 Adding data markers 087 4.4.1 Add data of line chart or scatter chart Mark 087 4.4.2 Example 3: marking different products Quarterly sales 089 4.5 Setting font 090 4.5.1 Setting font style 090 4.5.2 Example 4: Temperature and Temperature (set font style) 091 4.6 Switching theme style 092 4.7 Filling area 093 4.7.1 Filling the area between polygons or curves 093 4.7.2 Example 5: colored "snowflake" 095 4.8 Summary of this chapter 096 4.9 Exercise 096 Chapter 5 Drawing of Sub graph and Sharing of Coordinate Axes 099 5.1 Drawing a sub drawing of a fixed area100 5.1.1 Drawing single sub diagram100 5.1.2 Example 1: A factory product A and Sales analysis of product B last year 103 5.1.3 Drawing multiple subgraphs 105 5.1.4 Example 2: cat population ratio in some countries Analysis of the proportion of cases and dog owners 106 5.2 Drawing a Subfigure of a Custom Area107 5.2.1 Drawing a single subgraph 107 5.2.2 Example 3: 2017 and 2018 Treble user analysis 109 5.3 Coordinate axis of shared subgraph 110 5.3.1 Sharing coordinate axes of adjacent subgraphs 111 5.3.2 Coordinate axis for sharing non adjacent subgraphs 112 5.3.3 Example 4: annual average temperature of a region Relationship with precipitation and evaporation 114 5.4 Layout of subgraphs 115 5.4.1 Constraint Layout115 5.4.2 Tight layout 117 5.4.3 Custom Layout118 5.4.4 Example 5: A brand in the first half of 2018 Automobile sales 120 5.5 Summary of this chapter 121 5.6 Exercise122 Chapter 6 Customization of Coordinate Axes125 6.1 Overview of coordinate axis 126 6.2 Adding an axis to any location127 6.3 Custom scale 128 6.3.1 Position and format of customized scale 128 6.3.2 Customized scale style 130 6.3.3 Example 1: 24-hour Average wind speed 131 6.4 Hidden Ridge133 6.4.1 Hiding All Ridges 133 6.4.2 Hiding part of the ridge 134 6.4.3 Example 2: 24-hour Average wind speed (hidden ridge) 135 6.5 Moving the spine 136 6.5.1 Position of moving shaft ridge136 6.5.2 Example 3: Sine and cosine curves 137 6.6 Summary of this chapter 138 6.7 Exercise 139 Chapter 7 Drawing 3D Charts and Statistical Maps 141 7.1 Plotting 3D charts using mplot3d 142 7.1.1 Overview of mplot3d 142 7.1.2 Drawing common 3D charts143 7.1.3 Example 1: Stars in 3D space145 7.2 Using animation to make moving pictures 146 7.2.1 Overview of animation147 7.2.2 Example 2: 3D space flickering Stars 149 7.3 Using basemap to draw statistical map150 7.3.1 Overview of basemap 150 7.3.2 Example 3: Some urban populations in the United States Distribution 153 7.4 Summary of this chapter 155 7.5 Exercise 155 Chapter 8 Advanced Drawing with Matplotlib Chart 158 8.1 Preparation of contour map 159 8.2 Draw vector field streamline diagram161 8.3 Drawing a Cotton Stick Figure 163 8.4 Drawing dumbbell diagram165 8.5 Drawing Gantt Chart168 8.6 Mapping the population pyramid 169 8.7 Draw funnel diagram171 8.8 Drawing the Sankey Chart173 8.9 Drawing a tree diagram 176 8.10 Drawing a waffle chart 179 8.11 Summary of this chapter 181 8.12 Exercises181 Chapter 9 The Rising Star of Data Visualization pyecharts 184 9.1 Overview of pyecharts 185 9.2 Basic knowledge of pyecharts 187 9.2.1 Quick chart drawing 187 9.2.2 Understanding charts 188 9.2.3 Understanding configuration items189 9.2.4 Rendering Chart192 9.3 Drawing Common Charts192 9.3.1 Draw a line diagram192 9.3.2 Draw pie chart or doughnut chart 194 9.3.3 Draw scatter plots 196 9.3.4 Drawing 3D Column Chart198 9.3.5 Drawing statistical map199 9.3.6 Draw funnel diagram200 9.3.7 Drawing a Sankey Diagram 201 9.4 Drawing combination chart 203 9.4.1 Parallel multi graph 203 9.4.2 Sequence Multiple Diagram205 9.4.3 Tabbed Multifigure206 9.4.4 Timeline Carousel Multigraph 208 9.5 Customizing Chart Theme211 9.6 Integrated Web framework212 9.7 Example: Hupu Community Analysis 214 9.8 Summary of this chapter 218 9.9 Exercise 219

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Python Scientific Computing Best Practices: Scipy Guide: label: python scipy; Points: 1 Type: Technical document Uploader: throw away a brick in order to get a gem Upload time: July 30, 2023; Introduction: This book aims to introduce the open source Python algorithm library and mathematical toolkit Scipy. In recent years, complete ecosystems based on NumPy and SciPy have developed rapidly and been widely used in astronomy, biology, meteorology, climate science, materials science and other disciplines. This book, combined with a large number of code examples, shows in detail the powerful scientific computing capabilities of SciPy, including quantile standardization with NumPy and SciPy, image area network with ndimage, frequency and fast Fourier transform, contingency table with sparse coordinate matrix, linear algebra in SciPy, function optimization in SciPy, etc. Preface ix Chapter 1 Elegant NumPy: Foundation of Python Scientific Application 1 1.1 Data introduction: what is gene expression 2 1.2 N-dimensional array of NumPy 6 1.2.1 Why use N-dimensional arrays instead of Python lists 7 1.2.2 Vectorization 9 1.2.3 Broadcasting 9 1.3 Exploring gene expression data sets10 1.4 Standardization 13 1.4.1 Standardization between samples 13 1.4.2 Intergenic standardization 19 1.4.3 Sample and gene standardization: RPKM 21 1.5 Summary 27 Chapter 2 Quantile standardization with NumPy and SciPy 28 2.1 Obtaining Data 30 2.2 Differences in gene expression distribution among independent samples 30 2.3 Bi directional clustering of count data 33 2.4 Visualization of Clusters35 2.5 Predicting Survivors 37 2.5.1 Further work: using TCGA patient clusters 41 2.5.2 Further work: regenerate TCGA clusters 41 Chapter 3 Implementation of image area network with ndimage 42 3.1 The image is a NumPy array 43 3.2 Filters in signal processing48 3.3 Image filtering (two-dimensional filter) 53 3.4 Universal filter: arbitrary function of adjacent value55 3.4.1 Exercise: Kangwei's Life Game 56 3.4.2 Exercise: Sobel gradient amplitude 56 3.5 Diagram and NetworkX Library57 3.6 Regional adjacency diagram 60 3.7 Elegant ndimage: how to create image objects according to image area63 3.8 Summary: average color segmentation 65 Chapter 4 Frequency and Fast Fourier Transformation67 4.1 Frequency introduction 67 4.2 Example: Bird song spectrogram 69 4.3 History 74 4.4 Realizing 75 4.5 Selecting the length of the discrete Fourier transform 75 4.6 More Discrete Fourier Transform Concepts 77 4.6.1 Frequency and its sequence77 4.6.2 Windowing 83 4.7 Practical application: analyzing radar data86 4.7.1 Signal properties in frequency frequency91 4.7.2 After windowing 93 4.7.3 Radar image95 4.7.4 Further application of FFT 99 4.7.5 More Reading 99 4.7.6 Exercise: image convolution 100 Chapter 5 Realization of contingency table with sparse coordinate matrix 101 5.1 Train connection table102 5.1.1 Exercise: Calculation complexity of confusion matrix 103 5.1.2 Exercise: Another way to calculate the confusion matrix 103 5.1.3 Exercise: Multi class confusion matrix 104 5.2 Scipy.sparse data format 104 5.2.1 COO format 104 5.2.2 Exercise: COO represents 105 5.2.3 Sparse row compression format106 5.3 Sparse matrix application: image conversion 108 5.4 Return to the contingency table112 5.5 Contingency table in image segmentation 113 5.6 Introduction to information theory114 5.7 Information theory in image segmentation: information variation 117 5.8 Converting NumPy Array Code to Use Sparse Matrice119 5.9 Use information variation 120 Chapter 6 Linear Algebra in SciPy128 6.1 Fundamentals of Linear Algebra 128 6.2 Laplacian matrix of figure 129 6.3 Laplacian matrix of brain data 134 6.3.1 Exercise: Displaying a Nearest Neighbor View138 6.3.2 Exercise challenge: sparse matrix linear algebra 138 6.4 PageRank: Linear Algebra for Reputation and Importance139 6.4.1 Exercise: handling suspended nodes 144 6.4.2 Exercise: equivalence of different eigenvector methods 144 6.5 Conclusion 144 Chapter 7 Function Optimization in SciPy 145 7.1 SciPy optimization module: size.optimize 146 7.2 Image registration with optimize 152 7.3 Use the basin hopping algorithm to avoid the local minimum value155 7.4 Selecting the correct objective function156 Chapter 8 Using Toolz to Play Big Data on Notebook163 8.1 Stream processing with yield 164 8.2 Introducing Toolz stream library167 8.3 k-mer count and error correction 169 8.4 Curry: streaming seasoning173 8.5 Return to k-mer count 175 8.6 Genome wide Markov model177 Postscript 182 Appendix Exercise Answers 186 About the Author 206 Cover Introduction 206

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Pandas: use Python for data processing and analysis: label: python data processing; Points: 1 Type: Technical document Uploader: throw away a brick in order to get a gem Upload time: May 21, 2023; Introduction: If you want to give full play to the powerful role of Python, and if you want to become a good Python engineer, you should first learn Pandas well. This is a book that comprehensively covers the general needs and pain points of Pandas users. Based on the principle of practicality and easy learning, Pandas is comprehensively explained in detail from multiple dimensions such as function, use, and principle. It is not only a rare primer for beginners to systematically learn Pandas, but also an essential query manual for experienced Python engineers. This book consists of 17 chapters and is divided into seven parts. Part I (Chapter 1-2) Introduction to Pandas First, it introduces the functions, usage scenarios and learning methods of Pandas. Then it explains in detail the building of the Python development environment. After Z, it introduces a lot of basic functions of Pandas, aiming to guide readers to get a quick start. Part II (Chapter 3-5) Fundamentals of Pandas Data Analysis Pandas read and output data, index operation, data type conversion, query filtering, statistical calculation, sorting, displacement, data modification, data iteration, function application, etc. are explained in detail. Part III (Chapter 6-9) Data Form Change This paper explains Pandas' grouping and aggregation operations, consolidation operations, comparison operations, pivoting, transposition, normalization, standardization, etc., and how to use multi-level indexes to raise and lower dimensions of data. Part IV (Chapter 10-12) Data cleaning It explains the identification, deletion and filling of missing and duplicate values, the replacement and format conversion of data, the extraction, connection, matching, segmentation, replacement, formatting and virtual variable of text, as well as the application scenarios and operation methods of classified data. Part V (Chapter 13-14) Time series data analysis It explains the processing and analysis of various time type data in Pandas, as well as the window calculation often used in time series data processing. Part VI (Chapter 15-16) Visualization It explains how the style function of Pandas makes data tables more expressive, and how the drawing function of Pandas makes data speak for itself. Part VII (Chapter 17) Practical Cases This paper introduces the thinking process from demand to code, how to use the chain programming idea to improve the efficiency of code writing and data analysis, as well as the basic methods of data analysis and the data analysis tools and technology stacks that need to be mastered. In addition, it gives a large number of application cases and code explanations from the perspectives of data processing and data analysis. preface Part I Introduction to Pandas Chapter 1 Introduction to Pandas and Quick Start 1.1 What is Pandas 1.1.1 Introduction to Python 1.1.2 Application of Python 1.1.3 Why not select R 1.1.4 Introduction to Pandas 1.1.5 Users of Pandas 1.1.6 Basic Functions of Pandas 1.1.7 Learning methods of Pandas 1.1.8 Summary 1.2 Environment construction and installation 1.2.1 Python Environment Installation 1.2.2 Introduction to Anaconda 1.2.3 Installation of miniconda 1.2.4 Multi Python version environment 1.2.5 Installing the Editor 1.2.6 Jupyter Notebook 1.2.7 Installing the third-party library with pip 1.2.8 Installing Jupyter Notebook 1.2.9 Start Jupyter Notebook 1.2.10 Using Jupyter Notebook 1.2.11 Installing Pandas 1.2.12 Summary 1.3 Pandas Quick Start 1.3.1 Installation Import 1.3.2 Preparing Datasets 1.3.3 Reading data 1.3.4 View Data 1.3.5 Validation data 1.3.6 Indexing 1.3.7 Data selection 1.3.8 Sorting 1.3.9 Group Aggregation 1.3.10 Data conversion 1.3.11 Add Column 1.3.12 Statistical analysis 1.3.13 Drawing 1.3.14 Export 1.3.15 Summary 1.4 Summary of this chapter Chapter 2 Data Structure 2.1 Overview of data structure 2.1.1 What is data 2.1.2 What is data structure 2.1.3 Summary 2.2 Python data structure 2.2.1 Figures 2.2.2 String 2.2.3 Boolean 2.2.4 List 2.2.5 tuples …… Part II Fundamentals of Pandas Data Analysis Part III Data Format Change Part IV Data cleaning Part V Time series data analysis Part VI Visualization Part VII Practical Cases

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Python scientific computing and data analysis: label: python; Points: 1 Type: Technical document Uploader: Hyperplatinum Upload time: May 30, 2021; Introduction: This book will lead readers to understand all aspects of the most popular scientific computing library NumPy. The book not only introduces the installation, use and various related concepts of NumPy, but also introduces how to use this latest open source software library to write code with good readability, high implementation efficiency and fast running speed in a way as close as possible to traditional mathematical languages. Finally, several NumPy related scientific computing projects are explored. In addition, this book will lay a solid foundation for you to master NumPy arrays and general functions. It will also teach you to draw with Matplotlib through examples and learn about Scipy related projects. This book will help you: • Learn advanced indexing techniques and linear algebra • Understand the adjustment of array shape and image size • Explore broadcast mechanisms and histograms • Analyze NumPy code and visually present the analysis results • Speed up code with Python • Use array interface to share data • Use of common functions and interoperability • Learn Matplotlib and SciPy, which is often used with NumPy

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Python for Data Analysis: label: python; Points: 1 Type: Technical document Uploader: Hyperplatinum Upload time: May 30, 2021; Introduction: Still looking for a complete course of controlling, processing, sorting and analyzing structured data with Python? This book contains a large number of practical cases. You will learn how to use various Python libraries (including NumPy, Pandas, matplotlib and IPython) to efficiently solve various data analysis problems. Since the author Wes McKinney is the main author of the pandas library, this book can also be used as a practical guide for scientific computing to implement data intensive applications using Python. This book is suitable for analysts who are new to Python and Python programmers who are new to scientific computing. • Use the interactive shell IPython as your primary development environment. • Learn the basic and advanced knowledge of NumPy (Numerical Python). • Start with the data analysis tool of Pandas library. • Use high-performance tools to load, clean, transform, merge, and reshape data. • Use matplotlib to create scatter plots and static or interactive visualization results. • Use the groupby function of Pandas to slice, slice, and summarize data sets. • Processing various time series data. • Learn how to solve problems in Web analysis, social science, finance, economics and other fields through detailed cases.

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Python Data Visualization Matplotlib Practice (Liu Dacheng): label: python matplot; Points: 1 Type: Technical document Uploader: throw away a brick in order to get a gem Upload time: May 24, 2023; Introduction: This book uses Matplotlib to explain the key knowledge and skills required for Python data visualization practice. This book mainly consists of Matplotlib introduction, refinement, exercise and expansion. At the same time, in order to facilitate readers to effectively practice the contents of the book, a large number of typical comprehensive cases will be attached to the relevant chapters. The code used in the book is the basic content of Python programming knowledge, which helps readers to focus their time and energy on the practice of data visualization itself. Therefore, this book is suitable for people of insight in various industries and fields who are interested in Python data visualization. Chapter 1 Introduction Chapter 1 Use Functions to Draw the Chart Elements of Matplotlib 2 1.1 Main functions for drawing matplotlib diagram elements 2 1.2 Preparing Data 3 1.3 Function usage for drawing matplotlib diagram elements 4 1.3.1 Function plot () - display the trend change of variable 4 1.3.2 Function scatter() -- find the relationship between variables 5 1.3.3 Function xlim() - set the numerical display range of x-axis 6 1.3.4 Function xlabel() -- set the label text of x-axis 7 1.3.5 Function grid() -- Draw grid lines of tick marks 8 1.3.6 Function axhline() - draw a horizontal reference line parallel to the x-axis 9 1.3.7 Function axvspan() - plot the reference area perpendicular to the x-axis 11 1.3.8 Function annotate () - add directional annotation text of graphic content details 12 1.3.9 Function text() -- Non directional annotation text for adding graphic content details 13 1.3.10 Function title () -- add the title of graphic content 15 1.3.11 Function legend() - text label legend for different graphics 16 1.4 Function combination application 17 Chapter 2 Using Statistical Functions to Draw Simple Graphs 20 2.1 Function bar () - used to draw histogram 20 2.2 Function barh() - used to draw bar chart 22 2.3 Function hist() - used to draw histograms 23 2.4 Function pie() -- used to draw pie chart 25 2.5 Function polar () - used to draw polar diagram 26 2.6 Function scatter() -- used to draw bubble chart 27 2.7 Function stem () - used to draw cotton swab figure 29 2.8 Function boxplot () -- used to plot boxplot 30 2.9 Function errorbar() -- used to draw error bar chart 31 Chapter 3 Drawing Statistical Graphs 33 3.1 Histogram 33 3.1.1 Application scenario - distribution display of qualitative data 33 3.1.2 Drawing Principle 33 3.2 Bar Chart35 3.3 Stacking Chart37 3.3.1 Stacking Histogram 37 3.3.2 Stacked Bar Chart38 3.4 Block diagram 39 3.4.1 Multi data Parallel Histogram 40 3.4.2 Multiple Data Parallel Bar Chart41 3.5 Parameter exploration 42 3.6 Stacked Line Chart, Discontinuous Bar Chart and Ladder Chart 44 3.6.1 Use stackplot() to draw a stacked line chart 44 3.6.2 Draw discontinuous bar graph with function broker_barh() 45 3.6.3 Draw ladder diagram with function step() 47 3.7 Histogram 48 3.7.1 Application scenario - distribution display of quantitative data 48 3.7.2 Drawing Principle 49 3.7.3 Relationship between Histogram and Histogram 50 3.7.4 Stacked Histogram 51 3.7.5 Different shapes of histograms 53 3.8 Pie Chart55 3.8.1 Application Scenario - Proportional Display of Qualitative Data 55 3.8.2 Drawing Principle 55 3.8.3 Extended Reading - Non Split Pie Chart 57 3.8.4 Case - Drawing an Embedded Circular Pie Chart 58 3.9 Box line diagram 60 3.9.1 Application scenario - distribution comparison of multiple sets of quantitative data 60 3.9.2 Drawing principle61 3.9.3 Extended reading meaning and calculation method of box, box whisker and outlier 63 3.9.4 Case 1 - Horizontal Box Line Diagram 65 3.9.5 Case 2 - Horizontal Box Line Diagram without Outliers 66 3.10 Error bar diagram67 3.10.1 Application scenario - error range of quantitative data67 3.10.2 Drawing principle68 3.10.3 Case 1 - Histogram with Error Bars 69 3.10.4 Case 2 - Bar chart with error bar 71 3.10.5 Case 3 - Multiple data parallel histogram with error bar72 3.10.6 Case 4 - Stacked Histogram with Error Bars 74 Chapter 4 Improving Statistical Graphs 77 4.1 Adding Legend and Title77 4.1.1 Legend and title setting method77 4.1.2 Case 1 - Adjustment of display style of legend 79 4.1.3 Case 2 - Adjustment of display style of title 80 4.1.4 Case 3 - Pie chart with legend 82 4.2 Adjusting the scale range and scale label83 4.2.1 Method of adjusting scale range and scale label84 4.2.2 Extended reading - function subplot() 85 4.2.3 Case - Setting Axis Scale Labels in Reverse Order 86 4.3 Adding Tables to Statistical Graphs 87 Chapter 2 Progress Chapter 5 Advanced Statistical Graph Drawing: Graph Styles 92 5.1 Setting the scale style of the axis 92 5.1.1 Usage of scale locator and scale formatter 92 5.1.2 Call the function in the module pyplot to set the scale style 95 5.1.3 Case 1 - Customization of scale labels and scale line styles 95 5.1.4 Case 2 - Scale labels in currency and time series style 96 5.2 Adding Notes with and without Indication 98 5.2.1 Adding methods with and without indication notes 98 5.2.2 Case 1 - Setting of Rounded Text Box 100 5.2.3 Case 2 - Watermark effect of text 101 5.2.4 Case 3 - Notes with radian indication on rounded wireframe 102 5.2.5 Case 4 - Trend line with arrow indication 104 5.2.6 Case 5 - Sankitu 105 5.3 Realize the projection effect of title and axis labels 107 5.3.1 Operation method for realizing projection effect of title and coordinate axis label 107 5.3.2 Case - Adding a text box to the coordinate axis label 109 Chapter 6 Main Functions to Divide Canvas 111 6.1 Function subplot(): plot the sub area layout with the same geometry in the grid area111 6.1.1 How to use the function subplot() 112 6.1.2 Case 1 - Draw a line chart on the polar axis 113 6.1.3 Case 2 - Scatter plot on polar axis 114 6.1.4 Case 3 - Realize graphic display on the drawing area of non equidistant canvas115 6.2 Function subplot2grid(): Make the subarea span the fixed grid layout 116 6.2.1 How to use the function subplot2grid() 116 6.2.2 Extended reading - how to use the GridSpec class in the module gridspec 118 6.3 Function subplots(): Create a drawing mode with multiple sub areas on a canvas 120 6.3.1 Case 1 - Create a drawing mode of a canvas and a sub area 120 6.3.2 Case 2 - Create a drawing mode of one canvas and two sub areas 122 6.3.3 Case 3 - Combination display of multiple statistical graphs 124 Chapter 7 Coordinate Axis of Shared Drawing Area128 7.1 Axis sharing a single drawing area128 7.2 Sharing coordinate axes of different sub area drawing areas130 7.2.1 Setting method130 7.2.2 Case - Remove the gap between sub areas sharing the coordinate axis 135 7.3 Coordinate axis of drawing area sharing individual subareas 136 7.3.1 Setting method136 7.3.2 Extended reading - adjust the coordinate axis range with the function autoscale() 138 Part 3 Exercise Chapter 8 Advanced Application of Coordinate Axes 142 8.1 Setting the position and display form of the coordinate axle142 8.1.1 Case 1 - Add any number of coordinate axes to any position in the canvas142 8.1.2 Case 2 - Adjust the display, hiding and scale range of the determined coordinate axis 144 8.1.3 Extended reading - plot the coordinate axis 145 using the function axis() 8.2 Use two methods to control the display of coordinate axis scale 146 8.2.1 Method 1 - Call Axes. set_xticks() and Axes. set_yticks() instance methods 146 8.2.2 Method 2 -- Call function setp() 147 8.2.3 Case 1 - Customized display of cotton swab diagram 149 8.2.4 Case 2 - Customized display of the style and position of the coordinate axis 150 8.3 Control the display of coordinate axle152 8.4 Move the position of the coordinate axis 154 Chapter 9 Setting the Display Style of Line Type and Mark Type 158 9.1 How to use dictionaries with different calling signatures 158 9.1.1 Method 1 -- Call the setting form of keyword parameter in signature "fontdict=font" 158 9.1.2 Method 2 - Keyword parameter setting form "* * font" 160 9.2 Display style setting method of line type161 9.3 Display style setting method of mark type162 9.3.1 Method 1 - Single character mode162 9.3.2 Method 2 - mathtext mode 164 9.4 Extended reading 166 9.4.1 Case 1 - Setting methods for different presentation forms of "dash" line style 166 9.4.2 Case 2 - Setting method of marking filling pattern 168 9.4.3 Case 3 - Setting method for calling signature of function plot () 170 Part 4 Expansion Chapter 10 Matplotlib Configuration174 10.1 Modifying the Matplotlib Configuration at the Code Level 174 10.1.1 Method 1 - Call function matplotlib. rc() 175 10.1.2 Method 2 - Call attribute dictionary matplotlib.rcParams 175 10.2 Modifying the Matplotlib Configuration at the Project Level 176 10.2.1 Path of configuration file 176 10.2.2 Setting method177 Chapter 11 Text Attribute Setting179 11.1 Setting font attributes and text attributes179 11.1.1 Method 1 - Change the font attribute value and text attribute value of the configuration file matplotlibrc 181 11.1.2 Method 2 - Adjust font attribute value and text attribute value through attribute dictionary rcParams 182 11.1.3 Method 3 - By setting keyword parameters of function183 11.2 Extended reading - manually adding fonts 184 11.3 Case - Visual display of main attributes of typeface 185 Chapter 12 Use of Color188 12.1 Using color parameters and color mapping table188 12.1.1 Use of color parameter188 12.1.2 Use of color mapping table190 12.2 Comprehensive cases193 12.2.1 Case 1 - Color use mode of mimic diagram 193 12.2.2 Case 2 - Color Usage Mode of Scatter Chart194 12.2.3 Case 3 - Color usage patterns of polar maps 195 12.2.4 Case 4 - Color use mode of contour map 197 12.2.5 Case 5 - Color usage mode of color scale198 Chapter 13 Display and Save of Output Graphs 200 13.1 Run command line to display output graphics 200 13.1.1 Method 1 - Python shell mode 200 13.1.2 Method 2 - IPython shell mode 203 13.2 Saving output graphic205 13.2.1 Method 1 - Use the "Save" button to store 205 13.2.2 Method 2 - Saving by executing code statement207 Appendix A Python Basics 208 Appendix B NumPy Basics 213 Appendix C Installation Methods of Matplotlib, NumPy and IPython217

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Python English original classic album: OReilly. - Python for Data Analysis: label: python; Points: 1 Type: academic paper Uploader: baidu_linker Upload time: November 7, 2018; Introduction: python English original classic album: OReilly. - Python for Data Analysis

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Matplotlib refinement of Python data visualization: label: python matplot; Points: 1 Type: Technical document Uploader: throw away a brick in order to get a gem Upload time: May 24, 2023; Introduction: Matplotlib Practice of Python Data Visualization uses a large number of Matplotlib utility cases to explain the implementation methods of Python data visualization in various application directions. By learning these practical cases, readers can better master the advanced skills of Python data visualization. This book is mainly composed of five parts: graphics, elements, interaction, exploration and expansion. The practical cases in each part are conducive to expanding the application vision of matplotlib, and the example code in the case only involves the basic knowledge of Python. In this way, in the practice of Python data visualization, it is beneficial for readers to focus their time and energy on systematically mastering matplotlib knowledge and skills, and comprehensively improve their understanding and application level of matplotlib. Part 1 Graphics Chapter 1 Filling Geometric Figures with Colors two 1.1 Color filling of polygon two 1.1.1 Color filling of regular polygon two 1.1.2 Color filling of irregular polygon four 1.2 Color filling of cross curve five 1.3 Extended Reading six 1.3.1 Color filling method of horizontal cross curve six 1.3.2 Color filling method of vertical cross curve eight 1.4 Comprehensive case: color filling of intersecting and discontinuous curves nine Chapter 2 Drawing Geometric Figures Using Module Patches twelve 2.1 Realization method of circle twelve 2.2 Implementation method of ellipse fifteen 2.3 Implementation method of rectangle seventeen 2.4 Drawing method of arc and wedge nineteen 2.5 Extended Reading twenty-two 2.5.1 Using polylines to draw circles twenty-two 2.5.2 Drawing a Circle Using an Ellipse twenty-five 2.5.3 Using Wedges to Draw Pie Charts twenty-six 2.5.4 Using Wedges to Draw a Doughnut Pie twenty-eight Chapter 3 Combination of Statistical Figures thirty-one 3.1 Diagram of discriminant analysis in machine learning thirty-one 3.2 Date type time series chart thirty-three 3.3 Adding probability density curve to histogram thirty-five 3.4 Drawing area nested sub drawing area thirty-nine 3.5 Extended reading: setting the general date scale mark forty-two Chapter 2 Elements Chapter 4 Setting the Style and Layout of Text Content forty-five 4.1 Presentation style of text annotation forty-five 4.1.1 Style of text box forty-six 4.1.2 Style of arrow for text notes forty-seven 4.2 Layout of text content forty-nine 4.3 Extended Reading fifty-four 4.3.1 Text Wrapping fifty-four 4.3.2 Rotation angle of text content fifty-seven 4.3.3 Rotation mode of text content fifty-nine 4.3.4 Alignment of Multiline Text sixty-three 4.3.5 Connecting Style of Text Annotation Arrows sixty-six Chapter 5 Adjustment of Measurement Unit and Method seventy-six 5.1 Realization method of different measurement units seventy-six 5.1.1 Realization method of radian and angle seventy-six 5.1.2 Realization method of cm and inch seventy-eight 5.1.3 Realization method of second, Hz and minute eighty 5.1.4 Setting method of coordinate system of text annotation position eighty-one 5.2 Operating principle of different measuring methods eighty-three Chapter 6 Adjusting the Display Effect of Scale Marks, Scale Labels and Axis Ridges eighty-seven 6.1 Position adjustment of scale mark, scale label and axis label eighty-seven 6.2 Dynamic adjustment of scale mark position and value ninety 6.3 Adjustment of major scale mark and minor scale mark ninety-two 6.4 Display and hiding of axis ridge ninety-five 6.5 Adjustment of axis ridge position ninety-eight Chapter 3 Interaction Chapter 7 Realize the Animation Effect of Graphics one hundred and four 7.1 Using module animation to draw animation one hundred and four 7.2 Calling API of module pyplot to draw animation one hundred and six Chapter 8 Realizing GUI Effects one hundred and ten Usage of Class 8.1 RadioButtons one hundred and ten 8.2 Usage of Cursor one hundred and thirteen 8.3 Use of CheckButtons one hundred and fourteen Chapter 9 Realization of Event Handling Effect one hundred and eighteen 9.1 Event result prompt appears after clicking Close Canvas one hundred and eighteen 9.2 Implementation method of canvas local magnification effect one hundred and twenty Chapter 4 Exploration Chapter 10 Importing Images from the Outside to Load into the Drawing Area one hundred and twenty-four 10.1 Diversified display of external images one hundred and twenty-four 10.2 Topography one hundred and twenty-six 10.3 Thermal diagram one hundred and twenty-seven 10.4 Set the picture to have hyperlink function one hundred and thirty-one 10.5 Adding an External Image on a Canvas Layer one hundred and thirty-six 10.6 Diversified display effects of images with the help of filters one hundred and forty 10.6.1 Color reversal one hundred and forty-five 10.6.2 Converting RGB channel NumPy array to single channel NumPy array one hundred and forty-six Chapter 11 Drawing 3D Graphics one hundred and fifty 11.1 Drawing a colored surface with a color scale one hundred and fifty 11.2 Layered display of 2D histogram projected onto the designated plane in 3D space one hundred and fifty-two 11.3 Drawing Scatter Chart in 3D one hundred and fifty-four Chapter 12 Mapping one hundred and fifty-six 12.1 Population of Australia's capital city and capital city one hundred and fifty-six 12.2 Distribution map of day and night geographical regions at the current time point one hundred and sixty 12.3 Visual presentation of distance between cities one hundred and sixty-two Chapter 13 Comprehensive Cross Application Scenarios one hundred and sixty-seven 13.1 Input data can use string instead of variable one hundred and sixty-seven 13.2 Storing Canvas Graphics in PDF File Format one hundred and sixty-nine 13.3 Calling pyplot's API and object-oriented API to set graph properties one hundred and seventy-one 13.4 Displaying the file size in the folder with a tree diagram one hundred and seventy-two 13.5 Setting method of matplotlib style set one hundred and seventy-six 13.6 Configuration Method of Matplotlib Backend Type one hundred and eighty-one Part 5 Expansion Chapter 14 Rendering Text Content with the TeX Function of LaTeX and matplotlib one hundred and eighty-seven 14.1 Preparation steps one hundred and eighty-seven 14.2 Case presentation one hundred and eighty-eight 14.3 Extended Reading one hundred and ninety Chapter 15 Methods and Techniques of Using Matplotlib to Write Mathematical Expressions one hundred and ninety-three 15.1 Rules for editing strings one hundred and ninety-three 15.2 Setting the font effect of the output string one hundred and ninety-four 15.3 Learning TeX symbol writing rules through mathematical formulas and mathematical expressions one hundred and ninety-five 15.4 Learn the writing rules of TeX symbols through mathematical symbols and Greek letters two hundred and four Appendix A Installation Method of SciPy two hundred and nine Appendix B Usage of IPython two hundred and eleven Appendix C Installation and Use of mpl_toolkits two hundred and twenty-one Appendix D Suggestions on the Use of Python 2 and Python 3 Software Versions two hundred and twenty-six

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Python data analysis: Plotly based dynamic visual plotting (Sun Yangyang): label: python; Points: 1 Type: Technical document Uploader: throw away a brick in order to get a gem Upload time: May 28, 2023; Introduction: With the development of information technology and the reduction of the cost of hardware equipment, there are massive data on the Internet today. To quickly obtain more effective information from these data, data visualization is an important part. For Python, Matplotlib is the traditional data visualization module, but it is not beautiful, static, and difficult to share, which limits the development of Python in data visualization. In order to solve this problem, a new dynamic visualization open source module Plotly came into being. Because Plotly is dynamic, beautiful, easy to use, rich in variety and other characteristics, it has been loved by developers since its inception. This book mainly introduces the application of Plotly in various fields of visualization, including basic drawing, data processing, web page development, program GUI, machine learning and quantitative investment, so that readers can quickly learn about Plotly. Most of the code in this book is written in Python language, and the application cases of Plotly in R language, MATLAB and JavaScript are also given. 1.1 Introduction to Plotly 1.2 Installation and Installation Environment 1.3 Online initialization 1.4 Privacy instructions for online drawing 1.5 Start online mapping 1.6 Using offline drawing library 1.7 Parameter interpretation 1.8 View Help Chapter 2 Plotly Basic Graphics 2.1 Interpretation of basic cases 2.2 Basic drawing process 2.3 Scatter plot 2.3.1 Basic case 2.3.2 Style setting 2.3.3 Application cases 2.3.4 Parameter interpretation 2.4 Bubble Chart 2.4.1 Basic case 2.4.2 Style Setting 2.4.3 Zoom Settings 2.4.4 Parameter interpretation 2.5 Line Diagram 2.5.1 Basic case 2.5.2 Data gap and connection 2.5.3 Data interpolation 2.5.4 Filled Line Diagram 2.5.5 Application cases 2.5.6 Parameter interpretation 2.6 Histogram 2.6.1 Basic bar chart 2.6.2 Columnar clusters 2.6.3 Stacked Histogram 2.6.4 Waterfall bar chart 2.6.5 Graphic Style Settings 2.6.6 Application cases 2.6.7 Parameter interpretation 2.7 Horizontal bar chart 2.7.1 Basic case 2.7.2 Application cases 2.7.3 Parameter interpretation 2.8 Gantt Chart 2.8.1 Basic Gantt Chart 2.8.2 Gantt Chart (indexed by numbers) 2.8.3 Gantt Chart (indexed by category) 2.8.4 Application cases 2.9 Area map 2.9.1 Basic area diagram 2.9.2 Internal filled area diagram 2.9.3 Stacking area chart 2.10 Histogram 2.10.1 Basic histogram 2.10.2 Overlapping Histograms 2.10.3 Stacked Histogram 2.10.4 Cumulative histogram 2.10.5 Application cases 2.10.6 Parameter interpretation 2.11 Pie Chart 2.11.1 Basic Pie Chart 2.11.2 Circular Pie Chart 2.11.3 Style Setting 2.11.4 Application cases 2.11.5 Parameter interpretation 2.12 More cases 2.13 Overview of Plotly objects Chapter 3 Plotly Advanced Graphics 3.1 Time series 3.1.1 Usage 3.1.2 Time range constraints 3.2 Sliding selection control 3.3 Forms 3.3.1 Introduction case 3.3.2 Add link 3.3.3 Using Pandas 3.3.4 Changing size and color 3.3.5 Tables and Figures 3.4 Multiple charts 3.5 Multiple axes 3.5.1 Double coordinate axes 3.5.2 Multi axis 3.5.3 Shared Axis 3.6 Multiple sub graphs 3.6.1 Double sub graph (method 1) 3.6.2 Double sub graph (method 2) 3.6.3 Multiple subgraphs (method 1) 3.6.4 Multiple subgraphs (method 2) 3.6.5 Split view interval 3.6.6 Sub drawing shared coordinate axis (method 1) 3.6.7 Sub drawing shared coordinate axis (method 2) 3.6.8 Customization of sub graph coordinate axis 3.6.9 Embedded sub diagram 3.6.10 Mixed diagram 3.7 Draw SVG 3.7.1 Line drawing 3.7.2 Line graph application: create tangent line of graph 3.7.3 Drawing of rectangular chart 3.7.4 Rectangular chart application: set time series area to highlight 3.7.5 Drawing of circular chart 3.7.6 Circular chart application: highlight cluster of scatter chart Chapter 4 Plotly and Pandas 4.1 Simple quick start 4.1.1 Basic line diagram 4.1.2 Basic scatter diagram 4.1.3 Basic histogram 4.2 Drawing with cufflinks 4.2.1 Installing cufflinks 4.2.2 Quick Start 4.2.3 Quick data acquisition 4.2.4 Custom Drawing 4.2.5 Common classic graphics 4.2.6 More cases Chapter 5 Financial Mapping 5.1 Quick drawing of K-line diagram 5.1.1 Checking the Plotly Version 5.1.2 Quickly draw OHLC (American Line) diagram 5.1.3 Quick drawing of candle chart 5.2 Optimization of K line diagram 5.2.1 Filter non transaction time 5.2.2 Set shape, color and annotation 5.2.3 Add technical indicators 5.3 Financial mapping using custom data 5.4 Advanced financial mapping 5.4.1 Introduction case 5.4.2 Comprehensive cases Chapter 6 Matplotlib 6.1 Introduction to Matplotlib 6.2 Installing Matplotlib 6.3 Adjusting Matplotlib Parameters 6.4 Common API Functions 6.5 Linear function 6.6 Add sub graph 6.7 Determination of coordinate range 6.8 Probability Diagram 6.9 Scatter Chart 6.10 Histogram 6.11 More extensions Chapter 7 Plotly and Web Page Development 7.1 Application of Plotly in Django 7.1.1 Installation environment construction 7.1.2 Installation environment test 7.1.3 Introduction Case I 7.1.4 Introduction Case II 7.1.5 Extension of more cases 7.1.6 Application Case I 7.1.7 Application Case II 7.2 Application of Plotly in Flask 7.2.1 Installing Flask 7.2.2 The smallest web application 7.2.3 Template rendering 7.2.4 Introduction Case I 7.2.5 Introduction Case II 7.2.6 Application cases Chapter 8 Plotly and GUI Development 8.1 Installation of PyQt 8.2 Case interpretation 8.3 Setting promoted widgets 8.4 Use of Plotly_PyQt 5 8.5 More extensions (Plotly) 8.6 Combination of Plotly and PyQt 5.6 8.7 More extensions (Matplotlib) 8.8 Application case: display product portfolio information Chapter 9 Plotly and Machine Learning 9.1 Application of Plotly in Sklearn 9.1.1 Classification 9.1.2 Regression problem 9.1.3 Clustering 9.2 PyTorch Visualizer 9.2.1 Introduction to Visdom 9.2.2 Installing Visdom 9.2.3 Visdom and Plotly 9.2.4 Basic concepts of Visdom 9.2.5 Classic case of Visdom 9.2.6 Visdom and PyTorch Chapter 10 Application of Plotly in Quantitative Investment Chapter 11 Application of Plotly in Other Languages 11.1 Application of Plotly in R language 11.1.1 Installation of R language 11.1.2 Installing the Plotly module 11.1.3 Plotly application analysis 11.1.4 More extensions 11.2 Application of Plotly in MATLAB 11.2.1 Download and installation 11.2.2 Introduction to foundation 11.2.3 Classic cases 11.2.4 More extensions 11.3 Application of Plotly in JavaScript Language 11.3.1 Introduction to foundation 11.3.2 Scatter diagram 11.3.3 Bar chart 11.3.4 Sector diagram 11.3.5 More extensions

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Python Pandas Tutorial: label: pandas; Points: 5 Type: Technical document Uploader: Ha ha 88 Upload time: July 14, 2019; Introduction: Use Pandas for data analysis. Pandas can realize your unexpected data processing world. With Pandas, you can simply operate all data

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Python Data Analysis Practice: label: Python Data analysis; Points: 1 Type: Technical document Uploader: to glance over things hurriedly Upload time: January 29, 2024; Introduction: Author: Fabio Nelli Translator: Du Chunxiao Year of publication: 2016 Python is easy to learn, has rich libraries, and is extremely inclusive. This book shows how to use the powerful functions of Python language to extract, process and analyze data at the minimum programming cost. The main contents include: data analysis and basic introduction to Python, NumPy library, Pandas library, how to use pandas to read, write and extract data, and how to use matplotlib library and scikit-learn library to realize data visualization and machine learning respectively. Examples are used to demonstrate how to obtain information from raw data, embed D3 library and recognize handwritten digits.

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Fundamentals of machine learning numpy and pandas: label: machine learning; Points: 1 Type: Application Document Uploader: Hyperplatinum Upload time: 2021-05-30; Introduction: Fundamentals of machine learning numpy and pandas