| 000 | 08268nam a22001697a 4500 | ||
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| 999 |
_c22705 _d22705 |
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| 020 | _a9789354641336 | ||
| 082 |
_a005.133 _bACH |
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| 100 | _aAcharya, Seema | ||
| 245 |
_aReimagining date visualization using Python / _cSeema Acharya |
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| 260 |
_aNew Delhi : _bWiley Publication, _c2022. |
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| 300 |
_axxiii, 348 pages : _bcolor illustrations ; _c25 cm. |
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| 505 | _aPreface Acknowledgments About the Authors Chapter 1 Introduction to Data Visualization 1.1 What is Data Visualization? 1.2 Evolution of Data Visualization 1.2.1 Era of Statistical Chart Types 1.2.2 Era of Visual Display of Quantitative Information 1.2.3 The Era of Convergence 1.3 Why do We Need Data Visualization? 1.4 Difference between Data Visualization and Infographics 1.5 Principles of Gestalt’s Theory of Visual Perception 1.5.1 Why is Gestalt’s Principle Important? 1.5.2 Explanation of the Gestalt’s Principle 1.6 Advantages of Data Visualization 1.7 Benefits of Data Visualization Multiple-Choice Questions Answers Chapter 2 Types of Digital Data 2.1 What is in Store? 2.2 Classification of Digital Data 2.2.1 Structured Data 2.2.1.1 Sources of Structured Data 2.2.1.2 Benefits of Structured Data 2.2.1.3 Disadvantages of Structured Data 2.2.1.4 Classifying Attributes of Structured Data 2.2.2 Semi-Structured Data 2.2.2.1 Sources of Semi-Structured Data 2.2.3 Unstructured Data 2.2.3.1 Issues with “Unstructured” Terminology 2.2.3.2 How to Deal with Unstructured Data 2.3 Structured versus Unstructured Data Summary Review Questions Exercise Resources Answers Chapter 3 Reading Data from Varied Data Sources into Python DataFrame 3.1 Read from Excel Data Source 3.1.1 Example: Read Data from “Superstore.xlsx” 3.2 Read Data from .csv 3.2.1 Example: Read Data from “iris.csv” into Pandas Data Frame 3.3 Load a Python Dictionary into a DataFrame 3.3.1 Cleaning up Data 3.3.1.1 Remove Rows that Contain Empty Cells 3.3.1.2 Cleaning Wrong Format 3.3.1.3 Cleaning Wrong Data 3.3.1.4 Removing Duplicates 3.4 Reading JSON data into a Pandas DataFrame 3.5 Reading Data from Microsoft Access Database 3.6 Reading Data from .txt File 3.7 Reading Data from XML File Summary Exercises Chapter 4 Pros and Cons of Charts 4.1 Pie Chart 4.1.1 When to Use a Pie Chart? 4.1.2 How to Read a Pie Chart? 4.1.3 Pros and Cons of a Pie Chart 4.1.4 Five Tips for Using Pie Charts 4.1.5 A Critique’s View 4.2 Tree Map 4.2.1 Pros of Treemap 4.3 Heat Map 4.4 Scatter Plot 4.4.1 Correlation Coefficient 4.4.2 Why Use a Scatter Plot? 4.5 Histogram 4.5.1 What is Required to Plot a Histogram? 4.5.2 Pros of Histogram 4.6 Word Cloud 4.6.1 For What Should You Use a Word Cloud? 4.6.2 Where Should You Not Use a Word Cloud? 4.7 Box Plot 4.7.1 Advantages of a Box Plot 4.7.2 Disadvantages of Box Plot 4.7.3 When to Use a Box and Whisker Plot 4.7.4 What All It Can Help You With? 4.7.5 Box Plot Symbol Explanation Diagram 4.8 Chart Chooser Summary Review Questions Resources Answers Chapter 5 Good Chart Designs 5.1 Mistakes That Can Be Avoided 5.1.1 Example 1 5.1.2 Example 2 5.1.3 Example 3 5.1.4 Example 4 5.1.5 Example 5 5.1.6 Example 6 5.1.7 Example 7 5.1.8 Example 8 5.1.9 Example 9 5.2 Less Is More 5.2.1 Example 1 5.2.2 Example 2 5.2.3 Example 3 5.3 Tables versus Charts 5.3.1 Column Charts 5.3.2 Bar Charts 5.3.3 Line Charts 5.3.4 Area Charts 5.3.5 Pie Charts 5.3.6 Scatter Plot Summary Resources Answers Chapter 6 Data Wrangling in Python 6.1 Pandas Data Manipulation 6.1.1 Pandas 6.1.1.1 Pandas Series 6.1.1.2 Pandas DataFrame 6.1.1.3 Comparison between Pandas Series and Pandas DataFrame 6.1.2 Series 6.1.2.1 Create a Series from an Array Using pandas.Series() Method 6.1.2.2 Create a Series from a List Using pandas.Series() Method 6.1.2.3 Access Elements from the Series by Providing the Position 6.1.2.4 Access Elements from the Series by Providing the Position 6.1.2.5 Sort and Display the Values from the Series Using sort_values() 6.1.2.6 Display the Values from the Series Using iloc[] Method 6.1.2.7 Count the Number of Entries in the Series Using count() Method 6.1.2.8 Display Features of the Series such as “size”, “shape”, “ndim”, “memory_usage” 6.1.2.9 Converting dtypes Using astype 6.1.2.10 Change the Name of the Column 6.1.3 Timedelta 6.2 Dealing with Missing Values 6.3 Date Reshaping 6.4 Filtering Data 6.5 Merging Data 6.6 Subsetting DataFrames in Pandas 6.7 Reshaping the Data and Pivot Tables 6.8 Backfill 6.9 Forward Fill Summary Exercises Chapter 7 Functions in Python Pandas 7.1 Pandas DataFrame Functions 7.1.1 Groupby 7.1.1.1 Groupby and Aggregate Functions 7.1.1.2 Splitting the Data into Multiple Groups 7.1.1.3 Splitting the Data and Running an Aggregation Function 7.1.1.4 Nested Groups 7.1.1.5 Loop over Group by Groups 7.1.1.6 Display the Indices in Each Group 7.1.1.7 Iterate over All of the Groups 7.1.1.8 Finding the Values Contained in the Particular Group 7.1.2 Pandas Correlations 7.1.3 Pandas DataFrame All Method 7.1.4 Pandas DataFrame Any Method 7.1.5 Pandas DataFrame Columns Property 7.1.6 Pandas DataFrame count() Method 7.1.7 Pandas DataFrame describe() Method 7.1.8 Pandas DataFrame drop_duplicates() Method 7.1.9 Pandas DataFrame empty Property 7.1.10 Pandas DataFrame filter() Method 7.1.11 Pandas DataFrame equals() Method Summary Exercises Chapter 8 Matplotlib for Data Visualization 8.1 Exploratory Data Analysis using Python 8.1.1 What is Exploratory Data Analysis? 8.1.2 Steps Involved in EDA 8.1.3 Libraries in Python Used in EDA 8.2 Matplotlib 8.2.1 Understanding Matplotlib’s Pyplot API 8.2.2 Box Plot 8.2.2.1 Example 1 8.2.2.2 Example 2 8.2.3 Pie Chart 8.2.3.1 Example 1 8.2.3.2 Example 2 8.2.4 Scatter Plot 8.2.5 Treemaps 8.2.5.1 Example 1 8.2.6 Heat Maps 8.2.7 Waterfall Chart 8.2.7.1 Example 1 8.2.7.2 Example 2 8.2.8 Bubble Chart 8.2.9 Histogram 8.2.9.1 Example 1 8.2.10 Line Plot 8.2.10.1 Example 1 8.2.10.2 Example 2 8.2.11 Interactive Features of Matplotlib 8.2.11.1 Clearing the Figure with clf() Method 8.2.11.2 Clearing Axes with cla() Method Summary Exercises Answers Chapter 9 Plotly for Data Visualization 9.1 Plotly Python Package 9.1.1 Scatter Plot 9.1.1.1 Example 1 9.1.1.2 Example 2 9.1.1.3 Example 3 9.1.2 Bar Plot 9.1.2.1 Example 1 9.1.2.2 Example 2 9.1.2.3 Example 3 9.1.2.4 Example 4 9.1.3 Pie Chart 9.1.3.1 Example 1 9.1.3.2 Example 2 9.1.3.3 Example 3 9.1.4 Word Cloud 9.1.4.1 Example 1 9.1.4.2 Example 2 9.1.5 Treemap 9.1.6 Choropleth Chart 9.1.6.1 Example 1 9.1.6.2 Example 2 9.1.6.3 Example 3 9.1.7 Area Chart 9.1.8 Bubble Chart 9.1.8.1 Example 1 9.1.8.2 Example 2 9.1.9 Gantt Chart 9.1.10 Boxplot 9.1.11 Violin plot 9.1.12 Histogram Summary Exercises Chapter 10 Seaborn for Data Visualization 10.1 Seaborn Plots Using “iris” Dataset 10.1.1 Scatter Plot 10.1.2 Strip Plot 10.1.3 Swarm Plot 10.1.4 Count Plot 10.1.5 Box Plot 10.1.6 Violin Plot 10.1.7 Pair Plot 10.1.8 Pie Chart 10.2 Seaborn Plots Using “Superstore” Dataset 10.2.1 Bar Plot 10.2.2 Cat Plot 10.2.3 Countplot 10.2.4 Box Plot 10.2.5 Pair Plot 10.2.6 Implot Plot 10.3 Seaborn Plots Using “OLYMPIC” Dataset 10.3.1 DistPlot 10.4 Seaborn Plots Using “Passengers Flights” Dataset 10.4.1 Scatter Plot 10.4.2 Line Plot 10.4.3 Bar Plot Summary Chapter 11 Cases 11.1 Case Study 1 11.1.1 Analysis of Wine Quality Dataset 11.2 Case Study 2 11.2.1 Analysis of Titanic Dataset Appendix Python Assignments Index | ||
| 520 | _a : Reimagining Data Visualization Using Python is an extensive discourse on data visualization. It details how to perform data visualization on a variety of datasets using various data visualization libraries written in Python programming language. Understanding, visualizing, and presenting data is slowly and gradually becoming a must have skills for professionals in all disciplines. This book is designed for learners who are beginners in visualization using python. It is a guide with detailed out steps to write and execute command/code. | ||
| 650 | _aData visualization | ||
| 650 | _aPython (Programming Language.) | ||
| 942 |
_2ddc _cBK |
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