Reimagining date visualization using Python / (Record no. 22705)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 08268nam a22001697a 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9789354641336 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.133 |
| Item number | ACH |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Author name | Acharya, Seema |
| 245 ## - TITLE STATEMENT | |
| Title | Reimagining date visualization using Python / |
| Statement of responsibility, etc | Seema Acharya |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | New Delhi : |
| Name of publisher | Wiley Publication, |
| Year of publication | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xxiii, 348 pages : |
| Other physical details | color illustrations ; |
| Dimensions | 25 cm. |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Preface<br/>Acknowledgments<br/>About the Authors<br/>Chapter 1 Introduction to Data Visualization<br/>1.1 What is Data Visualization?<br/>1.2 Evolution of Data Visualization<br/>1.2.1 Era of Statistical Chart Types<br/>1.2.2 Era of Visual Display of Quantitative Information<br/>1.2.3 The Era of Convergence<br/>1.3 Why do We Need Data Visualization?<br/>1.4 Difference between Data Visualization and Infographics<br/>1.5 Principles of Gestalt’s Theory of Visual Perception<br/>1.5.1 Why is Gestalt’s Principle Important?<br/>1.5.2 Explanation of the Gestalt’s Principle<br/>1.6 Advantages of Data Visualization<br/>1.7 Benefits of Data Visualization<br/>Multiple-Choice Questions<br/>Answers<br/>Chapter 2 Types of Digital Data<br/>2.1 What is in Store?<br/>2.2 Classification of Digital Data<br/>2.2.1 Structured Data<br/>2.2.1.1 Sources of Structured Data<br/>2.2.1.2 Benefits of Structured Data<br/>2.2.1.3 Disadvantages of Structured Data<br/>2.2.1.4 Classifying Attributes of Structured Data<br/>2.2.2 Semi-Structured Data<br/>2.2.2.1 Sources of Semi-Structured Data<br/>2.2.3 Unstructured Data<br/>2.2.3.1 Issues with “Unstructured” Terminology<br/>2.2.3.2 How to Deal with Unstructured Data<br/>2.3 Structured versus Unstructured Data<br/>Summary<br/>Review Questions<br/>Exercise<br/>Resources<br/>Answers<br/>Chapter 3 Reading Data from Varied Data Sources into Python DataFrame<br/>3.1 Read from Excel Data Source<br/>3.1.1 Example: Read Data from “Superstore.xlsx”<br/>3.2 Read Data from .csv<br/>3.2.1 Example: Read Data from “iris.csv” into Pandas Data Frame<br/>3.3 Load a Python Dictionary into a DataFrame<br/>3.3.1 Cleaning up Data<br/>3.3.1.1 Remove Rows that Contain Empty Cells<br/>3.3.1.2 Cleaning Wrong Format<br/>3.3.1.3 Cleaning Wrong Data<br/>3.3.1.4 Removing Duplicates<br/>3.4 Reading JSON data into a Pandas DataFrame<br/>3.5 Reading Data from Microsoft Access Database<br/>3.6 Reading Data from .txt File<br/>3.7 Reading Data from XML File<br/>Summary<br/>Exercises<br/>Chapter 4 Pros and Cons of Charts<br/>4.1 Pie Chart<br/>4.1.1 When to Use a Pie Chart?<br/>4.1.2 How to Read a Pie Chart?<br/>4.1.3 Pros and Cons of a Pie Chart<br/>4.1.4 Five Tips for Using Pie Charts<br/>4.1.5 A Critique’s View<br/>4.2 Tree Map<br/>4.2.1 Pros of Treemap<br/>4.3 Heat Map<br/>4.4 Scatter Plot<br/>4.4.1 Correlation Coefficient<br/>4.4.2 Why Use a Scatter Plot?<br/>4.5 Histogram<br/>4.5.1 What is Required to Plot a Histogram?<br/>4.5.2 Pros of Histogram<br/>4.6 Word Cloud<br/>4.6.1 For What Should You Use a Word Cloud?<br/>4.6.2 Where Should You Not Use a Word Cloud?<br/>4.7 Box Plot<br/>4.7.1 Advantages of a Box Plot<br/>4.7.2 Disadvantages of Box Plot<br/>4.7.3 When to Use a Box and Whisker Plot<br/>4.7.4 What All It Can Help You With?<br/>4.7.5 Box Plot Symbol Explanation Diagram<br/>4.8 Chart Chooser<br/>Summary<br/>Review Questions<br/>Resources<br/>Answers<br/>Chapter 5 Good Chart Designs<br/>5.1 Mistakes That Can Be Avoided<br/>5.1.1 Example 1<br/>5.1.2 Example 2<br/>5.1.3 Example 3<br/>5.1.4 Example 4<br/>5.1.5 Example 5<br/>5.1.6 Example 6<br/>5.1.7 Example 7<br/>5.1.8 Example 8<br/>5.1.9 Example 9<br/>5.2 Less Is More<br/>5.2.1 Example 1<br/>5.2.2 Example 2<br/>5.2.3 Example 3<br/>5.3 Tables versus Charts<br/>5.3.1 Column Charts<br/>5.3.2 Bar Charts<br/>5.3.3 Line Charts<br/>5.3.4 Area Charts<br/>5.3.5 Pie Charts<br/>5.3.6 Scatter Plot<br/>Summary<br/>Resources<br/>Answers<br/>Chapter 6 Data Wrangling in Python<br/>6.1 Pandas Data Manipulation<br/>6.1.1 Pandas<br/>6.1.1.1 Pandas Series<br/>6.1.1.2 Pandas DataFrame<br/>6.1.1.3 Comparison between Pandas Series and Pandas DataFrame<br/>6.1.2 Series<br/>6.1.2.1 Create a Series from an Array Using pandas.Series() Method<br/>6.1.2.2 Create a Series from a List Using pandas.Series() Method<br/>6.1.2.3 Access Elements from the Series by Providing the Position<br/>6.1.2.4 Access Elements from the Series by Providing the Position<br/>6.1.2.5 Sort and Display the Values from the Series Using sort_values()<br/>6.1.2.6 Display the Values from the Series Using iloc[] Method<br/>6.1.2.7 Count the Number of Entries in the Series Using count() Method<br/>6.1.2.8 Display Features of the Series such as “size”, “shape”, “ndim”, “memory_usage”<br/>6.1.2.9 Converting dtypes Using astype<br/>6.1.2.10 Change the Name of the Column<br/>6.1.3 Timedelta<br/>6.2 Dealing with Missing Values<br/>6.3 Date Reshaping<br/>6.4 Filtering Data<br/>6.5 Merging Data<br/>6.6 Subsetting DataFrames in Pandas<br/>6.7 Reshaping the Data and Pivot Tables<br/>6.8 Backfill<br/>6.9 Forward Fill<br/>Summary<br/>Exercises<br/>Chapter 7 Functions in Python Pandas<br/>7.1 Pandas DataFrame Functions<br/>7.1.1 Groupby<br/>7.1.1.1 Groupby and Aggregate Functions<br/>7.1.1.2 Splitting the Data into Multiple Groups<br/>7.1.1.3 Splitting the Data and Running an Aggregation Function<br/>7.1.1.4 Nested Groups<br/>7.1.1.5 Loop over Group by Groups<br/>7.1.1.6 Display the Indices in Each Group<br/>7.1.1.7 Iterate over All of the Groups<br/>7.1.1.8 Finding the Values Contained in the Particular Group<br/>7.1.2 Pandas Correlations<br/>7.1.3 Pandas DataFrame All Method<br/>7.1.4 Pandas DataFrame Any Method<br/>7.1.5 Pandas DataFrame Columns Property<br/>7.1.6 Pandas DataFrame count() Method<br/>7.1.7 Pandas DataFrame describe() Method<br/>7.1.8 Pandas DataFrame drop_duplicates() Method<br/>7.1.9 Pandas DataFrame empty Property<br/>7.1.10 Pandas DataFrame filter() Method<br/>7.1.11 Pandas DataFrame equals() Method<br/>Summary<br/>Exercises<br/>Chapter 8 Matplotlib for Data Visualization<br/>8.1 Exploratory Data Analysis using Python<br/>8.1.1 What is Exploratory Data Analysis?<br/>8.1.2 Steps Involved in EDA<br/>8.1.3 Libraries in Python Used in EDA<br/>8.2 Matplotlib<br/>8.2.1 Understanding Matplotlib’s Pyplot API<br/>8.2.2 Box Plot<br/>8.2.2.1 Example 1<br/>8.2.2.2 Example 2<br/>8.2.3 Pie Chart<br/>8.2.3.1 Example 1<br/>8.2.3.2 Example 2<br/>8.2.4 Scatter Plot<br/>8.2.5 Treemaps<br/>8.2.5.1 Example 1<br/>8.2.6 Heat Maps<br/>8.2.7 Waterfall Chart<br/>8.2.7.1 Example 1<br/>8.2.7.2 Example 2<br/>8.2.8 Bubble Chart<br/>8.2.9 Histogram<br/>8.2.9.1 Example 1<br/>8.2.10 Line Plot<br/>8.2.10.1 Example 1<br/>8.2.10.2 Example 2<br/>8.2.11 Interactive Features of Matplotlib<br/>8.2.11.1 Clearing the Figure with clf() Method<br/>8.2.11.2 Clearing Axes with cla() Method<br/>Summary<br/>Exercises<br/>Answers<br/>Chapter 9 Plotly for Data Visualization<br/>9.1 Plotly Python Package<br/>9.1.1 Scatter Plot<br/>9.1.1.1 Example 1<br/>9.1.1.2 Example 2<br/>9.1.1.3 Example 3<br/>9.1.2 Bar Plot<br/>9.1.2.1 Example 1<br/>9.1.2.2 Example 2<br/>9.1.2.3 Example 3<br/>9.1.2.4 Example 4<br/>9.1.3 Pie Chart<br/>9.1.3.1 Example 1<br/>9.1.3.2 Example 2<br/>9.1.3.3 Example 3<br/>9.1.4 Word Cloud<br/>9.1.4.1 Example 1<br/>9.1.4.2 Example 2<br/>9.1.5 Treemap<br/>9.1.6 Choropleth Chart<br/>9.1.6.1 Example 1<br/>9.1.6.2 Example 2<br/>9.1.6.3 Example 3<br/>9.1.7 Area Chart<br/>9.1.8 Bubble Chart<br/>9.1.8.1 Example 1<br/>9.1.8.2 Example 2<br/>9.1.9 Gantt Chart<br/>9.1.10 Boxplot<br/>9.1.11 Violin plot<br/>9.1.12 Histogram<br/>Summary<br/>Exercises<br/>Chapter 10 Seaborn for Data Visualization<br/>10.1 Seaborn Plots Using “iris” Dataset<br/>10.1.1 Scatter Plot<br/>10.1.2 Strip Plot<br/>10.1.3 Swarm Plot<br/>10.1.4 Count Plot<br/>10.1.5 Box Plot<br/>10.1.6 Violin Plot<br/>10.1.7 Pair Plot<br/>10.1.8 Pie Chart<br/>10.2 Seaborn Plots Using “Superstore” Dataset<br/>10.2.1 Bar Plot<br/>10.2.2 Cat Plot<br/>10.2.3 Countplot<br/>10.2.4 Box Plot<br/>10.2.5 Pair Plot<br/>10.2.6 Implot Plot<br/>10.3 Seaborn Plots Using “OLYMPIC” Dataset<br/>10.3.1 DistPlot<br/>10.4 Seaborn Plots Using “Passengers Flights” Dataset<br/>10.4.1 Scatter Plot<br/>10.4.2 Line Plot<br/>10.4.3 Bar Plot<br/>Summary<br/>Chapter 11 Cases<br/>11.1 Case Study 1<br/>11.1.1 Analysis of Wine Quality Dataset<br/>11.2 Case Study 2<br/>11.2.1 Analysis of Titanic Dataset<br/>Appendix Python Assignments<br/>Index |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | : 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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Subject | Data visualization |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Subject | Python (Programming Language.) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Bill Date | Full call number | Accession Number | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Institute of Public Enterprise, Library | Institute of Public Enterprise, Library | S Campus | 08/26/2023 | Overseas Press India | 909.00 | 19-08-2023 | 005.133 ACH | 47763 | 08/26/2023 | Books | ||||
| Dewey Decimal Classification | Institute of Public Enterprise, Library | Institute of Public Enterprise, Library | S Campus | 08/26/2023 | Overseas Press India | 909.00 | 19-08-2023 | 005.133 ACH | 47764 | 08/26/2023 | Books | ||||
| Dewey Decimal Classification | Institute of Public Enterprise, Library | Institute of Public Enterprise, Library | S Campus | 08/26/2023 | Overseas Press India | 909.00 | 19-08-2023 | 005.133 ACH | 47765 | 08/26/2023 | Books | ||||
| Dewey Decimal Classification | Institute of Public Enterprise, Library | Institute of Public Enterprise, Library | S Campus | 08/26/2023 | Overseas Press India | 909.00 | 19-08-2023 | 005.133 ACH | 47766 | 08/26/2023 | Books | ||||
| Dewey Decimal Classification | Institute of Public Enterprise, Library | Institute of Public Enterprise, Library | S Campus | 08/26/2023 | Overseas Press India | 909.00 | 19-08-2023 | 005.133 ACH | 47767 | 08/26/2023 | Books |
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