Using Python Analyze Data to Create Visualizations for BI Systems
Content
- About the author
- About technical analysts
- Introduction
- Chapter 1: Introduction to Data Science with Python
- Part 2: The importance of visualization in cognitive behavior
- Chapter 3: Data Collection Format
- Chapter 4: File I/O Handling and Standard Imaging
- Chapter 5: Data collection and purification
- Chapter 6: Data Collection and Analysis
- Chapter 7: Obtaining Information
- Chapter 8: Lessons
- Summary
- Phone book
Preface
This book looks at Python from a scientific perspective and teaches the reader proven data collection techniques used to make critical business decisions. Starting with an introduction to data science using Python, the book then covers the Python environment and introduces authors such as Jupiter Notebook and Spyder.ID. After an introduction to Python programming, you will understand the basic Python techniques used in data science. By moving towards data visualization, you will understand how it impacts current business operations and is key to decision making. You will also see popular libraries to access libraries in Python. You will learn different aspects of data collection from a scientific perspective by changing the focus of data collection and also review data collection methods in Python. You will then learn file I/O handling and routines in Python, followed by data collection and cleaning techniques. To explore and analyze data, you look at different data in Python. You will then dive directly into the fundamentals of visualization technology through various programming methods in Python. At the end, you will go through two detailed exercises where you will have the opportunity to review the concepts you have understood so far.
This book is aimed at people who want to learn Python in a scientific context and become scientists. No special programming skills are required other than having basic beginner skills.
In particular, the following list reflects the content of the book:
• Chapter 1 introduces the fundamentals and lifecycle of information science. It also shows the importance of Python programming and key libraries for data processing. You will learn how different Python frameworks are used in science. You will learn how to implement abstract arrays and data arrays as the main core of Python. You will learn how to use basic Python techniques for data manipulation and manipulation. You will learn methods to perform basic statistical analysis. Additionally, exercises with sample results for real-life application are presented.
• Chapter 2 shows how to apply video in today’s business environment. You’ll learn how to identify the role of data visualization in decision-making and how to install and use key Python libraries to access data. Additionally, exercises with sample results for real-life application are presented.
• Chapter 3 shows how to collect and apply data in Python. You will learn how to identify different compilers in Python. You will learn how to create a list and manage the content of the list. You will learn the purpose of creating a dictionary as a data source and its manipulation. You will learn how to store data in tuple format and the difference between tuple format and dictionary as well as basic operations.
INNGENTRYother data collection methods. You will know how to do it data frames from different data collection methods and other sources. You will know how creates a panel as a collection of 3D data from series or Information framework. Plus, exercises and sample results is given to practice normal life.
• Chapter 4 shows users how to read and send data, how to read and retrieve data stored in history files, and how to open files to read, write, or both. You will learn how to access file attributes and manage sessions. You will learn to read user information and use the dump. You will learn how to apply regular expressions to retrieve data, how to use regular expressions, and how to use parentheses and recursive expressions to retrieve data. Additionally, models for exercises and practice are presented.
• Chapter 5 covers data collection and cleaning to obtain reliable data for analysis. You will learn how to use data cleaning techniques to resolve missing values. You can read CSV data format online or download it directly from the cloud. You will learn how to integrate and integrate information from different sources. You will learn to read and extract data in JSON, HTML, and XML formats. You will also be provided with exercises that include sample results for real-world application.
• Chapter 6 shows you how to use Python scripts to search and analyze data in various data sets. You will learn how to apply Python techniques to search and analyze data series and to create series.
Extract data from a time series and apply statistical methods to the series. You will learn how to find and analyze data in a data structure, create a data frame, and update and open data in a data structure. You will learn how to use the information in it.
Data frames, such as adding columns, selecting rows, adding or deleting data, and applying statistical operations to rows of data. You will learn how to use online statistical methods to search and analyze archived data. You will learn how to perform statistical analysis of data, grouping and ungrouping, transformation, and filtering techniques. Exercises with sample results for real-life application are also provided.
• Chapter 7 shows how to obtain images from different collections. You’ll learn how to plot data from a series, data frame, or table using Python drawing tools such as line charts, bar charts, box plots, histograms, and scatter plots. You will learn how to implement the Seaborn planning system using plots, plots, boxes, and multiple plots. You will learn how to implement Matplotlib using line plots, line plots, histograms, scatter plots, plots, and tables. Additionally, exercises with sample results for real-life application are presented.
• Chapter 8 conducts two case studies, starting with data collection and subsequent cleaning.
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