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 toward data visualization, you will understand how it impacts current business operations and is key to decision-making. You will also be able to access popular libraries in Python. By shifting the focus of data collection and reviewing various data collection methods in Python, you will gain a scientific perspective on various aspects of data collection. 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 will look at different types of data in Python. You will then dive directly into the fundamentals of visualization technology through various programming methods in Python. Ending with two detailed exercises to review concepts you’ve learned.
People who want to learn Python in a scientific context and become scientists are the target audience for this book. 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 discover the various applications of Python frameworks in the field of science. You will learn how to implement abstract arrays and data arrays, which form the 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. We also present exercises with sample results for real-life application.
• 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. We also present exercises with sample results for real-life applications.
• 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 its contents. 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, understand the differences between tuple format and dictionary, and perform basic operations.
INNENTRY other data collection methods. You will learn how to utilize data frames from various data collection methods and other sources. You will learn how to create a panel that collects 3D data from a series or information framework. Additionally, you will receive exercises and sample results for practical application in everyday life.
• Chapter 4 guides users through the process of reading and sending data, retrieving data from history files, and opening files for either reading or writing. 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, we present models for exercises and practice.
• 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 the CSV data format online or download it directly from the cloud. You will learn how to integrate information from various sources. You will learn to read and extract data in JSON, HTML, and XML formats. We will also provide you with exercises that include sample results for real-world applications.
• 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 and 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.
You will learn how to manipulate data frames by adding columns, selecting rows, adding or deleting data, and applying statistical operations to data rows. 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, including grouping and ungrouping, transformation, and filtering techniques. We also provide exercises with sample results for real-life application.
• 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, histograms, scatter plots, and tables. We also present exercises that include sample results for real-life applications.
• Chapter 8 conducts two case studies, starting with data collection and subsequent cleaning.
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