Data Analysis and Visualization Using Python

Analyze Data to Create Visualizations for BI Systems

verfasst von: Dr. Ossama Embarak

Verlag: Apress

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python.
Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python.
In conclusion, you will complete a detailed case study, where you’ll get a chance to revisit the concepts you’ve covered so far.
What You Will LearnUse Python programming techniques for data science
Master data collections in Python
Create engaging visualizations for BI systems
Deploy effective strategies for gathering and cleaning data
Integrate the Seaborn and Matplotlib plotting systems
Who This Book Is For
Developers with basic Python programming knowledge looking to adopt key strategies for data analysis and visualizations using Python.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction to Data Science with Python

Abstract

This chapter introduced data science as the field that comprises everything related to cleaning, preparing, and analyzing unstructured, semi structured, and structured data. This field of science uses a combination of statistics, mathematics, programming, problem-solving, and data capture to extract insights and information from data. Python provides powerful libraries and mechanisms for data science applications as demonstrated in the following sections.

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Chapter 2. The Importance of Data Visualization in Business Intelligence

Abstract

Data visualization is the process of interpreting data and presenting it in a pictorial or graphical format. Currently, we are living in the era of big data, where data has been described as a raw material for business. The volume of data used in businesses, industries, research organizations, and technological development is massive, and it is rapidly growing every day. The more data we collect and analyze, the more capable we can be in making critical business decisions. However, with the enormous growth of data, it has become harder for businesses to extract crucial information from the available data. That is where the importance of data visualization becomes clear. Data visualization helps people understand the significance of data by summarizing and presenting a huge amount of data in a simple and easy-to-understand format in order to communicate the information clearly and effectively.

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Chapter 3. Data Collection Structures

Abstract

Lists, dictionaries, tuples, series, data frames, and panels are Python data collection structures that can be used to maintain a collection of data. This chapter will demonstrate these various structures in detail with practical examples.

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Chapter 4. File I/O Processing and Regular Expressions

Abstract

In this chapter, you’ll study input-output functions and file processing. In addition, you’ll study regular expressions and how to extract data that matches specific patterns.

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Chapter 5. Data Gathering and Cleaning

Abstract

In the 21st century, data is vital for decision-making and developing long-term strategic plans. Python provides numerous libraries and built-in features that make it easy to support data analysis and processing. Making business decisions, forecasting weather, studying protein structures in biology, and designing a marketing campaign are all examples that require collecting data and then cleaning, processing, and visualizing it.

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Chapter 6. Data Exploring and Analysis

Abstract

Nowadays, massive data is collected daily and distributed over various channels. This requires efficient and flexible data analysis tools. Python’s open source Pandas library fills that gap and deals with three different data structures: series, data frames, and panels. A series is a one-dimensional data structure such as a dictionary, array, list, tuple, and so on. A data frame is a two-dimensional data structure with heterogeneous data types, i.e., tabular data. A panel refers to a three-dimensional data structure such as a three-dimensional array. It should be clear that the higher-dimensional data structure is a container of its lower-dimensional data structure. In other words, a panel is a container of a data frame, and a data frame is a container of a series. This chapter demonstrates how to explore, manipulate and analyze data.

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Chapter 7. Data Visualization

Abstract

This chapter demonstrates various plottings for data visualization. Various Python libraries can be used for data visualization, such as Pandas, Seaborn, Bokeh, Pygal, and Ploty. Python Pandas is the simplest method for basic plotting. Python Seaborn is great for creating visually appealing statistical charts that include color. Python Bokeh works great for more complicated visualizations, especially for web-based interactive presentations. Python Pygal works well for generating vector and interactive files. However, it does not have the flexibility that other methods do. Python Plotly is the most useful and easiest option for creating highly interactive web-based visualizations.

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Chapter 8. Case Studies

Abstract

This chapter covers two case studies. I will provide some brief information about each case and then show how to gather the data needed for analysis, how to analyze the data, and how to visualize the data related to specific patterns.

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Backmatter

Titel: Data Analysis and Visualization Using Python
verfasst von: Dr. Ossama Embarak
Verlag: Apress
Electronic ISBN: 978-1-4842-4109-7
Print ISBN: 978-1-4842-4108-0
DOI: https://doi.org/10.1007/978-1-4842-4109-7