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Python for Data Visualization - A Beginner's Guide [Video]
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Free ChapterSetup and Installation
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Plotting Line Plots with Matplotlib
- Changing the Axis Scales
- Label Styling
- Adding a Legend
- Changing Colors, Line Styles, Line Width, and Markers
- Adding a Grid to the Chart
- Filling Only a Specific Area
- Filling Area on Line Plots and Filling Only Specific Areas
- Changing Fill Color of Different Areas (Negative Versus Positive, For Example)
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Plotting Histograms and Bar Charts with Matplotlib
- Changing Edge Color and Adding Shadow on the Edge
- Adding Legends, Titles, Location, and Rotating Pie Chart
- Histograms Versus Bar Charts (Part 1)
- Histograms Versus Bar Charts (Part 2)
- Changing Edge Color of the Histogram
- Changing the Axis Scale to Log Scale
- Adding Median to Histogram
- Advanced Histograms and Patches (Part 1)
- Advanced Histograms and Patches (Part 2)
- Overlaying Bar Plots on Top of Each Other (Part 1)
- Overlaying Bar Plots on Top of Each Other (Part 2)
- Creating Box and Whisker Plots
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Plotting Stack Plots and Stem Plots
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Plotting Scatter Plots with Matplotlib
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Time Series Data Visualization with Matplotlib
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Creating Multiple Subplots
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Plotting Charts Using Seaborn
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Plotly and Cufflinks
About this video
Python-based data visualization uses the Python programming language and its libraries to transform data into visual representations, such as charts, graphs, and interactive dashboards. Python’s libraries, including Matplotlib, Seaborn, Plotly, and Bokeh, offer customizable plot types and interactive features to craft compelling visual narratives. Through data storytelling and customization, Python shares insights and effectively communicates them, making it an indispensable skill for anyone working with data.
In this course, we will begin by grasping the importance of data visualization and exploring essential Python libraries such as Matplotlib, Seaborn, and Plotly. You will learn to customize and enhance visualizations, adjust colors, labels, and legends, and understand the principles of effective data storytelling. The course delves into advanced topics such as creating interactive dashboards and dynamic data plots. We will work on practical projects and real-world examples to equip us with the skills to turn raw data into informative visuals using Python.
Upon completion, we will master Python-based data visualization from core principles to practical skills, Matplotlib, Seaborn, and Plotly, and transform raw data into compelling visuals. We will acquire tools to create visuals, convey insights, and make data-driven decisions with confidence.
- Publication date:
- September 2023
- Publisher
- Packt
- Duration
- 3 hours 40 minutes
- ISBN
- 9781805127598