This tutorial demonstrates the fundamentals of creating pie charts using Matplotlib, along with customizing slices using labels and an explode effect. Below, you’ll find two examples: one showing a basic pie chart, and another with customized slice properties. Basic Pie Chart The following code creates a basic pie chart. In this example, sample data is […]
Histograms are a fundamental tool in data analysis, allowing you to visualize the distribution of a dataset. In this tutorial, we cover several aspects of histogram creation. Basic Histogram with Custom Bins The following example generates 1000 random values drawn from a normal distribution. By setting the bins parameter (here, to 30), you adjust the
Bar plots are one of the most widely used visualization techniques in data science and analytics. They provide a simple way to represent numerical values across different categories. In this tutorial, we will explore how to create basic vertical and horizontal bar plots, and then move on to comparing multiple datasets with grouped and stacked
Scatter plots are essential for visualizing the relationship between two numerical variables. In this tutorial, we will first create a basic scatter plot using random data and then demonstrate how to enhance the visualization by encoding additional dimensions with marker size, color, and transparency (alpha). This approach helps uncover patterns and insights in your dataset.
When working with Jupyter Notebook, displaying your plots directly in the notebook is highly valuable. Matplotlib offers different “modes” through magic commands like %matplotlib inline, which renders static images, and interactive modes like %matplotlib notebook or %matplotlib widget that allow you to zoom, pan, and update your visualizations dynamically. This tutorial explains both approaches in
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Matplotlib is a powerful and widely-used Python library for creating high-quality visualizations, making it an essential tool for data analysis and scientific computing. Among its many capabilities, Matplotlib excels at generating line plots, which are fundamental for displaying trends, relationships, and changes in data over a continuous range. With its intuitive interface and extensive customization
Understanding the anatomy of a Matplotlib plot is essential for creating clear and informative visualizations. In Matplotlib, every plot is built upon two core components: the Figure and the Axes. The Figure serves as the overall canvas or container for your plot, while the Axes represent the individual plotting areas within the Figure where the
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In this tutorial, we will learn how to create your first plot using Python’s Matplotlib library. You will discover how to use the basic plotting function plt.plot() to create a simple line plot from data and display it with plt.show(). We will also cover how to customize your visualization by adjusting line colors, styles, and
Creating Your First Plot with Matplotlib Read More »
In this comprehensive tutorial, we delve into Matplotlib—a powerful, versatile plotting library in Python that has become the cornerstone for data visualization in the scientific computing ecosystem. Matplotlib enables users to generate high-quality static, animated, and interactive visualizations, making it an essential tool for anyone working with data. Its seamless integration with NumPy, Pandas, and
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