![]() ![]() (Yes, I know we’ve all had enough of COVID-19, but it’s a great dataset!) This dataset contains COVID-19 cases and deaths over time for 237 countries. I decided to look at COVID-19 data from the World Health Organization (WHO). ![]() I’ll endeavor to answer the question posted in the blog title at the end of this post, so please read on. I recently went on a deep dive into the interactive plotting ecosystem of Python, and in this blog post I’m going to share my personal opinions on what works and what doesn’t within the most popular Python interactive packages available now. They can be incredibly useful tools for investigating your data and for sharing your data and research results with others. By learning how to effectively set axis ranges (xlim, ylim) in Matplotlib, you will be able to create visually appealing and informative plots, enhancing your data analysis and presentation skills.I love using, creating and teaching people about interactive figures. This book serves as a unique, practical guide to Data Visualization, offering in-depth knowledge of a wide range of tools that you may encounter and utilize throughout your career. Spanning 11 chapters, this book covers a total of 9 essential Python libraries: Pandas, Matplotlib, Seaborn, Bokeh, Altair, Plotly, GGPlot, GeoPandas, and VisPy. Additionally, it delves into declarative and experimental libraries like Altair. This in-depth guide will teach you everything you need to know about Pandas and Matplotlib, including how to create custom plot types that aren't readily available within the library itself.ĭata Visualization in Python, a book designed for beginner to intermediate Python developers, provides comprehensive guidance on data manipulation using Pandas and thoroughly explains core plotting libraries such as Matplotlib and Seaborn. It builds a strong foundation for advanced work with these libraries, covering a wide range of plotting techniques - from simple 2D plots to animated 3D plots with interactive buttons. ✅ Regularly updated for free (latest update in April 2021)ĭata Visualization in Python with Matplotlib and Pandas is a comprehensive book designed to guide absolute beginners with basic Python knowledge in mastering Pandas and Matplotlib. ✅ 30-day no-question money-back guarantee This code limits the view on the X-axis to the data between 25 and 50, as shown in the resulting plot: For example, if we wanted to truncate the view to only show the data in the range of 25-50 on the X-axis, we'd use xlim(): import matplotlib.pyplot as plt Both of these methods accept a tuple containing the left and right limits. Let's first set the X-limit using both the PyPlot and Axes instances. For example, if you want to focus on the range from 2 to 8, you can set the x-axis limits as follows: To set the x-axis range, you can use the xlim function, which takes two arguments: the lower and upper limits of the x-axis. These functions can be accessed either through the PyPlot instance or the Axes instance. To adjust the axis range, you can use the xlim and ylim functions. However, you might want to modify the axis range for better visualization or to focus on a specific region of the plot. The x-axis currently ranges from 0 to 100, and the y-axis ranges from -1 to 1. Running this code produces the following plot: The sequence starts at 0 and ends at 10 with a step of 0.1. In this example, we've plotted the values created by applying a sine and cosine function to the sequence generated using Numpy's arange() Function. Optionally, you could add ax.legend() to display the labels for each wave. In the above code, we create a figure and axis object with plt.subplots(), generate x, y, and z data points using numpy, and then plot the sine and cosine waves on the same axis. Let's first create a simple plot to work with: import matplotlib.pyplot as pltĪx.plot(y, color= 'blue', label= 'Sine wave')Īx.plot(z, color= 'black', label= 'Cosine wave') This can be useful when you want to focus on a particular portion of your data or to ensure consistency across multiple plots. ![]() In this tutorial, we'll take a look at how to set the axis range (xlim, ylim) in Matplotlib, to truncate or expand the view to specific limits. ![]() Much of Matplotlib's popularity comes from its customization options - you can tweak just about any element from its hierarchy of objects. Matplotlib is one of the most widely used data visualization libraries in Python. ![]()
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