![]() It offers a range of chart types similar to Matplotlib and Seaborn, including line plots, scatter plots, area charts, bar charts, and more. Plotly’s Python graphing library provides an effortless way to create interactive and high-quality graphs. Even though it does not have a wide collection as Matplotlib, seaborn makes popular plots such as bar plot, box plot, heatmap, etc look pretty in less code. Takeaway: Seaborn is a higher-level version of Matplotlib. While it excels in popular plot types, it may not offer the same breadth of options for more specialized or custom plots. Seaborn, despite its advantages, does not have as extensive a collection of plot types as Matplotlib. In the following example, the count plot appears more visually appealing due to Seaborn’s default settings: sns.set(style="darkgrid")Īx = sns.countplot(x="class", data=titanic) Cons Not only does Seaborn require less code to generate these plots, but they also have enhanced visual aesthetics. Seaborn is a popular choice for common plot types such as bar plots, box plots, count plots, and histograms. This results in a more visually appealing heatmap without the need for additional configuration. This means you can achieve similar visualizations with less code and a more visually pleasing design.įor instance, using the same data as before, we can create a heatmap without explicitly setting the x and y labels: correlation = new_rr() Seaborn provides a higher-level interface for generating similar plots as Matplotlib. It offers a higher-level interface, simplifying the process of creating visually appealing plots. Seaborn is a Python data visualization library built on top of Matplotlib. Takeaway: Matplotlib is capable of producing any plot, but creating complex plots often requires more code compared to other libraries. Plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") This is due to Matplotlib’s low-level interface. If you intend to present your data to others, customizing the x-axis, y-axis, and other plot elements may require substantial effort. While Matplotlib can plot virtually anything, generating non-basic plots or adjusting plots for aesthetic purposes can be complex. The Matplotlib’s website offers comprehensive documentation and a gallery of various graphs, making it easy to find tutorials for virtually any type of plot. Matplotlib is very versatile and capable of generating a wide range of graph types. Plt.bar(top_er_name, top_followers.followers)ĭespite Matplotlib’s suboptimal x-axis representation, the graph provides a clear understanding of the data distribution. Top_followers = new_profile.sort_values(by="followers", axis=0, ascending=False) When analyzing data, it’s often helpful to get a quick overview of its distribution.įor example, if you want to examine the distribution of the top 100 users with the most followers, Matplotlib is typically sufficient. ![]() ![]() Almost everyone interested in data science has likely utilized Matplotlib at least once. Matplotlib is probably the most common Python library for visualizing data. To explore each plot, we will use the data of GitHub users: import pandas as pdįeel free to fork and play with the code for this article in this Github repo: However, they come with a more complex API.ĭeclarative libraries like Altair simplify the mapping of data to visualizations, offering a more intuitive syntax.Īre you working with specialized use cases, such as geographical plots or large datasets? Consider whether a specific library supports the plot types or handles large datasets effectively. How does the syntax differ across libraries? Lower-level libraries such as Matplotlib provide extensive flexibility, allowing you to accomplish almost anything. ![]() We will do this by focusing on a few specific attributes:ĭo you want interactive visualization? Libraries like Altair, Bokeh, and Plotly allow you to create interactive graphs that users can explore and interact with.Īlternatively, some libraries like Matplotlib render visualizations as static images, making them suitable for explaining concepts in papers, slide decks, or presentations. By the end, you will gain a better understanding of their distinct features, making it easier for you to select the optimal library. This article will show the pros and cons of each library. When visualizing a DataFrame, choosing the right library can be challenging as different libraries excel in specific cases. ![]() Some popular libraries for visualization include Matplotlib, Seaborn, Plotly, Bokeh, Altair, and Folium. If you’re new to Python visualization, the vast number of libraries and examples available might seem overwhelming. ![]()
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