Best plotting library for python – The Top 5 Python Plotting and Graphing Libraries

Data visualization:

Best python plotting library: Data visualization is a key component of data analysis. After all, viewing the hidden patterns and layers in the data in a visual manner is the only way to grasp them! Don’t believe it? Assume you reviewed your organization’s statistics and discovered that a specific product was continuously losing money for the company. Your manager might not pay attention to a written report, but if you present a line chart with profits as a red line that is continually decreasing, your supervisor might pay much more attention! This exemplifies the power of data visualization.

Because humans are visual creatures, data visualization charts such as bar charts, scatterplots, line charts, geographical maps, and so on are incredibly significant. They tell you information just by glancing at them, whereas you would normally have to read spreadsheets or text reports to grasp the data. And Python is a popular programming language for both data analytics and data visualization. In recent years, various libraries for creating stunning and complicated data visualizations have been accessible. These libraries are so popular because they make it simple for analysts and statisticians to develop visual data models based on their specifications by providing an interface and data visualization capabilities all in one place!

The Top 5 Python Plotting and Graphing Libraries

  • Matplotlib: Its API makes it simple to plot graphs in any application.
  • Seaborn: A versatile matplotlib-based library that enables the comparison of numerous variables.
  • ggplot: Generates visuals for specific domains.
  • Bokeh: Preferred libraries for real-time data and streaming.
  • Plotly: With the help of JS, it is possible to create incredibly interactive graphs.

1)Matplotlib

Best plotting library for python: Matplotlib, Python’s most popular data visualization tool, is a 2D plotting library. It is the most extensively used charting package in the Python community and has been around for more than a decade. It includes a cross-platform interactive environment. Matplotlib is compatible with Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits. It is a highly adaptable visualization library. With this library, plots, bar charts, histograms, power spectra, stemplots, scatterplots, error charts, pie charts, and many other types can be generated with just a few lines of code. When paired with IPython, the pyplot package provides a MATLAB-like interface. Matplotlib predates HTML5’s capability for rich online apps, instead of focusing on static images for publication as well as interactive figures using desktop-GUI toolkits such as Qt and GTK.

Advantages of Using Matplotlib:

  • Recognizing the correlation between the factors
  • The model’s fitting of the data is communicated.
  • Scatter plots can be used to detect outliers.

Cons:

  • Matplotlib can plot anything, however, it can be difficult to plot non-basic plots or to make them appear attractive.
  • Even if the plot is adequate for visualizing the distribution, if you want to convey your data to others, you will need to correct the x-axis and y-axis, which requires considerable effort. This is due to Matplotlib’s incredibly low-level interface.

2)Seaborn:

Python best plotting library: Seaborn is a Python data visualization package based on matplotlib. Seaborn features an API that is built on datasets and allows for the comparison of numerous variables.

It allows multi-plot grids, which facilitates the creation of complicated visualizations. It provides univariate and bivariate visuals for comparing data subsets.

It employs a variety of color palettes to expose various patterns. It also automatically estimates linear regression models.

Matplotlib vs. Seaborn

Graphing library python: Seaborn seeks to create a well-defined set of hard things that are also easy. Matplotlib tries to make easy things easy and hard things possible. In fact, matplotlib is good, but seaborn is superior. Matplotlib generates less appealing plots, while seaborn addresses this issue with high-level interfaces and configurable themes.

Matplotlib does not work well with data frames when working with pandas. Seaborn functions, on the other hand, work with data frames.

Pros:

  • fewer lines of code
    It provides a higher-level interface for plots of a similar nature. In other words, seaborn delivers plots that are similar to matplotlib, but with less code and a nicer design.
    We obtain a better heatmap if we don’t set the x and y labels.
  • Make often-used plots more attractive.
    When it comes to popular plots like bar plots, box plots, count plots, histograms, and so on, many people prefer seaborn not only because they can be made with less code but also because they appear more beautiful. The colours seem nicer than Matplotlib’s defaults.

cons:

  • Seaborn is more limited and does not have as large a collection as matplotlib.
  • Seaborn is a more advanced version of Matplotlib. Despite not having as large a library as Matplotlib, seaborn makes popular plots like bar plots, box plots, heatmaps, and so on appear nice with less code.

3)ggplot

Python graphing libraries: ggplot is a Python data visualization package based on the ggplot2 implementation designed for the computer language R. Using a high-level API, Ggplot can generate data visualizations like as bar charts, pie charts, histograms, scatterplots, error charts, and so on. It also enables you to include many sorts of data visualization components or layers in a single visualization.

Once ggplot is given which variables to map to specific elements in the plot, it takes care of the rest, allowing the user to focus on analyzing the visualizations rather than designing them. However, this also means that highly customized graphics in ggplot are not viable. Because ggplot is closely related to pandas, it is best to maintain the data as DataFrames.

4)Bokeh

Python graphing libraries: Bokeh is a strong Python data visualization package for current web browsers, similar to Plotly. It is built within the Python programming language, which is why many Python developers prefer it over Plotly.

We can get Bokeh plotted graphs in HTML format, just like we can with Plotly. Bokeh is also extremely compatible with major Python web frameworks such as Django and Flask, and it is possible to integrate bokeh in Django and Flask web applications.

Users can choose from three levels of bokeh: High Level, Middle Level, and Low Level. High-level users may easily and quickly generate histograms and bar charts. The matplotlib framework can be used by intermediate users to make dots for scatter plots.

5)Plotly

Python charting libraries: Plotly is a library-supported online visualization tool. We may create interactive plots similar to Bokeh here, but with additional graphs such as contour plots, 3D charts, and dendrograms. Plotly also identifies mouse-over and cursor-click events, making it a unique library that supports both graphics and JavaScript.

Bokeh and Plotly are comparable libraries, however, Plotly requires you to turn data into dictionaries. Plotly, on the other hand, is more user-friendly when it comes to manipulating data frames with Pandas.

Plotly is a free open-source graphing framework for creating data visualizations. Plotly (plotly.py) is a Python library that is built on top of the Plotly JavaScript library (plotly.js) that can be used to generate web-based data visualizations that can be presented in Jupyter notebooks or web applications using Dash, or saved as individual HTML files.

Pros:

  • Similar to R:
    If you really like R charts and miss them when you move to Python, Plotly provides the same high-quality plots in Python!
    Plotly Express is a favorite for most people since it makes creating excellent charts from a single line of Python so simple and fast.
  • It is simple to make interactive charts:
    Plotly also makes it simple to design interactive charts. Interactive plots are not only visually appealing, but they also allow users to zoom in on specific data points.
  • Simplifying complex graphs:
    Plotly allows you to simply generate several plots that are often difficult to create.

Plotly is an excellent tool for quickly creating interactive and publication-quality graphs with a few lines of code.