A decent Python GUI has a far more effective purpose than ‘just looking pretty’. For the average person, assuming they have no unmitigated visual disabilities, data is much more easily understood when it is presented in the form of a chart or graph. People wants the facts and figures to tell a story, to inform them. to enrich their knowledge and good visualizations help illustrate the elements and facilitate their comprehension. Data science often works with structured or unstructured data that can make human analysis difficult. Our understanding of the data is greatly enhanced by visualizations. Our brains are exceptionally good at detecting patterns and absorbing abstract information when it’s in the form of an image. We process graphically presented data much more quickly than text.
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Why is it necessary to create data visualizations?
Python is the go-to language for data scientists, and there are plenty of options available for data analysis, including various data visualization libraries. Large data sets are cumbersome to go through by hand. Still, it is critical to verify that any missing data entries are corrected or excluded.
Visual analysis of entire datasets helps with getting a feeling for the task at hand, which is important for choosing the right data analysis model. Correct data is also very important in machine learning and data analytics since any deviation may lead to a poor model, and ultimately the whole project may fail.
What Python data visualization libraries are available?
In a recent post, we covered the top 5 graph tools for Python. There are plenty of practical examples to get you started with visual data analysis.
One of the first Python plotting libraries the newbie programmer meets is matplotlib. It is robust and has a variety of options for customization; however, it can be complicated. Creating data visualizations is made easier with modern Python packages such as
- matplotlib
- pandas
- seaborn
- Plotnine (ggplot)
- Plotly
- Bokeh
- Altair
- geoplotlib
- pygal
- Gleam
An important interactive data visualization designs are Jupyter notebooks. They provide the means of interacting with the plots created using many of the above Python libraries.
What kind of plotting methods are there?
The type of plot is determined by the type of data. Some data sets are best represented as bar charts or as scatter plots. In other cases, it is necessary to make error charts to see, for example, deviations or measurement errors. Other more specific cases require complex data visualizations, such as statistical analyses displayed on box charts and plots on geographical maps.
How to visualize data with simple plots?
Nearly all python data visualization libraries are capable of creating simple plots of numerical data. Going to the next level is simple with some libraries than others. But most importantly, calling some Python libraries inside Jupyter notebooks makes it possible to generate interactive visualizations and even web apps.
What is matplotlib?
Matplotlib is the standard Python data visualization library, and there are countless examples online on how to present your data in a clear way.
Cons: matplotlib is old, and as such, it can be cumbersome at times. Sometimes seemingly simple things require plenty of coding or tweaking.
What is pandas?
Pandas runs a matplotlib-based engine to create plots, though the commands the user needs to enter are simplified. Most Python software engineers are familiar with pandas data structures. Still, any special additions that are present in matplotlib can be added to a pandas plot.
Cons: pandas can be slow with very large data sets.
What is seaborn?
Seaborn is a modern library that extends the functionality of matplotlib and pandas. There is a wide variety of plots to create with just a few lines of code.
Cons: None really.
Creating a scatter plot coloured by species only takes one line.
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sns.set_style("whitegrid") sns.scatterplot(data=iris, x = "sepal_length", y = "petal_length", hue = 'species') plt.show() |
Creating a histogram is equally simple.
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sns.set_style("whitegrid") iris["sepal_length"].plot(kind = "hist", label = "sepal_length") iris["sepal_width"].plot(kind = "hist", label = "sepal_width") plt.legend() plt.show() |
What is Plotnine (ggplot)?
With its simple commands and easy integration with pandas data frames, plotnine (also known as ggplot among the users of the R programming language) is among the easy Python data visualization libraries to get started with. There is detailed documentation to get familiar with its features.
Cons: Plotnine lacks some complex features present in matplotlib-based packages. It takes a slightly different approach than other data visualization libraries when defining graphs. At first, this might be confusing to experienced data analysts.
What is Plotly?
Plotly is a hugely popular Python data visualization package. Excellent for interactive web applications due to its integration with the Javascript library with the same name. Plots are easy to share with others online.
Cons: Plots are displayed via the browser rather than the traditional way of defining a file name and exporting the picture. Also, pandas integration is not functioning as smoothly as with the previously mentioned Python libraries. This makes it suitable for smaller datasets but not really for creating statistical graphics, for example, in machine learning.
What is Bokeh?
Bokeh can create excellent dynamic and interactive graphs. Plots can be exported in various formats.
Cons: While it is easy to make data plots like a bar graph or a line chart, Bokeh still lacks the options to create advanced representations like using matplotlib-based libraries. The documentation could also be better.
What is Altair?
Even though Altair is not based on matplotlib, it can be easily used inside Jupyter notebooks and IDEs such as PyScripter. The plots it creates are actually JSON objects, and the output charts can actually be saved in HTML format.
Cons: It lacks some types of plots used in other common data visualization tools, though it generally handles routine plotting successfully.
What is geoplotlib?
Geoplotlib is a Python visualization library specifically meant for geographical data and creating maps. It can generate elegant interactive graphs using data from Open Street Map. For example, it is possible to plot population data by country or city as dot density maps.
Cons: It is not suitable for general-purpose plots and lacks data visualization components for line charts with a trend line or bar plots, for example, as it is specialised for plotting geographical data only.
Below is a plot obtained with the example code from the GitHub repository of geoplotlib called taxi.py. It plots data from taxis in China in animated form. Here, I am showing one of the frames.
What is Pygal?
Pygal is a Python charting library that can be used for interactive web-based data visualizations.
Cons: Compared to other libraries for data visualization in Python, Pygal can be a bit clumsy when using many data points.
What is Gleam?
Gleam is an excellent data visualization library for interactive web apps created by using only Python scripts.
Cons: Hardly any. It certainly deserves more attention.
How to create interactive data visualizations in Python?
Interactive plots can be among the best ways of displaying data since a better overview of the data set is achieved with just several mouse clicks. These plots are typically executed in a browser so that the user can drag sliders, zoom in and out, and more. Interactive web applications can be hosted on a server, so the end user does not have to know any programming to understand the data.
Interactive charts are also critical for real-time data analysis. It is highly impractical to have to re-run a Python script by hand just to see how the oncoming data looks like. Many Python libraries can be run inside a notebook with a few exceptions. Some of the above mentioned packages such as Plotly and Altair open the chart in a new browser window.
What is the best tool to write and test Python data visualizations?
There are many Python data visualization libraries to make data analysis easy, efficient and productive. We recommend looking for examples and trying them out to make sure that you find the tools that fit your needs best.
By far the best available tool for writing, debugging and testing Python code is PyScripter. PyScripter is super-fast and easy to use. It comes with advanced features like a unit test wizard which will generate tests for your code to ensure everything works as expected. The best part is it’s free!
