The ability to create graphics programmatically is a very sought-after skill set due to the rising demand for Data Science and Analytics skill sets. Combining specific well-known Python data visualization modules with Python4Delphi will help you quickly resolve the issue (P4D). P4D is a collection of free, powerful data visualization tools in Python that you can use to interact with Delphi scripts, modules, and types to construct Windows GUIs quickly. So, naturally, in this article, we will learn how to use the best data visualization tools Python has available along with some best practices. So, let’s get started.
Table of Contents
Why is data visualization essential today?
Data visualization is a multidisciplinary field that deals with the graphic representation of data. It is an effective communication method when the info is varied and may be confusing. Furthermore, data visualization is essential because it makes the data understandable to a wider audience, whether it takes the shape of charts, graphs, or other representations. So, developers must know how to use data visualization tools Python.
Users of data for business can gain insight into vast amounts of data through data visualization. As developers or users, we benefit from identifying fresh patterns and flaws. As users we can concentrate on areas that signal danger or advancement. The company advances thanks to this procedure.
What are the best libraries for data visualization in Python?
Python has numerous libraries that provide the best data visualizations, but some of the best include Matplotlib, Bokeh, and Plotly.
Matplotlib is a comprehensive and popular Python library for creating static, animated, and interactive visualizations. Matplotlib creates publication-quality figures in various physical formats and cross-platform interactive settings. The Python and IPython shells, web application servers, and several graphical user interface toolkits (in this post, Python GUI by Delphi’s VCL using P4D!) all support the use of Matplotlib.
For contemporary web browsers, Bokeh is an interactive visualization library. It allows for high-performance interactivity over large or streaming datasets and offers elegant, concise construction of versatile graphics. Anyone who wants to create interactive plots, dashboards, and data applications quickly and simply can do so with the aid of Bokeh.
A browser-based, interactive, open-source data visualization library for Python is called Plotly or plotly.py. It is a high-level, declarative charting library built on top of plotly.js. Plotly.js comes with more than 30 different chart types, including financial, scientific, and 3D graphs. Plotly is MIT licensed software.
How can you use Matplotlib with P4D?
The Python Matplotlib library provides multiple tools for working with graphics. Using this library, you can design graphics, legends, style sheets, color schemes, and manipulate images.
Thanks to matplotlib, users also can create, adapt, and extend existing functionalities.
Is it possible to create annotated heatmaps with Python? Is there an example?
Yes, this is just one of the many possible real-life use cases that Maplotlob is used for.
Let’s see a code example:
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
import numpy as np import matplotlib.pyplot as plt category_names = ['Strongly disagree', 'Disagree', 'Neither agree nor disagree', 'Agree', 'Strongly agree'] results = { 'Question 1': [10, 15, 17, 32, 26], 'Question 2': [26, 22, 29, 10, 13], 'Question 3': [35, 37, 7, 2, 19], 'Question 4': [32, 11, 9, 15, 33], 'Question 5': [21, 29, 5, 5, 40], 'Question 6': [8, 19, 5, 30, 38] } def survey(results, category_names): """ Parameters ---------- results : dict A mapping from question labels to a list of answers per category. It is assumed all lists contain the same number of entries and that it matches the length of *category_names*. category_names : list of str The category labels. """ labels = list(results.keys()) data = np.array(list(results.values())) data_cum = data.cumsum(axis=1) category_colors = plt.get_cmap('RdYlGn')( np.linspace(0.15, 0.85, data.shape[1])) fig, ax = plt.subplots(figsize=(9.2, 5)) ax.invert_yaxis() ax.xaxis.set_visible(False) ax.set_xlim(0, np.sum(data, axis=1).max()) for i, (colname, color) in enumerate(zip(category_names, category_colors)): widths = data[:, i] starts = data_cum[:, i] - widths ax.barh(labels, widths, left=starts, height=0.5, label=colname, color=color) xcenters = starts + widths / 2 r, g, b, _ = color text_color = 'white' if r * g * b < 0.5 else 'darkgrey' for y, (x, c) in enumerate(zip(xcenters, widths)): ax.text(x, y, str(int(c)), ha='center', va='center', color=text_color) ax.legend(ncol=len(category_names), bbox_to_anchor=(0, 1), loc='lower left', fontsize='small') return fig, ax survey(results, category_names) plt.show() |
How do I start visualizing data with Python in Windows?
With so many online tools and libraries for data visualization, the entry barrier for producing engaging data visualization is lower than ever.
However, Python has so many options available that choosing where to start can be challenging. Any Python data visualization module can be combined with Embarcadero’s Delphi using Python4Delphi (P4D), allowing you to start developing Windows GUI applications.
If you know how to use P4D and Matplotlib, the only other tool you’ll need is an advanced IDE. Without a doubt, PyScripter is the most widely used Python scripting tool.
PyScripter is a lightweight IDE to add to the existing excellent Python for Delphi (P4D) components. It provides a state-of-the-art scripting solution for Delphi applications making it a popular choice amongst veteran developers.
Are you ready to use data visualization tools in Python?
The sheer number of available modules may be overwhelming if you’re new to Python visualization. Additionally, some libraries might be better suited for a given circumstance than others. You should be able to distinguish between the varied features of each library and make an informed decision.
You have now learned how to use Python for Delphi to run the three key data visualization libraries, and you can use plots produced by the Plotly library and Python4Delphi to solve various real-world problems.
PyScripter offers all the features one would anticipate from a contemporary Python IDE in a compact package. Additionally, it is natively compiled for Windows to use little memory while performing at its best. The IDE is also open-source and was created entirely in Delphi with Python scripts for extensibility. Most Python data analysts favor PyScripter because of its many outstanding features.
