The need for graphing arises in various types of business, as they are often used to solve various tasks. That’s why it’s important to have a simple and convenient graph tool. Python offers a number of such tools. Using built-in functions, you can easily build and customize complex graphs of different types. So, what business problems do graphs solve, and what is the best graph tool Python available to build them? You will get the answers to these questions after reading this article.
What business problems can you solve with graphs and the right graph tool?
To solve most business problems, it is necessary to analyze some amount of data. With a graph, you can combine different sets of data into a structure that allows you to discover relationships and find the cause of a problem. Let’s look at some types of problems that can be solved using a graph.
- Problems with the computer network. With the help of a graph, you can conveniently model a computer network, determine what parts it consists of, what connection method is used, etc. Using this information, you can easily find bottlenecks and ways to optimize the network.
- Path problem. Graphs are used to solve the known problem of finding the shortest and longest path from a starting point to a final destination.
- Fraud detection. Customer data and business events can be displayed graphically. Looking for suspicious patterns in customer activity data and cross-references to previously identified fraud will help spot potential fraud that may be ongoing.
- Project Management. With the help of a graph, you can depict the sequence of events, which allows you to sort them according to priority, find the most important event, and calculate the number of resources that are necessary for the implementation of the project.
- The problem of satisfying constraints. This is a common AI problem. To solve it you need to find a specific goal that satisfies a list of all constraints. Such problems are often modeled and solved using graphs.
What are the chart types?
Different types of charts are used to display data graphically. Each of them is used for different types of tasks. Below we list the most common types of charts.
- Histogram. This graph type is used to quickly compare data by category, highlight differences, show trends, and identify historical highs and lows.
- Line chart. It connects several different data points and is used to visualize trends in data over time.
- Scatter chart. Represents many different data points on a single chart. It is used to study the relationship between different variables.
- Treemap. Connects different segments of your data set to the whole. In this diagram, each rectangle in the treemap is subdivided into smaller rectangles or sub-rectangles depending on its proportion to the whole.
- Bar chart. Used to compare different items or to show a comparison of items over time.
- Area chart. This is a line chart, but the space between the horizontal axis and the line is filled with color or pattern. It is used to display the part-whole connection, display changes over time, and visualize large trends.
What are the best Python data visualization tools?
There are various Python graph tools that allow you to easily construct beautiful and user-friendly graphs. Let’s consider the most popular of them.
Matplotlib is a popular Python package used to build interactive 2D graphics. It allows you to create static, interactive, and dynamic presentations. With Matplotlib, you can plot line graphs, scatter plots, bar graphs, and more.
The Matplotlib package is easy to understand and use. It allows you to create high-quality images in various formats including png, pdf, etc. In addition, Matplotlib controls various aspects of the image such as color, font size, etc.
This library requires additional code to be written to create a visualization, as it is low-level. Also, it is not suitable for time series data because it requires importing all the helper classes for the year, month, week, and day format.
Seaborn is a popular Python data visualization framework based on Matplotlib. It allows you to create beautiful and important statistical visual images. Seaborn offers a large library of graphical objects that includes bar graphs, pie charts, histograms, scatter charts, and more. It also includes many color palette selection tools. Seaborn is used for visualizing univariate and bivariate data, plotting time series data, and more.
This library is easy to use and offers simple graphing tools. You can easily change the data display format. Seaborn creates a dynamic and informative graph for data presentation, which simplifies the process of data analysis.
Among the disadvantages of Seaborn, it is worth noting that it has limited customization options and rarely uses interactive visualizations.
The Ggplot graph tool
Ggplot is a Python data visualization package that can create histograms, pie charts, scatter plots, error plots, etc. using a high-level API. It also allows the combining of many types of data visualization components or layers into a single visualization.
The Ggplot library is easy to use and has detailed documentation. You can use its save method if you need to show your presentations or discuss your ideas with colleagues.
Ggplot is not recommended for building highly specialized visualizations.
Plotly graph tool
Plotly is an open-source Python 3D data visualization library. With Plotly, you can create web data visualizations, plot scatter charts, histograms, line charts, bar charts, box charts, line charts, multiple axes, 3D charts, and more.
You can share finished graphs without revealing the code. Plotly has a simple syntax and also allows you to create visualizations using a graphical user interface. Using a variety of interactive tools, you can build 3D graphs. An important advantage of Plotly is its beautiful and convenient design.
Plotly’s weaknesses are its outdated documentation and confusing initial setup. In addition, it is quite difficult to associate graphs with one data source.
Geoplotlib is a Python data visualization library that allows you to plot geographic data and create maps of various formats. It can zoom in and out of the map, allowing them to show more detail. It handles full dataset import, map projection, and map fragments. Geoplotlib has hardware acceleration enabled.
Bokeh graph tool
Bokeh is a popular Python data visualization library that allows you to create detailed, highly interactive images for a variety of datasets. With Bokeh, you can easily create responsive graphics with high-performance interactivity for large or streaming datasets.
This library supports zooming, panning, selection, etc. It provides a low-level interface with additional flexibility for chart customization. Interactive graphics created with Bokeh can be saved in PNG and SVG formats.
Bokeh lets you create many of the same plots as Matplotlib, but with less code and higher resolution.
The main disadvantages of Bokeh are the lack of a three-dimensional graphics function and limited interactivity for work.
Pygal is a popular Python package for creating interactive graphs that can be pasted into a web browser. It supports various types of charts such as line, bar, histogram, radar, pie charts, tree charts, etc.
With Pygal, you can create dynamic and interactive graphics on a web page. It allows you to create beautiful graphics using minimal coding. You can export finished graphics to various formats, including SVG, PNG, and others.
Pygal is not recommended for working with large data sets that contain thousands of points.
How to build graphs in Python easily using a graph tool?
PyScripter is a fast, feature-rich, and powerful open-source Python IDE. It packs a lot of features to make writing and debugging code easier. Download PyScripter for free and get a powerful tool for building graphs in Python.
What is a graph algorithm in Python?
Python graph algorithm is a set of instructions that are executed to render a graph. Some graph algorithms implement the search for a specific node or a path between two given nodes, searching for maximum and minimum values.
Is there a graph tool module in Python?
Python has some great libraries for working with graphs. For example, NetworkX, python-igraph, graph-tool, and others.
Is Python good for plotting graphs?
Python has several simple and useful libraries that allow you to construct and manipulate beautiful graphs of various types, so it is good for graphing.
Which Python module is used to create graphs?
Matplotlib, Pygal, Bokeh, Geoplotlib, Plotly, Ggplot, and others.