It has been said since ancient times, “A picture is more telling than a thousand words.”
In today’s world, a lot of data is generated daily. Analyzing this data, especially if the data is raw, can seem overwhelming. This is where data visualization enters the scene to make data analysis easy and understandable for us. Visualized data provides us with a well-organized visual representation of data that makes it easier to understand, view, and analyze. Data visualization in Python is perhaps one of the most widely used features of the Python programming language, especially in the field of data analysis.
In this article from the collection of Python training articles, we discuss how to visualize data using Python.
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Data visualization in Python
Data visualization is the attempt to understand data by transforming it into a visual form to reveal patterns, trends, and correlations that are not normally identifiable. In other words, data visualization is a field in data analysis that deals with the visual representation of data. This method plots the data graphically and is an effective way to make inferences from the data.
Using data visualization, we can get a visual summary of our data. The human mind communicates more easily with images, maps and charts to process and understand data. When we have a large collection, it is impossible to see all the data, let alone process and understand this data manually.
You may ask why we use Python for data visualization.
This is because the Python language provides a variety of drawing libraries with different features to create understandable, attractive and customizable charts to present data in the simplest and most effective way.
Visualization libraries in Python
Python provides various libraries that have different features for data visualization. All these libraries have different features and can support different types of charts. In this article, we will introduce four of the most used libraries.
Matplotlib
Matplotlib is a low-level, easy-to-use data visualization library built on NumPy arrays, introduced by John Hunter in 2002. This library contains various 2D charts such as scatter chart, line chart, histogram, etc. Matplotlib provides a lot of flexibility.
Seaborn
Seaborn is a Python data visualization library based on Matplotlib. This library provides a high-level user interface for creating attractive charts. Seaborn has a lot to offer. The difference between the Seaborn library and Matplotlib is that in Seaborn you can create graphs in one line, which may require dozens of lines of code to draw the same graph in Matplotlib. Another strength of this library is the beautiful design styles and diverse color palettes to create attractive charts.
Bokeh
Bokeh is mainly known for its interactive visualization of graphs. Charts drawn with Bokeh are rendered using HTML and JavaScript, making it a powerful tool for creating projects, custom charts, and web design-based applications. The Bokeh library also supports real-time data streaming. Bokeh offers three interfaces with different levels of control for different types of users. The highest level is for quick chart creation, which includes methods for creating common charts such as bar charts, box charts, and histograms. The middle layer has the same feature as Matplotlib and allows you to control the main blocks of any plot (eg the points in a scatter plot).
The lowest level is for software developers and engineers and requires you to define each element of the diagram.
Plotly
Plotly.py is an open-source, high-level, browser-based interactive visualization library (like bokeh). This library has a collection including scientific charts, 3D charts, statistical charts, financial charts, etc. plotly has floating tool capabilities that allow us to detect any outliers or anomalies in the data.
Which visualization method should we use for data analysis?
To extract the required information from images and graphs, we should try to use the correct representation based on the type of data. Below we will introduce a collection of the most used shows and how to use them.
Bar chart
A bar graph is used when we want to compare the values of a metric in different subgroups. A bar chart can be created using the bar() function.
Column chart
Column charts are mostly used when we need to compare a set of data between separate sub-items, for example, comparing income in different regions.
Histogram chart
Histogram is used to display data in group format. This chart is a type of bar chart where the X-axis shows the bin range while the Y-axis gives information about frequency. To create a hairy histogram, the hist() function is used, and by sending classified data, it automatically calculates the frequency of that data.
Scatter plot
Scatter plots are used to identify relationships between two variables.
Linear graph
A line chart is used to display data points continuously. When we want to understand the change process of a variable over time, we can use this graph.
Pie chart
A pie chart is used to identify the proportions of different components in a given whole.
Environmental diagram
An environmental chart is used to track changes over time for one or more groups. Pie charts are preferred over line charts when we want to record changes over time for more than 1 group.