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Python is a powerful programming language that is widely used in many industries today. Data visualisation is a process of representing data in a graphical form. When these two technologies are combined, the results can be incredibly powerful.
We’ll take a look at why exactly you should consider Python for data visualisation, along with some libraries that you can use to get started.
What is Data Visualisation?
Data visualisation is a process of transforming data into a graphical form that can be easily interpreted. The purpose of data visualisation is to help you understand and make decisions using data.
Data Visualisation is a way of representing data in visual form. It’s used to understand and interpret data, allowing you to make decisions from the information. Data Visualisation can be used in many industries and has been for decades.
Graphs have been used for years because they are easy to read, convey information quickly and give you an overview of the trends over time. If you want to show how sales are increasing over time, it’s much easier to show a line graph increasing instead of a spreadsheet of increasing numbers.
Data visualisation techniques can be used for:
- Showing the relationships between different data sets. It’s easier to use the data if you are able to see the different relationships that are present, and how the data sets are related.
- Identifying patterns and trends. Finding trends and patterns is much easier by looking at a graph.
- Highlighting important information. Important information is much more likely to stick out when using a graph instead of a long list of numbers.
- Understanding your data better. Data visualisation will allow you to analyse data more efficiently and effectively by creating graphs and charts that show trends in the numbers over time.
- Share your findings with others more clearly than just using raw figures alone – graphs are much easier and clearer for any almost audience to read.
Why Use Python for Data Visualisation?
There are many reasons to use Python for data visualisation. One of the main reasons is that Python is a very versatile language. It can be used for a wide range of tasks, including data analysis, web development, and machine learning.
Python also has a large community of developers. This means that there is a lot of support available for Python users, and there are many libraries and tools that can be used for data visualisation. Python is also a free and open-source language. This means that it can be used for any purpose, and it can be modified for your needs.
Importance of Data Visualisation
The importance of data visualisation is shown in its ability to help in decision making, understanding complex data, communicating data and representing it through visual means.
Data visualisation is the process of creating a graphic representation of the main characteristics of a set of data. Data visualisation helps understand the information in a simpler manner by using images, graphs or other forms to make sense of numbers.
Data visualisation is a great way to communicate information in a simple, easy-to-understand manner. Data visualisation can help you understand your data better by providing insights into what it means and how it relates to other factors that might affect its outcome.
There are several types of data visualisation tools, such as charts, graphs, heat maps and infographics. The most common type is the bar chart or line graph. This means that you can make comparisons and see what works.
Popular Data Visualisation Libraries for Python:
Python has a number of popular data visualisation libraries. Some of these include: Matplotlib, Bokeh, and Seaborn. Pandas is used for data analysis, but is also very useful for data visualisation.
Matplotlib is one of the most commonly used data visualisation libraries for Python. It offers a wide variety of chart types, and it is very powerful It provides an extensive list of features that is effective for data visualisation in Python. This includes being able to create various forms of graphs, and plot in different formats such as png and pdf.
Bokeh is a newer data visualisation library for Python. It’s a very powerful library, and it offers various other features that are not found in Matplotlib. The main feature that Bokeh includes is the interactive graphs and appealing visuals that are great for presentation.
Pandas is one of the most popular data analysis libraries in Python. It helps you to manipulate, explore and analyse data. Pandas can also be used to convert your data into a format that can easily be visualised through different methods like Matplotlib or Seaborn.
Seaborn is another data visualisation library for Python, which is built on top of Matplotlib. It offers a number of additional features, such as various styling features that you can add to your graphs. It provides a high-level interface to create clear and informative graphics.
Getting Started with Data Visualisation in Python:
There are many different ways to get started with data visualisation in Python. However, the best library to start with is Matplotlib. This is because of how extensively used the library is, and the versatility that it provides.
Additionally, you can also use Seaborn, which is built on top of Matplotlib. That means it’s a good idea to be familiar with Matplotlib first before learning Seaborn. Matplotlib gives you control over almost every detail, and has a lot of documentation on it if you ever get stuck.
However, Matplotlib falls short in creating interactive graphs. Using Bokeh for creating interactive graphs where Matplotlib falls short is ideal.
Conclusion:
Python is a versatile programming language that can be used even for data visualisation. Data visualisation is essential because using graphs to communicate data to others is much easier than just showing raw numbers. It also can show relationships and trends that you may have not noticed otherwise.
Some of the best libraries for data visualisation in Python include Matplotlib, Bokeh and Seaborn. It’s recommended to start with Matplotlib, then use Bokeh for creating interactive graphs and Seaborn for any additional features that you might want.
Need help with using Python for data visualisation? Feel free to reach out to the Raw Jam team for help.