When working in a field full of data, you will understand the reason why the data doesn't sound meaningful every time. The average amount of data generated every day is estimated to be 2.5 quintillion bytes. Anyxing large data volumes is not easy, especially when doing it manually. This is why you need data visualization to make the data analysis process easier and more straightforward. 

Data visualization refers to the technique of encoding data into visuals which are more helpful in gaining vital insights from complex data. This technique plays a fundamental role in any data science process. Given that the amount of data generated across the globe is steadily increasing, data visualization is also advancing from time to time. 

This is why you need to learn and master data visualization techniques in Python and all other features that matter. If you are aiming to learn and explore more about data visualization techniques in Python, this blog post covers everything you need to know. 

Why Use Python for Data Visualization? 

Even though there are multiple tools that you can use for data visualization, Python is one of the best options available in the market. What makes Python stand out from the crowd is the fact that it has several libraries that makes data visualization extremely easy.  Besides, the techniques used for data visualization in Python are easily affordable and can be used for both small-scale and large-scale businesses. 

In addition, you acquire courses all over the internet that can help you learn data visualization in Python. The good news is that you can learn these techniques in a matter of days, and you are good to go. If you have massive amounts of data that you intend to visualize and extract insights from, these data visualization techniques can be greatly beneficial.  Let's check them out. 

1. Boxplot

A box plot is mostly used to showcase the distribution of a particular variable. Also, this is considered a standardized method of showcasing data distribution based on a five-number summary. This includes the minimum, first quartile, median, third quartile, and maximum. You can also use this technique when showcasing the distribution of quantitative data by comparing different elements. 

You can plot a boxplot using a horizontal orientation depending on the nature of your data. In most cases, the boxplot drawn using the horizontal orientation is used to showcase the distribution of data sets that come with four quartiles. The technique can represent the data by showing maximum and minimum values. 

2. Bar Chart 

The bar chart, also known as a bar plot, is used when presenting categorical data to your readers. A chart such as this can be used to illustrate variances in different sets of data like depicting the price of car insurance by state or the popularity of products across age ranges.

The chart utilizes a general plot format, allowing you to aggregate categorical data depending on some functions using a default methodology. However, a bar chart can be outlined in different formats depending on the nature of the data. There are different types of bar charts that you can use when visualizing data in Python. 

The data variables you are presenting are outlined in the y and x-axis. Before you start interpreting the data presented on a bar chart, you should read the data points on both axes to understand what it means. This will give you a better picture of the data under visualization and the question under analysis. 

3. Countplot 

Countplot works the same way as a bat chart. The only thing that makes these two different is that when analyzing the data outlined in the countplot, you pass the x and y-axis and only focus on counting the number of occurrences. Every bar is used to represent the count for every category of the data presented. 

When interpreting data presented on a countplot, you only focus on counting the number of occurrences across the chart. Note that this is one of Python's most popular data visualization techniques. 

4. Scatterplot 

A scatterplot is one of the most popular data visualization techniques in Python. It's used when finding out the relationship in bivariate data. The chart works best when you want to find out correlations between different data elements that are continuous in nature. A scatterplot is pretty simple to use when using Python since it outlines data in a more clear and precise manner. 

When using a scatter plot to visualize your data, you don't necessarily need to have any special skill to read it. The chart is designed to outline data in an easily readable manner. 

5. Sankey Chart 

A Sankey chart is a type of flow diagram in Python that is used to represent data in proportions. It's among the most commonly used charts when dealing with categorical data. As the name suggests, the pie is divided into different segments in form of nodes that are equal to the number of data categories. The size of every node depends on the size of the data presented. 

6. Heatmap 

Heatmap is a matrix plot used in Python that enables you to plot encoded data. This is a data visualization technique that allows users to use any of their preferred colors. This technique is only used when finding multicollinearity in a given data set. When you want to plot a heatmap, you should ensure that your data is in a matrix format. 

The only thing that the heatmap dies is only to color the data for you depending on the message presented. The chart can as well play a key role when you want to find correlations in your data during data analysis. A heatmap is good at grabbing the attention of the reader using its attractive color combination. 

7. Jointplot 

A jointplot is used to showcase the distribution of one variable with the aim of matching with the distribution of another variable. The jointplot enables you to match different types of distplots when evaluating bivariate data. This technique is tailored to uncover detailed insights from your data and give you access to vital insights that can help you take your business to the next level. 

8. Histogram

A histogram is good at showcasing the distribution of continuous data variables. The chart has the capability to discover the frequency distribution for a single data variable during a univariate data analysis. The chart works better when visualizing data in Python it offers a better methodology for breaking down complex data sets. 

9. Distplot

A distplot is used to showcase the distribution of univariate data. The chart enables you to evaluate the normality of data regardless of its complexity. When analyzing data in Python, you can utilize this technique to break down complex data variables into simple aspects that suit your needs. 

Bottom Line 

Learning data visualization techniques in Python is essential for the well-being of your data analysis needs. The good news is that you can easily learn how to use these techniques and get your data analysis framework complete. The technique you choose entirely depends on the nature of your data and the target end goal. Provided that you have an awesome grasp of what you want to achieve, the process is easy to navigate. 

However, it's essential to ensure that you understand all these techniques to be better during data analysis. It's no secret that data analysis is critical for the smooth running of businesses. This can only be achieved if you understand how to apply various techniques in Python and extract the right insights from your data.