5 Easy Methods to Visualize Data in Python Better

Apart from gathering and analysing the data, a data scientist has to present the result of the analysis in a form that is understandable and discernible. Visualizing the data with clear representations and in a neat and concise presentation is essential to drive home the conclusion of the analysis.

As much as the analysis is important, taking time to present the final takeaway points to the appropriate persons is also equally important. A data scientist may need to explain the result of the analysis with the proof to some non-technical bosses or clients and the best way to help them understand is with data visualizations.

As Python is one of the preferred languages for machine learning algorithms, there is a lot of analysis that needs to be represented and delivered in an understandable form. Python has made this easier by having its own libraries, especially for data visualization matplotlib and seaborn.

These libraries offer 2D and 3D visualization graphics to create quality representations of data sets with complete options of customization like themes, colours, filters, palettes and various other tools to visualize functions and other complex mathematical computations in its simplest forms.

There are generally five types of data visualization techniques and all these five visualizations can be represented with the libraries available with Python. Let’s take a look at the five forms of visualization with Python.

1. Line Plot

Line plots are usually used to represent the clear relationship of one variable with another. Line plots are also best when a variable needs to be visualized at regular intervals of time. For example, the x-axis can represent the time while the y-axis can represent the percentage of sales with respect to the time.

Line plots come in use to depict the observations and the impact between two individual variables or find a particular pattern or sequence between two variables. A single line plot can also be a combination of a single parameter and its impact on many numbers of variables. This is the simplest way to show the result of the analysis in an uncomplicated manner and it can also be used to compare the data over a period of time.

2. Scatter Plots

Scatter plots are used to visualize the distribution of the data in relation to two variables. Scatter plot will have color codes for different categories of data compared between the two variables and the frequency of the dots or the little marks on the scatter plots indicate every data set.

A scatter plot can also be used to display a range of data set with color codes, thereby helping to understand the relationship of the data set with the two variables.

3. Bar Plots

Bar plots are used to represent categorical data across a variety. Bar plots may get a little confusing if there are more than 10 categories in a single bar chart. Generally, the x-axis of a bar chart represents the categories and the y-axis will represent a variable against which it is mapped.

There are three main types of bar plots: regular, grouped and stacked.

In the regular bar chart, there will be separate categories across the x-axis and the data will be mapped to the variable on the y-axis. While in a grouped bar chart, each category will have two or more divisions, read bars, which will be again mapped against the variable in the y-axis. Each category will be colour coded to compare within each category or with the other similar sub-categories from the neighbouring categories. It is best not to include more than 3 sub-categories in each category, else it could lead to a lot of clutter on the chart.

In the stacked chart, the chart will have a single bar in each category, which will be divided into color codes based on the data sets. This is simple and easy for comparing different data sets from the same chart.

4. Box Chart

The box chart is used to represent the distribution of a data sample. As a rule, the box chart starts at the 25% and ends at the 75% of the data set and a middle line, i.e. the median line, is drawn to indicate the 50%. The two whiskers on the top and the bottom of the box represents the two ends of the data sets to indicate its complete range of the data. Box charts are sometimes used for analysis rather than the end of an analysis to observe the unknown distribution of the data.

5. Histogram

The histogram is another form of representing the data distribution. The x-axis generally represents the intervals while the y-axis represents the frequency of the data. In a histogram, it is easy to pick out the median as the data thin around it in varying intervals which helps to see the overall picture without all the nitty-gritty detailing.