Python Data Visualization Tips that will help you!

In this article, we are going to take a look at various types of charts for data visualization for beginners and share some tips as well to represent the data better.

INTRODUCTION

Data visualization is an important tool used by scientists, engineers, and business professionals to help them make sense of large amounts of data. Data visualization is the process of representing data in graphical forms such as charts, diagrams, and maps. It is used to identify patterns, trends, and correlations among data points that may not be immediately apparent from the raw data.

Data visualization helps people interpret data quickly, allowing them to make decisions more easily and accurately. It allows people to visualize the relationships between different variables and see how they interact. This helps people analyze the data more effectively and make better decisions. It also helps people identify outliers and anomalies that can provide valuable insights.

Source: visme

Data visualization can be used in many different fields, from medicine and finance to engineering and marketing. It is used to help researchers identify correlations between different variables and to identify patterns in data. It is also used to help business professionals make decisions based on data analysis.

Data visualization helps people understand complex relationships between data points, allowing them to make more informed decisions. Data visualization is an invaluable tool for anyone dealing with large amounts of data.

Visualizing data is an essential part of data analysis and machine learning, but choosing the right type of visualization is often challenging.

This guide provides an introduction to popular data visualization graphs, where the graphs can be used, and some of the tips that can make the graphs better for audiences. (Link to the entire code is at the bottom)

TYPES OF GRAPHS

• Line graph — A line graph is a type of chart that is commonly used to represent changes over time. It uses a series of points to illustrate the changes in values for a particular variable, providing information about the rate at which the changes occur. Line graphs are useful for showing trends and patterns, or for showing relationships between two or more variables, and can be used in a variety of situations, such as in research or in sales and marketing.

• Scatter plot — A scatter plot is a type of graph used to display the relationship between two variables. It is useful for exploring the relationship between two variables, such as the effect of one on the other. Scatter plots can be used to identify any patterns or trends among the data points, such as clustering, outliers, and linear relationships. Scatter plots are best used in exploratory data analysis, hypothesis testing, and establishing cause-and-effect relationships.

Scatter Plot

Scatter Plot made using Plotly library

• Histogram and Frequency Distribution- A histogram is a graphical representation of a frequency distribution that shows the frequency of occurrences for a given range of values. It has two axes, one indicating the range of values, and the other indicating the frequency of each value in the given range. It is used to show the distribution of numerical data. Frequency distribution is the set of frequency of occurrence of each value in a given range. It is usually used to understand the underlying patterns in the data. Histograms and frequency distributions are mainly used in descriptive statistics to analyze the shapes of data and look for relationships between data sets. They can be used for exploring the data, as well as for comparison of distributions between groups of data.

Histogram plot

Frequency Distribution Plot

• Heatmap — A Heatmap is a graphical representation of data that uses different colors to show the relative values of different points. Heatmaps are commonly used to visualize patterns of data and to represent the relative importance of each point. Heatmaps are often used to find correlations and patterns in datasets, to understand the distribution of variables in a dataset, and to reveal clusters or outliers in the data.

Heatmap

• Contour Plot — A contour plot is a graphical representation of a 3-dimensional surface by plotting constant z slices, called contours, on a 2-dimensional format. It is a type of graph used to illustrate the relationships among three numeric variables in a three-dimensional space. Contour plots can be used wherever there are three or more variables whose values need to be compared, such as to measure the performance of a machine. For example, a contour plot could be used to compare a mechanical device’s vibration over time, or to illustrate motion data across a three-dimensional surface. Contour plots are also beneficial in geography, where they provide a graphical way of visualizing the surface of the earth.

Contour Plot

• Box Plot — A boxplot is a type of graph used in statistics to represent the distribution of a given set of numerical data. It is visualized as a rectangle divided into quartiles. It illustrates several important features of the data, including the median, quartiles, extremes, and outliers. Boxplots can be used to compare multiple data sets on a single graph, detect outliers, and assess symmetry and other characteristics of the data. They can be used in many fields, such as engineering, economics, and medicine, to describe and compare data distributions.

Box Plot

• Bar Chart — A bar chart is a type of chart used to compare values across categories by using vertical or horizontal bars. Bar charts are often used to compare the different components of a data set, such as different types of expenses, market share of products, or prevalence of diseases. They can also be used to compare individual items, such as performance over time.

Bar Chart

There are several libraries in Python that help you make these plots but the below ones are the most widely used:-

• Matplotlib: Plotting and visualization library for Python. We used the pyplot module from matplotlib. As a convention, it is often imported as plt.
• Seaborn: An easy-to-use visualization library that builds on top of Matplotlib and lets you create beautiful charts with just a few lines of code.
• Plotly: An Interactive visualization library that provides users the chance to interact with the plots and discover more insights.
• GGPlot: An R library for visualization (package now available for python as well) that’s been known to create beautiful charts.

USEFUL VISUALIZATION TIPS

1. Consistency — Whenever you are making charts, be sure to be consistent throughout the length of the jupyter notebook. This will help the user absorb the data in an easy manner and not give the user any shocks or surprises. This could mean using the same color schemes, font sizes, etc.
2. Annotations — Adding annotations to your visualizations is a great way of letting the user extract the relevant information as quickly as possible. For E.g if you have made a bar chart, you use can use plt.annotate function in matplotlib. Below is an example:-

Adding an Average line and annotating the line

Adding Data Labels

3. Use colors to distinguish — Taking the example of the above chart, we could see that the medical charges in the first bin of the histogram are the highest. We could have left the bar the same color but in cases where the two bars are equally close and it's hard to figure out, plotting the bar with the highest value in a different color might help the audience.

4. Data-Ink Ratio — Data-ink ratio is the ratio of the data points (ink) to the non-data elements (non-ink) on a chart or graph. The idea is that the more ink devoted to data and the less ink devoted to non-data elements, the more efficient and effective the chart or graph is.

This helps to ensure that viewers are not distracted by unnecessary clutter and are quickly able to identify the most important elements of the chart. Data-ink ratio is important because it helps to ensure that charts and graphs are clean, clutter-free, and easy to read and understand.

Example of a high data-ink ratio

Example of low data-ink ratio

5. Interactivity:- Use as many interactive plots as possible since these allow the users to engage and extract the information that they are interested in. There are libraries that help you with making the plots interactive but Plotly is the top most library that is widely used by data science folks.

The link to the entire code is here. That’s it for this article

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