When working with data, it can be challenging to understand your data when it's only in tabular form. To understand what our data is notifying us and to better clean it and select appropriate models, we need to visualize or represent it in pictorial form. This helps uncover patterns, correlations, and trends that cannot be obtained from data in a spreadsheet or CSV file.
Data visualization is finding trends and correlations in our data using a visual representation. To conduct data visualization in Python, we can use different python data visualization modules such as Matplotlib, Seaborn, Plotly, etc. In this article, Complete Guide to Python Data Visualization, we will discuss how to work with some Python data visualization modules.
 

What are data visualizations?


Simply put, data visualization allows people to explore data in many different ways and see patterns and insights that wouldn't be possible when looking at it in its raw form. People crave storytelling, and visualizations allow us to pull the story out of our data stores.
The phrase "A picture value is a thousand words" is accurate when converting vast piles of data into images from which the viewer can actually understand and derive meaning. Children's storybooks contain many ideas but very few words. As children, we don't know many words, but visuals allow us to quickly understand a story.
In our modern digital world, we have a vast amount of data. Data scientists and ML engineers get most of the data they work with in a structured or unstructured format. However, it is difficult for humans to understand and analyze. Data visualizations (or graphical representations) are essential to understanding data. They help users explore data through visuals such as tables, charts, graphs, maps, and other visualizations.

 

Different types of exploratory data analysis


Each data set has many variables (functions, input, or independent variables) and target/output variables (labels, dependent variables, classes, or class labels). The responsibility of a data scientist is to thoroughly understand each feature individually and the relationship between the various features. The goal is to prepare a dataset for the implementation of ML algorithms.
We have three ways for exploratory data analysis:
 
Univariate analysis
In univariate analysis, each variable is analyzed separately. This will give us complete statistical data for each element. There are a variety of data visualization techniques for univariate analysis, including Box Plot, Histogram, PDF, and CDF.
 
Bivariate analysis
Bivariate analysis is performed to find the relationship between each element and the target variable. Data visualization techniques for bivariate analysis are Scatter plots and Heatmap.
 
Multivariate analysis
As the name suggests, multivariate analysis is performed to understand the relationship between different features of a data set. The Pair Plot is one of the main techniques for visualizing data with multiple variables.
 

The importance of data visualization


We are living in an era of visual information, and visual content plays a vital role in every moment of our lives. Research by SHIFT Disruptive Learning has shown that we typically process images 60,000 times faster than tables or text and that our brains remember them better in the future. The studies found that after three days, the subjects analyzed maintained between 10% and 20% of written or spoken data compared to 65% of visual communication.
The human brain can grasp images in just 13 milliseconds and store information if it is associated with a concept. Our eyes can catch 36,000 visual messages per hour. 40% of the nerve fibers are connected to the retina.
This shows that people are better at visual processing information stored in their long-term memory. As an outcome, visual presentation using pictures is a more effective way of communicating information than text or a table; and takes up very little space. Data visibility is more attractive, easier to work with, and easier to remember.

 

Data visualization process


Several fields are involved in the data recognition process to facilitate or reveal existing relationships or discover something new in a dataset.
1. Filtering and processing.
Refining and refining data turns it into information by analyzing, interpreting, summarizing, comparing, and examining.
2. Translation and visual representation.
Creating a visual representation by describing image sources, language, context, and opening word, all for the recipient.
3. Visualization and interpretation.
Finally, visual acuity is adequate if it has a cognitive impact on knowledge creation.
 

Data visualization in Python


Python offers various plotting libraries, namely Matplotlib, Seaborn, and many other data visualization packages with multiple features to create informative, customized, and attractive graphs for the easiest and most effective presentation of data.

 

Matplotlib and Seaborn

 
Matplotlib and Seaborn are python libraries used for data visualization. They have built-in modules for drawing various graphs. While Matplotlib embeds graphs into applications, Seaborn primarily uses statistical graphs.
But when should we use one of the two? Let's understand this with a comparative analysis. The table below compares the well-known Python visualization packages, Matplotlib and Seaborn.
Matplotlib
"      It is mainly used for statistical visualization and can perform complex visualizations with fewer commands.
"      Works with entire datasets.
"      Matplotlib works productively with data arrays and frames. It considers aces and pieces as objects.
"      Matplotlib is more personalized and pairs well with Pandas and Numpy for exploratory data analysis.
Seaborn
"      It is used for basic plotting such as line graphs, bar graphs, etc.
"      It mainly works with datasets and arrays.
"      Seaborn is much more organized and functional than Matplotlib, treating the entire data set as a standalone unit.
"      Seaborn has multiple built-in themes and is mainly used for statistical analysis.

 

Data visualization formats


1. Bar Charts
Bar charts are one of the most famous ways to visualize data because they present quickly set data in an easy-to-understand format that permits viewers to notice height and depth at a glimpse.
They are miscellaneous and often used to compare different categories, analyze changes over time, or compare certain parts. The three types of a bar chart are:
 
"      Vertical Column: Data is also used chronologically, it should be in a left-to-right format.
"      Horizontal column: Used to visualize categories
"      Fully Stacked Column: Used to visualize types that add up to 100%
 
2. Histograms
Histograms present flexibility in the form of bars, where the area of ??each bar is equal to the number of values ??represented. They offer an overview of the demographic or sample distribution with a specific aspect. The two distinctions in the histogram are:
"      Standing columns
"      Horizontal columns
 
3. Pie charts
A pie chart contains a circle divided into categories representing part of the topic. They can be split into more than five data groups. They can help compare disparate or continuous data.
The two differences in a pie chart are:
"      Standard: Used to display relationships between components.
"      Donut: A style variation that makes it easy to include a full-size or design element in the center.
 
4. Scatter plots
Scatter plots use a point spanning the Cartesian integration plane to show the relationship between the two variables. They also help us determine whether different groups of data are related or not.
 
5. Heat maps
Heatmaps represent individual values ??from a dataset in a matrix using color variation or intensity. They usually use color to help viewers compare and contrast data in two distinct categories. They help display web pages where the areas most users encounter are represented by "hot" colors, and the least clicked pages are displayed in "cool" shades.
 
6. Line plot
This is used to show changes or trends in data over time. They help establish relationships, accelerations, decelerations, and instabilities in a data set.
 

Color schemes for data visualization in Python


Color is one of the most useful data sources for visual acuity and is essential if we are to properly understand detail. Color can separate elements, balance or represent values, and interact with cultural symbols associated with a particular color. Again, it guides our understanding, to analyze it, we must first comprehend its types:
 
Hue: This is what we frequently think of when we upload the color of a photo. There is no color order; they can only be distinguished by their properties (blue, red, yellow, etc.).
 
Brightness: This is an average approximation that describes the amount of light reflected from one object to another. Light is estimated on a scale, and we can discuss light and dark values ??in one color.
 
Saturation: indicates the intensity of the given color. It varies according to the light. Dark colors are less saturated and approach gray when a color is less saturated. In other words, it is close to a neutral (empty) color. The following diagram provides a summary of color application.
 

Winding Up!


In our modern big data world, data visualizations are essential. They can give direction and vision to data scientists and business front-end users. This article just gives you examples of different visualizations you can create in Python and code to get you started.
We hope you will find them easy to understand and implement. How data can be visualized are endless. This is just the beginning. Python and R visualizations give you a lot of options to explore. Just take the data and start experimenting. You will be amazed at the beautiful and informative images you can create.