Data Visualization

Data visualization is the graphical representation of information and data in a pictorial or graphical format like line chart, bar graph, pie chart etc. Data visualization helps to gain insights from the data to understand the underlying trends in the data helps the organization to make data-driven decisions. Reasons why data visulization is important:

• Pattern Recognition: Visual representation of data helps to identify the patterns and trends in the data
• Data Analysis: Visual representation of data allows users to filter information, and gain deeper insights.
• Decision Making: Improves the decision making of an organization by identifying the opportunities and potential risks in a pictorial view.
• Memory Retention: People tends to remember more graphical information rather than numerical or text.
• Efficieny: Allows user to quickly identify key insights, trends, and outliers and saves alot of time.

Types of Data Visualizations

• Bar Chart- Rectangular bars or columns are used in bar charts to depict data.They can be used to highlight discrete categories or show changes over time. Horizontal bar charts and vertical bar charts are examples of different types.
• Line Chart- Data is shown in line charts as a collection of data points connected by lines. They are perfect for displaying trends and patterns along an ongoing axis, like time.
• Pie Chart- Pie charts show data as slices of a circle, each slice representing the percentage of each category. They are useful for showing parts of a whole or contrasting relative sizes.
• Scatter Plot- Individual data points are displayed as dots on a two-dimensional grid in scatter plots. They are used to identify correlations and illustrate the link between two variables.
• Histogram- The frequency or distribution of the data within each bin is shown by a histogram, which groups the data into bins. For displaying the distribution of continuous data, they work well.
• Heatmap- In a matrix or table format, data values are represented by color intensity in heatmaps. They help show the connections between two category variables.
• Bubble Chart- Similar to scatter plots, bubble charts also include a size dimension to indicate a third variable. They are useful for showing three dimensional data in a two dimensional plot.
• Box Plot (Box-and-Whisker Plot)- Box plots use quartiles, outliers, and a box to represent the data distribution. They are useful for illustrating the skewness and distribution of data.
• Tree Map- Tree maps separate the data into nested rectangles, each of which stands for a different category.They are helpful for displaying proportions within categories and for hierarchical data.
• Choropleth Map- Color-coded regions are used in choropleth maps to display the data values for various geographic locations.They are excellent for displaying data variances by area.
• Word Cloud- The size of each word in a word cloud indicates its frequency or significance. Word clouds are used to show words or phrases.They are frequently employed in sentiment analysis and text data.
• Radar Chart- Data points are shown on spokes radiating from a central location on radar charts.They are helpful in comparing numerous factors between various groups.
• Network Diagram- Network diagrams show the links and connections between nodes.They work well for showing intricate relationships in networks or social networks.
• Some other types of data visualization- Donut Chart, Half Donut Chart, Multi-Layer Pie Chart, Flowchart, Table, Geographic Map, Icon Array, Percentage Bar, Timeline, Venn Diagram, Funnel Chart, Polar Graph

Data Prepration: It is also known as data preprocessing it is a method to convert the raw data into meaninful data it is a multi-level process it includes data collection, data cleaning, data transformation.

In this article, we will learn how to visualize data in Jupyter Notebook there are different libraries available in Python for data visualization like Matplotlib, seaborn, Plotly, GGPlot, Bokeh, etc. But in this article, we will use different libraries like Matplotlib, searborn, and Plotly which are widely used for data visualization. We will generate different graphs and plots in Jupyter Notebook using these libraries such as bar graphs, pie charts, line charts, scatter graphs, histograms, and box plots. We will also discuss how to install these libraries and use examples to understand each graph.

Jupyter Notebook

The Jupyter Notebook is the original web application for creating and sharing computational documents that contain live code, equations, visualizations, and narrative text. It offers a simple, streamlined, document-centric experience. Jupyter has support for over 40 different programming languages and Python is one of them.

Prerequisites

In this article, we will use different libraries to create graphs and plots and you have to install the library to function the below example you can use the following code snippetes to install the dependencies.

Install matplotlib

pip install matplotlib

Install Seaborn

pip install seaborn

Install Plotly

pip install plotly