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https://github.com/kinshuk-code-1729/data-visualisation-using-python

This Repository consists of several python snippets for creating Two-Dimensional (2D) Graphics
https://github.com/kinshuk-code-1729/data-visualisation-using-python

data-analysis data-science data-visualization matplotlib visualization

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This Repository consists of several python snippets for creating Two-Dimensional (2D) Graphics

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# Data-Visualisation-Using-Python

This Repository consists of several python snippets for creating Two-Dimensional (2D)  Graphics 

## *What Is Data Visualization ?*

- Data visualization is a graphical representation of quantitative information and data by using visual elements like graphs, charts, and maps.

- Data visualization convert large and small data sets into visuals, which is easy to understand and process for humans.

- By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

- In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions.

- Data visualizations are common in your everyday life, but they always appear in the form of graphs and charts. The combination of multiple visualizations and bits of information are still referred to as Infographics.

- Data visualizations are used to discover unknown facts and trends. You can see visualizations in the form of line charts to display change over time. Bar and column charts are useful for observing relationships and making comparisons. A pie chart is a great way to show parts-of-a-whole. And maps are the best way to share geographical data visually.

- Today's data visualization tools go beyond the charts and graphs used in the Microsoft Excel spreadsheet, which displays the data in more sophisticated ways such as dials and gauges, geographic maps, heat maps, pie chart, and fever chart.

## *What makes Data Visualization Effective ?*

- Effective data visualization are created by communication, data science, and design collide. Data visualizations did right key insights into complicated data sets into meaningful and natural.

- To craft an effective data visualization, you need to start with clean data that is well-sourced and complete. After the data is ready to visualize, you need to pick the right chart.

- After you have decided the chart type, you need to design and customize your visualization to your liking. Simplicity is essential - you don't want to add any elements that distract from the data.

## *History of Data Visualization*

- The concept of using picture was launched in the 17th century to understand the data from the maps and graphs, and then in the early 1800s, it was reinvented to the pie chart.

- Several decades later, one of the most advanced examples of statistical graphics occurred when Charles Minard mapped Napoleon's invasion of Russia. The map represents the size of the army and the path of Napoleon's retreat from Moscow - and that information tied to temperature and time scales for a more in-depth understanding of the event.

- Computers made it possible to process a large amount of data at lightning-fast speeds. Nowadays, data visualization becomes a fast-evolving blend of art and science that certain to change the corporate landscape over the next few years.

## *Importance of Data Visualization :*

Data visualization is important because of the processing of information in human brains. Using graphs and charts to visualize a large amount of the complex data sets is more comfortable in comparison to studying the spreadsheet and reports.



Data visualization is an easy and quick way to convey concepts universally. You can experiment with a different outline by making a slight adjustment.

### *Data visualization have some more specialties such as :*

- Data visualization can identify areas that need improvement or modifications.

- Data visualization can clarify which factor influence customer behavior.

- Data visualization helps you to understand which products to place where.

- Data visualization can predict sales volumes.

## *Why We Use Data Visualization ?*

- To make easier in understand and remember.

- To discover unknown facts, outliers, and trends.

- To visualize relationships and patterns quickly.

- To ask a better question and make better decisions.

- To competitive analyze.

- To improve insights.

## *Why Python for Data Visualization ?*

Though there are lots of tools available for Data Visualization, Python has few best libraries that make Python Visualization easy for any dataset. These libraries make Python Visualization affordable for large and small datasets.  There are several courses available on the internet that just focuses on Data Visualization with Python and especially with Matplotlib. Matplotlib is very useful to create and present Python Visualization.

## *Popular Libraries For Data Visualization in Python :*

### Some of the most popular Libraries for Python Data Visualizations are :

- Matplotlib

- Seaborn

- Pandas

- Plotly

## * A little description about the library module ```matplotlib.pyplot()``` :

- Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

  Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library.

- Pyplot is a collection of command style functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc.