Data Visualization Presentation using python visualization libraries
Visualization \ Visualisation
It is any technique for creating images, diagrams, or animations to communicate a message.
Types of Data Visualizations :
Explanatory:
The aim of explanatory visualizations is to tell stories—they’re carefully constructed to surface key findings.
Exploratory:
Create an interface into a dataset … they facilitate the user exploring the data, letting them unearth their own insights.
Exploratory visualizations are interactive. While there are many Python plotting libraries, only a handful can create interactive charts that you can embed online and distribute
Python Visualisation Libraries
• Matplotlib
o https://matplotlib.org/
• Pandas built-in plotting
• ggpy
o https://github.com/yhat/ggpy
• Altair
o https://altair-viz.github.io/
• Seaborn
o https://seaborn.pydata.org/
• Plotly
o https://plot.ly/python/
• Bokeh
o https://bokeh.pydata.org/en/latest/
• HoloViews
o http://holoviews.org/
• VisPy
o http://vispy.org/
• Lightning
o http://lightning-viz.org/
Visualization methods :
• Distribution
• Comparison
• Relationship
• Composition
Distribution
It is commonly used at the initial stage of data exploration i.e. when we get started with understanding the variable. Variables are of two types: Continuous and Categorical. For continuous variable, we look at the centre, spread, outlier. For categorical variable we look at frequency table.
Histogram : It is used for showing the distribution of continuous variables.
Box-Plot : It is used to display full range of variation from min to max and useful to identify outlier values.
Comparison
It is used to compare values across different categories.
Common charts to represent these information are Bar and Line chart.
Bar Chart : It is used to compare values across different categories
Line Chart : It is used to compare values over quantitative variable
Composition
It is used to show distribution of a variable across categories of another variable
Pie Chart : It can be created by passing the values representing each of the slices of the pie.
Relationship
It is widely used to understand the correlation between two or more continuous variables
Scatter Plot : It clearly shows the relationship between two variables
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Please watch: "Non proctored Solutions Python for Data Science Question and Answers"
• Python for Data Science Non proctored Solu...
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