Pie Chart
A pie chart shows a static number and how categories represent part of a whole the composition of something. A pie chart represents numbers in percentages, and the total sum of all segments needs to equal 100%.
Python3
plt.pie(df[ 'Age' ], labels = { "A" , "B" , "C" , "D" , "E" , "F" , "G" , "H" , "I" , "J" }, autopct = '% 1.1f %%' , shadow = True ) plt.show() plt.pie(df[ 'Income' ], labels = { "A" , "B" , "C" , "D" , "E" , "F" , "G" , "H" , "I" , "J" }, autopct = '% 1.1f %%' , shadow = True ) plt.show() plt.pie(df[ 'Sales' ], labels = { "A" , "B" , "C" , "D" , "E" , "F" , "G" , "H" , "I" , "J" }, autopct = '% 1.1f %%' , shadow = True ) plt.show() |
Output:
Basic Python Charts
Python Chart is part of data visualization to present data in a graphical format. It helps people understand the significance of data by summarizing and presenting huge amounts of data in a simple and easy-to-understand format and helps communicate information clearly and effectively.
In this article, we will be discussing various Python Charts that help to visualize data in various dimensions such as Histograms, Column charts, Box plot charts, Line charts, and so on.
Table of Content
- Python Charts for Data Visualization
- Histogram
- Column Chart
- Box plot chart
- Pie Chart
- Scatter Chart
- Line Chart
- Area Chart
- Heatmap
- Bubble Chart
- Radar Chart
- Conclusion