Python Data Science Tutorial
Python Data Processing
Python Data Visualization
Statistical Data Analysis
Selected Reading
- Python Data Science - Matplotlib
- Python Data Science - SciPy
- Python Data Science - Numpy
- Python Data Science - Pandas
- Python Data Science - Environment Setup
- Python Data Science - Getting Started
- Python Data Science - Home
Python Data Processing
- Python Stemming and Lemmatization
- Python word tokenization
- Python Processing Unstructured Data
- Python Reading HTML Pages
- Python Data Aggregation
- Python Data Wrangling
- Python Date and Time
- Python NoSQL Databases
- Python Relational databases
- Python Processing XLS Data
- Python Processing JSON Data
- Python Processing CSV Data
- Python Data cleansing
- Python Data Operations
Python Data Visualization
- Python Graph Data
- Python Geographical Data
- Python Time Series
- Python 3D Charts
- Python Bubble Charts
- Python Scatter Plots
- Python Heat Maps
- Python Box Plots
- Python Chart Styling
- Python Chart Properties
Statistical Data Analysis
- Python Linear Regression
- Python Chi-square Test
- Python Correlation
- Python P-Value
- Python Bernoulli Distribution
- Python Poisson Distribution
- Python Binomial Distribution
- Python Normal Distribution
- Python Measuring Variance
- Python Measuring Central Tendency
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
Python Bubble Charts
Python - Bubble Charts
Bubble charts display data as a cluster of circles. The required data to create bubble chart needs to have the xy coordinates, size of the bubble and the colour of the bubbles. The colours can be suppped by the pbrary itself.
Drawing a Bubble Chart
Bubble chart can be created using the DataFrame.plot.scatter() methods.
import matplotpb.pyplot as plt import numpy as np # create data x = np.random.rand(40) y = np.random.rand(40) z = np.random.rand(40) colors = np.random.rand(40) # use the scatter function plt.scatter(x, y, s=z*1000,c=colors) plt.show()
Its output is as follows −
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