
- Python Data Science Tutorial
- Python Data Science
- Python Data Science Introduction
- Python Data Science Environment Setup
- Python Pandas
- Python Numpy
- Python SciPy
- Python Matplotlib
- Python Data Processing
- Python Data Operations
- Python Data cleansing
- Python Processing CSV Data
- Python Processing JSON Data
- Python Processing XLS Data
- Python Relational databases
- Python NoSQL Databases
- Python Date and Time
- Python Data Wrangling
- Python Data Aggregation
- Python Reading HTML Pages
- Python Processing Unstructured Data
- Python word tokenization
- Python Stemming and Lemmatization
- Python Data Visualization
- Python Chart Properties
- Python Chart Styling
- Python Box Plots
- Python Heat Maps
- Python Scatter Plots
- Python Bubble Charts
- Python 3D Charts
- Python Time Series
- Python Geographical Data
- Python Graph Data
- Statistical Data Analysis
- Python Measuring Central Tendency
- Python Measuring Variance
- Python Normal Distribution
- Python Binomial Distribution
- Python Poisson Distribution
- Python Bernoulli Distribution
- Python P-Value
- Python Correlation
- Python Chi-square Test
- Python Linear Regression
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Python - Binomial Distribution
The binomial distribution model deals with finding the probability of success of an event which has only two possible outcomes in a series of experiments. For example, tossing of a coin always gives a head or a tail. The probability of finding exactly 3 heads in tossing a coin repeatedly for 10 times is estimated during the binomial distribution.
We use the seaborn python library which has in-built functions to create such probability distribution graphs. Also, the scipy package helps is creating the binomial distribution.
from scipy.stats import binom import seaborn as sb binom.rvs(size=10,n=20,p=0.8) data_binom = binom.rvs(n=20,p=0.8,loc=0,size=1000) ax = sb.distplot(data_binom, kde=True, color='blue', hist_kws={"linewidth": 25,'alpha':1}) ax.set(xlabel='Binomial', ylabel='Frequency')
Its output is as follows −

Useful Video Courses
Advertisements