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 Box Plots
Python - Box Plots
Boxplots are a measure of how well distributed the data in a data set is. It spanides the data set into three quartiles. This graph represents the minimum, maximum, median, first quartile and third quartile in the data set. It is also useful in comparing the distribution of data across data sets by drawing boxplots for each of them.
Drawing a Box Plot
Boxplot can be drawn calpng Series.box.plot() and DataFrame.box.plot(), or DataFrame.boxplot() to visuapze the distribution of values within each column.
For instance, here is a boxplot representing five trials of 10 observations of a uniform random variable on [0,1).
import pandas as pd import numpy as np df = pd.DataFrame(np.random.rand(10, 5), columns=[ A , B , C , D , E ]) df.plot.box(grid= True )
Its output is as follows −
Advertisements