Understanding different Box Plot with visualization Last Updated : 14 Nov, 2019 Comments Improve Suggest changes Like Article Like Report Let's see how can boxplot be useful in different ways. Loading Libraries Python3 1== import numpy as np import pandas as pd import matplotlib.pyplot as plt Preparing Data Python3 1== spread = np.random.rand(50) * 100 center = np.ones(25) * 50 flier_high = np.random.rand(10) * 100 + 100 flier_low = np.random.rand(10) * -100 data = np.concatenate((spread, center, flier_high, flier_low), 0) print (data) Output : [ 35.94741387 98.49500418 37.2487085 93.19618571 6.34263359 49.10532713 53.86860981 58.59362227 36.96325746 62.27757508 65.44118887 73.79592156 95.15399991 79.94114982 16.64273792 88.35737021 14.84581489 0.76759854 91.61486239 16.03299406 73.12589808 8.63636833 33.25606049 46.05712779 81.60993207 95.0390852 43.94169286 2.96961334 38.21446718 12.15763603 8.79716665 61.18542821 70.93695599 48.90136391 54.6233727 77.27315695 14.63597135 68.22763576 52.23548596 14.34491407 55.53669512 93.63144771 15.66242535 72.47360029 67.82493039 0.34568417 63.39884046 0.46750944 70.39370656 83.42420235 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 50. 134.61039367 133.42423423 132.77938791 157.75858139 105.99552891 159.1713425 190.9938417 118.33354777 142.13310114 113.54291724 -32.73427425 -34.92884623 -49.28116565 -15.24891626 -14.57460618 -9.48256045 -46.74250253 -36.3992666 -88.14980994 -64.49187441] Code #1: Normal Box Plot Python3 1== plt.figure(figsize = (7, 5)) plt.boxplot(data) plt.show() Output : Code #2: Notch Box Plot Python3 1== plt.figure(figsize = (7, 5)) plt.boxplot(data, 1) plt.show() Output : Code #3: Box Plot showing Outliers Python3 1== plt.figure(figsize = (7, 5)) plt.boxplot(data, 0, 'gD') plt.show() Output : Code #4: Box Plot without Outliers Python3 1== plt.figure(figsize = (7, 5)) plt.boxplot(data, 0, '') plt.show() Output : Code #5: Horizontal Box Plot Python3 1== plt.figure(figsize = (7, 5)) plt.boxplot(data, 0, 'rs', 0) plt.show() Output : Code #6: Horizontal Box Plot changing Whiskers length Python3 1== plt.figure(figsize = (7, 5)) plt.boxplot(data, 0, 'rs', 0, 0.75) plt.show() Output : Comment More infoAdvertise with us Next Article Understanding different Box Plot with visualization M mohit gupta_omg :) Follow Improve Article Tags : Data Visualization AI-ML-DS AI-ML-DS With Python Python Data Visualization Similar Reads Box plot visualization with Pandas and Seaborn Box Plot is the visual representation of the depicting groups of numerical data through their quartiles. Boxplot is also used for detect the outlier in data set. It captures the summary of the data efficiently with a simple box and whiskers and allows us to compare easily across groups. Boxplot summ 3 min read Getting started with Data Visualization in R Data visualization is the practice of representing data through visual elements like graphs, charts, and maps. It helps in understanding large datasets more easily, making it possible to identify patterns and trends that support better decision-making. R is a language designed for statistical analys 5 min read Data Visualization Using Bqplot in Python Bqplot is a powerful and flexible tool for creating interactive visualizations within Jupyter Notebooks. Its integration with ipywidgets and use of D3.js make it a valuable library for data scientists and developers who need to build interactive data visualization applications. What is BqPlot in Pyt 9 min read Python Bokeh tutorial - Interactive Data Visualization with Bokeh Python Bokeh is a Data Visualization library that provides interactive charts and plots. Bokeh renders its plots using HTML and JavaScript that uses modern web browsers for presenting elegant, concise construction of novel graphics with high-level interactivity. Features of Bokeh: Flexibility: Boke 15+ min read Data Visualization using Matplotlib in Python Matplotlib is a widely-used Python library used for creating static, animated and interactive data visualizations. It is built on the top of NumPy and it can easily handles large datasets for creating various types of plots such as line charts, bar charts, scatter plots, etc. These visualizations he 10 min read Like