Data Visualization using python libraries | matplotlib I Seaborn | plotly with examples
Learn how to create stunning and insightful data visualizations using Python libraries like Matplotlib, Seaborn, and Plotly! 📊✨ This video walks you through the basics and advanced techniques to transform raw data into meaningful charts, graphs, and interactive plots. Perfect for data analysts, scientists, and enthusiasts looking to present data effectively. Unlock the power of Python for data visualization and make your data tell a story! 🚀🐍 #DataScience #Python #dataviz
Visualization is any technique for creating images, diagrams, or animations to communicate a message.
Types of Data Visualizations :
Explanatory:
Exploratory:
#jaganinfo
Python Visualisation Libraries
• Matplotlib
o https://matplotlib.org/
• Pandas built-in plotting
• ggpy
o https://github.com/yhat/ggpy
• Altair
o https://altair-viz.github.io/
• Seaborn
o https://seaborn.pydata.org/
• Plotly
o https://plot.ly/python/
• Bokeh
o https://bokeh.pydata.org/en/latest/
• HoloViews
o http://holoviews.org/
• VisPy
o http://vispy.org/
• Lightning
o http://lightning-viz.org/
Visualization methods :
Distribution
It is commonly used at the initial stage of data exploration i.e. when we get started with understanding the variable. Variables are of two types: Continuous and Categorical. For continuous variable, we look at the center, spread, outlier. For categorical variable we look at frequency table.
Histogram : It is used for showing the distribution of continuous variables.
Box-Plot : It is used to display full range of variation from min to max and useful to identify outlier values.
Comparison
It is used to compare values across different categories.
Common charts to represent these information are Bar and Line chart.
Bar Chart : It is used to compare values across different categories
Line Chart : It is used to compare values over quantitative variable
Composition
It is used to show distribution of a variable across categories of another variable
Pie Chart : It can be created by passing the values representing each of the slices of the pie.
Relationship
It is widely used to understand the correlation between two or more continuous variables
Scatter Plot : It clearly shows the relationship between two variables
Demo 1 : Basic Plot
import matplotlib.pyplot as plt
plt.plot([1,2,4],[5,7,4])
plt.show()
Demo 2 : Basic Plot Legend Title Labels
import matplotlib.pyplot as plt
x,y = [1,2,4],[5,7,4]
x2,y2 = [1,2,5],[8,11,5]
plt.plot(x,y, label = "Firstline")
plt.plot(x2,y2, label = "Secondline")
plt.xlabel('X-axis label')
plt.ylabel('Y-axis label')
plt.title('Graph Title')
plt.legend()
plt.show()
Demo 3 : Bar Chart
import matplotlib.pyplot as plt
x = [2,4,6,8,10]
y = [6,7,8,2,4]
x2 = [1,3,5,7,9]
y2 = [7,8,2,4,2]
plt.bar(x, y, label="FirstBar", color='lightblue')
plt.bar(x2, y2, label="SecondBar", color='c')
plt.xlabel('Bar Number')
plt.ylabel('Bar Height')
plt.title('Graph: Two Bars')
plt.legend()
plt.show()
Demo 4 : Hist Chart
import matplotlib.pyplot as plt
population_ages = [22,55,62,45,21,22,34,42,42,4,99,102,110,120,122,130,111,151,115,112,80,75,65,54,44,42,48]
bins = [0,10,20,30,40,50,60,70,80,90,100,120]
plt.hist(population_ages, bins , histtype='bar', rwidth=0.8)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Graph: Hist Chart')
plt.legend()
plt.show()
Demo 5 : Scatter Plot
import matplotlib.pyplot as plt
x = [1,2,3,4,5,6,7,8]
y = [5,2,4,2,1,4,5,2]
plt.scatter(x,y,label='skitcat', color='r',s=250)
google matplotlib marker option
plt.scatter(x,y,label='skitcat', color='r',s=200, marker = '*')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Graph: Scatter Plot')
plt.show()
Demo 6 : Stack Plot
import matplotlib.pyplot as plt
days = [1,2,3,4,5]
sleeping = [7,8,6,11,7]
eating = [2,3,4,3,2]
working = [7,8,7,2,2]
playing = [8,5,7,8,13]
plt.plot([],[],color = 'm',label = 'sleeping')
plt.plot([],[],color = 'c',label = 'eating')
plt.plot([],[],color = 'r',label = 'working')
plt.plot([],[],color = 'y',label = 'playing') # linewidth
plt.stackplot(days,sleeping,eating,working,playing ,colors =['m','c','r','y'])
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.legend()
plt.title('Graph: Stack Plot')
plt.show()
Tags:
NPTEL Data Science Course
Data Science Proctored Videos
Data Analyst Tutorials
Data Engineer Training Videos
Learn Data Science Online
NPTEL Data Science Lectures
Data Science for Beginners
Advanced Data Science Videos
Data Science Certification Course
Free Data Science Resources
Data Science Career Guide
Data Analysis Techniques
Data Engineering Skills
NPTEL Online Learning
Data Science Practical Training
En esta página del sitio puede ver el video en línea Data Visualization using python libraries | matplotlib I Seaborn | plotly with examples de Duración hora minuto segunda en buena calidad , que subió el usuario jaganinfo 22 marzo 2020, comparta el enlace con amigos y conocidos, en youtube este video ya ha sido visto 790 veces y le gustó 15 a los espectadores. Disfruta viendo!
