title
Python Seaborn Tutorial | Data Visualization in Python Using Seaborn | Edureka

description
** Python Certification Training: https://www.edureka.co/data-science-python-certification-course ** This Edureka video on 'Python Seaborn Tutorial' is to educate you about data visualizations using Seaborn in Python. Below are the topics covered in this video: Introduction to Seaborn Seaborn vs Matplotlib How to install Seaborn Installing dependencies Seaborn Plotting functions Multi-plot grids Plot-Aesthetics Python Tutorial Playlist: https://goo.gl/WsBpKe Blog Series: http://bit.ly/2sqmP4s #Edureka #PythonEdureka #PythonSeabornTutorial #pythonProgramming #pythonTutorial #PythonTraining PG in Artificial Intelligence and Machine Learning with NIT Warangal : https://www.edureka.co/post-graduate/machine-learning-and-ai Post Graduate Certification in Data Science with IIT Guwahati - https://www.edureka.co/post-graduate/data-science-program (450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies) Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka ----------------------------------------------------------------------------------------------------------- How it Works? 1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Master the Basic and Advanced Concepts of Python 2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs 3. Master the Concepts of Sequences and File operations 4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python 5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 7. Master the concepts of MapReduce in Hadoop 8. Learn to write Complex MapReduce programs 9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python 10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics 11. Master the concepts of Web scraping in Python 12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. - - - - - - - - - - - - - - - - - - - Who should go for python? Edureka’s Data Science certification course in Python is a good fit for the below professionals: · Programmers, Developers, Technical Leads, Architects · Developers aspiring to be a ‘Machine Learning Engineer' · Analytics Managers who are leading a team of analysts · Business Analysts who want to understand Machine Learning (ML) Techniques · Information Architects who want to gain expertise in Predictive Analytics · 'Python' professionals who want to design automatic predictive models For more information, Please write back to us at sales@edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll free)

detail
{'title': 'Python Seaborn Tutorial | Data Visualization in Python Using Seaborn | Edureka', 'heatmap': [{'end': 434.97, 'start': 359.286, 'weight': 1}, {'end': 527.344, 'start': 486.398, 'weight': 0.827}, {'end': 760.488, 'start': 731.556, 'weight': 0.965}], 'summary': 'Provides an introduction to seaborn, highlighting its advantages over matplotlib, covering installation, plotting functions, multi-plot grids, and plot aesthetics, emphasizing its simplicity and varied functionalities. it also covers importing dependencies, loading datasets, and visualizing with various plot types such as scatter, line, catplot, distplot, jointplot, facetgrid, and pairgrid, with customization options.', 'chapters': [{'end': 272.108, 'segs': [{'end': 74.73, 'src': 'embed', 'start': 29.585, 'weight': 0, 'content': [{'end': 36.37, 'text': 'Coming back towards this session, we shall first begin with a small introduction to Seaborn and the advantages of Seaborn over Matplotlib.', 'start': 29.585, 'duration': 6.785}, {'end': 41.455, 'text': "Then I'll be showing you all the installation of seaborn along with its dependencies.", 'start': 36.954, 'duration': 4.501}, {'end': 48.197, 'text': 'following that, we shall take a look at the various plotting functions in seaborn and how you can create multi-plot grids, and finally,', 'start': 41.455, 'duration': 6.742}, {'end': 51.618, 'text': 'we shall be studying the various plot aesthetics available in seaborn.', 'start': 48.197, 'duration': 3.421}, {'end': 53.899, 'text': "So I hope everyone's ready to start with this.", 'start': 52.078, 'duration': 1.821}, {'end': 56.78, 'text': 'Just give me a quick confirmation in the chat box guys.', 'start': 54.519, 'duration': 2.261}, {'end': 63.562, 'text': 'Okay, I see many of your responses.', 'start': 61.881, 'duration': 1.681}, {'end': 64.441, 'text': "So let's get started.", 'start': 63.622, 'duration': 0.819}, {'end': 70.108, 'text': 'Seaborn as we all know is a data visualization library available in Python.', 'start': 65.706, 'duration': 4.402}, {'end': 74.73, 'text': "It's based on matplotlib and allows creation of statistical graphics.", 'start': 70.908, 'duration': 3.822}], 'summary': "Introduction to seaborn, installation, plotting functions, and plot aesthetics in python's data visualization library.", 'duration': 45.145, 'max_score': 29.585, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TLdXM0A7SR8/pics/TLdXM0A7SR829585.jpg'}, {'end': 190.017, 'src': 'embed', 'start': 122.293, 'weight': 2, 'content': [{'end': 125.694, 'text': 'Factually, matplotlib is good, but seaborn is better.', 'start': 122.293, 'duration': 3.401}, {'end': 130.634, 'text': 'There are basically two shortcomings of matplotlib that seaborn fixes.', 'start': 126.414, 'duration': 4.22}, {'end': 137.537, 'text': "matplotlib can be personalized, but it's difficult to figure out what settings are required to make plots more attractive.", 'start': 130.634, 'duration': 6.903}, {'end': 143.159, 'text': 'on the other hand, seaborn comes with numerous customized themes and high-level interfaces to solve this issue.', 'start': 137.537, 'duration': 5.622}, {'end': 152.168, 'text': "When working with pandas matplotlib doesn't serve well when it comes to dealing with data frames while seaborn functions actually work on data frames.", 'start': 143.945, 'duration': 8.223}, {'end': 154.649, 'text': 'Now that you have an idea about seaborn.', 'start': 152.868, 'duration': 1.781}, {'end': 159.97, 'text': "Let's move on to see how you can actually install this library to install seaborn.", 'start': 154.989, 'duration': 4.981}, {'end': 165.992, 'text': 'You can use the simple pip install seaborn command or if you are using an anaconda platform,', 'start': 160.19, 'duration': 5.802}, {'end': 170.574, 'text': 'you can just go to the anaconda prompt and type conda install seaborn.', 'start': 165.992, 'duration': 4.582}, {'end': 175.393, 'text': 'Since I already have it installed over here.', 'start': 173.573, 'duration': 1.82}, {'end': 176.894, 'text': "I'm not going to redo it again.", 'start': 175.614, 'duration': 1.28}, {'end': 182.735, 'text': "Now let's get back to our presentation and take a look at the dependencies of seaborn.", 'start': 177.734, 'duration': 5.001}, {'end': 190.017, 'text': 'seaborn basically has four mandatory dependencies, which is numpy, scipy, matplotlib and the pandas library.', 'start': 182.735, 'duration': 7.282}], 'summary': 'Seaborn improves upon matplotlib, excelling in customization and data frame functionality, with four mandatory dependencies.', 'duration': 67.724, 'max_score': 122.293, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TLdXM0A7SR8/pics/TLdXM0A7SR8122293.jpg'}, {'end': 276.551, 'src': 'embed', 'start': 251.661, 'weight': 5, 'content': [{'end': 258.084, 'text': 'relplot is a figure level function that makes use of two other access functions for visualizing statistical relationships.', 'start': 251.661, 'duration': 6.423}, {'end': 267.645, 'text': 'These access level functions are scatter plot and line plot which can be specified using the kind parameter of the relplot to explain this in detail.', 'start': 258.999, 'duration': 8.646}, {'end': 269.767, 'text': 'Let me jump onto my Jupiter notebook from here.', 'start': 267.885, 'duration': 1.882}, {'end': 272.108, 'text': "I'll just open a new Python notebook.", 'start': 270.587, 'duration': 1.521}, {'end': 276.551, 'text': "I hope everyone's familiar with Jupiter.", 'start': 274.79, 'duration': 1.761}], 'summary': 'Relplot is a figure level function using scatter and line plots for statistical relationships.', 'duration': 24.89, 'max_score': 251.661, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TLdXM0A7SR8/pics/TLdXM0A7SR8251661.jpg'}], 'start': 11.491, 'title': 'Seaborn data visualization', 'summary': 'Provides an introduction to seaborn, highlighting its advantages over matplotlib, covering installation, plotting functions, multi-plot grids, and plot aesthetics, emphasizing its simplicity and varied functionalities. it also outlines the process of installing seaborn and its mandatory dependencies.', 'chapters': [{'end': 99.3, 'start': 11.491, 'title': 'Introduction to seaborn data visualization', 'summary': 'Introduces seaborn as a data visualization library in python, highlighting its advantages over matplotlib, and covers its installation, plotting functions, multi-plot grids, and plot aesthetics, emphasizing its simplicity and varied functionalities.', 'duration': 87.809, 'highlights': ['Seaborn is a data visualization library available in Python, based on matplotlib and allows creation of statistical graphics, offering an API for dataset-based comparison between multiple variables, supporting multiplot grids, univariate and bivariate visualizations, and different color palettes to reveal various patterns.', 'The chapter includes an introduction to Seaborn and its advantages over Matplotlib, covers the installation of seaborn along with its dependencies, explores various plotting functions in seaborn, demonstrates the creation of multi-plot grids, and studies the various plot aesthetics available in seaborn.', 'Vajeeha from Edureka presents a live Python Seaborn tutorial, highlighting the simplicity of Seaborn in tackling the challenging task of data visualization, and encourages the audience to subscribe to the channel and stay updated with the latest videos.', 'The session involves a small introduction to Seaborn, emphasizing its simplicity and varied functionalities, and requests quick confirmation from the audience to proceed, demonstrating audience engagement and readiness to begin the tutorial.']}, {'end': 272.108, 'start': 100.041, 'title': 'Understanding seaborn for data visualization', 'summary': 'Introduces seaborn, a library that simplifies data visualization and provides a well-defined set of hard things easy to, offering numerous customized themes and high-level interfaces to solve the shortcomings of matplotlib, and it also outlines the process of installing seaborn and its mandatory dependencies.', 'duration': 172.067, 'highlights': ['Seaborn fixes the shortcomings of matplotlib by providing numerous customized themes and high-level interfaces, making it easier to create attractive plots. Seaborn offers solutions to the shortcomings of matplotlib by providing numerous customized themes and high-level interfaces, making it easier to create attractive plots.', "Seaborn functions work well on data frames, unlike matplotlib, which doesn't serve well when dealing with data frames. Seaborn functions work well on data frames, unlike matplotlib, which doesn't serve well when dealing with data frames.", 'Seaborn has four mandatory dependencies: numpy, scipy, matplotlib, and the pandas library. Seaborn has four mandatory dependencies: numpy, scipy, matplotlib, and the pandas library.', 'Relplot is a figure level function in seaborn that uses scatter plot and line plot to visualize statistical relationships. Relplot is a figure level function in seaborn that uses scatter plot and line plot to visualize statistical relationships.']}], 'duration': 260.617, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TLdXM0A7SR8/pics/TLdXM0A7SR811491.jpg', 'highlights': ['Seaborn is a data visualization library in Python, based on matplotlib, offering an API for dataset-based comparison between multiple variables, supporting multiplot grids, univariate and bivariate visualizations, and different color palettes.', 'The chapter includes an introduction to Seaborn and its advantages over Matplotlib, covers the installation of seaborn along with its dependencies, explores various plotting functions in seaborn, demonstrates the creation of multi-plot grids, and studies the various plot aesthetics available in seaborn.', 'Seaborn fixes the shortcomings of matplotlib by providing numerous customized themes and high-level interfaces, making it easier to create attractive plots.', "Seaborn functions work well on data frames, unlike matplotlib, which doesn't serve well when dealing with data frames.", 'Seaborn has four mandatory dependencies: numpy, scipy, matplotlib, and the pandas library.', 'Relplot is a figure level function in seaborn that uses scatter plot and line plot to visualize statistical relationships.']}, {'end': 1165.019, 'segs': [{'end': 326.117, 'src': 'embed', 'start': 274.79, 'weight': 0, 'content': [{'end': 276.551, 'text': "I hope everyone's familiar with Jupiter.", 'start': 274.79, 'duration': 1.761}, {'end': 281.134, 'text': "Okay, I'll just rename this to Python seaborn tutorial.", 'start': 278.232, 'duration': 2.902}, {'end': 287.439, 'text': "I'll just say Python seaborn and I'll rename this.", 'start': 284.997, 'duration': 2.442}, {'end': 291.005, 'text': 'Before we begin data visualization with seaborn.', 'start': 288.684, 'duration': 2.321}, {'end': 294.267, 'text': "We'll have to import seaborn along with its dependencies.", 'start': 291.265, 'duration': 3.002}, {'end': 300.75, 'text': "So the first thing I'm going to import is numpy.", 'start': 294.867, 'duration': 5.883}, {'end': 326.117, 'text': "import numpy as NP, then I'll import pandas as PD, import matplotlib dot pyplot as plt and then I'll import seaborn.", 'start': 300.75, 'duration': 25.367}], 'summary': 'Introduction to data visualization using python seaborn, including import of numpy, pandas, matplotlib, and seaborn.', 'duration': 51.327, 'max_score': 274.79, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TLdXM0A7SR8/pics/TLdXM0A7SR8274790.jpg'}, {'end': 434.97, 'src': 'heatmap', 'start': 345.057, 'weight': 1, 'content': [{'end': 349.98, 'text': 'Seaborn also allows you to load any data set from git using the load data set function.', 'start': 345.057, 'duration': 4.923}, {'end': 352.742, 'text': 'So let me just load the flights data set.', 'start': 350.801, 'duration': 1.941}, {'end': 357.124, 'text': "So I'll just say SNS dot load underscore data set.", 'start': 353.442, 'duration': 3.682}, {'end': 360.987, 'text': "And I'll specify the name which is flights.", 'start': 359.286, 'duration': 1.701}, {'end': 365.97, 'text': 'Okay So now let me just go to the GitHub website.', 'start': 363.528, 'duration': 2.442}, {'end': 371.413, 'text': 'And open the flights data set over here.', 'start': 369.812, 'duration': 1.601}, {'end': 379.099, 'text': 'As you can see there are a number of data sets that are available which you can use along with seaborn.', 'start': 374.176, 'duration': 4.923}, {'end': 383.242, 'text': 'So let me just open the flights data set and scroll down.', 'start': 379.399, 'duration': 3.843}, {'end': 389.245, 'text': 'So as you can see over here, there are three columns for the year month and the number of passengers.', 'start': 384.182, 'duration': 5.063}, {'end': 394.608, 'text': "So I'll just come back to my Jupiter notebook and I'll use SNS dot relplot.", 'start': 389.886, 'duration': 4.722}, {'end': 399.031, 'text': "And I'll specify what I want my x-axis to be.", 'start': 396.55, 'duration': 2.481}, {'end': 401.893, 'text': "So I'll just give x-axis as the number of passengers.", 'start': 399.351, 'duration': 2.542}, {'end': 406.391, 'text': 'and to the y-axis.', 'start': 405.47, 'duration': 0.921}, {'end': 409.773, 'text': "I'll give month as the parameter and for the data.", 'start': 406.491, 'duration': 3.282}, {'end': 415.277, 'text': "I'll use a which is the variable wherein I'm loading the data set and I'll execute this.", 'start': 410.614, 'duration': 4.663}, {'end': 419.419, 'text': 'Okay, so I hope you clear with this.', 'start': 417.558, 'duration': 1.861}, {'end': 426.024, 'text': 'So as you can see over here the y-axis has month and the x-axis has the number of passengers.', 'start': 420.36, 'duration': 5.664}, {'end': 429.026, 'text': 'These points are plotted in two dimensions.', 'start': 426.744, 'duration': 2.282}, {'end': 431.668, 'text': 'To add another dimension.', 'start': 430.407, 'duration': 1.261}, {'end': 433.229, 'text': 'You can add the hue semantic.', 'start': 431.888, 'duration': 1.341}, {'end': 434.97, 'text': "So I'll just copy this control C.", 'start': 433.569, 'duration': 1.401}], 'summary': 'Using seaborn to load and visualize flights dataset with 3 columns and 2 dimensions.', 'duration': 26.356, 'max_score': 345.057, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TLdXM0A7SR8/pics/TLdXM0A7SR8345057.jpg'}, {'end': 527.344, 'src': 'heatmap', 'start': 451.852, 'weight': 2, 'content': [{'end': 459.015, 'text': 'The year has been marked as the hue semantic month, which is in the y-axis and the number of passengers which is on the x-axis.', 'start': 451.852, 'duration': 7.163}, {'end': 463.017, 'text': 'So as you can see on the screen, this is nothing but the scatterplot.', 'start': 459.876, 'duration': 3.141}, {'end': 469.851, 'text': 'So in case you want a line plot all you have to do is specify the kind parameter of relplot with the keyword line.', 'start': 463.709, 'duration': 6.142}, {'end': 472.352, 'text': 'So let me just load another data set over here.', 'start': 470.372, 'duration': 1.98}, {'end': 481.015, 'text': "So I'll say B is equal to SNS dot load underscore data set and I load the tips data set this time.", 'start': 472.652, 'duration': 8.363}, {'end': 485.177, 'text': "After this, I'll use SNS dot relplot.", 'start': 482.776, 'duration': 2.401}, {'end': 499.794, 'text': "I'll specify the x-axis to be the time and y-axis to be the tip Data is nothing but B and kind is line.", 'start': 486.398, 'duration': 13.396}, {'end': 501.495, 'text': 'Now, let me hit run.', 'start': 500.754, 'duration': 0.741}, {'end': 504.056, 'text': "Okay, so I hope you're clear with this.", 'start': 502.475, 'duration': 1.581}, {'end': 508.559, 'text': 'If you have any doubts, please do let me know in the chat box and my team is here to help you.', 'start': 504.757, 'duration': 3.802}, {'end': 516.384, 'text': "Now, let's get back to our presentation and see how you can plot categorical data in seaborn.", 'start': 511.541, 'duration': 4.843}, {'end': 522.741, 'text': 'This approach basically comes into picture when our main variable is divided into discrete groups.', 'start': 517.296, 'duration': 5.445}, {'end': 527.344, 'text': "to achieve this, we'll be using the cat plot function available in seaborn.", 'start': 522.741, 'duration': 4.603}], 'summary': 'Demonstrating scatterplot and line plot using seaborn.', 'duration': 52.204, 'max_score': 451.852, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TLdXM0A7SR8/pics/TLdXM0A7SR8451852.jpg'}, {'end': 608.128, 'src': 'embed', 'start': 581.738, 'weight': 4, 'content': [{'end': 587.86, 'text': "However, you can change the kind of plot you want by specifying the kind parameter, like we've done previously.", 'start': 581.738, 'duration': 6.122}, {'end': 594.202, 'text': 'you can choose between any options, which is strip plot, swarm plot, box plot, violent plot, etc.', 'start': 587.86, 'duration': 6.342}, {'end': 598.844, 'text': 'Let me just show you what happens when I change the kind parameter of this cat plot.', 'start': 594.783, 'duration': 4.061}, {'end': 601.845, 'text': "So I'll just copy this and I'll paste it over here.", 'start': 599.364, 'duration': 2.481}, {'end': 608.128, 'text': "and I'll specify kind to be as violin plot.", 'start': 602.825, 'duration': 5.303}], 'summary': 'Changing the kind parameter can alter the type of plot, such as violin plot, in data visualization.', 'duration': 26.39, 'max_score': 581.738, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TLdXM0A7SR8/pics/TLdXM0A7SR8581738.jpg'}, {'end': 767.91, 'src': 'heatmap', 'start': 716.11, 'weight': 5, 'content': [{'end': 718.771, 'text': 'So this is an example of a univariate distribution.', 'start': 716.11, 'duration': 2.661}, {'end': 723.131, 'text': "So now let's move on and see how a bivariate graph is going to look.", 'start': 719.648, 'duration': 3.483}, {'end': 726.553, 'text': 'To plot bivariate distributions.', 'start': 724.832, 'duration': 1.721}, {'end': 728.814, 'text': 'You can make use of the joint plot function.', 'start': 726.773, 'duration': 2.041}, {'end': 731.536, 'text': 'I already have a small example typed over here.', 'start': 729.475, 'duration': 2.061}, {'end': 735.259, 'text': "So I'll just copy paste that and I'll show you all how it actually looks.", 'start': 731.556, 'duration': 3.703}, {'end': 741.023, 'text': 'Okay, so this is basically a bivariate distribution.', 'start': 737.581, 'duration': 3.442}, {'end': 742.784, 'text': 'So I hope you see the difference.', 'start': 741.603, 'duration': 1.181}, {'end': 747.928, 'text': 'This was our univariate distribution and this is our bivariate distribution.', 'start': 743.104, 'duration': 4.824}, {'end': 753.747, 'text': 'Okay, if you have any doubts, please do let me know in the chat box and my team is here to help you.', 'start': 749.366, 'duration': 4.381}, {'end': 760.488, 'text': 'Moving on Python c1 also allows you to plot multiple grids side by side.', 'start': 755.687, 'duration': 4.801}, {'end': 767.91, 'text': 'These are basically plots or graphs that are plotted using the same scale and access to it comparison between them.', 'start': 761.028, 'duration': 6.882}], 'summary': 'Introduction to univariate and bivariate distributions in python, with the use of joint plot function and comparison of plots.', 'duration': 51.8, 'max_score': 716.11, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TLdXM0A7SR8/pics/TLdXM0A7SR8716110.jpg'}, {'end': 786.844, 'src': 'embed', 'start': 761.028, 'weight': 7, 'content': [{'end': 767.91, 'text': 'These are basically plots or graphs that are plotted using the same scale and access to it comparison between them.', 'start': 761.028, 'duration': 6.882}, {'end': 773.031, 'text': 'this in turn helps the programmer to differentiate between the plots and obtain large amounts of information.', 'start': 767.91, 'duration': 5.121}, {'end': 774.976, 'text': 'To plot multiple graphs.', 'start': 773.675, 'duration': 1.301}, {'end': 779.679, 'text': 'You can either use the facet grid function or the pair grid function of seaborn.', 'start': 775.316, 'duration': 4.363}, {'end': 783.962, 'text': 'Let me jump on to my Jupiter notebook and show you all an example of each of these.', 'start': 780.2, 'duration': 3.762}, {'end': 786.844, 'text': "So first I'll be using the facet plot function.", 'start': 784.623, 'duration': 2.221}], 'summary': 'Plot multiple graphs for comparison using seaborn functions.', 'duration': 25.816, 'max_score': 761.028, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TLdXM0A7SR8/pics/TLdXM0A7SR8761028.jpg'}, {'end': 1005.637, 'src': 'embed', 'start': 977.271, 'weight': 8, 'content': [{'end': 983.273, 'text': 'you can change the style parameter to any of the available themes, which is dark grid, dark white, Etc.', 'start': 977.271, 'duration': 6.002}, {'end': 990.315, 'text': 'So if I just specify dark and I hit run you can see that the grids have vanished but the background is dark.', 'start': 984.093, 'duration': 6.222}, {'end': 1000.534, 'text': 'Not just this guys you can make many other changes to your graphs such as adding or removing access the colors that are available Etc.', 'start': 992.89, 'duration': 7.644}, {'end': 1005.637, 'text': 'Now, let me just show you all how you can remove the axis lines that are present in your graphs.', 'start': 1001.095, 'duration': 4.542}], 'summary': 'Options for changing graph style and removing axis lines demonstrated.', 'duration': 28.366, 'max_score': 977.271, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TLdXM0A7SR8/pics/TLdXM0A7SR8977271.jpg'}], 'start': 274.79, 'title': 'Python seaborn tutorial', 'summary': 'Covers the process of importing dependencies, loading datasets, and visualizing with relplot, as well as various plot types such as scatter, line, catplot, distplot, jointplot, facetgrid, and pairgrid, with customization options.', 'chapters': [{'end': 451.212, 'start': 274.79, 'title': 'Python seaborn tutorial', 'summary': "Covers the process of importing dependencies, loading a dataset from github using seaborn, and visualizing the dataset with seaborn's relplot function, demonstrating the plot of number of passengers against month and the addition of another dimension using the hue semantic.", 'duration': 176.422, 'highlights': ['Importing dependencies for data visualization with Seaborn The tutorial covers the process of importing numpy, pandas, matplotlib, and seaborn for data visualization with Seaborn.', "Loading a dataset from GitHub using Seaborn The tutorial demonstrates the usage of Seaborn's load_dataset function to load the flights dataset from GitHub for visualization.", "Visualizing dataset with Seaborn's relplot function The tutorial showcases the use of Seaborn's relplot function to plot number of passengers against month, and demonstrates adding another dimension using the hue semantic."]}, {'end': 1165.019, 'start': 451.852, 'title': 'Python seaborn tutorial', 'summary': 'Covers the usage of relplot for scatter and line plots, catplot for categorical data, distplot for univariate distributions, jointplot for bivariate distributions, facetgrid and pairgrid for multiple plots, and customization of plots like changing background style, removing axis lines, and exploring color palettes.', 'duration': 713.167, 'highlights': ['The chapter explains the usage of relplot for scatter and line plots, with an example of loading the tips dataset and plotting the time vs. tip data as a line plot. usage of relplot, example of scatter and line plots, loading and plotting tips dataset', 'The chapter demonstrates the usage of catplot for plotting categorical data, with examples of scatter plot and violin plot using the kind parameter. usage of catplot, examples of scatter and violin plots, usage of kind parameter', 'The chapter covers the usage of distplot for visualizing univariate distributions, with an example of plotting a univariate distribution using distplot. usage of distplot, example of plotting univariate distribution', 'The chapter explains the usage of jointplot for visualizing bivariate distributions, with an example of plotting a bivariate distribution using jointplot. usage of jointplot, example of plotting bivariate distribution', 'The chapter demonstrates the usage of facetgrid and pairgrid for plotting multiple grids side by side, with examples using the iris and flights datasets. usage of facetgrid and pairgrid, examples using iris and flights datasets', 'The chapter covers customization of plots, including changing background style, removing axis lines, and exploring color palettes using functions like set, despine, and color_palette. customization of plots, changing background style, removing axis lines, exploring color palettes']}], 'duration': 890.229, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/TLdXM0A7SR8/pics/TLdXM0A7SR8274790.jpg', 'highlights': ['Covers the process of importing numpy, pandas, matplotlib, and seaborn for data visualization with Seaborn.', "Demonstrates the usage of Seaborn's load_dataset function to load the flights dataset from GitHub for visualization.", "Showcases the use of Seaborn's relplot function to plot number of passengers against month and demonstrates adding another dimension using the hue semantic.", 'Explains the usage of relplot for scatter and line plots, with an example of loading the tips dataset and plotting the time vs. tip data as a line plot.', 'Demonstrates the usage of catplot for plotting categorical data, with examples of scatter plot and violin plot using the kind parameter.', 'Covers the usage of distplot for visualizing univariate distributions, with an example of plotting a univariate distribution using distplot.', 'Explains the usage of jointplot for visualizing bivariate distributions, with an example of plotting a bivariate distribution using jointplot.', 'Demonstrates the usage of facetgrid and pairgrid for plotting multiple grids side by side, with examples using the iris and flights datasets.', 'Covers customization of plots, including changing background style, removing axis lines, and exploring color palettes using functions like set, despine, and color_palette.']}], 'highlights': ['Seaborn is a data visualization library in Python, based on matplotlib, offering an API for dataset-based comparison between multiple variables, supporting multiplot grids, univariate and bivariate visualizations, and different color palettes.', 'The chapter includes an introduction to Seaborn and its advantages over Matplotlib, covers the installation of seaborn along with its dependencies, explores various plotting functions in seaborn, demonstrates the creation of multi-plot grids, and studies the various plot aesthetics available in seaborn.', 'Covers the process of importing numpy, pandas, matplotlib, and seaborn for data visualization with Seaborn.', 'Seaborn fixes the shortcomings of matplotlib by providing numerous customized themes and high-level interfaces, making it easier to create attractive plots.', "Seaborn functions work well on data frames, unlike matplotlib, which doesn't serve well when dealing with data frames.", 'Relplot is a figure level function in seaborn that uses scatter plot and line plot to visualize statistical relationships.', "Demonstrates the usage of Seaborn's load_dataset function to load the flights dataset from GitHub for visualization.", "Showcases the use of Seaborn's relplot function to plot number of passengers against month and demonstrates adding another dimension using the hue semantic.", 'Explains the usage of relplot for scatter and line plots, with an example of loading the tips dataset and plotting the time vs. tip data as a line plot.', 'Demonstrates the usage of catplot for plotting categorical data, with examples of scatter plot and violin plot using the kind parameter.', 'Covers the usage of distplot for visualizing univariate distributions, with an example of plotting a univariate distribution using distplot.', 'Explains the usage of jointplot for visualizing bivariate distributions, with an example of plotting a bivariate distribution using jointplot.', 'Demonstrates the usage of facetgrid and pairgrid for plotting multiple grids side by side, with examples using the iris and flights datasets.', 'Covers customization of plots, including changing background style, removing axis lines, and exploring color palettes using functions like set, despine, and color_palette.']}