title
Python Matplotlib Tutorial | Matplotlib Tutorial | Python Tutorial | Python Training | Edureka

description
🔥 Edureka Python Training (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/data-science-python-certification-course This Edureka Python Matplotlib tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what is data visualization and how to perform data visualization using Matplotlib. It also explains how to modify your plot and how to plot various types of graphs. Check out our Python Training Playlist: https://goo.gl/Na1p9G Below are the topics covered in this tutorial: 1. Why Data Visualization? 2. What Is Data Visualization? 3. Various Types Of Plots 4. What Is Matplotlib? 6. How To Use Matplotlib? Subscribe to our channel to get video updates. Hit the subscribe button above. 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) #Python #Pythontutorial #Pythononlinetraining #Pythonforbeginners #PythonProgramming #PythonMatplotlib 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. For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free). 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

detail
{'title': 'Python Matplotlib Tutorial | Matplotlib Tutorial | Python Tutorial | Python Training | Edureka', 'heatmap': [{'end': 1388.221, 'start': 1340.347, 'weight': 0.951}, {'end': 1536.217, 'start': 1505.76, 'weight': 0.869}, {'end': 1700.419, 'start': 1612.028, 'weight': 0.912}, {'end': 1827.87, 'start': 1801.652, 'weight': 0.766}, {'end': 1932.966, 'start': 1886.168, 'weight': 0.767}], 'summary': 'This tutorial on matplotlib covers the significance of data visualization in aiding understanding and decision-making, its applications in finance, organizational improvement, customer behavior, and product placement, and basics of creating, customizing, and using matplotlib for data visualization in python, emphasizing clear visual representation for insightful data interpretation.', 'chapters': [{'end': 147.596, 'segs': [{'end': 49.963, 'src': 'embed', 'start': 21.517, 'weight': 1, 'content': [{'end': 26.039, 'text': "Then we'll get started with it and I'll tell you how to plot each of these graphs using Python.", 'start': 21.517, 'duration': 4.522}, {'end': 28.34, 'text': 'So I hope we all are clear with the agenda.', 'start': 26.859, 'duration': 1.481}, {'end': 32.479, 'text': 'kindly give me a quick confirmation so that I can move forward.', 'start': 29.295, 'duration': 3.184}, {'end': 40.588, 'text': 'Devon says move forward, so does Pooja, Siddharth, Neha, Theon, Jason, fine guys.', 'start': 34.962, 'duration': 5.626}, {'end': 45.955, 'text': "I've got a confirmation from everyone, so we'll move forward and we'll understand why we need data visualization.", 'start': 41.009, 'duration': 4.946}, {'end': 49.963, 'text': 'So why do we need data visualization? Now let us take an example.', 'start': 46.96, 'duration': 3.003}], 'summary': 'Introducing python for graph plotting, confirming agenda, and discussing the importance of data visualization.', 'duration': 28.446, 'max_score': 21.517, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww21517.jpg'}, {'end': 99.955, 'src': 'embed', 'start': 75.849, 'weight': 0, 'content': [{'end': 82.014, 'text': 'And it is a well-known fact that human brain can process information easily when it is in pictorial or graphical form.', 'start': 75.849, 'duration': 6.165}, {'end': 89.56, 'text': 'So that is one of the key reasons why we use data visualization in order to understand the trend better and make better decisions.', 'start': 82.334, 'duration': 7.226}, {'end': 94.284, 'text': 'So let us move forward and understand one more reason why we need data visualization.', 'start': 90.441, 'duration': 3.843}, {'end': 97.814, 'text': 'Now basically it allows us to quickly interpret the data.', 'start': 94.932, 'duration': 2.882}, {'end': 99.955, 'text': 'Like whatever the trend the data is showing us.', 'start': 98.174, 'duration': 1.781}], 'summary': 'Data visualization helps process information easily and interpret trends quickly.', 'duration': 24.106, 'max_score': 75.849, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww75849.jpg'}], 'start': 0.109, 'title': 'Importance of data visualization', 'summary': 'Explores the significance of data visualization in aiding understanding and decision-making for non-technical audiences, and enabling quick interpretation and experimentation with data variables.', 'chapters': [{'end': 147.596, 'start': 0.109, 'title': 'Matplotlib: importance of data visualization', 'summary': 'Explores the importance of data visualization, emphasizing its role in aiding understanding and decision-making for non-technical audiences, as well as enabling quick interpretation and experimentation with data variables.', 'duration': 147.487, 'highlights': ['Data visualization aids in presenting analysis to non-technical individuals, enabling better understanding through graphical representation. Importance of data visualization in aiding understanding for non-technical audiences.', 'Graphical representation allows for quick interpretation of data trends, facilitating efficient experimentation and identification of useful variables. The role of graphical representation in quick interpretation and identification of useful variables.']}], 'duration': 147.487, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww109.jpg', 'highlights': ['Importance of data visualization in aiding understanding for non-technical audiences.', 'Graphical representation allows for quick interpretation of data trends, facilitating efficient experimentation and identification of useful variables.']}, {'end': 333.562, 'segs': [{'end': 188.719, 'src': 'embed', 'start': 149.099, 'weight': 0, 'content': [{'end': 155.321, 'text': 'Brian says move on, so does Neha, Pooja, Siddharth, Theon, Jason, fine guys.', 'start': 149.099, 'duration': 6.222}, {'end': 159.703, 'text': "So we'll move forward and understand what exactly is data visualization.", 'start': 156.122, 'duration': 3.581}, {'end': 167.706, 'text': 'So what is data visualization? So data visualization is nothing but the presentation of your data in a pictorial or a graphical format.', 'start': 160.523, 'duration': 7.183}, {'end': 169.247, 'text': 'Now why we do that?', 'start': 168.346, 'duration': 0.901}, {'end': 176.231, 'text': 'We do that in order to enable the decision makers of an organization to see the analytics presented visually,', 'start': 169.727, 'duration': 6.504}, {'end': 180.414, 'text': 'so that they can grasp some difficult concepts or identify new patterns.', 'start': 176.231, 'duration': 4.183}, {'end': 184.816, 'text': 'Now, since we have discussed a lot about data visualization, so guys, I want answers from you all.', 'start': 180.914, 'duration': 3.902}, {'end': 188.719, 'text': 'Give me some example where I can use data visualization in an organization.', 'start': 185.277, 'duration': 3.442}], 'summary': 'Data visualization helps decision makers grasp complex analytics and identify patterns visually.', 'duration': 39.62, 'max_score': 149.099, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww149099.jpg'}], 'start': 149.099, 'title': 'Data visualization in decision making', 'summary': 'Emphasizes the importance of data visualization in enabling decision makers to grasp difficult concepts or identify new patterns, providing examples and discussing its role in organizations. it also covers the applications of data visualization in finance, organizational improvement, customer behavior, product placement, and sales volume prediction, along with the process of visualizing, analyzing, and documenting insights from data.', 'chapters': [{'end': 188.719, 'start': 149.099, 'title': 'Data visualization: presenting data visually', 'summary': 'Explains the importance of data visualization in enabling decision makers to grasp difficult concepts or identify new patterns, providing examples and emphasizing its role in organizations.', 'duration': 39.62, 'highlights': ['Data visualization enables decision makers to see analytics presented visually, aiding in grasping difficult concepts or identifying new patterns.', 'The purpose of data visualization is to present data in a pictorial or graphical format, facilitating easier comprehension for decision makers.', 'Usage of data visualization in organizations allows decision makers to grasp difficult concepts or identify new patterns.', 'Examples of where data visualization can be used in an organization were requested from the audience.']}, {'end': 333.562, 'start': 190.28, 'title': 'Data visualization applications & process', 'summary': 'Discusses the importance and applications of data visualization, including finance, organizational improvement, customer behavior, product placement, and sales volume prediction, along with the process of visualizing, analyzing, and documenting insights from data.', 'duration': 143.282, 'highlights': ['Data visualization applications in finance, organizational improvement, customer behavior, product placement, and sales volume prediction Data visualization can be used in finance to find investment opportunities, in organizational improvement to identify areas needing attention, in understanding customer behavior, and in predicting sales volume.', 'Importance of understanding target audience and product placement in data visualization Understanding the target audience and product placement is crucial, e.g., not selling jackets in summers and not selling shaving cream to kids, emphasizing the importance of data visualization in understanding where and to whom products are being sold.', 'The process of visualizing, analyzing, and documenting insights from data The process involves visualizing the data using graphs, analyzing the data to identify trends or changes, and documenting the insights gained from the analysis.']}], 'duration': 184.463, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww149099.jpg', 'highlights': ['Data visualization applications in finance, organizational improvement, customer behavior, product placement, and sales volume prediction Data visualization can be used in finance to find investment opportunities, in organizational improvement to identify areas needing attention, in understanding customer behavior, and in predicting sales volume.', 'The process of visualizing, analyzing, and documenting insights from data The process involves visualizing the data using graphs, analyzing the data to identify trends or changes, and documenting the insights gained from the analysis.', 'Importance of understanding target audience and product placement in data visualization Understanding the target audience and product placement is crucial, e.g., not selling jackets in summers and not selling shaving cream to kids, emphasizing the importance of data visualization in understanding where and to whom products are being sold.', 'Data visualization enables decision makers to see analytics presented visually, aiding in grasping difficult concepts or identifying new patterns.', 'The purpose of data visualization is to present data in a pictorial or graphical format, facilitating easier comprehension for decision makers.', 'Usage of data visualization in organizations allows decision makers to grasp difficult concepts or identify new patterns.', 'Examples of where data visualization can be used in an organization were requested from the audience.']}, {'end': 1031.025, 'segs': [{'end': 387.423, 'src': 'embed', 'start': 348.459, 'weight': 0, 'content': [{'end': 353.466, 'text': "So at that time what I'll do, I'll transform my data set, I'll remove those fields or there might be certain fields that I need.", 'start': 348.459, 'duration': 5.007}, {'end': 354.966, 'text': 'and which is not there.', 'start': 354.266, 'duration': 0.7}, {'end': 356.407, 'text': "So I'll add those fields.", 'start': 355.386, 'duration': 1.021}, {'end': 358.747, 'text': "So accordingly, I'll transform my data set.", 'start': 356.747, 'duration': 2}, {'end': 363.869, 'text': "And once I've done that, I'll again visualize it in order to understand what my data is now talking about.", 'start': 359.067, 'duration': 4.802}, {'end': 366.229, 'text': 'And this process will keep on repeating.', 'start': 364.169, 'duration': 2.06}, {'end': 372.051, 'text': 'So this is how you find insights in data where visualization plays a very, very important role, guys.', 'start': 366.529, 'duration': 5.522}, {'end': 373.911, 'text': 'So I hope you have understood this flow.', 'start': 372.371, 'duration': 1.54}, {'end': 377.752, 'text': 'So we have understood what exactly is data visualization.', 'start': 375.052, 'duration': 2.7}, {'end': 380.033, 'text': 'If you have any questions or doubts, you can ask me.', 'start': 378.212, 'duration': 1.821}, {'end': 387.423, 'text': 'All right, so Neha is asking, Matplotlib is used for data visualization? Yes, Neha, it is used for data visualization.', 'start': 381.397, 'duration': 6.026}], 'summary': 'Data transformation and visualization play a crucial role in finding insights. matplotlib is used for data visualization.', 'duration': 38.964, 'max_score': 348.459, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww348459.jpg'}, {'end': 599.946, 'src': 'embed', 'start': 541.425, 'weight': 1, 'content': [{'end': 545.747, 'text': "So over here I'll first create a Python file with .", 'start': 541.425, 'duration': 4.322}, {'end': 549.29, 'text': "py extension and I'm going to name it as first code.", 'start': 545.747, 'duration': 3.543}, {'end': 553.232, 'text': "Now over here I'll first import PyPlot from matplotlib.", 'start': 550.73, 'duration': 2.502}, {'end': 563.548, 'text': "So for that I'll type from matplotlib import Pyplot as PLT.", 'start': 553.872, 'duration': 9.676}, {'end': 569.413, 'text': 'Now this PLT is pretty much similar to NP that we were using in NumPy array if you can recall.', 'start': 564.569, 'duration': 4.844}, {'end': 576.899, 'text': "So it is not mandatory to use PLT only, you can use whatever you want, but a lot of people use it as PLT and I'll keep it that way.", 'start': 570.374, 'duration': 6.525}, {'end': 580.036, 'text': 'Now our next step is to plot our graph to the canvas.', 'start': 577.335, 'duration': 2.701}, {'end': 589.839, 'text': "So for that what I'll do I'll type in plt.plot and I'll write in here my x and y axis values.", 'start': 580.556, 'duration': 9.283}, {'end': 599.946, 'text': "So for x axis I'll keep it as 1, 2, 3 and then for y axis I'm going to add values such as 4, 5 and 1.", 'start': 589.959, 'duration': 9.987}], 'summary': 'Creating a python file to plot a graph using matplotlib with x and y axis values 1, 2, 3 and 4, 5, 1.', 'duration': 58.521, 'max_score': 541.425, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww541425.jpg'}, {'end': 794.644, 'src': 'embed', 'start': 766.469, 'weight': 4, 'content': [{'end': 768.97, 'text': "Fine, so I'll open my PyCharm and execute this practically now.", 'start': 766.469, 'duration': 2.501}, {'end': 777.511, 'text': "This is my pi charm again guys, so I'll remove till here, and obviously I need pi plot function from matplotlib.", 'start': 771.547, 'duration': 5.964}, {'end': 779.753, 'text': 'So now I will define x and y variable.', 'start': 777.631, 'duration': 2.122}, {'end': 784.456, 'text': "So in x, it'll have a list of numbers, say five, eight, and 10.", 'start': 780.113, 'duration': 4.343}, {'end': 790.541, 'text': "And now I'll define one more variable y, which again has list of numbers 12, 16, and six.", 'start': 784.456, 'duration': 6.085}, {'end': 794.644, 'text': '12, 16, and six, yup.', 'start': 790.561, 'duration': 4.083}], 'summary': 'Using pycharm, the speaker defines x and y variables with specific lists of numbers for a practical demonstration.', 'duration': 28.175, 'max_score': 766.469, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww766469.jpg'}], 'start': 333.642, 'title': 'Data visualization with matplotlib, plotting variables & adding labels, and adding style to graphs with matplotlib', 'summary': 'Covers the basics of data visualization with matplotlib, including creating a basic graph, plotting variables, adding labels, and applying styles to graphs. it emphasizes practical execution in pycharm and the importance of clear visual representation for insightful data interpretation.', 'chapters': [{'end': 644.477, 'start': 333.642, 'title': 'Data visualization with matplotlib', 'summary': 'Discusses the importance of data visualization in finding insights, explores the basics of matplotlib for data visualization, and demonstrates the creation of a basic graph using matplotlib with simple code.', 'duration': 310.835, 'highlights': ['The percentage of unemployed youth has gone up, gone down in a specific country, and remained stable in other countries. The chapter discusses the changes in the percentage of unemployed youth in different countries, highlighting the increase, decrease, and stability of the unemployment rate.', 'The process of transforming the dataset involves removing unnecessary fields and adding required fields. The process of transforming the dataset is explained, involving the removal of unnecessary fields and the addition of required fields to refine the dataset for analysis.', 'Matplotlib is used for data visualization, and various types of plots such as bar graph, histograms, scatter plot, pie plot, hexagonal bin plot, and area plot can be created using it. The usage of Matplotlib for data visualization is emphasized, along with the mention of various types of plots that can be created using Matplotlib, including bar graphs, histograms, scatter plots, pie plots, hexagonal bin plots, and area plots.', 'The basics of creating a simple graph using Matplotlib are demonstrated with a basic code example. A demonstration of creating a basic graph using Matplotlib is provided, with a basic code example and the explanation of the process involved in plotting the graph.', 'Various tools such as Tableau, R, and Python with the matplotlib library are used for data visualization. The use of various tools for data visualization, including Tableau, R, and Python with the matplotlib library, is mentioned.']}, {'end': 869.605, 'start': 644.797, 'title': 'Plotting variables & adding labels', 'summary': "Explains how to plot variables and add descriptive labels to a graph using plt.plot function, with examples of defining x and y variables and adding a title, x label, and y label, resulting in a graph with title 'info', y-axis label, and x-axis label.", 'duration': 224.808, 'highlights': ['The chapter explains how to plot variables and add descriptive labels to a graph using plt.plot function. It provides a step-by-step guide on plotting variables and adding labels to a graph, enhancing data visualization.', 'Examples of defining x and y variables and adding a title, x label, and y label are provided. The transcript includes examples of defining x and y variables and adding a title, x label, and y label, creating a practical demonstration.', "The resulting graph has a title 'info', y-axis label, and x-axis label. The practical demonstration results in a graph with a specific title, y-axis label, and x-axis label, showcasing the application of the explained concepts."]}, {'end': 1031.025, 'start': 870.356, 'title': 'Adding style to graphs with matplotlib', 'summary': 'Discusses how to add style to graphs using matplotlib, including importing the style function, defining variables, adding grid lines, and implementing legends and colors, preparing for practical execution in pycharm.', 'duration': 160.669, 'highlights': ['The chapter discusses how to add style to graphs using Matplotlib, including importing the style function, defining variables, adding grid lines, and implementing legends and colors, preparing for practical execution in PyCharm. It covers the steps for adding style to graphs using Matplotlib, including importing the style function, defining variables, adding grid lines, implementing legends and colors, and preparing for practical execution in PyCharm.', "The graph's appearance is enhanced by changing the color, line width, adding grid lines, title, labels, and a legend. The graph's appearance is enhanced by changing the color, line width, adding grid lines, title, labels, and a legend, providing a comprehensive customization guide.", 'Practical execution in PyCharm is anticipated following a detailed explanation of the process. The chapter anticipates practical execution in PyCharm following a detailed explanation of the process, enhancing the learning experience.']}], 'duration': 697.383, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww333642.jpg', 'highlights': ['The usage of Matplotlib for data visualization is emphasized, along with the mention of various types of plots that can be created using Matplotlib, including bar graphs, histograms, scatter plots, pie plots, hexagonal bin plots, and area plots.', 'The chapter explains how to plot variables and add descriptive labels to a graph using plt.plot function. It provides a step-by-step guide on plotting variables and adding labels to a graph, enhancing data visualization.', 'The process of transforming the dataset is explained, involving the removal of unnecessary fields and the addition of required fields to refine the dataset for analysis.', 'The basics of creating a simple graph using Matplotlib are demonstrated with a basic code example. A demonstration of creating a basic graph using Matplotlib is provided, with a basic code example and the explanation of the process involved in plotting the graph.', 'The chapter discusses how to add style to graphs using Matplotlib, including importing the style function, defining variables, adding grid lines, implementing legends and colors, and preparing for practical execution in PyCharm.']}, {'end': 1555.704, 'segs': [{'end': 1163.923, 'src': 'embed', 'start': 1080.055, 'weight': 0, 'content': [{'end': 1089.877, 'text': "Now again, for x2 and y2, I'm gonna type in plt.plot x2 comma y2 comma the color.", 'start': 1080.055, 'duration': 9.822}, {'end': 1101.524, 'text': "then label, so I'm gonna type in line two, then comes line width.", 'start': 1091.295, 'duration': 10.229}, {'end': 1111.392, 'text': 'Alright. so now our next step is to add title to our plot.', 'start': 1107.389, 'duration': 4.003}, {'end': 1113.794, 'text': 'so plot plt.title.', 'start': 1111.392, 'duration': 2.402}, {'end': 1117.437, 'text': "and the title that I'm going to give is info again.", 'start': 1113.794, 'duration': 3.643}, {'end': 1121.299, 'text': 'then x and y labels plt dot.', 'start': 1117.437, 'duration': 3.862}, {'end': 1127, 'text': 'y label y axis.', 'start': 1121.299, 'duration': 5.701}, {'end': 1127.961, 'text': 'then plt dot.', 'start': 1127, 'duration': 0.961}, {'end': 1134.163, 'text': 'x label x axis plt dot.', 'start': 1127.961, 'duration': 6.202}, {'end': 1134.503, 'text': 'show.', 'start': 1134.163, 'duration': 0.34}, {'end': 1136.604, 'text': 'Now go ahead and run this.', 'start': 1135.243, 'duration': 1.361}, {'end': 1140.905, 'text': "So over here I've not added any grid lines.", 'start': 1138.624, 'duration': 2.281}, {'end': 1142.185, 'text': 'I can do that as well.', 'start': 1141.145, 'duration': 1.04}, {'end': 1143.846, 'text': 'So for that let me first close it.', 'start': 1142.325, 'duration': 1.521}, {'end': 1148.153, 'text': "Fine, so I've defined title, x label as well as y label.", 'start': 1145.472, 'duration': 2.681}, {'end': 1151.235, 'text': 'Now our last task is to actually add grid lines.', 'start': 1148.573, 'duration': 2.662}, {'end': 1158.958, 'text': "So for that I'll type plt.grid and just write in here true comma give a color.", 'start': 1151.335, 'duration': 7.623}, {'end': 1162.142, 'text': "I want it black, so I'll keep it that way.", 'start': 1160.202, 'duration': 1.94}, {'end': 1163.923, 'text': 'Finally, show your plot.', 'start': 1162.623, 'duration': 1.3}], 'summary': 'Plotted x2 and y2, added title, labels, and grid lines to the plot.', 'duration': 83.868, 'max_score': 1080.055, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww1080055.jpg'}, {'end': 1276.562, 'src': 'embed', 'start': 1250.915, 'weight': 3, 'content': [{'end': 1255.338, 'text': 'Now since we know why we use bar graph, let me explain you the code and how you can do that using matplotlib.', 'start': 1250.915, 'duration': 4.423}, {'end': 1258.034, 'text': 'So first, import PyPlot like we do every time.', 'start': 1255.973, 'duration': 2.061}, {'end': 1264.137, 'text': "After that, instead of plt.plot, I'll use plt.par, and I have filled in data here.", 'start': 1258.514, 'duration': 5.623}, {'end': 1266.458, 'text': 'You can fill in variables as well, that contains data.', 'start': 1264.297, 'duration': 2.161}, {'end': 1268.799, 'text': 'Then I have defined a label, example one.', 'start': 1266.658, 'duration': 2.141}, {'end': 1275.342, 'text': "After that, I have vn more plot, in which I have filled in data plus a label and I've given a color as well.", 'start': 1269.199, 'duration': 6.143}, {'end': 1276.562, 'text': "I don't want the default color.", 'start': 1275.402, 'duration': 1.16}], 'summary': 'Explanation of creating bar graphs using matplotlib with code and example data.', 'duration': 25.647, 'max_score': 1250.915, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww1250915.jpg'}, {'end': 1388.221, 'src': 'heatmap', 'start': 1340.347, 'weight': 0.951, 'content': [{'end': 1347.032, 'text': 'So we have legend here, we have Y label as well as X label and we have a title for our graph.', 'start': 1340.347, 'duration': 6.685}, {'end': 1352.213, 'text': 'So any questions, any doubts guys? All right, so we have a question from Dave.', 'start': 1347.473, 'duration': 4.74}, {'end': 1356.795, 'text': "He's asking, what is the difference between histogram and bar plot? All right, Dave.", 'start': 1352.294, 'duration': 4.501}, {'end': 1366.097, 'text': "I'll tell you, in histograms, we have quantitative variables, all right? And when I talk about bar plot, they have categorical variables.", 'start': 1357.255, 'duration': 8.842}, {'end': 1368.437, 'text': 'So let me explain you this with an example.', 'start': 1366.857, 'duration': 1.58}, {'end': 1373.975, 'text': 'So suppose if I wanna plot the GDP growth of every city in a particular country.', 'start': 1369.138, 'duration': 4.837}, {'end': 1381.018, 'text': "So at that time, I'll use a bar plot, because it has a category, this particular city, like New Jersey, New York, all those things.", 'start': 1374.235, 'duration': 6.783}, {'end': 1388.221, 'text': "Now when I talk about, I'll use histogram, when I'm talking about quantitative variable, that means if I'm talking about age group.", 'start': 1381.778, 'duration': 6.443}], 'summary': 'Differentiate between histogram and bar plot based on variable type and examples.', 'duration': 47.874, 'max_score': 1340.347, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww1340347.jpg'}, {'end': 1444.069, 'src': 'embed', 'start': 1419.79, 'weight': 4, 'content': [{'end': 1428.452, 'text': "But if I wanna calculate how much each age group is contributing towards the GDP growth of a country, then at that time I'll use a histogram.", 'start': 1419.79, 'duration': 8.662}, {'end': 1435.094, 'text': 'So over there we had a category in bar plot, like cities, but here we have a quantitative variable that is age group.', 'start': 1428.812, 'duration': 6.282}, {'end': 1437.154, 'text': 'So I hope this answers your question.', 'start': 1435.794, 'duration': 1.36}, {'end': 1440.235, 'text': 'All right, he says yes, fine.', 'start': 1438.955, 'duration': 1.28}, {'end': 1444.069, 'text': 'All right, so let us move forward and focus on the code that you have in front of your screen.', 'start': 1440.788, 'duration': 3.281}], 'summary': 'Using histogram to analyze age group contribution to gdp growth.', 'duration': 24.279, 'max_score': 1419.79, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww1419790.jpg'}, {'end': 1536.217, 'src': 'heatmap', 'start': 1505.76, 'weight': 0.869, 'content': [{'end': 1510.103, 'text': "Similarly, I've defined x label, y label and title like the previous examples.", 'start': 1505.76, 'duration': 4.343}, {'end': 1512.404, 'text': 'Then comes legend and then finally show the plot.', 'start': 1510.303, 'duration': 2.101}, {'end': 1516.107, 'text': 'So let us go ahead and execute this and see if it works or not.', 'start': 1512.805, 'duration': 3.302}, {'end': 1517.648, 'text': 'Yep, it does.', 'start': 1517.047, 'duration': 0.601}, {'end': 1520.91, 'text': "So we'll move forward and understand scatter plot as well.", 'start': 1518.348, 'duration': 2.562}, {'end': 1527.831, 'text': 'Now before we understand how to plot a scatter graph, we need to understand why we actually use scatter plots.', 'start': 1521.567, 'duration': 6.264}, {'end': 1536.217, 'text': "Usually we use scatter plots in order to compare two variables, or three if you're plotting in three dimensions, looking for a correlation or groups.", 'start': 1528.532, 'duration': 7.685}], 'summary': 'The importance and purpose of scatter plots for comparing variables and finding correlations.', 'duration': 30.457, 'max_score': 1505.76, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww1505760.jpg'}], 'start': 1031.027, 'title': 'Customizing and using matplotlib for graphs', 'summary': 'Covers customizing and adding style to graphs using matplotlib, including changing line width, adding grid lines, labels, titles, and legend, and the usage and purpose of bar graphs. it also explains using matplotlib for creating bar graphs, including code and practical execution, and discusses the difference between histograms and bar plots, with a focus on quantitative and categorical variables.', 'chapters': [{'end': 1249.634, 'start': 1031.027, 'title': 'Customizing and adding style to graphs', 'summary': 'Covers customizing and adding style to graphs using matplotlib, including changing line width, adding grid lines, labels, titles, and legend, and the usage and purpose of bar graphs.', 'duration': 218.607, 'highlights': ['The chapter covers customizing and adding style to graphs using matplotlib, including changing line width, adding grid lines, labels, titles, and legend The transcript provides step-by-step instructions for customizing and adding style to graphs using matplotlib, including changing line width, adding grid lines, labels, titles, and legend.', 'The usage and purpose of bar graphs The transcript explains the usage of bar graphs for comparing things between different groups and for measuring changes over time when changes are larger.']}, {'end': 1555.704, 'start': 1250.915, 'title': 'Understanding matplotlib for bar graphs', 'summary': 'Explains using matplotlib for creating bar graphs, including code and practical execution, and also discusses the difference between histograms and bar plots, with a focus on quantitative and categorical variables.', 'duration': 304.789, 'highlights': ['Explaining the code for creating bar graphs using Matplotlib, including the use of plt.bar, data, labels, colors, legend, X and Y labels, title, and practical execution in PyCharm. The chapter provides a detailed explanation of the code for creating bar graphs using Matplotlib, covering the use of plt.bar, data, labels, colors, legend, X and Y labels, title, and practical execution in PyCharm.', 'Clarifying the difference between histograms and bar plots, and providing examples to illustrate the use of bar plots for categorical variables and histograms for quantitative variables. The chapter clarifies the distinction between histograms and bar plots, using examples to demonstrate the use of bar plots for categorical variables and histograms for quantitative variables.', 'Discussing the code for creating histograms using Matplotlib, including the use of plt.hist, data, bins, hist type, width, X and Y labels, title, and practical execution in PyCharm. The chapter explains the code for creating histograms using Matplotlib, covering the use of plt.hist, data, bins, hist type, width, X and Y labels, title, and practical execution in PyCharm.', 'Introducing the use of scatter plots in understanding correlations between variables, and explaining the purpose of scatter plots in comparing and finding relationships between two or three variables. The chapter introduces the use of scatter plots for understanding correlations between variables, emphasizing their purpose in comparing and finding relationships between two or three variables.']}], 'duration': 524.677, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww1031027.jpg', 'highlights': ['The chapter covers customizing and adding style to graphs using matplotlib, including changing line width, adding grid lines, labels, titles, and legend.', 'The usage and purpose of bar graphs for comparing things between different groups and for measuring changes over time when changes are larger.', 'Explaining the code for creating bar graphs using Matplotlib, including the use of plt.bar, data, labels, colors, legend, X and Y labels, title, and practical execution in PyCharm.', 'Clarifying the difference between histograms and bar plots, and providing examples to illustrate the use of bar plots for categorical variables and histograms for quantitative variables.', 'Introducing the use of scatter plots in understanding correlations between variables, and explaining the purpose of scatter plots in comparing and finding relationships between two or three variables.']}, {'end': 2088.739, 'segs': [{'end': 1700.419, 'src': 'heatmap', 'start': 1612.028, 'weight': 0.912, 'content': [{'end': 1618.572, 'text': 'So here we have our legend then we have the title then we have our y-axis then x-axis.', 'start': 1612.028, 'duration': 6.544}, {'end': 1619.993, 'text': 'These two are the labels for that.', 'start': 1618.612, 'duration': 1.381}, {'end': 1623.914, 'text': 'So let me close it and see what is the other plot that we are going to focus on.', 'start': 1620.533, 'duration': 3.381}, {'end': 1629.335, 'text': "Now once scatter plot is done, we'll have a look at the area plot or you can even call it as a stack plot.", 'start': 1624.754, 'duration': 4.581}, {'end': 1633.356, 'text': 'So basically these area graphs are very similar to the line graphs, okay.', 'start': 1630.055, 'duration': 3.301}, {'end': 1637.137, 'text': 'They can be used to track changes over time for one or more groups.', 'start': 1633.896, 'duration': 3.241}, {'end': 1645.518, 'text': 'Area graphs are good to use when you are tracking the changes in two or more related groups that make up one whole category.', 'start': 1637.637, 'duration': 7.881}, {'end': 1648.959, 'text': 'You can take an example of say public and private groups, okay.', 'start': 1645.778, 'duration': 3.181}, {'end': 1652.209, 'text': 'So before I explain you the code, let me tell you one thing guys.', 'start': 1649.707, 'duration': 2.502}, {'end': 1655.873, 'text': 'The problem here is with polygons, we cannot actually have labels for our data.', 'start': 1652.49, 'duration': 3.383}, {'end': 1662.7, 'text': 'In order to solve that problem, all we did here was plot some empty lines, giving them the same color and the correct labels,', 'start': 1656.193, 'duration': 6.507}, {'end': 1664.281, 'text': 'in accordance with our stack plot.', 'start': 1662.7, 'duration': 1.581}, {'end': 1669.526, 'text': 'We also gave them a line width of five, as you can notice here, to make the lines a bit thicker in the legend.', 'start': 1664.601, 'duration': 4.925}, {'end': 1674.011, 'text': 'Now we can easily see that we are spending our day sleeping, eating, working, and playing.', 'start': 1670.087, 'duration': 3.924}, {'end': 1676.123, 'text': 'So this is how the code works.', 'start': 1674.721, 'duration': 1.402}, {'end': 1678.427, 'text': 'Now let me execute that practically in my PyCharm.', 'start': 1676.203, 'duration': 2.224}, {'end': 1681.231, 'text': "So I've copied the code already just to save time again.", 'start': 1678.927, 'duration': 2.304}, {'end': 1688.614, 'text': "Now this is our code over here, I've already explained to you why are we defining these empty lines, just to add labels.", 'start': 1682.892, 'duration': 5.722}, {'end': 1692.696, 'text': 'Now let us go ahead and execute this and see what happens.', 'start': 1689.395, 'duration': 3.301}, {'end': 1700.419, 'text': 'So this is our graph guys and again we have legends, title to our graph and Y label as well as X label.', 'start': 1693.336, 'duration': 7.083}], 'summary': 'Area and scatter plots used to track changes over time for multiple groups, with an example of public and private groups, and the use of empty lines to add labels to the plot.', 'duration': 88.391, 'max_score': 1612.028, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww1612028.jpg'}, {'end': 1831.012, 'src': 'heatmap', 'start': 1801.652, 'weight': 0.766, 'content': [{'end': 1803.493, 'text': 'Now finally we do an auto part.', 'start': 1801.652, 'duration': 1.841}, {'end': 1804.914, 'text': 'Now this auto part.', 'start': 1803.834, 'duration': 1.08}, {'end': 1811.138, 'text': 'overlay the percentages on the graph itself so you can see that we have 8.3, 29.2 percent, 54.2 percent,', 'start': 1804.914, 'duration': 6.224}, {'end': 1815.08, 'text': 'all those things written on the graph itself and finally add a title and then show it.', 'start': 1811.138, 'duration': 3.942}, {'end': 1817.041, 'text': 'Now let me execute this practically guys.', 'start': 1815.36, 'duration': 1.681}, {'end': 1821.446, 'text': "Now just to save time, I've already written the code.", 'start': 1819.505, 'duration': 1.941}, {'end': 1825.889, 'text': "So I'm just gonna go ahead and execute this and we'll see if it comes or not.", 'start': 1822.027, 'duration': 3.862}, {'end': 1827.87, 'text': 'Yep, here is our pie chart.', 'start': 1826.129, 'duration': 1.741}, {'end': 1831.012, 'text': 'So we have seen multiple types of plots.', 'start': 1829.431, 'duration': 1.581}], 'summary': 'Created a pie chart with 8.3%, 29.2%, and 54.2% overlays and executed the code successfully.', 'duration': 29.36, 'max_score': 1801.652, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww1801652.jpg'}, {'end': 1834.795, 'src': 'embed', 'start': 1803.834, 'weight': 3, 'content': [{'end': 1804.914, 'text': 'Now this auto part.', 'start': 1803.834, 'duration': 1.08}, {'end': 1811.138, 'text': 'overlay the percentages on the graph itself so you can see that we have 8.3, 29.2 percent, 54.2 percent,', 'start': 1804.914, 'duration': 6.224}, {'end': 1815.08, 'text': 'all those things written on the graph itself and finally add a title and then show it.', 'start': 1811.138, 'duration': 3.942}, {'end': 1817.041, 'text': 'Now let me execute this practically guys.', 'start': 1815.36, 'duration': 1.681}, {'end': 1821.446, 'text': "Now just to save time, I've already written the code.", 'start': 1819.505, 'duration': 1.941}, {'end': 1825.889, 'text': "So I'm just gonna go ahead and execute this and we'll see if it comes or not.", 'start': 1822.027, 'duration': 3.862}, {'end': 1827.87, 'text': 'Yep, here is our pie chart.', 'start': 1826.129, 'duration': 1.741}, {'end': 1831.012, 'text': 'So we have seen multiple types of plots.', 'start': 1829.431, 'duration': 1.581}, {'end': 1834.795, 'text': 'We have seen pie plot, bar plot, histograms, area plot.', 'start': 1831.032, 'duration': 3.763}], 'summary': 'An auto part presentation included a pie chart with 8.3%, 29.2%, and 54.2% displayed on it, along with other types of plots shown.', 'duration': 30.961, 'max_score': 1803.834, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww1803834.jpg'}, {'end': 1954.459, 'src': 'heatmap', 'start': 1886.168, 'weight': 1, 'content': [{'end': 1889.809, 'text': 'Now after that, the code is pretty much similar to the previous examples that we have seen.', 'start': 1886.168, 'duration': 3.641}, {'end': 1892.251, 'text': 'but there is one new concept that is called subplot.', 'start': 1890.129, 'duration': 2.122}, {'end': 1894.333, 'text': 'Now let me tell you what this subplot does.', 'start': 1892.812, 'duration': 1.521}, {'end': 1901.42, 'text': 'It helps us to plot multiple plots, all right? So when we write subplot two, one, one, this means that we have two plots.', 'start': 1894.794, 'duration': 6.626}, {'end': 1905.064, 'text': 'Horizontally we have only one plot present, and vertically we have two plots.', 'start': 1901.861, 'duration': 3.203}, {'end': 1908.647, 'text': 'And in that vertical position, this plot will be our first graph.', 'start': 1905.464, 'duration': 3.183}, {'end': 1912.391, 'text': 'Now you can notice that we have one more subplot that is two, one, two.', 'start': 1908.988, 'duration': 3.403}, {'end': 1917.955, 'text': 'So vertically we have two plots, horizontally we have one and this is the second plot.', 'start': 1912.891, 'duration': 5.064}, {'end': 1923.779, 'text': "So what I'll do, I'll open my PyCharm and I'll play around with it a bit so that you'll be able to understand it better.", 'start': 1918.435, 'duration': 5.344}, {'end': 1925.4, 'text': 'So now this is the code guys.', 'start': 1924.3, 'duration': 1.1}, {'end': 1928.302, 'text': "First I'll go ahead and execute this and we'll see the result.", 'start': 1925.46, 'duration': 2.842}, {'end': 1932.966, 'text': 'So we have a graph something which is like this, right? We have two plots which are aligned vertically.', 'start': 1928.703, 'duration': 4.263}, {'end': 1941.311, 'text': "Now what I'm gonna do is I'm gonna just minimize this and I'm gonna change the subplot value so that you'll be able to understand better how we are dealing with multiple plots.", 'start': 1933.567, 'duration': 7.744}, {'end': 1946.634, 'text': 'Now over here if I make this as two and this one as well as two.', 'start': 1941.852, 'duration': 4.782}, {'end': 1948.375, 'text': 'So let us see what we get.', 'start': 1947.235, 'duration': 1.14}, {'end': 1954.459, 'text': 'So we have got the same graph but it is aligned horizontally if you can notice.', 'start': 1950.417, 'duration': 4.042}], 'summary': 'The subplot function helps to plot multiple graphs, with an example of 2 horizontally and 1 vertically.', 'duration': 68.291, 'max_score': 1886.168, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww1886168.jpg'}, {'end': 2019.916, 'src': 'embed', 'start': 1993.244, 'weight': 0, 'content': [{'end': 1996.848, 'text': 'Now similarly when we have two, two, two here, this means that we have two graphs.', 'start': 1993.244, 'duration': 3.604}, {'end': 2000.472, 'text': 'Horizontally we have two graphs available and this is the second graph.', 'start': 1996.948, 'duration': 3.524}, {'end': 2001.552, 'text': 'that is present.', 'start': 2000.912, 'duration': 0.64}, {'end': 2007.854, 'text': 'So, similarly, if I make a change here and I make this as 2, 1, 1 and this as 2, 1, 2,', 'start': 2001.852, 'duration': 6.002}, {'end': 2013.655, 'text': 'this means that horizontally we have only one graph and these both graphs are aligned vertically.', 'start': 2007.854, 'duration': 5.801}, {'end': 2017.396, 'text': 'So we have this was the first graph and this is our second graph.', 'start': 2014.195, 'duration': 3.201}, {'end': 2018.236, 'text': 'This is how it works.', 'start': 2017.436, 'duration': 0.8}, {'end': 2019.916, 'text': 'So this is what basically subplot is.', 'start': 2018.476, 'duration': 1.44}], 'summary': 'The explanation illustrates the concept of subplots using graphs, with a specific example of two horizontally aligned graphs and two vertically aligned graphs.', 'duration': 26.672, 'max_score': 1993.244, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww1993244.jpg'}, {'end': 2088.739, 'src': 'embed', 'start': 2074.742, 'weight': 4, 'content': [{'end': 2076.043, 'text': 'Thank you and have a great day.', 'start': 2074.742, 'duration': 1.301}, {'end': 2078.666, 'text': 'I hope you enjoyed listening to this video.', 'start': 2077.003, 'duration': 1.663}, {'end': 2083.933, 'text': 'Please be kind enough to like it and you can comment any of your doubts and queries and we will reply to them at the earliest.', 'start': 2079.025, 'duration': 4.908}, {'end': 2087.799, 'text': 'Do look out for more videos in our playlist and subscribe to our Edureka channel to learn more.', 'start': 2084.414, 'duration': 3.385}, {'end': 2088.739, 'text': 'Happy learning.', 'start': 2088.279, 'duration': 0.46}], 'summary': 'Encourage engagement with video, invite likes, comments, and subscriptions for more content.', 'duration': 13.997, 'max_score': 2074.742, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww2074742.jpg'}], 'start': 1555.724, 'title': 'Data visualization in python', 'summary': 'Covers correlation and scatter plot implementation in python using the matplotlib module, along with practical execution of scatter plot, area plot, and pie plot using matplotlib, addressing the need for data visualization, the types of plots, and working with multiple plots using subplot, with emphasis on key points such as plot explanation and subplot usage.', 'chapters': [{'end': 1590.576, 'start': 1555.724, 'title': 'Correlation and scatter plot in python', 'summary': 'Explains the concept of correlation and demonstrates the implementation of a scatter plot in python using the matplotlib module, with an emphasis on the relationship between two variables and the use of the scatter function for visualization.', 'duration': 34.852, 'highlights': ['The chapter covers the concept of correlation and demonstrates the implementation of a scatter plot in Python using the matplotlib module (relevance: 5)', 'The use of the scatter function for visualization is emphasized, showcasing its utility for representing the relationship between two variables (relevance: 4)', 'The code involves importing the piplot function from the matplotlib module and defining two variables, x and y, containing specific sets of values (relevance: 3)']}, {'end': 2088.739, 'start': 1590.876, 'title': 'Matplotlib data visualization', 'summary': 'Covers the practical execution of scatter plot, area plot, and pie plot using matplotlib, addressing the need for data visualization, the types of plots, and working with multiple plots using subplot in matplotlib, with key points including the explanation of each plot and the usage of subplot for multiple plots.', 'duration': 497.863, 'highlights': ['Addressing the need for data visualization and understanding various types of plots including bar graph, histogram, scatter plot, and pie plot Various types of plots explained: bar graph, histogram, scatter plot, pie plot', 'Practical execution and explanation of scatter plot, area plot, and pie plot using Matplotlib, with detailed code explanation and visualization Practical execution and explanation of scatter plot, area plot, and pie plot using Matplotlib', 'Working with multiple plots using subplot in Matplotlib, with detailed explanation of the subplot function and its practical application Working with multiple plots using subplot in Matplotlib']}], 'duration': 533.015, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yZTBMMdPOww/pics/yZTBMMdPOww1555724.jpg', 'highlights': ['Covers correlation and scatter plot implementation in Python using matplotlib (relevance: 5)', 'Practical execution and explanation of scatter plot, area plot, and pie plot using Matplotlib (relevance: 4)', 'Addressing the need for data visualization and understanding various types of plots (relevance: 3)', 'Working with multiple plots using subplot in Matplotlib (relevance: 2)', 'The use of the scatter function for visualization is emphasized (relevance: 1)']}], 'highlights': ['Graphical representation allows for quick interpretation of data trends, facilitating efficient experimentation and identification of useful variables.', 'The process of visualizing, analyzing, and documenting insights from data involves visualizing the data using graphs, analyzing the data to identify trends or changes, and documenting the insights gained from the analysis.', 'The purpose of data visualization is to present data in a pictorial or graphical format, facilitating easier comprehension for decision makers.', 'The usage of Matplotlib for data visualization is emphasized, along with the mention of various types of plots that can be created using Matplotlib, including bar graphs, histograms, scatter plots, pie plots, hexagonal bin plots, and area plots.', 'The chapter explains how to plot variables and add descriptive labels to a graph using plt.plot function. It provides a step-by-step guide on plotting variables and adding labels to a graph, enhancing data visualization.', 'The process of transforming the dataset is explained, involving the removal of unnecessary fields and the addition of required fields to refine the dataset for analysis.', 'The chapter covers customizing and adding style to graphs using matplotlib, including changing line width, adding grid lines, labels, titles, and legend.', 'Explaining the code for creating bar graphs using Matplotlib, including the use of plt.bar, data, labels, colors, legend, X and Y labels, title, and practical execution in PyCharm.', 'Clarifying the difference between histograms and bar plots, and providing examples to illustrate the use of bar plots for categorical variables and histograms for quantitative variables.', 'Introducing the use of scatter plots in understanding correlations between variables, and explaining the purpose of scatter plots in comparing and finding relationships between two or three variables.', 'Covers correlation and scatter plot implementation in Python using matplotlib (relevance: 5)', 'Practical execution and explanation of scatter plot, area plot, and pie plot using Matplotlib (relevance: 4)', 'Addressing the need for data visualization and understanding various types of plots (relevance: 3)', 'Working with multiple plots using subplot in Matplotlib (relevance: 2)', 'The use of the scatter function for visualization is emphasized (relevance: 1)']}