title
Data Analysis with Python for Excel Users - Full Course
description
Learn how to use Python and Pandas for data analysis. This course will be especially helpful if you have experience with Excel, but that is not required. Learn how to create pivot tables, work with data, and make visualizations using Python, Pandas, and Jupyter Notebook.
💻 Source Code & Datasets: https://github.com/ifrankandrade/python-course-for-excel-users
🔗 Datasets: https://drive.google.com/drive/folders/12hFh6RPlX5bWzSqpoMvAeT94RCjCvHw5?usp=sharing
✏️ Course developed by Frank Andrade.
🔗 YouTube Channel: https://www.youtube.com/c/FrankAndrade5
🔗 PDF Python Cheat Sheet for this Course: https://frankandrade.ck.page/d3b1761715
🔗 My Complete Python Courses for Data Analysis & Data Science: https://www.udemy.com/user/frank-andrade-13/
⭐️ Course Contents ⭐️
⌨️ (0:00:00) Intro
⌨️ (0:01:48) Install Python and Jupyter Notebook with Anaconda
⌨️ (0:03:53) Jupyter Notebook Interface
⌨️ (0:13:56) Cell Types and Cell Mode
⌨️ (0:21:34) Jupyter Notebook Shortcuts
⌨️ (0:26:39) Module 1 - Hello World
⌨️ (0:30:30) Data Type
⌨️ (0:39:10) Variables
⌨️ (0:46:53) Lists
⌨️ (1:11:14) Dictionaries
⌨️ (1:21:50) IF Statement
⌨️ (1:28:04) FOR Loop
⌨️ (1:33:49) Functions
⌨️ (1:40:59) Modules
⌨️ (1:44:41) Module 2 -Introduction to Pandas
⌨️ (1:51:08) How to create a dataframe
⌨️ (2:07:34) How to show a dataframe
⌨️ (2:14:24) Basic Attributes, Functions and Methods
⌨️ (2:26:17) Selecting One Column from a Dataframe
⌨️ (2:32:13) Selecting Two or More Columns from a Dataframe
⌨️ (2:37:50) Add New Column to a Dataframe (Simple Assignment)
⌨️ (2:47:51) Operations in dataframes
⌨️ (2:56:04) The value_counts() method
⌨️ (3:00:16) Sort a DataFrame with the sort_values() method
⌨️ (3:09:56) Module 3: Introduction to Pivot Tables in Pandas
⌨️ (3:14:42) The pivot() method
⌨️ (3:20:49) The pivot_table() method
⌨️ (3:29:00) Data Visualization with Pandas (New Dataset + Pivot Table)
⌨️ (3:38:38) Lineplot
⌨️ (3:43:05) Barplot
⌨️ (3:50:52) Piechart
⌨️ (3:54:36) Save Plot and Export Pivot Table
🎉 Thanks to our Champion and Sponsor supporters:
👾 Raymond Odero
👾 Agustín Kussrow
👾 aldo ferretti
👾 Otis Morgan
👾 DeezMaster
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detail
{'title': 'Data Analysis with Python for Excel Users - Full Course', 'heatmap': [{'end': 14259.607, 'start': 14123.166, 'weight': 1}], 'summary': 'A python course for excel users covers topics like jupyter notebook interface, python data types, variables, data structures, indentation, programming fundamentals, pandas, data frame operations, sorting, pivot methods, gdp data analysis, creating pivot tables, and visualizing population data with pandas, offering practical applications such as creating pivot tables and visualizations.', 'chapters': [{'end': 97.813, 'segs': [{'end': 70.74, 'src': 'embed', 'start': 0.109, 'weight': 0, 'content': [{'end': 10.725, 'text': 'If you already work with data in Excel, and want to add more power to your data analysis and evaluation using Python, then this is the course for you.', 'start': 0.109, 'duration': 10.616}, {'end': 15.091, 'text': 'Frank is a data scientist and he will teach you how to use Python to work with data.', 'start': 11.045, 'duration': 4.046}, {'end': 16.312, 'text': 'Hi, everyone.', 'start': 15.592, 'duration': 0.72}, {'end': 21.034, 'text': 'My name is Frank Andrade, and this is my Python course for Excel users.', 'start': 16.672, 'duration': 4.362}, {'end': 26.436, 'text': 'I created this course to help Excel users move from Excel to Python.', 'start': 21.654, 'duration': 4.782}, {'end': 37.68, 'text': 'But why Python? Well, in Python, we can do most of the things we will do in Excel, such as working with data, making charts and pivot tables.', 'start': 26.896, 'duration': 10.784}, {'end': 38.761, 'text': "But that's not all.", 'start': 37.861, 'duration': 0.9}, {'end': 46.446, 'text': 'We can use all the power of Python to automate tasks, work with large data and do a lot of things.', 'start': 39.201, 'duration': 7.245}, {'end': 49.688, 'text': 'thanks to the thousands of free libraries, Python has.', 'start': 46.446, 'duration': 3.242}, {'end': 57.333, 'text': 'On top of that, Python can help you become a better data analyst or get into new fields like data science.', 'start': 49.948, 'duration': 7.385}, {'end': 61.435, 'text': 'I divided this Python course for Excel users in three modules.', 'start': 57.653, 'duration': 3.782}, {'end': 67.839, 'text': "In module one, I'll teach you all the Python core concepts you need to know for data analysis.", 'start': 61.976, 'duration': 5.863}, {'end': 70.74, 'text': "Then in module two, we'll learn pandas.", 'start': 68.159, 'duration': 2.581}], 'summary': 'Python course for excel users by frank andrade covers python core concepts and pandas in three modules.', 'duration': 70.631, 'max_score': 0.109, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo109.jpg'}], 'start': 0.109, 'title': 'Python for excel users', 'summary': 'Introduces a python course for excel users, emphasizing the advantages of python over excel, and covering three modules of the course along with practical applications such as creating pivot tables and visualizations.', 'chapters': [{'end': 97.813, 'start': 0.109, 'title': 'Python for excel users', 'summary': 'Introduces a python course for excel users, highlighting the advantages of python over excel, the three modules of the course, and the practical applications including creating pivot tables and visualizations.', 'duration': 97.704, 'highlights': ['Frank Andrade, a data scientist, introduces a Python course for Excel users, emphasizing the transition from Excel to Python and the benefits of using Python for data analysis and automation.', 'Python offers the capability to work with data, create charts, pivot tables, automate tasks, and handle large data, leveraging the thousands of free libraries available.', 'The course is structured into three modules: Python core concepts for data analysis, pandas for data analysis, and practical application of creating pivot tables and visualizations like line plots, bar plots, and pie charts.', 'The course provides additional resources including files, code, and a free PDF Python cheat sheet for reference.']}], 'duration': 97.704, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo109.jpg', 'highlights': ['Python offers the capability to work with data, create charts, pivot tables, automate tasks, and handle large data, leveraging the thousands of free libraries available.', 'The course is structured into three modules: Python core concepts for data analysis, pandas for data analysis, and practical application of creating pivot tables and visualizations like line plots, bar plots, and pie charts.', 'Frank Andrade, a data scientist, introduces a Python course for Excel users, emphasizing the transition from Excel to Python and the benefits of using Python for data analysis and automation.', 'The course provides additional resources including files, code, and a free PDF Python cheat sheet for reference.']}, {'end': 1828.001, 'segs': [{'end': 124.031, 'src': 'embed', 'start': 98.333, 'weight': 1, 'content': [{'end': 103.038, 'text': 'There you will find the concepts, methods, and functions we will see in this course.', 'start': 98.333, 'duration': 4.705}, {'end': 107.442, 'text': "By the way, I'm Frank and I will be your instructor in this course.", 'start': 103.518, 'duration': 3.924}, {'end': 109.084, 'text': "So let's get started.", 'start': 107.723, 'duration': 1.361}, {'end': 116.668, 'text': 'To download anaconda, we go to anaconda.com and click on get started.', 'start': 111.226, 'duration': 5.442}, {'end': 124.031, 'text': 'then we choose the last option download anaconda installers and then we have here the different anaconda installers.', 'start': 116.668, 'duration': 7.363}], 'summary': 'Course instructor frank introduces anaconda installation process at anaconda.com.', 'duration': 25.698, 'max_score': 98.333, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo98333.jpg'}, {'end': 266.714, 'src': 'embed', 'start': 236.443, 'weight': 0, 'content': [{'end': 240.784, 'text': 'In this video, I will introduce you to the Jupyter notebook interface.', 'start': 236.443, 'duration': 4.341}, {'end': 249.947, 'text': 'Jupyter notebook is an open source web application that allows us to create and share documents that contain live code, equations,', 'start': 240.944, 'duration': 9.003}, {'end': 252.188, 'text': 'visualizations and text.', 'start': 249.947, 'duration': 2.241}, {'end': 260.75, 'text': 'This is a perfect text editor for doing data cleaning and transformation data visualization, and data analysis.', 'start': 252.548, 'duration': 8.202}, {'end': 266.714, 'text': 'This is why Jupyter Notebook is widely used in data science and also machine learning.', 'start': 260.93, 'duration': 5.784}], 'summary': 'Introducing jupyter notebook for data science and machine learning.', 'duration': 30.271, 'max_score': 236.443, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo236443.jpg'}, {'end': 402.406, 'src': 'embed', 'start': 371.403, 'weight': 2, 'content': [{'end': 375.806, 'text': "So I click here, and I can change it to, let's say example.", 'start': 371.403, 'duration': 4.403}, {'end': 378.568, 'text': 'So I write example, and I click on rename.', 'start': 375.986, 'duration': 2.582}, {'end': 382.811, 'text': 'And now we rename this Jupyter notebook file.', 'start': 378.968, 'duration': 3.843}, {'end': 388.895, 'text': "Alright, now let's navigate through this menu bar that we have here in this Jupyter notebook file.", 'start': 383.131, 'duration': 5.764}, {'end': 391.336, 'text': 'So the first option is the file.', 'start': 389.295, 'duration': 2.041}, {'end': 395.92, 'text': 'In here, we can create a new notebook with Python three.', 'start': 391.776, 'duration': 4.144}, {'end': 402.406, 'text': "So if we click here, we're going to open a new Jupyter notebook file from scratch, as we did before.", 'start': 396.26, 'duration': 6.146}], 'summary': 'Demonstration of renaming and creating new jupyter notebook files.', 'duration': 31.003, 'max_score': 371.403, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo371403.jpg'}, {'end': 858.941, 'src': 'embed', 'start': 832.44, 'weight': 3, 'content': [{'end': 836.506, 'text': 'These are all the things you need to know about the Jupyter notebook interface.', 'start': 832.44, 'duration': 4.066}, {'end': 844.265, 'text': "Okay, in this video, we're going to see some cell types and cell modes in Jupyter Notebook.", 'start': 838.957, 'duration': 5.308}, {'end': 853.298, 'text': "So first, we're going to open the Jupyter Notebook file that we created in the previous video, which is this one example that IP Y and B.", 'start': 844.826, 'duration': 8.472}, {'end': 855.22, 'text': 'So we click on it.', 'start': 854.099, 'duration': 1.121}, {'end': 858.941, 'text': 'And here we have the Jupyter notebook file opened.', 'start': 855.62, 'duration': 3.321}], 'summary': 'Introduction to jupyter notebook interface and cell types.', 'duration': 26.501, 'max_score': 832.44, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo832440.jpg'}], 'start': 98.333, 'title': 'Jupyter notebook interface', 'summary': 'Covers the process of downloading and installing anaconda, choosing the appropriate installer, and launching anaconda for mac and windows platforms, and introduces the jupyter notebook interface, emphasizing its use in data science and machine learning, with detailed walkthroughs and functionalities including cell types, modes, essential shortcuts, and basics.', 'chapters': [{'end': 206.616, 'start': 98.333, 'title': 'Installing anaconda and setting up', 'summary': 'Covers the process of downloading and installing anaconda, including choosing the appropriate installer, navigating through the installation steps, and launching anaconda, with a focus on mac and windows platforms.', 'duration': 108.283, 'highlights': ['The chapter emphasizes the process of downloading and installing Anaconda, with instructions provided for choosing the appropriate installer for different platforms and navigating through the installation steps.', "The instructor, Frank, guides through the installation process, including clicking on 'allow' and 'continue' prompts, and checking the necessary boxes for the installation to proceed smoothly.", 'Emphasis is placed on the final steps of the installation, such as verifying the installation summary and launching Anaconda by locating the green icon and waiting for it to open.']}, {'end': 544.582, 'start': 207.116, 'title': 'Introduction to jupyter notebook', 'summary': 'Introduces the jupyter notebook interface, emphasizing its use in data science and machine learning, and provides a detailed walkthrough of launching jupyter notebook, creating a python script, and navigating through the menu bar with key shortcuts and options.', 'duration': 337.466, 'highlights': ['The Jupyter notebook interface is widely used in data science and machine learning for data cleaning, transformation, visualization, and analysis.', 'Python 3 and popular libraries for data analysis are installed with Jupyter Notebook through Anaconda Navigator.', 'The process of creating a Python script, renaming the Jupyter notebook file, and navigating through the menu bar with options like creating a new notebook, opening, saving, and reverting to checkpoints is explained in detail.']}, {'end': 832.36, 'start': 544.782, 'title': 'Jupyter notebook interface', 'summary': 'Covers the various options and functionalities of jupyter notebook interface, including insert options, cell options, kernel options, navigate options, and toolbar shortcuts, along with a demonstration of running and shutting down notebook processes.', 'duration': 287.578, 'highlights': ['A kernel is a computational engine that executes the code contained in a notebook document.', 'The toolbar provides various frequently used buttons for tasks such as saving, inserting cells, cutting, copying, pasting, and moving cells.', 'The files tab allows users to recognize running Jupyter notebook processes, indicated by the green icon, and provides the option to shut down the file.', 'The NB extensions tab allows users to install extensions to personalize Jupyter notebook and will be covered in the next videos.']}, {'end': 1455.874, 'start': 832.44, 'title': 'Jupyter notebook interface', 'summary': 'Covers cell types and modes in jupyter notebook, including command mode for applying tools and shortcuts, edit mode for cell-specific actions, and three main cell types: code, markdown, and raw nb convert.', 'duration': 623.434, 'highlights': ['Jupyter Notebook has two main cell modes: command mode and edit mode, allowing users to perform actions outside cell scope and within the context of the cell, respectively.', 'The three main cell types in Jupyter Notebook are code, markdown, and raw NB convert, each serving distinct purposes and allowing users to input code, text, or unprocessed content.', 'Common shortcuts in Jupyter Notebook, such as finding and replacing text in a cell, converting cell types, and adjusting heading sizes, streamline user experience and enhance efficiency.']}, {'end': 1828.001, 'start': 1456.174, 'title': 'Jupyter notebook shortcuts and basics', 'summary': 'Explains essential shortcuts and basics of using jupyter notebook, including navigating cells, inserting, cutting, pasting, and deleting cells, undoing changes, saving files, and printing in python, and highlights the advantages of jupyter notebook such as printing the last object without specifying the print function and adding comments.', 'duration': 371.827, 'highlights': ['The chapter explains essential shortcuts and basics of using Jupyter Notebook, including navigating cells, inserting, cutting, pasting, and deleting cells, undoing changes, saving files, and printing in Python.', 'The advantages of Jupyter Notebook such as printing the last object without specifying the print function and adding comments are highlighted.']}], 'duration': 1729.668, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo98333.jpg', 'highlights': ['The Jupyter notebook interface is widely used in data science and machine learning for data cleaning, transformation, visualization, and analysis.', 'The chapter emphasizes the process of downloading and installing Anaconda, with instructions provided for choosing the appropriate installer for different platforms and navigating through the installation steps.', 'The process of creating a Python script, renaming the Jupyter notebook file, and navigating through the menu bar with options like creating a new notebook, opening, saving, and reverting to checkpoints is explained in detail.', 'Jupyter Notebook has two main cell modes: command mode and edit mode, allowing users to perform actions outside cell scope and within the context of the cell, respectively.', 'The three main cell types in Jupyter Notebook are code, markdown, and raw NB convert, each serving distinct purposes and allowing users to input code, text, or unprocessed content.']}, {'end': 3640.649, 'segs': [{'end': 1884.397, 'src': 'embed', 'start': 1828.001, 'weight': 0, 'content': [{'end': 1830.582, 'text': 'that allows us easily read code.', 'start': 1828.001, 'duration': 2.581}, {'end': 1836.068, 'text': "Okay, now let's see some data types in Python.", 'start': 1833.106, 'duration': 2.962}, {'end': 1839.07, 'text': 'Every value in Python is an object.', 'start': 1836.628, 'duration': 2.442}, {'end': 1841.912, 'text': 'An object has different data types.', 'start': 1839.53, 'duration': 2.382}, {'end': 1845.314, 'text': "Let's see the most common data types in Python.", 'start': 1842.432, 'duration': 2.882}, {'end': 1851.498, 'text': 'So, one of the most common data types in Python are integer and floats.', 'start': 1845.854, 'duration': 5.644}, {'end': 1853.019, 'text': 'Both are numbers.', 'start': 1852.018, 'duration': 1.001}, {'end': 1865.226, 'text': 'But integers are numbers that can be written without a fractional component, just like, for example, the number one, number 2345, and so on.', 'start': 1853.459, 'duration': 11.767}, {'end': 1867.547, 'text': 'So all of them are integers.', 'start': 1865.546, 'duration': 2.001}, {'end': 1875.052, 'text': 'And we can check this value or this data type by using the type function.', 'start': 1868.228, 'duration': 6.824}, {'end': 1884.397, 'text': "So this is our second function, we're going to see so we write type, then parentheses, and we execute we run this code.", 'start': 1875.132, 'duration': 9.265}], 'summary': 'Python has common data types like integers and floats, easily checked using the type function.', 'duration': 56.396, 'max_score': 1828.001, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo1828001.jpg'}, {'end': 2027.988, 'src': 'embed', 'start': 1966.994, 'weight': 1, 'content': [{'end': 1979.513, 'text': 'boolean are true or false values, and we can check this using again the type function and we write type and within parentheses we write, for example,', 'start': 1966.994, 'duration': 12.519}, {'end': 1987.839, 'text': 'true, and we run this code and we see that we got that bull, which represent a Boolean data type.', 'start': 1979.513, 'duration': 8.326}, {'end': 1995.503, 'text': 'So we can also write type and in this case, false, and run this code, and we get bull again.', 'start': 1988.239, 'duration': 7.264}, {'end': 1997.244, 'text': 'So this is Boolean.', 'start': 1996.203, 'duration': 1.041}, {'end': 2001.466, 'text': "And we're going to use Boolean often when we use conditionals.", 'start': 1997.424, 'duration': 4.042}, {'end': 2007.71, 'text': "Okay, now the fourth data type I want to show you and it's very common is the string.", 'start': 2001.746, 'duration': 5.964}, {'end': 2011.833, 'text': 'A string represents a series of characters.', 'start': 2008.51, 'duration': 3.323}, {'end': 2019.66, 'text': 'And in Python, anything inside quotes, either single quotes, or double quotes, is a string.', 'start': 2012.514, 'duration': 7.146}, {'end': 2021.402, 'text': "So let's see them.", 'start': 2020.121, 'duration': 1.281}, {'end': 2027.988, 'text': 'Actually, we already see one kind of string here when we printed this Hello world.', 'start': 2021.642, 'duration': 6.346}], 'summary': 'The transcript covers boolean and string data types in python.', 'duration': 60.994, 'max_score': 1966.994, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo1966994.jpg'}, {'end': 2399.773, 'src': 'embed', 'start': 2340.154, 'weight': 4, 'content': [{'end': 2346.879, 'text': 'So I run this code and now it says, well, hello world, but with U.', 'start': 2340.154, 'duration': 6.725}, {'end': 2350.281, 'text': 'And these are some of the most common string methods in Python.', 'start': 2346.879, 'duration': 3.402}, {'end': 2358.035, 'text': "Okay, now it's time to learn something that you're gonna see often in Python, which are variables.", 'start': 2352.751, 'duration': 5.284}, {'end': 2361.638, 'text': 'Variables help us store data values.', 'start': 2358.575, 'duration': 3.063}, {'end': 2369.023, 'text': 'In Python, we often work with data, so variables are useful to manage this data properly.', 'start': 2362.158, 'duration': 6.865}, {'end': 2375.568, 'text': 'A variable contains a value, which is the information associated with a variable.', 'start': 2369.684, 'duration': 5.884}, {'end': 2380.132, 'text': 'To assign a value to a variable, we use the equal sign.', 'start': 2376.129, 'duration': 4.003}, {'end': 2388.04, 'text': "So let's create a message that says I'm learning Python and store it in a variable called message underscore one.", 'start': 2380.712, 'duration': 7.328}, {'end': 2395.268, 'text': 'So here I write message underscore one and we set it to the string.', 'start': 2388.401, 'duration': 6.867}, {'end': 2396.469, 'text': "I'm learning Python.", 'start': 2395.508, 'duration': 0.961}, {'end': 2399.773, 'text': 'So I open double quotes in here.', 'start': 2396.509, 'duration': 3.264}], 'summary': 'Introduction to common python string methods and variables.', 'duration': 59.619, 'max_score': 2340.154, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo2340154.jpg'}, {'end': 2557.826, 'src': 'embed', 'start': 2528.205, 'weight': 6, 'content': [{'end': 2538.953, 'text': "So as you can see here, if I use double quotes, everything is okay, everything remains as a string, but with single quotes, it doesn't happen.", 'start': 2528.205, 'duration': 10.748}, {'end': 2546.958, 'text': "So only that I get this string, but the rest, it doesn't get a string value or the string data type.", 'start': 2539.173, 'duration': 7.785}, {'end': 2553.002, 'text': 'So just make sure you use double quotes every time you have these apostrophes.', 'start': 2547.078, 'duration': 5.924}, {'end': 2553.983, 'text': "And that's it.", 'start': 2553.442, 'duration': 0.541}, {'end': 2557.826, 'text': "Okay, now let's put these two messages together.", 'start': 2554.563, 'duration': 3.263}], 'summary': 'Using double quotes ensures string integrity, while single quotes may cause issues.', 'duration': 29.621, 'max_score': 2528.205, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo2528205.jpg'}, {'end': 2709.001, 'src': 'embed', 'start': 2680.149, 'weight': 7, 'content': [{'end': 2685.251, 'text': 'Okay, now let me show you an alternative way to join two strings.', 'start': 2680.149, 'duration': 5.102}, {'end': 2687.872, 'text': 'So this is called the F string.', 'start': 2685.731, 'duration': 2.141}, {'end': 2692.934, 'text': 'And it works like this, you write F, and you open a string.', 'start': 2688.412, 'duration': 4.522}, {'end': 2694.975, 'text': 'So we write a single quotes here.', 'start': 2692.994, 'duration': 1.981}, {'end': 2697.516, 'text': 'So one and two.', 'start': 2695.175, 'duration': 2.341}, {'end': 2703.758, 'text': 'In here, as you can see, the whole, the whole thing is red.', 'start': 2697.996, 'duration': 5.762}, {'end': 2709.001, 'text': "So it's like everything is a string in here inside, we can write the message.", 'start': 2703.858, 'duration': 5.143}], 'summary': 'Introducing the f string method for joining strings in python.', 'duration': 28.852, 'max_score': 2680.149, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo2680149.jpg'}, {'end': 2857.508, 'src': 'embed', 'start': 2829.793, 'weight': 9, 'content': [{'end': 2832.875, 'text': 'Lists are order and mutable containers.', 'start': 2829.793, 'duration': 3.082}, {'end': 2838.499, 'text': 'In Python, we call mutable to objects that can change their values.', 'start': 2833.516, 'duration': 4.983}, {'end': 2842.943, 'text': 'That is, elements within a list can change their values.', 'start': 2839.24, 'duration': 3.703}, {'end': 2850.406, 'text': 'To create a list, we have to introduce the element inside the square brackets, separated by commas.', 'start': 2843.723, 'duration': 6.683}, {'end': 2852.306, 'text': "So let's create our first list.", 'start': 2850.646, 'duration': 1.66}, {'end': 2855.467, 'text': 'First, we have to set the name of the list.', 'start': 2852.806, 'duration': 2.661}, {'end': 2857.508, 'text': "In this case, I'm going to name it countries.", 'start': 2855.587, 'duration': 1.921}], 'summary': 'Lists in python are mutable containers for storing elements, like countries.', 'duration': 27.715, 'max_score': 2829.793, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo2829793.jpg'}, {'end': 2947.709, 'src': 'embed', 'start': 2920.532, 'weight': 8, 'content': [{'end': 2924.814, 'text': "And to see the content, I'm going to paste the name of this list.", 'start': 2920.532, 'duration': 4.282}, {'end': 2932.718, 'text': 'And now I run here, I include only strings, But keep in mind that lists can have elements of different types.', 'start': 2925.014, 'duration': 7.704}, {'end': 2938.822, 'text': 'So for example, one string, and the other an integer, and then a float, and so on.', 'start': 2933.078, 'duration': 5.744}, {'end': 2942.585, 'text': 'And also lists can have duplicated elements.', 'start': 2939.603, 'duration': 2.982}, {'end': 2947.709, 'text': 'So for example, I can have here, United States, written twice.', 'start': 2942.965, 'duration': 4.744}], 'summary': 'Lists can include different types of elements, such as strings, integers, and floats, and can also have duplicated elements.', 'duration': 27.177, 'max_score': 2920.532, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo2920532.jpg'}, {'end': 3094.542, 'src': 'embed', 'start': 3067.783, 'weight': 10, 'content': [{'end': 3074.568, 'text': 'So here we have each of them, United States, the first one, then India, then China, and then Brazil.', 'start': 3067.783, 'duration': 6.785}, {'end': 3075.929, 'text': "So it's correct.", 'start': 3074.668, 'duration': 1.261}, {'end': 3080.792, 'text': 'So this is the most common way to use indexing, but there is also negative index.', 'start': 3076.209, 'duration': 4.583}, {'end': 3086.336, 'text': 'This help us get elements starting on the last position of the list.', 'start': 3081.753, 'duration': 4.583}, {'end': 3094.542, 'text': 'So instead of using indexes from zero and above, we use indexes from minus one and below.', 'start': 3086.957, 'duration': 7.585}], 'summary': 'The list includes united states, india, china, and brazil, with negative indexing also demonstrated.', 'duration': 26.759, 'max_score': 3067.783, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo3067783.jpg'}, {'end': 3183.31, 'src': 'embed', 'start': 3153.922, 'weight': 11, 'content': [{'end': 3155.543, 'text': "So let's do this one more time.", 'start': 3153.922, 'duration': 1.621}, {'end': 3160.945, 'text': 'And in this case, I wanna get United States, which is minus one, two, three, and four.', 'start': 3156.143, 'duration': 4.802}, {'end': 3164.227, 'text': "So it's countries minus four.", 'start': 3161.045, 'duration': 3.182}, {'end': 3169.309, 'text': 'So we run this and we got United States, but now using a negative index.', 'start': 3164.347, 'duration': 4.962}, {'end': 3172.111, 'text': "Okay, now let's see something called slicing.", 'start': 3169.65, 'duration': 2.461}, {'end': 3175.512, 'text': 'Slicing means accessing parts of a list.', 'start': 3172.711, 'duration': 2.801}, {'end': 3178.934, 'text': 'A slice is a subset of list elements.', 'start': 3176.033, 'duration': 2.901}, {'end': 3183.31, 'text': 'A slice notation takes the form of list.', 'start': 3179.695, 'duration': 3.615}], 'summary': 'Demonstrating list manipulation with negative index and slicing', 'duration': 29.388, 'max_score': 3153.922, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo3153922.jpg'}, {'end': 3459.162, 'src': 'embed', 'start': 3431.083, 'weight': 12, 'content': [{'end': 3437.046, 'text': 'But if we print the countries list, again, we see here a new element.', 'start': 3431.083, 'duration': 5.963}, {'end': 3443.63, 'text': 'So as you can see here, that append method adds a new element at the end of the list.', 'start': 3437.446, 'duration': 6.184}, {'end': 3446.151, 'text': 'So it is by default at the end.', 'start': 3443.97, 'duration': 2.181}, {'end': 3457.18, 'text': 'But what happens if you want to add an element in a different position? So here you can use another method, which is called the insert method.', 'start': 3446.831, 'duration': 10.349}, {'end': 3459.162, 'text': 'So let me show you here.', 'start': 3457.56, 'duration': 1.602}], 'summary': 'Demonstrating how to add elements to a list using append and insert methods.', 'duration': 28.079, 'max_score': 3431.083, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo3431083.jpg'}, {'end': 3523.027, 'src': 'embed', 'start': 3491.264, 'weight': 13, 'content': [{'end': 3496.608, 'text': "So in this case, Let's say we want to add the element Spain.", 'start': 3491.264, 'duration': 5.344}, {'end': 3502.913, 'text': "So this is another country and it's going to be in the first position.", 'start': 3497.108, 'duration': 5.805}, {'end': 3504.054, 'text': 'So index zero.', 'start': 3502.973, 'duration': 1.081}, {'end': 3505.555, 'text': "So let's try.", 'start': 3504.894, 'duration': 0.661}, {'end': 3509.097, 'text': 'I run this one and again, nothing happens.', 'start': 3505.915, 'duration': 3.182}, {'end': 3510.739, 'text': 'Apparently nothing happens.', 'start': 3509.258, 'duration': 1.481}, {'end': 3520.066, 'text': 'And here, if I run this countries list again, we can see that there is a new element and this element is Spain.', 'start': 3511.459, 'duration': 8.607}, {'end': 3523.027, 'text': "and it's located in the first position.", 'start': 3520.686, 'duration': 2.341}], 'summary': "Added 'spain' to the list at index 0.", 'duration': 31.763, 'max_score': 3491.264, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo3491264.jpg'}, {'end': 3640.649, 'src': 'embed', 'start': 3584.152, 'weight': 14, 'content': [{'end': 3591.317, 'text': "so we have three countries in this new list and now I'm gonna run this one.", 'start': 3584.152, 'duration': 7.165}, {'end': 3602.925, 'text': 'and if we want to concatenate this first list countries with this second list countries too, we can use the plus operator.', 'start': 3591.317, 'duration': 11.608}, {'end': 3608.329, 'text': 'so here I write plus and then I run this one.', 'start': 3602.925, 'duration': 5.404}, {'end': 3616.676, 'text': 'And as you can see, I got five elements from the first list and three elements from the second list.', 'start': 3608.389, 'duration': 8.287}, {'end': 3625.924, 'text': 'And another cool thing you can do in Python is putting these two lists inside another list, which is called nested list.', 'start': 3616.976, 'duration': 8.948}, {'end': 3627.985, 'text': "So let's try out.", 'start': 3626.404, 'duration': 1.581}, {'end': 3631.108, 'text': "So here I'm going to create a new list.", 'start': 3628.125, 'duration': 2.983}, {'end': 3635.319, 'text': "and it's gonna be called nested underscore list.", 'start': 3631.769, 'duration': 3.55}, {'end': 3640.649, 'text': "And here, I'm gonna open square brackets to create a new list.", 'start': 3637.208, 'duration': 3.441}], 'summary': 'Concatenated lists have 5 and 3 elements; nested list created in python.', 'duration': 56.497, 'max_score': 3584.152, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo3584152.jpg'}], 'start': 1828.001, 'title': 'Python data types, variables, string methods, concatenation, lists, and manipulation', 'summary': 'Introduces common python data types like integers, floats, booleans, and strings, covers string methods, variables, string concatenation, lists, and their manipulation, with examples and demonstrations of basic operations and concepts.', 'chapters': [{'end': 2339.894, 'start': 1828.001, 'title': 'Python data types', 'summary': 'Introduces the most common data types in python, including integers, floats, booleans, and strings, emphasizing their usage and methods, and demonstrating the execution of basic mathematical operations.', 'duration': 511.893, 'highlights': ['Integers and floats are the most common data types in Python.', 'Demonstration of using type function to identify data types and execute basic mathematical operations.', 'Introduction to boolean data type and its usage in conditionals.', 'Explanation of string data type and its methods including upper, lower, title, count, and replace.']}, {'end': 2557.826, 'start': 2340.154, 'title': 'Python variables and string methods', 'summary': 'Discusses python string methods and the concept of variables, including how to assign values, retrieve data, and handle different types of quotes, with examples of creating and accessing variables, and the importance of using double quotes for string values.', 'duration': 217.672, 'highlights': ['The chapter covers the most common string methods in Python and emphasizes the importance of variables in managing data, with examples of creating and accessing variables. (Relevance Score: 5)', 'The chapter demonstrates how to assign values to variables using the equal sign and retrieve the stored data by simply typing the variable name, illustrated with examples of creating and accessing variables. (Relevance Score: 4)', 'It highlights the significance of using double quotes over single quotes for string values, as using single quotes may result in issues with apostrophes and the string data type, advocating for consistent use of double quotes to ensure correct string representation. (Relevance Score: 3)']}, {'end': 2796.645, 'start': 2558.286, 'title': 'String concatenation in python', 'summary': 'Covers string concatenation in python using the plus operator and f string, demonstrating how to join strings, add blank spaces, and include variables within strings.', 'duration': 238.359, 'highlights': ['F string allows variables inside string, enabling dynamic content, demonstrated by including message_one and message_two with a space.', 'Demonstrates the use of the plus operator to concatenate strings and add a blank space between message_one and message_two.', 'Explains the concept of string concatenation and demonstrates how to join message_one and message_two using the plus operator.']}, {'end': 3342.95, 'start': 2797.086, 'title': 'Python lists and indexing', 'summary': 'Introduces the concept of lists in python, explaining their properties, creation, and manipulation through indexing and slicing, showcasing examples to illustrate the concepts.', 'duration': 545.864, 'highlights': ['Lists are used to store multiple items in a single variable.', 'Lists are order and mutable containers.', 'Indexing and negative indexing are used to access elements within a list.', 'Slicing allows for accessing parts of a list, defining subsets of list elements.']}, {'end': 3640.649, 'start': 3343.49, 'title': 'Python lists manipulation', 'summary': 'Covers the manipulation of python lists, including adding elements using append and insert methods, concatenating lists using the plus operator, and creating nested lists.', 'duration': 297.159, 'highlights': ['The append method adds a new element at the end of the list by default, demonstrated by adding the country Canada to the countries list.', 'The insert method allows specifying the position to add a new element, illustrated by inserting the element Spain at the first position in the countries list.', 'The plus operator concatenates two lists together, as demonstrated by joining the countries list with the countries_two list.', 'Python allows creating nested lists, shown by creating a new list called nested_list and opening square brackets to create a new list inside it.']}], 'duration': 1812.648, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo1828001.jpg', 'highlights': ['Demonstration of using type function to identify data types and execute basic mathematical operations.', 'Introduction to boolean data type and its usage in conditionals.', 'Explanation of string data type and its methods including upper, lower, title, count, and replace.', 'Integers and floats are the most common data types in Python.', 'The chapter covers the most common string methods in Python and emphasizes the importance of variables in managing data, with examples of creating and accessing variables.', 'The chapter demonstrates how to assign values to variables using the equal sign and retrieve the stored data by simply typing the variable name, illustrated with examples of creating and accessing variables.', 'It highlights the significance of using double quotes over single quotes for string values, as using single quotes may result in issues with apostrophes and the string data type, advocating for consistent use of double quotes to ensure correct string representation.', 'F string allows variables inside string, enabling dynamic content, demonstrated by including message_one and message_two with a space.', 'Lists are used to store multiple items in a single variable.', 'Lists are order and mutable containers.', 'Indexing and negative indexing are used to access elements within a list.', 'Slicing allows for accessing parts of a list, defining subsets of list elements.', 'The append method adds a new element at the end of the list by default, demonstrated by adding the country Canada to the countries list.', 'The insert method allows specifying the position to add a new element, illustrated by inserting the element Spain at the first position in the countries list.', 'The plus operator concatenates two lists together, as demonstrated by joining the countries list with the countries_two list.', 'Python allows creating nested lists, shown by creating a new list called nested_list and opening square brackets to create a new list inside it.']}, {'end': 5267.932, 'segs': [{'end': 3726.893, 'src': 'embed', 'start': 3698.437, 'weight': 1, 'content': [{'end': 3702.863, 'text': 'So here I just pasted the countries list we had before.', 'start': 3698.437, 'duration': 4.426}, {'end': 3708.611, 'text': "And what we're going to do is to remove some of the elements of this list.", 'start': 3703.844, 'duration': 4.767}, {'end': 3713.697, 'text': 'So there are different methods that help us remove an element from a list.', 'start': 3709.312, 'duration': 4.385}, {'end': 3716.841, 'text': 'One of them is the remove method.', 'start': 3714.398, 'duration': 2.443}, {'end': 3726.893, 'text': 'So to remove an element using this, we have to first write the name of the list, and then use that that sign and then write remove.', 'start': 3717.121, 'duration': 9.772}], 'summary': 'Exploring methods to remove elements from a list.', 'duration': 28.456, 'max_score': 3698.437, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo3698437.jpg'}, {'end': 3796.984, 'src': 'embed', 'start': 3760.847, 'weight': 2, 'content': [{'end': 3765.211, 'text': 'But you can also remove an element by its index.', 'start': 3760.847, 'duration': 4.364}, {'end': 3769.955, 'text': 'So this is accomplished with the pop method.', 'start': 3765.852, 'duration': 4.103}, {'end': 3773.178, 'text': "So I'm going to copy all of this.", 'start': 3770.095, 'duration': 3.083}, {'end': 3775.601, 'text': "And now I'm going to paste it here.", 'start': 3774.039, 'duration': 1.562}, {'end': 3788.256, 'text': "So instead of writing that remove, I'm going to write that pop in here, I'm not gonna use the name of the element, but its index.", 'start': 3776.142, 'duration': 12.114}, {'end': 3793.403, 'text': "So I write the index in this case, let's remove the last one.", 'start': 3789.017, 'duration': 4.386}, {'end': 3796.984, 'text': "So it's gonna be index minus one.", 'start': 3794.183, 'duration': 2.801}], 'summary': 'The pop method can remove an element by its index, e.g., the last element can be removed using index minus one.', 'duration': 36.137, 'max_score': 3760.847, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo3760847.jpg'}, {'end': 3852.981, 'src': 'embed', 'start': 3822.19, 'weight': 3, 'content': [{'end': 3825.711, 'text': "And that's how you remove an element using the pop method.", 'start': 3822.19, 'duration': 3.521}, {'end': 3833.512, 'text': "But there's still another way to remove an item using an a specific index, and it's the Dell.", 'start': 3826.491, 'duration': 7.021}, {'end': 3840.354, 'text': "So I'm going to show you here, Dell, it's the function del function.", 'start': 3834.072, 'duration': 6.282}, {'end': 3845.577, 'text': 'In here, we have to write the countries list.', 'start': 3840.954, 'duration': 4.623}, {'end': 3850.099, 'text': 'And then again, open square brackets in here, write that index.', 'start': 3845.957, 'duration': 4.142}, {'end': 3852.981, 'text': 'So I write here the index.', 'start': 3850.159, 'duration': 2.822}], 'summary': 'Demonstrated removing elements using pop and del methods.', 'duration': 30.791, 'max_score': 3822.19, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo3822190.jpg'}, {'end': 3912.582, 'src': 'embed', 'start': 3885.92, 'weight': 0, 'content': [{'end': 3890.863, 'text': 'And there you have it three different ways to remove an element from a list.', 'start': 3885.92, 'duration': 4.943}, {'end': 3893.365, 'text': "Okay, now let's see how to sort a list.", 'start': 3891.103, 'duration': 2.262}, {'end': 3897.969, 'text': 'we can easily sort a list using the sort method.', 'start': 3893.985, 'duration': 3.984}, {'end': 3904.214, 'text': "let's create a new list called numbers and then sort it from the smallest to the largest number.", 'start': 3897.969, 'duration': 6.245}, {'end': 3909.259, 'text': 'so here first i write numbers and then open square brackets.', 'start': 3904.214, 'duration': 5.045}, {'end': 3912.582, 'text': "so i'm going to write some random numbers.", 'start': 3909.259, 'duration': 3.323}], 'summary': 'Three methods to remove elements from a list, followed by sorting using the sort method.', 'duration': 26.662, 'max_score': 3885.92, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo3885920.jpg'}, {'end': 4332.499, 'src': 'embed', 'start': 4299.989, 'weight': 4, 'content': [{'end': 4304.333, 'text': 'And to create this dictionary, we have to use these curly braces.', 'start': 4299.989, 'duration': 4.344}, {'end': 4318.936, 'text': 'So we open curly braces and inside we write our first item and the first item consists of a key here on the left and then a value here.', 'start': 4305.034, 'duration': 13.902}, {'end': 4322.313, 'text': "and it's separated with the colon.", 'start': 4319.651, 'duration': 2.662}, {'end': 4326.856, 'text': 'So here we have the key, then column and then the value.', 'start': 4322.573, 'duration': 4.283}, {'end': 4329.998, 'text': 'And then we have here the second item.', 'start': 4327.656, 'duration': 2.342}, {'end': 4332.499, 'text': 'So the second key and the second value.', 'start': 4330.378, 'duration': 2.121}], 'summary': 'Creating a dictionary with key-value pairs using curly braces.', 'duration': 32.51, 'max_score': 4299.989, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo4299989.jpg'}, {'end': 4493.693, 'src': 'embed', 'start': 4456.23, 'weight': 6, 'content': [{'end': 4457.912, 'text': 'So this is the first item.', 'start': 4456.23, 'duration': 1.682}, {'end': 4460.454, 'text': 'And this is the second item.', 'start': 4458.592, 'duration': 1.862}, {'end': 4465.178, 'text': 'So we can say that the item is a pair of key and volume.', 'start': 4460.514, 'duration': 4.664}, {'end': 4470.382, 'text': 'So we can get this by using the items method.', 'start': 4465.658, 'duration': 4.724}, {'end': 4477.047, 'text': "So instead of writing that values, I'm going to write here that items, and then run this one.", 'start': 4470.482, 'duration': 6.565}, {'end': 4479.628, 'text': 'So here we got the first item.', 'start': 4477.687, 'duration': 1.941}, {'end': 4493.693, 'text': 'So the first pair, key and value, which is my name, and well, that key name, and then my name, Frank, and then the second item, so the key name H,', 'start': 4480.448, 'duration': 13.245}], 'summary': 'Demonstration of accessing items using the items method.', 'duration': 37.463, 'max_score': 4456.23, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo4456230.jpg'}, {'end': 4997.916, 'src': 'embed', 'start': 4973.225, 'weight': 5, 'content': [{'end': 4978.849, 'text': 'So here one little detail that most beginners forget to write is the column.', 'start': 4973.225, 'duration': 5.624}, {'end': 4984.734, 'text': "So it's sometimes easy to forget it's there, but you have to include it.", 'start': 4979.169, 'duration': 5.565}, {'end': 4990.995, 'text': 'And one other things some people miss is this indentation.', 'start': 4985.474, 'duration': 5.521}, {'end': 4997.916, 'text': 'So here, there is an indentation, you have to include after the column.', 'start': 4991.355, 'duration': 6.561}], 'summary': 'Beginners often forget to include a column and indentation in their writing.', 'duration': 24.691, 'max_score': 4973.225, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo4973225.jpg'}, {'end': 5222.903, 'src': 'embed', 'start': 5189.4, 'weight': 7, 'content': [{'end': 5193.782, 'text': "So in this case, I'm going to write another age, so 30.", 'start': 5189.4, 'duration': 4.382}, {'end': 5197.384, 'text': 'And again, 30 is greater than 18.', 'start': 5193.782, 'duration': 3.602}, {'end': 5199.005, 'text': 'So this is executed.', 'start': 5197.384, 'duration': 1.621}, {'end': 5200.366, 'text': "So you're an adult.", 'start': 5199.105, 'duration': 1.261}, {'end': 5202.948, 'text': "So now let's add a new block.", 'start': 5200.826, 'duration': 2.122}, {'end': 5206.35, 'text': "And I'm gonna use that elif.", 'start': 5203.428, 'duration': 2.922}, {'end': 5215.917, 'text': "So I write elif, and then age and then greater than, let's say 13.", 'start': 5207.11, 'duration': 8.807}, {'end': 5219.9, 'text': 'And then column, press Enter, and we got this indentation.', 'start': 5215.917, 'duration': 3.983}, {'end': 5222.903, 'text': 'And then we print another message.', 'start': 5220.681, 'duration': 2.222}], 'summary': 'Using python, a conditional block is created to determine adult status based on age, with an additional condition for age greater than 13.', 'duration': 33.503, 'max_score': 5189.4, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo5189400.jpg'}], 'start': 3641.33, 'title': 'Python data structures and indentation', 'summary': "Explains nested lists and methods for removing elements, covering 'remove', 'pop', and 'del', list operations like remove, sort, update, and copy, working with dictionaries including key-value pairs and methods like keys, values, items, and the update method, and the importance of indentation in python programming for proper code execution, with practical examples.", 'chapters': [{'end': 3885.7, 'start': 3641.33, 'title': 'Nested lists and methods for removing elements', 'summary': "Explains the concept of nested lists and demonstrates methods for removing elements, including the 'remove' method, the 'pop' method, and the 'del' function, with specific examples and outcomes.", 'duration': 244.37, 'highlights': ["The 'remove' method can be used to eliminate a specific element from a list, such as removing 'United States' from the 'countries' list.", "The 'pop' method can remove an element based on its index and return the removed element, for instance, removing the last element from the list.", "The 'del' function can delete an element at a specific index directly from the list, as demonstrated by removing the element at index zero from the 'countries' list."]}, {'end': 4272.367, 'start': 3885.92, 'title': 'List operations: remove, sort, update, and copy', 'summary': 'Covers three methods to remove elements from a list, sorting a list using the sort method, updating values in a list using indexing, and creating copies of a list using slicing and the copy method.', 'duration': 386.447, 'highlights': ['The chapter covers three methods to remove elements from a list', 'Sorting a list using the sort method', 'Updating values in a list using indexing', 'Creating copies of a list using slicing and the copy method']}, {'end': 4972.744, 'start': 4272.507, 'title': 'Working with dictionaries in python', 'summary': 'Explains how to create, manipulate, and remove data from dictionaries in python, highlighting the use of key-value pairs, methods like keys, values, items, and the update method, and the importance of using the copy method for making independent copies.', 'duration': 700.237, 'highlights': ['The chapter explains how to create a dictionary in Python using key-value pairs, demonstrating the use of curly braces and the colon to separate key and value, allowing for mixing different data types like strings and integers.', 'It discusses the usage of methods like keys, values, and items to retrieve keys, values, and key-value pairs from a dictionary, showcasing the process of getting and updating specific items in the dictionary.', 'It emphasizes the significance of using the copy method for making independent copies of dictionaries in Python, highlighting the difference between simple assignment and using the copy method to ensure changes in the original dictionary do not affect the new copy.', 'The chapter also delves into removing elements from a dictionary, demonstrating the use of methods like pop, del, and clear to remove specific items or clear all elements from the dictionary, showcasing practical examples for each method.']}, {'end': 5267.932, 'start': 4973.225, 'title': 'Indentation in python programming', 'summary': 'Discusses the importance of including a column and indentation in python programming to ensure proper execution of code, and provides examples of if statements with different age values resulting in specific messages being printed based on the conditions.', 'duration': 294.707, 'highlights': ['The importance of including a column and indentation in Python programming to ensure proper execution of code is emphasized, with specific guidance on using the Tab key for indentation.', "Examples of if statements with different age values are provided, demonstrating the execution of specific messages based on the conditions, such as 'you're an adult' for age greater than or equal to 18 and 'you're a kid' for age less than 18.", "The utilization of 'elif' for additional conditional blocks is explained, with a specific example of printing 'you're a teenager' for ages between 13 and 17."]}], 'duration': 1626.602, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo3641330.jpg', 'highlights': ['The chapter covers three methods to remove elements from a list', "The 'remove' method can be used to eliminate a specific element from a list, such as removing 'United States' from the 'countries' list.", "The 'pop' method can remove an element based on its index and return the removed element, for instance, removing the last element from the list.", "The 'del' function can delete an element at a specific index directly from the list, as demonstrated by removing the element at index zero from the 'countries' list.", 'The chapter explains how to create a dictionary in Python using key-value pairs, demonstrating the use of curly braces and the colon to separate key and value, allowing for mixing different data types like strings and integers.', 'The importance of including a column and indentation in Python programming to ensure proper execution of code is emphasized, with specific guidance on using the Tab key for indentation.', 'It discusses the usage of methods like keys, values, and items to retrieve keys, values, and key-value pairs from a dictionary, showcasing the process of getting and updating specific items in the dictionary.', "The utilization of 'elif' for additional conditional blocks is explained, with a specific example of printing 'you're a teenager' for ages between 13 and 17."]}, {'end': 6269.299, 'segs': [{'end': 5330.423, 'src': 'embed', 'start': 5300.863, 'weight': 8, 'content': [{'end': 5304.285, 'text': 'One example of an iterable object is a list.', 'start': 5300.863, 'duration': 3.422}, {'end': 5311.25, 'text': 'So we can loop through each element of a list and perform the same action on each element of that list.', 'start': 5304.485, 'duration': 6.765}, {'end': 5321.617, 'text': 'Here you can see the syntax of the for loop and, as you can see here, is the four keyword and then we have to use a variable,', 'start': 5311.45, 'duration': 10.167}, {'end': 5326.02, 'text': 'then we have to write that in keyword and then that iterable.', 'start': 5321.617, 'duration': 4.403}, {'end': 5330.423, 'text': 'In this case, as I told you before, the most common is the list.', 'start': 5326.38, 'duration': 4.043}], 'summary': 'The for loop iterates through list elements, demonstrating syntax and common usage.', 'duration': 29.56, 'max_score': 5300.863, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo5300863.jpg'}, {'end': 5441.879, 'src': 'embed', 'start': 5380.337, 'weight': 1, 'content': [{'end': 5387.499, 'text': 'So for country in, and then we have to write the name of that iterable, which is, in this case, a list.', 'start': 5380.337, 'duration': 7.162}, {'end': 5389, 'text': 'So countries.', 'start': 5387.679, 'duration': 1.321}, {'end': 5396.089, 'text': 'So for country in countries and then column and then enter and we get this indentation.', 'start': 5389.64, 'duration': 6.449}, {'end': 5400.995, 'text': 'Then we say print country.', 'start': 5397.07, 'duration': 3.925}, {'end': 5412.766, 'text': "So for this variable in this iterable, which is a list, print each element, this is what we're saying in this for loop.", 'start': 5402.722, 'duration': 10.044}, {'end': 5414.627, 'text': 'So we run this.', 'start': 5413.127, 'duration': 1.5}, {'end': 5420.09, 'text': 'And as you can see, each element of the list country is printed.', 'start': 5415.308, 'duration': 4.782}, {'end': 5425.232, 'text': "So we're looking through the countries list and printing each element.", 'start': 5420.41, 'duration': 4.822}, {'end': 5430.251, 'text': 'So the first is United States, then India, then China and Brazil.', 'start': 5425.808, 'duration': 4.443}, {'end': 5432.673, 'text': 'And this is how the for loop works.', 'start': 5430.671, 'duration': 2.002}, {'end': 5437.456, 'text': 'Now let me show you a new function that you can implement along with the for loop.', 'start': 5433.053, 'duration': 4.403}, {'end': 5439.357, 'text': "And it's called enumerate.", 'start': 5437.996, 'duration': 1.361}, {'end': 5441.879, 'text': "So I'm going to write here enumerate.", 'start': 5439.898, 'duration': 1.981}], 'summary': "Demonstration of for loop iterating through list of countries, printing each element. introduction to 'enumerate' function.", 'duration': 61.542, 'max_score': 5380.337, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo5380337.jpg'}, {'end': 5561.607, 'src': 'embed', 'start': 5534.489, 'weight': 11, 'content': [{'end': 5540.153, 'text': "So let's use that dictionary we created before that was my underscore data.", 'start': 5534.489, 'duration': 5.664}, {'end': 5542.054, 'text': 'Well, this is empty.', 'start': 5540.753, 'duration': 1.301}, {'end': 5544.876, 'text': "So I'm gonna use the original dictionary.", 'start': 5542.174, 'duration': 2.702}, {'end': 5549.739, 'text': "So here I have the original dictionary, and it's here.", 'start': 5545.356, 'duration': 4.383}, {'end': 5551.801, 'text': "So I'm just going to print it.", 'start': 5550, 'duration': 1.801}, {'end': 5553.942, 'text': 'So this is a dictionary.', 'start': 5552.401, 'duration': 1.541}, {'end': 5556.944, 'text': "And now we're going to loop through this dictionary.", 'start': 5554.222, 'duration': 2.722}, {'end': 5558.805, 'text': 'So let me show you here.', 'start': 5557.505, 'duration': 1.3}, {'end': 5561.607, 'text': 'First, we have to write four.', 'start': 5559.666, 'duration': 1.941}], 'summary': 'Using the original dictionary to loop through and print its contents.', 'duration': 27.118, 'max_score': 5534.489, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo5534489.jpg'}, {'end': 5825.069, 'src': 'embed', 'start': 5798.342, 'weight': 9, 'content': [{'end': 5804.623, 'text': 'Python has lots of built in functions that can help us perform a specific task.', 'start': 5798.342, 'duration': 6.281}, {'end': 5806.683, 'text': "Let's have a look at some of them.", 'start': 5804.943, 'duration': 1.74}, {'end': 5812.165, 'text': "So let's start with the land function, we only have to write the word land.", 'start': 5807.324, 'duration': 4.841}, {'end': 5814.445, 'text': 'And then we open parentheses.', 'start': 5812.925, 'duration': 1.52}, {'end': 5819.246, 'text': 'And as you can see here, Jupyter Notebook gives the green color to functions.', 'start': 5814.925, 'duration': 4.321}, {'end': 5823.308, 'text': "And now let's calculate the length of the countries list.", 'start': 5820.026, 'duration': 3.282}, {'end': 5825.069, 'text': 'So I have here the countries list.', 'start': 5823.328, 'duration': 1.741}], 'summary': 'Python has various built-in functions, including the len function for calculating the length of lists.', 'duration': 26.727, 'max_score': 5798.342, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo5798342.jpg'}, {'end': 5884.54, 'src': 'embed', 'start': 5854.224, 'weight': 10, 'content': [{'end': 5856.724, 'text': "Now let's see a different function.", 'start': 5854.224, 'duration': 2.5}, {'end': 5862.186, 'text': "In this case, I'm going to create a new list that contains only numbers.", 'start': 5857.085, 'duration': 5.101}, {'end': 5866.448, 'text': "So I'm going to write random numbers here, 10 6381, then one there 99.", 'start': 5862.206, 'duration': 4.242}, {'end': 5867.548, 'text': 'So this is my new list.', 'start': 5866.448, 'duration': 1.1}, {'end': 5878.675, 'text': 'And I created this list with only numbers to try the max and min function.', 'start': 5872.63, 'duration': 6.045}, {'end': 5884.54, 'text': 'So the max function is this one, we write max and then parentheses.', 'start': 5879.416, 'duration': 5.124}], 'summary': 'Creating a list of numbers to demonstrate the use of the max function.', 'duration': 30.316, 'max_score': 5854.224, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo5854224.jpg'}, {'end': 5943.044, 'src': 'embed', 'start': 5913.828, 'weight': 7, 'content': [{'end': 5917.171, 'text': 'So we run and we get one.', 'start': 5913.828, 'duration': 3.343}, {'end': 5922.113, 'text': 'Okay, another common function used in Python is the type function.', 'start': 5917.511, 'duration': 4.602}, {'end': 5928.136, 'text': 'And this function, give us the type of the object, we only have to write type.', 'start': 5922.273, 'duration': 5.863}, {'end': 5933.439, 'text': 'And what this function does is to return the type of an object.', 'start': 5929.137, 'duration': 4.302}, {'end': 5937.801, 'text': "So in this case, let's copy and paste the countries object.", 'start': 5933.939, 'duration': 3.862}, {'end': 5943.044, 'text': 'And if we run this, we can see that this object is a list.', 'start': 5938.461, 'duration': 4.583}], 'summary': 'Python type function returns object type, e.g. countries is a list.', 'duration': 29.216, 'max_score': 5913.828, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo5913828.jpg'}, {'end': 5995.549, 'src': 'embed', 'start': 5971.22, 'weight': 2, 'content': [{'end': 5979.623, 'text': "First, that start number, this one, I'm going to write one, then the number where the sequence stops.", 'start': 5971.22, 'duration': 8.403}, {'end': 5983.084, 'text': "In this case, I'm going to write, let's say 10.", 'start': 5979.903, 'duration': 3.181}, {'end': 5985.785, 'text': 'And then the last argument is the increment.', 'start': 5983.084, 'duration': 2.701}, {'end': 5995.549, 'text': "So how that this sequence is going to grow by how much so in this case, I'm going to say that this sequence is going to grow by two.", 'start': 5986.145, 'duration': 9.404}], 'summary': 'Explaining how to create a sequence from 1 to 10 with an increment of 2.', 'duration': 24.329, 'max_score': 5971.22, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo5971220.jpg'}, {'end': 6059.715, 'src': 'embed', 'start': 6031.509, 'weight': 12, 'content': [{'end': 6037.933, 'text': 'So one plus two is three, then three plus two, five, then seven, and then nine.', 'start': 6031.509, 'duration': 6.424}, {'end': 6040.575, 'text': 'And then we should get 11.', 'start': 6038.613, 'duration': 1.962}, {'end': 6043.997, 'text': "But the last element here, it's 10.", 'start': 6040.575, 'duration': 3.422}, {'end': 6047.341, 'text': 'So this sequence stops at 10.', 'start': 6043.997, 'duration': 3.344}, {'end': 6049.824, 'text': 'So we only get until number nine.', 'start': 6047.341, 'duration': 2.483}, {'end': 6054.689, 'text': "And that's how the range function works in Python.", 'start': 6050.645, 'duration': 4.044}, {'end': 6055.811, 'text': "And that's it.", 'start': 6055.23, 'duration': 0.581}, {'end': 6059.715, 'text': 'Now you know the most common built in functions in Python.', 'start': 6056.011, 'duration': 3.704}], 'summary': 'Python range function stops at 10, common built-in functions explained.', 'duration': 28.206, 'max_score': 6031.509, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo6031509.jpg'}, {'end': 6179.02, 'src': 'embed', 'start': 6088.835, 'weight': 0, 'content': [{'end': 6092.138, 'text': "And this one comes with Python, so you don't need to install it.", 'start': 6088.835, 'duration': 3.303}, {'end': 6098.042, 'text': 'So to get access to this OS module, we have to write import OS.', 'start': 6092.638, 'duration': 5.404}, {'end': 6100.504, 'text': "And that's it, we only write this.", 'start': 6098.723, 'duration': 1.781}, {'end': 6104.107, 'text': "And now let's see some functionalities of this module.", 'start': 6101.065, 'duration': 3.042}, {'end': 6110.472, 'text': "So the first one that we're going to see is the get current directory method.", 'start': 6104.287, 'duration': 6.185}, {'end': 6120.402, 'text': 'So to get access to that method, we write OS, then get C W D, and then parentheses.', 'start': 6110.652, 'duration': 9.75}, {'end': 6125.428, 'text': 'So this C W D stands for current working directory.', 'start': 6120.803, 'duration': 4.625}, {'end': 6130.894, 'text': "So we're going to get the directory where our Jupyter notebook file is located.", 'start': 6125.928, 'duration': 4.966}, {'end': 6136.578, 'text': "So this file I'm working with right now, So let's run and let's see what happens.", 'start': 6130.994, 'duration': 5.584}, {'end': 6142.82, 'text': 'So as you can see here, I have the path where the Jupyter Notebook is located.', 'start': 6137.158, 'duration': 5.662}, {'end': 6145.962, 'text': 'So this is the complete path.', 'start': 6142.92, 'duration': 3.042}, {'end': 6151.163, 'text': 'And you can see it by using the get CWD method.', 'start': 6146.422, 'duration': 4.741}, {'end': 6154.885, 'text': "So now let's see another method.", 'start': 6151.824, 'duration': 3.061}, {'end': 6163.149, 'text': "And in this case, we're going to list all the elements in the folder where this Jupyter notebook file is located.", 'start': 6155.205, 'duration': 7.944}, {'end': 6167.832, 'text': "So here to do that, we're going to use the method list dir.", 'start': 6163.569, 'duration': 4.263}, {'end': 6172.035, 'text': "So this means list directory, and I'm going to run it.", 'start': 6168.633, 'duration': 3.402}, {'end': 6179.02, 'text': 'And as you can see here, I have this Jupyter notebook file that is named untitled.', 'start': 6172.715, 'duration': 6.305}], 'summary': "Using python's os module for accessing current directory and listing folder elements.", 'duration': 90.185, 'max_score': 6088.835, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo6088835.jpg'}, {'end': 6269.299, 'src': 'embed', 'start': 6240.621, 'weight': 5, 'content': [{'end': 6249.985, 'text': 'But now if we use this list your method to list all the elements in my folder, we can see that there is a new folder.', 'start': 6240.621, 'duration': 9.364}, {'end': 6259.551, 'text': 'So here, if we compare this result we got before with this new result, we can see that there is one new element.', 'start': 6250.445, 'duration': 9.106}, {'end': 6268.498, 'text': 'And this element is that new folder element, which is the folder we created using the make duress method.', 'start': 6260.051, 'duration': 8.447}, {'end': 6269.299, 'text': "And that's it.", 'start': 6268.738, 'duration': 0.561}], 'summary': 'Using list method, 1 new folder element found.', 'duration': 28.678, 'max_score': 6240.621, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo6240621.jpg'}], 'start': 5267.932, 'title': 'Python programming fundamentals', 'summary': 'Covers python for loops and the enumerate function, working with functions and built-in functions such as len, max, and min, explaining python built-in functions including type, range, and print, and exploring os module functionalities like retrieving the current working directory and creating a new folder.', 'chapters': [{'end': 5533.789, 'start': 5267.932, 'title': 'Python for loop and enumerate function', 'summary': 'Explains the working of python for loops, demonstrating how to iterate through a list using a for loop and implement the enumerate function to enumerate elements, resulting in printing each element of the list along with its index.', 'duration': 265.857, 'highlights': ['The enumerate function in Python returns the index and the element of the iterable as it loops through it, as demonstrated with the countries list, printing the index and the element for each iteration.', 'The for loop in Python is used to iterate through an iterable object, such as a list, performing the same action for each entry, as shown in the example of looping through the countries list and printing each element.', 'The chapter explains the syntax and usage of the for loop in Python, highlighting its ability to loop through an iterable object and execute the designated code for each iteration.']}, {'end': 5913.328, 'start': 5534.489, 'title': 'Working with python functions and built-in functions', 'summary': 'Covers looping through a dictionary, creating and using functions in python, and demonstrating the use of built-in functions such as len, max, and min with quantifiable results including the length of a list and the maximum and minimum values in a new list.', 'duration': 378.839, 'highlights': ['Demonstrating the use of built-in functions such as len, max, and min', 'Creating and using functions in Python', 'Looping through a dictionary']}, {'end': 6059.715, 'start': 5913.828, 'title': 'Python built-in functions explained', 'summary': 'Covers the type, range, and print functions in python, demonstrating their usage and output, with the range function explained in detail, including a specific example and its output.', 'duration': 145.887, 'highlights': ['The range function returns a sequence of numbers that start with a specified number and end with another specified number, with an optional increment value, demonstrated through an example where a sequence starting from 1, ending at 10, and growing by 2 is shown in a for loop, resulting in the output of numbers 1, 3, 5, 7, and 9.', 'The type function in Python returns the type of an object when invoked, as demonstrated by using it to identify the type of a list object, showing that the object is a list.', 'The print function in Python is briefly mentioned and used as an example in the explanation of the range function, highlighting its usage in a for loop to display the elements of the generated sequence.']}, {'end': 6269.299, 'start': 6062.279, 'title': 'Python modules: os module functionalities', 'summary': 'Explores the functionalities of the os module in python, demonstrating methods to retrieve the current working directory, list directory contents, and create a new folder, showcasing practical examples and their outcomes.', 'duration': 207.02, 'highlights': ['The OS module in Python allows accessing functionalities such as retrieving the current working directory, listing folder contents, and creating new folders, providing practical demonstrations of each method. (3 functionalities demonstrated)', 'The getcwd method is used to retrieve the current working directory, providing the complete path where the Jupyter Notebook is located. (quantifiable outcome: complete path displayed)', 'The listdir method is utilized to list all the elements in the folder where the Jupyter notebook file is located, showcasing practical examples and outcomes. (quantifiable outcome: listing all elements in the folder)', 'The makedirs method is employed to create a new folder, successfully demonstrating the creation of a new element in the designated folder. (quantifiable outcome: successful creation of a new folder)']}], 'duration': 1001.367, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo5267932.jpg', 'highlights': ['The OS module in Python allows accessing functionalities such as retrieving the current working directory, listing folder contents, and creating new folders, providing practical demonstrations of each method. (3 functionalities demonstrated)', 'The enumerate function in Python returns the index and the element of the iterable as it loops through it, as demonstrated with the countries list, printing the index and the element for each iteration.', 'The range function returns a sequence of numbers that start with a specified number and end with another specified number, with an optional increment value, demonstrated through an example where a sequence starting from 1, ending at 10, and growing by 2 is shown in a for loop, resulting in the output of numbers 1, 3, 5, 7, and 9.', 'The getcwd method is used to retrieve the current working directory, providing the complete path where the Jupyter Notebook is located. (quantifiable outcome: complete path displayed)', 'The listdir method is utilized to list all the elements in the folder where the Jupyter notebook file is located, showcasing practical examples and outcomes. (quantifiable outcome: listing all elements in the folder)', 'The makedirs method is employed to create a new folder, successfully demonstrating the creation of a new element in the designated folder. (quantifiable outcome: successful creation of a new folder)', 'The for loop in Python is used to iterate through an iterable object, such as a list, performing the same action for each entry, as shown in the example of looping through the countries list and printing each element.', 'The type function in Python returns the type of an object when invoked, as demonstrated by using it to identify the type of a list object, showing that the object is a list.', 'The chapter explains the syntax and usage of the for loop in Python, highlighting its ability to loop through an iterable object and execute the designated code for each iteration.', 'Creating and using functions in Python', 'Demonstrating the use of built-in functions such as len, max, and min', 'Looping through a dictionary', 'The print function in Python is briefly mentioned and used as an example in the explanation of the range function, highlighting its usage in a for loop to display the elements of the generated sequence.']}, {'end': 8566.988, 'segs': [{'end': 6437.842, 'src': 'embed', 'start': 6359.567, 'weight': 0, 'content': [{'end': 6367.472, 'text': 'Excel can handle around one million rows, while Python can handle millions and millions of rows.', 'start': 6359.567, 'duration': 7.905}, {'end': 6374.697, 'text': 'Another benefit that Python and pandas have over Excel is the complex data transformation.', 'start': 6367.733, 'duration': 6.964}, {'end': 6386.741, 'text': 'So, in Excel, memory-intensive computations can crash a workbook, while in Python, when you work with pandas,', 'start': 6375.698, 'duration': 11.043}, {'end': 6390.703, 'text': 'you can handle complex computations without any major problem.', 'start': 6386.741, 'duration': 3.962}, {'end': 6397.585, 'text': 'Also, Python is good for automation, while Excel was not designed to automate tasks.', 'start': 6391.163, 'duration': 6.422}, {'end': 6404.227, 'text': "You can create a macro or use VBA to simplify some tasks, but that's the limit.", 'start': 6398.105, 'duration': 6.122}, {'end': 6410.53, 'text': 'However, Python can go beyond that with its hundred of free libraries available.', 'start': 6404.767, 'duration': 5.763}, {'end': 6414.612, 'text': 'And finally, Python has gross platform capabilities.', 'start': 6411.09, 'duration': 3.522}, {'end': 6422.835, 'text': 'This means that Python code remains the same regardless of the operating system or language set on your computer.', 'start': 6415.392, 'duration': 7.443}, {'end': 6428.238, 'text': 'Okay, before we start writing code, let me explain to you the core concepts of pandas.', 'start': 6423.376, 'duration': 4.862}, {'end': 6432.54, 'text': "So we're gonna start seeing the concepts of arrays.", 'start': 6428.878, 'duration': 3.662}, {'end': 6437.842, 'text': 'So arrays in Python are a data structure like lists.', 'start': 6433.34, 'duration': 4.502}], 'summary': 'Python can handle millions of rows, automate tasks, and has platform capabilities, unlike excel.', 'duration': 78.275, 'max_score': 6359.567, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo6359567.jpg'}, {'end': 7680.203, 'src': 'embed', 'start': 7646.434, 'weight': 8, 'content': [{'end': 7650.621, 'text': 'And this when we read it with pandas is a data frame.', 'start': 7646.434, 'duration': 4.187}, {'end': 7651.383, 'text': "And that's it.", 'start': 7650.822, 'duration': 0.561}, {'end': 7654.388, 'text': 'These are the three ways to create a pandas data frame.', 'start': 7651.523, 'duration': 2.865}, {'end': 7661.145, 'text': "Okay, now it's time to see how to display a data frame in pandas.", 'start': 7656.761, 'duration': 4.384}, {'end': 7666.27, 'text': 'So here I have the CSV file we use before to create a data frame.', 'start': 7661.786, 'duration': 4.484}, {'end': 7671.274, 'text': 'And a little detail I forgot to mention before,', 'start': 7667.191, 'duration': 4.083}, {'end': 7680.203, 'text': 'is that this CSV file should be located in the same directory where your computer notebook script is located.', 'start': 7671.274, 'duration': 8.929}], 'summary': 'Three ways to create a pandas data frame, and how to display it.', 'duration': 33.769, 'max_score': 7646.434, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo7646434.jpg'}, {'end': 7965.115, 'src': 'embed', 'start': 7938.805, 'weight': 9, 'content': [{'end': 7947.868, 'text': "So now, I'm going to show you how to display all the rows of this data frame, as you will do it in Excel or in Google Sheets.", 'start': 7938.805, 'duration': 9.063}, {'end': 7952.65, 'text': 'To do so first, we have to know how many columns this data frame has.', 'start': 7948.288, 'duration': 4.362}, {'end': 7957.512, 'text': 'So an easy way to get the number of columns is using the shape attribute.', 'start': 7953.15, 'duration': 4.362}, {'end': 7960.153, 'text': 'To get the shape attribute.', 'start': 7958.252, 'duration': 1.901}, {'end': 7962.494, 'text': 'First, we write the name of the data frame.', 'start': 7960.213, 'duration': 2.281}, {'end': 7965.115, 'text': 'So in this case, DF underscore exams.', 'start': 7962.614, 'duration': 2.501}], 'summary': 'Demonstrating how to display all rows of a data frame, using the shape attribute to determine the number of columns.', 'duration': 26.31, 'max_score': 7938.805, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo7938805.jpg'}, {'end': 8138.335, 'src': 'embed', 'start': 8110.71, 'weight': 2, 'content': [{'end': 8118.796, 'text': 'So now we have a function, a function is a group of related statements that performs a specific task.', 'start': 8110.71, 'duration': 8.086}, {'end': 8121.958, 'text': "So we've seen functions before in Python.", 'start': 8119.036, 'duration': 2.922}, {'end': 8131.107, 'text': "we've seen some Python built in functions like the max that gets the maximum value of a list or mean that gets the minimum value,", 'start': 8121.958, 'duration': 9.149}, {'end': 8133.43, 'text': 'or length that gets the length of a list.', 'start': 8131.107, 'duration': 2.323}, {'end': 8138.335, 'text': 'So those are some Python building functions that we can use in pandas too.', 'start': 8134.03, 'duration': 4.305}], 'summary': 'Functions in python can perform specific tasks, such as finding max and min values or list length.', 'duration': 27.625, 'max_score': 8110.71, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo8110710.jpg'}, {'end': 8483.284, 'src': 'embed', 'start': 8454.565, 'weight': 4, 'content': [{'end': 8457.007, 'text': "And as you might know, it's with the head method.", 'start': 8454.565, 'duration': 2.442}, {'end': 8461.81, 'text': 'So we only write the name of that attribute, sorry, the name of that data frame.', 'start': 8457.127, 'duration': 4.683}, {'end': 8464.191, 'text': 'And then we write the head method.', 'start': 8462.49, 'duration': 1.701}, {'end': 8465.712, 'text': 'So head and parentheses.', 'start': 8464.231, 'duration': 1.481}, {'end': 8469.855, 'text': 'So we run this and we obtain the first five rows.', 'start': 8466.313, 'duration': 3.542}, {'end': 8477.52, 'text': 'So we can also obtain some summary info the data frame by using the info method.', 'start': 8470.416, 'duration': 7.104}, {'end': 8483.284, 'text': 'So here we write the name of the data frame info parentheses and execute this.', 'start': 8478.201, 'duration': 5.083}], 'summary': 'Using the head method to get the first five rows, and the info method for summary info.', 'duration': 28.719, 'max_score': 8454.565, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo8454565.jpg'}, {'end': 8539.691, 'src': 'embed', 'start': 8506.644, 'weight': 3, 'content': [{'end': 8514.891, 'text': 'Okay, now if we want to get some basic statistics of a data frame, we have to use that describe method.', 'start': 8506.644, 'duration': 8.247}, {'end': 8522.097, 'text': 'So we write the name of the data frame and write describe parentheses to execute this.', 'start': 8515.571, 'duration': 6.526}, {'end': 8523.938, 'text': 'So we run this code.', 'start': 8522.617, 'duration': 1.321}, {'end': 8526.62, 'text': 'And we have some basic statistics.', 'start': 8524.639, 'duration': 1.981}, {'end': 8529.202, 'text': 'So first, the count.', 'start': 8526.76, 'duration': 2.442}, {'end': 8533.766, 'text': 'So this indicates how many rows each column has.', 'start': 8529.603, 'duration': 4.163}, {'end': 8539.691, 'text': 'So each of them have 1000 rows, then we have the mean.', 'start': 8534.427, 'duration': 5.264}], 'summary': 'Using the describe method on a data frame provides basic statistics including count and mean for each column.', 'duration': 33.047, 'max_score': 8506.644, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo8506644.jpg'}], 'start': 6269.679, 'title': 'Introduction to pandas and data frame creation', 'summary': 'Introduces the benefits of using pandas over excel, including the ability to handle millions of rows and perform complex data transformations without crashing. it also covers creating pandas data frames, working with csv files in jupyter notebook, and introduction to pandas functions and methods.', 'chapters': [{'end': 6414.612, 'start': 6269.679, 'title': 'Introduction to pandas benefits over excel', 'summary': 'Introduces the benefits of using pandas over excel, including the ability to handle millions of rows, perform complex data transformations without crashing, automate tasks, and its cross-platform capabilities.', 'duration': 144.933, 'highlights': ['Pandas can handle millions of rows, while Excel is limited to around one million rows, showcasing the scalability of Pandas over Excel.', 'Python and Pandas allow complex data transformations without crashing, unlike Excel which can experience memory-intensive computations crashing a workbook.', 'Python is good for automation with hundreds of free libraries available, surpassing the limitation of Excel which can only be automated to a certain extent using macros or VBA.', "Python offers cross-platform capabilities, providing more flexibility compared to Excel's platform limitations."]}, {'end': 7281.955, 'start': 6415.392, 'title': 'Pandas data frame creation', 'summary': 'Introduces the core concepts of pandas, including arrays, series, and data frames, and illustrates three ways to create a pandas data frame with arrays, dictionaries, and csv files, using numpy and pandas libraries to handle data and create data frames.', 'duration': 866.563, 'highlights': ['The chapter introduces the core concepts of pandas, including arrays, series, and data frames', 'Illustrates three ways to create a pandas data frame with arrays, dictionaries, and CSV files', 'Demonstrates the use of NumPy and pandas libraries to handle data and create data frames']}, {'end': 7671.274, 'start': 7282.295, 'title': 'Creating pandas data frames', 'summary': 'Discusses creating pandas data frames, starting with creating lists of states and their populations, then creating a dictionary and finally importing a data frame from a csv file, while showcasing the process and the resulting data frame.', 'duration': 388.979, 'highlights': ['Creating a dictionary from lists of states and populations', 'Importing a data frame from a CSV file', 'Displaying a data frame and differentiating between a data set and a data frame']}, {'end': 8109.809, 'start': 7671.274, 'title': 'Working with csv files in jupyter notebook', 'summary': 'Explains how to read a csv file in jupyter notebook using pandas, including demonstrating methods to display and manipulate the data frame, such as using head and tail to view the first and last rows, modifying display options, and understanding attributes, methods, and functions in pandas.', 'duration': 438.535, 'highlights': ['The chapter explains how to read a CSV file in Jupyter Notebook using pandas', 'Demonstrating methods to display and manipulate the data frame', 'Understanding attributes, methods, and functions in pandas']}, {'end': 8566.988, 'start': 8110.71, 'title': 'Introduction to pandas functions and methods', 'summary': 'Introduces functions and methods in pandas, including built-in functions like max and mean, methods like head and tail, and attributes like shape and index, with examples and explanations of how to use them, along with some basic statistics of a data frame.', 'duration': 456.278, 'highlights': ['The chapter introduces functions and methods in pandas, including built-in functions like max and mean, methods like head and tail, and attributes like shape and index.', 'The describe method provides basic statistics for each column, including count, mean, standard deviation, minimum and maximum values, and percentiles.', 'The head method is used to obtain the first five rows of the data frame, providing a quick overview of the data.', 'The D types attribute provides the data type of each column, showing object for string types and int64 for numerical types.', "The shape attribute provides the number of rows and columns in the data frame, giving a fundamental understanding of the data frame's structure."]}], 'duration': 2297.309, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo6269679.jpg', 'highlights': ['Pandas can handle millions of rows, while Excel is limited to around one million rows, showcasing the scalability of Pandas over Excel.', 'Python and Pandas allow complex data transformations without crashing, unlike Excel which can experience memory-intensive computations crashing a workbook.', 'The chapter introduces functions and methods in pandas, including built-in functions like max and mean, methods like head and tail, and attributes like shape and index.', 'The describe method provides basic statistics for each column, including count, mean, standard deviation, minimum and maximum values, and percentiles.', 'The head method is used to obtain the first five rows of the data frame, providing a quick overview of the data.', 'Python is good for automation with hundreds of free libraries available, surpassing the limitation of Excel which can only be automated to a certain extent using macros or VBA.', "Python offers cross-platform capabilities, providing more flexibility compared to Excel's platform limitations.", 'The chapter introduces the core concepts of pandas, including arrays, series, and data frames', 'Illustrates three ways to create a pandas data frame with arrays, dictionaries, and CSV files', "The shape attribute provides the number of rows and columns in the data frame, giving a fundamental understanding of the data frame's structure."]}, {'end': 9910.545, 'segs': [{'end': 8631.579, 'src': 'embed', 'start': 8567.528, 'weight': 0, 'content': [{'end': 8570.729, 'text': 'And we see that the maximum score is 100 on each of them.', 'start': 8567.528, 'duration': 3.201}, {'end': 8589.817, 'text': 'So the describe method is a useful method whenever we want to get some basic statistics of the data frame,', 'start': 8582.273, 'duration': 7.544}, {'end': 8594.379, 'text': 'especially of the numerical data that we have in our data frame.', 'start': 8589.817, 'duration': 4.562}, {'end': 8598.461, 'text': "Okay, now let's see some functions that we can use in pandas.", 'start': 8594.699, 'duration': 3.762}, {'end': 8603.183, 'text': 'We can use some built in functions that Python has in pandas.', 'start': 8599.201, 'duration': 3.982}, {'end': 8612.148, 'text': 'For example, if we want to get the length of a data frame, we only have to write length, and then inside parentheses, the name of the data frame.', 'start': 8603.603, 'duration': 8.545}, {'end': 8618.391, 'text': 'So we run this, and we obtain that the length of this data frame is 1000.', 'start': 8612.748, 'duration': 5.643}, {'end': 8623.014, 'text': 'Actually, the length of a data frame indicates only the number of rows.', 'start': 8618.391, 'duration': 4.623}, {'end': 8626.336, 'text': 'So here I made a mistake is rows.', 'start': 8623.714, 'duration': 2.622}, {'end': 8631.579, 'text': 'And this is how we obtain the number of rows of data frame.', 'start': 8627.436, 'duration': 4.143}], 'summary': 'The maximum score is 100 on each feature. the data frame contains 1000 rows.', 'duration': 64.051, 'max_score': 8567.528, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo8567528.jpg'}, {'end': 8729.817, 'src': 'embed', 'start': 8664.325, 'weight': 2, 'content': [{'end': 8670.15, 'text': "So if we use the max function, we're going to get the maximum or the highest index here.", 'start': 8664.325, 'duration': 5.825}, {'end': 8674.974, 'text': 'So run n is 999.', 'start': 8670.43, 'duration': 4.544}, {'end': 8680.138, 'text': 'So we can also get the lowest index of a data frame, we only have to copy this.', 'start': 8674.974, 'duration': 5.164}, {'end': 8686.119, 'text': 'And instead of writing the max function, we write min.', 'start': 8682.253, 'duration': 3.866}, {'end': 8691.747, 'text': 'So in this case, we get the minimum index and is zero.', 'start': 8686.52, 'duration': 5.227}, {'end': 8698.077, 'text': 'So now we can obtain the data type of the data frame.', 'start': 8692.609, 'duration': 5.468}, {'end': 8704.541, 'text': 'Well, the data frame has a data frame type, but we can verify that using the type function.', 'start': 8698.377, 'duration': 6.164}, {'end': 8710.864, 'text': 'So we write type, then sorry, write only the name of the data frame.', 'start': 8705.061, 'duration': 5.803}, {'end': 8712.445, 'text': 'And we run.', 'start': 8711.925, 'duration': 0.52}, {'end': 8718.309, 'text': 'So here, as you can see, the type of this object is a data frame.', 'start': 8712.966, 'duration': 5.343}, {'end': 8725.374, 'text': 'And finally, we can use a common function that is the round function.', 'start': 8718.829, 'duration': 6.545}, {'end': 8727.155, 'text': 'So we write only round.', 'start': 8725.814, 'duration': 1.341}, {'end': 8729.817, 'text': 'And this has two arguments.', 'start': 8727.875, 'duration': 1.942}], 'summary': 'Using max and min functions to find maximum index (999) and minimum index (0) of a data frame, verifying data type as data frame, and utilizing round function with two arguments.', 'duration': 65.492, 'max_score': 8664.325, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo8664325.jpg'}, {'end': 8819.715, 'src': 'embed', 'start': 8791.828, 'weight': 6, 'content': [{'end': 8794.829, 'text': "And well, let's import pandas.", 'start': 8791.828, 'duration': 3.001}, {'end': 8797.79, 'text': "And let's read this CSV file.", 'start': 8794.929, 'duration': 2.861}, {'end': 8800.611, 'text': 'So I have this in the same data frame.', 'start': 8797.87, 'duration': 2.741}, {'end': 8804.232, 'text': "And I'm just showing the first five rows.", 'start': 8801.411, 'duration': 2.821}, {'end': 8809.413, 'text': 'So now to select one of the columns of this data frame, we have two options.', 'start': 8804.632, 'duration': 4.781}, {'end': 8811.593, 'text': "So let's see the first option.", 'start': 8809.813, 'duration': 1.78}, {'end': 8815.594, 'text': 'The first option is using the square brackets.', 'start': 8812.073, 'duration': 3.521}, {'end': 8819.715, 'text': 'This is the preferred way to select a column in pandas.', 'start': 8816.174, 'duration': 3.541}], 'summary': 'Using pandas to read csv file, select columns, and display data.', 'duration': 27.887, 'max_score': 8791.828, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo8791828.jpg'}, {'end': 8933.595, 'src': 'embed', 'start': 8909.379, 'weight': 13, 'content': [{'end': 8917.565, 'text': 'And as you can see, here, we get a serious and serious, just like pandas dataframes have attributes and methods.', 'start': 8909.379, 'duration': 8.186}, {'end': 8920.706, 'text': 'So we can access those attributes and methods.', 'start': 8917.904, 'duration': 2.802}, {'end': 8927.951, 'text': 'And actually, the attributes and methods between a series in a data frames are very similar.', 'start': 8921.246, 'duration': 6.705}, {'end': 8933.595, 'text': 'So, for example, if we want to get the index attribute of this series,', 'start': 8928.151, 'duration': 5.444}], 'summary': 'Pandas dataframes have attributes and methods, similar to series in data frames.', 'duration': 24.216, 'max_score': 8909.379, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo8909379.jpg'}, {'end': 8990.024, 'src': 'embed', 'start': 8962.441, 'weight': 14, 'content': [{'end': 8966.505, 'text': 'So as you can see, here, we get the first five rows of this series.', 'start': 8962.441, 'duration': 4.064}, {'end': 8969.067, 'text': "Alright, that's it for the first syntax.", 'start': 8966.965, 'duration': 2.102}, {'end': 8970.989, 'text': 'This is my favorite syntax.', 'start': 8969.407, 'duration': 1.582}, {'end': 8979.857, 'text': "And actually, most people use it, because it's the most practical, you know, it's time to see the second syntax to select a column from a data frame.", 'start': 8971.149, 'duration': 8.708}, {'end': 8990.024, 'text': "So this syntax involves writing that that sign, which is here, So let's say we want to get the same gender column.", 'start': 8980.397, 'duration': 9.627}], 'summary': 'Introduction to two syntaxes for selecting column data from a data frame.', 'duration': 27.583, 'max_score': 8962.441, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo8962441.jpg'}, {'end': 9042.509, 'src': 'embed', 'start': 9009.557, 'weight': 16, 'content': [{'end': 9014.302, 'text': 'And probably now you might be thinking that this is more practical than the first syntax.', 'start': 9009.557, 'duration': 4.745}, {'end': 9017.966, 'text': 'But this syntax has some pitfalls.', 'start': 9014.903, 'duration': 3.063}, {'end': 9019.728, 'text': 'So now let me show you here.', 'start': 9018.306, 'duration': 1.422}, {'end': 9026.594, 'text': 'So what if you want to get one column that has two words?', 'start': 9020.749, 'duration': 5.845}, {'end': 9029.797, 'text': 'for example, what if you want to get?', 'start': 9026.594, 'duration': 3.203}, {'end': 9034.522, 'text': 'let me show you here this column that has as name math score.', 'start': 9029.797, 'duration': 4.725}, {'end': 9042.509, 'text': "So now let's try to get access to this column, I'm going to copy this column name, you know, scroll down and, you know, let's try.", 'start': 9034.662, 'duration': 7.847}], 'summary': 'Comparing practicality of syntax; demonstrating accessing a column with two words.', 'duration': 32.952, 'max_score': 9009.557, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo9009557.jpg'}, {'end': 9251.221, 'src': 'embed', 'start': 9225.309, 'weight': 18, 'content': [{'end': 9233.173, 'text': "And by the way, the order that we write these columns is the same order that we're going to get that data frame.", 'start': 9225.309, 'duration': 7.864}, {'end': 9237.935, 'text': 'I mean, we can define the order of the columns inside this square bracket.', 'start': 9233.453, 'duration': 4.482}, {'end': 9240.976, 'text': "So here, we're saying that first is the gender column.", 'start': 9237.975, 'duration': 3.001}, {'end': 9243.858, 'text': 'And second, it should be the math score column.', 'start': 9241.357, 'duration': 2.501}, {'end': 9245.478, 'text': "So now, let's run this.", 'start': 9243.938, 'duration': 1.54}, {'end': 9251.221, 'text': 'And as you can see, here, we obtain first the gender column and second, the math score column.', 'start': 9246.099, 'duration': 5.122}], 'summary': 'Defining column order for data frame: gender first, math score second.', 'duration': 25.912, 'max_score': 9225.309, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo9225309.jpg'}, {'end': 9326.204, 'src': 'embed', 'start': 9300.93, 'weight': 7, 'content': [{'end': 9307.315, 'text': 'But when we use only single pair of square brackets, as we did in the previous video, we get a series.', 'start': 9300.93, 'duration': 6.385}, {'end': 9314.04, 'text': "So one pair of square brackets is for a series and two pairs of square brackets, it's for a data frame.", 'start': 9307.715, 'duration': 6.325}, {'end': 9316.321, 'text': 'Okay, now to continue with the video.', 'start': 9314.26, 'duration': 2.061}, {'end': 9322.183, 'text': "I'm going to select two or more columns using these two pairs of square brackets.", 'start': 9317.102, 'duration': 5.081}, {'end': 9326.204, 'text': "So now let's choose the columns that we're going to get.", 'start': 9322.643, 'duration': 3.561}], 'summary': 'Using single pair of square brackets yields series, while two pairs yield a data frame for selecting columns.', 'duration': 25.274, 'max_score': 9300.93, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo9300930.jpg'}, {'end': 9671.385, 'src': 'embed', 'start': 9586.365, 'weight': 9, 'content': [{'end': 9589.288, 'text': "In this case, I'm going to add a value of 70.", 'start': 9586.365, 'duration': 2.923}, {'end': 9599.795, 'text': "So now if we run this code, we're going to see that nothing happens that if we Now show the data frame, we're going to see that we have a new column.", 'start': 9589.288, 'duration': 10.507}, {'end': 9602.537, 'text': 'And this column is named language score.', 'start': 9600.296, 'duration': 2.241}, {'end': 9607.079, 'text': 'And the value that this column has is the same value.', 'start': 9603.217, 'duration': 3.862}, {'end': 9609.501, 'text': "So it's 70 in all its rows.", 'start': 9607.239, 'duration': 2.262}, {'end': 9611.462, 'text': 'So we have 70 in row zero.', 'start': 9609.561, 'duration': 1.901}, {'end': 9617.204, 'text': "And if we scroll down, we're going to see that it's 70 in all the rows.", 'start': 9612.242, 'duration': 4.962}, {'end': 9620.185, 'text': 'So even in row 999.', 'start': 9617.324, 'duration': 2.861}, {'end': 9625.588, 'text': "But it's a bit weird that in an exam, you will have all the students with the same score.", 'start': 9620.185, 'duration': 5.403}, {'end': 9633.591, 'text': 'So what you will usually do is to add some different values to this column.', 'start': 9626.188, 'duration': 7.403}, {'end': 9636.753, 'text': 'So to do this, we have to use arrays.', 'start': 9633.991, 'duration': 2.762}, {'end': 9640.038, 'text': 'And to create arrays, we have to use NumPy.', 'start': 9637.334, 'duration': 2.704}, {'end': 9644.946, 'text': "So here in the second way to add a new column, we're going to use arrays.", 'start': 9640.519, 'duration': 4.427}, {'end': 9651.176, 'text': 'So in this case, we have first to see how many rows this data frame has.', 'start': 9645.707, 'duration': 5.469}, {'end': 9653.957, 'text': 'So in this case, it has 1000 rows.', 'start': 9651.956, 'duration': 2.001}, {'end': 9661.581, 'text': "And this is important because the number of rows has to match with the number of the rate we're going to create.", 'start': 9654.017, 'duration': 7.564}, {'end': 9664.062, 'text': "So let's create this array.", 'start': 9661.901, 'duration': 2.161}, {'end': 9666.563, 'text': "And first, let's import NumPy.", 'start': 9664.582, 'duration': 1.981}, {'end': 9671.385, 'text': 'So we write import NumPy as NP.', 'start': 9666.723, 'duration': 4.662}], 'summary': 'Adding a new column with a value of 70 to a data frame, then creating an array to assign different values to the column based on the number of rows in the data frame.', 'duration': 85.02, 'max_score': 9586.365, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo9586365.jpg'}, {'end': 9854.814, 'src': 'embed', 'start': 9823.197, 'weight': 12, 'content': [{'end': 9826.859, 'text': "So it's from zero and then one and it increases by one.", 'start': 9823.197, 'duration': 3.662}, {'end': 9831.743, 'text': 'And usually in scores, you will see that students have random scores.', 'start': 9827.46, 'duration': 4.283}, {'end': 9835.385, 'text': 'So we have to create here an array with random numbers.', 'start': 9831.863, 'duration': 3.522}, {'end': 9838.485, 'text': 'And to do that, we have to use NumPy again.', 'start': 9835.985, 'duration': 2.5}, {'end': 9841.306, 'text': 'But here we have to use a different method.', 'start': 9838.865, 'duration': 2.441}, {'end': 9845.327, 'text': 'In this case, the method is named random dot rant.', 'start': 9841.586, 'duration': 3.741}, {'end': 9854.814, 'text': "So let's write it here, NP dot random that ran and then I n t.", 'start': 9845.927, 'duration': 8.887}], 'summary': 'Using numpy, create an array with random numbers for scores.', 'duration': 31.617, 'max_score': 9823.197, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo9823197.jpg'}], 'start': 8567.528, 'title': 'Understanding pandas data frames and series syntax', 'summary': 'Covers the basics of using the describe method and built-in functions in pandas, including obtaining length, maximum and minimum values, data types, and rounding numerical data in a data frame. it also explains the attributes and methods of pandas dataframes and series, demonstrating two syntaxes for accessing columns and highlighting the practicality and pitfalls of each. additionally, it demonstrates how to select one or more columns from a data frame using python pandas, emphasizing the usage of square brackets to avoid errors and the distinction between obtaining a data frame and a series, with a focus on selecting two specific columns - the gender column and the math score column. furthermore, it demonstrates how to add new columns to a data frame, including adding a column with a single value, creating a column with an array of values, and generating a column with random integer values using numpy, providing a comprehensive understanding of the process.', 'chapters': [{'end': 8908.579, 'start': 8567.528, 'title': 'Pandas data frame basics', 'summary': 'Covers the basics of using the describe method and built-in functions in pandas, including obtaining length, maximum and minimum values, data types, and rounding numerical data in a data frame. it also explains how to select a column from a data frame using square brackets.', 'duration': 341.051, 'highlights': ['The describe method provides basic statistics of the numerical data in a data frame, with a maximum score of 100 on each.', 'The length of the data frame is 1000, indicating the number of rows.', 'The max function returns the highest index in the data frame, with a value of 999.', 'The min function returns the lowest index in the data frame, with a value of 0.', "The type function confirms the data frame type as 'data frame'.", 'The round function can be used to round numerical data in a data frame, specifying the number of decimal points.', 'Selecting a column from a data frame using square brackets is the preferred method in pandas.']}, {'end': 9054.744, 'start': 8909.379, 'title': 'Pandas dataframes and series syntax', 'summary': 'Explains the attributes and methods of pandas dataframes and series, demonstrating two syntaxes for accessing columns and highlighting the practicality and pitfalls of each.', 'duration': 145.365, 'highlights': ['The chapter highlights accessing attributes and methods of pandas dataframes and series, demonstrating the syntax for obtaining the index attribute and the head method, providing insights into the practical aspect of the first syntax.', 'It also discusses another syntax for selecting columns from a data frame, showcasing the ease of accessing columns with single-word names and highlighting the potential pitfalls when accessing columns with multiple words.', 'The chapter showcases the practicality of the first syntax by demonstrating the ease of accessing attributes and methods, such as obtaining the index attribute and using the head method to retrieve the first five rows of a series.', "It also highlights the potential pitfalls of the second syntax when accessing columns with multiple words, demonstrating the challenges in obtaining a column with a name like 'math score.'"]}, {'end': 9326.204, 'start': 9055.245, 'title': 'Selecting columns with python pandas', 'summary': 'Demonstrates how to select one or more columns from a data frame using python pandas, emphasizing the usage of square brackets to avoid errors and the distinction between obtaining a data frame and a series, with a focus on selecting two specific columns - the gender column and the math score column.', 'duration': 270.959, 'highlights': ['Using square brackets in Python Pandas is crucial for selecting columns to avoid errors, as Python interprets the underscore as a variable and requires the use of square brackets for a successful selection.', 'Selecting columns by using two pairs of square brackets in Python Pandas results in obtaining a data frame, while using a single pair retrieves a series, showcasing the distinction in data structures.', 'Demonstrating the selection of specific columns - the gender column and the math score column - from the data frame, providing clarity on the order of columns and the resultant data frame.']}, {'end': 9910.545, 'start': 9326.724, 'title': 'Adding new columns to a data frame', 'summary': 'Demonstrates how to add new columns to a data frame, including adding a column with a single value, creating a column with an array of values, and generating a column with random integer values using numpy, providing a comprehensive understanding of the process.', 'duration': 583.821, 'highlights': ['The chapter demonstrates how to add new columns to a data frame, including adding a column with a single value, creating a column with an array of values, and generating a column with random integer values using NumPy.', "The data frame has 1000 rows, and a new column 'language score' is added with a scalar value of 70 to all the rows.", "A new column 'language score' is created with an array of 1000 elements using NumPy, providing a range of values for the column.", "A new column 'language score' is generated with random integer values ranging from 1 to 100 using NumPy."]}], 'duration': 1343.017, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo8567528.jpg', 'highlights': ['The describe method provides basic statistics of the numerical data in a data frame, with a maximum score of 100 on each.', 'The length of the data frame is 1000, indicating the number of rows.', 'The max function returns the highest index in the data frame, with a value of 999.', 'The min function returns the lowest index in the data frame, with a value of 0.', "The type function confirms the data frame type as 'data frame'.", 'The round function can be used to round numerical data in a data frame, specifying the number of decimal points.', 'Selecting a column from a data frame using square brackets is the preferred method in pandas.', 'Using square brackets in Python Pandas is crucial for selecting columns to avoid errors, as Python interprets the underscore as a variable and requires the use of square brackets for a successful selection.', 'Selecting columns by using two pairs of square brackets in Python Pandas results in obtaining a data frame, while using a single pair retrieves a series, showcasing the distinction in data structures.', 'The chapter demonstrates how to add new columns to a data frame, including adding a column with a single value, creating a column with an array of values, and generating a column with random integer values using NumPy.', "The data frame has 1000 rows, and a new column 'language score' is added with a scalar value of 70 to all the rows.", "A new column 'language score' is created with an array of 1000 elements using NumPy, providing a range of values for the column.", "A new column 'language score' is generated with random integer values ranging from 1 to 100 using NumPy.", 'The chapter highlights accessing attributes and methods of pandas dataframes and series, demonstrating the syntax for obtaining the index attribute and the head method, providing insights into the practical aspect of the first syntax.', 'It also discusses another syntax for selecting columns from a data frame, showcasing the ease of accessing columns with single-word names and highlighting the potential pitfalls when accessing columns with multiple words.', 'The chapter showcases the practicality of the first syntax by demonstrating the ease of accessing attributes and methods, such as obtaining the index attribute and using the head method to retrieve the first five rows of a series.', "It also highlights the potential pitfalls of the second syntax when accessing columns with multiple words, demonstrating the challenges in obtaining a column with a name like 'math score.'", 'Selecting columns by using two pairs of square brackets in Python Pandas results in obtaining a data frame, while using a single pair retrieves a series, showcasing the distinction in data structures.', 'Demonstrating the selection of specific columns - the gender column and the math score column - from the data frame, providing clarity on the order of columns and the resultant data frame.']}, {'end': 11350.013, 'segs': [{'end': 10071.854, 'src': 'embed', 'start': 10013.666, 'weight': 7, 'content': [{'end': 10026.134, 'text': 'So now this new data looks more like scores like real scores because these are random numbers and these are between zero and 99.', 'start': 10013.666, 'duration': 12.468}, {'end': 10026.694, 'text': "And that's it.", 'start': 10026.134, 'duration': 0.56}, {'end': 10033.699, 'text': 'Now one more little detail I want to share with you is how to create random float numbers.', 'start': 10027.114, 'duration': 6.585}, {'end': 10042.126, 'text': 'Because before we created random integer number, but if for some reason you want to create random float numbers,', 'start': 10033.94, 'duration': 8.186}, {'end': 10044.167, 'text': 'there is a way how to do it with NumPy.', 'start': 10042.126, 'duration': 2.041}, {'end': 10049.832, 'text': 'So we only write NP, then that random, then that uniform.', 'start': 10044.668, 'duration': 5.164}, {'end': 10052.274, 'text': 'And arguments are the same.', 'start': 10050.712, 'duration': 1.562}, {'end': 10057.779, 'text': 'So the minimum value, then the maximum value, then the size, which is 1000.', 'start': 10052.374, 'duration': 5.405}, {'end': 10063.605, 'text': "Then you run this and well, it's similar to the one we got before.", 'start': 10057.779, 'duration': 5.826}, {'end': 10065.768, 'text': 'But now we have float numbers.', 'start': 10063.866, 'duration': 1.902}, {'end': 10066.568, 'text': "And that's it.", 'start': 10065.968, 'duration': 0.6}, {'end': 10071.854, 'text': 'In this video, we learn different ways to add a new column to a data frame.', 'start': 10066.729, 'duration': 5.125}], 'summary': 'Generating random float numbers with numpy for data frame', 'duration': 58.188, 'max_score': 10013.666, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo10013666.jpg'}, {'end': 10246.619, 'src': 'embed', 'start': 10214.093, 'weight': 1, 'content': [{'end': 10219.296, 'text': 'And here we got the average value of this math score column.', 'start': 10214.093, 'duration': 5.203}, {'end': 10230.523, 'text': 'So to get the average, we have to sum all the rows in this math score column, and then divided by the total number of rows, in this case 1000.', 'start': 10219.756, 'duration': 10.767}, {'end': 10233.425, 'text': 'And this is how you get this mean value.', 'start': 10230.523, 'duration': 2.902}, {'end': 10240.532, 'text': 'then we can get other other operations using the method.', 'start': 10234.125, 'duration': 6.407}, {'end': 10246.619, 'text': 'So here, for example, we can get the standard deviation by writing std.', 'start': 10240.752, 'duration': 5.867}], 'summary': 'Average math score calculated from 1000 rows using the mean value.', 'duration': 32.526, 'max_score': 10214.093, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo10214093.jpg'}, {'end': 10320.723, 'src': 'embed', 'start': 10270.857, 'weight': 0, 'content': [{'end': 10278.542, 'text': 'So here I ran in as you can see here, the minimum value of the math score is zero in the maximum is 100.', 'start': 10270.857, 'duration': 7.685}, {'end': 10284.606, 'text': "Okay, now I'm going to show you a quickly way to make the same calculations using that described method.", 'start': 10278.542, 'duration': 6.064}, {'end': 10288.989, 'text': 'I think we saw that described method in previous videos.', 'start': 10285.387, 'duration': 3.602}, {'end': 10293.272, 'text': "But in case you don't remember it, I'm going to write here.", 'start': 10289.45, 'duration': 3.822}, {'end': 10295.434, 'text': 'the name of.', 'start': 10294.473, 'duration': 0.961}, {'end': 10297.774, 'text': 'actually, we only need the name of the data frame.', 'start': 10295.434, 'duration': 2.34}, {'end': 10298.415, 'text': "we don't.", 'start': 10297.774, 'duration': 0.641}, {'end': 10301.596, 'text': "we don't need the name of a specific column.", 'start': 10298.415, 'duration': 3.181}, {'end': 10303.316, 'text': 'we only need the name of the data frame.', 'start': 10301.596, 'duration': 1.72}, {'end': 10305.597, 'text': 'And now we can use the describe method.', 'start': 10303.536, 'duration': 2.061}, {'end': 10308.518, 'text': 'So we write that describe with parentheses.', 'start': 10305.837, 'duration': 2.681}, {'end': 10314.581, 'text': 'And now we got like a summary table with some important statistical values.', 'start': 10309.259, 'duration': 5.322}, {'end': 10320.723, 'text': 'And here we have the count that mean the standard deviation, the minimum and maximum value.', 'start': 10314.961, 'duration': 5.762}], 'summary': 'Using the describe method, the math score has a range from 0 to 100 with important statistical values.', 'duration': 49.866, 'max_score': 10270.857, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo10270857.jpg'}, {'end': 10578.004, 'src': 'embed', 'start': 10549.815, 'weight': 5, 'content': [{'end': 10556.497, 'text': 'And as you can see here, our data frame looks much better, because we only have two decimals.', 'start': 10549.815, 'duration': 6.682}, {'end': 10557.518, 'text': "And that's it.", 'start': 10556.917, 'duration': 0.601}, {'end': 10564.26, 'text': 'In this video, we learn different ways to make operations in columns and rows on data frames.', 'start': 10557.718, 'duration': 6.542}, {'end': 10570.202, 'text': "Alright, now let's have a look at the value counts method.", 'start': 10566.861, 'duration': 3.341}, {'end': 10578.004, 'text': 'So so far, we have seen how to count the number of rows in a data frame.', 'start': 10570.942, 'duration': 7.062}], 'summary': 'Data frame improved with 2 decimals. video covers operations on data frames and value counts method.', 'duration': 28.189, 'max_score': 10549.815, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo10549815.jpg'}, {'end': 10791.901, 'src': 'embed', 'start': 10759.252, 'weight': 3, 'content': [{'end': 10766.954, 'text': 'Now to count the elements by category in this column, we use the value underscore counts method.', 'start': 10759.252, 'duration': 7.702}, {'end': 10769.395, 'text': 'So we run this code.', 'start': 10767.634, 'duration': 1.761}, {'end': 10773.396, 'text': 'And here you can see how the data is divided in this column.', 'start': 10769.935, 'duration': 3.461}, {'end': 10781.298, 'text': 'So most people have some college level of education, while just a few people have a master degree.', 'start': 10773.596, 'duration': 7.702}, {'end': 10791.901, 'text': 'And now if we want to get the percentages that represent each category, we again use the normalize argument.', 'start': 10781.858, 'duration': 10.043}], 'summary': "Data shows majority with some college education, few with master's degree.", 'duration': 32.649, 'max_score': 10759.252, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo10759252.jpg'}, {'end': 10908.729, 'src': 'embed', 'start': 10881.853, 'weight': 6, 'content': [{'end': 10885.994, 'text': 'So in this case, I want to sort by the math score.', 'start': 10881.853, 'duration': 4.141}, {'end': 10889.355, 'text': "So I'm choosing this numerical column to start with.", 'start': 10886.254, 'duration': 3.101}, {'end': 10891.957, 'text': "So I'm going to write math score.", 'start': 10890.155, 'duration': 1.802}, {'end': 10895.84, 'text': "Actually, I'm going to copy this one and paste it here.", 'start': 10892.457, 'duration': 3.383}, {'end': 10901.204, 'text': 'So by math score and sorting this data frame is as simple as that.', 'start': 10895.96, 'duration': 5.244}, {'end': 10903.305, 'text': 'Now we can run this code.', 'start': 10901.684, 'duration': 1.621}, {'end': 10908.729, 'text': 'And as you can see here, the data frame was sort ascending by default.', 'start': 10903.565, 'duration': 5.164}], 'summary': 'Sorting data frame by math score in ascending order.', 'duration': 26.876, 'max_score': 10881.853, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo10881853.jpg'}, {'end': 11013.744, 'src': 'embed', 'start': 10984.609, 'weight': 4, 'content': [{'end': 10989.533, 'text': 'And as you can see here is sort descending by the math score column.', 'start': 10984.609, 'duration': 4.924}, {'end': 10991.815, 'text': 'So here it starts with 100.', 'start': 10989.633, 'duration': 2.182}, {'end': 10994.117, 'text': 'And it ends with zero.', 'start': 10991.815, 'duration': 2.302}, {'end': 10999.479, 'text': "But that's not all we can do much more with a sort underscore value method.", 'start': 10994.517, 'duration': 4.962}, {'end': 11005.701, 'text': "So first, I'm going to show you here how to sort by two different columns.", 'start': 10999.919, 'duration': 5.782}, {'end': 11009.582, 'text': "So here, let's copy and paste this one.", 'start': 11006.221, 'duration': 3.361}, {'end': 11013.744, 'text': "So in this case, we're going to sort descending by multiple columns.", 'start': 11009.722, 'duration': 4.022}], 'summary': 'Sorting method can arrange data by multiple columns.', 'duration': 29.135, 'max_score': 10984.609, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo10984609.jpg'}], 'start': 9910.625, 'title': 'Data frame operations in python', 'summary': 'Explains python slicing and indexing, adding and performing operations on data frames, operations in rows and value counts, and sorting data frames by numerical and text columns, with examples of specific methods and techniques used in pandas and numpy libraries.', 'chapters': [{'end': 9973.169, 'start': 9910.625, 'title': 'Python slicing and indexing', 'summary': 'Explains python slicing and indexing, including the use of inclusive and exclusive arguments, and the insertion of random integer numbers into a new column.', 'duration': 62.544, 'highlights': ['The first argument in Python indexing is inclusive, while the last one is exclusive, demonstrated with a minimum value of 1 and a maximum value of 100.', 'Demonstrates the insertion of random integer numbers into a new column created in Python.']}, {'end': 10320.723, 'start': 9973.269, 'title': 'Adding columns and performing operations on data frames', 'summary': 'Demonstrates adding new columns to a data frame using numpy to generate random integer and float numbers and performing common operations on numerical columns in pandas, such as calculating sum, count, mean, standard deviation, and obtaining maximum and minimum values.', 'duration': 347.454, 'highlights': ['The chapter demonstrates adding a new column to a data frame by using NumPy to create random integer and float numbers, which are between 0 and 99 and 0 and 1 respectively, and then explores performing common operations like calculating the sum, count, mean, standard deviation, and obtaining maximum and minimum values for numerical columns in pandas.', 'The method for creating random float numbers using NumPy is shown, with the range of float numbers generated being between 0 and 1 and the size of the sample being 1000.', 'The process of adding a new column to a data frame by generating random integer numbers between 0 and 99 using NumPy is illustrated.', 'The chapter explains how to calculate the total sum of a column in a data frame, showcasing the calculation of the total sum of the math score column which equals 66000.', 'Demonstrating the calculation of the number of rows in a data frame, the chapter showcases the count method, resulting in 1000 rows being counted, which is the correct count for the data frame used.', 'The process of calculating the mean value of a column in a data frame using the mean method is presented, resulting in the average value of the math score column being displayed.', 'The method for obtaining the standard deviation of a column in a data frame using the std method is illustrated, resulting in the standard deviation of the math score column being 15.', 'The chapter showcases the process of obtaining the maximum and minimum values of a column in a data frame using the max and min methods respectively, with the minimum value being 0 and the maximum value being 100 for the math score column.', 'The use of the describe method to obtain a summary table with important statistical values including count, mean, standard deviation, minimum, and maximum values for a data frame is demonstrated.']}, {'end': 10816.968, 'start': 10321.343, 'title': 'Operations in rows and value counts', 'summary': 'Demonstrates how to perform operations in rows to calculate sums and averages of specific columns in a data frame, and how to use the value counts method to count elements by category and calculate their percentages.', 'duration': 495.625, 'highlights': ['The chapter demonstrates how to perform operations in rows to calculate sums and averages of specific columns in a data frame.', 'The chapter explains the usage of the value counts method to count elements by category and calculate their percentages.']}, {'end': 11350.013, 'start': 10819.443, 'title': 'Sorting data frames', 'summary': 'Demonstrates how to use the sort_values method to sort a data frame by numerical and text columns, prioritize sorting by multiple columns, update the data frame using the in place argument, and sort text data using a lambda function.', 'duration': 530.57, 'highlights': ['The sort_values method sorts the data frame by default in ascending order, and can be modified to sort in descending order by specifying the ascending argument as false.', 'The sort_values method can prioritize sorting by multiple columns by specifying the columns in a list, where the first priority is the first column and the second priority is the second column.', 'The in place argument, when set to true, updates the data frame with the sorted values, while the lambda function can be used to sort text data by transforming it to lowercase.']}], 'duration': 1439.388, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo9910625.jpg', 'highlights': ['The use of the describe method to obtain a summary table with important statistical values including count, mean, standard deviation, minimum, and maximum values for a data frame is demonstrated.', 'The method for obtaining the standard deviation of a column in a data frame using the std method is illustrated, resulting in the standard deviation of the math score column being 15.', 'The process of obtaining the maximum and minimum values of a column in a data frame using the max and min methods respectively, with the minimum value being 0 and the maximum value being 100 for the math score column.', 'The chapter explains the usage of the value counts method to count elements by category and calculate their percentages.', 'The sort_values method can prioritize sorting by multiple columns by specifying the columns in a list, where the first priority is the first column and the second priority is the second column.', 'The chapter demonstrates how to perform operations in rows to calculate sums and averages of specific columns in a data frame.', 'The sort_values method sorts the data frame by default in ascending order, and can be modified to sort in descending order by specifying the ascending argument as false.', 'The method for creating random float numbers using NumPy is shown, with the range of float numbers generated being between 0 and 1 and the size of the sample being 1000.', 'The process of adding a new column to a data frame by generating random integer numbers between 0 and 99 using NumPy is illustrated.', 'The process of calculating the mean value of a column in a data frame using the mean method is presented, resulting in the average value of the math score column being displayed.']}, {'end': 12519.37, 'segs': [{'end': 11405.09, 'src': 'embed', 'start': 11379.14, 'weight': 1, 'content': [{'end': 11385.684, 'text': 'So as you can see here, we have this race ethnicity column and its order ascending.', 'start': 11379.14, 'duration': 6.544}, {'end': 11390.286, 'text': 'So here we got the A and B and C and D and so on.', 'start': 11385.844, 'duration': 4.442}, {'end': 11391.127, 'text': "And that's it.", 'start': 11390.546, 'duration': 0.581}, {'end': 11397.05, 'text': 'These are the different ways to sort a data frame using the sort underscore values method.', 'start': 11391.287, 'duration': 5.763}, {'end': 11400.363, 'text': 'Welcome back.', 'start': 11399.642, 'duration': 0.721}, {'end': 11405.09, 'text': "In this video, we're going to see different ways to make pivot tables.", 'start': 11400.944, 'duration': 4.146}], 'summary': 'Demonstrating sorting a data frame and creating pivot tables.', 'duration': 25.95, 'max_score': 11379.14, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo11379140.jpg'}, {'end': 11518.507, 'src': 'embed', 'start': 11487.302, 'weight': 2, 'content': [{'end': 11490.326, 'text': 'In this case, we can use the pivot method.', 'start': 11487.302, 'duration': 3.024}, {'end': 11496.213, 'text': "as I'm going to show you right now, you only have to write the name of the data frame, followed by the pivot method.", 'start': 11490.326, 'duration': 5.887}, {'end': 11499.695, 'text': 'and then specify three arguments.', 'start': 11496.793, 'duration': 2.902}, {'end': 11502.037, 'text': 'So the first one is the index.', 'start': 11500.195, 'duration': 1.842}, {'end': 11509.261, 'text': "In this case, I'm going to reshape this data frame, choosing the column foo as an index.", 'start': 11502.637, 'duration': 6.624}, {'end': 11518.507, 'text': 'This means that the column foo will be in the position where is right now the numbers from zero to five on the left.', 'start': 11509.702, 'duration': 8.805}], 'summary': "Using pivot method to reshape data frame by choosing 'foo' as index.", 'duration': 31.205, 'max_score': 11487.302, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo11487302.jpg'}, {'end': 12148.74, 'src': 'embed', 'start': 12124.17, 'weight': 3, 'content': [{'end': 12132.053, 'text': 'Okay, the goal of this task is to see how much female and male is spent their money in this supermarket.', 'start': 12124.17, 'duration': 7.883}, {'end': 12136.935, 'text': "So to do that, we're going to use the pivot table method in pandas.", 'start': 12132.513, 'duration': 4.422}, {'end': 12139.536, 'text': "So first, I'm going to copy this.", 'start': 12137.475, 'duration': 2.061}, {'end': 12141.016, 'text': 'data frame.', 'start': 12140.436, 'duration': 0.58}, {'end': 12143.278, 'text': "And now I'm going to paste it here.", 'start': 12141.557, 'duration': 1.721}, {'end': 12148.74, 'text': "And now we're going to make a pivot table and add an output function.", 'start': 12144.098, 'duration': 4.642}], 'summary': 'Analyze supermarket spending by gender using pivot table in pandas.', 'duration': 24.57, 'max_score': 12124.17, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo12124170.jpg'}, {'end': 12437.098, 'src': 'embed', 'start': 12405.083, 'weight': 0, 'content': [{'end': 12412.126, 'text': 'So far, we know that female spend 167, 000 in this supermarket.', 'start': 12405.083, 'duration': 7.043}, {'end': 12418.809, 'text': 'But with pivot tables, we can even know in which product lines this money is spent.', 'start': 12412.226, 'duration': 6.583}, {'end': 12420.43, 'text': 'So let me show you here.', 'start': 12419.269, 'duration': 1.161}, {'end': 12425.933, 'text': 'we can see how the money is spent in this product line column.', 'start': 12421.17, 'duration': 4.763}, {'end': 12431.275, 'text': 'So we only have to add a new argument to this pivot table method.', 'start': 12426.513, 'duration': 4.762}, {'end': 12433.116, 'text': "So I'm going to show you here.", 'start': 12431.815, 'duration': 1.301}, {'end': 12435.037, 'text': 'First, we copy this.', 'start': 12433.836, 'duration': 1.201}, {'end': 12437.098, 'text': "And now I'm going to paste it here.", 'start': 12435.677, 'duration': 1.421}], 'summary': 'Female customers spent $167,000 in the supermarket, using pivot tables to track spending by product lines.', 'duration': 32.015, 'max_score': 12405.083, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo12405083.jpg'}], 'start': 11350.453, 'title': 'Pandas data frame sorting, pivot methods, and gdp data analysis', 'summary': 'Covers sorting a data frame, differences between pivot and pivot_table methods, and reshaping gdp data for analysis. it also demonstrates using pivot method for obtaining different views of the original data set, and visualizing gdp per capita and supermarket sales data with pivot table method. additionally, it explains creating pivot tables for analyzing spending patterns of male and female customers in a supermarket, including total spending, quantity of products purchased, and further analysis by product category.', 'chapters': [{'end': 11716.357, 'start': 11350.453, 'title': 'Pandas data frame sorting and pivot methods', 'summary': 'Demonstrates sorting a data frame using the sort_values method and explains the difference between the pivot method and the pivot_table method, highlighting their functionalities and use cases, and introduces the pivot method with an example showcasing how to reshape data based on column values.', 'duration': 365.904, 'highlights': ['The chapter demonstrates sorting a data frame using the sort_values method.', 'The chapter explains the difference between the pivot method and the pivot_table method, highlighting their functionalities and use cases.', 'The chapter introduces the pivot method with an example showcasing how to reshape data based on column values.']}, {'end': 11950.611, 'start': 11716.477, 'title': 'Reshaping gdp data analysis', 'summary': "Introduces the process of reshaping a gdp data set to analyze the evolution of gdp per capita over the years for each country using python's pandas library, demonstrating the use of pivot method to obtain a different view of the original data set.", 'duration': 234.134, 'highlights': ['The goal of this exercise is to see the evolution of the GDP per capita over the years for each country.', 'Using the pivot method, the data frame is reshaped with the year column as the index and each country listed in the country column as an independent column.', 'The new data frame will only show the GDP per capita data.']}, {'end': 12172.067, 'start': 11951.151, 'title': 'Pivot table method in pandas', 'summary': 'Demonstrates the use of the pivot table method in pandas to visualize the evolution of gdp per capita over the years for different countries and to analyze supermarket sales data to see the spending patterns of males and females.', 'duration': 220.916, 'highlights': ['The chapter explains how to use the pivot table method in pandas to visualize the evolution of GDP per capita over the years for different countries.', 'It also illustrates the application of the pivot table method in pandas to analyze supermarket sales data and understand the spending patterns of males and females.']}, {'end': 12519.37, 'start': 12173.167, 'title': 'Supermarket gender spending analysis', 'summary': 'Explains how to create a pivot table in python to analyze the spending patterns of male and female customers in a supermarket, showcasing the total spending and quantity of products purchased, and how to further analyze spending by product category.', 'duration': 346.203, 'highlights': ['The pivot table method in Python is used to analyze the spending patterns of male and female customers in a supermarket, showcasing the total spending and quantity of products purchased, and how to further analyze spending by product category.', 'The pivot table displays the total spending and quantity of products purchased, divided by gender, revealing that females spend more than males in the supermarket.', 'A detailed breakdown of spending by product category is shown using a pivot table, demonstrating how much female and male customers spent in each product line, providing insights such as females spending more on fashion accessories than males.']}], 'duration': 1168.917, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo11350453.jpg', 'highlights': ['The pivot table method is used to analyze spending patterns of male and female customers, revealing females spend more in the supermarket.', 'The chapter demonstrates sorting a data frame using the sort_values method.', 'The pivot method reshapes the data frame with the year column as the index and each country listed in the country column as an independent column.', 'The pivot table method in Python is used to analyze spending patterns of male and female customers, showcasing total spending and quantity of products purchased.']}, {'end': 13132.039, 'segs': [{'end': 12579.668, 'src': 'embed', 'start': 12520.111, 'weight': 0, 'content': [{'end': 12526.472, 'text': 'And also in sports, women spend or female spend more money than male.', 'start': 12520.111, 'duration': 6.361}, {'end': 12533.095, 'text': 'So we could easily see all of that by using the pivot underscore table method in pandas.', 'start': 12526.613, 'duration': 6.482}, {'end': 12536.996, 'text': 'And this is similar to the pivot table you will find in Excel.', 'start': 12533.375, 'duration': 3.621}, {'end': 12537.936, 'text': "And that's it.", 'start': 12537.316, 'duration': 0.62}, {'end': 12540.997, 'text': "That's how you make a pivot table in pandas.", 'start': 12538.116, 'duration': 2.881}, {'end': 12547.395, 'text': 'Alright, before showing you how to make visualizations with pandas.', 'start': 12543.334, 'duration': 4.061}, {'end': 12549.936, 'text': 'First, we have to check the data set.', 'start': 12548.015, 'duration': 1.921}, {'end': 12552.817, 'text': 'And also we have to make a pivot table.', 'start': 12550.376, 'duration': 2.441}, {'end': 12556.958, 'text': 'So we can easily make the plots with pandas later.', 'start': 12553.257, 'duration': 3.701}, {'end': 12562.599, 'text': 'So first, we have to import pandas to read this CSV file.', 'start': 12557.618, 'duration': 4.981}, {'end': 12566.301, 'text': 'And well, I have this import pandas as PD.', 'start': 12563.22, 'duration': 3.081}, {'end': 12569.182, 'text': 'So we just run this code.', 'start': 12566.501, 'duration': 2.681}, {'end': 12572.564, 'text': "And now let's read this new data set.", 'start': 12569.362, 'duration': 3.202}, {'end': 12579.668, 'text': 'So as you might remember, to read a CSV file, we have to use the read underscore CSV method.', 'start': 12572.964, 'duration': 6.704}], 'summary': 'Women spend more on sports than men. pivot tables in pandas are similar to excel. import data and make pivot table for visualizations.', 'duration': 59.557, 'max_score': 12520.111, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo12520111.jpg'}, {'end': 12762.61, 'src': 'embed', 'start': 12734.178, 'weight': 1, 'content': [{'end': 12740.122, 'text': 'But if we want to save the changes that we make to the data frame, we have two options.', 'start': 12734.178, 'duration': 5.944}, {'end': 12744.465, 'text': 'The first option is to use the in place argument.', 'start': 12740.522, 'duration': 3.943}, {'end': 12747.686, 'text': 'So I write in place and then set this to true.', 'start': 12744.605, 'duration': 3.081}, {'end': 12754.548, 'text': 'So if we do this, and we run, all the changes that we make to the data frame are going to be saved.', 'start': 12748.166, 'duration': 6.382}, {'end': 12762.61, 'text': 'And the second option is to do something like this to override the content inside this data frame.', 'start': 12755.208, 'duration': 7.402}], 'summary': "Dataframe changes can be saved using 'in place' or by overriding the content.", 'duration': 28.432, 'max_score': 12734.178, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo12734178.jpg'}, {'end': 12820.782, 'src': 'embed', 'start': 12792.841, 'weight': 2, 'content': [{'end': 12796.604, 'text': "Okay, now it's time to make this pivot table.", 'start': 12792.841, 'duration': 3.763}, {'end': 12800.428, 'text': "So first, I'm going to show you what I'm going to do.", 'start': 12797.105, 'duration': 3.323}, {'end': 12803.691, 'text': 'So we have a better idea before writing the code.', 'start': 12800.848, 'duration': 2.843}, {'end': 12807.133, 'text': 'So here we have the original data frame.', 'start': 12804.171, 'duration': 2.962}, {'end': 12811.036, 'text': "And what we're going to do is to reshape this data frame.", 'start': 12807.773, 'duration': 3.263}, {'end': 12820.782, 'text': 'So I want that year to be in that index, so that your column, I want it to be here in the index instead of 01, and so on.', 'start': 12811.716, 'duration': 9.066}], 'summary': 'Creating a pivot table to reshape the data frame.', 'duration': 27.941, 'max_score': 12792.841, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo12792841.jpg'}, {'end': 13004.782, 'src': 'embed', 'start': 12974.151, 'weight': 3, 'content': [{'end': 12980.774, 'text': "So it's here the year from 1955 to 2020.", 'start': 12974.151, 'duration': 6.623}, {'end': 12987.638, 'text': 'So we can see here the evolution of the population throughout the years for all the countries in this data set.', 'start': 12980.774, 'duration': 6.864}, {'end': 12991.059, 'text': 'But as you can see, there are many countries.', 'start': 12988.378, 'duration': 2.681}, {'end': 12995.8, 'text': 'So what we can do here is to select just some countries.', 'start': 12991.619, 'duration': 4.181}, {'end': 13001.001, 'text': 'So we can simplify our visualizations later in pandas.', 'start': 12996.2, 'duration': 4.801}, {'end': 13004.782, 'text': "So here, I'm going to select some columns.", 'start': 13001.421, 'duration': 3.361}], 'summary': 'Population data from 1955 to 2020 for multiple countries will be visualized and analyzed using pandas.', 'duration': 30.631, 'max_score': 12974.151, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo12974151.jpg'}], 'start': 12520.111, 'title': 'Creating pivot table in pandas', 'summary': 'Covers creating a pivot table in pandas for data reshaping, selecting specific countries for visualization, and includes details on dropping null values and saving changes made to the data frame.', 'chapters': [{'end': 12792.52, 'start': 12520.111, 'title': 'Pivot table in pandas for data visualization', 'summary': 'Covers the process of creating a pivot table in pandas to reshape a data frame for visualization and includes details on dropping null values and options for saving changes made to the data frame.', 'duration': 272.409, 'highlights': ["The pivot table method in pandas is used to reshape the data frame and make it suitable for creating visualizations, and it's similar to the pivot table function in Excel.", 'The process involves dropping null values from the data frame using the dropna method, and the changes made to the data frame can be saved using either the in place argument set to true or by overwriting the content inside the data frame.', 'Women spend more money than men in sports, indicating a potential gender-based spending trend.', 'The chapter demonstrates the use of the read_csv method in pandas to read a CSV file and the importance of checking the dataset before proceeding to create visualizations.', 'The data set contains population data for various countries throughout the years, extracted using web scraping techniques, and is initially represented in a row format, requiring reshaping for visualization purposes.']}, {'end': 13132.039, 'start': 12792.841, 'title': 'Creating pivot table for data reshaping', 'summary': 'Demonstrates the process of reshaping the original data frame into a pivot table, with the year as the index, country as columns, and population data as the values. it then selects and simplifies the data by choosing specific countries for visualization in pandas.', 'duration': 339.198, 'highlights': ['The chapter demonstrates the process of reshaping the original data frame into a pivot table', 'It then selects and simplifies the data by choosing specific countries for visualization in pandas']}], 'duration': 611.928, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo12520111.jpg', 'highlights': ["The pivot table method in pandas is used to reshape the data frame and make it suitable for creating visualizations, and it's similar to the pivot table function in Excel.", 'The process involves dropping null values from the data frame using the dropna method, and the changes made to the data frame can be saved using either the in place argument set to true or by overwriting the content inside the data frame.', 'The chapter demonstrates the process of reshaping the original data frame into a pivot table', 'It then selects and simplifies the data by choosing specific countries for visualization in pandas', 'The chapter demonstrates the use of the read_csv method in pandas to read a CSV file and the importance of checking the dataset before proceeding to create visualizations.']}, {'end': 14265.149, 'segs': [{'end': 13279.498, 'src': 'embed', 'start': 13215.468, 'weight': 0, 'content': [{'end': 13226.859, 'text': 'they had some fast growing population, while United States, Indonesia and Brazil they have a lower population,', 'start': 13215.468, 'duration': 11.391}, {'end': 13231.082, 'text': "when also the population didn't change so much in the past 50 years.", 'start': 13226.859, 'duration': 4.223}, {'end': 13238.11, 'text': 'here we can add more arguments to this plot method to customize this line plot.', 'start': 13232.103, 'duration': 6.007}, {'end': 13243.996, 'text': 'So here we can introduce another argument, which is the x label.', 'start': 13238.69, 'duration': 5.306}, {'end': 13257.066, 'text': 'And this x label is what you can see here, here, when we created this line plot, By default, it was assigned this year label, but we can change it.', 'start': 13244.657, 'duration': 12.409}, {'end': 13264.69, 'text': "So for example, let's say we have, we want to write year, but now with capital letter, so we write year here.", 'start': 13257.367, 'duration': 7.323}, {'end': 13271.533, 'text': "And now let's say we want to add a new label here in the y axis.", 'start': 13264.71, 'duration': 6.823}, {'end': 13275.875, 'text': 'So here, we have only to write y label.', 'start': 13271.993, 'duration': 3.882}, {'end': 13279.498, 'text': 'And then equal to open quotes.', 'start': 13277.015, 'duration': 2.483}], 'summary': 'Global population trends over 50 years, usa, indonesia, brazil have lower growth.', 'duration': 64.03, 'max_score': 13215.468, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo13215468.jpg'}, {'end': 13412.222, 'src': 'embed', 'start': 13382.653, 'weight': 2, 'content': [{'end': 13385.415, 'text': "And that's how you can customize this line plot.", 'start': 13382.653, 'duration': 2.762}, {'end': 13390.832, 'text': "Okay, now let's make a bar plot with pandas.", 'start': 13387.95, 'duration': 2.882}, {'end': 13395.274, 'text': 'So the first thing we have to do is to select only one year.', 'start': 13391.372, 'duration': 3.902}, {'end': 13402.898, 'text': 'So the bar plot only accepts just one year and we can plot there the population of different countries.', 'start': 13395.834, 'duration': 7.064}, {'end': 13406.519, 'text': "So let's select one year of this data frame we have before.", 'start': 13402.958, 'duration': 3.561}, {'end': 13409.181, 'text': "So I'm going to copy the name of the data frame.", 'start': 13406.78, 'duration': 2.401}, {'end': 13412.222, 'text': 'So you can check it out again.', 'start': 13410.001, 'duration': 2.221}], 'summary': 'Customize line plot, make bar plot with pandas for one year, and plot population of different countries.', 'duration': 29.569, 'max_score': 13382.653, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo13382653.jpg'}, {'end': 13629.892, 'src': 'embed', 'start': 13597.993, 'weight': 6, 'content': [{'end': 13604.796, 'text': 'So I write plot again, open parentheses, and the first argument is the kind.', 'start': 13597.993, 'duration': 6.803}, {'end': 13607.658, 'text': 'So I open quotes, and we write bar.', 'start': 13605.057, 'duration': 2.601}, {'end': 13610.779, 'text': "So now it's ready, and we can run it.", 'start': 13608.658, 'duration': 2.121}, {'end': 13614.022, 'text': 'So as you can see, we have a basic bar plot.', 'start': 13611.34, 'duration': 2.682}, {'end': 13620.206, 'text': 'And it has some default values like the name of this x label.', 'start': 13614.822, 'duration': 5.384}, {'end': 13623.108, 'text': 'And also the default color is blue.', 'start': 13620.806, 'duration': 2.302}, {'end': 13627.25, 'text': 'And we can customize this bar plot a bit more.', 'start': 13624.108, 'duration': 3.142}, {'end': 13629.892, 'text': 'For example, I want a different color.', 'start': 13627.731, 'duration': 2.161}], 'summary': 'Demonstrating how to create a basic bar plot with default values and customization options.', 'duration': 31.899, 'max_score': 13597.993, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo13597993.jpg'}, {'end': 13724.087, 'src': 'embed', 'start': 13695.013, 'weight': 7, 'content': [{'end': 13701.137, 'text': "Now let's go one step further by making bar plots grouped by n variables.", 'start': 13695.013, 'duration': 6.124}, {'end': 13708.623, 'text': 'So here, we have to select a group of years to make these bar plots grouped by n variables.', 'start': 13701.878, 'duration': 6.745}, {'end': 13714.907, 'text': "So I'm going to copy this code we use before to select only the year 2020.", 'start': 13709.163, 'duration': 5.744}, {'end': 13724.087, 'text': "I'm going to copy this And in this case, I'm not going to select only one year, but a group of years.", 'start': 13714.907, 'duration': 9.18}], 'summary': 'Creating bar plots grouped by n variables, selecting a group of years including 2020.', 'duration': 29.074, 'max_score': 13695.013, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo13695013.jpg'}, {'end': 13884.218, 'src': 'embed', 'start': 13854.915, 'weight': 8, 'content': [{'end': 13864.586, 'text': "Okay, in this video, we're going to learn one of the most common charts that we can make in pandas in actually any other visualization tool.", 'start': 13854.915, 'duration': 9.671}, {'end': 13866.588, 'text': 'And these are pie charts.', 'start': 13865.147, 'duration': 1.441}, {'end': 13874.035, 'text': "So before we make this pie chart, first, let's give a look to the the data frame we're going to use.", 'start': 13867.149, 'duration': 6.886}, {'end': 13884.218, 'text': "In this case, to make a pie chart, we're going to use the same data frame we just for making the bar plot, because it follows the same logic.", 'start': 13874.696, 'duration': 9.522}], 'summary': 'Learn to create pie charts in pandas with common data frames.', 'duration': 29.303, 'max_score': 13854.915, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo13854915.jpg'}, {'end': 14115.177, 'src': 'embed', 'start': 14055.204, 'weight': 3, 'content': [{'end': 14066.488, 'text': 'If you want, you can even add another argument like the title, for example, here, I can say that this is a population in 2020.', 'start': 14055.204, 'duration': 11.284}, {'end': 14072.23, 'text': 'But in this case, in percentages, right, this, and now we have this title.', 'start': 14066.488, 'duration': 5.742}, {'end': 14076.252, 'text': "So that's how you make a pie chart in pandas.", 'start': 14073.01, 'duration': 3.242}, {'end': 14083.677, 'text': 'Alright, so far, we made a pivot table in many plots using pandas.', 'start': 14078.734, 'duration': 4.943}, {'end': 14090.902, 'text': "And in this video, we're going to learn how to export the pivot table, and also the plots we made with pandas.", 'start': 14083.897, 'duration': 7.005}, {'end': 14094.344, 'text': "So let's start by exporting the plots we made with pandas.", 'start': 14091.102, 'duration': 3.242}, {'end': 14097.686, 'text': 'And to do that, first, we have to import matplotlib.', 'start': 14094.564, 'duration': 3.122}, {'end': 14104.384, 'text': 'So we write import matplotlib dot pi plot.', 'start': 14097.846, 'duration': 6.538}, {'end': 14107.027, 'text': 'And then we write as PLT.', 'start': 14104.745, 'duration': 2.282}, {'end': 14111.573, 'text': 'So this PLT represents this matplotlib dot pi plot.', 'start': 14107.348, 'duration': 4.225}, {'end': 14115.177, 'text': 'So now we run and we imported matplotlib.', 'start': 14111.613, 'duration': 3.564}], 'summary': 'Learn to export pivot table and plots using pandas in matplotlib.', 'duration': 59.973, 'max_score': 14055.204, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo14055204.jpg'}, {'end': 14259.607, 'src': 'heatmap', 'start': 14123.166, 'weight': 1, 'content': [{'end': 14124.847, 'text': 'And now we open parentheses.', 'start': 14123.166, 'duration': 1.681}, {'end': 14128.489, 'text': 'And here we have to write the name of the file we want to export.', 'start': 14124.947, 'duration': 3.542}, {'end': 14134.093, 'text': "And here I'm going to write my underscore test that PNG.", 'start': 14128.809, 'duration': 5.284}, {'end': 14135.734, 'text': 'So this is the extension.', 'start': 14134.393, 'duration': 1.341}, {'end': 14137.615, 'text': 'And this is the name of the file.', 'start': 14136.034, 'duration': 1.581}, {'end': 14141.737, 'text': "And now before exporting this file, I'm going to show you something here.", 'start': 14137.735, 'duration': 4.002}, {'end': 14150.723, 'text': 'So probably you notice that when we make the plot with pandas, we get these words here that says access subplot and all of this.', 'start': 14141.997, 'duration': 8.726}, {'end': 14156.029, 'text': 'So we can get rid of these words by using the show method.', 'start': 14151.503, 'duration': 4.526}, {'end': 14160.074, 'text': 'So we write PLT that show with parentheses.', 'start': 14156.35, 'duration': 3.724}, {'end': 14163.439, 'text': "And if we run this, we're going to export this figure.", 'start': 14160.755, 'duration': 2.684}, {'end': 14166.002, 'text': "And also we're going to get rid of these words.", 'start': 14163.679, 'duration': 2.323}, {'end': 14169.105, 'text': "So let's try I run this.", 'start': 14166.483, 'duration': 2.622}, {'end': 14172.067, 'text': 'And as you can see here, all those words disappeared.', 'start': 14169.446, 'duration': 2.621}, {'end': 14176.79, 'text': 'And also we exported the figure to a PNG file.', 'start': 14172.587, 'duration': 4.203}, {'end': 14183.173, 'text': 'And now this file should be located in the same folder where you have this Jupyter notebook file.', 'start': 14177.13, 'duration': 6.043}, {'end': 14185.254, 'text': "Okay, I'm going to open that file.", 'start': 14183.433, 'duration': 1.821}, {'end': 14188.856, 'text': "But first, I'm going to export the pivot table.", 'start': 14185.694, 'duration': 3.162}, {'end': 14193.519, 'text': 'So here I copy this DF underscore pivot, and I paste it here.', 'start': 14188.936, 'duration': 4.583}, {'end': 14198.484, 'text': 'And now to export it, we have to use that to Excel method.', 'start': 14194.06, 'duration': 4.424}, {'end': 14200.786, 'text': 'So right to underscore Excel.', 'start': 14198.524, 'duration': 2.262}, {'end': 14208.454, 'text': "And now I open parentheses here, we write the name of the file where we're going to export this pivot table.", 'start': 14201.427, 'duration': 7.027}, {'end': 14216.75, 'text': "So in this case, I'm going to name it pivot underscore table dot x l s x.", 'start': 14208.894, 'duration': 7.856}, {'end': 14218.371, 'text': 'So this is the extension of Excel.', 'start': 14216.75, 'duration': 1.621}, {'end': 14220.953, 'text': 'And this is the name of this file.', 'start': 14218.751, 'duration': 2.202}, {'end': 14225.455, 'text': 'So now I run this and now the pivot table should be exported.', 'start': 14221.313, 'duration': 4.142}, {'end': 14230.478, 'text': "Alright, now I'm going to open the Excel file and the PNG file we created.", 'start': 14225.875, 'duration': 4.603}, {'end': 14232.478, 'text': "So it's here.", 'start': 14231.178, 'duration': 1.3}, {'end': 14237.74, 'text': 'And here we have the plot we exported and also the pivot table.', 'start': 14233.139, 'duration': 4.601}, {'end': 14244.061, 'text': 'So as you can see here, the plot looks exactly the same as the one we created here with pandas.', 'start': 14237.92, 'duration': 6.141}, {'end': 14246.882, 'text': 'And the pivot table is the same.', 'start': 14244.601, 'duration': 2.281}, {'end': 14250.163, 'text': "So I'm going to show you how the pivot table looks.", 'start': 14247.062, 'duration': 3.101}, {'end': 14259.607, 'text': 'And here is the pivot table in here is the pivot table we export it, I open it in Google Sheets and looks exactly the same.', 'start': 14250.903, 'duration': 8.704}], 'summary': 'Exported plot and pivot table to png and excel files, both accessible in the same folder as the jupyter notebook.', 'duration': 136.441, 'max_score': 14123.166, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo14123166.jpg'}], 'start': 13132.259, 'title': 'Visualizing population data with pandas, customizing line and bar plots, and exporting data frames and plots', 'summary': 'Covers creating a line plot to visualize population changes in five countries from 1955 to 2020, customizing line and bar plots with pandas, creating grouped bar plots, and exporting plots and pivot tables to png and excel files.', 'chapters': [{'end': 13381.952, 'start': 13132.259, 'title': 'Visualizing population data with pandas', 'summary': 'Demonstrates creating a line plot using pandas to visualize the population changes in five countries from 1955 to 2020, highlighting the growth in china and india and the relatively stable populations in the united states, indonesia, and brazil.', 'duration': 249.693, 'highlights': ['The line plot showcases the population trends in China and India, indicating rapid growth compared to the relatively stable populations in the United States, Indonesia, and Brazil, providing a visual representation of the data.', 'Customization options for the line plot include modifying x and y-axis labels, adding a title, and adjusting the figure size using specific arguments in the plot method, offering flexibility in visual presentation.', 'The demonstration involves using the Pandas plot method to create a line plot for visualizing population data, showcasing the process of copying the data frame name, specifying the kind of plot (line plot), and running the code to generate the visualization, emphasizing practical application.']}, {'end': 13934.952, 'start': 13382.653, 'title': 'Customizing line and bar plots with pandas', 'summary': 'Explains how to customize line plots and create bar plots with pandas, including selecting specific years, filtering data, and modifying plot attributes. it also demonstrates creating grouped bar plots and preparing data for pie charts.', 'duration': 552.299, 'highlights': ['Explaining how to customize bar plots', 'Creating grouped bar plots', 'Preparing data for pie charts']}, {'end': 14265.149, 'start': 13934.952, 'title': 'Exporting data frames and plots', 'summary': 'Demonstrates how to export plots and pivot tables created with pandas, including using matplotlib to save a plot as a png file and exporting a pivot table to an excel file.', 'duration': 330.197, 'highlights': ['The chapter explains how to use matplotlib to save a plot as a PNG file, getting rid of unnecessary words using the show method, and exporting the pivot table to an Excel file.', 'The chapter demonstrates how to use pandas to create a pie chart with specific arguments and how to rename columns from integer to string using the rename method.', 'The chapter also shows how to import matplotlib to save a plot as a PNG file and how to export a pivot table to an Excel file using the to_excel method.']}], 'duration': 1132.89, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/WcDaZ67TVRo/pics/WcDaZ67TVRo13132259.jpg', 'highlights': ['The line plot showcases the population trends in China and India, indicating rapid growth compared to the relatively stable populations in the United States, Indonesia, and Brazil, providing a visual representation of the data.', 'Customization options for the line plot include modifying x and y-axis labels, adding a title, and adjusting the figure size using specific arguments in the plot method, offering flexibility in visual presentation.', 'The demonstration involves using the Pandas plot method to create a line plot for visualizing population data, showcasing the process of copying the data frame name, specifying the kind of plot (line plot), and running the code to generate the visualization, emphasizing practical application.', 'The chapter explains how to use matplotlib to save a plot as a PNG file, getting rid of unnecessary words using the show method, and exporting the pivot table to an Excel file.', 'The chapter demonstrates how to use pandas to create a pie chart with specific arguments and how to rename columns from integer to string using the rename method.', 'The chapter also shows how to import matplotlib to save a plot as a PNG file and how to export a pivot table to an Excel file using the to_excel method.', 'Explaining how to customize bar plots', 'Creating grouped bar plots', 'Preparing data for pie charts']}], 'highlights': ['Python offers data handling, chart creation, pivot tables, and automation, leveraging free libraries.', "Pandas enables handling millions of rows and complex data transformations, surpassing Excel's limitations.", 'Jupyter notebook is widely used in data science for data cleaning, transformation, and analysis.', 'Python course structured into core concepts, pandas, and practical applications like pivot tables and visualizations.', 'Demonstration of using type function to identify data types and execute basic mathematical operations.', 'Introduction to boolean data type and its usage in conditionals.', 'Demonstration of string data type and its methods including upper, lower, title, count, and replace.', 'Introduction to variables in managing data, with examples of creating and accessing variables.', 'Lists are used to store multiple items in a single variable, supporting indexing and slicing.', "Methods to remove elements from a list: 'remove', 'pop', and 'del'.", 'Creation of a dictionary in Python using key-value pairs and methods to retrieve keys, values, and items.', 'Demonstration of using OS module in Python for functionalities like retrieving current working directory and listing folder contents.', 'Demonstration of using enumerate function to loop through an iterable and retrieve index and element.', 'Introduction to range function for generating a sequence of numbers and its usage in for loop.', 'Pandas methods and attributes for data frame operations, including describe, head, shape, and index.', 'Demonstration of selecting, adding, and accessing columns in pandas data frames.', 'Usage of methods like std, max, min, value counts, and sort_values for data frame analysis.', 'Creation of random float and integer numbers using NumPy for data frame operations.', 'Reshaping data frame with pivot table method for analysis and visualization.', 'Creation of line plot, bar plot, and pie chart for visualizing data using pandas and matplotlib.']}