title
Python Interview Questions And Answers | Python Interview Questions | Python Training | Edureka

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🔵 Edureka Python Training (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/data-science-python-certification-course This video on Python Interview Questions and Answers will help you prepare for Python interviews. Start your preparation by going through the most frequently asked Python interview questions in most Python interviews. Below are topics covered in this Python Interview Questions video: Basic Python Interview Questions Django - Python Interview Questions Web Scraping - Python Interview Questions Data Analysis - Python Interview Questions Check out our Python Training Playlist: https://goo.gl/Na1p9G 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 🔵 Python Online Training: http://bit.ly/3Oubt8M 🔵 Data Science Online Training: http://bit.ly/3V3nLrc 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🔵 Data Scientist Masters Program: http://bit.ly/3tUAOiT 🔵 Python Developer Masters Program: http://bit.ly/3EV6kDv 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬 🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh 🌕 Artificial and Machine Learning PGD with E&ICT Academy NIT Warangal: http://bit.ly/3OuZ3xs 🔴 Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV #Python #pythoninterviewquestions #EdurekaPython #Pythontutorial #Pythononlinetraining #pythoninterview How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Master the Basic and Advanced Concepts of Python 2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs 3. Master the Concepts of Sequences and File operations 4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python 5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 7. Master the concepts of MapReduce in Hadoop 8. Learn to write Complex MapReduce programs 9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python 10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics 11. Master the concepts of Web scraping in Python 12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka

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{'title': 'Python Interview Questions And Answers | Python Interview Questions | Python Training | Edureka', 'heatmap': [{'end': 717.332, 'start': 655.075, 'weight': 1}, {'end': 1045.961, 'start': 985.01, 'weight': 0.914}, {'end': 1325.159, 'start': 1258.093, 'weight': 0.744}, {'end': 4067.094, 'start': 3936.971, 'weight': 0.895}], 'summary': "Covers python's versatility, popularity in the job market, and applications, with starting salaries ranging from 2.5 to 8 lakhs per annum and experienced professionals earning up to 12 lakhs per annum. it also discusses python interview questions, list vs tuple, multi-threading, dynamic function patching, data manipulation, inheritance types, django architecture, web scraping, numpy, scipy, charting apis, pandas data frame, subsetting, file handling, and data analysis in python.", 'chapters': [{'end': 306.402, 'segs': [{'end': 114.999, 'src': 'embed', 'start': 56.1, 'weight': 0, 'content': [{'end': 67.533, 'text': 'If you just take a look at Python jobs these days, you will be able to see that there are a whole plethora of jobs available as such.', 'start': 56.1, 'duration': 11.433}, {'end': 70.536, 'text': 'So let me just take up one single example.', 'start': 68.174, 'duration': 2.362}, {'end': 76.658, 'text': "So you've got people looking for jobs in duty-based development.", 'start': 71.994, 'duration': 4.664}, {'end': 77.858, 'text': "You've got DevOps.", 'start': 76.678, 'duration': 1.18}, {'end': 81.881, 'text': "You've got software development with other languages.", 'start': 78.439, 'duration': 3.442}, {'end': 87.085, 'text': "You've got jQuery, which means to say this is front-end development.", 'start': 82.322, 'duration': 4.763}, {'end': 90.948, 'text': "You've got for SOA, distributed architecture.", 'start': 87.826, 'duration': 3.122}, {'end': 93.19, 'text': "So you've got it in cloud computing.", 'start': 91.409, 'duration': 1.781}, {'end': 97.393, 'text': 'You see there is NumPy, CMAT, which means to say It is data analytics.', 'start': 93.55, 'duration': 3.843}, {'end': 103.055, 'text': "So you've got a whole lot of domains where Python can actually apply.", 'start': 97.433, 'duration': 5.622}, {'end': 112.278, 'text': 'So, from the industrial standpoint, Python is heavily used in web application, in backend development,', 'start': 104.215, 'duration': 8.063}, {'end': 114.999, 'text': 'in core application development all these particular things.', 'start': 112.278, 'duration': 2.721}], 'summary': 'Python offers diverse job opportunities in web and software development, data analytics, and cloud computing.', 'duration': 58.899, 'max_score': 56.1, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI56100.jpg'}, {'end': 170.749, 'src': 'embed', 'start': 142.492, 'weight': 4, 'content': [{'end': 148.215, 'text': 'Also, please remember this that, you know, a couple of years ago, Python was not so predominant as it is right now.', 'start': 142.492, 'duration': 5.723}, {'end': 151.738, 'text': 'So the quality of interviews were much more lower.', 'start': 148.616, 'duration': 3.122}, {'end': 154.739, 'text': 'So basically they would typically ask you simple questions.', 'start': 151.778, 'duration': 2.961}, {'end': 162.164, 'text': 'Okay These days you get to hear domain specific questions, core Python questions and a whole lot more.', 'start': 155.32, 'duration': 6.844}, {'end': 170.749, 'text': 'Okay So what we will do is we will go into the core Python questions and then delve into more of the domain specific questions.', 'start': 162.464, 'duration': 8.285}], 'summary': "Python's rise led to higher interview standards with core python and domain-specific questions.", 'duration': 28.257, 'max_score': 142.492, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI142492.jpg'}, {'end': 220.391, 'src': 'embed', 'start': 187.893, 'weight': 2, 'content': [{'end': 189.473, 'text': "Alright, so let's move forward.", 'start': 187.893, 'duration': 1.58}, {'end': 192.414, 'text': 'So as for indeed.com, okay.', 'start': 189.813, 'duration': 2.601}, {'end': 200.799, 'text': "just taking at the python trench, you'll see the percentage of matching job postings across the various years.", 'start': 192.791, 'duration': 8.008}, {'end': 202.16, 'text': 'so 2012, i span back way before 2012.', 'start': 200.799, 'duration': 1.361}, {'end': 208.388, 'text': 'i go back, you know, into 2007,, 2008,, even before that.', 'start': 202.16, 'duration': 6.228}, {'end': 213.629, 'text': 'So back then it was a little lower, much lower actually because it was not predominant.', 'start': 208.908, 'duration': 4.721}, {'end': 220.391, 'text': "Now you'll see that there are heavy spikes, there are a couple of falls, but it is still spiking upwards.", 'start': 214.13, 'duration': 6.261}], 'summary': 'Python job postings on indeed.com have shown significant growth since 2012.', 'duration': 32.498, 'max_score': 187.893, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI187893.jpg'}, {'end': 265.358, 'src': 'embed', 'start': 239.407, 'weight': 3, 'content': [{'end': 246.049, 'text': 'So to actually show you the details, you see that the annual salaries of developers based on languages.', 'start': 239.407, 'duration': 6.642}, {'end': 249.45, 'text': "You have ASP.NET, you've got PHP, Python, Ruby.", 'start': 246.789, 'duration': 2.661}, {'end': 252.511, 'text': 'This is just a general statistics that we have got over here.', 'start': 249.59, 'duration': 2.921}, {'end': 257.091, 'text': 'they have been moving on pretty much the same level.', 'start': 253.609, 'duration': 3.482}, {'end': 265.358, 'text': 'but you see that python has never reduced, and in comparison, the salaries in lakhs per annum, this is in Indian lakhs per annum.', 'start': 257.091, 'duration': 8.267}], 'summary': 'Annual salaries of developers based on languages: asp.net, php, python, ruby, with python showing consistent high salaries in indian lakhs per annum.', 'duration': 25.951, 'max_score': 239.407, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI239407.jpg'}], 'start': 0.131, 'title': "Python's versatility, popularity, and job market", 'summary': 'Covers the versatility of python, its popularity in the job market, and its applications in diverse domains, such as web development and devops. it also discusses the increasing prominence of python in interviews and its high demand, with starting salaries ranging from 2.5 to 8 lakhs per annum and experienced professionals earning up to 12 lakhs per annum.', 'chapters': [{'end': 87.085, 'start': 0.131, 'title': 'Python interview questions', 'summary': 'Discusses the versatility of python, its popularity in the job market, and its applications in diverse domains, such as web development and devops, highlighting the high demand for python skills in various job roles.', 'duration': 86.954, 'highlights': ['Python is a highly sought after language, with a wide range of job opportunities, including duties-based development, DevOps, software development with other languages, and front-end development using jQuery.', 'Python is not limited to just scripting or web application development, but spans across various domains, showcasing its versatility in the current job market.', 'The current job market trend demonstrates a plethora of job opportunities for Python, indicating its high demand and popularity among employers and organizations.', 'Python is highly sought after in the job market, with a wide range of opportunities available, including duty-based development, DevOps, and front-end development using jQuery.']}, {'end': 187.233, 'start': 87.826, 'title': "Python's versatility and dominance in industry", 'summary': "Discusses python's versatility in various domains such as soa, cloud computing, data analytics, web development, and its increasing prominence in interviews, with a shift from simple to domain-specific questions.", 'duration': 99.407, 'highlights': ["Python's application in various domains such as SOA, cloud computing, and data analytics Python is used in SOA, distributed architecture, cloud computing, and data analytics, making it versatile across different domains.", "Python's usage in web application, backend development, and core application development Python is heavily utilized in web application, backend development, and core application development, showcasing its significance in the industry.", "Shift in interview quality and questions Interview quality has improved over the years, with a shift from simple to domain-specific questions, reflecting Python's increasing dominance in the industry."]}, {'end': 306.402, 'start': 187.893, 'title': 'Python job market and salaries', 'summary': 'Discusses the growth of python job postings over the years, highlighting its increasing popularity and high salaries, with starting salaries ranging from 2.5 to 8 lakhs per annum and experienced professionals earning up to 12 lakhs per annum.', 'duration': 118.509, 'highlights': ['Python job postings have seen heavy spikes and continuous growth across the years, indicating its increasing popularity and adoption. Increased percentage of matching job postings over the years.', 'Python is known for its ease of use, simplicity, and high pay, with experienced professionals earning up to 12 lakhs per annum. Starting salaries ranging from 2.5 to 8 lakhs per annum, with experienced professionals earning up to 12 lakhs per annum.', 'The starting salaries for Python developers range from 2.5 to 8 lakhs per annum, with experienced professionals earning close to 10 to 12 lakhs per annum. Starting salaries for 1-2 years of experience: 2.5-4 lakhs per annum, for 2 years of experience: 4 plus lakhs per annum, and for 4 plus years of experience: close to 8 lakhs per annum.']}], 'duration': 306.271, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI131.jpg', 'highlights': ['Python is highly sought after in the job market, with a wide range of opportunities available, including duty-based development, DevOps, and front-end development using jQuery.', "Python's application in various domains such as SOA, cloud computing, and data analytics Python is used in SOA, distributed architecture, cloud computing, and data analytics, making it versatile across different domains.", 'Python job postings have seen heavy spikes and continuous growth across the years, indicating its increasing popularity and adoption. Increased percentage of matching job postings over the years.', 'The starting salaries for Python developers range from 2.5 to 8 lakhs per annum, with experienced professionals earning close to 10 to 12 lakhs per annum. Starting salaries for 1-2 years of experience: 2.5-4 lakhs per annum, for 2 years of experience: 4 plus lakhs per annum, and for 4 plus years of experience: close to 8 lakhs per annum.', "Shift in interview quality and questions Interview quality has improved over the years, with a shift from simple to domain-specific questions, reflecting Python's increasing dominance in the industry."]}, {'end': 1286.593, 'segs': [{'end': 404.71, 'src': 'embed', 'start': 374.795, 'weight': 2, 'content': [{'end': 376.097, 'text': 'a quick introduction into.', 'start': 374.795, 'duration': 1.302}, {'end': 381.724, 'text': 'you know basically what you are looking for in this session as such, and you know what do you want as a takeaway.', 'start': 376.097, 'duration': 5.627}, {'end': 386.681, 'text': "While you answer that, let's tackle a couple of questions.", 'start': 382.919, 'duration': 3.762}, {'end': 389.362, 'text': 'So as I said, basic Python questions.', 'start': 387.361, 'duration': 2.001}, {'end': 396.526, 'text': 'My suggestion would be to get yourself familiarized with the various data structures that you have in Python.', 'start': 389.682, 'duration': 6.844}, {'end': 398.887, 'text': 'That is the most heavily asked.', 'start': 397.026, 'duration': 1.861}, {'end': 400.608, 'text': "It's heavily asked as such.", 'start': 399.687, 'duration': 0.921}, {'end': 404.71, 'text': "So you've got questions like what is the difference between lists and tuples?", 'start': 401.008, 'duration': 3.702}], 'summary': 'Python session focuses on data structures, emphasizing lists and tuples.', 'duration': 29.915, 'max_score': 374.795, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI374795.jpg'}, {'end': 625.24, 'src': 'embed', 'start': 597.427, 'weight': 0, 'content': [{'end': 601.568, 'text': 'but what i can tell you is, if you want to tackle the question, this is what you need to say.', 'start': 597.427, 'duration': 4.141}, {'end': 610.413, 'text': 'So, basically, your shallow copy copies all the reference pointers, all the values, and it creates a new memory location.', 'start': 602.688, 'duration': 7.725}, {'end': 614.395, 'text': 'So as to, you know, you have a copy of the entire set which you created.', 'start': 610.753, 'duration': 3.642}, {'end': 620.078, 'text': 'Whereas a deep copy, it takes care of all the references of the references and a whole lot more.', 'start': 614.775, 'duration': 5.303}, {'end': 625.24, 'text': 'Which means to say, it has to make, you know, pointers to each and everything.', 'start': 620.418, 'duration': 4.822}], 'summary': 'Shallow copy copies all reference pointers and values, creating a new memory location, while deep copy takes care of references of references and more.', 'duration': 27.813, 'max_score': 597.427, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI597427.jpg'}, {'end': 717.332, 'src': 'heatmap', 'start': 655.075, 'weight': 1, 'content': [{'end': 662.94, 'text': 'whereas shallow copy does not copy all the admin structures, just the top level references and the original values.', 'start': 655.075, 'duration': 7.865}, {'end': 663.681, 'text': "that's it.", 'start': 662.94, 'duration': 0.741}, {'end': 666.503, 'text': 'are you all clear on the copy and the deep copy?', 'start': 663.681, 'duration': 2.822}, {'end': 677.01, 'text': 'yeah, so the example for deep copy would be something like this okay, l4 copy, dot, deep copy, in case you were asked about that.', 'start': 666.503, 'duration': 10.507}, {'end': 678.225, 'text': 'so l2.', 'start': 677.645, 'duration': 0.58}, {'end': 683.266, 'text': 'okay, l4 id of l4.', 'start': 678.225, 'duration': 5.041}, {'end': 686.106, 'text': "you will see, it's got a whole different id altogether.", 'start': 683.266, 'duration': 2.84}, {'end': 691.007, 'text': 'okay. so this is how you create a shallow copy and this is how you create a deep copy.', 'start': 686.106, 'duration': 4.901}, {'end': 698.449, 'text': "please remember this, because it does come up not frequently, but you know, it's good to know something like this particular thing.", 'start': 691.007, 'duration': 7.442}, {'end': 706.25, 'text': 'what is the difference between copy, you know a regular copy, dot copy, what is a deep copy and what is a regular reference?', 'start': 698.449, 'duration': 7.801}, {'end': 706.81, 'text': 'are you all clear?', 'start': 706.25, 'duration': 0.56}, {'end': 708.446, 'text': 'alright, great.', 'start': 707.665, 'duration': 0.781}, {'end': 709.366, 'text': "so let's move on.", 'start': 708.446, 'duration': 0.92}, {'end': 710.387, 'text': "okay, let's move on.", 'start': 709.366, 'duration': 1.021}, {'end': 717.332, 'text': 'so the next question this is a very frequently asked question, that is, what is the difference between a list and a tuple?', 'start': 710.387, 'duration': 6.945}], 'summary': 'Shallow copy only copies top level references, deep copy creates entirely new id. understanding difference between list and tuple is frequently asked.', 'duration': 62.257, 'max_score': 655.075, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI655075.jpg'}, {'end': 839.234, 'src': 'embed', 'start': 798.81, 'weight': 3, 'content': [{'end': 810.136, 'text': 'okay, whereas your lists are allocated space which know is dynamic in nature, we can append, we can remove, for example, list.l1.remove of 3,', 'start': 798.81, 'duration': 11.326}, {'end': 814.537, 'text': "and you'll see that row 3 has been removed, so you can keep on modifying it.", 'start': 810.136, 'duration': 4.401}, {'end': 817.198, 'text': 'whereas your tuples are fixed in nature.', 'start': 814.537, 'duration': 2.661}, {'end': 827.41, 'text': "when you're thinking about fixed amount of data coming in, fixed amount of data going out, and you are thinking about memory intensive tasks,", 'start': 817.198, 'duration': 10.212}, {'end': 829.651, 'text': 'always go for tuples.', 'start': 827.41, 'duration': 2.241}, {'end': 838.514, 'text': 'when you are thinking about buffers, when you are thinking about queues, when you are thinking about, you know, stacks, always think about lists.', 'start': 829.651, 'duration': 8.863}, {'end': 839.234, 'text': 'am i clear on that?', 'start': 838.514, 'duration': 0.72}], 'summary': 'Lists are dynamic, tuples are fixed. use tuples for fixed data, lists for buffers/queues/stacks.', 'duration': 40.424, 'max_score': 798.81, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI798810.jpg'}, {'end': 898.267, 'src': 'embed', 'start': 859.808, 'weight': 5, 'content': [{'end': 871.326, 'text': 'so, for example, i have got fixed columns okay, column with id, column with name and a column with marks.', 'start': 859.808, 'duration': 11.518}, {'end': 873.007, 'text': 'a, okay.', 'start': 871.326, 'duration': 1.681}, {'end': 876.59, 'text': 'so the list represents the rows okay.', 'start': 873.007, 'duration': 3.583}, {'end': 877.811, 'text': 'so this is one row.', 'start': 876.59, 'duration': 1.221}, {'end': 892.403, 'text': 'this is the second row, something like this okay, when you know exactly how many columns you have, you go for a tuple.', 'start': 877.811, 'duration': 14.592}, {'end': 896.507, 'text': "when you don't know how many rows you have, you go for a particular list.", 'start': 892.403, 'duration': 4.104}, {'end': 898.267, 'text': 'That is how it has been.', 'start': 897.045, 'duration': 1.222}], 'summary': 'Illustrates fixed columns and rows, using tuples and lists for data storage.', 'duration': 38.459, 'max_score': 859.808, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI859808.jpg'}, {'end': 1068.782, 'src': 'heatmap', 'start': 985.01, 'weight': 8, 'content': [{'end': 989.331, 'text': 'will you be able to modify this list which is inside the tuple, though?', 'start': 985.01, 'duration': 4.321}, {'end': 992.232, 'text': "so you say i don't think so, but it is.", 'start': 989.331, 'duration': 2.901}, {'end': 997.574, 'text': 'you are able to modify it because it is referenced, as i said earlier.', 'start': 992.232, 'duration': 5.342}, {'end': 1000.615, 'text': 'so if you see l2 now, it would have been modified.', 'start': 997.574, 'duration': 3.041}, {'end': 1011.338, 'text': 'remember, lists are referenced when, even inside this, the structure, the attributes of a list, do not change, even if they are inside a tuple.', 'start': 1001.115, 'duration': 10.223}, {'end': 1017.18, 'text': "it's a tricky question, for you know beginners, it does come across all right now.", 'start': 1011.338, 'duration': 5.842}, {'end': 1022.242, 'text': 'how do we achieve multi-threading in python is a question which comes up.', 'start': 1017.18, 'duration': 5.062}, {'end': 1026.262, 'text': 'okay, so python does have a threading package.', 'start': 1022.242, 'duration': 4.02}, {'end': 1027.703, 'text': 'okay, so as to.', 'start': 1026.262, 'duration': 1.441}, {'end': 1028.824, 'text': "it's got a threading package.", 'start': 1027.703, 'duration': 1.121}, {'end': 1030.304, 'text': "it's got a multi-processing package.", 'start': 1028.824, 'duration': 1.48}, {'end': 1040.906, 'text': 'There are two different concepts threads are related to specifically threads and multiprocessing is specifically related to the number of cores which you have on your system.', 'start': 1030.731, 'duration': 10.175}, {'end': 1045.961, 'text': 'ok, now python has something called a gil global interpreter lock.', 'start': 1041.656, 'duration': 4.305}, {'end': 1047.603, 'text': 'this is a question which comes up.', 'start': 1045.961, 'duration': 1.642}, {'end': 1052.71, 'text': 'ok, typically, when it comes to threading, this is a question which comes up as to what is a gil.', 'start': 1047.603, 'duration': 5.107}, {'end': 1060.919, 'text': 'the gil is a global interpreter lock which makes sure that only one thread can execute at a certain point of time in python.', 'start': 1052.71, 'duration': 8.209}, {'end': 1068.782, 'text': 'okay, so what happens is that the thread acquires the gil, it completes the work and moves on to the next thread.', 'start': 1061.5, 'duration': 7.282}], 'summary': 'Python allows modification of lists inside tuples; it supports threading and multiprocessing. gil ensures only one thread executes at a time.', 'duration': 39.958, 'max_score': 985.01, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI985010.jpg'}, {'end': 1160.85, 'src': 'embed', 'start': 1130.476, 'weight': 6, 'content': [{'end': 1134.057, 'text': 'okay, so you have to be very careful while using the threading package as such.', 'start': 1130.476, 'duration': 3.581}, {'end': 1140.18, 'text': "So when you're turning off your GIL, you have to make sure that you write your semaphores, your mutexes,", 'start': 1134.477, 'duration': 5.703}, {'end': 1143.702, 'text': 'properly so that it does not mess with your memory management.', 'start': 1140.18, 'duration': 3.522}, {'end': 1149.284, 'text': 'Okay Because Python memory is internally managed by, you know, the Python interpreter itself.', 'start': 1144.202, 'duration': 5.082}, {'end': 1154.107, 'text': 'Unlike, you know, something like C or C++ where, you know, you have to do the memory management part.', 'start': 1149.484, 'duration': 4.623}, {'end': 1160.85, 'text': 'Okay So is that clear to you all? So GIL can always add an overhead to your execution.', 'start': 1154.687, 'duration': 6.163}], 'summary': 'Be cautious when using the threading package to avoid gil overhead and memory management issues in python.', 'duration': 30.374, 'max_score': 1130.476, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI1130476.jpg'}, {'end': 1215.489, 'src': 'embed', 'start': 1187.277, 'weight': 7, 'content': [{'end': 1194.843, 'text': 'so if you say i have got four core processor, you can make use of all those four cores and run parallel jobs on all those four cores.', 'start': 1187.277, 'duration': 7.566}, {'end': 1198.945, 'text': "okay, so you've got something called multi-processing for that.", 'start': 1194.843, 'duration': 4.102}, {'end': 1204.389, 'text': 'okay, library called multi-processing for threading, you have something called threading everyone.', 'start': 1198.945, 'duration': 5.444}, {'end': 1204.85, 'text': 'are you all clear?', 'start': 1204.389, 'duration': 0.461}, {'end': 1207.042, 'text': 'okay, great.', 'start': 1206.101, 'duration': 0.941}, {'end': 1209.424, 'text': 'now, basic python questions.', 'start': 1207.042, 'duration': 2.382}, {'end': 1215.489, 'text': "when it comes to ternary operation so i wouldn't call it exactly ternary it behaves like a ternary operation.", 'start': 1209.424, 'duration': 6.065}], 'summary': 'Utilize all four cores with multi-processing and threading in python.', 'duration': 28.212, 'max_score': 1187.277, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI1187277.jpg'}], 'start': 307.824, 'title': 'Python interview questions, list vs tuple, and multi-threading in python', 'summary': 'Covers python interview questions, differences between lists and tuples, including mutability, memory usage, and specific use cases, and concepts of multi-threading and multi-processing in python, highlighting the use of threading and multiprocessing packages, gil, and the distinction between parallelism and mimicked parallelism.', 'chapters': [{'end': 710.387, 'start': 307.824, 'title': 'Python interview questions', 'summary': 'Covers various interview questions related to python, including basic python questions, the difference between deep and shallow copy, and the implications of each, alongside practical examples.', 'duration': 402.563, 'highlights': ['The chapter covers various interview questions related to Python, including basic Python questions, the difference between deep and shallow copy, and the implications of each, alongside practical examples. The chapter covers various interview questions related to Python, including basic Python questions, the difference between deep and shallow copy, and the implications of each, alongside practical examples.', 'It is important to be familiar with various data structures in Python as they are heavily asked about in interviews. It is important to be familiar with various data structures in Python as they are heavily asked about in interviews.', 'The difference between shallow and deep copy is explained, with practical examples demonstrating the implications of each. The difference between shallow and deep copy is explained, with practical examples demonstrating the implications of each.', 'The difference between shallow and deep copy is important as it impacts memory usage and performance, with deep copy being slower due to copying all references and values. The difference between shallow and deep copy is important as it impacts memory usage and performance, with deep copy being slower due to copying all references and values.', 'The chapter emphasizes the significance of understanding the differences between shallow copy, deep copy, and regular references when handling instances in Python. The chapter emphasizes the significance of understanding the differences between shallow copy, deep copy, and regular references when handling instances in Python.']}, {'end': 1000.615, 'start': 710.387, 'title': 'List vs tuple: mutability and usage', 'summary': 'Discusses the differences between lists and tuples, emphasizing mutability, memory usage, and specific use cases, such as fixed data and database representation.', 'duration': 290.228, 'highlights': ['Lists can be modified at runtime using methods like append, while tuples cannot be modified, highlighting the mutability difference between the two. (e.g. list.append(10) vs. inability to modify tuples)', 'Tuples are recommended for fixed data with known size to save memory space, while lists are suitable for dynamic data manipulation and tasks like buffers and queues, emphasizing the specific use cases for each data structure. (e.g. fixed data and memory-intensive tasks favor tuples, while dynamic data and tasks like buffers suit lists)', 'The chapter also clarifies the usage of tuples for representing fixed columns in a database, while lists are used for representing rows with variable length, providing a clear distinction between their roles in database representation. (e.g. tuples for fixed columns and lists for variable rows in databases)']}, {'end': 1286.593, 'start': 1001.115, 'title': 'Multi-threading and multi-processing in python', 'summary': 'Explains the concepts of multi-threading and multi-processing in python, highlighting the use of the threading and multiprocessing packages, the impact of gil (global interpreter lock), and the distinction between parallelism and mimicked parallelism.', 'duration': 285.478, 'highlights': ['Python has a threading package and a multi-processing package, each related to threads and the number of CPU cores respectively. Python has a threading package and a multi-processing package, each related to threads and the number of CPU cores respectively.', 'GIL (Global Interpreter Lock) ensures only one thread can execute at a certain point of time in Python, impacting multi-threading execution. GIL (Global Interpreter Lock) ensures only one thread can execute at a certain point of time in Python, impacting multi-threading execution.', 'Multi-threading in Python mimics parallelism but actually takes turns using the CPU core, and turning off the GIL can lead to memory management issues. Multi-threading in Python mimics parallelism but actually takes turns using the CPU core, and turning off the GIL can lead to memory management issues.', 'Multi-processing in Python takes advantage of the number of CPU cores for parallel jobs using a library called multi-processing. Multi-processing in Python takes advantage of the number of CPU cores for parallel jobs using a library called multi-processing.', 'The ternary operation in Python behaves like a ternary operation, evaluating conditions and returning specific values based on the evaluation. The ternary operation in Python behaves like a ternary operation, evaluating conditions and returning specific values based on the evaluation.']}], 'duration': 978.769, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI307824.jpg', 'highlights': ['The chapter emphasizes the significance of understanding the differences between shallow copy, deep copy, and regular references when handling instances in Python.', 'The difference between shallow and deep copy is important as it impacts memory usage and performance, with deep copy being slower due to copying all references and values.', 'It is important to be familiar with various data structures in Python as they are heavily asked about in interviews.', 'Lists can be modified at runtime using methods like append, while tuples cannot be modified, highlighting the mutability difference between the two.', 'Tuples are recommended for fixed data with known size to save memory space, while lists are suitable for dynamic data manipulation and tasks like buffers and queues, emphasizing the specific use cases for each data structure.', 'The chapter also clarifies the usage of tuples for representing fixed columns in a database, while lists are used for representing rows with variable length, providing a clear distinction between their roles in database representation.', 'Multi-threading in Python mimics parallelism but actually takes turns using the CPU core, and turning off the GIL can lead to memory management issues.', 'Multi-processing in Python takes advantage of the number of CPU cores for parallel jobs using a library called multi-processing.', 'GIL (Global Interpreter Lock) ensures only one thread can execute at a certain point of time in Python, impacting multi-threading execution.', 'Python has a threading package and a multi-processing package, each related to threads and the number of CPU cores respectively.']}, {'end': 1578.057, 'segs': [{'end': 1315.76, 'src': 'embed', 'start': 1286.593, 'weight': 0, 'content': [{'end': 1290.695, 'text': 'you know how to mimic a ternary operation.', 'start': 1286.593, 'duration': 4.102}, {'end': 1294.066, 'text': 'great, Now, what is monkey patching in Python?', 'start': 1290.695, 'duration': 3.371}, {'end': 1298.589, 'text': 'This is something which appears when you have gotten into classes as such.', 'start': 1294.106, 'duration': 4.483}, {'end': 1304.993, 'text': "Okay Now, so basically it means to say that, you know, you're applying a patch at runtime.", 'start': 1298.689, 'duration': 6.304}, {'end': 1309.876, 'text': 'Okay Or, you know, basically dynamic modifications of class or a module at runtime.', 'start': 1305.353, 'duration': 4.523}, {'end': 1315.76, 'text': "So basically what happens here? So let's quickly create this example, recreate this example over here.", 'start': 1310.116, 'duration': 5.644}], 'summary': 'Monkey patching in python involves dynamic runtime modifications of classes or modules.', 'duration': 29.167, 'max_score': 1286.593, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI1286593.jpg'}, {'end': 1360.636, 'src': 'embed', 'start': 1333.064, 'weight': 2, 'content': [{'end': 1337.247, 'text': 'Do I use old style classes or new style classes? This is something called an old style class.', 'start': 1333.064, 'duration': 4.183}, {'end': 1338.748, 'text': 'This is using new style classes.', 'start': 1337.287, 'duration': 1.461}, {'end': 1341.33, 'text': 'Always use new style classes.', 'start': 1339.389, 'duration': 1.941}, {'end': 1345.213, 'text': 'It is only legacy architecture which makes use of old style classes.', 'start': 1341.751, 'duration': 3.462}, {'end': 1352.078, 'text': 'New style classes where the classes inherit from objects and object parent class as such.', 'start': 1346.114, 'duration': 5.964}, {'end': 1354.474, 'text': 'def fself.', 'start': 1352.694, 'duration': 1.78}, {'end': 1360.636, 'text': 'okay, I am creating a particular, you know, function print.', 'start': 1354.474, 'duration': 6.162}], 'summary': 'Prefer new style classes over old style classes for better functionality and compatibility with modern architecture.', 'duration': 27.572, 'max_score': 1333.064, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI1333064.jpg'}, {'end': 1494.668, 'src': 'embed', 'start': 1449.973, 'weight': 1, 'content': [{'end': 1452.234, 'text': 'are you clear as to what happened exactly?', 'start': 1449.973, 'duration': 2.261}, {'end': 1459.578, 'text': 'so we are applying a dynamic change for a particular function in a particular class at runtime.', 'start': 1452.234, 'duration': 7.344}, {'end': 1462.22, 'text': 'did you all understand how it is applied as such?', 'start': 1459.578, 'duration': 2.642}, {'end': 1469.183, 'text': 'we are, you know, forcing a particular function into a particular function attribute of a particular class at runtime.', 'start': 1462.22, 'duration': 6.963}, {'end': 1469.904, 'text': 'did you all understand?', 'start': 1469.183, 'duration': 0.721}, {'end': 1476.663, 'text': 'so what happened here is that you have a particular class which contains a particular function f.', 'start': 1470.802, 'duration': 5.861}, {'end': 1483.645, 'text': 'okay, so this particular function is completely independent of everything else which is part of this class.', 'start': 1476.663, 'duration': 6.982}, {'end': 1485.365, 'text': 'it is only part of this class.', 'start': 1483.645, 'duration': 1.72}, {'end': 1494.108, 'text': 'now you create an additional function called monkey f, so which you want to patch into this particular function of this particular class.', 'start': 1485.365, 'duration': 8.743}, {'end': 1494.668, 'text': 'do you follow me?', 'start': 1494.108, 'duration': 0.56}], 'summary': 'Applying dynamic changes to a function in a class at runtime.', 'duration': 44.695, 'max_score': 1449.973, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI1449973.jpg'}], 'start': 1286.593, 'title': 'Dynamic function patching in python', 'summary': 'Discusses dynamic function patching in python, covering the application of patches to specific functions within classes at runtime and its use as a quick fix for code issues.', 'chapters': [{'end': 1360.636, 'start': 1286.593, 'title': 'Understanding monkey patching in python', 'summary': 'Discusses the concept of monkey patching in python, which involves dynamic modifications of classes or modules at runtime and the use of new style classes over old style classes in python.', 'duration': 74.043, 'highlights': ['Monkey patching in Python involves applying a patch at runtime for dynamic modifications of classes or modules.', "The use of new style classes is recommended over old style classes in Python for inheritance from the 'object' parent class.", 'Monkey patching allows for dynamic modifications of classes or modules at runtime, providing flexibility in the Python code structure.', "The distinction between old style classes and new style classes is emphasized, with a recommendation to use new style classes due to their inheritance from the 'object' parent class."]}, {'end': 1578.057, 'start': 1360.636, 'title': 'Dynamic function patching', 'summary': 'Discusses the concept of dynamic function patching in python, demonstrating how to apply a patch to a specific function within a class at runtime, and its use as a quick fix for issues in the code.', 'duration': 217.421, 'highlights': ['The process involves applying a dynamic change for a particular function in a particular class at runtime, allowing for a quick fix in case of code issues.', "Demonstrates the process of creating a patch function, 'monkey_f', and applying it to the original function 'f' within the class 'my_class', showcasing the ability to force a specific function attribute onto a class at runtime.", "Explains the independence of the original function 'f' within the class 'my_class' and the process of creating an additional function 'monkey_f' to be patched onto 'f' as a quick fix for issues in the code.", "Illustrates the use of 'self' as a definition for a particular constructor and its relevance in the context of dynamic function patching in Python."]}], 'duration': 291.464, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI1286593.jpg', 'highlights': ['Monkey patching allows for dynamic modifications of classes or modules at runtime, providing flexibility in the Python code structure.', "Demonstrates the process of creating a patch function, 'monkey_f', and applying it to the original function 'f' within the class 'my_class', showcasing the ability to force a specific function attribute onto a class at runtime.", "The use of new style classes is recommended over old style classes in Python for inheritance from the 'object' parent class.", 'The process involves applying a dynamic change for a particular function in a particular class at runtime, allowing for a quick fix in case of code issues.']}, {'end': 2393.107, 'segs': [{'end': 1628.418, 'src': 'embed', 'start': 1578.982, 'weight': 0, 'content': [{'end': 1586.004, 'text': 'okay, so to create instances as such, okay now, so, basically, what you do is you create, you know,', 'start': 1578.982, 'duration': 7.022}, {'end': 1593.366, 'text': 'patches so as to get dynamic attributes at runtime, which you were initially not thinking about, or you know,', 'start': 1586.004, 'duration': 7.362}, {'end': 1598.587, 'text': 'applying quick fixes so as to you know at runtime, so that things do not go wrong.', 'start': 1593.366, 'duration': 5.221}, {'end': 1599.527, 'text': 'maybe you know something.', 'start': 1598.587, 'duration': 0.94}, {'end': 1602.088, 'text': 'there was a functionality and it is not working right.', 'start': 1599.527, 'duration': 2.561}, {'end': 1605.029, 'text': 'so you go ahead and apply a patch for that, something like this.', 'start': 1602.088, 'duration': 2.941}, {'end': 1610.112, 'text': 'okay, this is how you do it without changing the class itself.', 'start': 1605.751, 'duration': 4.361}, {'end': 1612.173, 'text': 'okay, now here is a question.', 'start': 1610.112, 'duration': 2.061}, {'end': 1616.794, 'text': 'okay, how can you randomize the items of a list in place in python?', 'start': 1612.173, 'duration': 4.621}, {'end': 1619.735, 'text': 'okay, so this is a question which makes use of.', 'start': 1616.794, 'duration': 2.941}, {'end': 1621.756, 'text': 'you know something called a random.', 'start': 1619.735, 'duration': 2.021}, {'end': 1628.418, 'text': 'we have a particular module in python called random from random import, shuffle.', 'start': 1621.756, 'duration': 6.662}], 'summary': "Creating dynamic attributes at runtime to fix functionality issues, also discussing randomizing items in a list using python's random module.", 'duration': 49.436, 'max_score': 1578.982, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI1578982.jpg'}, {'end': 1687.337, 'src': 'embed', 'start': 1659.296, 'weight': 2, 'content': [{'end': 1663.419, 'text': 'when you print x, you see that the you know items have been shuffled.', 'start': 1659.296, 'duration': 4.123}, {'end': 1664.34, 'text': 'you do it again.', 'start': 1663.419, 'duration': 0.921}, {'end': 1667.262, 'text': 'you see that again it has been shuffled.', 'start': 1664.34, 'duration': 2.922}, {'end': 1671.225, 'text': 'okay, this is how you shuffle items in place.', 'start': 1667.262, 'duration': 3.963}, {'end': 1671.786, 'text': 'are you all clear?', 'start': 1671.225, 'duration': 0.561}, {'end': 1678.032, 'text': 'all right, super, now, write a sorting algorithm for a numerical data set in python.', 'start': 1673.109, 'duration': 4.923}, {'end': 1687.337, 'text': 'so maybe the question is not totally clear, but what we are trying to say here is write a sort for a data set which is a string.', 'start': 1678.032, 'duration': 9.305}], 'summary': 'Demonstrated shuffling of items in place and requested to write a sorting algorithm for a string data set in python.', 'duration': 28.041, 'max_score': 1659.296, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI1659296.jpg'}, {'end': 1972.17, 'src': 'embed', 'start': 1931.648, 'weight': 3, 'content': [{'end': 1944.376, 'text': 'when you do dict of zip, of t1, comma, t2, you actually get a particular, you know, zip dictionary which is created from the lists or the tuples.', 'start': 1931.648, 'duration': 12.728}, {'end': 1945.416, 'text': 'is it clear with everyone?', 'start': 1944.376, 'duration': 1.04}, {'end': 1949.799, 'text': 'ok, so this is the first thing you want to create.', 'start': 1945.416, 'duration': 4.383}, {'end': 1951.16, 'text': 'a list of items.', 'start': 1949.799, 'duration': 1.361}, {'end': 1960.923, 'text': 'ok, list of integers from 0 to 9, you do it like this using range range of 10, okay.', 'start': 1951.16, 'duration': 9.763}, {'end': 1972.17, 'text': 'Now, A2 is equal to sorted off I for I in A1, if I in A naught, okay.', 'start': 1961.543, 'duration': 10.627}], 'summary': 'Creating a zip dictionary from lists or tuples and sorting a list a1 to get a2.', 'duration': 40.522, 'max_score': 1931.648, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI1931648.jpg'}, {'end': 2142.814, 'src': 'embed', 'start': 2106.582, 'weight': 4, 'content': [{'end': 2110.883, 'text': 'now A5 is a dict comprehension.', 'start': 2106.582, 'duration': 4.301}, {'end': 2112.624, 'text': 'this was a list comprehension.', 'start': 2110.883, 'duration': 1.741}, {'end': 2114.816, 'text': 'this is a dict comprehension.', 'start': 2112.624, 'duration': 2.192}, {'end': 2116.537, 'text': 'now i colon.', 'start': 2114.816, 'duration': 1.721}, {'end': 2119.679, 'text': 'so i would be your particular key.', 'start': 2116.537, 'duration': 3.142}, {'end': 2121.34, 'text': 'okay, i colon i into i.', 'start': 2119.679, 'duration': 1.661}, {'end': 2130.926, 'text': 'that is, we are creating a particular square of the item for i in a1.', 'start': 2121.34, 'duration': 9.586}, {'end': 2137.37, 'text': 'a5 will give you 0, 0, 1, 1, 2, 4, 3, 9, 4, 16, so on till 9.', 'start': 2130.926, 'duration': 6.444}, {'end': 2142.814, 'text': 'okay, so we are creating a dictionary comprehension by using this for i in a1.', 'start': 2137.37, 'duration': 5.444}], 'summary': 'Creating a dictionary comprehension to generate squares of numbers in a1.', 'duration': 36.232, 'max_score': 2106.582, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI2106582.jpg'}, {'end': 2202.243, 'src': 'embed', 'start': 2175.018, 'weight': 5, 'content': [{'end': 2179.419, 'text': 'there are some more things now when it comes to regular expressions in python.', 'start': 2175.018, 'duration': 4.401}, {'end': 2182.46, 'text': 'okay, how do we do various things in?', 'start': 2179.419, 'duration': 3.041}, {'end': 2183.38, 'text': 'you know python.', 'start': 2182.46, 'duration': 0.92}, {'end': 2188.521, 'text': "so we've got an re module, so we've got split.", 'start': 2183.38, 'duration': 5.141}, {'end': 2192.841, 'text': 'uses a regex pattern to split a given string into a list.', 'start': 2188.521, 'duration': 4.32}, {'end': 2196.382, 'text': 'okay, so split it uses a regex pattern.', 'start': 2192.841, 'duration': 3.541}, {'end': 2202.243, 'text': 'sub will find all the substrings where the regex pattern matches and replace them with a different string.', 'start': 2196.382, 'duration': 5.861}], 'summary': "Python's re module includes split and sub functions for regex operations.", 'duration': 27.225, 'max_score': 2175.018, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI2175018.jpg'}], 'start': 1578.982, 'title': 'Python data manipulation', 'summary': 'Covers dynamic patching, sorting, data structures, and regular expressions in python. it includes creating dynamic patches, implementing sorting algorithms, using list comprehension and map, creating dictionaries from lists or tuples using zip, list comprehension, dictionary comprehension, and regular expressions, with examples and explanations.', 'chapters': [{'end': 1895.478, 'start': 1578.982, 'title': 'Dynamic patching and sorting in python', 'summary': 'Covers creating dynamic patches for runtime attributes, using the random module to shuffle items in a list, and implementing sorting algorithms for numerical datasets in python, along with explaining the use of list comprehension and map in sorting.', 'duration': 316.496, 'highlights': ['The chapter covers creating dynamic patches for runtime attributes The speaker discusses the process of creating dynamic patches to acquire dynamic attributes at runtime, or applying quick fixes to prevent runtime errors.', "Using the random module to shuffle items in a list The speaker explains the usage of the random module in Python, specifically the 'shuffle' function, to randomize the items of a list in place.", 'Implementing sorting algorithms for numerical datasets in Python The speaker demonstrates two methods for sorting a numerical dataset in Python, using list comprehension and the map function to convert integer strings to integers before sorting.']}, {'end': 2393.107, 'start': 1895.478, 'title': 'Python data structures & regular expressions', 'summary': 'Covers creating dictionaries from lists or tuples using zip, list comprehension, dictionary comprehension, and regular expressions in python, with examples and explanations.', 'duration': 497.629, 'highlights': ['Creating dictionaries from lists or tuples using zip The chapter covers creating dictionaries from lists or tuples using the zip function, providing a clear explanation of the process and its application in Python.', 'List comprehension and dictionary comprehension Explains the use of list comprehension and dictionary comprehension in Python with examples, demonstrating the creation of lists and dictionaries from given input with detailed explanations.', 'Regular expressions in Python Discusses the use of regular expressions in Python, covering split, sub, and subn functionalities, providing clear examples and explanations for each, including a step-by-step guide on their application.']}], 'duration': 814.125, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI1578982.jpg', 'highlights': ['Covers creating dynamic patches for runtime attributes and applying quick fixes.', 'Demonstrates using the random module to shuffle items in a list.', 'Explains implementing sorting algorithms for numerical datasets in Python.', 'Covers creating dictionaries from lists or tuples using the zip function.', 'Explains list comprehension and dictionary comprehension in Python with examples.', 'Discusses the use of regular expressions in Python, covering split, sub, and subn functionalities.']}, {'end': 2800.406, 'segs': [{'end': 2454.448, 'src': 'embed', 'start': 2424.065, 'weight': 0, 'content': [{'end': 2427.989, 'text': 'okay now, so there are different types of inheritances.', 'start': 2424.065, 'duration': 3.924}, {'end': 2433.774, 'text': 'okay, so there is single, there is multilevel, there is hierarchical and multiple.', 'start': 2427.989, 'duration': 5.785}, {'end': 2439.759, 'text': 'so typically the highly used ones are either single inheritance or multiple inheritance.', 'start': 2433.774, 'duration': 5.985}, {'end': 2441.26, 'text': 'now, how does it actually function?', 'start': 2439.759, 'duration': 1.501}, {'end': 2447.662, 'text': 'so where a derived class acquires the members of a single superclass is known as a single inheritance.', 'start': 2441.26, 'duration': 6.402}, {'end': 2454.448, 'text': 'Multi level is you know one class derives from you know another class which derives again from another class.', 'start': 2448.343, 'duration': 6.105}], 'summary': 'Different types of inheritance include single, multilevel, and hierarchical; highly used ones are single and multiple inheritance.', 'duration': 30.383, 'max_score': 2424.065, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI2424065.jpg'}, {'end': 2586.582, 'src': 'embed', 'start': 2507.386, 'weight': 1, 'content': [{'end': 2514.228, 'text': 'You have to be careful while using hierarchical inheritance, because if you are instantiating both the classes,', 'start': 2507.386, 'duration': 6.842}, {'end': 2518.189, 'text': 'you have to be sure as to which one you are going to use.', 'start': 2514.228, 'duration': 3.961}, {'end': 2525.692, 'text': 'If it is going to be using from m class or is it going to be from m2 class if it is deriving from the same one.', 'start': 2518.77, 'duration': 6.922}, {'end': 2532.994, 'text': 'You have to be very careful, especially with the functions which have been derived from the base class as such.', 'start': 2526.392, 'duration': 6.602}, {'end': 2538.498, 'text': 'ok, now, multiple inheritance is where you derive from multiple classes.', 'start': 2533.655, 'duration': 4.843}, {'end': 2555.188, 'text': 'ok, it is from multiple classes, so my class.', 'start': 2538.498, 'duration': 16.69}, {'end': 2571.957, 'text': 'so another class will derive from my class, something like this.', 'start': 2555.188, 'duration': 16.769}, {'end': 2580.18, 'text': 'so child will derived from two different, you know, super classes as such, are you clear as to what exactly inheritance is?', 'start': 2571.957, 'duration': 8.223}, {'end': 2586.582, 'text': 'so you got an example here v1, v2, child class, parent class pass, and v1 and v2.', 'start': 2580.18, 'duration': 6.402}], 'summary': 'Be cautious with hierarchical and multiple inheritance in classes.', 'duration': 79.196, 'max_score': 2507.386, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI2507386.jpg'}, {'end': 2643.548, 'src': 'embed', 'start': 2612.914, 'weight': 3, 'content': [{'end': 2617.817, 'text': "so you've got MVP, you've got MVC, you've got MVT.", 'start': 2612.914, 'duration': 4.903}, {'end': 2623.159, 'text': 'now MVT is, you know, a model view and template.', 'start': 2617.817, 'duration': 5.342}, {'end': 2627.3, 'text': 'it is similar to that of MVC, which is model view controller.', 'start': 2623.159, 'duration': 4.141}, {'end': 2634.301, 'text': 'but here what happens is that model is similar to both of them, both MVC and MVT.', 'start': 2627.3, 'duration': 7.001}, {'end': 2643.548, 'text': 'the view over here functions as a controller itself, okay, and this V, the view in django, functions as a regular controller.', 'start': 2634.301, 'duration': 9.247}], 'summary': "Comparison of mvp, mvc, and mvt architecture with emphasis on django's view as a controller.", 'duration': 30.634, 'max_score': 2612.914, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI2612914.jpg'}, {'end': 2737.058, 'src': 'embed', 'start': 2668.447, 'weight': 4, 'content': [{'end': 2674.674, 'text': "now you've got your model, which is part of your database, where you know you define your database.", 'start': 2668.447, 'duration': 6.227}, {'end': 2682.862, 'text': 'as such, your database schema is defined in the model, the view, and the template maps the model.', 'start': 2674.674, 'duration': 8.188}, {'end': 2690.319, 'text': 'so basically, the developer provides a model and the view, and the template then maps it to the url and django does the magic to serve it as such.', 'start': 2682.862, 'duration': 7.457}, {'end': 2691.66, 'text': 'ok, you got the model.', 'start': 2690.319, 'duration': 1.341}, {'end': 2693.021, 'text': 'you got the template.', 'start': 2691.66, 'duration': 1.361}, {'end': 2699.506, 'text': 'it is then taken up into the view and given as a url through django to the user.', 'start': 2693.021, 'duration': 6.485}, {'end': 2704.811, 'text': 'are you all clear regarding the model, view, template structure as such?', 'start': 2699.506, 'duration': 5.305}, {'end': 2710.395, 'text': 'so templates would be your regular html pages which contain dynamic elements in them.', 'start': 2704.811, 'duration': 5.584}, {'end': 2715.638, 'text': 'okay, for example, let me just give you a simple template as such.', 'start': 2710.874, 'duration': 4.764}, {'end': 2737.058, 'text': 'for example, I have something like this HTML slash, HTML head slash, head body.', 'start': 2715.638, 'duration': 21.42}], 'summary': 'In django, the developer provides a model and view, which is then mapped to the url for user access.', 'duration': 68.611, 'max_score': 2668.447, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI2668447.jpg'}], 'start': 2393.107, 'title': 'Python inheritance types and django mvc vs mvt architecture', 'summary': "Explains python inheritance types including single, multi-level, hierarchical, and multiple inheritance, and discusses implications for class derivation. it also covers the difference between django's mvc and mvt architecture, highlighting the unique roles of model, view, and template.", 'chapters': [{'end': 2586.582, 'start': 2393.107, 'title': 'Python inheritance types', 'summary': 'Explains the concept of inheritance in python, discussing single, multi-level, hierarchical, and multiple inheritance and their implications for class derivation, cautioning against potential issues with hierarchical inheritance and illustrating multiple inheritance with an example.', 'duration': 193.475, 'highlights': ['Explaining different types of inheritance The chapter explains single, multi-level, hierarchical, and multiple inheritance, highlighting single and multiple inheritance as the most commonly used types.', 'Cautioning about potential issues with hierarchical inheritance The chapter warns about the careful usage of hierarchical inheritance, emphasizing the need to be mindful while instantiating classes and using functions derived from the base class.', 'Illustrating multiple inheritance with an example The chapter provides an example to illustrate multiple inheritance, showing how a child class can derive from two different super classes, emphasizing the concept through the v1, v2, child class, parent class pass, and v1 and v2 example.']}, {'end': 2800.406, 'start': 2587.731, 'title': 'Django mvc vs mvt architecture', 'summary': 'Explains the difference between the mvc and mvt architecture in django, where mvt has the model similar to mvc but the view functions as a controller, and the template is similar to the view in mvc, serving dynamic html templates from the model.', 'duration': 212.675, 'highlights': ['The MVT architecture in Django has the model similar to both MVC and MVT, where the view functions as a controller and the template serves as the view in MVC, allowing dynamic elements to be pulled from the model.', 'The template in Django consists of HTML pages containing dynamic elements, which are populated with data from the model, demonstrating the structure of a template with dynamic variables and information pulled from the model in the database.', "Django's architecture includes the model, view, and template, with the model defining the database schema, the view functioning as a controller, and the template serving dynamic HTML templates, allowing a single template to serve multiple items from the model."]}], 'duration': 407.299, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI2393107.jpg', 'highlights': ['Explaining different types of inheritance The chapter explains single, multi-level, hierarchical, and multiple inheritance, highlighting single and multiple inheritance as the most commonly used types.', 'Illustrating multiple inheritance with an example The chapter provides an example to illustrate multiple inheritance, showing how a child class can derive from two different super classes, emphasizing the concept through the v1, v2, child class, parent class pass, and v1 and v2 example.', 'Cautioning about potential issues with hierarchical inheritance The chapter warns about the careful usage of hierarchical inheritance, emphasizing the need to be mindful while instantiating classes and using functions derived from the base class.', 'The MVT architecture in Django has the model similar to both MVC and MVT, where the view functions as a controller and the template serves as the view in MVC, allowing dynamic elements to be pulled from the model.', "Django's architecture includes the model, view, and template, with the model defining the database schema, the view functioning as a controller, and the template serving dynamic HTML templates, allowing a single template to serve multiple items from the model.", 'The template in Django consists of HTML pages containing dynamic elements, which are populated with data from the model, demonstrating the structure of a template with dynamic variables and information pulled from the model in the database.']}, {'end': 3364.165, 'segs': [{'end': 2850.636, 'src': 'embed', 'start': 2823.752, 'weight': 0, 'content': [{'end': 2831.458, 'text': 'For example, you might have to use something like SQLAlchemy or something like that to create your database as such for your project.', 'start': 2823.752, 'duration': 7.706}, {'end': 2836.962, 'text': 'So basically, with your database in place, it tells Django how to use it.', 'start': 2832.238, 'duration': 4.724}, {'end': 2841.253, 'text': 'okay, this is where your settings.py files comes in.', 'start': 2837.492, 'duration': 3.761}, {'end': 2850.636, 'text': 'so you, in your setting.py file, is where you do all the configuration regarding that particular database, or you know your web server information.', 'start': 2841.253, 'duration': 9.383}], 'summary': 'Using sqlalchemy to create database in django, configuring settings.py for web server.', 'duration': 26.884, 'max_score': 2823.752, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI2823752.jpg'}, {'end': 2940.435, 'src': 'embed', 'start': 2909.709, 'weight': 1, 'content': [{'end': 2915.233, 'text': 'are you all clear on that all right now, how do you write a view in django?', 'start': 2909.709, 'duration': 5.524}, {'end': 2916.494, 'text': 'so we talked about templates.', 'start': 2915.233, 'duration': 1.261}, {'end': 2919.637, 'text': 'we, i also showed you a simple template, which we have.', 'start': 2916.494, 'duration': 3.143}, {'end': 2921.859, 'text': 'okay, how do you write a view?', 'start': 2919.637, 'duration': 2.222}, {'end': 2931.087, 'text': 'okay, a view is similar to a controller which pulls up information from your model and your template and serves it to the user.', 'start': 2921.859, 'duration': 9.228}, {'end': 2940.435, 'text': 'an example is like this so you you have a, you know, views.py file where you put from django.http, import http response,', 'start': 2931.087, 'duration': 9.348}], 'summary': 'Creating a view in django involves pulling data from the model and template to serve to the user.', 'duration': 30.726, 'max_score': 2909.709, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI2909709.jpg'}, {'end': 3132.648, 'src': 'embed', 'start': 3103.243, 'weight': 2, 'content': [{'end': 3107.806, 'text': 'Okay, so how do I, you know, this is another way of doing the same thing regarding session.', 'start': 3103.243, 'duration': 4.563}, {'end': 3112.189, 'text': 'How do I see to it that the user logs out after a particular time?', 'start': 3108.446, 'duration': 3.743}, {'end': 3116.663, 'text': 'so that is where you make use of something known as sessions in Django.', 'start': 3112.622, 'duration': 4.041}, {'end': 3124.686, 'text': 'okay, you know, Django provides a session that will allow you to store and retrieve data as per site visitor basis.', 'start': 3116.663, 'duration': 8.023}, {'end': 3128.027, 'text': 'so you know, you will create a random session.', 'start': 3124.686, 'duration': 3.341}, {'end': 3132.648, 'text': 'it could be a random keyword as such, which would have, you know, the session data.', 'start': 3128.027, 'duration': 4.621}], 'summary': 'Django allows creating random session data for user logout after a particular time.', 'duration': 29.405, 'max_score': 3103.243, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI3103243.jpg'}, {'end': 3224.917, 'src': 'embed', 'start': 3194.758, 'weight': 4, 'content': [{'end': 3197.463, 'text': "You've got multiple table inheritance and you've got proxy model.", 'start': 3194.758, 'duration': 2.705}, {'end': 3205.959, 'text': 'So what is an ABC? So the style is used when you want parents class to hold information that you do not want to type out for each child model.', 'start': 3197.483, 'duration': 8.476}, {'end': 3208.882, 'text': 'So basically you will have a particular parent class.', 'start': 3206.3, 'duration': 2.582}, {'end': 3217.67, 'text': 'Okay And you would want the parent class to put information and you do not want to create it for each and every child class as such.', 'start': 3209.182, 'duration': 8.488}, {'end': 3220.132, 'text': 'That is where you use an ABC as such.', 'start': 3217.73, 'duration': 2.402}, {'end': 3224.917, 'text': 'So ABC is also used to create particular classes.', 'start': 3220.633, 'duration': 4.284}], 'summary': 'Abc is used for multiple table inheritance and proxy models to avoid redundant typing for child classes.', 'duration': 30.159, 'max_score': 3194.758, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI3194758.jpg'}, {'end': 3281.746, 'src': 'embed', 'start': 3257.987, 'weight': 5, 'content': [{'end': 3269.281, 'text': "So, you've got an existing model and you want to create a model based on that existing table, another particular model based on that existing table.", 'start': 3257.987, 'duration': 11.294}, {'end': 3272.025, 'text': "So, you don't have to write each and everything down.", 'start': 3269.321, 'duration': 2.704}, {'end': 3275.944, 'text': 'you can inherit from multiple table as such.', 'start': 3272.503, 'duration': 3.441}, {'end': 3278.805, 'text': 'okay, you want to say you know, you have a class called book.', 'start': 3275.944, 'duration': 2.861}, {'end': 3280.206, 'text': "you've got an article.", 'start': 3278.805, 'duration': 1.401}, {'end': 3281.746, 'text': "you've got two different classes.", 'start': 3280.206, 'duration': 1.54}], 'summary': 'Inherit from multiple tables to create new models based on an existing table.', 'duration': 23.759, 'max_score': 3257.987, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI3257987.jpg'}, {'end': 3330.101, 'src': 'embed', 'start': 3300.516, 'weight': 6, 'content': [{'end': 3308.562, 'text': 'so it allows you to override basically existing functions as such, but in multi-table inheritance it does not allow you to do that.', 'start': 3300.516, 'duration': 8.046}, {'end': 3311.527, 'text': 'okay, are you all clear on that now?', 'start': 3308.562, 'duration': 2.965}, {'end': 3313.669, 'text': 'what are proxy models now?', 'start': 3311.527, 'duration': 2.142}, {'end': 3315.03, 'text': 'proxy models.', 'start': 3313.669, 'duration': 1.361}, {'end': 3321.595, 'text': 'so you can use this model if you want to modify the python level behavior without changing the models field.', 'start': 3315.03, 'duration': 6.565}, {'end': 3330.101, 'text': 'so you can create proxies where you are making changes to the actual database via an existing you know what do you say?', 'start': 3321.595, 'duration': 8.506}], 'summary': 'Django allows function overrides, except for multi-table inheritance. proxy models modify python behavior without changing fields.', 'duration': 29.585, 'max_score': 3300.516, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI3300516.jpg'}], 'start': 2800.406, 'title': 'Setting up a database in java and writing views in django', 'summary': 'Covers setting up a database in java, focusing on sqlite, and explains the configuration in settings.py, alongside discussing writing a view in django and the components of a django template. it also covers using sessions in django for user logouts and explains three inheritance styles: abstract based classes, multiple table inheritance, and proxy models.', 'chapters': [{'end': 3079.48, 'start': 2800.406, 'title': 'Setting up a database in java and writing views in django', 'summary': 'Covers setting up a database in java, focusing on sqlite, and explains the configuration in the settings.py file, alongside discussing how to write a view in django and the components of a django template.', 'duration': 279.074, 'highlights': ['Setting up a database in Java, focusing on SQLite, and configuring the settings.py file The chapter explains how to set up a default database in Java, such as SQLite, and mentions the use of administrative tools like SQLAlchemy for creating new databases. It also delves into the configuration of the settings.py file for the database and web server information.', 'Writing a view in Django and the components of a Django template The chapter discusses writing a view in Django, which is similar to a controller, and explains the process of pulling information from the model and template to serve it to the user. Additionally, it covers the components of a Django template, such as XML, CSV, HTML, variables, and tags.', 'Explanation of the use of session in Django framework The chapter addresses the use of session in the Django framework, providing an opportunity to delve into the management of session data in Django applications.']}, {'end': 3364.165, 'start': 3079.94, 'title': 'Django sessions & inheritance', 'summary': 'Covers using sessions in django for user logouts and explains three inheritance styles: abstract based classes, multiple table inheritance, and proxy models.', 'duration': 284.225, 'highlights': ['Using Sessions in Django Django provides a session that allows you to store and retrieve data per site visitor basis, with a default timeout of two hours for user logouts.', 'Abstract Based Classes (ABC) ABC is used when you want parent class to hold information not typed out for each child model, and it cannot be instantiated in Python.', 'Multiple Table Inheritance Allows creating a model based on an existing table without writing everything down, enabling inheritance from multiple tables but not allowing to override existing functions.', "Proxy Models Proxy models can modify Python level behavior without changing the model's field, allowing multiple behaviors without altering the base model itself."]}], 'duration': 563.759, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI2800406.jpg', 'highlights': ['Setting up a database in Java, focusing on SQLite, and configuring the settings.py file', 'Writing a view in Django and the components of a Django template', 'Explanation of the use of session in Django framework', 'Using Sessions in Django with a default timeout of two hours for user logouts', 'Abstract Based Classes (ABC) for holding information not typed out for each child model', 'Multiple Table Inheritance for creating a model based on an existing table', "Proxy Models for modifying Python level behavior without changing the model's field"]}, {'end': 3976.689, 'segs': [{'end': 3456.959, 'src': 'embed', 'start': 3364.165, 'weight': 1, 'content': [{'end': 3370.958, 'text': 'if it is integer string, where char or anything like that, The default HTML widget avail.', 'start': 3364.165, 'duration': 6.793}, {'end': 3380.149, 'text': "while rendering a form field, whether it's a text box, whether it's a particular email.", 'start': 3370.958, 'duration': 9.191}, {'end': 3383.333, 'text': "you've got an email text field, something like that.", 'start': 3380.149, 'duration': 3.184}, {'end': 3384.934, 'text': "You've got different types of field classes.", 'start': 3383.373, 'duration': 1.561}, {'end': 3387.918, 'text': "You've got a password field, anything like this.", 'start': 3385.996, 'duration': 1.922}, {'end': 3393.727, 'text': 'so that is the default html widget and the minimum validation requirements.', 'start': 3388.342, 'duration': 5.385}, {'end': 3401.794, 'text': 'so number of characters which you can enter, number of lines which can be in the text box all of this come in the field class.', 'start': 3393.727, 'duration': 8.067}, {'end': 3409.221, 'text': 'please remember this minimum validation requirements, default html widget and the database column type.', 'start': 3401.794, 'duration': 7.427}, {'end': 3415.322, 'text': 'okay, coming to the next section, that is web scraping using python, these questions.', 'start': 3409.881, 'duration': 5.441}, {'end': 3421.884, 'text': 'so how do you save an image locally using python whose url address you already know?', 'start': 3415.322, 'duration': 6.562}, {'end': 3424.504, 'text': 'okay, so there are various ways of doing it.', 'start': 3421.884, 'duration': 2.62}, {'end': 3426.565, 'text': 'so this will work directly.', 'start': 3424.504, 'duration': 2.061}, {'end': 3432.166, 'text': 'if you are using from you know your local, so your local, it would actually work directly.', 'start': 3426.565, 'duration': 5.601}, {'end': 3450.634, 'text': 'so typically what i would do is something like this python requests my URL is equal to www.http.google.com.', 'start': 3432.166, 'duration': 18.468}, {'end': 3456.959, 'text': 'this requests get URL.', 'start': 3450.634, 'duration': 6.325}], 'summary': 'Default html widget and minimum validation requirements for form fields. python web scraping to save image locally.', 'duration': 92.794, 'max_score': 3364.165, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI3364165.jpg'}, {'end': 3574.207, 'src': 'embed', 'start': 3542.667, 'weight': 2, 'content': [{'end': 3546.131, 'text': 'So if you use webcast.google user content.', 'start': 3542.667, 'duration': 3.464}, {'end': 3549.094, 'text': "So I'll open this up.", 'start': 3546.872, 'duration': 2.222}, {'end': 3554.64, 'text': 'You can actually do this.', 'start': 3552.838, 'duration': 1.802}, {'end': 3558.965, 'text': 'So your URL, for example, www.edu.com.', 'start': 3556.382, 'duration': 2.583}, {'end': 3567.644, 'text': 'So you will be able to get this information.', 'start': 3564.302, 'duration': 3.342}, {'end': 3569.505, 'text': 'So you will be getting the cache content.', 'start': 3567.704, 'duration': 1.801}, {'end': 3574.207, 'text': 'This is the cache content which has been provided by your CDN.', 'start': 3569.525, 'duration': 4.682}], 'summary': 'Webcast.google allows access to cache content for specified urls.', 'duration': 31.54, 'max_score': 3542.667, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI3542667.jpg'}, {'end': 3640.758, 'src': 'embed', 'start': 3602.873, 'weight': 0, 'content': [{'end': 3603.953, 'text': 'You might get a simple one.', 'start': 3602.873, 'duration': 1.08}, {'end': 3613.316, 'text': 'So how do you approach this? So you would have to get into imdb.com first, and get the information first, this particular thing.', 'start': 3604.713, 'duration': 8.603}, {'end': 3614.897, 'text': 'Are you all following me so far?', 'start': 3613.817, 'duration': 1.08}, {'end': 3619.533, 'text': 'so you go.', 'start': 3618.993, 'duration': 0.54}, {'end': 3622.614, 'text': 'imdb.com. top 250 movies.', 'start': 3619.533, 'duration': 3.081}, {'end': 3625.755, 'text': 'ok, so there are various ways of doing it.', 'start': 3622.614, 'duration': 3.141}, {'end': 3629.676, 'text': 'ok, but for this you have to start with beautiful soup.', 'start': 3625.755, 'duration': 3.921}, {'end': 3640.758, 'text': 'on this beautiful soup from bs4, import beautiful soup.', 'start': 3629.676, 'duration': 11.082}], 'summary': 'Access imdb.com to gather information on top 250 movies using beautiful soup.', 'duration': 37.885, 'max_score': 3602.873, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI3602873.jpg'}], 'start': 3364.165, 'title': 'Python web scraping and image saving', 'summary': 'Covers html widgets, form field validation, and web scraping in python, including techniques for saving images locally, obtaining google cache age of a url, and data scraping from imdb top 250 pages using beautiful soup and requests library.', 'chapters': [{'end': 3415.322, 'start': 3364.165, 'title': 'Field classes and web scraping in python', 'summary': 'Covers the default html widgets and minimum validation requirements for form fields, as well as web scraping using python.', 'duration': 51.157, 'highlights': ['The chapter discusses the default HTML widget available for rendering form fields and the minimum validation requirements, including the number of characters and lines allowed in the text box.', 'It explains the different types of field classes such as text box, email, and password fields, and their respective functionalities.', 'The section also touches on web scraping using Python, indicating its relevance in the chapter.']}, {'end': 3976.689, 'start': 3415.322, 'title': 'Python image saving and web scraping', 'summary': 'Covers saving an image locally using python, obtaining google cache age of a url, and scraping data from imdb top 250 pages using beautiful soup and requests library.', 'duration': 561.367, 'highlights': ["To save an image locally using Python, you can use the requests library to fetch the image from a URL and then write the encoded content to a file, such as image.png, using 'open' with 'wb' mode, ensuring the image is successfully saved.", 'Obtaining the Google cache age of a URL involves accessing the webcache.googleusercontent.com and inputting the desired URL to retrieve the cache content provided by the CDN.', "For scraping data from IMDB top 250 pages, the process begins with importing Beautiful Soup from bs4 and using requests to fetch the webpage's content, followed by parsing the HTML and identifying and extracting the required fields such as movie name, year, and rating using specific classes and tags."]}], 'duration': 612.524, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI3364165.jpg', 'highlights': ["The process of scraping data from IMDB top 250 pages involves importing Beautiful Soup from bs4 and using requests to fetch the webpage's content, followed by parsing the HTML and identifying and extracting the required fields such as movie name, year, and rating using specific classes and tags.", "To save an image locally using Python, you can use the requests library to fetch the image from a URL and then write the encoded content to a file, such as image.png, using 'open' with 'wb' mode, ensuring the image is successfully saved.", 'Obtaining the Google cache age of a URL involves accessing the webcache.googleusercontent.com and inputting the desired URL to retrieve the cache content provided by the CDN.', 'The chapter discusses the default HTML widget available for rendering form fields and the minimum validation requirements, including the number of characters and lines allowed in the text box.', 'It explains the different types of field classes such as text box, email, and password fields, and their respective functionalities.', 'The section also touches on web scraping using Python, indicating its relevance in the chapter.']}, {'end': 4437.722, 'segs': [{'end': 4007.874, 'src': 'embed', 'start': 3976.689, 'weight': 0, 'content': [{'end': 3979.27, 'text': 'this is how you pull all the information.', 'start': 3976.689, 'duration': 2.581}, {'end': 3982.772, 'text': 'are you all clear on how, to you know, approach this particular problem?', 'start': 3979.27, 'duration': 3.502}, {'end': 3985.854, 'text': 'okay, next, data analysis user.', 'start': 3982.772, 'duration': 3.082}, {'end': 3991.097, 'text': 'now, numpy is one of the modules which are heavily used in python.', 'start': 3985.854, 'duration': 5.243}, {'end': 3996.725, 'text': 'okay, so how do you go about using this?', 'start': 3992.702, 'duration': 4.023}, {'end': 4003.77, 'text': 'so you will get a particular question saying how to get indices of n maximum values in a numpy array.', 'start': 3996.725, 'duration': 7.045}, {'end': 4007.874, 'text': 'okay, so you have to use these particular things.', 'start': 4003.77, 'duration': 4.104}], 'summary': 'Discussion on using numpy for data analysis and finding indices of n maximum values in a numpy array.', 'duration': 31.185, 'max_score': 3976.689, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI3976689.jpg'}, {'end': 4261.35, 'src': 'embed', 'start': 4239.181, 'weight': 2, 'content': [{'end': 4247.564, 'text': 'So you get a lot of vector and matrix operations so that you know you have to, you can avoid unnecessary work of recreating those vector operations.', 'start': 4239.181, 'duration': 8.383}, {'end': 4256.968, 'text': 'NumPy array is faster, you get a lot of built-in and built-in with NumPy, FFTs, convolution, searching, linear algebra, histograms, etc.', 'start': 4248.264, 'duration': 8.704}, {'end': 4259.589, 'text': "You've got a whole lot of stuff which is included.", 'start': 4257.068, 'duration': 2.521}, {'end': 4261.35, 'text': 'So what about the lists?', 'start': 4260.069, 'duration': 1.281}], 'summary': 'Numpy array offers faster vector and matrix operations with built-in functions like ffts, convolution, searching, linear algebra, and histograms.', 'duration': 22.169, 'max_score': 4239.181, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI4239181.jpg'}, {'end': 4356.783, 'src': 'embed', 'start': 4331.481, 'weight': 1, 'content': [{'end': 4340.047, 'text': 'okay, but you would see that numpy and scipy are quite compatible with each other because scipy is built on top of numpy.', 'start': 4331.481, 'duration': 8.566}, {'end': 4345.837, 'text': 'so where scipy has retained all the how do you say operations of numpy?', 'start': 4340.714, 'duration': 5.123}, {'end': 4348.798, 'text': "but is it's got a whole lot of its own operations?", 'start': 4345.837, 'duration': 2.961}, {'end': 4356.783, 'text': 'for example, numpy contains linear algebra functions, even though they are more properly utilized and they more properly belong to scipy.', 'start': 4348.798, 'duration': 7.985}], 'summary': 'Numpy and scipy are compatible, with scipy having additional operations. numpy contains linear algebra functions, properly utilized in scipy.', 'duration': 25.302, 'max_score': 4331.481, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI4331481.jpg'}], 'start': 3976.689, 'title': 'Numpy and scipy', 'summary': 'Provides an overview of numpy and scipy, including calculating percentile with python, differences between numpy and lists, and usage for scientific computing and visualizations.', 'chapters': [{'end': 4114.825, 'start': 3976.689, 'title': 'Numpy array and list comparison', 'summary': 'Explains how to use numpy to find the indices of the n maximum values in a numpy array, with an example of finding the top three indices and the difference between numpy arrays and lists.', 'duration': 138.136, 'highlights': ['The chapter demonstrates using numpy to find the indices of the n maximum values in a numpy array, with an example of finding the top three indices (3, 4, 2) from a given array.', 'It also addresses the difference between numpy arrays and lists, showcasing how to recreate an array and retrieve specific indices using arg sort.']}, {'end': 4437.722, 'start': 4115.325, 'title': 'Numpy and scipy overview', 'summary': 'Provides an overview of numpy and scipy, including calculating percentile with python, differences between numpy and lists, and the usage of numpy and scipy for scientific computing and visualizations.', 'duration': 322.397, 'highlights': ['Numpy contains array, data type and basic operations, while Scipy has more fully featured versions of linear algebra modules and provides integration and differentiation functions. Numpy contains array, data type and basic operations, while Scipy has more fully featured versions of linear algebra modules and provides integration and differentiation functions.', 'Numpy array is faster and supports vector and matrix operations, while lists are general purpose containers effective in regular insertion, deletion, appending, concatenation, and dynamic in nature. Numpy array is faster and supports vector and matrix operations, while lists are general purpose containers effective in regular insertion, deletion, appending, concatenation, and dynamic in nature.', 'Numpy and Scipy are quite compatible as Scipy is built on top of Numpy, making it advisable to use both for scientific computing. Numpy and Scipy are quite compatible as Scipy is built on top of Numpy, making it advisable to use both for scientific computing.']}], 'duration': 461.033, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI3976689.jpg', 'highlights': ['The chapter demonstrates using numpy to find the indices of the n maximum values in a numpy array, with an example of finding the top three indices (3, 4, 2) from a given array.', 'Numpy and Scipy are quite compatible as Scipy is built on top of Numpy, making it advisable to use both for scientific computing.', 'Numpy array is faster and supports vector and matrix operations, while lists are general purpose containers effective in regular insertion, deletion, appending, concatenation, and dynamic in nature.']}, {'end': 5478.825, 'segs': [{'end': 4501.715, 'src': 'embed', 'start': 4437.722, 'weight': 0, 'content': [{'end': 4439.523, 'text': "3d you've got.", 'start': 4437.722, 'duration': 1.801}, {'end': 4447.807, 'text': "for 2d, you've got bokeh, which is dependent on the bokeh.js library.", 'start': 4439.523, 'duration': 8.284}, {'end': 4448.868, 'text': "you've got.", 'start': 4447.807, 'duration': 1.061}, {'end': 4449.648, 'text': 'so the 3d.', 'start': 4448.868, 'duration': 0.78}, {'end': 4453.83, 'text': "you've got mayavi as well.", 'start': 4449.648, 'duration': 4.182}, {'end': 4462.019, 'text': 'you also got nvd3, which is part of the D3JS library, okay?', 'start': 4453.83, 'duration': 8.189}, {'end': 4467.321, 'text': "So you've got different beautiful charting APIs which you can use, okay?", 'start': 4462.359, 'duration': 4.962}, {'end': 4472.083, 'text': 'Now, how do I find indices of an array where some condition is true?', 'start': 4467.841, 'duration': 4.242}, {'end': 4475.744, 'text': 'So you can simply do this np.array of.', 'start': 4472.723, 'duration': 3.021}, {'end': 4480.306, 'text': 'you have an array of one, two, three, four, five, six, seven, eight, nine, print a greater than three.', 'start': 4475.744, 'duration': 4.562}, {'end': 4482.007, 'text': 'you will find all the.', 'start': 4480.306, 'duration': 1.701}, {'end': 4482.907, 'text': 'you know, what do you see??', 'start': 4482.007, 'duration': 0.9}, {'end': 4485.368, 'text': "You'll automatically create a Boolean.", 'start': 4483.227, 'duration': 2.141}, {'end': 4487.009, 'text': 'you know Boolean matrix as such.', 'start': 4485.368, 'duration': 1.641}, {'end': 4492.391, 'text': 'np dot non zero of a greater than three will give you the indices.', 'start': 4487.589, 'duration': 4.802}, {'end': 4494.692, 'text': 'so the output comes up this way.', 'start': 4492.391, 'duration': 2.301}, {'end': 4501.715, 'text': 'so let me just take that up.', 'start': 4494.692, 'duration': 7.023}], 'summary': 'Various charting apis available, np.array used to find array indices based on condition.', 'duration': 63.993, 'max_score': 4437.722, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI4437722.jpg'}, {'end': 4701.015, 'src': 'embed', 'start': 4660.089, 'weight': 2, 'content': [{'end': 4662.89, 'text': 'that is commander date score.', 'start': 4660.089, 'duration': 2.801}, {'end': 4670.763, 'text': 'you just simply do this data time score.', 'start': 4662.89, 'duration': 7.873}, {'end': 4673.725, 'text': 'you do this and check for df.', 'start': 4670.763, 'duration': 2.962}, {'end': 4675.526, 'text': 'it would have changed column names.', 'start': 4673.725, 'duration': 1.801}, {'end': 4679.729, 'text': 'this is how you change your column names in pandas.', 'start': 4675.526, 'duration': 4.203}, {'end': 4681.39, 'text': 'are you all clear?', 'start': 4679.729, 'duration': 1.661}, {'end': 4688.095, 'text': 'ok, now, what is the function used to iterate through values in a manner that one also retrieves the index?', 'start': 4681.39, 'duration': 6.705}, {'end': 4701.015, 'text': 'so for index comma row in df, dot iter rows, print index comma row.', 'start': 4688.095, 'duration': 12.92}], 'summary': 'The transcript covers using pandas to change column names and iterate through values in a dataframe.', 'duration': 40.926, 'max_score': 4660.089, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI4660089.jpg'}, {'end': 4757.038, 'src': 'embed', 'start': 4725.828, 'weight': 4, 'content': [{'end': 4730.092, 'text': 'how do you convert a list of dictionaries into a pandas data frame?', 'start': 4725.828, 'duration': 4.264}, {'end': 4733.975, 'text': 'so we had that question earlier and we did that you know directly.', 'start': 4730.092, 'duration': 3.883}, {'end': 4736.196, 'text': 'so you had wait.', 'start': 4733.975, 'duration': 2.221}, {'end': 4738.658, 'text': 'let me go back data.', 'start': 4736.196, 'duration': 2.462}, {'end': 4742.421, 'text': 'so you have got data over here, which is a dictionary containing all these items.', 'start': 4738.658, 'duration': 3.763}, {'end': 4751.355, 'text': 'so print pd dot data frame of data.', 'start': 4742.421, 'duration': 8.934}, {'end': 4757.038, 'text': 'so you will get a data frame which is created just doing pd dot data frame.', 'start': 4751.355, 'duration': 5.683}], 'summary': 'Convert a list of dictionaries into a pandas dataframe using pd.dataframe.', 'duration': 31.21, 'max_score': 4725.828, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI4725828.jpg'}, {'end': 4989.945, 'src': 'embed', 'start': 4962.342, 'weight': 5, 'content': [{'end': 4966.704, 'text': 'but it makes sense to use something like this df of a is in 3, 6.', 'start': 4962.342, 'duration': 4.362}, {'end': 4968.305, 'text': 'it will give you whatever.', 'start': 4966.704, 'duration': 1.601}, {'end': 4972.648, 'text': 'is there for that particular subset wherever there is 3 and 6?', 'start': 4968.305, 'duration': 4.343}, {'end': 4974.309, 'text': 'ok, in a.', 'start': 4972.648, 'duration': 1.661}, {'end': 4979.772, 'text': 'ok, so if you change it to b, you would probably get the value for only b.', 'start': 4974.309, 'duration': 5.463}, {'end': 4981.053, 'text': 'subset for b.', 'start': 4979.772, 'duration': 1.281}, {'end': 4982.294, 'text': 'are you all clear?', 'start': 4981.053, 'duration': 1.241}, {'end': 4983.955, 'text': 'you can also do this.', 'start': 4982.294, 'duration': 1.661}, {'end': 4989.285, 'text': "ok, Now, you've got a data frame which contains both.", 'start': 4983.955, 'duration': 5.33}, {'end': 4989.945, 'text': 'you know what you see.', 'start': 4989.285, 'duration': 0.66}], 'summary': "Using a data frame, it's possible to obtain values for subsets, such as 3 and 6, and switch between subsets like a and b.", 'duration': 27.603, 'max_score': 4962.342, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI4962342.jpg'}, {'end': 5041.455, 'src': 'embed', 'start': 5013.302, 'weight': 6, 'content': [{'end': 5017.105, 'text': 'in that case, how do you get only the ones which are not a number?', 'start': 5013.302, 'duration': 3.803}, {'end': 5027.109, 'text': 'so in that particular case, you just give this df dot is infinite, is is finite of so and so, for example, If we do it here,', 'start': 5017.105, 'duration': 10.004}, {'end': 5035.693, 'text': 'so it will give you only the finite values.', 'start': 5027.109, 'duration': 8.584}, {'end': 5038.134, 'text': 'This is just an example of what we had done earlier.', 'start': 5035.733, 'duration': 2.401}, {'end': 5040.275, 'text': 'So I do not know if I can replicate this.', 'start': 5038.694, 'duration': 1.581}, {'end': 5041.455, 'text': 'Just give me a second.', 'start': 5040.455, 'duration': 1}], 'summary': 'Filter non-numeric values using df.isfinite() to obtain finite values.', 'duration': 28.153, 'max_score': 5013.302, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI5013302.jpg'}, {'end': 5155.503, 'src': 'embed', 'start': 5129.071, 'weight': 7, 'content': [{'end': 5139.316, 'text': 'Similarly, f dot write, if you want to do a write binary, you simply do f is equal to open of wb and you can write information to it.', 'start': 5129.071, 'duration': 10.245}, {'end': 5141.717, 'text': 'You can also write image information to it.', 'start': 5139.356, 'duration': 2.361}, {'end': 5145.019, 'text': 'So an example which I have would be this.', 'start': 5142.217, 'duration': 2.802}, {'end': 5146.439, 'text': 'Just a moment.', 'start': 5145.939, 'duration': 0.5}, {'end': 5150.461, 'text': 'So one more important library is the OS library.', 'start': 5146.779, 'duration': 3.682}, {'end': 5151.782, 'text': "I'll give you another question.", 'start': 5150.541, 'duration': 1.241}, {'end': 5155.503, 'text': 'so how do you get the current working directory?', 'start': 5153.041, 'duration': 2.462}], 'summary': "Using 'f.write' and 'open(wb)' to write binary data, including image information. 'os library' is also important. another question: how to get the current working directory?", 'duration': 26.432, 'max_score': 5129.071, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI5129071.jpg'}, {'end': 5381.3, 'src': 'embed', 'start': 5341.39, 'weight': 8, 'content': [{'end': 5344.913, 'text': 'Okay, so I have a dictionary which says Roma.', 'start': 5341.39, 'duration': 3.523}, {'end': 5350.037, 'text': 'So you said where to use lists, when to use dictionaries.', 'start': 5347.015, 'duration': 3.022}, {'end': 5353.12, 'text': 'So basically a dictionary which contains lists as the values.', 'start': 5350.077, 'duration': 3.043}, {'end': 5362.688, 'text': 'For example, Roma at the rate gmail.com is going to be the email ID and the ID itself is going to be 112.', 'start': 5353.6, 'duration': 9.088}, {'end': 5366.091, 'text': 'Then I would probably have, what do you see, something like Hitesh.', 'start': 5362.688, 'duration': 3.403}, {'end': 5381.3, 'text': 'So that you know when you are accessing.', 'start': 5378.058, 'duration': 3.242}], 'summary': 'Using dictionaries with lists as values for managing email ids and corresponding ids.', 'duration': 39.91, 'max_score': 5341.39, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI5341390.jpg'}, {'end': 5434.718, 'src': 'embed', 'start': 5406.315, 'weight': 9, 'content': [{'end': 5408.456, 'text': 'so that would be from a developer perspective.', 'start': 5406.315, 'duration': 2.141}, {'end': 5412.018, 'text': 'so I am a pythonista, so I would specifically go for python.', 'start': 5408.456, 'duration': 3.562}, {'end': 5414.358, 'text': 'it depends on the ease of use.', 'start': 5412.018, 'duration': 2.34}, {'end': 5415.999, 'text': 'I am happier with python.', 'start': 5414.358, 'duration': 1.641}, {'end': 5419.241, 'text': 'I have an ease of use with python as compared to R.', 'start': 5415.999, 'duration': 3.242}, {'end': 5420.301, 'text': 'I have taken a look at R,', 'start': 5419.241, 'duration': 1.06}, {'end': 5429.417, 'text': 'But I still feel the what do you say learning curve for Python is much more flat compared to that of R if I was to take up R immediately.', 'start': 5420.956, 'duration': 8.461}, {'end': 5433.058, 'text': 'And with Python, I can do so much more as compared to R as well.', 'start': 5429.878, 'duration': 3.18}, {'end': 5434.718, 'text': 'So I could do R.', 'start': 5433.638, 'duration': 1.08}], 'summary': 'Python is preferred over r due to ease of use and learning curve, enabling more capabilities.', 'duration': 28.403, 'max_score': 5406.315, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI5406315.jpg'}], 'start': 4437.722, 'title': 'Charting apis, data manipulation, pandas data frame, subsetting, file handling, and data analysis in python', 'summary': 'Covers the usage of charting apis like bokeh, mayavi, and nvd3, techniques for finding indices of an array, renaming column headers in pandas data frame, and iterating through values with their respective indices. it also discusses creating pandas data frames from dictionaries, subsetting data frames, filtering for finite values, file handling, reading and writing image files, accessing the current working directory, copying files, and the usage of dictionaries and lists in python. additionally, it mentions the preference for python over r in data analysis, citing ease of use and capabilities.', 'chapters': [{'end': 4725.828, 'start': 4437.722, 'title': 'Charting apis and data manipulation in python', 'summary': 'Covers the usage of charting apis like bokeh, mayavi, and nvd3 in python, along with techniques for finding indices of an array and renaming column headers in pandas data frame, and iterating through values with their respective indices.', 'duration': 288.106, 'highlights': ['Explanation of charting APIs like bokeh, mayavi, and nvd3 in Python. The chapter discusses various beautiful charting APIs including bokeh, mayavi, and nvd3 for creating 2D and 3D visualizations.', 'Technique for finding indices of an array where a certain condition is true. The method np.nonzero() is explained for finding indices of an array where a condition is true, with a specific example of finding indices of numbers greater than 3 in an array.', 'Procedure for renaming column headers in pandas data frame. The process of renaming column headers in a pandas data frame using the df.columns attribute and reassigning new column names is demonstrated.', 'Usage of a function to iterate through values along with their indices in a pandas data frame. The function df.iterrows() is introduced for iterating through values in a pandas data frame while also retrieving their respective indices.']}, {'end': 5088.946, 'start': 4725.828, 'title': 'Pandas data frame and subsetting', 'summary': 'Discusses creating a pandas data frame from a list of dictionaries, subsetting a data frame based on a list of values, and filtering for finite values in a data frame.', 'duration': 363.118, 'highlights': ["Creating a pandas data frame from a list of dictionaries involves using 'pd.DataFrame(data)' to directly convert the dictionary into a data frame.", "Subsetting a data frame based on a list of values can be done using 'df[df['a'].isin([3, 6])]', which returns the subset of values in column 'a' that match the list of values [3, 6].", "Filtering for finite values in a data frame can be achieved using 'df.isfinite()', which returns only the finite rows in the data frame."]}, {'end': 5478.825, 'start': 5088.946, 'title': 'Python file handling and data analysis', 'summary': 'Covers python file handling using read binary and write binary mode, including examples for reading and writing image files, accessing current working directory, and copying files. it also discusses the usage of dictionaries and lists in python, and the preference for python over r in data analysis, citing ease of use and capabilities.', 'duration': 389.879, 'highlights': ['The chapter covers Python file handling using read binary and write binary mode, including examples for reading and writing image files, accessing current working directory, and copying files. Python file handling, read binary mode, write binary mode, reading image files, accessing current working directory, copying files', 'It also discusses the usage of dictionaries and lists in Python. Usage of dictionaries and lists in Python', 'The preference for Python over R in data analysis, citing ease of use and capabilities. Preference for Python over R in data analysis, ease of use, capabilities']}], 'duration': 1041.103, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/B_17_RORoiI/pics/B_17_RORoiI4437722.jpg', 'highlights': ['Covers the usage of charting APIs like bokeh, mayavi, and nvd3 in Python', 'Technique for finding indices of an array where a certain condition is true using np.nonzero()', 'Procedure for renaming column headers in pandas data frame using df.columns attribute', 'Usage of a function to iterate through values along with their indices in a pandas data frame using df.iterrows()', "Creating a pandas data frame from a list of dictionaries using 'pd.DataFrame(data)'", "Subsetting a data frame based on a list of values using 'df[df['a'].isin([3, 6])]'", "Filtering for finite values in a data frame using 'df.isfinite()'", 'Python file handling using read binary and write binary mode, including examples for reading and writing image files, accessing current working directory, and copying files', 'Usage of dictionaries and lists in Python', 'Preference for Python over R in data analysis, citing ease of use and capabilities']}], 'highlights': ['Python job postings have seen heavy spikes and continuous growth across the years, indicating its increasing popularity and adoption. Increased percentage of matching job postings over the years.', "Covers python's versatility, popularity in the job market, and applications, with starting salaries ranging from 2.5 to 8 lakhs per annum and experienced professionals earning up to 12 lakhs per annum.", 'The starting salaries for Python developers range from 2.5 to 8 lakhs per annum, with experienced professionals earning close to 10 to 12 lakhs per annum. Starting salaries for 1-2 years of experience: 2.5-4 lakhs per annum, for 2 years of experience: 4 plus lakhs per annum, and for 4 plus years of experience: close to 8 lakhs per annum.', 'Python is highly sought after in the job market, with a wide range of opportunities available, including duty-based development, DevOps, and front-end development using jQuery.', "Python's application in various domains such as SOA, cloud computing, and data analytics Python is used in SOA, distributed architecture, cloud computing, and data analytics, making it versatile across different domains.", 'The chapter emphasizes the significance of understanding the differences between shallow copy, deep copy, and regular references when handling instances in Python.', 'It is important to be familiar with various data structures in Python as they are heavily asked about in interviews.', 'Lists can be modified at runtime using methods like append, while tuples cannot be modified, highlighting the mutability difference between the two.', 'Tuples are recommended for fixed data with known size to save memory space, while lists are suitable for dynamic data manipulation and tasks like buffers and queues, emphasizing the specific use cases for each data structure.', "Demonstrates the process of creating a patch function, 'monkey_f', and applying it to the original function 'f' within the class 'my_class', showcasing the ability to force a specific function attribute onto a class at runtime.", "The use of new style classes is recommended over old style classes in Python for inheritance from the 'object' parent class.", 'Explains implementing sorting algorithms for numerical datasets in Python.', 'Covers creating dictionaries from lists or tuples using the zip function.', 'The chapter explains single, multi-level, hierarchical, and multiple inheritance, highlighting single and multiple inheritance as the most commonly used types.', 'The MVT architecture in Django has the model similar to both MVC and MVT, where the view functions as a controller and the template serves as the view in MVC, allowing dynamic elements to be pulled from the model.', 'Setting up a database in Java, focusing on SQLite, and configuring the settings.py file', "The process of scraping data from IMDB top 250 pages involves importing Beautiful Soup from bs4 and using requests to fetch the webpage's content, followed by parsing the HTML and identifying and extracting the required fields such as movie name, year, and rating using specific classes and tags.", 'The chapter demonstrates using numpy to find the indices of the n maximum values in a numpy array, with an example of finding the top three indices (3, 4, 2) from a given array.', 'Covers the usage of charting APIs like bokeh, mayavi, and nvd3 in Python', 'Python file handling using read binary and write binary mode, including examples for reading and writing image files, accessing current working directory, and copying files', 'Preference for Python over R in data analysis, citing ease of use and capabilities']}