title
Python Interview Questions | Python Tutorial | Intellipaat
description
🔥Intellipaat Python training course: https://intellipaat.com/python-certification-training-online/
This python interview questions and answers will help you ace your next Python Job Interview. We have collected these Python Interview Questions video based on the new curriculum for Python which was recently updated. This Python tutorial has Python interview tips as well so that you can include this in your Python interview preparation and excel in the interview. We have tried to cover almost all the concepts so that you clear the interview with ease.
#PythonInterviewQuestions #PythonInterviewQuestionsandanswers #PythonTutorial #PythonTutorialforBeginners #Intellipaat
đź“Ś Do subscribe to Intellipaat channel & get regular updates on videos: http://bit.ly/Intellipaat
đź’ˇ Know top 5 reasons to learn python: https://bit.ly/2IjH1Ng
đź”— Watch complete Python tutorials here: https://www.youtube.com/watch?v=5GYeia8IRbg&list=PLVHgQku8Z935Qq0h3SZpSOwSrUMx1y3c9
đź“• Read complete Python tutorial here: https://intellipaat.com/tutorial/python-tutorial/
đź“•Read insightful blog on Python certification: https://intellipaat.com/blog/python-certification/
đź“ťFollowing topics are covered in this video:
Python Job Trend - 00:38
Basic Questions - 1:10
Questions on OOPS - 5:27
Questions on NumPy - 16:02
Questions on Pandas - 22:17
File Handling in Python - 31:11
Lambda Function in Python - 32:05
Questions on Matplotlib - 33:23
Module in Python - 37:47
Random Questions - 38:47
Machine Learning with Python - 49:16
If you’ve enjoyed this python interview questions and answers for freshers tutorial, Like us and Subscribe to our channel for more similar informative tutorials.
Got any questions about python training? Ask us in the comment section below.
----------------------------
Intellipaat Edge
1. 24*7 Life time Access & Support
2. Flexible Class Schedule
3. Job Assistance
4. Mentors with +14 yrs
5. Industry Oriented Course ware
6. Life time free Course Upgrade
------------------------------
Why should you watch this Python interview questions video?
Python is one of the top programming language offering high-paying jobs. If you are looking to clear the Python interview then this Python interview questions and answers is a must watch for you. In this Python interview questions video you will learn what are the most probable questions that will be asked in the interview.
What is included in this Python interview preparation video?
You will find that this Python certification interview questions tips video is clearly segregated into its logical components. This way you will be in a better position to clear the interview.
Check this segregation of interview questions here:
1. Generic Questions
2. Questions on OOPS
3. Questions on NumPy
4. Questions on Pandas
5. Questions on File Handling
6. Questions on Lambda Function
7. Questions on Matplotlib
8. Random Questions
9. Machine Learning with Python Questions
Who is eligible to watch this Python interview preparation video?
This Python interview questions and answers video is both for experienced and freshers in the technology.
What makes this Python interview questions video so unique?
This Python interview questions and answers video is not prepared by academicians. This Python questions and answers video has been exclusively created by professionals who are working in Python domain. This way they have the first-hand idea of what are the questions that are being asked in the Python interview. Due to this you will be in a better position to clear the Python interview and land your dream job after watching this video.
------------------------------
For more Information:
Please write us to sales@intellipaat.com, or call us at: +91- 7847955955
Website: https://intellipaat.com/python-certification-training-online/
Facebook: https://www.facebook.com/intellipaatonline
LinkedIn: https://www.linkedin.com/in/intellipaat/
Twitter: https://twitter.com/Intellipaat
detail
{'title': 'Python Interview Questions | Python Tutorial | Intellipaat', 'heatmap': [{'end': 143.505, 'start': 100.424, 'weight': 0.889}, {'end': 349.408, 'start': 309.616, 'weight': 0.747}, {'end': 668.133, 'start': 625.094, 'weight': 1}, {'end': 838.352, 'start': 799.254, 'weight': 0.775}, {'end': 976.739, 'start': 938.471, 'weight': 0.862}, {'end': 1221.729, 'start': 1145.922, 'weight': 0.704}, {'end': 1361.287, 'start': 1275.682, 'weight': 0.734}, {'end': 1879.273, 'start': 1842.131, 'weight': 0.716}, {'end': 1949.249, 'start': 1907.478, 'weight': 0.841}, {'end': 2299.068, 'start': 2260.552, 'weight': 0.97}, {'end': 2679.679, 'start': 2640.371, 'weight': 0.714}], 'summary': 'This python tutorial video covers python job trends, keywords, dictionary creation, manipulation, classes, inheritance, numpy arrays, data manipulation using pandas, lambda functions, matplotlib, model building with machine learning reaching a root mean squared error of 0.0 and a decision tree classifier accuracy of 91.11%.', 'chapters': [{'end': 198.794, 'segs': [{'end': 65.447, 'src': 'embed', 'start': 24.045, 'weight': 0, 'content': [{'end': 30.648, 'text': "So in today's session, we've come up with this Python interview questions so that you can ace any Python interview that you attend.", 'start': 24.045, 'duration': 6.603}, {'end': 36.93, 'text': 'And you guys also have to make sure that you implement all of the coding part by yourselves so that you gain the required expertise.', 'start': 31.028, 'duration': 5.902}, {'end': 42.312, 'text': "Now let's actually start off by having a glance at the job trends of different programming languages.", 'start': 37.569, 'duration': 4.743}, {'end': 46.975, 'text': 'So here we are comparing Python, R, Angular and C.', 'start': 42.872, 'duration': 4.103}, {'end': 51.898, 'text': "And it's very obvious that Python is the most preferred language across various industries.", 'start': 46.975, 'duration': 4.923}, {'end': 54.019, 'text': 'So you see this blue color line over here.', 'start': 52.318, 'duration': 1.701}, {'end': 57.101, 'text': 'So this blue colored line is for the Python programming language.', 'start': 54.16, 'duration': 2.941}, {'end': 65.447, 'text': 'And if you closely observe this blue colored line, you will observe that the popularity of Python has been steadily increasing over the years.', 'start': 57.722, 'duration': 7.725}], 'summary': 'Python is the most preferred language with steadily increasing popularity in job trends.', 'duration': 41.402, 'max_score': 24.045, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk24045.jpg'}, {'end': 143.505, 'src': 'heatmap', 'start': 85.338, 'weight': 3, 'content': [{'end': 91.882, 'text': 'So, these are some of the keywords which exist in Python, such as true, false, not, continue and so on.', 'start': 85.338, 'duration': 6.544}, {'end': 96.283, 'text': 'And in total, there are 33 keywords in Python 3.7.', 'start': 92.262, 'duration': 4.021}, {'end': 100.084, 'text': 'Now you also need to keep in mind that these keywords are case sensitive.', 'start': 96.283, 'duration': 3.801}, {'end': 106.446, 'text': 'That is, if you look at the keyword true over here, then you see that T needs to be capital.', 'start': 100.424, 'duration': 6.022}, {'end': 112.128, 'text': "So our next question is what are literals in Python, and then we'd have to explain about the different types of literals.", 'start': 106.766, 'duration': 5.362}, {'end': 120.134, 'text': 'So literals are the constants used in python, or in other words, this is the data which is stored in a variable,', 'start': 112.708, 'duration': 7.426}, {'end': 122.136, 'text': 'and there are four types of literals in python.', 'start': 120.134, 'duration': 2.002}, {'end': 126.599, 'text': 'So we have string literals, numeric literals, boolean literals and special literals.', 'start': 122.436, 'duration': 4.163}, {'end': 128.881, 'text': "So let's look at string literals.", 'start': 127.28, 'duration': 1.601}, {'end': 133.842, 'text': 'so you can create string literals by just enclosing the text within quotes.', 'start': 129.321, 'duration': 4.521}, {'end': 138.504, 'text': 'so here we have created two string literals john and james.', 'start': 133.842, 'duration': 4.662}, {'end': 143.505, 'text': 'so we see that john is enclosed double quotes and james is enclosed within single quote.', 'start': 138.504, 'duration': 5.001}], 'summary': 'Python 3.7 has 33 keywords, including true and false, and 4 types of literals: string, numeric, boolean, and special.', 'duration': 41.261, 'max_score': 85.338, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk85338.jpg'}, {'end': 201.176, 'src': 'embed', 'start': 173.527, 'weight': 5, 'content': [{'end': 177.207, 'text': 'So these Boolean literals comprise of just true and false values.', 'start': 173.527, 'duration': 3.68}, {'end': 183.469, 'text': 'They are generally used when we are dealing with some condition whose output is either true or false.', 'start': 177.527, 'duration': 5.942}, {'end': 186.269, 'text': "Now we'll head on to special literal.", 'start': 184.309, 'duration': 1.96}, {'end': 193.571, 'text': 'So Python consists of this special literal called as none, and it is used to specify a field that is not created.', 'start': 186.649, 'duration': 6.922}, {'end': 198.794, 'text': 'Here in this example, I have assigned none to the variable val2.', 'start': 194.031, 'duration': 4.763}, {'end': 201.176, 'text': 'So this variable would basically be empty.', 'start': 199.095, 'duration': 2.081}], 'summary': 'Python has boolean literals true and false, and a special literal called none used for specifying empty fields.', 'duration': 27.649, 'max_score': 173.527, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk173527.jpg'}], 'start': 3.285, 'title': 'Python job trends and keywords', 'summary': 'Covers the high demand for python programmers, its versatility in web development and machine learning, providing interview questions, and discusses the 33 keywords and various types of literals in python.', 'chapters': [{'end': 65.447, 'start': 3.285, 'title': 'Python interview questions and job trends', 'summary': 'Discusses the high demand for python programmers, its versatility in web development and machine learning, and provides python interview questions to help ace interviews, while also highlighting the increasing popularity of python in job trends.', 'duration': 62.162, 'highlights': ['Python is in high demand for web development, machine learning, and deep learning, and is the most preferred language across various industries, as shown by job trends comparing Python, R, Angular, and C.', 'The popularity of Python has been steadily increasing over the years, as indicated by the blue colored line in the job trends graph.', 'The session provides Python interview questions to help attendees ace any Python interview and emphasizes the importance of implementing the coding part independently to gain the required expertise.']}, {'end': 198.794, 'start': 66.067, 'title': 'Python keywords, literals, and their types', 'summary': 'Discusses python keywords, including 33 total keywords in python 3.7 and their case sensitivity, followed by an explanation of literals in python, which include string literals, numeric literals, boolean literals, and special literals.', 'duration': 132.727, 'highlights': ['Python has 33 keywords in Python 3.7. There are 33 keywords in Python 3.7, including true, false, not, continue, and others.', 'Literals in Python include string literals, numeric literals, boolean literals, and special literals. Literals in Python encompass string, numeric, boolean, and special literals, each serving specific data storage purposes.', 'Boolean literals in Python consist of true and false values. Boolean literals in Python are limited to true and false values, commonly utilized for conditional evaluations.', "Special literal 'none' in Python is used to specify a field that is not created. Python includes a special literal 'none' to indicate a non-existing field, often employed to denote absence of value."]}], 'duration': 195.509, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk3285.jpg', 'highlights': ['Python is the most preferred language for web development, machine learning, and deep learning, evident from job trends comparing Python, R, Angular, and C.', 'The popularity of Python has been steadily increasing, as indicated by the blue colored line in the job trends graph.', 'The session provides Python interview questions to help attendees ace any Python interview and emphasizes the importance of independent coding practice.', 'Python 3.7 has 33 keywords, including true, false, not, and continue.', 'Literals in Python encompass string, numeric, boolean, and special literals, each serving specific data storage purposes.', 'Boolean literals in Python are limited to true and false values, commonly utilized for conditional evaluations.', "Python includes a special literal 'none' to indicate a non-existing field, often employed to denote absence of value."]}, {'end': 941.253, 'segs': [{'end': 264.54, 'src': 'embed', 'start': 238.493, 'weight': 0, 'content': [{'end': 246.559, 'text': "Right, now let's head on to Jupyter Notebook and create our own dictionary where the key is fruit name and the values would be four fruit names.", 'start': 238.493, 'duration': 8.066}, {'end': 254.405, 'text': "So I'll name the dictionary as my dictionary and we can create a dictionary with the help of these curly braces over here.", 'start': 246.999, 'duration': 7.406}, {'end': 264.54, 'text': "So I'll give in the key name, which would be fruit name, and after this i'd have to give in values of four fruit names.", 'start': 254.925, 'duration': 9.615}], 'summary': 'Create a dictionary in jupyter notebook with fruit names and their values.', 'duration': 26.047, 'max_score': 238.493, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk238493.jpg'}, {'end': 309.236, 'src': 'embed', 'start': 284.199, 'weight': 1, 'content': [{'end': 292.644, 'text': 'So we have created a dictionary with the name, my dictionary, where the key is fruit name and the values are apple, mango, orange and guava.', 'start': 284.199, 'duration': 8.445}, {'end': 297.748, 'text': 'And if you want to extract the individual key and individual values, this is how we can do it.', 'start': 293.085, 'duration': 4.663}, {'end': 300.89, 'text': "Now I'll type in the name of the dictionary, which is my dictionary.", 'start': 298.208, 'duration': 2.682}, {'end': 304.472, 'text': "I'll put in dot, and then I'll just type in keys.", 'start': 301.27, 'duration': 3.202}, {'end': 309.236, 'text': "I'll click on run and we see that the key for this dictionary is fruit name.", 'start': 305.152, 'duration': 4.084}], 'summary': "A dictionary named 'my dictionary' contains fruit names as keys and 'apple', 'mango', 'orange', and 'guava' as values.", 'duration': 25.037, 'max_score': 284.199, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk284199.jpg'}, {'end': 349.408, 'src': 'heatmap', 'start': 309.616, 'weight': 0.747, 'content': [{'end': 314.021, 'text': 'Similarly, if I want all of the values, I just have to type in values over here.', 'start': 309.616, 'duration': 4.405}, {'end': 315.922, 'text': 'Now let me click on run.', 'start': 314.821, 'duration': 1.101}, {'end': 320.286, 'text': 'So for this dictionary, the values are apple, mango, orange and guava.', 'start': 316.383, 'duration': 3.903}, {'end': 324.53, 'text': 'So our next question what are classes and objects in Python?', 'start': 320.767, 'duration': 3.763}, {'end': 334.197, 'text': 'So, simply put, you can consider a class to be a blueprint and objects to be real world entities which are defined and created from classes.', 'start': 325.211, 'duration': 8.986}, {'end': 339.041, 'text': 'For example, over here you can see the actual blueprint of a house.', 'start': 334.658, 'duration': 4.383}, {'end': 344.805, 'text': 'Now this blueprint can be used for the rapid creation of unlimited number of copies.', 'start': 339.581, 'duration': 5.224}, {'end': 349.408, 'text': 'So these copies of the blueprint are nothing but your objects.', 'start': 345.385, 'duration': 4.023}], 'summary': 'Python class is a blueprint for creating objects. objects are real-world entities created from classes.', 'duration': 39.792, 'max_score': 309.616, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk309616.jpg'}, {'end': 583.951, 'src': 'embed', 'start': 557.531, 'weight': 2, 'content': [{'end': 561.735, 'text': "So let's say I create an object and name that object to be person1.", 'start': 557.531, 'duration': 4.204}, {'end': 566.52, 'text': 'Now this would be our first instance and I just have to call in human.', 'start': 562.176, 'duration': 4.344}, {'end': 570.204, 'text': "So I'm creating a person instance of the human class.", 'start': 566.54, 'duration': 3.664}, {'end': 576.309, 'text': 'Now from this person object, I will invoke the get name and get age methods.', 'start': 571.027, 'duration': 5.282}, {'end': 579.51, 'text': 'So person one dot get name.', 'start': 576.749, 'duration': 2.761}, {'end': 581.83, 'text': 'Let me click on run.', 'start': 580.77, 'duration': 1.06}, {'end': 583.951, 'text': "I'd have to enter the name of the person.", 'start': 582.21, 'duration': 1.741}], 'summary': "Creating an instance 'person1' of the 'human' class and invoking its methods.", 'duration': 26.42, 'max_score': 557.531, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk557531.jpg'}, {'end': 668.133, 'src': 'heatmap', 'start': 625.094, 'weight': 1, 'content': [{'end': 629.738, 'text': 'Right So we have successfully created our class, which would print out the name of the person in the age of the person.', 'start': 625.094, 'duration': 4.644}, {'end': 636.644, 'text': "Right So next question, what do you understand by the init method in Python? And after that, we'd have to give an example of it.", 'start': 630.059, 'duration': 6.585}, {'end': 641.288, 'text': 'So you can just consider the init method to be sort of constructed in Python.', 'start': 637.285, 'duration': 4.003}, {'end': 646.313, 'text': 'So it is a special method in a Python class which is used to initialize the variables.', 'start': 641.649, 'duration': 4.664}, {'end': 653.677, 'text': "so now that we've understood what init method is and what it is used for, let's go ahead and work with this init method.", 'start': 646.773, 'duration': 6.904}, {'end': 659.321, 'text': "so now over here, what I'm going to do is create a student class with the init method in it.", 'start': 653.677, 'duration': 5.644}, {'end': 668.133, 'text': "so let me do that class, student, and over here I'll just create the init method.", 'start': 659.321, 'duration': 8.812}], 'summary': 'Created a python class with an init method for initializing variables.', 'duration': 43.039, 'max_score': 625.094, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk625094.jpg'}, {'end': 668.133, 'src': 'embed', 'start': 641.649, 'weight': 3, 'content': [{'end': 646.313, 'text': 'So it is a special method in a Python class which is used to initialize the variables.', 'start': 641.649, 'duration': 4.664}, {'end': 653.677, 'text': "so now that we've understood what init method is and what it is used for, let's go ahead and work with this init method.", 'start': 646.773, 'duration': 6.904}, {'end': 659.321, 'text': "so now over here, what I'm going to do is create a student class with the init method in it.", 'start': 653.677, 'duration': 5.644}, {'end': 668.133, 'text': "so let me do that class, student, and over here I'll just create the init method.", 'start': 659.321, 'duration': 8.812}], 'summary': 'Python init method initializes class variables.', 'duration': 26.484, 'max_score': 641.649, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk641649.jpg'}, {'end': 838.352, 'src': 'heatmap', 'start': 799.254, 'weight': 0.775, 'content': [{'end': 800.975, 'text': 'and then which branch is he studying?', 'start': 799.254, 'duration': 1.721}, {'end': 803.918, 'text': "so let's say, this guy is studying engineering.", 'start': 800.975, 'duration': 2.943}, {'end': 806.821, 'text': 'let me just put in engineering over here.', 'start': 803.918, 'duration': 2.903}, {'end': 812.614, 'text': 'run now student one, dot.', 'start': 808.052, 'duration': 4.562}, {'end': 817.035, 'text': 'i will call in the print student method over here, right.', 'start': 812.614, 'duration': 4.421}, {'end': 818.996, 'text': 'so we have successfully created this instance.', 'start': 817.035, 'duration': 1.961}, {'end': 821.016, 'text': 'student one, right.', 'start': 818.996, 'duration': 2.02}, {'end': 824.337, 'text': 'so what do you understand by inheritance in python?', 'start': 821.016, 'duration': 3.321}, {'end': 826.318, 'text': "and then we'd have to give an example of it.", 'start': 824.337, 'duration': 1.981}, {'end': 832.79, 'text': 'So inheritance refers to the property of one class acquiring the properties of another class.', 'start': 827.048, 'duration': 5.742}, {'end': 838.352, 'text': "For example, let's say you have inherited your features or properties from your parents.", 'start': 833.27, 'duration': 5.082}], 'summary': 'Introducing inheritance in python, with an example. one class acquiring properties from another class.', 'duration': 39.098, 'max_score': 799.254, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk799254.jpg'}, {'end': 891.764, 'src': 'embed', 'start': 860.601, 'weight': 4, 'content': [{'end': 864.423, 'text': "Now let's go ahead to Jupiter notebook and work with an example of inheritance.", 'start': 860.601, 'duration': 3.822}, {'end': 873.489, 'text': 'So here, what we are doing is we have a base class with the name fruit, and this base class is being inherited by another class citrus.', 'start': 865.044, 'duration': 8.445}, {'end': 877.252, 'text': 'Now this is our base class fruit, which has a constructor.', 'start': 874.01, 'duration': 3.242}, {'end': 882.036, 'text': 'this constructor just prints out I am a fruit.', 'start': 877.852, 'duration': 4.184}, {'end': 891.764, 'text': 'now, after this, what we are doing is we are creating another class with the name citrus, and this class inherits from the fruit class right.', 'start': 882.036, 'duration': 9.728}], 'summary': "Working in jupiter notebook with an example of inheritance, creating a base class 'fruit' and a derived class 'citrus.'", 'duration': 31.163, 'max_score': 860.601, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk860601.jpg'}], 'start': 199.095, 'title': 'Python dictionary creation and manipulation and understanding classes and inheritance in python', 'summary': 'Explains the creation and manipulation of dictionaries in python, highlighting key-value pair creation and access, and introduces classes and objects. it also covers creating classes, understanding the init method, and demonstrating inheritance with examples.', 'chapters': [{'end': 324.53, 'start': 199.095, 'title': 'Python dictionary creation and manipulation', 'summary': 'Explains the creation and manipulation of dictionaries in python, highlighting the process of creating a dictionary with key-value pairs and accessing keys and values, and then introduces the topic of classes and objects.', 'duration': 125.435, 'highlights': ["A dictionary is an unordered collection of elements stored as key-value pairs, demonstrated by the creation of 'MyDictionary' with three key-value pairs.", 'The process of creating a dictionary using curly braces and assigning key-value pairs for fruit names, such as apple, mango, orange, and guava, is explained and demonstrated.', "Accessing individual keys and values from the created dictionary is shown by using the 'keys' and 'values' methods, highlighting the retrieval of the key 'fruit name' and the corresponding values apple, mango, orange, and guava."]}, {'end': 941.253, 'start': 325.211, 'title': 'Understanding classes and inheritance in python', 'summary': 'Explains the concept of creating classes and objects in python, including a detailed example of creating a class for a person, understanding the init method, and demonstrating inheritance with a fruit and citrus example.', 'duration': 616.042, 'highlights': ['Creating a Class for a Person The instructor explains the process of creating a class for a person, including defining variables for name and age, creating methods to get and print the name and age, and demonstrating the creation of objects from the class, providing a clear understanding of classes and objects in Python.', 'Understanding the Init Method The chapter provides a comprehensive explanation of the init method in Python, highlighting its role as a constructor to initialize variables, and demonstrates its usage by creating a student class with the init method and a method to print the values, facilitating a deeper understanding of the init method.', "Demonstrating Inheritance The instructor illustrates the concept of inheritance in Python by using the example of inheriting traits from parents and grandparents, and demonstrates inheritance in Python by creating a base class 'fruit' and a derived class 'citrus' through which the super method is used to invoke the init method from the base class, providing a clear and practical example of inheritance in Python."]}], 'duration': 742.158, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk199095.jpg', 'highlights': ['The process of creating a dictionary using curly braces and assigning key-value pairs for fruit names is explained and demonstrated.', "Accessing individual keys and values from the created dictionary is shown by using the 'keys' and 'values' methods.", 'Creating a Class for a Person, defining variables for name and age, creating methods to get and print the name and age, and demonstrating the creation of objects from the class.', 'Understanding the init method in Python, highlighting its role as a constructor to initialize variables, and demonstrating its usage by creating a student class with the init method and a method to print the values.', "Illustrating the concept of inheritance in Python by using the example of inheriting traits from parents and grandparents, and demonstrating inheritance in Python by creating a base class 'fruit' and a derived class 'citrus' through which the super method is used to invoke the init method from the base class."]}, {'end': 1322.805, 'segs': [{'end': 988.041, 'src': 'embed', 'start': 961.388, 'weight': 2, 'content': [{'end': 970.715, 'text': 'Well, NumPy is the most widely used Python library for linear algebra and it is used for performing mathematical and logical operations on arrays.', 'start': 961.388, 'duration': 9.327}, {'end': 976.739, 'text': 'And to import the NumPy library in Python, you just have to use the command import NumPy.', 'start': 971.195, 'duration': 5.544}, {'end': 981.863, 'text': "So again, let's head to Jupyter Notebook and create a 1D NumPy array and a 2D NumPy array.", 'start': 977.179, 'duration': 4.684}, {'end': 988.041, 'text': "So I'll start off by typing import numpy as np.", 'start': 982.739, 'duration': 5.302}], 'summary': 'Numpy is the most widely used python library for linear algebra, used for mathematical and logical operations on arrays.', 'duration': 26.653, 'max_score': 961.388, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk961388.jpg'}, {'end': 1221.729, 'src': 'heatmap', 'start': 1145.922, 'weight': 0.704, 'content': [{'end': 1149.005, 'text': "So my first parameter would be the numpy arrays which I'd have to add.", 'start': 1145.922, 'duration': 3.083}, {'end': 1155.59, 'text': 'So I want to add a and P and I will set the axis to be equal to zero.', 'start': 1149.145, 'duration': 6.445}, {'end': 1160.274, 'text': 'So when I set the axis value to be equal to zero, this would individually add the elements.', 'start': 1155.85, 'duration': 4.424}, {'end': 1162.777, 'text': 'So this will do four plus one, five plus two and six plus three.', 'start': 1160.334, 'duration': 2.443}, {'end': 1164.995, 'text': 'And this is what we have.', 'start': 1164.194, 'duration': 0.801}, {'end': 1169.078, 'text': '4 plus 1 is 5, 5 plus 2 is 7 and 6 plus 3 is 9.', 'start': 1165.015, 'duration': 4.063}, {'end': 1173.22, 'text': "Now let me actually change the axis value to be 1 and let's see what do we get.", 'start': 1169.078, 'duration': 4.142}, {'end': 1178.984, 'text': 'So when I change the axis value to be 1, then the addition happens across the row.', 'start': 1173.24, 'duration': 5.744}, {'end': 1184.568, 'text': 'So when you do 3 plus 2 plus 1, you get 6 and when you do 6 plus 4 plus 5, you get 15.', 'start': 1179.425, 'duration': 5.143}, {'end': 1187.75, 'text': "So now we'd have to get the n largest values from a numpy array.", 'start': 1184.568, 'duration': 3.182}, {'end': 1196.175, 'text': 'So this is our numpy array over here, and this comprise of 103, 456, 7 elements, 12, 43, 200, 54, 5 and 68,', 'start': 1188.23, 'duration': 7.945}, {'end': 1201.718, 'text': "and I'd have to get the first two largest values, which over here are 100 and 68..", 'start': 1196.175, 'duration': 5.543}, {'end': 1204.119, 'text': "Now let's go to Jupyter notebook and let's see how can we do this.", 'start': 1201.718, 'duration': 2.401}, {'end': 1207.381, 'text': "Right. so again we'll start off by importing the numpy array.", 'start': 1204.6, 'duration': 2.781}, {'end': 1210.603, 'text': "import numpy as np and I'll create this array.", 'start': 1207.381, 'duration': 3.222}, {'end': 1213.705, 'text': "x is equal to np.array and I'll give in all of these values.", 'start': 1210.603, 'duration': 3.102}, {'end': 1221.729, 'text': 'Now to get the indices of values which are arranged in ascending order we can use the np.arg sort function.', 'start': 1214.065, 'duration': 7.664}], 'summary': 'Using numpy, demonstrated adding arrays, finding largest values, and sorting indices.', 'duration': 75.807, 'max_score': 1145.922, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk1145922.jpg'}, {'end': 1221.729, 'src': 'embed', 'start': 1179.425, 'weight': 0, 'content': [{'end': 1184.568, 'text': 'So when you do 3 plus 2 plus 1, you get 6 and when you do 6 plus 4 plus 5, you get 15.', 'start': 1179.425, 'duration': 5.143}, {'end': 1187.75, 'text': "So now we'd have to get the n largest values from a numpy array.", 'start': 1184.568, 'duration': 3.182}, {'end': 1196.175, 'text': 'So this is our numpy array over here, and this comprise of 103, 456, 7 elements, 12, 43, 200, 54, 5 and 68,', 'start': 1188.23, 'duration': 7.945}, {'end': 1201.718, 'text': "and I'd have to get the first two largest values, which over here are 100 and 68..", 'start': 1196.175, 'duration': 5.543}, {'end': 1204.119, 'text': "Now let's go to Jupyter notebook and let's see how can we do this.", 'start': 1201.718, 'duration': 2.401}, {'end': 1207.381, 'text': "Right. so again we'll start off by importing the numpy array.", 'start': 1204.6, 'duration': 2.781}, {'end': 1210.603, 'text': "import numpy as np and I'll create this array.", 'start': 1207.381, 'duration': 3.222}, {'end': 1213.705, 'text': "x is equal to np.array and I'll give in all of these values.", 'start': 1210.603, 'duration': 3.102}, {'end': 1221.729, 'text': 'Now to get the indices of values which are arranged in ascending order we can use the np.arg sort function.', 'start': 1214.065, 'duration': 7.664}], 'summary': 'Using numpy, finding n largest values from an array.', 'duration': 42.304, 'max_score': 1179.425, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk1179425.jpg'}], 'start': 941.253, 'title': 'Python inheritance and numpy arrays', 'summary': 'Covers single level inheritance in python and demonstrates the creation of 1d and 2d numpy arrays, as well as performing mathematical operations including addition, finding the n largest values, and sorting indices in ascending and descending order.', 'chapters': [{'end': 1322.805, 'start': 941.253, 'title': 'Python inheritance and numpy arrays', 'summary': 'Covers single level inheritance in python and demonstrates the creation of 1d and 2d numpy arrays, as well as performing mathematical operations including addition, finding the n largest values, and sorting indices in ascending and descending order.', 'duration': 381.552, 'highlights': ['The chapter demonstrates the creation of 1D and 2D NumPy arrays in Python, with specific examples of array initialization and manipulation.', 'It also illustrates performing mathematical operations on NumPy arrays, including addition of individual elements and obtaining the n largest values from a NumPy array.', 'Additionally, the chapter covers sorting indices in ascending and descending order using the np.arg sort function for NumPy arrays.']}], 'duration': 381.552, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk941253.jpg', 'highlights': ['The chapter covers sorting indices in ascending and descending order using the np.argsort function for NumPy arrays.', 'It illustrates performing mathematical operations on NumPy arrays, including addition of individual elements and obtaining the n largest values from a NumPy array.', 'The chapter demonstrates the creation of 1D and 2D NumPy arrays in Python, with specific examples of array initialization and manipulation.']}, {'end': 1925.228, 'segs': [{'end': 1371.364, 'src': 'embed', 'start': 1342.134, 'weight': 3, 'content': [{'end': 1346.796, 'text': "Similarly, we'd have to create a simple dictionary and convert that dictionary into a data frame.", 'start': 1342.134, 'duration': 4.662}, {'end': 1352.26, 'text': "So I'll type in import pandas as pd.", 'start': 1348.618, 'duration': 3.642}, {'end': 1355.262, 'text': "So I'd have to start off by importing the pandas library.", 'start': 1352.621, 'duration': 2.641}, {'end': 1358.004, 'text': 'Now I will go ahead and create a list.', 'start': 1355.803, 'duration': 2.201}, {'end': 1361.287, 'text': "So I'll name this list to be equal to l1.", 'start': 1358.545, 'duration': 2.742}, {'end': 1369.723, 'text': 'So l1 equals 1, comma 2, comma 3, comma 4, comma 5.', 'start': 1362.968, 'duration': 6.755}, {'end': 1371.364, 'text': 'so we have created our list.', 'start': 1369.723, 'duration': 1.641}], 'summary': 'Create a dictionary, convert to dataframe, import pandas, create list l1 with 5 elements.', 'duration': 29.23, 'max_score': 1342.134, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk1342134.jpg'}, {'end': 1459.579, 'src': 'embed', 'start': 1428.737, 'weight': 4, 'content': [{'end': 1440.08, 'text': "so our second key value pair would be count and the values would be, let's say, 12, 24 and 36, right.", 'start': 1428.737, 'duration': 11.343}, {'end': 1441.54, 'text': 'so we have created this dictionary.', 'start': 1440.08, 'duration': 1.46}, {'end': 1453.892, 'text': "now again, to convert this dictionary into data frame, we'd have to use pd dot data frame and i will pass in dt1 inside this right.", 'start': 1441.54, 'duration': 12.352}, {'end': 1459.579, 'text': 'so we have created this data frame where our first column is fruit name and the second column is count.', 'start': 1453.892, 'duration': 5.687}], 'summary': 'Created a dictionary with fruit names and counts, then converted it to a dataframe.', 'duration': 30.842, 'max_score': 1428.737, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk1428737.jpg'}, {'end': 1577.341, 'src': 'embed', 'start': 1542.412, 'weight': 0, 'content': [{'end': 1552.62, 'text': "again, iris, and from this I'd have to select only those sepal length columns where it is greater than 5.", 'start': 1542.412, 'duration': 10.208}, {'end': 1556.563, 'text': 'so sepal.lngdh.', 'start': 1552.62, 'duration': 3.943}, {'end': 1558.684, 'text': 'this value needs to be greater than 5.', 'start': 1556.563, 'duration': 2.121}, {'end': 1561.066, 'text': "so I've given my first condition.", 'start': 1558.684, 'duration': 2.382}, {'end': 1563.968, 'text': "after this I'll go ahead and give my second condition,", 'start': 1561.066, 'duration': 2.902}, {'end': 1577.341, 'text': "and this time I'd have to extract only those records where sepal.width Let me type this out and this needs to be greater than 3..", 'start': 1563.968, 'duration': 13.373}], 'summary': 'Filtering sepal length > 5 and sepal width > 3', 'duration': 34.929, 'max_score': 1542.412, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk1542412.jpg'}, {'end': 1772.161, 'src': 'embed', 'start': 1720.203, 'weight': 1, 'content': [{'end': 1724.606, 'text': 'so iris1.head right.', 'start': 1720.203, 'duration': 4.403}, {'end': 1732.469, 'text': 'so we see that this is our original data frame and with the help of np.nan I have introduced any values from actually the first row.', 'start': 1724.606, 'duration': 7.863}, {'end': 1735.91, 'text': 'So let me actually change this to be 0 over here.', 'start': 1732.509, 'duration': 3.401}, {'end': 1737.09, 'text': "I'll click on run.", 'start': 1736.31, 'duration': 0.78}, {'end': 1740.271, 'text': 'So now I have any values for the first 10 records.', 'start': 1737.71, 'duration': 2.561}, {'end': 1745.653, 'text': "Similarly I'll also go ahead and introduce any values in the petal length column.", 'start': 1740.811, 'duration': 4.842}, {'end': 1756.736, 'text': 'So over here I just have to change the index of the column which would be 2.', 'start': 1748.354, 'duration': 8.382}, {'end': 1759.157, 'text': 'Let me have a glance at the head iris dot head.', 'start': 1756.736, 'duration': 2.421}, {'end': 1765.697, 'text': "Right, so now we'd have to get the number of NAN values present in each column of this NHANES data frame.", 'start': 1760.313, 'duration': 5.384}, {'end': 1768.799, 'text': 'So this is our data frame over here, which comprises of these columns.', 'start': 1766.097, 'duration': 2.702}, {'end': 1772.161, 'text': 'So we have age, BMI, HYP, and CHL.', 'start': 1768.819, 'duration': 3.342}], 'summary': 'Introduced nan values to first 10 records and petal length column, then checked for nan values in nhanes data frame.', 'duration': 51.958, 'max_score': 1720.203, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk1720203.jpg'}, {'end': 1879.273, 'src': 'heatmap', 'start': 1842.131, 'weight': 0.716, 'content': [{'end': 1846.433, 'text': 'so wherever you see true values, it basically represents all those nan values.', 'start': 1842.131, 'duration': 4.302}, {'end': 1852.766, 'text': 'and if i want the sum of all of these nan values, I just have to type in sum and now I click on run.', 'start': 1846.433, 'duration': 6.333}, {'end': 1855.435, 'text': 'Right So we see that.', 'start': 1854.472, 'duration': 0.963}, {'end': 1865.57, 'text': 'H column has no NAN values, BMI column has 9 NAN values, HYP column has 8 NAN values and CHL column has 10 NAN values.', 'start': 1856.108, 'duration': 9.462}, {'end': 1869.671, 'text': "Now we'd have to open and read a file in Python.", 'start': 1866.27, 'duration': 3.401}, {'end': 1871.992, 'text': "So let's see how can we do that.", 'start': 1870.331, 'duration': 1.661}, {'end': 1877.153, 'text': 'So I actually have this file with the name Sparta and it is present in my D drive.', 'start': 1872.452, 'duration': 4.701}, {'end': 1879.273, 'text': 'So let me actually copy the path over here.', 'start': 1877.453, 'duration': 1.82}], 'summary': 'Identified nan values in columns: bmi=9, hyp=8, chl=10.', 'duration': 37.142, 'max_score': 1842.131, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk1842131.jpg'}, {'end': 1913.861, 'src': 'embed', 'start': 1879.933, 'weight': 6, 'content': [{'end': 1882.054, 'text': 'So this is just the path which I have to copy.', 'start': 1879.933, 'duration': 2.121}, {'end': 1886.178, 'text': "So first to open a file in Python, I'd have to use the open function.", 'start': 1882.814, 'duration': 3.364}, {'end': 1888.421, 'text': "So I'll just type in f equals open.", 'start': 1886.218, 'duration': 2.203}, {'end': 1890.403, 'text': 'And this takes in two parameters.', 'start': 1888.841, 'duration': 1.562}, {'end': 1892.125, 'text': 'The first parameter is just the path.', 'start': 1890.443, 'duration': 1.682}, {'end': 1896.41, 'text': "And after the path, I'll give in the name of the file, which would be sparta.txt.", 'start': 1892.646, 'duration': 3.764}, {'end': 1901.454, 'text': 'And the second parameter is the mode which I want to open this file.', 'start': 1898.112, 'duration': 3.342}, {'end': 1904.096, 'text': 'So I want to open this file in the read mode.', 'start': 1901.814, 'duration': 2.282}, {'end': 1907.478, 'text': "So again, I'll just give in double quotes and I'll type in R.", 'start': 1904.656, 'duration': 2.822}, {'end': 1913.861, 'text': "So R basically means that I am opening this file in the read mode and I'm storing this in this object F.", 'start': 1907.478, 'duration': 6.383}], 'summary': 'Opening a file in python using the open function with path and mode parameters.', 'duration': 33.928, 'max_score': 1879.933, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk1879933.jpg'}], 'start': 1323.125, 'title': 'Data manipulation in python', 'summary': 'Covers creating and extracting data frames using pandas library, with examples such as converting lists and dictionaries into data frames, and extracting specified rows from a dataset based on conditions, along with introduction of nan values and file handling in python.', 'chapters': [{'end': 1459.579, 'start': 1323.125, 'title': 'Creating data frames in python', 'summary': 'Covers creating data frames from lists and dictionaries using pandas library, a common question in python interviews, providing examples with quantifiable data such as converting a list into a data frame and creating a data frame from a dictionary.', 'duration': 136.454, 'highlights': ['Creating a data frame from a list using pandas library and printing the resulting data frame. The speaker demonstrates creating a data frame from a list containing values 1, 2, 3, 4, and 5 using the pandas library and prints the resulting data frame.', "Converting a dictionary into a data frame with the keys 'fruit name' and 'count' and their respective values, using the pd.dataframe function. The process of converting a dictionary, containing keys 'fruit name' and 'count' with respective values like 'apple', 'mango', 'orange' and '12', '24', '36' into a data frame using the pd.dataframe function is explained.", 'Importing the pandas library and creating a list for conversion into a data frame. The initial step of importing the pandas library and creating a list containing values 1, 2, 3, 4, and 5 for conversion into a data frame is mentioned.']}, {'end': 1925.228, 'start': 1459.579, 'title': 'Data extraction with pandas in python', 'summary': 'Covers extracting specified rows from the iris dataset where sepal length is greater than 5 and sepal width is greater than 3, introducing nan values in specific columns, and finding the count of nan values in each column, as well as the process of opening and reading a file in python.', 'duration': 465.649, 'highlights': ['Extracting specified rows based on conditions from the iris dataset Demonstrates the process of extracting rows from the iris dataset where sepal length is greater than 5 and sepal width is greater than 3.', 'Introducing NaN values in specific columns of the iris dataset Shows the process of introducing NaN values in the first 10 rows of sepal width and petal length columns of the iris dataset using the np.nan method.', 'Finding the count of NaN values in each column of the NHANES dataset Illustrates the process of finding the count of NaN values in each column of the NHANES dataset, where BMI column has 9 NAN values, HYP column has 8 NAN values, and CHL column has 10 NAN values.', "Opening and reading a file in Python Demonstrates the process of opening and reading a file named 'Sparta.txt' in Python using the open function and reading the contents using F.read."]}], 'duration': 602.103, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk1323125.jpg', 'highlights': ['Extracting specified rows based on conditions from the iris dataset Demonstrates the process of extracting rows from the iris dataset where sepal length is greater than 5 and sepal width is greater than 3.', 'Introducing NaN values in specific columns of the iris dataset Shows the process of introducing NaN values in the first 10 rows of sepal width and petal length columns of the iris dataset using the np.nan method.', 'Finding the count of NaN values in each column of the NHANES dataset Illustrates the process of finding the count of NaN values in each column of the NHANES dataset, where BMI column has 9 NAN values, HYP column has 8 NAN values, and CHL column has 10 NAN values.', 'Creating a data frame from a list using pandas library and printing the resulting data frame. The speaker demonstrates creating a data frame from a list containing values 1, 2, 3, 4, and 5 using the pandas library and prints the resulting data frame.', "Converting a dictionary into a data frame with the keys 'fruit name' and 'count' and their respective values, using the pd.dataframe function. The process of converting a dictionary, containing keys 'fruit name' and 'count' with respective values like 'apple', 'mango', 'orange' and '12', '24', '36' into a data frame using the pd.dataframe function is explained.", 'Importing the pandas library and creating a list for conversion into a data frame. The initial step of importing the pandas library and creating a list containing values 1, 2, 3, 4, and 5 for conversion into a data frame is mentioned.', "Opening and reading a file in Python Demonstrates the process of opening and reading a file named 'Sparta.txt' in Python using the open function and reading the contents using F.read."]}, {'end': 2609.946, 'segs': [{'end': 2003.739, 'src': 'embed', 'start': 1974.752, 'weight': 2, 'content': [{'end': 1977.856, 'text': 'so this is how we can create a simple lambda function.', 'start': 1974.752, 'duration': 3.104}, {'end': 1981.463, 'text': "now I'll call the function and pass in a number.", 'start': 1977.856, 'duration': 3.607}, {'end': 1984.125, 'text': "So let's say I'll pass in 8.", 'start': 1981.663, 'duration': 2.462}, {'end': 1986.126, 'text': 'Now this is returning 18.', 'start': 1984.125, 'duration': 2.001}, {'end': 1989.388, 'text': "So all I'm doing is adding 10 to the number which I'm passing into this.", 'start': 1986.126, 'duration': 3.262}, {'end': 1993.752, 'text': "Now again, let's say if I pass 5 into this, I'll get 15.", 'start': 1989.889, 'duration': 3.863}, {'end': 1997.374, 'text': "Similarly, let's say if I pass 100, I'll get 110.", 'start': 1993.752, 'duration': 3.622}, {'end': 2003.739, 'text': 'So we have successfully created a lambda function which takes in a parameter and adds 10 to the given parameter.', 'start': 1997.374, 'duration': 6.365}], 'summary': 'Created lambda function adds 10 to input parameter.', 'duration': 28.987, 'max_score': 1974.752, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk1974752.jpg'}, {'end': 2194.252, 'src': 'embed', 'start': 2166.467, 'weight': 4, 'content': [{'end': 2172.149, 'text': "so we've got apple, banana and orange and we've got the cost of the fruits on the y-axis.", 'start': 2166.467, 'duration': 5.682}, {'end': 2179.008, 'text': 'so let me start off by loading the required library from matplotlib.', 'start': 2172.149, 'duration': 6.859}, {'end': 2184.309, 'text': "i'll be importing by plot as plt.", 'start': 2179.008, 'duration': 5.301}, {'end': 2186.59, 'text': 'after this i just have to create my data.', 'start': 2184.309, 'duration': 2.281}, {'end': 2194.252, 'text': "so i'll be creating a simple dictionary over here and i'll name this dictionary as data i'll put in braces over here,", 'start': 2186.59, 'duration': 7.662}], 'summary': 'Data visualization of apple, banana, and orange costs using matplotlib library.', 'duration': 27.785, 'max_score': 2166.467, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk2166467.jpg'}, {'end': 2299.068, 'src': 'heatmap', 'start': 2260.552, 'weight': 0.97, 'content': [{'end': 2267.421, 'text': 'right. so we have successfully created a bar plot and on the x-axis we have the names and on the y-axis we have the cost.', 'start': 2260.552, 'duration': 6.869}, {'end': 2271.145, 'text': 'next question so what do you understand by a module in python?', 'start': 2267.421, 'duration': 3.724}, {'end': 2277.35, 'text': 'So when we write everything in a single page, it becomes difficult to track, and not just this.', 'start': 2271.966, 'duration': 5.384}, {'end': 2285.475, 'text': "Let's say if you want to make a change in a certain place in the project, then it would affect the entire project and may prove to be disastrous.", 'start': 2277.85, 'duration': 7.625}, {'end': 2288.577, 'text': "And this is where we'll be using the concept of modules.", 'start': 2285.835, 'duration': 2.742}, {'end': 2295.343, 'text': 'So instead of writing one big software in one page, you would have to break it down into parts.', 'start': 2289.078, 'duration': 6.265}, {'end': 2299.068, 'text': 'so a module basically helps us to organize our python code.', 'start': 2295.343, 'duration': 3.725}], 'summary': 'Created a bar plot with names on x-axis and cost on y-axis. modules help organize python code.', 'duration': 38.516, 'max_score': 2260.552, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk2260552.jpg'}, {'end': 2303.172, 'src': 'embed', 'start': 2277.85, 'weight': 3, 'content': [{'end': 2285.475, 'text': "Let's say if you want to make a change in a certain place in the project, then it would affect the entire project and may prove to be disastrous.", 'start': 2277.85, 'duration': 7.625}, {'end': 2288.577, 'text': "And this is where we'll be using the concept of modules.", 'start': 2285.835, 'duration': 2.742}, {'end': 2295.343, 'text': 'So instead of writing one big software in one page, you would have to break it down into parts.', 'start': 2289.078, 'duration': 6.265}, {'end': 2299.068, 'text': 'so a module basically helps us to organize our python code.', 'start': 2295.343, 'duration': 3.725}, {'end': 2303.172, 'text': "now let's say you want to write a program to create a calculator.", 'start': 2299.068, 'duration': 4.104}], 'summary': 'Using modules to organize python code, reducing complexity, and enabling efficient program development.', 'duration': 25.322, 'max_score': 2277.85, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk2277850.jpg'}, {'end': 2385.56, 'src': 'embed', 'start': 2344.588, 'weight': 1, 'content': [{'end': 2348.31, 'text': 'And to randomize the items of a list, we can use the shuffle function.', 'start': 2344.588, 'duration': 3.722}, {'end': 2351.251, 'text': 'And the shuffle function is part of the random library.', 'start': 2348.85, 'duration': 2.401}, {'end': 2356.554, 'text': "So I'll type in from random import shuffle.", 'start': 2351.431, 'duration': 5.123}, {'end': 2359.495, 'text': 'So I have loaded the function.', 'start': 2358.035, 'duration': 1.46}, {'end': 2361.696, 'text': 'Now let me go ahead and create the list.', 'start': 2359.815, 'duration': 1.881}, {'end': 2374.577, 'text': 'Mary had a little lamb.', 'start': 2364.598, 'duration': 9.979}, {'end': 2382.84, 'text': 'right now I will just pass in this inside the shuffle function.', 'start': 2374.577, 'duration': 8.263}, {'end': 2385.56, 'text': 'now let me print in X right.', 'start': 2382.84, 'duration': 2.72}], 'summary': 'The shuffle function from the random library randomizes a list.', 'duration': 40.972, 'max_score': 2344.588, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk2344588.jpg'}, {'end': 2526.402, 'src': 'embed', 'start': 2492.803, 'weight': 0, 'content': [{'end': 2496.685, 'text': 'Right So this is our numpy array, which comprise of the numbers from zero to nine.', 'start': 2492.803, 'duration': 3.882}, {'end': 2498.867, 'text': "And we'd have to replace all of the odd numbers.", 'start': 2497.146, 'duration': 1.721}, {'end': 2502.75, 'text': 'So one would become minus one, three would become minus one, five would become minus one.', 'start': 2498.947, 'duration': 3.803}, {'end': 2507.253, 'text': 'So wherever odd numbers are present, all of those odd numbers would become minus one.', 'start': 2503.15, 'duration': 4.103}, {'end': 2509.614, 'text': 'let me load the numpy library.', 'start': 2507.933, 'duration': 1.681}, {'end': 2512.675, 'text': 'import numpy as np.', 'start': 2509.614, 'duration': 3.061}, {'end': 2516.477, 'text': 'after this, what I have to do is create my numpy array.', 'start': 2512.675, 'duration': 3.802}, {'end': 2526.402, 'text': 'so arr equals np dot a range, and it will go from 0 to 10.', 'start': 2516.477, 'duration': 9.925}], 'summary': 'Replace odd numbers in a numpy array with -1. range from 0 to 9.', 'duration': 33.599, 'max_score': 2492.803, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk2492803.jpg'}], 'start': 1925.928, 'title': 'Lambda function, matplotlib, and numpy array manipulation', 'summary': 'Discusses lambda function for adding 10 to a number, creating line and bar plots with matplotlib, and string length determination and numpy array manipulation, showcasing functionalities with multiple examples.', 'chapters': [{'end': 1997.374, 'start': 1925.928, 'title': 'Lambda function: adding 10 to a number', 'summary': 'Discusses the concept of a lambda function, which is an anonymous function that can take any number of arguments and should have only one expression. it demonstrates the creation of a simple lambda function to add 10 to a given number and provides examples of adding 10 to different numbers, showcasing its functionality.', 'duration': 71.446, 'highlights': ['Lambda function is an anonymous function that takes any number of arguments and should have only one expression. Explains the concept of a Lambda function and its criteria for number of arguments and expression.', "Demonstrates creation of a simple Lambda function to add 10 to a given number using the syntax 'lambda a: a + 10'. Illustrates the creation of a Lambda function to add 10 to a given number using the provided syntax.", 'Examples of adding 10 to different numbers are provided, such as adding 10 to 8 resulting in 18, adding 10 to 5 resulting in 15, and adding 10 to 100 resulting in 110. Provides specific examples of adding 10 to different numbers using the created Lambda function, demonstrating its functionality.']}, {'end': 2402.944, 'start': 1997.374, 'title': 'Creating line and bar plots with matplotlib', 'summary': 'Demonstrates creating a line plot with x-axis and y-axis values ranging from 0 to 10 and a bar plot representing the cost of fruits, using matplotlib package, and explains the concept of modules in python, along with randomizing items of a list using the shuffle function.', 'duration': 405.57, 'highlights': ['The chapter demonstrates creating a line plot with x-axis and y-axis values ranging from 0 to 10. Demonstrates the process of creating a line plot; x and y axis values range from 0 to 10.', 'The chapter also demonstrates creating a bar plot representing the cost of fruits using the Matplotlib package. Illustrates the process of creating a bar plot; represents the cost of fruits.', 'Explains the concept of modules in Python, emphasizing the organization of code to make it easier to manage and maintain. Describes the concept of modules in Python; emphasizes organizing code for easier management.', 'Explains how to randomize items of a list in place using the shuffle function from the random library. Describes the process of randomizing items inside a list using the shuffle function.']}, {'end': 2609.946, 'start': 2402.944, 'title': 'String length and numpy array manipulation', 'summary': 'Demonstrates how to find the length of a string without using the len function by iterating through the characters using a for loop, resulting in a string length of 13, and then illustrates replacing odd numbers in a numpy array with -1 using the numpy library and modulo operation.', 'duration': 207.002, 'highlights': ["The chapter illustrates how to find the length of a string without using the len function by iterating through the characters using a for loop, resulting in a string length of 13. By iterating through the characters of the string 'ophthalmology' using a for loop, the chapter demonstrates obtaining the length of the string without using the len function, resulting in a string length of 13.", 'The chapter demonstrates replacing odd numbers in a numpy array with -1 using the numpy library and modulo operation. Using the numpy library and modulo operation, the chapter illustrates the process of replacing odd numbers in a numpy array with -1, providing an example of replacing 1, 3, 5, 7, and 9 with -1 in the numpy array.']}], 'duration': 684.018, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk1925928.jpg', 'highlights': ['Demonstrates replacing odd numbers in a numpy array with -1 using the numpy library and modulo operation.', 'Describes the process of randomizing items inside a list using the shuffle function.', 'Illustrates the creation of a Lambda function to add 10 to a given number using the provided syntax.', 'Describes the concept of modules in Python; emphasizes organizing code for easier management.', 'Describes the process of creating a bar plot; represents the cost of fruits.']}, {'end': 3471.872, 'segs': [{'end': 2637.267, 'src': 'embed', 'start': 2610.406, 'weight': 4, 'content': [{'end': 2615.252, 'text': 'Now we have to perform an operation so that we get the common items between two NumPy arrays.', 'start': 2610.406, 'duration': 4.846}, {'end': 2617.073, 'text': 'This is our first numpy array.', 'start': 2615.712, 'duration': 1.361}, {'end': 2618.514, 'text': 'This is our second numpy array.', 'start': 2617.173, 'duration': 1.341}, {'end': 2620.855, 'text': "Now let's actually check the common items.", 'start': 2618.874, 'duration': 1.981}, {'end': 2629.62, 'text': 'So if we look closely at these two arrays, we see that 2 and 4 are the only two common items present among these two arrays.', 'start': 2621.396, 'duration': 8.224}, {'end': 2631.882, 'text': "And I'd want to extract these two.", 'start': 2630.041, 'duration': 1.841}, {'end': 2634.685, 'text': 'Right, so we have created our arrays over here.', 'start': 2632.382, 'duration': 2.303}, {'end': 2637.267, 'text': "This is the first numpy array and I'm storing it in A.", 'start': 2634.745, 'duration': 2.522}], 'summary': 'Identified 2 common items (2 and 4) between two numpy arrays.', 'duration': 26.861, 'max_score': 2610.406, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk2610406.jpg'}, {'end': 2693.267, 'src': 'heatmap', 'start': 2640.371, 'weight': 3, 'content': [{'end': 2645.296, 'text': 'Now to get the common elements, I have the np.intersect1d.', 'start': 2640.371, 'duration': 4.925}, {'end': 2655.023, 'text': 'method. so np, dot, intersect 1d and i just have to pass in the two numpy arrays inside this as the parameters.', 'start': 2647.458, 'duration': 7.565}, {'end': 2657.385, 'text': "so a comma b, i'll pass in these two.", 'start': 2655.023, 'duration': 2.362}, {'end': 2658.826, 'text': "i'll click on, run right.", 'start': 2657.385, 'duration': 1.441}, {'end': 2662.688, 'text': "so i've got the common elements present in these two arrays right.", 'start': 2658.826, 'duration': 3.862}, {'end': 2669.293, 'text': "so we have a panda series over here and we'd have to convert each of these elements into title case.", 'start': 2662.688, 'duration': 6.605}, {'end': 2670.674, 'text': 'so miri had a little lamp.', 'start': 2669.293, 'duration': 1.381}, {'end': 2673.556, 'text': 'so we see that all of this are in small cases.', 'start': 2670.674, 'duration': 2.882}, {'end': 2676.698, 'text': "now i'd have to convert all of these elements into title case.", 'start': 2673.556, 'duration': 3.142}, {'end': 2679.679, 'text': 'so let me start off by loading the pandas library.', 'start': 2677.498, 'duration': 2.181}, {'end': 2683.762, 'text': 'import pandas as pd.', 'start': 2679.679, 'duration': 4.083}, {'end': 2693.267, 'text': "now i'll create the series pd dot series and i'll pass in the values which are basically.", 'start': 2683.762, 'duration': 9.505}], 'summary': 'Using np.intersect1d to find common elements, then converting panda series elements to title case.', 'duration': 52.896, 'max_score': 2640.371, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk2640371.jpg'}, {'end': 2915.305, 'src': 'embed', 'start': 2890.711, 'weight': 2, 'content': [{'end': 2897.795, 'text': "so we have all of these columns over here and i'd have to change the name of the sepal dot length column to s length.", 'start': 2890.711, 'duration': 7.084}, {'end': 2904.46, 'text': 'so to rename the columns of a data frame i have the pandas dot rename method.', 'start': 2897.795, 'duration': 6.665}, {'end': 2910.544, 'text': "so first i'd have to give in the name of the data frame, which is iris, and then i will invoke the rename method.", 'start': 2904.46, 'duration': 6.084}, {'end': 2915.305, 'text': "now i'd have to give in all of the column names which i'd want to change.", 'start': 2911.641, 'duration': 3.664}], 'summary': 'Using pandas dot rename method to change column names in the iris data frame.', 'duration': 24.594, 'max_score': 2890.711, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk2890711.jpg'}, {'end': 3203.46, 'src': 'embed', 'start': 3175.692, 'weight': 1, 'content': [{'end': 3180.834, 'text': "i'll use regressor dot predict and the parameter which i'm passing inside.", 'start': 3175.692, 'duration': 5.142}, {'end': 3186.016, 'text': "this is x underscore test and i'll store this in y underscore pred.", 'start': 3180.834, 'duration': 5.182}, {'end': 3191.038, 'text': "now, once you've predicted the values, i have to find out the root mean square error.", 'start': 3186.016, 'duration': 5.022}, {'end': 3197.538, 'text': "So I'll import metrics from sklearn and this is how I'll get the root mean squared error.", 'start': 3191.897, 'duration': 5.641}, {'end': 3203.46, 'text': 'So metrics dot mean squared error and this takes in two parameters ytest and ypred.', 'start': 3197.578, 'duration': 5.882}], 'summary': 'Using regressor to predict values and calculate root mean squared error.', 'duration': 27.768, 'max_score': 3175.692, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk3175692.jpg'}, {'end': 3387.233, 'src': 'embed', 'start': 3363.809, 'weight': 0, 'content': [{'end': 3374.4, 'text': "So I will go ahead and create an instance of this decision tree classifier and I'll name that instance to be classifier and I will fit this classifier on top of the training set.", 'start': 3363.809, 'duration': 10.591}, {'end': 3378.884, 'text': "So classifier.fit and I'll pass in X train and Y train inside this.", 'start': 3374.52, 'duration': 4.364}, {'end': 3383.429, 'text': 'So we have successfully fit the model on top of the train set.', 'start': 3379.545, 'duration': 3.884}, {'end': 3387.233, 'text': "Now we'll go ahead and predict the values on top of the test set.", 'start': 3384.01, 'duration': 3.223}], 'summary': 'Created decision tree classifier, fitted on training set, and predicted values on test set.', 'duration': 23.424, 'max_score': 3363.809, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk3363809.jpg'}], 'start': 2610.406, 'title': 'Python data manipulation and model building', 'summary': 'Covers numpy array operations, pandas series manipulation, renaming columns in a data frame, and building machine learning models with a root mean squared error of 0.0 and a decision tree classifier accuracy of 91.11%.', 'chapters': [{'end': 2854.952, 'start': 2610.406, 'title': 'Numpy array operations and pandas series manipulation', 'summary': 'Explains how to find common items between two numpy arrays and manipulate elements in a pandas series to convert them to title case and calculate the number of characters in each word, using methods like np.intersect1d and ser.map, with examples showing 2 common items and the lengths of words.', 'duration': 244.546, 'highlights': ['The chapter explains how to find common items between two NumPy arrays using np.intersect1d method. It shows that 2 and 4 are the only common items between the two arrays.', 'It demonstrates the manipulation of elements in a Pandas series to convert them to title case using the map method. It showcases the conversion of all elements into title case and the use of the map method with a lambda function.', 'The chapter also covers the calculation of the number of characters in each word of a Pandas series using the map method. It illustrates the use of the map method with a lambda function to find the length of each word in the Pandas series.']}, {'end': 3471.872, 'start': 2855.632, 'title': 'Python data frame renaming and machine learning model building', 'summary': 'Covers renaming columns in a data frame and building a linear regression model with a root mean squared error of 0.0. it also involves building a decision tree classifier with an accuracy of 91.11%.', 'duration': 616.24, 'highlights': ['Building a decision tree classifier with an accuracy of 91.11% The chapter concludes with building a decision tree classifier on the iris data frame, achieving an accuracy of 91.11% by correctly classifying species and obtaining a confusion matrix.', 'Building a linear regression model with a root mean squared error of 0.0 The chapter begins with building a linear regression model on the Boston data frame, achieving a root mean squared error of 0.0 while predicting the MEDV column values based on the RM column.', "Renaming columns in a data frame The process of renaming columns in a data frame is demonstrated using the pandas rename method, where the 'sepal length' column is changed to 's_length' in the iris data frame."]}], 'duration': 861.466, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XwcJ9_hijdk/pics/XwcJ9_hijdk2610406.jpg', 'highlights': ['Building a decision tree classifier with an accuracy of 91.11%', 'Building a linear regression model with a root mean squared error of 0.0', 'Renaming columns in a data frame using the pandas rename method', 'Demonstrating the manipulation of elements in a Pandas series to convert them to title case using the map method', 'Explaining how to find common items between two NumPy arrays using np.intersect1d method']}], 'highlights': ['Python is the most preferred language for web development, machine learning, and deep learning, evident from job trends comparing Python, R, Angular, and C.', 'The popularity of Python has been steadily increasing, as indicated by the blue colored line in the job trends graph.', 'The session provides Python interview questions to help attendees ace any Python interview and emphasizes the importance of independent coding practice.', 'Python 3.7 has 33 keywords, including true, false, not, and continue.', 'The chapter covers sorting indices in ascending and descending order using the np.argsort function for NumPy arrays.', 'Building a decision tree classifier with an accuracy of 91.11%', 'Building a linear regression model with a root mean squared error of 0.0']}