title
Python Pandas Tutorial | Pandas Library - Python Programming | Python Tutorial | Edureka

description
๐Ÿ”ฅEdureka Python Certification Training (Use Code "๐˜๐Ž๐”๐“๐”๐๐„๐Ÿ๐ŸŽ") : https://www.edureka.co/data-science-python-certification-course This Edureka video on 'Python Pandas Tutorial' will help you get started with Python Pandas Library for various applications including Data analysis. Following are the topics covered in this Pandas Library Python Tutorial: Introduction to Pandas DataFrames and Series How To View Data? Selecting Data Handling Missing Data Pandas Operations Merge, Group, Reshape Data Time Series And Categoricals Plotting Using Pandas ๐Ÿ”นPython Tutorial Playlist: https://goo.gl/WsBpKe ๐Ÿ”นBlog Series: http://bit.ly/2sqmP4s ๐Ÿ”ด ๐„๐๐ฎ๐ซ๐ž๐ค๐š ๐Ž๐ง๐ฅ๐ข๐ง๐ž ๐“๐ซ๐š๐ข๐ง๐ข๐ง๐  ๐š๐ง๐ ๐‚๐ž๐ซ๐ญ๐ข๐Ÿ๐ข๐œ๐š๐ญ๐ข๐จ๐ง๐ฌ ๐Ÿ”ต Python Online Training: http://bit.ly/3Oubt8M ๐Ÿ”ต Data Science Online Training: http://bit.ly/3V3nLrc ๐Ÿ”ด ๐„๐๐ฎ๐ซ๐ž๐ค๐š ๐‘๐จ๐ฅ๐ž-๐๐š๐ฌ๐ž๐ ๐‚๐จ๐ฎ๐ซ๐ฌ๐ž๐ฌ ๐Ÿ”ต Data Scientist Masters Program: http://bit.ly/3tUAOiT ๐Ÿ”ต Python Developer Masters Program: http://bit.ly/3EV6kDv ๐Ÿ”ด ๐„๐๐ฎ๐ซ๐ž๐ค๐š ๐”๐ง๐ข๐ฏ๐ž๐ซ๐ฌ๐ข๐ญ๐ฒ ๐๐ซ๐จ๐ ๐ซ๐š๐ฆ๐ฌ ๐Ÿ”ต Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh ๐ŸŒ• Artificial and Machine Learning PGD with E&ICT Academy NIT Warangal: http://bit.ly/3OuZ3xs ๐Ÿ”ด Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV ------------------------------------------------------------------------------- Edureka Community: https://bit.ly/EdurekaCommunity 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 Castbox: https://castbox.fm/networks/505?country=in SlideShare: https://www.slideshare.net/EdurekaIN #Edureka #PythonEdureka #pythonPandas #pandas #pythonprojects #pythonprogramming #pythontutorial #PythonTraining 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 and 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 licence. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. ------------------------------------------------------------------------------- Who should go for python? The Python Programming Certification Course is a good fit for the below professionals: Programmers, Developers, Technical Leads, Architects, Freshers Data Scientists, Data Analysts Statisticians and Analysts Business Analysts Project Managers Business Intelligence Managers ------------------------------------------------------------------------------- For more information, Please write back to us at sales@edureka.in or call us at IND: 9606058406 / US: 18338555775

detail
{'title': 'Python Pandas Tutorial | Pandas Library - Python Programming | Python Tutorial | Edureka', 'heatmap': [{'end': 612.646, 'start': 566.513, 'weight': 1}, {'end': 1589.913, 'start': 1541.525, 'weight': 0.731}, {'end': 1790.981, 'start': 1750.203, 'weight': 0.743}], 'summary': 'This tutorial covers the installation and applications of python pandas in data analysis and science, data manipulation, creating data structures and frames, viewing and manipulating data, merging data frames, dataframe reshaping, working with pandas for data analysis, and categorical data manipulation. it also includes practical implementations of data visualization using pandas.', 'chapters': [{'end': 280.603, 'segs': [{'end': 101.822, 'src': 'embed', 'start': 68.846, 'weight': 1, 'content': [{'end': 72.069, 'text': 'and then we will see how we explore the data using pandas.', 'start': 68.846, 'duration': 3.223}, {'end': 72.71, 'text': 'moving further,', 'start': 72.069, 'duration': 0.641}, {'end': 76.593, 'text': 'We will learn pandas operations merging grouping reshaping Etc.', 'start': 72.77, 'duration': 3.823}, {'end': 80.017, 'text': 'And then I will discuss time series and categorical data.', 'start': 77.034, 'duration': 2.983}, {'end': 85.132, 'text': 'After this I will talk about plotting with pandas and finally to sum up this session.', 'start': 80.669, 'duration': 4.463}, {'end': 88.594, 'text': 'I will tell you about reading and writing files using pandas.', 'start': 85.532, 'duration': 3.062}, {'end': 90.555, 'text': 'I hope you are clear with the agenda.', 'start': 89.174, 'duration': 1.381}, {'end': 101.822, 'text': "Also, don't forget to subscribe to edureka for more exciting tutorials and press the bell icon to get the latest updates on edureka and enroll to edureka Python programming certification program.", 'start': 91.136, 'duration': 10.686}], 'summary': 'Learn pandas operations, time series, plotting, and file handling in python with edureka.', 'duration': 32.976, 'max_score': 68.846, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx468846.jpg'}, {'end': 138.168, 'src': 'embed', 'start': 114.147, 'weight': 2, 'content': [{'end': 120.971, 'text': 'and python pandas is well suited for different kinds of data, such as we can work on tabular data with heterogeneously typed columns.', 'start': 114.147, 'duration': 6.824}, {'end': 127.795, 'text': 'We can work on ordered and unordered time series data arbitrary matrix data with rows and column labels.', 'start': 121.351, 'duration': 6.444}, {'end': 134.78, 'text': 'We can work on unlabeled data and we can also work on any other form of observational or statistical data sets.', 'start': 128.276, 'duration': 6.504}, {'end': 138.168, 'text': "Now, I'm going to tell you how you can install pandas on your systems guys.", 'start': 135.526, 'duration': 2.642}], 'summary': 'Python pandas can handle various data types, including tabular, time series, and matrix data with labels. it can also handle unlabeled and other observational or statistical data sets.', 'duration': 24.021, 'max_score': 114.147, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx4114147.jpg'}, {'end': 216.738, 'src': 'embed', 'start': 178.192, 'weight': 0, 'content': [{'end': 181.213, 'text': "It's already there in my system because I have already installed pandas,", 'start': 178.192, 'duration': 3.021}, {'end': 185.774, 'text': "since I have already worked on various data analysis projects and it's a very integral part of it.", 'start': 181.213, 'duration': 4.561}, {'end': 189.648, 'text': 'because to walk on a data set, to read a data set, you require pandas,', 'start': 186.186, 'duration': 3.462}, {'end': 195.57, 'text': "and it's just that you cannot work without pandas if you are working with any data related project.", 'start': 189.648, 'duration': 5.922}, {'end': 199.292, 'text': 'So this is how important python pandas it actually is.', 'start': 196.211, 'duration': 3.081}, {'end': 202.693, 'text': "I'm going to tell you a few applications of funders as well.", 'start': 199.872, 'duration': 2.821}, {'end': 211.073, 'text': "So first of all, You can just say that python pandas is an integral part of data, whichever project you're working on so you can work on economics.", 'start': 202.714, 'duration': 8.359}, {'end': 214.836, 'text': 'You can use a python pandas for stock prediction.', 'start': 211.413, 'duration': 3.423}, {'end': 216.738, 'text': 'You can use it for recommendation systems.', 'start': 214.896, 'duration': 1.842}], 'summary': 'Python pandas is essential for data projects and can be used in economics, stock prediction, and recommendation systems.', 'duration': 38.546, 'max_score': 178.192, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx4178192.jpg'}, {'end': 293.602, 'src': 'embed', 'start': 258.202, 'weight': 5, 'content': [{'end': 262.406, 'text': 'So data frame is a two-dimensional and the size of the data frame is mutable.', 'start': 258.202, 'duration': 4.204}, {'end': 266.92, 'text': 'potentially heterogeneous data or we can call it heterogeneous tabular data.', 'start': 262.739, 'duration': 4.181}, {'end': 270.901, 'text': 'So the data structure, which is data frame, also contained.', 'start': 267.56, 'duration': 3.341}, {'end': 276.602, 'text': 'labeled axis, which is rows and columns, and arithmetic operations align on both rows and column labels.', 'start': 270.901, 'duration': 5.701}, {'end': 280.603, 'text': 'It can be taught as a dictionary like container for series objects.', 'start': 277.182, 'duration': 3.421}, {'end': 283.863, 'text': 'Now, what exactly the series, guys?', 'start': 281.283, 'duration': 2.58}, {'end': 293.602, 'text': 'so a series or a panda series is a one-dimensional labeled array capable of holding data of any type, which is integer, can be string float,', 'start': 283.863, 'duration': 9.739}], 'summary': 'Data frame is a mutable, two-dimensional structure containing potentially heterogeneous tabular data, with labeled axis for rows and columns, and series objects as a dictionary-like container for one-dimensional labeled arrays capable of holding integer, string, and float data.', 'duration': 35.4, 'max_score': 258.202, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx4258202.jpg'}], 'start': 16.206, 'title': 'Python pandas installation & applications', 'summary': 'Covers the significance of python pandas in data analysis and data science, introduces the installation process on different platforms, and highlights its applications in economics, stock prediction, recommendation systems, neuroscience, statistics, advertising, and analytics.', 'chapters': [{'end': 134.78, 'start': 16.206, 'title': 'Introduction to python pandas', 'summary': 'Introduces python pandas library, covering its significance in data analysis and data science, and outlining the agenda for the session, including basic introduction, exploration, operations, time series, categorical data, plotting, and reading/writing files using pandas.', 'duration': 118.574, 'highlights': ['Python pandas library is an integral part of data analysis and serves as the building block of data analysis and data science. It is emphasized that the pandas library is essential for data analysis and data science, highlighting its significance in these fields.', 'The agenda for the session includes basic introduction to pandas in Python, data frames and series with examples, data exploration, operations like merging and grouping, time series and categorical data, plotting, and reading/writing files using pandas. The agenda for the session is outlined, covering key topics such as data frames, data exploration, operations, time series, categorical data, and plotting using pandas.', 'Pandas is well suited for different kinds of data, including tabular data with heterogeneously typed columns, ordered and unordered time series data, arbitrary matrix data with rows and column labels, unlabeled data, and other forms of observational or statistical data sets. The versatility of pandas is highlighted, indicating its suitability for various types of data including tabular data, time series, matrix data, and other observational or statistical data sets.']}, {'end': 280.603, 'start': 135.526, 'title': 'Install python pandas & applications', 'summary': 'Explains the process of installing python pandas on different platforms, emphasizing its importance in data analysis, and highlights its applications in economics, stock prediction, recommendation systems, neuroscience, statistics, advertising, and analytics.', 'duration': 145.077, 'highlights': ['Python Pandas is essential for data analysis projects, as it is required for reading and working with datasets, and is considered integral for data-related projects.', 'Applications of Python Pandas include its usage in economics, stock prediction, recommendation systems, neuroscience, statistics, advertising, and analytics.', 'Data frames in Python Pandas are described as two-dimensional, mutable, potentially heterogeneous tabular data, containing labeled axis, and supporting arithmetic operations aligned on both rows and columns.']}], 'duration': 264.397, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx416206.jpg', 'highlights': ['Pandas library is essential for data analysis and data science.', 'Agenda includes introduction to pandas, data frames, data exploration, operations, time series, categorical data, and plotting.', 'Pandas is well suited for tabular data, time series, matrix data, and other observational or statistical data sets.', 'Pandas is integral for reading and working with datasets in data analysis projects.', 'Python Pandas is used in economics, stock prediction, recommendation systems, neuroscience, statistics, advertising, and analytics.', 'Data frames in Python Pandas are two-dimensional, mutable, potentially heterogeneous tabular data.']}, {'end': 625.955, 'segs': [{'end': 310.791, 'src': 'embed', 'start': 281.283, 'weight': 0, 'content': [{'end': 283.863, 'text': 'Now, what exactly the series, guys?', 'start': 281.283, 'duration': 2.58}, {'end': 293.602, 'text': 'so a series or a panda series is a one-dimensional labeled array capable of holding data of any type, which is integer, can be string float,', 'start': 283.863, 'duration': 9.739}, {'end': 302.026, 'text': 'Python objects, Etc, and the access labels are collectively called index, and Panda series is nothing but a column in an excel sheet.', 'start': 293.602, 'duration': 8.424}, {'end': 308.049, 'text': "So let's just take a look at a few examples and then you'll be able to understand this better what I'm talking about like pandas and CDs.", 'start': 302.506, 'duration': 5.543}, {'end': 310.791, 'text': 'What exactly they are will be working on a example now.', 'start': 308.069, 'duration': 2.722}], 'summary': 'Pandas series: 1d labeled array in python for holding various data types. acts like a column in excel sheet.', 'duration': 29.508, 'max_score': 281.283, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx4281283.jpg'}, {'end': 346.012, 'src': 'embed', 'start': 316.262, 'weight': 2, 'content': [{'end': 322.227, 'text': 'If you are not familiar with Jupiter notebook guys, we have a full tutorial on how to use Jupiter notebook all the cheat sheet and everything,', 'start': 316.262, 'duration': 5.965}, {'end': 328.872, 'text': 'so you can just refer to those in our Erica YouTube channel and make sure you subscribe to Erica for more exciting tutorials,', 'start': 322.227, 'duration': 6.645}, {'end': 330.994, 'text': 'because we have a lot of content on python guys.', 'start': 328.872, 'duration': 2.122}, {'end': 336.438, 'text': 'So if you want to learn python inside out or any other technology for that matter, we have a lot of content on YouTube.', 'start': 331.054, 'duration': 5.384}, {'end': 338.6, 'text': 'You can refer also if you are at it.', 'start': 336.458, 'duration': 2.142}, {'end': 346.012, 'text': 'Be sure to check out our courses on edureco.co and we have a full Python programming certification program that you should also check out.', 'start': 339.068, 'duration': 6.944}], 'summary': "Learn python and other technologies with tutorials and courses on erica's youtube channel and edureco.co.", 'duration': 29.75, 'max_score': 316.262, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx4316262.jpg'}, {'end': 500.795, 'src': 'embed', 'start': 468.774, 'weight': 3, 'content': [{'end': 470.836, 'text': "I'm going to tell you how you create a data frame.", 'start': 468.774, 'duration': 2.062}, {'end': 472.017, 'text': 'So for that also,', 'start': 471.236, 'duration': 0.781}, {'end': 478.163, 'text': "I'm going to tell you how you create a data frame using a dictionary object and how you can create a data frame using series as well.", 'start': 472.017, 'duration': 6.146}, {'end': 486.112, 'text': 'So now what we are going to do is we are going to create a data frame by passing a numpy array with the daytime index and label columns.', 'start': 479.71, 'duration': 6.402}, {'end': 489.072, 'text': "So I'll take one variable.", 'start': 486.672, 'duration': 2.4}, {'end': 490.633, 'text': "Let's say date or dates.", 'start': 489.092, 'duration': 1.541}, {'end': 495.974, 'text': "I'll just type it as D and I'm going to take PD dot.", 'start': 491.993, 'duration': 3.981}, {'end': 500.795, 'text': "So we're going to date the date range and after this I'm going to pass a few value.", 'start': 496.574, 'duration': 4.221}], 'summary': 'Creating a data frame using dictionary, series, and numpy array with a daytime index and label columns.', 'duration': 32.021, 'max_score': 468.774, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx4468774.jpg'}, {'end': 612.646, 'src': 'heatmap', 'start': 566.513, 'weight': 1, 'content': [{'end': 572.156, 'text': "So I'll pass the columns as a list and I'm going to take let's say four columns.", 'start': 566.513, 'duration': 5.643}, {'end': 574.437, 'text': "So I'm just going to take okay.", 'start': 572.176, 'duration': 2.261}, {'end': 584.922, 'text': 'Wait a minute a a B C D.', 'start': 574.477, 'duration': 10.445}, {'end': 586.563, 'text': 'All right, do we have any errors? No.', 'start': 584.922, 'duration': 1.641}, {'end': 588.984, 'text': "So now I'm going to print my data frame.", 'start': 587.303, 'duration': 1.681}, {'end': 595.087, 'text': 'So I have a data frame guys, which I have created using in a passing a numpy array and I have a date time index.', 'start': 589.004, 'duration': 6.083}, {'end': 598.723, 'text': 'with labeled columns, which are A B C and D.', 'start': 595.882, 'duration': 2.841}, {'end': 602.763, 'text': 'This is my index guys and I have all these random values using NP array.', 'start': 598.723, 'duration': 4.04}, {'end': 606.424, 'text': 'So this is how you create a data frame is just a simple example,', 'start': 603.404, 'duration': 3.02}, {'end': 612.646, 'text': "and I'm going to show you how you can create a data frame by passing a dictionary of objects that can be, you know, converted into a series also.", 'start': 606.424, 'duration': 6.222}], 'summary': 'Creating a data frame with labeled columns a, b, c, and d from a numpy array and a date time index.', 'duration': 46.133, 'max_score': 566.513, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx4566513.jpg'}], 'start': 281.283, 'title': 'Python pandas data manipulation', 'summary': 'Introduces pandas series, a one-dimensional labeled array in python, and covers the installation of pandas, creating series and data frames using numpy arrays and dictionaries, with a demonstration of using alias and importing libraries, as well as explaining the use of alias, and creating a data frame with a date time index and labeled columns, and creating a data frame using dictionary objects.', 'chapters': [{'end': 330.994, 'start': 281.283, 'title': 'Pandas series in python', 'summary': 'Introduces pandas series, a one-dimensional labeled array in python capable of holding various data types, and provides insights into its functionality and applications in data analysis.', 'duration': 49.711, 'highlights': ['Pandas Series is a one-dimensional labeled array capable of holding data of any type, including integers, strings, floats, and Python objects. The series can hold data of any type, such as integers, strings, floats, and Python objects.', 'Pandas Series is analogous to a column in an Excel sheet. Pandas Series is akin to a column in an Excel sheet, providing a practical analogy for understanding its functionality.', "The chapter emphasizes the importance of understanding Jupyter notebook for working with Pandas Series and offers resources for learning about it on Erica's YouTube channel. The chapter underscores the significance of familiarizing with Jupyter notebook and provides resources for learning about it on Erica's YouTube channel."]}, {'end': 625.955, 'start': 331.054, 'title': 'Python pandas data manipulation', 'summary': 'Covers the installation of pandas, creating series and data frames using numpy arrays and dictionaries, with a demonstration of using alias and importing libraries, as well as explaining the use of alias, and creating a data frame with a date time index and labeled columns, and creating a data frame using dictionary objects.', 'duration': 294.901, 'highlights': ['The chapter covers creating a data frame using a dictionary object and a numpy array, with a demonstration of using alias and importing libraries, and explaining the use of alias. (Relevance: 5)', 'The chapter demonstrates the creation of a data frame with a date time index and labeled columns. (Relevance: 4)', 'The chapter explains the process of creating a series in Python using pandas. (Relevance: 3)', 'The chapter covers the installation of pandas and importing it as PD, ensuring the successful import of pandas. (Relevance: 2)']}], 'duration': 344.672, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx4281283.jpg', 'highlights': ['Pandas Series is a one-dimensional labeled array capable of holding data of any type, including integers, strings, floats, and Python objects.', 'Pandas Series is analogous to a column in an Excel sheet, providing a practical analogy for understanding its functionality.', "The chapter emphasizes the importance of understanding Jupyter notebook for working with Pandas Series and offers resources for learning about it on Erica's YouTube channel.", 'The chapter covers creating a data frame using a dictionary object and a numpy array, with a demonstration of using alias and importing libraries, and explaining the use of alias.', 'The chapter demonstrates the creation of a data frame with a date time index and labeled columns.']}, {'end': 851.118, 'segs': [{'end': 692.357, 'src': 'embed', 'start': 626.575, 'weight': 1, 'content': [{'end': 630.136, 'text': "So first value is let's say a now today.", 'start': 626.575, 'duration': 3.561}, {'end': 632.537, 'text': 'I have to pass something right? Okay.', 'start': 630.256, 'duration': 2.281}, {'end': 639.158, 'text': "I'm going to write let's say a list of 1 2 3 and 4.", 'start': 632.557, 'duration': 6.601}, {'end': 641.859, 'text': "After this my next value is going to be let's say B.", 'start': 639.158, 'duration': 2.701}, {'end': 646.36, 'text': "And I'm going to pass a timestamp.", 'start': 641.859, 'duration': 4.501}, {'end': 650.4, 'text': "Let's say and for timestamp.", 'start': 646.62, 'duration': 3.78}, {'end': 655.896, 'text': "I'm going to use same I have used over here 2020 zero, three, zero, one.", 'start': 650.42, 'duration': 5.476}, {'end': 669.608, 'text': "I'll use the, right, and after this, I'm gonna pass one more value, let's say C, and I'm going to use a series now, a series object.", 'start': 655.916, 'duration': 13.692}, {'end': 672.991, 'text': 'Inside this.', 'start': 672.37, 'duration': 0.621}, {'end': 680.858, 'text': "I'm gonna pass one and the index is going to be, let's say, range.", 'start': 672.991, 'duration': 7.867}, {'end': 690.997, 'text': 'Alright, index is equal to a list with a range of four, because we have only four values over here.', 'start': 682.475, 'duration': 8.522}, {'end': 692.357, 'text': "We don't want any null values.", 'start': 691.117, 'duration': 1.24}], 'summary': 'Values a, b, and c are passed along with a list and a timestamp in the given format.', 'duration': 65.782, 'max_score': 626.575, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx4626575.jpg'}, {'end': 833.413, 'src': 'embed', 'start': 785.085, 'weight': 0, 'content': [{'end': 797.054, 'text': "So I take another value, and for this let's just say I give the value and you rake car, All right.", 'start': 785.085, 'duration': 11.969}, {'end': 799.616, 'text': 'so our Dictionary is done over here.', 'start': 797.054, 'duration': 2.562}, {'end': 801.318, 'text': 'So we have created our data frameways.', 'start': 799.696, 'duration': 1.622}, {'end': 802.879, 'text': "There's no error.", 'start': 801.858, 'duration': 1.021}, {'end': 806.965, 'text': 'Now when I print this So we have our data frame guys.', 'start': 803.219, 'duration': 3.746}, {'end': 813.307, 'text': 'So a b c d e and f so we have all these values using different data types or we can call it objects as well.', 'start': 806.985, 'duration': 6.322}, {'end': 820.709, 'text': "So for that also we can check the data frame and we write d types and it's going to give us all the data types that we have.", 'start': 813.627, 'duration': 7.082}, {'end': 826.851, 'text': 'So we have date time stamps over here integer float integer category and an object because I have used a string over here.', 'start': 821.229, 'duration': 5.622}, {'end': 831.412, 'text': "That's why it is giving us an object but in the new release that is Python 1.0.", 'start': 827.631, 'duration': 3.781}, {'end': 833.413, 'text': "0 It's not going to be an object.", 'start': 831.412, 'duration': 2.001}], 'summary': 'Created a data frame with data types including datetime, integer, float, category, and string in python 1.0.0', 'duration': 48.328, 'max_score': 785.085, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx4785085.jpg'}], 'start': 626.575, 'title': 'Creating data structures and frames', 'summary': 'Discusses creating data structures with values, timestamps, and series objects, with a range of four values and no null values. it also demonstrates creating a data frame using different data types including float 32, integer 32, and categorical objects, showcasing the use of numpy arrays and data frames to create a dictionary with various data types.', 'chapters': [{'end': 692.357, 'start': 626.575, 'title': 'Creating data structure with values and timestamps', 'summary': 'Discusses the process of creating a data structure by passing values, timestamps, and series objects, with a range of four values and no null values.', 'duration': 65.782, 'highlights': ["Creating a list of values 1, 2, 3, and 4, followed by passing a timestamp '2020-03-01'.", 'Using a series object with a range of four values and no null values.']}, {'end': 851.118, 'start': 693.438, 'title': 'Creating data frame with different data types', 'summary': 'Demonstrates the creation of a data frame using different data types including float 32, integer 32, and categorical objects, showcasing the use of numpy arrays and data frames to create a dictionary with various data types.', 'duration': 157.68, 'highlights': ['The chapter showcases the creation of a data frame using different data types such as float 32, integer 32, and categorical objects, demonstrating the use of numpy arrays and data frames to create a dictionary with various data types.', 'The speaker discusses the use of different data types, including datetime stamps, integers, floats, categories, and objects within the created data frame, highlighting the compatibility with the latest Python pandas 1.0. 0 release.']}], 'duration': 224.543, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx4626575.jpg', 'highlights': ['Creating a data frame using different data types such as float 32, integer 32, and categorical objects', 'Using a series object with a range of four values and no null values', "Creating a list of values 1, 2, 3, and 4, followed by passing a timestamp '2020-03-01'", 'Demonstrating the use of numpy arrays and data frames to create a dictionary with various data types', 'Discussing the use of different data types, including datetime stamps, integers, floats, categories, and objects within the created data frame']}, {'end': 1769.574, 'segs': [{'end': 961.742, 'src': 'embed', 'start': 933.153, 'weight': 2, 'content': [{'end': 939.637, 'text': 'So what this function is going to do is give you the first five values inside your data frame or the first five rows, and similarly,', 'start': 933.153, 'duration': 6.484}, {'end': 941.958, 'text': 'for the last rows, you can use the tail method.', 'start': 939.637, 'duration': 2.321}, {'end': 945.28, 'text': 'So this is how you get the first and last values inside your data frame.', 'start': 942.438, 'duration': 2.842}, {'end': 951.604, 'text': "So it's going to display all the five values that you have at your beginning and the end of your data set.", 'start': 946.101, 'duration': 5.503}, {'end': 954.8, 'text': 'After this we have DF dot index.', 'start': 952.239, 'duration': 2.561}, {'end': 961.742, 'text': 'So what this will do is it will give you all the values from your index and similarly we have DF dot columns,', 'start': 955.88, 'duration': 5.862}], 'summary': 'Function displays first and last 5 values in data frame, and provides index and columns.', 'duration': 28.589, 'max_score': 933.153, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx4933153.jpg'}, {'end': 1043.09, 'src': 'embed', 'start': 1002.705, 'weight': 0, 'content': [{'end': 1009.61, 'text': "I don't want this and then we have data frame dot describe which is going to give you some word like this.", 'start': 1002.705, 'duration': 6.905}, {'end': 1016.856, 'text': 'which is going to give you go on the count the mean the standard deviation minimum 25% 50% 70% and maximum.', 'start': 1010.094, 'duration': 6.762}, {'end': 1019.936, 'text': 'So these are values, using the describe you can have,', 'start': 1017.436, 'duration': 2.5}, {'end': 1027.738, 'text': 'which is going to give you an idea or a perspective of how your data is actually is and what kind of calculations are already there that you can think of.', 'start': 1019.936, 'duration': 7.802}, {'end': 1029.439, 'text': 'then we have sorting by an axis.', 'start': 1027.738, 'duration': 1.701}, {'end': 1031.239, 'text': 'We can sort our data using an axis.', 'start': 1029.459, 'duration': 1.78}, {'end': 1043.09, 'text': 'So for that you have to just write can just show you guys just have to write the F dot sort by and inside this you have to give the value of the axis.', 'start': 1031.719, 'duration': 11.371}], 'summary': "Using 'dataframe.describe' provides statistical insights, sorting data by axis is done with 'f.sortby'.", 'duration': 40.385, 'max_score': 1002.705, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx41002705.jpg'}, {'end': 1124.235, 'src': 'embed', 'start': 1094.534, 'weight': 4, 'content': [{'end': 1098.357, 'text': "It's very simple guys to get a value from your data frame using only a single column.", 'start': 1094.534, 'duration': 3.823}, {'end': 1103.642, 'text': "You can add a or let's say see it's going to give all the values from C over here.", 'start': 1098.678, 'duration': 4.964}, {'end': 1106.164, 'text': 'It has actually given you frequency.', 'start': 1104.723, 'duration': 1.441}, {'end': 1108.326, 'text': 'It has given you name data type as well.', 'start': 1106.545, 'duration': 1.781}, {'end': 1113.07, 'text': 'So this is how you actually get a single column from your data frame.', 'start': 1108.586, 'duration': 4.484}, {'end': 1116.493, 'text': 'Now, let me show you how you can slice the rows as well.', 'start': 1113.951, 'duration': 2.542}, {'end': 1124.235, 'text': "So for that you're going to use the slicing If you have actually worked on list comprehension, so we have slicing the data over here.", 'start': 1117.034, 'duration': 7.201}], 'summary': 'Easily extract single column values from dataframe and slice rows using slicing.', 'duration': 29.701, 'max_score': 1094.534, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx41094534.jpg'}, {'end': 1633.646, 'src': 'heatmap', 'start': 1541.525, 'weight': 5, 'content': [{'end': 1555.899, 'text': 'So instead of dates, I have taken be guys so D of 0 and D of 1 at E is equal to 1.', 'start': 1541.525, 'duration': 14.374}, {'end': 1556.519, 'text': "Now, let's check.", 'start': 1555.899, 'duration': 0.62}, {'end': 1560.36, 'text': 'What is df2s? We have two null values over here.', 'start': 1556.599, 'duration': 3.761}, {'end': 1564.262, 'text': "So this is how I'm going to show you how to handle missing values inside your data frame.", 'start': 1560.6, 'duration': 3.662}, {'end': 1566.342, 'text': 'So we have done reindexing.', 'start': 1565.122, 'duration': 1.22}, {'end': 1570.944, 'text': "So first of all, I'm going to check for null values.", 'start': 1566.963, 'duration': 3.981}, {'end': 1576.746, 'text': 'So we have to over here and we can get the count as well is null.', 'start': 1571.704, 'duration': 5.042}, {'end': 1579.665, 'text': 'and we count these null values.', 'start': 1577.984, 'duration': 1.681}, {'end': 1582.207, 'text': 'All right.', 'start': 1579.685, 'duration': 2.522}, {'end': 1589.913, 'text': 'Right now we are going to drop a few columns.', 'start': 1588.192, 'duration': 1.721}, {'end': 1594.276, 'text': "So we're going to drop the any that is the any values.", 'start': 1590.433, 'duration': 3.843}, {'end': 1606.325, 'text': 'So as you can see from our data frame all the values that had null values are dropped actually the whole column has been dropped.', 'start': 1598.779, 'duration': 7.546}, {'end': 1609.941, 'text': 'or we can do one thing fill in the missing data guys.', 'start': 1607.501, 'duration': 2.44}, {'end': 1613.582, 'text': 'Just do one thing.', 'start': 1612.862, 'duration': 0.72}, {'end': 1622.484, 'text': 'Okay, check df2 we have so we can do one thing we can fill the missing values and we are going to provide some value.', 'start': 1613.642, 'duration': 8.842}, {'end': 1631.666, 'text': "Let's say value is equal to 2 right? So we have actually filled the value with some of the value wherever there is a missing value.", 'start': 1622.504, 'duration': 9.162}, {'end': 1633.646, 'text': 'We have given a value that is going to fill over there.', 'start': 1631.686, 'duration': 1.96}], 'summary': 'Demonstrated handling missing values in dataframe using reindexing, dropping, and filling methods.', 'duration': 55.662, 'max_score': 1541.525, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx41541525.jpg'}], 'start': 852.079, 'title': 'Data manipulation in pandas', 'summary': 'Covers viewing and manipulating data in pandas, including using functions like head, tail, describe method, sorting, selection, reduction, handling missing values, and applying descriptive statistics and string methods.', 'chapters': [{'end': 1002.645, 'start': 852.079, 'title': 'Viewing data in pandas', 'summary': 'Explains how to view data in pandas using functions like head, tail, index, columns, and to_numpy, providing insights into the first and last rows, index values, columns, and numpy representation of the data.', 'duration': 150.566, 'highlights': ['The head method is used to display the first five values or rows of the data frame, providing a quick overview of the initial data distribution.', 'The tail method displays the last rows of the data frame, aiding in understanding the final data distribution.', 'The to_numpy function creates a numpy array representation of the data, offering a fast and efficient method for handling floating-point values.', "The index method retrieves all the values from the index of the data frame, aiding in understanding the data's indexing structure.", 'The columns method retrieves all the columns from the data frame, providing an overview of the available features in the dataset.']}, {'end': 1436.87, 'start': 1002.705, 'title': 'Manipulating data with pandas', 'summary': 'Covers the usage of describe method to obtain statistics of data, sorting data by axis and values, selecting single columns and slicing rows, selecting data using labels and multi-axis, reducing dimensions of the return object, obtaining scalar values, selecting values using position, boolean indexing, and setting new values inside the data frame.', 'duration': 434.165, 'highlights': ['The describe method provides statistics of data including count, mean, standard deviation, minimum, 25th, 50th, 75th percentile, and maximum. Using the describe method, one can obtain key statistics of the data including count, mean, standard deviation, minimum, 25th, 50th, 75th percentile, and maximum, providing a comprehensive perspective of the dataset.', 'Sorting data by axis and values using the sort by method allows arranging the data frame based on the specified axis and values. The sort by method facilitates sorting the data frame based on the specified axis and values, aiding in organizing the dataset for analysis and presentation.', 'Selecting single columns from the data frame using the specified column label provides access to the values, frequency, and data type of the selected column. By selecting single columns using the specified column label, one can access the values, frequency, and data type of the selected column, enabling targeted analysis and manipulation of specific data.', 'Slicing rows using the slicing method allows extracting a specific range of rows based on the specified index values. The slicing method enables the extraction of a specific range of rows based on the specified index values, facilitating the isolation of particular segments of the dataset for analysis and processing.', 'Selecting data using labels and multi-axis by label allows for targeted extraction of data based on specified labels and multi-axis criteria. The use of labels and multi-axis by label enables the targeted extraction of data based on specified labels and multi-axis criteria, providing flexibility in accessing specific data subsets within the dataset.', 'Boolean indexing enables the retrieval of data based on specified conditions, aiding in filtering and applying functions to the data frame. Boolean indexing facilitates the retrieval of data based on specified conditions, supporting data filtering and the application of functions to the data frame for efficient analysis and processing.', 'The is in method checks if a particular value is present in the data frame, providing a mechanism to verify the presence of specific values within the dataset. The is in method serves to verify the presence of specific values within the dataset, offering a valuable mechanism to check for the existence of particular values within the data frame.']}, {'end': 1769.574, 'start': 1437.45, 'title': 'Pandas data manipulation', 'summary': 'Covers data alignment, handling missing values, and pandas operations, demonstrating reindexing, dropping null values, filling missing values, and applying descriptive statistics and string methods, while showcasing np dot any n and pd dot series functionalities.', 'duration': 332.124, 'highlights': ['The reindexing process is demonstrated, where a new column is added, and null values are handled by checking, dropping, and filling them, resulting in a data frame with missing values being addressed and manipulated.', 'The process of checking for null values and dropping them is shown, resulting in the removal of the entire column with null values in the data frame.', 'The method of filling missing values in the data frame is showcased, where the missing values are filled with a specified value, effectively addressing the issue of missing data.', 'The concept of pandas operations, including descriptive statistics and string methods, is introduced, showcasing the application of these operations on a data frame and other pandas objects.']}], 'duration': 917.495, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx4852079.jpg', 'highlights': ['The describe method provides statistics of data including count, mean, standard deviation, minimum, 25th, 50th, 75th percentile, and maximum, offering a comprehensive perspective of the dataset.', 'Sorting data by axis and values using the sort by method facilitates organizing the dataset for analysis and presentation.', 'The head method is used to display the first five values or rows of the data frame, providing a quick overview of the initial data distribution.', 'The tail method displays the last rows of the data frame, aiding in understanding the final data distribution.', 'Selecting single columns from the data frame using the specified column label provides access to the values, frequency, and data type of the selected column.', 'The process of checking for null values and dropping them results in the removal of the entire column with null values in the data frame.', 'The method of filling missing values in the data frame effectively addresses the issue of missing data.', 'The reindexing process is demonstrated, where a new column is added, and null values are handled by checking, dropping, and filling them, resulting in a data frame with missing values being addressed and manipulated.']}, {'end': 2392.776, 'segs': [{'end': 1802.969, 'src': 'embed', 'start': 1769.575, 'weight': 6, 'content': [{'end': 1773.637, 'text': 'Yes Now when I print s over here, we have all these values.', 'start': 1769.575, 'duration': 4.062}, {'end': 1777.473, 'text': 'Now we can do one more thing.', 'start': 1776.092, 'duration': 1.381}, {'end': 1790.981, 'text': "So we write it as DF dot sub and we pass the S over here, which is our C's and we make an axis write it as index.", 'start': 1778.113, 'duration': 12.868}, {'end': 1797.506, 'text': 'So we have operated with objects that have different dimensionality and needed alignment.', 'start': 1792.963, 'duration': 4.543}, {'end': 1802.969, 'text': 'So in addition pandas actually helped us automatically broadcast the specified dimension.', 'start': 1797.526, 'duration': 5.443}], 'summary': 'Using pandas, data with different dimensions were aligned and broadcasted automatically.', 'duration': 33.394, 'max_score': 1769.575, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx41769575.jpg'}, {'end': 1913.229, 'src': 'embed', 'start': 1863.04, 'weight': 0, 'content': [{'end': 1867.861, 'text': "I'm sure most of you must be aware of the Lambda functions that we have in Python.", 'start': 1863.04, 'duration': 4.821}, {'end': 1876.124, 'text': "If you don't have any prior knowledge on Lambda function guys, there is a full tutorial on how Lambda function works in Python guys.", 'start': 1867.901, 'duration': 8.223}, {'end': 1885.187, 'text': 'So this is how we applied Lambda function to get the subtraction between the x max and x minimum, right for all the columns.', 'start': 1876.264, 'duration': 8.923}, {'end': 1888.788, 'text': 'We have subtraction mean all these values.', 'start': 1885.687, 'duration': 3.101}, {'end': 1892.423, 'text': 'So this is how we apply functions to our data guys.', 'start': 1889.622, 'duration': 2.801}, {'end': 1894.343, 'text': "Now, I'm going to talk about histogramming.", 'start': 1892.843, 'duration': 1.5}, {'end': 1897.484, 'text': 'So histogram is a representation of the distribution of data.', 'start': 1894.824, 'duration': 2.66}, {'end': 1905.007, 'text': 'So this function we have which is my plot lip dot pi plot dot hist on each series in the data frame resulting in one histogram per column.', 'start': 1897.865, 'duration': 7.142}, {'end': 1913.229, 'text': "So what we do is we'll make a series and it's going to give us value counts for histogramming.", 'start': 1906.187, 'duration': 7.042}], 'summary': 'Lambda functions applied for data subtraction and histogramming in python.', 'duration': 50.189, 'max_score': 1863.04, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx41863040.jpg'}, {'end': 1972.802, 'src': 'embed', 'start': 1937.964, 'weight': 2, 'content': [{'end': 1941.046, 'text': 'that actually makes it easy to operate on each element of the array.', 'start': 1937.964, 'duration': 3.082}, {'end': 1943.647, 'text': "So let's just move ahead with the example.", 'start': 1941.326, 'duration': 2.321}, {'end': 1951.574, 'text': "So I'll make a series guys PD dot let's say series and inside this I'm gonna pass a few string values.", 'start': 1943.667, 'duration': 7.907}, {'end': 1966.139, 'text': "So I'm gonna start with edureka, write Python next, let's write Jupyter, give it a few null values as well.", 'start': 1954.055, 'duration': 12.084}, {'end': 1972.802, 'text': 'You may get a little or slightly different from perfect.', 'start': 1968.18, 'duration': 4.622}], 'summary': 'Demonstrating array operations using pd.series with string values and null values.', 'duration': 34.838, 'max_score': 1937.964, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx41937964.jpg'}, {'end': 2035.39, 'src': 'embed', 'start': 2003.958, 'weight': 3, 'content': [{'end': 2006.8, 'text': 'These are all the operations that you can perform on pandas guys.', 'start': 2003.958, 'duration': 2.842}, {'end': 2010.459, 'text': "So let's move ahead to the next topic that we have which is merging.", 'start': 2007.318, 'duration': 3.141}, {'end': 2015.662, 'text': 'So in merging we are basically going to merge two data frames together.', 'start': 2011.06, 'duration': 4.602}, {'end': 2018.963, 'text': 'So we have two functions, which is concat and join.', 'start': 2016.422, 'duration': 2.541}, {'end': 2024.125, 'text': 'So concat Pandas objects along particular axis with optional set logic along the other axis.', 'start': 2019.143, 'duration': 4.982}, {'end': 2029.727, 'text': 'It can also add a layer of hierarchical indexing on the concatenation axis,', 'start': 2024.945, 'duration': 4.782}, {'end': 2035.39, 'text': 'which may be useful if the labels are the same or overlapping on the past axis number.', 'start': 2029.727, 'duration': 5.663}], 'summary': 'Introduction to merging in pandas, including concat and join functions', 'duration': 31.432, 'max_score': 2003.958, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx42003958.jpg'}, {'end': 2346.324, 'src': 'embed', 'start': 2318.513, 'weight': 4, 'content': [{'end': 2323.954, 'text': 'Now we can actually group the data by multiple columns and form a hierarchical index.', 'start': 2318.513, 'duration': 5.441}, {'end': 2327.815, 'text': 'So for that and just copy this guys.', 'start': 2324.654, 'duration': 3.161}, {'end': 2330.908, 'text': "Or over here only, I'll just do one thing.", 'start': 2329.099, 'duration': 1.809}, {'end': 2333.379, 'text': "Two and let's say three.", 'start': 2331.912, 'duration': 1.467}, {'end': 2339.923, 'text': 'So this is how you actually combine multiple columns to form a hierarchical index.', 'start': 2334.642, 'duration': 5.281}, {'end': 2344.164, 'text': "But here again, we don't have actually categorical values for these columns.", 'start': 2340.463, 'duration': 3.701}, {'end': 2346.324, 'text': "If we had, we'd be able to do that.", 'start': 2344.284, 'duration': 2.04}], 'summary': 'Group data by multiple columns to form a hierarchical index, lacking categorical values.', 'duration': 27.811, 'max_score': 2318.513, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx42318513.jpg'}, {'end': 2407.085, 'src': 'embed', 'start': 2382.13, 'weight': 5, 'content': [{'end': 2388.794, 'text': "So what exactly is the stack guys in pandas? I'm sure most of you must have heard about some of the definitions of stack.", 'start': 2382.13, 'duration': 6.664}, {'end': 2392.776, 'text': "So I'm going to tell you about this with perspective of pandas library here.", 'start': 2388.814, 'duration': 3.962}, {'end': 2407.085, 'text': 'So the stack function is used to stack the prescribed levels from columns to index and it returns a reshape data frame or a series having a multi-level index with one or more new innermost wet levels compared to the current data frame.', 'start': 2393.557, 'duration': 13.528}], 'summary': 'Pandas stack function reshapes data frame, returning a multi-level index with new innermost levels.', 'duration': 24.955, 'max_score': 2382.13, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx42382130.jpg'}], 'start': 1769.575, 'title': 'Data manipulation and merging in pandas', 'summary': 'Covers data manipulation using pandas, including aligning objects, applying functions, and string processing. it also discusses merging data frames using concat and join functions, grouping data, and the concept of stack in pandas.', 'chapters': [{'end': 2003.058, 'start': 1769.575, 'title': 'Manipulating data with pandas', 'summary': 'Explores the manipulation of data using pandas, including aligning objects with different dimensionality, applying functions such as lambda functions and histogramming, and using string processing methods to operate on each element of the array.', 'duration': 233.483, 'highlights': ['The chapter explains how Pandas automatically broadcasts specified dimension when operating with objects of different dimensionality. Pandas helps automatically broadcast the specified dimension when operating with objects of different dimensionality.', 'The application of functions, including lambda functions, such as subtraction, mean, and others, to the data is demonstrated. Demonstration of applying functions, including lambda functions, like subtraction, mean, and others, to the data.', 'The chapter discusses histogramming, including the representation of data distribution and using value counts for histogramming. Discussion of histogramming, representation of data distribution, and usage of value counts for histogramming.', 'The usage of string processing methods in Pandas for operating on each element of the array is illustrated. Illustration of using string processing methods in Pandas to operate on each element of the array.']}, {'end': 2392.776, 'start': 2003.958, 'title': 'Pandas merging and grouping', 'summary': 'Covers merging two data frames using concat and join functions, grouping data based on certain criteria, and the concept of stack in pandas.', 'duration': 388.818, 'highlights': ['The chapter explains how to merge two data frames using the concat and join functions, such as breaking a data frame into pieces and then concatenating them using the concat function.', 'The chapter also demonstrates the process of grouping data inside a data frame based on certain criteria, like grouping by a specific column and forming a hierarchical index by combining multiple columns.', 'The concept of stack in Pandas is introduced, providing a perspective of stack with respect to the Pandas library.']}], 'duration': 623.201, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx41769575.jpg', 'highlights': ['Demonstration of applying functions, including lambda functions, like subtraction, mean, and others, to the data.', 'Discussion of histogramming, representation of data distribution, and usage of value counts for histogramming.', 'Illustration of using string processing methods in Pandas to operate on each element of the array.', 'The chapter explains how to merge two data frames using the concat and join functions, such as breaking a data frame into pieces and then concatenating them using the concat function.', 'The chapter also demonstrates the process of grouping data inside a data frame based on certain criteria, like grouping by a specific column and forming a hierarchical index by combining multiple columns.', 'Introduction of the concept of stack in Pandas, providing a perspective of stack with respect to the Pandas library.', 'Pandas helps automatically broadcast the specified dimension when operating with objects of different dimensionality.']}, {'end': 2769.774, 'segs': [{'end': 2415.403, 'src': 'embed', 'start': 2393.557, 'weight': 0, 'content': [{'end': 2407.085, 'text': 'So the stack function is used to stack the prescribed levels from columns to index and it returns a reshape data frame or a series having a multi-level index with one or more new innermost wet levels compared to the current data frame.', 'start': 2393.557, 'duration': 13.528}, {'end': 2409.747, 'text': "So you're going to understand this with an example.", 'start': 2407.705, 'duration': 2.042}, {'end': 2415.403, 'text': "So I'm just going to take a say my tuple is equal to.", 'start': 2409.767, 'duration': 5.636}], 'summary': 'The stack function reshapes data frame with multi-level index.', 'duration': 21.846, 'max_score': 2393.557, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx42393557.jpg'}, {'end': 2557.856, 'src': 'embed', 'start': 2508.325, 'weight': 1, 'content': [{'end': 2521.608, 'text': "So for this, we write pd.dataFrame, and inside this I'm gonna pass a few values, np.random.randomNumber.", 'start': 2508.325, 'duration': 13.283}, {'end': 2526.049, 'text': 'So we have eight values and two columns.', 'start': 2522.088, 'duration': 3.961}, {'end': 2529.91, 'text': 'And the index is going to be index.', 'start': 2527.069, 'duration': 2.841}, {'end': 2540.963, 'text': 'Columns is equal to A and B.', 'start': 2532.056, 'duration': 8.907}, {'end': 2542.764, 'text': 'All right, we have an attribute error, guys.', 'start': 2540.963, 'duration': 1.801}, {'end': 2543.685, 'text': 'I made a mistake.', 'start': 2542.844, 'duration': 0.841}, {'end': 2554.513, 'text': "So there's no error now, and I'm going to make one more data frame.", 'start': 2550.43, 'duration': 4.083}, {'end': 2557.856, 'text': "So inside this, I'm gonna pass this value.", 'start': 2555.354, 'duration': 2.502}], 'summary': 'Created a data frame with 8 values and 2 columns using pd.dataframe and np.random.randomnumber.', 'duration': 49.531, 'max_score': 2508.325, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx42508325.jpg'}, {'end': 2651.418, 'src': 'embed', 'start': 2619.892, 'weight': 2, 'content': [{'end': 2621.053, 'text': "Let's put it inside.", 'start': 2619.892, 'duration': 1.161}, {'end': 2630.103, 'text': 'variable and say a All right, so this is how you unstack guys.', 'start': 2621.937, 'duration': 8.166}, {'end': 2634.346, 'text': "We're getting different values there.", 'start': 2632.845, 'duration': 1.501}, {'end': 2638.068, 'text': "Now, we're going to talk about the pivot tables that we have in pandas.", 'start': 2635.046, 'duration': 3.022}, {'end': 2646.254, 'text': 'So it is nothing but the levels in the pivot table will be stored in multi index objects on the index are columns of the result data frame.', 'start': 2638.789, 'duration': 7.465}, {'end': 2651.418, 'text': "So we'll take a look at one example guys, which is going to be pretty clear.", 'start': 2646.955, 'duration': 4.463}], 'summary': 'Introduction to pivot tables in pandas for multi-level data analysis.', 'duration': 31.526, 'max_score': 2619.892, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx42619892.jpg'}], 'start': 2393.557, 'title': 'Pandas dataframe and reshaping', 'summary': 'Covers using stack function for multi-level indexing, reshaping data frames and pivot tables, and creating pandas dataframe with specific values and lists, including a demonstration of a multi-level index data frame with two columns and eight values. additionally, it explains the process of generating random numbers and multiplying values, resulting in an invalid syntax.', 'chapters': [{'end': 2557.856, 'start': 2393.557, 'title': 'Pandas stack function and multi-level indexing', 'summary': 'Discusses the usage of the stack function in pandas to reshape data frames or series with a multi-level index and demonstrates its application with an example of creating a multi-level index data frame with two columns and eight values.', 'duration': 164.299, 'highlights': ['The stack function in Pandas is used to stack prescribed levels from columns to index, returning a reshape data frame or a series with a multi-level index, demonstrated by creating a multi-level index data frame with two columns and eight values.', 'Demonstrates the creation of a multi-level index data frame with two columns and eight values using the stack function in Pandas.', 'The process involves creating a tuple, a multi index, and a data frame with eight values and two columns, showcasing the practical application of the stack function in Pandas.']}, {'end': 2646.254, 'start': 2558.356, 'title': 'Reshaping data frames and pivot tables', 'summary': 'Discusses reshaping data frames using stack and unstack methods, with an explanation of how to perform these operations and their applications in pivot tables in pandas.', 'duration': 87.898, 'highlights': ['Stack method compresses a level in the data frames columns, demonstrated by df2.stack.', "Unstack is the inverse operation of stack, and it's shown by df2.stack.", 'Pivot tables in Pandas store levels in multi index objects on the index and columns of the result data frame.']}, {'end': 2769.774, 'start': 2646.955, 'title': 'Pandas dataframe example', 'summary': 'Explains the process of creating a pandas dataframe using specific values and lists, and generating random numbers, with a focus on multiplying values and the total number of values, resulting in an invalid syntax.', 'duration': 122.819, 'highlights': ['Creating a Pandas DataFrame using specific values and lists, including multiplying values and generating a total of 12 values for one of the columns.', 'Generating random numbers using the NumPy library to create a column with 12 random numbers.', 'The chapter ends with an invalid syntax, indicating a potential error in the code.']}], 'duration': 376.217, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx42393557.jpg', 'highlights': ['The stack function in Pandas is used to stack prescribed levels from columns to index, returning a reshape data frame or a series with a multi-level index, demonstrated by creating a multi-level index data frame with two columns and eight values.', 'Creating a Pandas DataFrame using specific values and lists, including multiplying values and generating a total of 12 values for one of the columns.', 'Pivot tables in Pandas store levels in multi index objects on the index and columns of the result data frame.', 'Generating random numbers using the NumPy library to create a column with 12 random numbers.', 'The process involves creating a tuple, a multi index, and a data frame with eight values and two columns, showcasing the practical application of the stack function in Pandas.']}, {'end': 3305.185, 'segs': [{'end': 2907.764, 'src': 'embed', 'start': 2843.833, 'weight': 0, 'content': [{'end': 2847.015, 'text': "Now we're going to talk about the time series and categoricals.", 'start': 2843.833, 'duration': 3.182}, {'end': 2855.822, 'text': 'So pandas has simple, powerful and efficient functionality for performing resampling operations during a frequency conversion, which is, for example,', 'start': 2847.575, 'duration': 8.247}, {'end': 2863.097, 'text': 'converting secondly data into five-minute key data, and this is extremely common in, but not limited to, financial applications.', 'start': 2855.822, 'duration': 7.275}, {'end': 2865.357, 'text': "So we're going to take a look at a few examples.", 'start': 2863.137, 'duration': 2.22}, {'end': 2866.777, 'text': 'and for categorical data.', 'start': 2865.357, 'duration': 1.42}, {'end': 2874.158, 'text': "data that you collect can be either categorical or numerical, so numbers often don't make sense unless you assign meaning to those numbers.", 'start': 2866.777, 'duration': 7.381}, {'end': 2879.279, 'text': 'So for categorical data is when numbers are collected in groups or categories,', 'start': 2874.379, 'duration': 4.9}, {'end': 2884.26, 'text': 'and categorical data is also the data that is collected in an either or yes or no situation.', 'start': 2879.279, 'duration': 4.981}, {'end': 2887.321, 'text': 'For example, we have 0 or 1 we have true or false.', 'start': 2884.76, 'duration': 2.561}, {'end': 2889.121, 'text': "So that's going to be the category over there.", 'start': 2887.621, 'duration': 1.5}, {'end': 2894.978, 'text': "So let's take a look at a few examples to understand this guys the time series and categoricals.", 'start': 2889.775, 'duration': 5.203}, {'end': 2897.559, 'text': "So we'll take a look at a few examples for time series.", 'start': 2895.678, 'duration': 1.881}, {'end': 2902.041, 'text': "So first of all, what I'm going to do is I am going to make a time series guys.", 'start': 2897.579, 'duration': 4.462}, {'end': 2906.323, 'text': "So first of all, what we'll do is we'll convert the data into 5-minute Lee data.", 'start': 2902.881, 'duration': 3.442}, {'end': 2907.764, 'text': "So it's very common guys.", 'start': 2906.463, 'duration': 1.301}], 'summary': 'Pandas offers efficient resampling for time series, useful in financial applications, and explains categorical data.', 'duration': 63.931, 'max_score': 2843.833, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx42843833.jpg'}, {'end': 3220.29, 'src': 'embed', 'start': 3147.456, 'weight': 4, 'content': [{'end': 3149.416, 'text': 'So this is how you get the time zone representation.', 'start': 3147.456, 'duration': 1.96}, {'end': 3152.157, 'text': "So you're getting the date, the time and everything.", 'start': 3149.636, 'duration': 2.521}, {'end': 3156.189, 'text': 'And the data type is floor 64.', 'start': 3153.097, 'duration': 3.092}, {'end': 3158.131, 'text': 'And then you can also get the UTC as well.', 'start': 3156.189, 'duration': 1.942}, {'end': 3161.113, 'text': "So for that, I'll just write TS UTC.", 'start': 3158.171, 'duration': 2.942}, {'end': 3169.561, 'text': 'And we just write timestamp.tz, localize, and we get UTC.', 'start': 3162.114, 'duration': 7.447}, {'end': 3173.324, 'text': "It's UTC.", 'start': 3170.942, 'duration': 2.382}, {'end': 3179.869, 'text': "Now I'm gonna print this.", 'start': 3176.046, 'duration': 3.823}, {'end': 3183.853, 'text': 'So we get the UTC as well.', 'start': 3181.01, 'duration': 2.843}, {'end': 3188.279, 'text': 'So this is how you create the time zone representation.', 'start': 3185.137, 'duration': 3.142}, {'end': 3191.862, 'text': 'And now after this I want to show you how we can convert to another time zone.', 'start': 3188.439, 'duration': 3.423}, {'end': 3194.203, 'text': "So for that we don't have to do anything.", 'start': 3192.542, 'duration': 1.661}, {'end': 3202.909, 'text': 'We just write ts.utsc.tz which is time zone convert.', 'start': 3194.223, 'duration': 8.686}, {'end': 3208.813, 'text': 'And we write us eastern.', 'start': 3204.89, 'duration': 3.923}, {'end': 3219.39, 'text': 'So we have converted into the u.s. Times and converting between time span representations.', 'start': 3214.369, 'duration': 5.021}, {'end': 3220.29, 'text': 'Also, we can do that.', 'start': 3219.45, 'duration': 0.84}], 'summary': 'Demonstrating time zone representation and conversion using typescript, including utc and us eastern time zone.', 'duration': 72.834, 'max_score': 3147.456, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx43147456.jpg'}], 'start': 2770.728, 'title': 'Working with pandas for data analysis', 'summary': 'Covers creating pivot tables for data analysis, time series operations including resampling, and working with categoricals. also, it explains converting data to 5-minute intervals, handling date ranges, creating time zone representations, and converting between time span representations.', 'chapters': [{'end': 2887.321, 'start': 2770.728, 'title': 'Pivot tables, time series, and categoricals in pandas', 'summary': 'Covers creating pivot tables in pandas for data analysis, followed by discussions on time series and categoricals, including the functionality for resampling operations and the distinction between categorical and numerical data.', 'duration': 116.593, 'highlights': ['Pandas has functionality for performing resampling operations during a frequency conversion, common in financial applications, such as converting secondly data into five-minute key data.', 'Categorical data is collected in groups or categories, and can also be in an either-or situation, such as true or false, 0 or 1.']}, {'end': 3305.185, 'start': 2887.621, 'title': 'Time series and categoricals', 'summary': 'Covers the creation of time series, including converting data to 5-minute intervals, handling date ranges, creating time zone representations, and converting between time span representations.', 'duration': 417.564, 'highlights': ['The chapter covers the creation of time series, including converting data to 5-minute intervals, handling date ranges, creating time zone representations, and converting between time span representations.', 'The speaker demonstrates how to create a time series by converting the data into 5-minute intervals, with a common frequency.', 'The speaker provides a step-by-step guide on creating time zone representations, including getting the date, time, and data type, as well as obtaining the UTC representation.', 'The speaker explains the process of converting between time span representations, such as converting to another time zone and between period and timestamp, enabling convenient arithmetic functions to be used.']}], 'duration': 534.457, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx42770728.jpg', 'highlights': ['Pandas has functionality for performing resampling operations during a frequency conversion, common in financial applications, such as converting secondly data into five-minute key data.', 'The chapter covers the creation of time series, including converting data to 5-minute intervals, handling date ranges, creating time zone representations, and converting between time span representations.', 'The speaker demonstrates how to create a time series by converting the data into 5-minute intervals, with a common frequency.', 'Categorical data is collected in groups or categories, and can also be in an either-or situation, such as true or false, 0 or 1.', 'The speaker provides a step-by-step guide on creating time zone representations, including getting the date, time, and data type, as well as obtaining the UTC representation.', 'The speaker explains the process of converting between time span representations, such as converting to another time zone and between period and timestamp, enabling convenient arithmetic functions to be used.']}, {'end': 3903.436, 'segs': [{'end': 3424.027, 'src': 'embed', 'start': 3372.285, 'weight': 0, 'content': [{'end': 3374.266, 'text': "So we have our data frame, let's print this.", 'start': 3372.285, 'duration': 1.981}, {'end': 3376.786, 'text': 'Okay, so we have our data frame.', 'start': 3374.286, 'duration': 2.5}, {'end': 3380.827, 'text': "What I'm going to do is, I am going to get the grade.", 'start': 3376.926, 'duration': 3.901}, {'end': 3383.188, 'text': 'Right, so.', 'start': 3382.208, 'duration': 0.98}, {'end': 3388.629, 'text': 'We get the grade.', 'start': 3386.729, 'duration': 1.9}, {'end': 3397.722, 'text': 'is equal to DF grade.', 'start': 3393.621, 'duration': 4.101}, {'end': 3402.844, 'text': "Now this is going to be the category I'm going to make.", 'start': 3400.163, 'duration': 2.681}, {'end': 3407.286, 'text': 'All right.', 'start': 3406.906, 'duration': 0.38}, {'end': 3416.749, 'text': 'Now we print DF grade.', 'start': 3409.546, 'duration': 7.203}, {'end': 3419.21, 'text': 'So we have grades like ABC, BAE.', 'start': 3417.149, 'duration': 2.061}, {'end': 3424.027, 'text': "Now I'm gonna rename the categories to more meaningful names.", 'start': 3421.226, 'duration': 2.801}], 'summary': 'Data frame contains grades abc, bae; categories to be renamed.', 'duration': 51.742, 'max_score': 3372.285, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx43372285.jpg'}, {'end': 3607.019, 'src': 'embed', 'start': 3582.412, 'weight': 3, 'content': [{'end': 3591.355, 'text': 'then we have very good, very bad and good, and then we have any end, because we have not given any other category for that.', 'start': 3582.412, 'duration': 8.943}, {'end': 3593.635, 'text': 'And we have the five objects.', 'start': 3592.655, 'duration': 0.98}, {'end': 3596.996, 'text': 'So we are getting very good bad very bad good medium.', 'start': 3594.095, 'duration': 2.901}, {'end': 3601.457, 'text': "So that's how you use the categoricals in pandas guys.", 'start': 3598.276, 'duration': 3.181}, {'end': 3604.978, 'text': "Now, I'm going to talk about plotting using pandas.", 'start': 3602.758, 'duration': 2.22}, {'end': 3607.019, 'text': "So that's going to be very simple guys for that.", 'start': 3605.178, 'duration': 1.841}], 'summary': 'Utilizing categoricals in pandas for five objects and plotting using pandas.', 'duration': 24.607, 'max_score': 3582.412, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx43582412.jpg'}, {'end': 3758.573, 'src': 'embed', 'start': 3726.636, 'weight': 5, 'content': [{'end': 3729.698, 'text': 'So we have a plot over here using pandas guys.', 'start': 3726.636, 'duration': 3.062}, {'end': 3735.282, 'text': 'This is how we have created one series using the random numbers from numpy library and using the pi plot.', 'start': 3729.738, 'duration': 5.544}, {'end': 3744.568, 'text': 'We have plotted a graph for random values, which we have taken from 0 to 500 and the random range as well.', 'start': 3735.722, 'duration': 8.846}, {'end': 3752.193, 'text': 'So this is how you take or get a plot using pandas guys.', 'start': 3746.089, 'duration': 6.104}, {'end': 3758.573, 'text': 'Now last but not least we have another topic which is Reading and writing two files.', 'start': 3752.913, 'duration': 5.66}], 'summary': 'Using pandas, a graph for random values from 0 to 500 was plotted, and reading and writing two files was discussed.', 'duration': 31.937, 'max_score': 3726.636, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx43726636.jpg'}, {'end': 3841.2, 'src': 'embed', 'start': 3813.219, 'weight': 4, 'content': [{'end': 3819.045, 'text': 'Okay, we have an unicode error.', 'start': 3813.219, 'duration': 5.826}, {'end': 3822.93, 'text': "So just write out over here and I'm able to read from the CSV file guys.", 'start': 3819.226, 'duration': 3.704}, {'end': 3823.63, 'text': 'Look at this.', 'start': 3823.25, 'duration': 0.38}, {'end': 3832.941, 'text': "Instead of CSV, I can write Excel and it's going to create a excel file or a CSV file.", 'start': 3827.395, 'duration': 5.546}, {'end': 3833.882, 'text': "It's going to read from.", 'start': 3833.121, 'duration': 0.761}, {'end': 3841.2, 'text': "So that's how we actually read and write from files like a CSV file, which is a comma separated file basically.", 'start': 3834.454, 'duration': 6.746}], 'summary': 'Demonstrating the ability to read from and write to csv and excel files.', 'duration': 27.981, 'max_score': 3813.219, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx43813219.jpg'}], 'start': 3305.665, 'title': 'Categorical data and pandas analysis', 'summary': 'Covers creating and manipulating categorical data in a dataframe, focusing on grades a, b, c and their counts, and practical implementation of data visualization using pandas.', 'chapters': [{'end': 3424.027, 'start': 3305.665, 'title': 'Categorical data and data frame manipulation', 'summary': 'Discusses creating a data frame with categorical values and manipulating the categories, including renaming and printing, with a focus on the grades a, b, c, and the count of each grade.', 'duration': 118.362, 'highlights': ['The chapter covers creating a data frame with categorical values such as grades A, B, C, and manipulating the categories by renaming and printing them, with a focus on the count of each grade and the specific values associated with them.', "The process involves creating a data frame using Python's Pandas library and populating it with categorical values, including grades A, B, C, and then manipulating these categories by renaming them and printing the updated categories.", 'The data frame manipulation includes the creation of a category for grades A, B, C, as well as renaming and printing the categories, with a focus on the specific values associated with each grade, such as the count of each grade and the individual values within the categories.']}, {'end': 3903.436, 'start': 3424.947, 'title': 'Pandas data analysis', 'summary': 'Introduces how to create and set categories in a pandas dataframe, plot using pandas, and read and write from files, with an emphasis on practical implementation and data visualization.', 'duration': 478.489, 'highlights': ["The chapter introduces how to create and set categories in a Pandas dataframe, with examples of changing categories from 'pass', 'fail' to 'very good', 'very bad', and 'excellent'. Categories changed from 'pass', 'fail' to 'very good', 'very bad', and 'excellent'.", 'Demonstrates plotting using Pandas, including importing matplotlib.pyplot, creating a series with random values, and plotting a graph. Graph plotted for random values using Pandas.', 'Provides guidance on reading and writing files, including converting a series to a CSV file and reading from a CSV or Excel file. Demonstrates reading and writing from CSV and Excel files.']}], 'duration': 597.771, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/UB3DE5Bgfx4/pics/UB3DE5Bgfx43305665.jpg', 'highlights': ['The chapter covers creating a data frame with categorical values such as grades A, B, C, and manipulating the categories by renaming and printing them, with a focus on the count of each grade and the specific values associated with them.', "The process involves creating a data frame using Python's Pandas library and populating it with categorical values, including grades A, B, C, and then manipulating these categories by renaming them and printing the updated categories.", 'The data frame manipulation includes the creation of a category for grades A, B, C, as well as renaming and printing the categories, with a focus on the specific values associated with each grade, such as the count of each grade and the individual values within the categories.', "The chapter introduces how to create and set categories in a Pandas dataframe, with examples of changing categories from 'pass', 'fail' to 'very good', 'very bad', and 'excellent'.", 'Provides guidance on reading and writing files, including converting a series to a CSV file and reading from a CSV or Excel file. Demonstrates reading and writing from CSV and Excel files.', 'Demonstrates plotting using Pandas, including importing matplotlib.pyplot, creating a series with random values, and plotting a graph. Graph plotted for random values using Pandas.']}], 'highlights': ['Pandas library is essential for data analysis and data science.', 'Pandas is integral for reading and working with datasets in data analysis projects.', 'Pandas is well suited for tabular data, time series, matrix data, and other observational or statistical data sets.', 'The describe method provides statistics of data including count, mean, standard deviation, minimum, 25th, 50th, 75th percentile, and maximum, offering a comprehensive perspective of the dataset.', 'Demonstration of applying functions, including lambda functions, like subtraction, mean, and others, to the data.', 'Pandas helps automatically broadcast the specified dimension when operating with objects of different dimensionality.', 'Pandas has functionality for performing resampling operations during a frequency conversion, common in financial applications, such as converting secondly data into five-minute key data.', 'The chapter covers creating a data frame with categorical values such as grades A, B, C, and manipulating the categories by renaming and printing them, with a focus on the count of each grade and the specific values associated with them.']}