title
Python for Data Analysis Tutorial - Setup, Read File & First Chart

description
How can we get started with data analysis or data science - so for example read and change data and also create our first quick chart - in Python? Besides Python, all we need is Pandas and Matplotlib. Doesn't sound familiar to you? Let's clear things up and get started in this video! ---------- Learn Python from scratch: https://acad.link/python-blockchain Want to learn something totally different? Check out all other courses: https://academind.com/learn/our-courses ---------- • You can follow Max on Twitter (@maxedapps). • And you should of course also follow @academind_real. • You can also find us on Facebook.(https://www.facebook.com/academindchannel/) • Or visit our Website (https://www.academind.com) and subscribe to our newsletter! See you in the videos! ---------- Academind is your source for online education in the areas of web development, frontend web development, backend web development, programming, coding and data science! No matter if you are looking for a tutorial, a course, a crash course, an introduction, an online tutorial or any related video, we try our best to offer you the content you are looking for. Our topics include Angular, React, Vue, Html, CSS, JavaScript, TypeScript, Redux, Nuxt.js, RxJs, Bootstrap, Laravel, Node.js, Progressive Web Apps (PWA), Ionic, React Native, Regular Expressions (RegEx), Stencil, Power BI, Amazon Web Services (AWS), Firebase or other topics, make sure to have a look at this channel or at academind.com to find the learning resource of your choice!

detail
{'title': 'Python for Data Analysis Tutorial - Setup, Read File & First Chart', 'heatmap': [{'end': 495.208, 'start': 392.426, 'weight': 1}, {'end': 601.778, 'start': 554.331, 'weight': 0.711}, {'end': 878.472, 'start': 774.031, 'weight': 0.824}], 'summary': 'This tutorial series covers python basics for data analysis, installation options, jupyter notebook usage, key packages like numpy and pandas, data manipulation and visualization, log functionality in pandas, data selection, and creating charts with matplotlib, providing beginners with a comprehensive starting point for data analysis.', 'chapters': [{'end': 49.376, 'segs': [{'end': 49.376, 'src': 'embed', 'start': 2.301, 'weight': 0, 'content': [{'end': 3.823, 'text': 'Hello, welcome to this video.', 'start': 2.301, 'duration': 1.522}, {'end': 8.308, 'text': 'In this video, we will get started with Python for data analysis.', 'start': 4.544, 'duration': 3.764}, {'end': 15.856, 'text': "So if you're working with Excel, for example, and you always wondered, how can I use something like Python to analyze my data?", 'start': 8.688, 'duration': 7.168}, {'end': 18.979, 'text': 'Well, then this is exactly what we will dive into now.', 'start': 16.116, 'duration': 2.863}, {'end': 25.041, 'text': 'because we will have a look at how we can set up Python correctly to make sure we can use it for data analysis.', 'start': 19.54, 'duration': 5.501}, {'end': 31.623, 'text': "We'll then have a look at how we can access a CSV file, a typical Excel use case, for example.", 'start': 25.602, 'duration': 6.021}, {'end': 37.245, 'text': 'So how we can access the CSV file and how we can access specific columns or rows in that file.', 'start': 32.003, 'duration': 5.242}, {'end': 41.246, 'text': 'And finally, how we can also plot a quick chart using Python.', 'start': 37.645, 'duration': 3.601}, {'end': 42.807, 'text': "That's it actually.", 'start': 42.006, 'duration': 0.801}, {'end': 45.13, 'text': "That's just the basics of course.", 'start': 43.168, 'duration': 1.962}, {'end': 46.872, 'text': 'But you have to get started somehow.', 'start': 45.391, 'duration': 1.481}, {'end': 49.376, 'text': "So let's get started together in this video.", 'start': 47.093, 'duration': 2.283}], 'summary': 'Introduction to python for data analysis, including setting up, accessing csv files, and plotting charts.', 'duration': 47.075, 'max_score': 2.301, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg2301.jpg'}], 'start': 2.301, 'title': 'Python basics for data analysis', 'summary': 'Introduces python basics for data analysis, covering setting up python, accessing a csv file, and plotting a chart, providing beginners a starting point for data analysis.', 'chapters': [{'end': 49.376, 'start': 2.301, 'title': 'Introduction to python for data analysis', 'summary': 'Introduces the basics of using python for data analysis, including setting up python, accessing a csv file, and plotting a chart, providing beginners with a starting point for data analysis.', 'duration': 47.075, 'highlights': ['The chapter covers setting up Python for data analysis, accessing a CSV file, and plotting a chart using Python, serving as a starting point for beginners in data analysis.', 'Demonstrates how to access specific columns or rows in a CSV file, enabling practical data manipulation and analysis.', "Provides beginners with a foundational understanding of using Python for data analysis, catering to individuals working with Excel and seeking to explore Python's data analysis capabilities."]}], 'duration': 47.075, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg2301.jpg', 'highlights': ['The chapter covers setting up Python for data analysis, accessing a CSV file, and plotting a chart using Python, serving as a starting point for beginners in data analysis.', 'Demonstrates how to access specific columns or rows in a CSV file, enabling practical data manipulation and analysis.', "Provides beginners with a foundational understanding of using Python for data analysis, catering to individuals working with Excel and seeking to explore Python's data analysis capabilities."]}, {'end': 319.418, 'segs': [{'end': 91.597, 'src': 'embed', 'start': 69.345, 'weight': 3, 'content': [{'end': 78.408, 'text': 'Well, the direct download would mean that you go to python.org, download the Python language basically there and follow the installation guidelines.', 'start': 69.345, 'duration': 9.063}, {'end': 80.349, 'text': 'Pretty straightforward, actually.', 'start': 78.728, 'duration': 1.621}, {'end': 84.151, 'text': 'there is also no problem about doing it like that.', 'start': 81.249, 'duration': 2.902}, {'end': 91.597, 'text': 'the only problem, especially for beginners, is that python is an open source language and python is a really powerful language.', 'start': 84.151, 'duration': 7.446}], 'summary': 'To download python, go to python.org and follow the installation guidelines. python is a powerful open source language.', 'duration': 22.252, 'max_score': 69.345, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg69345.jpg'}, {'end': 143.272, 'src': 'embed', 'start': 114.106, 'weight': 4, 'content': [{'end': 118.587, 'text': 'because if you want to build a web page or if you want to analyze data well, as you can imagine,', 'start': 114.106, 'duration': 4.481}, {'end': 121.728, 'text': 'there are different things that you would like to do with that language.', 'start': 118.587, 'duration': 3.141}, {'end': 132.668, 'text': 'And for that Python comes with a lot of different packages and libraries which add such additional functionalities and such convenience functionalities to that language.', 'start': 122.344, 'duration': 10.324}, {'end': 138.49, 'text': "The problem is that the direct download Python version doesn't come with these packages.", 'start': 133.308, 'duration': 5.182}, {'end': 143.272, 'text': "It does come with pip Python's integrated package manager, though,", 'start': 139.09, 'duration': 4.182}], 'summary': "Python offers various packages and libraries for web page building and data analysis, but these are not included in the direct download python version. however, it does come with pip, python's integrated package manager.", 'duration': 29.166, 'max_score': 114.106, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg114106.jpg'}, {'end': 184.521, 'src': 'embed', 'start': 152.916, 'weight': 0, 'content': [{'end': 158.78, 'text': 'But using a Python distribution is a lot more convenient, especially because of these packages.', 'start': 152.916, 'duration': 5.864}, {'end': 166.146, 'text': 'Because you could go to anaconda.com, for example, download the Python distribution from there.', 'start': 159.461, 'duration': 6.685}, {'end': 167.827, 'text': "We'll have a look at that in a few seconds.", 'start': 166.346, 'duration': 1.481}, {'end': 175.192, 'text': 'And with that, you install Python and a lot of the most common packages that are used together with Python.', 'start': 168.327, 'duration': 6.865}, {'end': 184.521, 'text': 'And because of that increased convenience I definitely recommend using the Python distribution if you just get started with Python.', 'start': 176.093, 'duration': 8.428}], 'summary': 'Using a python distribution is convenient. anaconda.com offers python distribution with common packages. recommended for beginners.', 'duration': 31.605, 'max_score': 152.916, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg152916.jpg'}, {'end': 280.772, 'src': 'embed', 'start': 236.03, 'weight': 5, 'content': [{'end': 240.913, 'text': 'So you are basically ready to use Python now after installing this Python distribution.', 'start': 236.03, 'duration': 4.883}, {'end': 244.174, 'text': 'Sounds quite awesome, sounds quite easy, it definitely is.', 'start': 241.653, 'duration': 2.521}, {'end': 248.195, 'text': 'But before we dive into the Python code, we have to think about one more thing.', 'start': 244.594, 'duration': 3.601}, {'end': 255.898, 'text': 'What is the working environment we want to use in Python? Because writing Python code can be done in a lot of different ways.', 'start': 249.035, 'duration': 6.863}, {'end': 258.197, 'text': 'One way would be the REPL.', 'start': 256.838, 'duration': 1.359}, {'end': 261.858, 'text': 'REPL stands for Read Eval, Evaluate,', 'start': 258.757, 'duration': 3.101}, {'end': 269.442, 'text': 'Print and Loop and basically means that we can write Python code in our command prompt or in the terminal on the Mac.', 'start': 261.858, 'duration': 7.584}, {'end': 272.364, 'text': 'This is not a big issue in general.', 'start': 270.402, 'duration': 1.962}, {'end': 274.546, 'text': "You can do that and it's quite nice to get started.", 'start': 272.424, 'duration': 2.122}, {'end': 280.772, 'text': 'You only have to type Python 3 in your terminal and then you can basically get started.', 'start': 274.566, 'duration': 6.206}], 'summary': "After installing python distribution, one can use python, including using repl for writing python code, by typing 'python 3' in the terminal.", 'duration': 44.742, 'max_score': 236.03, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg236030.jpg'}, {'end': 328.042, 'src': 'embed', 'start': 302.188, 'weight': 6, 'content': [{'end': 309.833, 'text': 'These code editors are nice because they come with some additional convenience features like version control or debugging.', 'start': 302.188, 'duration': 7.645}, {'end': 315.696, 'text': "And especially if you're coming from a web development world, you're quite used to such code editors.", 'start': 310.413, 'duration': 5.283}, {'end': 319.418, 'text': 'So nothing wrong about these, by the way, you can use code editors.', 'start': 315.996, 'duration': 3.422}, {'end': 328.042, 'text': "But as we don't want to create a web page, but as we want to analyze data, there is a third and in my case, also preferable option,", 'start': 319.918, 'duration': 8.124}], 'summary': 'Code editors offer convenience features, but for data analysis, a preferable option exists.', 'duration': 25.854, 'max_score': 302.188, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg302188.jpg'}], 'start': 52.799, 'title': 'Python installation and distribution', 'summary': 'Covers two options for installing python - direct download from python.org and using a python distribution, emphasizing the ease of direct download and the benefits of python as an open source language. it also explores the advantages of python distributions such as anaconda, emphasizing the convenience of common packages, and describes various working environments for writing python code, suggesting the use of ides like pycharm or vs code.', 'chapters': [{'end': 91.597, 'start': 52.799, 'title': 'Install python options', 'summary': 'Discusses the two options for installing python, direct download from python.org and using a python distribution, highlighting the straightforward process of direct download and the power of python as an open source language.', 'duration': 38.798, 'highlights': ['The direct download option from python.org offers a straightforward installation process.', 'Python is a powerful open source language, which may be overwhelming for beginners.']}, {'end': 319.418, 'start': 91.597, 'title': 'Python distribution and working environment', 'summary': 'Discusses the advantages of using a python distribution like anaconda, highlighting its convenience in providing common packages, and also explains the various working environments for writing python code, recommending the use of ides like pycharm or vs code.', 'duration': 227.821, 'highlights': ['Anaconda distribution provides convenience by including common packages used with Python, making it a recommended choice for beginners. Increased convenience for beginners', 'Using Python distribution like Anaconda eliminates the need for manual installation of different packages through pip. Time-saving and hassle-free installation', 'Recommendation to use Python distribution if just getting started with Python due to its increased convenience and inclusion of common packages. Beginner-friendly recommendation', 'Explanation of the working environments for writing Python code, including the use of REPL and IDEs like PyCharm or VS Code. Introduction to different ways of writing Python code', 'Description of REPL as a code playground for writing Python code in the command prompt or terminal, highlighting its ease of use for getting started. Introduction to REPL as a beginner-friendly option', 'Introduction to using IDEs or code editors like PyCharm or VS Code, emphasizing their additional convenience features like version control and debugging. Highlighting the features of IDEs for more advanced usage']}], 'duration': 266.619, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg52799.jpg', 'highlights': ['Anaconda distribution provides convenience by including common packages used with Python, making it a recommended choice for beginners. Increased convenience for beginners', 'Using Python distribution like Anaconda eliminates the need for manual installation of different packages through pip. Time-saving and hassle-free installation', 'Recommendation to use Python distribution if just getting started with Python due to its increased convenience and inclusion of common packages. Beginner-friendly recommendation', 'The direct download option from python.org offers a straightforward installation process.', 'Python is a powerful open source language, which may be overwhelming for beginners.', 'Explanation of the working environments for writing Python code, including the use of REPL and IDEs like PyCharm or VS Code. Introduction to different ways of writing Python code', 'Introduction to using IDEs or code editors like PyCharm or VS Code, emphasizing their additional convenience features like version control and debugging. Highlighting the features of IDEs for more advanced usage', 'Description of REPL as a code playground for writing Python code in the command prompt or terminal, highlighting its ease of use for getting started. Introduction to REPL as a beginner-friendly option']}, {'end': 610.62, 'segs': [{'end': 346.368, 'src': 'embed', 'start': 319.918, 'weight': 3, 'content': [{'end': 328.042, 'text': "But as we don't want to create a web page, but as we want to analyze data, there is a third and in my case, also preferable option,", 'start': 319.918, 'duration': 8.124}, {'end': 335.744, 'text': 'especially if you want to get started with data analysis, and that is using a Jupyter Notebook as our code writing environment.', 'start': 328.042, 'duration': 7.702}, {'end': 346.368, 'text': 'A Jupyter Notebook in simple terms well simply means running Python code in the browser with a tailored or Python specific interface.', 'start': 336.724, 'duration': 9.644}], 'summary': 'Using jupyter notebook for data analysis is a preferable option, especially for beginners, as it allows running python code in the browser with a tailored interface.', 'duration': 26.45, 'max_score': 319.918, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg319918.jpg'}, {'end': 495.208, 'src': 'heatmap', 'start': 371.979, 'weight': 0, 'content': [{'end': 378.761, 'text': 'So if I enter code, I can see the result of this code also in this browser window.', 'start': 371.979, 'duration': 6.782}, {'end': 382.982, 'text': 'This, by the way, also includes visuals, as we will see throughout this video.', 'start': 379.141, 'duration': 3.841}, {'end': 387.724, 'text': "And therefore, it's, in my opinion, the best environment to get started with Python.", 'start': 383.443, 'duration': 4.281}, {'end': 391.886, 'text': 'Another question is how can we install such a Jupyter Notebook??', 'start': 388.504, 'duration': 3.382}, {'end': 403.01, 'text': 'Well, you can go to Jupyter.org and download it, or you can simply open the terminal or the command prompt and now type conda list.', 'start': 392.426, 'duration': 10.584}, {'end': 406.432, 'text': 'What does this mean? Well, we installed Anaconda.', 'start': 403.431, 'duration': 3.001}, {'end': 414.535, 'text': 'And just as Python comes with this pip, this integrated package manager, Anaconda also comes with an integrated package manager.', 'start': 407.032, 'duration': 7.503}, {'end': 416.575, 'text': 'This is simply called conda right here.', 'start': 414.915, 'duration': 1.66}, {'end': 420.177, 'text': 'And with this conda list command, and with hitting enter.', 'start': 417.156, 'duration': 3.021}, {'end': 427.561, 'text': 'you can find a list of all the packages we installed as part of our Anaconda distribution.', 'start': 421.537, 'duration': 6.024}, {'end': 430.863, 'text': 'So these packages are now installed on your system.', 'start': 427.961, 'duration': 2.902}, {'end': 439.775, 'text': 'And if we scroll up a bit to the J, Right here, we can see that we installed Jupyter already.', 'start': 431.684, 'duration': 8.091}, {'end': 442.557, 'text': 'So this is exactly this Jupyter notebook I was referring to.', 'start': 440.016, 'duration': 2.541}, {'end': 444.258, 'text': 'And what does this mean for us then?', 'start': 442.937, 'duration': 1.321}, {'end': 451.223, 'text': 'Well, this simply means that we can now immediately start writing our first Python code in such a Jupyter notebook.', 'start': 444.879, 'duration': 6.344}, {'end': 464.26, 'text': 'for that i will create a or open a new tab like this up here with new tab and now just enter jupiter notebook like this if we now hit enter,', 'start': 451.903, 'duration': 12.357}, {'end': 470.306, 'text': 'the jupiter notebook gets up and running And now you should see your Jupyter Notebook,', 'start': 464.26, 'duration': 6.046}, {'end': 476.433, 'text': 'so this browser window running with that specific tailored interface in the end.', 'start': 470.306, 'duration': 6.127}, {'end': 486.323, 'text': 'I navigated into a project folder already, so please do the same because you can create that notebook then in the folder of your choice.', 'start': 477.093, 'duration': 9.23}, {'end': 495.208, 'text': 'and if we now go right here to new, to the right part of that page, we can either create a new notebook, which is what we will do in a few seconds,', 'start': 487.063, 'duration': 8.145}], 'summary': 'Jupyter notebook allows visual code execution, best for python beginners. install via jupyter.org or conda list command.', 'duration': 44.596, 'max_score': 371.979, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg371979.jpg'}, {'end': 523.024, 'src': 'embed', 'start': 495.208, 'weight': 2, 'content': [{'end': 498.27, 'text': 'or you can also create a text file or a folder, for example.', 'start': 495.208, 'duration': 3.062}, {'end': 499.211, 'text': "we don't need that.", 'start': 498.27, 'duration': 0.941}, {'end': 503.874, 'text': "we just want to create a python free notebook, because that's the python version we installed.", 'start': 499.211, 'duration': 4.663}, {'end': 509.351, 'text': 'So if we click onto that, we see this new window with a so-called cell in here.', 'start': 504.847, 'duration': 4.504}, {'end': 513.495, 'text': 'The cell is the part in the Jupyter Notebook where we can write our Python code.', 'start': 509.551, 'duration': 3.944}, {'end': 520.221, 'text': 'And we can also rename that up here if we click onto untitled and maybe call it Python for data.', 'start': 514.176, 'duration': 6.045}, {'end': 523.024, 'text': 'analysis something like that.', 'start': 521.022, 'duration': 2.002}], 'summary': 'Creating a python jupyter notebook for data analysis.', 'duration': 27.816, 'max_score': 495.208, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg495208.jpg'}, {'end': 601.778, 'src': 'heatmap', 'start': 554.331, 'weight': 0.711, 'content': [{'end': 560.637, 'text': 'the notebook allows us to see both input and output immediately and on the same page, and this is quite cool actually.', 'start': 554.331, 'duration': 6.306}, {'end': 567.821, 'text': 'However, there are two more things that we have to change or understand before we can finally dive into our data analysis code.', 'start': 561.337, 'duration': 6.484}, {'end': 573.785, 'text': 'The first thing is we need an input file, a source file that we want to, well, get access to.', 'start': 568.402, 'duration': 5.383}, {'end': 578.768, 'text': 'For that you can find a link to the source file below the video in the video description.', 'start': 574.405, 'duration': 4.363}, {'end': 581.19, 'text': 'So simply click onto that link and download the file.', 'start': 579.028, 'duration': 2.162}, {'end': 592.697, 'text': "and then simply take that file, it's called Revenue Profit CSV, and drag and drop it into the folder where you created that Jupyter Notebook.", 'start': 582.455, 'duration': 10.242}, {'end': 601.778, 'text': 'In my case, this is this basics folder, and in this folder, you can see this Python for Data Analysis IPYNV file.', 'start': 593.277, 'duration': 8.501}], 'summary': "The notebook allows immediate input and output viewing. source file 'revenue profit csv' needs to be downloaded and placed in the jupyter notebook folder.", 'duration': 47.447, 'max_score': 554.331, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg554331.jpg'}, {'end': 592.697, 'src': 'embed', 'start': 561.337, 'weight': 4, 'content': [{'end': 567.821, 'text': 'However, there are two more things that we have to change or understand before we can finally dive into our data analysis code.', 'start': 561.337, 'duration': 6.484}, {'end': 573.785, 'text': 'The first thing is we need an input file, a source file that we want to, well, get access to.', 'start': 568.402, 'duration': 5.383}, {'end': 578.768, 'text': 'For that you can find a link to the source file below the video in the video description.', 'start': 574.405, 'duration': 4.363}, {'end': 581.19, 'text': 'So simply click onto that link and download the file.', 'start': 579.028, 'duration': 2.162}, {'end': 592.697, 'text': "and then simply take that file, it's called Revenue Profit CSV, and drag and drop it into the folder where you created that Jupyter Notebook.", 'start': 582.455, 'duration': 10.242}], 'summary': 'To start the data analysis, download revenue profit csv file and place it in the jupyter notebook folder.', 'duration': 31.36, 'max_score': 561.337, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg561337.jpg'}], 'start': 319.918, 'title': 'Jupyter notebook basics', 'summary': "Introduces jupyter notebook for data analysis, including installation using anaconda's conda. it also provides a guide for setting up and using jupyter notebook for python coding, covering creating new notebooks, writing and running python code, and managing input files.", 'chapters': [{'end': 444.258, 'start': 319.918, 'title': 'Introduction to jupyter notebook', 'summary': "Introduces the use of jupyter notebook as a preferred option for data analysis, outlining its features and installation process, including using anaconda's integrated package manager, conda, to install jupyter notebook.", 'duration': 124.34, 'highlights': ['Jupyter Notebook is recommended as the best environment to get started with Python, featuring an interactive browser interface for Python code, with the ability to view input and output simultaneously, including visuals. (Relevance: 5)', "The installation of Jupyter Notebook can be done by downloading it from Jupyter.org or using Anaconda's integrated package manager, conda, by typing 'conda list' in the terminal or command prompt to view the list of installed packages. (Relevance: 4)", "Anaconda comes with an integrated package manager, conda, similar to Python's pip, allowing the installation of various packages, with 'conda list' command displaying all installed packages as part of the Anaconda distribution. (Relevance: 3)"]}, {'end': 610.62, 'start': 444.879, 'title': 'Getting started with jupyter notebook', 'summary': 'Provides a step-by-step guide to set up and use jupyter notebook for python coding, including creating a new notebook, writing python code, running code, and managing input files.', 'duration': 165.741, 'highlights': ['Creating a new Jupyter Notebook The chapter explains the process of creating a new Jupyter Notebook and navigating to a specific folder, emphasizing the importance of choosing the right Python version and running the code.', 'Writing and running Python code in Jupyter Notebook The chapter demonstrates the process of writing and running Python code in a Jupyter Notebook, including the use of cells, renaming the notebook, and executing code to view the output immediately.', "Managing input files in Jupyter Notebook The chapter outlines the steps to acquire and manage input files, highlighting the importance of downloading the source file and dragging it into the notebook's folder for data analysis."]}], 'duration': 290.702, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg319918.jpg', 'highlights': ['Jupyter Notebook is recommended as the best environment to get started with Python, featuring an interactive browser interface for Python code, with the ability to view input and output simultaneously, including visuals.', "The installation of Jupyter Notebook can be done by downloading it from Jupyter.org or using Anaconda's integrated package manager, conda, by typing 'conda list' in the terminal or command prompt to view the list of installed packages.", 'Creating a new Jupyter Notebook The chapter explains the process of creating a new Jupyter Notebook and navigating to a specific folder, emphasizing the importance of choosing the right Python version and running the code.', 'Writing and running Python code in Jupyter Notebook The chapter demonstrates the process of writing and running Python code in a Jupyter Notebook, including the use of cells, renaming the notebook, and executing code to view the output immediately.', "Managing input files in Jupyter Notebook The chapter outlines the steps to acquire and manage input files, highlighting the importance of downloading the source file and dragging it into the notebook's folder for data analysis.", "Anaconda comes with an integrated package manager, conda, similar to Python's pip, allowing the installation of various packages, with 'conda list' command displaying all installed packages as part of the Anaconda distribution."]}, {'end': 956.38, 'segs': [{'end': 676.674, 'src': 'embed', 'start': 654.715, 'weight': 1, 'content': [{'end': 664.103, 'text': 'Now there are lots and lots of libraries and packages available, but three of the most common ones you will well probably use is NumPy,', 'start': 654.715, 'duration': 9.388}, {'end': 666.165, 'text': 'pandas and matplotlib.', 'start': 664.103, 'duration': 2.062}, {'end': 668.547, 'text': 'Now, what are these packages doing?', 'start': 666.606, 'duration': 1.941}, {'end': 676.674, 'text': 'Well, NumPy simply adds multidimensional array support, so basically being able to read columns and rows in Python in simple words.', 'start': 669.068, 'duration': 7.606}], 'summary': 'Numpy, pandas, and matplotlib are common packages for data analysis and visualization in python.', 'duration': 21.959, 'max_score': 654.715, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg654715.jpg'}, {'end': 757.059, 'src': 'embed', 'start': 702.427, 'weight': 0, 'content': [{'end': 710.131, 'text': 'Because remember what I said about Anaconda, that Anaconda comes with, well, actually all of the most popular packages by default.', 'start': 702.427, 'duration': 7.704}, {'end': 718.956, 'text': 'So if we go back to our terminal right here, so not into that part, but into that part where we had that conda list command.', 'start': 710.751, 'duration': 8.205}, {'end': 725.319, 'text': 'And as we saw Jupyter right here, we can also see that we have, for example, matplotlib right here.', 'start': 719.476, 'duration': 5.843}, {'end': 731.603, 'text': 'or pandas right there or numpy right there, not right there.', 'start': 726.98, 'duration': 4.623}, {'end': 732.803, 'text': 'this one is numpy.', 'start': 731.603, 'duration': 1.2}, {'end': 737.025, 'text': 'so this means we got these packages installed in our system or on our system.', 'start': 732.803, 'duration': 4.222}, {'end': 741.207, 'text': 'we only need to import these now to our project.', 'start': 737.025, 'duration': 4.182}, {'end': 748.794, 'text': "to do that, I'm back in my Jupyter notebook and now we can simply type import numpy as np.", 'start': 741.207, 'duration': 7.587}, {'end': 757.059, 'text': "that's the typical way how we import numpy to our projects, and we can also import pandas as pd.", 'start': 748.794, 'duration': 8.265}], 'summary': 'Anaconda comes with popular packages by default, such as matplotlib, pandas, and numpy, simplifying the import process to projects.', 'duration': 54.632, 'max_score': 702.427, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg702427.jpg'}, {'end': 878.472, 'src': 'heatmap', 'start': 774.031, 'weight': 0.824, 'content': [{'end': 779.616, 'text': "So let's maybe call this functionality this reading functionality content, maybe like that.", 'start': 774.031, 'duration': 5.585}, {'end': 783.48, 'text': 'And content should be equal to pd.', 'start': 780.577, 'duration': 2.903}, {'end': 793.306, 'text': 'This basically means that we now want to access a function that is available in pandas, and if you now hit tab,', 'start': 784.241, 'duration': 9.065}, {'end': 799.168, 'text': 'you can find a lot of different functionalities that are implemented into this pandas package.', 'start': 793.306, 'duration': 5.862}, {'end': 807.091, 'text': 'now, if we scroll down a bit right here, we can see this pd.read csv functionality.', 'start': 799.168, 'duration': 7.923}, {'end': 811.152, 'text': "as we want to access a csv file, this doesn't sound like the worst plan.", 'start': 807.091, 'duration': 4.061}, {'end': 815.573, 'text': 'so if we click onto that, we can basically access the csv file now.', 'start': 811.152, 'duration': 4.421}, {'end': 821.476, 'text': 'we only need to tell python or pandas the file name or the path of our file.', 'start': 815.573, 'duration': 5.903}, {'end': 829.301, 'text': "for that, let's add brackets right here and let's now insert another cell below our current one like this,", 'start': 821.476, 'duration': 7.825}, {'end': 836.146, 'text': 'because in our case the jupyter notebook file and the source file are in the same folder in the basics folder.', 'start': 829.301, 'duration': 6.845}, {'end': 844.528, 'text': 'so if you now type ls like that, you can see that we have our python file and we have our revenue profit csv file,', 'start': 836.146, 'duration': 8.382}, {'end': 846.749, 'text': "and that's exactly the name that we can select now.", 'start': 844.528, 'duration': 2.221}, {'end': 852.053, 'text': 'so select it and copy it and now paste it right here into the brackets.', 'start': 846.749, 'duration': 5.304}, {'end': 860.44, 'text': "make sure to also add single quotation marks, otherwise this doesn't work here and you can also select the cell right here with the ls.", 'start': 852.053, 'duration': 8.387}, {'end': 861.221, 'text': "we don't need it anymore.", 'start': 860.44, 'duration': 0.781}, {'end': 865.386, 'text': 'hit escape and press D two times like that.', 'start': 861.881, 'duration': 3.505}, {'end': 866.868, 'text': 'This deletes a cell.', 'start': 865.867, 'duration': 1.001}, {'end': 869.411, 'text': 'A nice feature, it might be helpful in some cases.', 'start': 867.288, 'duration': 2.123}, {'end': 878.472, 'text': 'So with that we now set that if we use content right here, we want to read the content of this csv file.', 'start': 870.172, 'duration': 8.3}], 'summary': 'Accessing pd.read_csv functionality in pandas for csv file processing.', 'duration': 104.441, 'max_score': 774.031, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg774031.jpg'}, {'end': 811.152, 'src': 'embed', 'start': 784.241, 'weight': 5, 'content': [{'end': 793.306, 'text': 'This basically means that we now want to access a function that is available in pandas, and if you now hit tab,', 'start': 784.241, 'duration': 9.065}, {'end': 799.168, 'text': 'you can find a lot of different functionalities that are implemented into this pandas package.', 'start': 793.306, 'duration': 5.862}, {'end': 807.091, 'text': 'now, if we scroll down a bit right here, we can see this pd.read csv functionality.', 'start': 799.168, 'duration': 7.923}, {'end': 811.152, 'text': "as we want to access a csv file, this doesn't sound like the worst plan.", 'start': 807.091, 'duration': 4.061}], 'summary': 'Access pandas package for various functionalities, including pd.read_csv for accessing csv files.', 'duration': 26.911, 'max_score': 784.241, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg784241.jpg'}, {'end': 933.23, 'src': 'embed', 'start': 909.456, 'weight': 4, 'content': [{'end': 916.538, 'text': 'including the file name if the file is not located in the same folder as it is the case for us, you can also add other parameters here.', 'start': 909.456, 'duration': 7.082}, {'end': 919.239, 'text': 'So if we enter or if we add a comma.', 'start': 917.178, 'duration': 2.061}, {'end': 926.504, 'text': 'and now say sep, like this, equals now single quotation marks.', 'start': 920.359, 'duration': 6.145}, {'end': 930.748, 'text': 'now we can define our separator, our delimiter, right here.', 'start': 926.504, 'duration': 4.244}, {'end': 933.23, 'text': 'in our case this should be a semicolon.', 'start': 930.748, 'duration': 2.482}], 'summary': 'Specify file location and delimiter for data processing.', 'duration': 23.774, 'max_score': 909.456, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg909456.jpg'}], 'start': 611.48, 'title': 'Python data analysis & visualization', 'summary': "Introduces the usage of python for data analysis, emphasizing the installation and application of three key packages: numpy, pandas, and matplotlib, highlighting the advantage of having these packages already installed through anaconda. it also demonstrates the process of importing numpy and pandas packages and accessing csv data, including using 'import numpy as np' and 'import pandas as pd' to import the packages, and the process of accessing and modifying the csv file using pd.read_csv and specifying the separator as a semicolon.", 'chapters': [{'end': 725.319, 'start': 611.48, 'title': 'Python data analysis & visualization', 'summary': 'Introduces the usage of python for data analysis, emphasizing the installation and application of three key packages: numpy, pandas, and matplotlib, which facilitate multidimensional array support, improved data manipulation and analysis, and data visualization respectively, and highlights the advantage of having these packages already installed through anaconda.', 'duration': 113.839, 'highlights': ['The chapter emphasizes the installation and application of three key packages: NumPy, pandas, and matplotlib, which facilitate multidimensional array support, improved data manipulation and analysis, and data visualization respectively, for data analysis purposes.', 'Python is an open source language designed for various purposes, enabling the installation of additional packages, with the most popular ones being NumPy, pandas, and matplotlib.', 'Anaconda comes with the most popular packages by default, including NumPy, pandas, and matplotlib, which are essential for data analysis purposes.']}, {'end': 956.38, 'start': 726.98, 'title': 'Importing numpy and pandas, accessing csv data', 'summary': "Demonstrates the process of importing numpy and pandas packages and accessing csv data, including using 'import numpy as np' and 'import pandas as pd' to import the packages, and the process of accessing and modifying the csv file using pd.read_csv and specifying the separator as a semicolon.", 'duration': 229.4, 'highlights': ["The process of importing Numpy and Pandas packages is demonstrated. The typical way of importing Numpy to projects is shown as 'import numpy as np', and the import of Pandas package as 'import pandas as pd'.", 'Accessing and modifying the CSV file using pd.read_csv and specifying the separator as a semicolon is explained. The process of accessing the CSV file using pd.read_csv and specifying the separator as a semicolon is demonstrated, ensuring the correct display of the file.', 'Demonstration of accessing a function available in Pandas and modifying the separator for a CSV file. The demonstration includes accessing a function available in Pandas for reading CSV files and modifying the separator to ensure correct file display.']}], 'duration': 344.9, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg611480.jpg', 'highlights': ['Anaconda comes with the most popular packages by default, including NumPy, pandas, and matplotlib, which are essential for data analysis purposes.', 'The chapter emphasizes the installation and application of three key packages: NumPy, pandas, and matplotlib, which facilitate multidimensional array support, improved data manipulation and analysis, and data visualization respectively, for data analysis purposes.', 'Python is an open source language designed for various purposes, enabling the installation of additional packages, with the most popular ones being NumPy, pandas, and matplotlib.', "The process of importing Numpy and Pandas packages is demonstrated. The typical way of importing Numpy to projects is shown as 'import numpy as np', and the import of Pandas package as 'import pandas as pd'.", 'Accessing and modifying the CSV file using pd.read_csv and specifying the separator as a semicolon is explained. The process of accessing the CSV file using pd.read_csv and specifying the separator as a semicolon is demonstrated, ensuring the correct display of the file.', 'Demonstration of accessing a function available in Pandas and modifying the separator for a CSV file. The demonstration includes accessing a function available in Pandas for reading CSV files and modifying the separator to ensure correct file display.']}, {'end': 1267.439, 'segs': [{'end': 1066.071, 'src': 'embed', 'start': 1006.73, 'weight': 0, 'content': [{'end': 1008.491, 'text': "Nothing we'll have a look at in this video though.", 'start': 1006.73, 'duration': 1.761}, {'end': 1014.533, 'text': "So let's just keep in mind that we have a data frame here, which is a structure that is created by pandas,", 'start': 1009.071, 'duration': 5.462}, {'end': 1021.436, 'text': 'and that we can use this data frame to basically access our columns and rows by certain indexes.', 'start': 1014.533, 'duration': 6.903}, {'end': 1023.477, 'text': "We'll see how this works in a few seconds.", 'start': 1021.836, 'duration': 1.641}, {'end': 1029.898, 'text': 'Now, with this data frame structure, we also get a lot of data frame functionalities, you could say.', 'start': 1024.936, 'duration': 4.962}, {'end': 1030.96, 'text': 'Now, what do I mean by that?', 'start': 1029.919, 'duration': 1.041}, {'end': 1040.294, 'text': 'If I enter content here once again and now type head and add brackets and press shift and enter,', 'start': 1032.38, 'duration': 7.914}, {'end': 1042.974, 'text': 'you can basically see the same structure we had before.', 'start': 1040.294, 'duration': 2.68}, {'end': 1051.72, 'text': "The difference now is that we don't have a preview of our entire content right here but that we only see a preview that we can define on our own.", 'start': 1043.535, 'duration': 8.185}, {'end': 1057.563, 'text': 'Now what do I mean by that? By default the head gives us back the first five rows of our file.', 'start': 1052.14, 'duration': 5.423}, {'end': 1066.071, 'text': 'But if I enter a 3 into these brackets right here and press shift and enter once again, you can see that we only see the first 3 rows.', 'start': 1058.183, 'duration': 7.888}], 'summary': 'Introduction to using pandas data frame to access and preview data, with examples of customizing the preview.', 'duration': 59.341, 'max_score': 1006.73, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg1006730.jpg'}, {'end': 1146.078, 'src': 'embed', 'start': 1116.958, 'weight': 3, 'content': [{'end': 1120.721, 'text': 'And what do we want to rename? Well, we want to rename a certain column.', 'start': 1116.958, 'duration': 3.763}, {'end': 1126.605, 'text': 'For that, we simply type columns like that equals and now curly braces.', 'start': 1121.361, 'duration': 5.244}, {'end': 1128.206, 'text': 'And now we need two things.', 'start': 1126.945, 'duration': 1.261}, {'end': 1132.889, 'text': 'We first need the name of the current name of the label.', 'start': 1128.867, 'duration': 4.022}, {'end': 1135.351, 'text': 'So this is no data in our case.', 'start': 1133.169, 'duration': 2.182}, {'end': 1141.135, 'text': 'Now we add a colon and now we define the new label name that we want to have, which is year in our case.', 'start': 1135.991, 'duration': 5.144}, {'end': 1142.156, 'text': 'I guess this makes sense.', 'start': 1141.375, 'duration': 0.781}, {'end': 1146.078, 'text': 'if we now press shift and enter, we can see nothing.', 'start': 1142.896, 'duration': 3.182}], 'summary': "Renaming a column to 'year' with no data present.", 'duration': 29.12, 'max_score': 1116.958, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg1116958.jpg'}, {'end': 1222.987, 'src': 'embed', 'start': 1195.281, 'weight': 4, 'content': [{'end': 1207.774, 'text': 'we can then set year equal to 2012 like that, and now press shift and enter, and if we now simply say single year like that,', 'start': 1195.281, 'duration': 12.493}, {'end': 1211.838, 'text': 'then you can see that we retrieve the data for the year 2012 only.', 'start': 1207.774, 'duration': 4.064}, {'end': 1216.021, 'text': 'So this is one way how we can access specific rows.', 'start': 1212.418, 'duration': 3.603}, {'end': 1222.987, 'text': 'Now, what about columns then? Well, we could also say that we want to access a single column.', 'start': 1216.621, 'duration': 6.366}], 'summary': 'Access specific rows and columns using python pandas.', 'duration': 27.706, 'max_score': 1195.281, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg1195281.jpg'}, {'end': 1275.323, 'src': 'embed', 'start': 1246.888, 'weight': 5, 'content': [{'end': 1251.432, 'text': 'If you want to access multiple columns, by the way, you could also write something like this.', 'start': 1246.888, 'duration': 4.544}, {'end': 1256.237, 'text': 'So you open the curly braces two times and now say you want to have revenue column.', 'start': 1251.713, 'duration': 4.524}, {'end': 1261.056, 'text': 'like this and profit like that.', 'start': 1258.035, 'duration': 3.021}, {'end': 1267.439, 'text': 'If you do that you can see that we have the revenue and the profit columns both in our table right here.', 'start': 1261.537, 'duration': 5.902}, {'end': 1275.323, 'text': "Now these ways to access data are fine but I don't think these are really clear and really well flexible as I would call them.", 'start': 1267.979, 'duration': 7.344}], 'summary': 'Demonstrates accessing multiple columns in a table using curly braces and discusses the limitations of this method.', 'duration': 28.435, 'max_score': 1246.888, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg1246888.jpg'}], 'start': 957.041, 'title': 'Pandas data frame functionality and data manipulation', 'summary': "Discusses creating a data frame in pandas, which provides access to columns and rows through specific indexes, and demonstrates the functionality of 'head' to preview a defined number of rows. it also covers renaming columns, accessing specific rows and columns, and retrieving data, with examples of renaming a column to 'year' and accessing data for the year 2012.", 'chapters': [{'end': 1090.776, 'start': 957.041, 'title': 'Pandas data frame functionality', 'summary': "Discusses the creation of a data frame structure by pandas, which allows access to columns and rows using specific indexes, and demonstrates the functionality of 'head' to preview a defined number of rows from the data frame.", 'duration': 133.735, 'highlights': ['The data frame structure created by pandas allows access to columns and rows using specific indexes, providing a tabular structure with indexed rows and columns.', "The 'head' functionality enables the preview of a defined number of rows from the data frame, offering flexibility in displaying the data, such as viewing the first 3, 4, or all 8 rows.", "Pandas provides various data frame functionalities, including the ability to define the number of rows to preview using the 'head' function, enhancing data exploration and analysis."]}, {'end': 1267.439, 'start': 1091.377, 'title': 'Pandas data manipulation', 'summary': "Demonstrates how to rename columns, access specific rows and columns, and retrieve data using pandas, with examples of renaming a column from 'no data' to 'year' and accessing data for the year 2012.", 'duration': 176.062, 'highlights': ["The chapter demonstrates how to rename columns, with an example of renaming a column from 'no data' to 'year'.", 'The chapter explains how to access specific rows and retrieve data, with an example of retrieving data for the year 2012.', "The chapter illustrates how to access single and multiple columns, with examples of accessing the 'revenue' column and both 'revenue' and 'profit' columns."]}], 'duration': 310.398, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg957041.jpg', 'highlights': ['The data frame structure created by pandas allows access to columns and rows using specific indexes, providing a tabular structure with indexed rows and columns.', "The 'head' functionality enables the preview of a defined number of rows from the data frame, offering flexibility in displaying the data, such as viewing the first 3, 4, or all 8 rows.", "Pandas provides various data frame functionalities, including the ability to define the number of rows to preview using the 'head' function, enhancing data exploration and analysis.", "The chapter demonstrates how to rename columns, with an example of renaming a column from 'no data' to 'year'.", 'The chapter explains how to access specific rows and retrieve data, with an example of retrieving data for the year 2012.', "The chapter illustrates how to access single and multiple columns, with examples of accessing the 'revenue' column and both 'revenue' and 'profit' columns."]}, {'end': 1501.842, 'segs': [{'end': 1297.535, 'src': 'embed', 'start': 1267.979, 'weight': 0, 'content': [{'end': 1275.323, 'text': "Now these ways to access data are fine but I don't think these are really clear and really well flexible as I would call them.", 'start': 1267.979, 'duration': 7.344}, {'end': 1285.408, 'text': 'because of that we have another functionality implemented into pandas which makes things like that a lot easier the so-called log argument.', 'start': 1276.323, 'duration': 9.085}, {'end': 1286.789, 'text': 'now, what does this mean?', 'start': 1285.408, 'duration': 1.381}, {'end': 1297.535, 'text': 'well, we can simply access our content, so up here, the content right here, once again by well typing content, and now we add log to it like that,', 'start': 1286.789, 'duration': 10.746}], 'summary': 'Pandas has a new log functionality that eases data access, making it more flexible and clearer.', 'duration': 29.556, 'max_score': 1267.979, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg1267979.jpg'}, {'end': 1393.978, 'src': 'embed', 'start': 1335.981, 'weight': 1, 'content': [{'end': 1349.477, 'text': 'now, what i could say here is that i would like to have the row with the label name 2 And I want to have the column with the label name revenue,', 'start': 1335.981, 'duration': 13.496}, {'end': 1350.057, 'text': 'something like that.', 'start': 1349.477, 'duration': 0.58}, {'end': 1354.879, 'text': 'If I do this, you can see that we get 130.', 'start': 1350.717, 'duration': 4.162}, {'end': 1361.161, 'text': "That's exactly what you can see right here because the revenue in the year 2012 was at 130.", 'start': 1354.879, 'duration': 6.282}, {'end': 1361.901, 'text': 'But we can do more.', 'start': 1361.161, 'duration': 0.74}, {'end': 1366.744, 'text': "Let's say we also want to add the year 2015.", 'start': 1362.442, 'duration': 4.302}, {'end': 1372.487, 'text': 'For that we can simply add 5 right here but now put both of these into curly braces.', 'start': 1366.744, 'duration': 5.743}, {'end': 1373.808, 'text': "Otherwise this wouldn't work.", 'start': 1372.847, 'duration': 0.961}, {'end': 1375.228, 'text': 'Like this and like that.', 'start': 1374.248, 'duration': 0.98}, {'end': 1384.793, 'text': 'So what we basically want to have is we want to display the revenue column or the revenue basically for the years with the label 2 and 5.', 'start': 1375.949, 'duration': 8.844}, {'end': 1388.035, 'text': 'So 2012 and 2015.', 'start': 1384.793, 'duration': 3.242}, {'end': 1393.978, 'text': 'If we now press shift and enter, well, you can see that we get 130 right here and 179 right there.', 'start': 1388.035, 'duration': 5.943}], 'summary': 'Display revenue for years 2012 and 2015, resulting in 130 and 179.', 'duration': 57.997, 'max_score': 1335.981, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg1335981.jpg'}, {'end': 1444.947, 'src': 'embed', 'start': 1417.131, 'weight': 3, 'content': [{'end': 1422.274, 'text': 'you could also say that you want to have the data for a specific range of years.', 'start': 1417.131, 'duration': 5.143}, {'end': 1427.838, 'text': "so let's say you want to start in the year 2012 and you want the data up to year 2017..", 'start': 1422.274, 'duration': 5.564}, {'end': 1428.178, 'text': 'for that.', 'start': 1427.838, 'duration': 0.34}, {'end': 1433.701, 'text': 'you could say you want to have two up to seven from the label name perspective to do this.', 'start': 1428.178, 'duration': 5.523}, {'end': 1439.644, 'text': 'you can get rid of the square brackets here and simply type 227 like that.', 'start': 1433.701, 'duration': 5.943}, {'end': 1444.947, 'text': 'important. this refers to the label name, so this includes all of these rows.', 'start': 1439.644, 'duration': 5.303}], 'summary': 'Data from 2012 to 2017 can be obtained by using 227 as the label name.', 'duration': 27.816, 'max_score': 1417.131, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg1417131.jpg'}, {'end': 1501.842, 'src': 'embed', 'start': 1479.827, 'weight': 4, 'content': [{'end': 1488.293, 'text': "This would also work, but it's not the best practice code from a Python perspective, because you should always be as precise, as clear as possible,", 'start': 1479.827, 'duration': 8.466}, {'end': 1489.934, 'text': 'about what we want to achieve with our code.', 'start': 1488.293, 'duration': 1.641}, {'end': 1492.656, 'text': 'So definitely make sure to add the colon right here.', 'start': 1490.234, 'duration': 2.422}, {'end': 1495.218, 'text': 'Same result, but better code in the end.', 'start': 1493.016, 'duration': 2.202}, {'end': 1501.842, 'text': 'So this is how we can basically tailor the columns and the rows that we want to display right here.', 'start': 1495.518, 'duration': 6.324}], 'summary': 'Python code should be precise and clear to achieve better results.', 'duration': 22.015, 'max_score': 1479.827, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg1479827.jpg'}], 'start': 1267.979, 'title': 'Pandas log functionality, data selection and custom filtering', 'summary': 'Introduces the log argument in pandas for easier data access, demonstrates data selection using label names with examples, resulting in specific revenue values, and covers custom data filtering and selection using python, emphasizing clear coding practices.', 'chapters': [{'end': 1314.442, 'start': 1267.979, 'title': 'Pandas log functionality', 'summary': 'Introduces the log argument in pandas, which provides an easier way to access data by allowing the selection of rows and columns by their label names, making it more flexible and clear.', 'duration': 46.463, 'highlights': ['The log argument in Pandas allows us to select rows and columns by their label names, making it easier to access data.', 'This functionality provides a more flexible and clear way to access data compared to other methods.']}, {'end': 1415.47, 'start': 1314.442, 'title': 'Data selection and displaying', 'summary': 'Demonstrates how to select specific rows and columns from a table using label names, with examples showing revenue values for specific years and multiple column selection, resulting in 130 and 179 for revenue in 2012 and 2015, and displaying revenue and profit for the second and fifth label names.', 'duration': 101.028, 'highlights': ['Demonstrates selecting specific rows and columns using label names, with examples showing revenue values for specific years and multiple column selection, resulting in 130 and 179 for revenue in 2012 and 2015.', 'Illustrates how to display revenue and profit for the second and fifth label names using label names, resulting in 130 and 179 for revenue in 2012 and 2015.']}, {'end': 1501.842, 'start': 1417.131, 'title': 'Custom data filtering and selection', 'summary': 'Covers custom data filtering and selection using python, demonstrating how to specify a specific range of years and include all columns for the selected rows, emphasizing the importance of precise and clear coding practices.', 'duration': 84.711, 'highlights': ['Demonstrating how to specify a specific range of years (2012-2017) for data filtering.', 'Emphasizing the importance of adding a colon for precise and clear coding practices in Python.', "Showing how to include all columns for the selected rows, although it's not the best practice code from a Python perspective."]}], 'duration': 233.863, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg1267979.jpg', 'highlights': ['The log argument in Pandas allows us to select rows and columns by their label names, making it easier to access data.', 'Demonstrates selecting specific rows and columns using label names, with examples showing revenue values for specific years and multiple column selection, resulting in 130 and 179 for revenue in 2012 and 2015.', 'Illustrates how to display revenue and profit for the second and fifth label names using label names, resulting in 130 and 179 for revenue in 2012 and 2015.', 'Demonstrating how to specify a specific range of years (2012-2017) for data filtering.', 'Emphasizing the importance of adding a colon for precise and clear coding practices in Python.']}, {'end': 1991.532, 'segs': [{'end': 1532.651, 'src': 'embed', 'start': 1502.443, 'weight': 0, 'content': [{'end': 1504.644, 'text': 'I also want to show you another functionality.', 'start': 1502.443, 'duration': 2.201}, {'end': 1510.588, 'text': 'This is not the log argument, which, just to keep that in mind, refers to the label names.', 'start': 1505.124, 'duration': 5.464}, {'end': 1516.772, 'text': 'So this one right here and the label names right here, our index that we have for the different rows.', 'start': 1510.828, 'duration': 5.944}, {'end': 1519.594, 'text': 'But we also have ilog like this.', 'start': 1517.433, 'duration': 2.161}, {'end': 1526.642, 'text': 'Ilog simply refers to the integers you could say of these different rows and columns.', 'start': 1520.695, 'duration': 5.947}, {'end': 1532.651, 'text': "Now, what do I mean by that? Well, let's simply add iLog and let's simply press Shift and Enter.", 'start': 1527.103, 'duration': 5.548}], 'summary': 'Demonstrating ilog functionality to refer to row and column indices.', 'duration': 30.208, 'max_score': 1502.443, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg1502443.jpg'}, {'end': 1743.231, 'src': 'embed', 'start': 1715.568, 'weight': 2, 'content': [{'end': 1720.111, 'text': 'You can see that we have five different years, so five different revenue data and cost data.', 'start': 1715.568, 'duration': 4.543}, {'end': 1727.157, 'text': 'You can see the mean, you can see the standard deviation and you can see a minimum and a maximum value, for example.', 'start': 1720.911, 'duration': 6.246}, {'end': 1728.899, 'text': "So I won't dive too deep into that.", 'start': 1727.498, 'duration': 1.401}, {'end': 1730.381, 'text': "I think it's quite straightforward.", 'start': 1728.919, 'duration': 1.462}, {'end': 1735.866, 'text': 'I just wanted to make sure that you are aware of this functionality also included in pandas, by the way.', 'start': 1730.881, 'duration': 4.985}, {'end': 1739.389, 'text': 'So this is one thing, the describe argument right here.', 'start': 1736.667, 'duration': 2.722}, {'end': 1743.231, 'text': 'Now let me conclude this video with plotting a quick chart.', 'start': 1739.709, 'duration': 3.522}], 'summary': 'The transcript discusses revenue and cost data for five years, including mean, standard deviation, and plotting a chart.', 'duration': 27.663, 'max_score': 1715.568, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg1715568.jpg'}, {'end': 1817.512, 'src': 'embed', 'start': 1785.412, 'weight': 1, 'content': [{'end': 1788.215, 'text': 'you can see a chart type and what it could look like in Python.', 'start': 1785.412, 'duration': 2.803}, {'end': 1792.359, 'text': 'And you can also see that we have some example code down there.', 'start': 1788.656, 'duration': 3.703}, {'end': 1796.143, 'text': 'So feel free to play around with this code and create your own charts.', 'start': 1792.66, 'duration': 3.483}, {'end': 1800.365, 'text': 'and as you can see also by this import, we will use matplotlib.', 'start': 1796.904, 'duration': 3.461}, {'end': 1804.747, 'text': 'well, specifically in connection with pyplot, which you can find right here,', 'start': 1800.365, 'duration': 4.382}, {'end': 1809.769, 'text': 'which basically provides a matlab like plotting framework for our python code.', 'start': 1804.747, 'duration': 5.022}, {'end': 1811.83, 'text': "we won't dive deeper into this right now.", 'start': 1809.769, 'duration': 2.061}, {'end': 1817.512, 'text': 'the important thing is that we can simply take this command right here and create our chart right there.', 'start': 1811.83, 'duration': 5.682}], 'summary': 'Learn to create charts in python using matplotlib and pyplot.', 'duration': 32.1, 'max_score': 1785.412, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg1785412.jpg'}, {'end': 1990.791, 'src': 'embed', 'start': 1963.167, 'weight': 3, 'content': [{'end': 1969.589, 'text': 'As you can imagine, we are just at the very, very basics of Python and its data analysis capabilities.', 'start': 1963.167, 'duration': 6.422}, {'end': 1976.812, 'text': 'But still, these are the first steps that should help you to get a grip onto Python in relation to data analysis.', 'start': 1970.109, 'duration': 6.703}, {'end': 1980.496, 'text': 'So I hope that you liked this video and that this was helpful for you.', 'start': 1977.532, 'duration': 2.964}, {'end': 1986.364, 'text': 'And of course, I hope to see you in one of the next videos, maybe also related to Python and data analysis.', 'start': 1980.777, 'duration': 5.587}, {'end': 1990.791, 'text': 'So thanks a lot for watching and see you in one of these next videos.', 'start': 1987.065, 'duration': 3.726}], 'summary': 'Intro to python data analysis, basic concepts covered.', 'duration': 27.624, 'max_score': 1963.167, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg1963167.jpg'}], 'start': 1502.443, 'title': 'Pandas ilog functionality and creating line chart with matplotlib', 'summary': 'Demonstrates the functionality of ilog in pandas, illustrating its differences from log and its usage to access data using index numbers, and also covers creating a line chart in python using matplotlib, including installation, documentation access, example code utilization, and basic chart creation as a foundation for data analysis.', 'chapters': [{'end': 1735.866, 'start': 1502.443, 'title': 'Pandas ilog functionality', 'summary': 'Demonstrates the functionality of ilog in pandas, showing how it differs from log and how to use it to access data using index numbers, ultimately leading to a demonstration of the describe function which provides basic statistics about the data.', 'duration': 233.423, 'highlights': ['The ilog functionality in pandas is demonstrated, showing its difference from log and how it affects the display of data, with specific examples of accessing columns and rows using index numbers.', 'The describe function in pandas is showcased, providing basic statistics such as mean, standard deviation, minimum, and maximum values for the data.']}, {'end': 1991.532, 'start': 1736.667, 'title': 'Creating line chart with matplotlib', 'summary': 'Demonstrates how to use matplotlib to create a line chart in python, covering the installation process, accessing the documentation, utilizing example code, and creating a basic chart, serving as a foundation for data analysis in python.', 'duration': 254.865, 'highlights': ['The chapter covers the process of creating a line chart using Matplotlib in Python, starting from the installation through accessing documentation and utilizing example code to finally creating a basic chart.', 'The video emphasizes the foundational steps in Python data analysis, including installation, accessing documentation, utilizing example code, and creating a basic chart using Matplotlib.', "The chapter concludes with an encouragement for viewers to explore further Python and data analysis-related videos, providing a helpful foundation for Python's data analysis capabilities."]}], 'duration': 489.089, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/cXP_i5-nTXg/pics/cXP_i5-nTXg1502443.jpg', 'highlights': ['The ilog functionality in pandas is demonstrated, showing its difference from log and how it affects the display of data, with specific examples of accessing columns and rows using index numbers.', 'The chapter covers the process of creating a line chart using Matplotlib in Python, starting from the installation through accessing documentation and utilizing example code to finally creating a basic chart.', 'The describe function in pandas is showcased, providing basic statistics such as mean, standard deviation, minimum, and maximum values for the data.', 'The video emphasizes the foundational steps in Python data analysis, including installation, accessing documentation, utilizing example code, and creating a basic chart using Matplotlib.', "The chapter concludes with an encouragement for viewers to explore further Python and data analysis-related videos, providing a helpful foundation for Python's data analysis capabilities."]}], 'highlights': ['Anaconda distribution provides convenience by including common packages used with Python, making it a recommended choice for beginners. Increased convenience for beginners', 'The chapter covers setting up Python for data analysis, accessing a CSV file, and plotting a chart using Python, serving as a starting point for beginners in data analysis.', 'Demonstrates how to access specific columns or rows in a CSV file, enabling practical data manipulation and analysis.', 'The data frame structure created by pandas allows access to columns and rows using specific indexes, providing a tabular structure with indexed rows and columns.', "The 'head' functionality enables the preview of a defined number of rows from the data frame, offering flexibility in displaying the data, such as viewing the first 3, 4, or all 8 rows.", 'The log argument in Pandas allows us to select rows and columns by their label names, making it easier to access data.', 'The chapter covers the process of creating a line chart using Matplotlib in Python, starting from the installation through accessing documentation and utilizing example code to finally creating a basic chart.']}