https://www.datacamp.com/community/news/word-frequency-the-difference-between-simple-and-weighted-counting-cc241l8k5qf

We go through the process of analyzing movie titles, and uncover some insights from two main metrics: 
- Absolute word frequency: which words are most frequently used in movie titles? 
- Weighted word frequency: which words are most associated with box-office revenue? 

Hope you like it! 

Word Frequency: The Difference Between Simple and Weighted Counting

https://www.datacamp.com/community/news/making-a-gif-of-solar-panel-installations-in-minnesota-and-wisconsin-540pnkjpx9y

In this post we demonstrate how to use the tidyverse and magick packages in R to make a GIF of maps over time! 

Making a GIF of Solar Panel Installations in Minnesota and Wisconsin

https://www.datacamp.com/community/news/object-oriented-programming-in-python-tutorial-tt4da4d8rac

Object-Oriented programming is a widely used concept to write powerful applications. As a data scientist, you will be required to write applications to process your data, among a range of other things.  In this tutorial, you will discover the basics of object-oriented programming in Python.

Object-Oriented Programming in Python: Tutorial

https://www.datacamp.com/community/news/pickle-in-python-object-serialization-6k4yuc17c6f

Discover the Python pickle module: learn about serialization, when (not) to use it, how to compress pickled objects, multiprocessing, and much more!

Pickle in Python: Object Serialization

<p>pandas is one of the most popular Python data science packages and is the standard tool for working with tabular data in Python. Luckily, a complete beginner can learn and start programming in pandas within a couple of weeks. Here’s how to get started.</p>
<h2 id="what-to-know-before-getting-started">What to know before getting started</h2>
<h3 id="what-is-pandas-">What is pandas?</h3>
<p>pandas is one of the first Python packages you should learn because it’s easy to use, open source, and will allow you to work with large quantities of data. It allows fast and efficient data manipulation, data aggregation and pivoting, flexible time series functionality, and more.</p>
<p>pandas has a lot of the same functionality as SQL or Excel, and can take in various data types—from CSV and text files to Microsoft Excel files, SQL databases, and more. It has the added benefit of allowing you to convert them to a DataFrame object, which simply represents columns and rows that resemble tabular data in Excel—but in Python.</p>
<p><center><a href="https://www.datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1606929736/pandasdataframe_ihqxej.png" /></a></p>
<h5 id="the-pandas-dataframe-in-action">The pandas DataFrame in action</h5>
<h3 id="how-does-pandas-fit-in-the-pydata-ecosystem-">How does pandas fit in the PyData ecosystem?</h3>
<p>When analyzing data with Python, pandas is often used in conjunction with other packages and tools. These packages range from machine learning packages for producing predictions on data like <a href="https://scikit-learn.org/">scikit-learn</a>, data visualization packages like <a href="https://matplotlib.org/">Matplotlib</a> and <a href="https://seaborn.pydata.org/">seaborn</a>, and tools like <a href="https://jupyter.org/">Jupyter Notebooks</a> for creating and sharing reports that contain code, visualizations, and text. All of these tools comprise what is usually referred to as the <a href="https://pandas.pydata.org/community/ecosystem.html">PyData ecosystem</a>. But you don’t need to worry about learning all of these tools at once—start by focusing on pandas.</p>
<h2 id="six-steps-for-your-pandas-learning-journey">Six steps for your pandas learning journey</h2>
<h3 id="1-learn-basic-python-syntax">1) Learn basic Python syntax</h3>
<p>pandas is a package built for Python, so you need to have a firm grasp of basic Python syntax before you get started with pandas. It’s very easy to get bogged down when learning syntax, as introductory courses often make learning a chore by focusing purely on Python syntax.</p>
<p>As a rule of thumb, you should spend as little time as possible on syntax and learn just enough syntax to get you started with simple tasks with pandas. Here’s a list of pure Python resources to get you started with the basics:</p>
<ul>
<li><a href="https://realpython.com/products/python-basics-book/">A Practical Introduction to Python 3</a></li>
<li><a href="https://docs.python.org/3/tutorial/">Python’s Main tutorial page</a></li>
<li><a href="https://www.coursera.org/learn/python-crash-course">Coursera&#39;s Crash Course in Python taught by Google</a></li>
<li><a href="http://www.datacamp.com/courses/intro-to-python-for-data-science">DataCamp’s Introduction to Python</a></li>
</ul>

<h3 id="2-set-up-your-python-environment">2) Set up your Python environment</h3>
<p>Before working with pandas, you need to have a Python environment set up on your machine. This will allow you to apply your newfound skills and experiment with pandas on your own data.</p>
<p>To set up your Python environment for data science, I recommend installing an <a href="https://www.anaconda.com/products/individual">Anaconda distribution</a> to conveniently install pandas along with relevant data science packages and tools like Jupyter Notebooks.</p>
<p>For a primer on setting up your Python environment with an Anaconda, watch my <a href="https://www.datacamp.com/resources/webinars/setting-up-your-python-environment">hands-on training</a> for a step-by-step tutorial. You can also follow beginner guides for installing Anaconda on <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-windows">Windows</a> and <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-mac-os-x">Mac OS X</a>.</p>
<h3 id="3-learn-by-doing">3) Learn by doing</h3>
<p>At DataCamp, we believe that learning by doing is the best way to learn and apply programming skills. Applying your pandas skills with guided projects will help you get out of your comfort zone, learn how to debug and read documentation, and gain confidence working with real data.</p>
<p>Becoming proficient in pandas requires knowing how to import and export data of different types, manipulating and reshaping data, pivoting and aggregating data, deriving simple insights from DataFrames, and more. Here’s a list of resources that cover these topics and more, containing easy-to-implement pandas code in the form of guided projects:</p>
<ul>
<li>The <a href="https://pandas.pydata.org/pandas-docs/stable/getting_started/intro_tutorials/01_table_oriented.html#min-tut-01-tableoriented">pandas</a> website offers a ton of tutorials on getting started with the package. More importantly, it has many <a href="https://pandas.pydata.org/pandas-docs/stable/getting_started/tutorials.html">community tutorials</a> to get new users started.</li>
<li><a href="https://www.amazon.com/Pandas-Cookbook-Scientific-Computing-Visualization/dp/1784393878">Pandas Cookbook</a> is an excellent book offering a code-rich, project-based approach to working with pandas. It also introduces other packages often used with pandas in the PyData Ecosystem, like seaborn for data visualization.</li>
<li><a href="https://datacamp.com">DataCamp</a> offers a range of pandas courses ranging from beginner to advanced. The <a href="http://www.datacamp.com/courses/pandas-foundations">pandas foundations</a> course shows you how to import DataFrames, conduct basic exploratory analysis, and analyze time series—all from your browser.</li>
</ul>

<h3 id="4-work-on-projects-with-real-world-data">4) Work on projects with real-world data</h3>
<p>Practicing the pandas skills you’ve learned on real-world data is an excellent way to retain your skills and apply them confidently in your daily work.</p>
<p>There’s a variety of ways to get started on your own project. For starters, if your daily work requires you to work with tabular data, start using pandas instead of spreadsheet software like Google Sheets and Microsoft Excel. While this could take more time initially, it will help you sharpen and retain your pandas skills, making you faster and more proficient in the long run.</p>
<p><a href="http://www.kaggle.com">Kaggle</a> contains a range of real-world datasets and machine learning competitions. While I encourage you to learn machine learning, it’s best suited for more advanced learners, so make sure to initially focus on projects focused on data analysis and exploration with pandas. You can do this by writing data analysis notebooks on Kaggle or blogging platforms like <a href="http://www.medium.com">Medium</a>.</p>
<h3 id="5-master-the-art-of-debugging">5) Master the art of debugging</h3>
<p>One of the most overlooked skills any aspiring programmer needs to hone is debugging. Every Python and pandas practitioner, regardless of skill level, faces errors and problems with their code. When you encounter errors in your pandas code, these resources are at your disposal:</p>
<ul>
<li><a href="http://google.com/">Google</a> is the first place I go when faced with errors with my code. Almost always, the answer to my pandas query will be in the first ten search results.</li>
<li><a href="https://stackoverflow.com/">Stack Overflow</a> is the largest community of developers where practitioners can seek help with debugging code by asking and answering programming questions. It currently contains around <a href="https://stackoverflow.com/questions/tagged/pandas?sort=MostVotes&amp;edited=true">180,000 questions</a> on pandas. If you want to post a question yourself, make sure to follow their <a href="https://stackoverflow.com/help/how-to-ask">best practices when phrasing questions</a>.</li>
<li>Documentation will always be your friend, and the <a href="https://pandas.pydata.org/docs/">pandas documentation</a> is extensive. It covers all the possible functionalities of pandas as well as best practices.</li>
</ul>

<h3 id="6-keep-expanding-your-pandas-skills">6) Keep expanding your pandas skills</h3>
<p>Keep on building and expanding your pandas skills. Many resources cover advanced pandas topics. For example, Ashish Kumar’s <a href="https://www.amazon.com/Mastering-pandas-complete-installation-techniques-ebook/dp/B07WFM4JXZ">Mastering pandas</a> covers techniques to speed up and simplify your pandas workflow. This <a href="https://realpython.com/python-pandas-tricks/">Real Python blog post</a> covers tips and tricks for advanced users. You can also start learning how other packages in the PyData Ecosystem extend your pandas ability, like <a href="https://seaborn.pydata.org/">seaborn</a> and <a href="https://plotly.com/">plotly</a>, for creating interactive plots.</p>
<p><center><a href="https://www.datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1607005373/How_to_Learn_Pandas_lqualj.gif" /></a>
<br></p>
<h5 id="an-interactive-plot-made-with-plotly">An interactive plot made with Plotly</h5>
<p>A great way to expand your pandas skills is by revisiting some of the pandas code you wrote at the beginning of your learning journey. Try to spot opportunities to simplify your code, adopt best practices, and leverage other packages in the PyData ecosystem.</p>
<p>I hope you feel inspired by this blog post and that these resources are useful to you. Mastering pandas is a lifelong learning journey—one that I am still on myself. At DataCamp, pandas is covered across our core curriculum, and we’re continually improving and adding to our content. Lastly, I invite you to join thousands of learners in the <a href="https://support.datacamp.com/hc/en-us/articles/360001915993-The-DataCamp-Slack-Community-An-Overview">DataCamp Slack Community</a> to get support on your pandas journey!</p>
<p><em>Adel Nehme is a Data Science Evangelist at DataCamp. He was previously a Content Developer where he collaborated with instructors on a range of Python courses and created our course <a href="http://datacamp.com/courses/cleaning-data-in-python">Cleaning Data in Python</a>.</em></p>


https://www.datacamp.com/community/blog/how-to-learn-pandas

Here’s all you need to know to get started with pandas.

How to Learn pandas

<p>Python is the most popular programming language today and is widely used across verticals from software and web development, game development, data science, machine learning, and more. Learning Python is imperative for aspiring data scientists, data analysts, data engineers, and machine learning scientists.</p>
<p>The good news is that a complete beginner can learn and start programming in Python in just a couple of weeks. Here’s how to get started.</p>
<h2 id="know-your-motivation-before-you-begin">Know your motivation before you begin</h2>
<p>Python is a highly versatile programming language with many use cases. It’s important to have a specific motivation in mind as you embark on your Python learning journey. Targeting an area of interest will help you get started quicker and filter out learning resources that don’t fit your use case. My own Python learning journey began when I needed to use it for school for data science projects. I had no Python or programming experience and needed to get up to speed fast. Luckily, with a few online resources, I was able to become proficient in just a few months. Here’s a list of relevant areas where Python is helpful:</p>
<p><center><a href="https://datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1606922669/Python_usecases_qfxctl.png" /></a> <br></p>
<h3 id="1-data-science-and-machine-learning">1. Data Science and Machine Learning</h3>
<p>Python’s rise in popularity can be attributed to its rich set of packages and tools for data science and machine learning. Researchers use data analysis packages like <a href="https://pandas.pydata.org/">pandas</a> to analyze <a href="https://www.sciencedirect.com/science/article/pii/S2352340920310726">Covid data</a>, practitioners can quickly apply machine learning with libraries like <a href="https://scikit-learn.org/">scikit-learn</a>, <a href="https://keras.io/">Keras</a>, and <a href="https://pytorch.org/">PyTorch</a> to detect fraud, classify images, and produce product recommendations.</p>
<h3 id="2-desktop-and-mobile-software-development">2. Desktop and Mobile Software Development</h3>
<p>Whether building simple software applications like calculators, or more <a href="https://codeinstitute.net/blog/7-popular-software-programs-written-in-python/">complex</a> ones like document editors and social networks. Python offers a plethora of tools to prototype, develop, and run desktop or mobile applications such as <a href="https://wxpython.org/">wxWidgets</a> and <a href="https://kivy.org/">Kivy</a>.</p>
<h3 id="3-web-development">3. Web Development</h3>
<p>Did you know that web applications like Uber, Netflix, and Spotify <a href="https://yourstory.com/mystory/top-twelve-python-applications-real-world-know/amp">were built with Python</a>? There are a variety of web frameworks on Python that simplify website development. Packages such as <a href="https://flask.palletsprojects.com/en/2.0.x/">Flask</a>, <a href="https://www.djangoproject.com/">Django</a>, <a href="https://bottlepy.org/docs/dev/">Bottle</a>, and <a href="https://trypyramid.com/">Pyramid</a> all offer tools to quickly prototype and build scalable web apps.</p>
<h3 id="4-game-development">4. Game Development</h3>
<p>This may come as a surprise, but games such as The Sims 4 and Civilization IV were built with Python. Python libraries such as <a href="https://www.pygame.org/download.shtml">PyGame</a> and <a href="https://docs.python.org/3/library/turtle.html">Turtle</a> simplify prototyping and development of games.</p>
<h3 id="5-internet-of-things-iot-">5. Internet of Things (IoT)</h3>
<p>The rise of embedded systems and the internet of things (IoT) has catalyzed innovation and business process improvement across domains. Python offers a set of packages that make it easy to design, create, and deploy an IoT device on <a href="https://www.arduino.cc/">Arduino</a>, <a href="https://www.raspberrypi.org/">Raspberry Pi</a>, and other <a href="https://www.home-assistant.io/integrations/">IoT devices</a> such as Alexa and Google Home.</p>
<h2 id="6-steps-for-your-python-learning-journey">6 steps for your Python learning journey</h2>
<p><center><a href="https://datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1606924547/6_steps_for_your_Python_learning_journey_-_no_title_kvijhj.png" /></a> <br></p>
<h3 id="1-spend-as-little-time-as-possible-on-basic-syntax">1. Spend as little time as possible on basic syntax</h3>
<p>When learning any programming language, it’s very easy to get bogged down by learning syntax. Introductory courses often make learning a chore by focusing on pure Python syntax rather than how Python can solve a specific problem. Don’t get me wrong: Learning Python syntax is essential for any beginner Python learner, but as a rule of thumb, you should learn just enough syntax to get started working on simple projects. When I began my Python journey for data science, I learned just enough syntax to be able to use <a href="https://pandas.pydata.org/">pandas</a> for simple data analysis tasks. Here’s a list of syntax resources:</p>
<ul>
<li><a href="https://realpython.com/products/python-basics-book/">A Practical Introduction to Python 3</a></li>
<li><a href="https://docs.python.org/3/tutorial/">Python’s Main tutorial page</a></li>
<li><a href="https://www.coursera.org/learn/python-crash-course">Coursera&#39;s Crash Course in Python taught by Google</a></li>
<li><a href="https://www.datacamp.com/courses/intro-to-python-for-data-science">DataCamp’s Introduction to Python course</a></li>
</ul>

<h3 id="2-set-up-your-python-environment">2. Set up your Python environment</h3>
<p>Before working on projects, you need to have a Python environment set up on your machine. Being able to work on projects on your own machine is the best way to apply your new skills. To set up your environment for data science, I recommend installing an <a href="https://www.anaconda.com/products/individual">Anaconda</a> distribution, which conveniently installs all relevant data science packages, and an integrated development environment for editing code in Python called <a href="https://www.spyder-ide.org/">Spyder</a>.</p>
<p>For a primer on setting up your Python environment with an Anaconda, watch my <a href="https://www.datacamp.com/resources/webinars/setting-up-your-python-environment">hands-on training</a> for a step-by-step tutorial. You can also follow beginner guides for installing Anaconda on <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-windows">Windows</a> and <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-mac-os-x">Mac OS X</a>.</p>
<p><center><a href="https://www.spyder-ide.org/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1606331451/spyderIDE_sakabc.png" /></a></p>
<h5 id="an-image-of-the-spyder-integrated-development-environment-https-www-spyder-ide-org-">An image of the <a href="https://www.spyder-ide.org/">Spyder Integrated Development Environment</a></h5>
<h3 id="3-learn-by-doing">3. Learn by doing</h3>
<p>At DataCamp, we believe that learning by doing is the best way to learn and apply programming skills. No matter your focus area, working on projects will help you get out of your comfort zone, learn how to debug and read documentation, and showcase your skills to potential employers.</p>
<h4 id="data-science-and-machine-learning">Data Science and Machine Learning</h4>
<p>Data Science and Machine Learning is one of the <a href="https://business.linkedin.com/content/dam/me/business/en-us/talent-solutions/emerging-jobs-report/Emerging_Jobs_Report_U.S._FINAL.pdf">fastest-growing</a> focus areas today. There are many solutions for online data skills training:</p>
<ul>
<li><a href="https://www.datacamp.com/">DataCamp</a> offers over 130 interactive Python courses and 39 projects across a wide range of data science and machine learning topics. You’ll build your skills from the ground up by coding directly in the browser, and apply your skills on a variety of datasets from Finance, Marketing, Sports, History and more.</li>
<li>The <a href="https://jakevdp.github.io/PythonDataScienceHandbook/">Python Data Science Handbook</a> provides an excellent overview of the main data science packages and techniques in Python. Conveniently, all of the content is available on <a href="https://github.com/jakevdp/PythonDataScienceHandbook">GitHub</a> in the form of Jupyter notebooks.</li>
<li>The <a href="https://www.coursera.org/professional-certificates/ibm-data-science">IBM Data Science Professional Certificate</a> specialization on Coursera contains nine courses covering a range of techniques in data analysis, data visualization, machine learning, and more. The <a href="https://www.coursera.org/learn/applied-data-science-capstone">final course</a> in the specialization is a capstone project combining these techniques on Foursquare data.</li>
<li>The <a href="https://www.codecademy.com/learn/paths/data-science">Codeacademy Data Scientist Path</a> offers a range of courses on data science and machine learning with Python, culminating in a final portfolio project.</li>
</ul>

<h4 id="desktop-and-mobile-software-development">Desktop and Mobile Software Development</h4>
<p>Python offers a range of tools to prototype and develop desktop and mobile applications. Here’s a list of learning resources to get you started:</p>
<ul>
<li>The <a href="https://zetcode.com/wxpython/">ZetCode wxWidgets tutorial</a> offers a step-by-step introduction to building graphical user interfaces with <a href="https://wxpython.org/">wxWidgets</a> and shows learners how to build a simple Tetris game.</li>
<li><a href="https://kivy.org/">Kivy</a> offers a range of tutorials on how to build simple mobile applications with Kivy on Python.</li>
</ul>

<h4 id="web-development">Web Development</h4>
<p>The two main packages for Python web development are Flask and Django. While there is <a href="https://steelkiwi.medium.com/flask-vs-django-how-to-understand-whether-you-need-a-hammer-or-a-toolbox-39b8b3a2e4a5">discussion</a> on the pros and cons of each, here’s a list of learning resources for both packages:</p>
<ul>
<li>The <a href="https://flask.palletsprojects.com/en/1.1.x/tutorial/">Flask tutorial page</a> explains how to build a simple blog website. If you’re a visual learner, <a href="https://www.youtube.com/watch?v=MwZwr5Tvyxo">this YouTube video</a> is an excellent introduction to building websites with Flask.</li>
<li>Learn to build web apps with the <a href="https://docs.djangoproject.com/en/3.1/intro/tutorial01/">Django tutorial page</a>. <a href="https://realpython.com/get-started-with-django-1/">Real Python</a> also explains this nicely, and includes a list of sample projects for a web developer portfolio.</li>
</ul>

<h4 id="game-development">Game Development</h4>
<p>While modern game development is dominated by game engines like Unreal Engine and Unity where most developers use JavaScript, C#, C++ or Java, the following python packages are a great way to get into game development:</p>
<ul>
<li>The <a href="http://www.pygame.org/wiki/tutorials">PyGame tutorial page</a> shows you how to build a range of different games with PyGame. Visual learners can follow <a href="https://www.youtube.com/watch?v=Q-__8Xw9KTM">this YouTube video</a> to build a space invaders game with Python.</li>
<li><a href="https://realpython.com/beginners-guide-python-turtle/">Real Python</a> offers an excellent overview of the <a href="https://docs.python.org/3/library/turtle.html">Turtle</a> package for game development in Python, ending with a turtle race game as a final project.</li>
</ul>

<h4 id="internet-of-things-iot-">Internet of Things (IoT)</h4>
<p>IoT devices are everywhere around us, collecting data about our environment and are only expected to rise in numbers, here’s a list of resources covering how to program IoT devices with Python:</p>
<ul>
<li>Coursera&#39;s <a href="https://www.coursera.org/specializations/iot">Introduction to Programming the Internet of Things (IOT) Specialization</a> offers a series of courses for beginners on how to interface with the Arduino and Raspberry Pi platforms with Python. The specialization ends with a <a href="https://www.coursera.org/learn/internet-of-things-project">capstone project</a> where learners will program an IoT system.</li>
<li>LinkedIn Learning’s <a href="https://www.linkedin.com/learning/raspberry-pi-essential-training?replacementOf=internet-of-things-with-python-and-raspberry-pi">Internet of Things with Python and Raspberry Pi</a> shows you how to program Raspberry Pi with Python by using different projects as case studies.</li>
</ul>

<h3 id="4-learn-how-to-debug-code-and-ask-for-help">4. Learn how to debug code and ask for help</h3>
<p>One of the most overlooked skills any aspiring programmer needs to hone is debugging. Every Python practitioner, regardless of skill level, faces errors and problems with their code. When you encounter with errors in your code, make sure to use these resources at your disposal:</p>
<ul>
<li><a href="http://www.google.com/">Google</a> is almost always the first place I go when faced with errors with my code. Almost always, the answer to my Python query will be in the first 10 search results.</li>
<li><a href="https://stackoverflow.com/">Stack Overflow</a> is the largest community of developers where practitioners can seek help with debugging code by asking and answering programming questions. Most of the time, your Python question has already been <a href="https://stackoverflow.com/questions/tagged/python">asked and answered</a>. If you want to post a question yourself, make sure to follow their <a href="https://stackoverflow.com/help/how-to-ask">best practices when phrasing questions</a>.</li>
<li>Documentation will always be your friend. Whether working with built-in <a href="https://docs.python.org/3/">Python</a> functions or working with a downloaded package like <a href="https://pandas.pydata.org/docs/">pandas</a>, you can always find documentation detailing functionality, possible errors, and tutorials on the website of the package you’re working with.</li>
</ul>

<h3 id="5-expand-your-learning-resources">5. Expand your learning resources</h3>
<p>Now that you’ve set up your Python environment, worked on guided projects, and learned how to debug, it’s time to expand your knowledge and continuously sharpen your skills. Here are a list of resources that will help you do that:</p>
<ul>
<li>Blogs can help you keep up with the latest trends and techniques in Python. One of my favorites is <a href="https://realpython.com/">Real Python</a>, as it hosts primers and tutorials on packages I wouldn’t otherwise use. They also have a bit-sized newsletter with Python tips and tricks.</li>
<li>Reading other people’s code is immensely useful when sharpening your Python skills. For data science and machine learning, <a href="https://www.kaggle.com/">Kaggle</a> is an excellent resource to see how experienced data scientists would solve a problem. You can also look at the source code of your favorite open-source packages on GitHub.</li>
<li>Attend <a href="https://python.geekle.us/">conferences</a>, <a href="https://www.meetup.com/topics/python/">meetups</a>, and other community gatherings with more experienced practitioners. This will give you the opportunity to network and learn from more experienced Python programmers.</li>
</ul>

<h3 id="6-build-a-portfolio-of-projects">6. Build a portfolio of projects</h3>
<p>Now you&#39;re ready to start tackling complex open-ended projects and showcase them in a portfolio for future employers. When working on open-ended projects, it&#39;s best to start simple  and work on Python projects directly related to your work. If you&#39;re looking for inspiration, <a href="https://www.edureka.co/blog/python-projects/">this Edureka blog post</a> contains a list of open-ended projects in game development, web development, data science and machine learning.</p>
<p>You can start building your portfolio by hosting your code on <a href="http://www.github.com">Github</a> and writing blog posts about your projects. This gives you the ability to showcase your technical writing ability and teach beginners how you&#39;ve developed your projects. You can write blog posts by developing your own website or by using a blog hosting service like <a href="http://www.medium.com">Medium</a>.</p>
<p>I hope you feel inspired by this blog post, and that these resources are useful to you. Mastering any programming language is a lifelong learning journey—one that I am still on myself. At DataCamp, Python is part of our core curriculum and we’re constantly improving and adding to our content. Lastly, I invite you to join thousands of learners in the <a href="https://support.datacamp.com/hc/en-us/articles/360001915993-The-DataCamp-Slack-Community-An-Overview">DataCamp Slack Community</a> to get support on your Python journey!</p>
<p><em>Adel Nehme is a Data Science Evangelist at DataCamp. He was previously a Content Developer where he collaborated with instructors on a range of Python courses, and created our course <a href="https://www.datacamp.com/courses/cleaning-data-in-python">Cleaning Data in Python</a>.</em></p>


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<p>As a data scientist or data analyst, data is naturally at the heart of the job. There are many sources from which you might gather your data. Often the data lives in a SQL database, in which case an understanding of SQL query commands can be essential in order to perform this role successfully. This article will introduce you to some of the more basic commands, right up to more advanced operations that will serve you well when working as a data analyst or data scientist.</p>
<nav><ul>
<li><a href="#simple-data-retrieval">Simple Data Retrieval</a>
<ul>
  <li><a href="#select-from">SELECT FROM</a></li>
<li><a href="#distinct">DISTINCT</a></li>
</ul>
</li>
<li><a href="#data-retrieval-with-simple-conditions">Data Retrieval with Simple Conditions</a>
<ul>
  <li><a href="#where">WHERE</a></li>
<li><a href="#order-by">ORDER BY</a></li>
<li><a href="#limit">LIMIT</a></li>
</ul>
</li>
<li><a href="#aggregations">Aggregations</a>
<ul>
  <li><a href="#group-by-count-">GROUP BY &amp; COUNT( )</a></li>
<li><a href="#sum-">SUM( )</a></li>
<li><a href="#avg-having">AVG( ) &amp; HAVING</a></li>
<li><a href="#min-alias">MIN( ) &amp; Alias</a></li>
</ul>
</li>
<li><a href="#joins">Joins</a>
<ul>
  <li><a href="#inner-join-join-">INNER JOIN (JOIN)</a></li>
</ul>
</li>
<li><a href="#changing-data-types">Changing Data Types</a>
<ul>
  <li><a href="#cast-">CAST( )</a></li>
<li><a href="#round-">ROUND( )</a></li>
</ul>
</li>
<li><a href="#complex-conditions">Complex Conditions</a>
<ul>
  <li><a href="#case-statement">CASE Statement</a></li>
<li><a href="#subqueries">Subqueries</a></li>
<li><a href="#common-table-expressions-ctes-">Common Table Expressions (CTEs)</a></li>
<li><a href="#window-functions">Window Functions</a></li>
</ul>
</li>
</ul>
<p>&nbsp;</p></nav>
<p>In order to understand the commands listed above, we are going to use the open source DVD rental database to show you how these commands can be used. You can find the entity relationship diagram (EDR) <a target="_blank" href="https://www.postgresqltutorial.com/wp-content/uploads/2018/03/printable-postgresql-sample-database-diagram.pdf">here</a>.</p>
<p>For the purpose of this article, we have set up the database locally and completed all queries using <a target="_blank" href="https://www.enterprisedb.com/downloads/postgres-postgresql-downloads">pgAdmin4</a>, so we are using postgreSQL. Many if not all the commands should work in other SQL servers. The <a target="_blank" href="https://www.datacamp.com/community/tutorials/installing-postgresql-windows-macosx">installation of postgreSQL</a> and the setting up of the database itself is outside the scope of this article; we encourage you to check out other resources such as DataCamp's <a target="_blank" href="https://www.datacamp.com/community/tutorials/beginners-introduction-postgresql?">Beginner&#39;s Guide to PostgreSQL</a> for advice on these processes.</p>
<h2 id="simple-data-retrieval">Simple Data Retrieval</h2>
<h3 id="select-from">SELECT FROM</h3>
<p>The simplest example of retrieval is to get all the content from a specific table in the database.
If we wanted to know all the film categories that are in the 'Category' table, you could try inputting this command:</p>
<pre><code class="lang-SQL hljs">SELECT *
FROM category
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102294/image9_zyfudf.png" alt="SELECT FROM"></center>


<p>Here we've specified the table name after FROM. Since we want all the content in the table, we've used * after SELECT to specify that we want everything.</p>
<h3 id="distinct">DISTINCT</h3>
<p>There are cases in which we are only interested in knowing unique values. For example, what if we want to know all the MPAA ratings from the film table? Using <b>DISTINCT</b> allows us to choose unique values alone:</p>
<pre><code class="lang-SQL hljs">SELECT DISTINCT(rating)
FROM film
</code></pre><p>Which returns:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102293/image4_nyxfur.png" alt="DISTINCT"></center>

<h2 id="data-retrieval-with-simple-conditions">Data Retrieval with Simple Conditions</h2>
<h3 id="where">WHERE</h3>
<p>There are also situations in which we only want to retrieve specific entries that meet specific conditions. We can provide conditions that have to be met before returning the data using the <b>WHERE</b> clause. In essence, we are filtering entries given the condition. For example, what if we want to know only movies that are rated PG-13?</p>
<pre><code class="lang-SQL hljs">SELECT title
FROM film
WHERE rating = &#39;PG-13&#39;
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102295/image12_zqzlbh.png" alt="Data Retrieval with Simple Conditions 1"></center>

<p>Additionally, we can figure out the list of movies that have a rental rating of 4.5 or higher:</p>
<pre><code class="lang-SQL hljs">SELECT title, rental_rate
FROM film
WHERE rental_rate &gt;= 4.5
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102294/image7_fvoyrh.png" alt="Data Retrieval with Simple Conditions 2"></center>

<h3 id="order-by">ORDER BY</h3>
<p>Ordering the returned result is often useful for us to understand the data better. How about if we want the same list above with PG-13 movies, but ordered by the movie length from longest to shortest, and the corresponding movie length? Using <b>ORDER BY</b>, this is easily accomplished:</p>
<pre><code class="lang-SQL hljs">SELECT title, length
FROM film
WHERE rating = &#39;PG-13&#39;
ORDER BY length DESC
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102294/image10_cklqha.png" alt="ORDER BY"></center>

<h3 id="limit">LIMIT</h3>
<p>Additionally, sometimes we are only interested in a limited number of entries. Here, we are actually only interested in the top 10 longest movies that are PG-13, so we can use the <b>LIMIT</b> clause.</p>
<pre><code class="lang-SQL hljs">SELECT title, length
FROM film
WHERE rating = &#39;PG-13&#39;
ORDER BY length DESC
LIMIT 10
</code></pre><p>Which returns:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102294/image8_h5jren.png" alt="LIMIT"></center>

<h2 id="aggregations">Aggregations</h2>
<h3 id="group-by-count-">GROUP BY &amp; COUNT( )</h3>
<p>Aggregations are often used to get a summary of the dataset to gain insights. Aggregations are often used with the <b>GROUP BY</b> clause. For example, if we need to know how many rentals each customer has rented so far, we can count the number of rentals and order the results with the most rentals on top.</p>
<pre><code class="lang-SQL hljs">SELECT customer_id, COUNT(rental_id)
FROM rental
GROUP BY customer_id
ORDER BY COUNT(rental_id) DESC
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102294/image11_agylng.png" alt="GROUP BY &amp; COUNT( )"></center>

<h3 id="sum-">SUM( )</h3>
<p>How about how much money each customer has spent? We can accomplish this using the SUM aggregator function. Here, we want the top spender on top.</p>
<pre><code class="lang-SQL hljs">SELECT customer_id, SUM(amount)
FROM payment
GROUP BY customer_id
ORDER BY SUM(amount) DESC
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102295/image17_nar8bk.png" alt="SUM( )"></center>

<h3 id="avg-having">AVG( ) &amp; HAVING</h3>
<p>We can add additional conditions using HAVING after you have calculated a grouped aggregation. For example, we want to know the MAAP rating categories that have movies that are longer than 115 minutes on average?</p>
<pre><code class="lang-SQL hljs">SELECT rating, AVG(length)
FROM film
GROUP BY rating
HAVING AVG(length) &gt; 115
</code></pre><p>Which returns:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102293/image5_wzlvp2.png" alt="AVG( ) &amp; HAVING"></center>

<h3 id="min-alias">MIN( ) &amp; Alias</h3>
<p>Grouping is not always necessary using an aggregation. If you want to know the shortest movie length, we can simply use the MIN function. We also want the column to read as 'shortest_movie_length', which can be accomplished by assigning an alias using 'AS'.</p>
<pre><code class="lang-SQL hljs">SELECT MIN(length) AS shortest_movie_length
FROM film
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102293/image3_kvqpfb.png" alt="MIN( ) &amp; Alias"></center>

<p>You can also use an alias for table names. This is useful when you are working with long table names, especially when joining tables (see below). We also want to be mindful of what kind of alias we use as it can drastically change the readability of the code .</p>
<p>There are other aggregation functions that are useful, and you can learn more about them in the <a target="_blank" href="https://www.datacamp.com/courses/introduction-to-sql">Introduction to SQL course</a>.</p>
<h2 id="joins">Joins</h2>
<h3 id="inner-join-join-">INNER JOIN (JOIN)</h3>
<p>You have probably noticed that there are only limited things we can do by looking at a single table. We often want to look at the data from multiple tables. This is where we use JOINs. We can join tables based on a common key. For example, if we were looking for films that are not in English:</p>
<pre><code class="lang-SQL hljs">SELECT film.title, language.name
FROM film
JOIN language
    ON film.language_id = language.language_id
WHERE language.name != &#39;English&#39;
</code></pre><p>(Please note that there are no movies other than those in English in this database. You will get an empty table with this query right above.)</p>
<p>You can also join multiple tables. For example, below we want to know the categories and their corresponding average rating. We've also returned values ordered by the average rating, with the highest first.</p>
<pre><code class="lang-SQL hljs">SELECT category.name, AVG(film.rental_rate) AS average_rating
FROM film
JOIN film_category
    ON film.film_id = film_category.film_id
JOIN category
    ON film_category.category_id = category.category_id
GROUP BY category.name
ORDER BY average_rating DESC
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102296/image6_eemfze.png" alt="INNER JOIN (JOIN)"></center>

<p>There are many types of joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, OUTER JOIN, CROSS JOIN. Here, we used JOIN, which by default is an INNER JOIN in which you get back entries only if they are present in both tables. If you would like to learn more about the differences between these joins, you can take a look at the <a target="_blank" href="https://www.datacamp.com/courses/joining-data-in-sql">Joining Data in SQL</a> course.</p>
<h2 id="changing-data-types">Changing Data Types</h2>
<h3 id="cast-">CAST( )</h3>
<p>In the example used above where we calculated dollar amounts, you might have noticed that SQL only sees the values as numbers. We can change these to dollar amounts using the CAST function:</p>
<pre><code class="lang-SQL hljs">SELECT customer_id, CAST(SUM(amount) AS money)
FROM payment
GROUP BY customer_id
ORDER BY SUM(amount) DESC
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102293/image2_p13a5u.png" alt="CAST( )"></center>

<p>Money is not the only thing that we can change values into. We can change numbers into floats, text, and even into date and time.</p>
<h3 id="round-">ROUND( )</h3>
<p>Similarly, we can also round numbers. In the example above in which we calculated the average rental rating, there are many decimal points in the calculated values. We can use the ROUND function to round to two decimals:</p>
<pre><code class="lang-SQL hljs">SELECT category.name, ROUND(AVG(film.rental_rate), 2) AS average_rating
FROM film
JOIN film_category
    ON film.film_id = film_category.film_id
JOIN category
    ON film_category.category_id = category.category_id
GROUP BY category.name
ORDER BY average_rating DESC
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102296/image14_jsv1oa.png"></center>

<h2 id="complex-conditions">Complex Conditions</h2>
<h3 id="case-statement">CASE Statement</h3>
<p>You can imagine that by combining many of the above functions and clauses, you can perform a fairly complex query. However, there are situations in which the basics are not sufficient. For example, if we wanted to add a column with movie-length categories. We can label movies as &#39;Short Film&#39; if less than 50 minutes, &#39;Long Film&#39; if more than 150 minutes, and &#39;Medium Film&#39; for the rest. We can accomplish this by using a CASE statement:</p>
<pre><code class="lang-SQL hljs">SELECT title, length,
CASE
    WHEN length &lt; 50 THEN &#39;Short Film&#39;
    WHEN length &gt; 150 THEN &#39;Long Film&#39;
    ELSE &#39;Medium Film&#39;
END AS Movie_Length_Category
FROM film
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102295/image16_hkiwbn.png" alt="CASE Statement"></center>

<p>As you can see, the result is returned when the specified condition is met. This is similar to 'if-else' statements in programming languages.</p>
<h3 id="subqueries">Subqueries</h3>
<p>We may now want to know which movies are the longest in length, and their associated titles. For this, we can use the WHERE clause that we introduced above, just with a more complex condition:</p>
<pre><code class="lang-SQL hljs">SELECT title, length
FROM film
WHERE length = (SELECT max(length)
                FROM film)
</code></pre><p>Which returns:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102295/image15_vuxcbi.png" alt="Subqueries"></center>

<p>Complex queries can happen using subqueries. In this example, we want to know the list of movie titles that have a higher rental rating than the average rental rating of all comedy films:</p>
<pre><code class="lang-SQL hljs">SELECT title, rental_rate
FROM film
WHERE rental_rate &gt;
(SELECT AVG(film.rental_rate) AS Average_Rating
FROM film
JOIN film_category AS cat_id
    ON film.film_id=cat_id.film_id
JOIN category AS cat
    ON cat_id.category_id=cat.category_id
GROUP BY cat.name, cat_id.category_id
HAVING cat.name = &#39;Comedy&#39;)
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102294/image7_fvoyrh.png" alt="Which returns (only the first 10 rows shown):"></center>

<p>In this example, we first find the average film rental rating for comedy films. The value found in this query (seen inside the parentheses above) is then used as a condition in the WHERE clause in which we can find the movie titles.</p>
<h3 id="common-table-expressions-ctes-">Common Table Expressions (CTEs)</h3>
<p>In a more complex example, we can use common table expression (CTE) to save a temporary table from which we extract the information needed. For this example, we want to know the average number of rentals each staff processes per week.</p>
<pre><code class="lang-SQL hljs">WITH weekly_rentals AS(
    SELECT
        staff_id,
        DATE_PART(&#39;week&#39;, payment_date) AS week,
        COUNT(rental_id) AS rental_numbers
    FROM payment
    GROUP BY staff_id, week)

SELECT staff_id, AVG(rental_numbers)
FROM weekly_rentals
GROUP BY staff_id
</code></pre><p>Which returns:</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102295/image18_d8ruge.png" alt="Common Table Expressions (CTEs)"></center>

<p>In the above case, we are saving the number of rentals per week in a temporary table called &#39;weekly_rentals&#39; upon which we can then perform another query to get the final result.</p>
<h3 id="window-functions">Window Functions</h3>
<p>Window functions are another set of SQL skills that are important and can take you to the next level. Even though window functions are considered intermediate to advanced level skills, many data analysts and scientists believe them to be essential for their jobs. It is therefore worthwhile for you to spend some time getting familiar with them.</p>
<p>OVER( ) &amp; PARTITION BY
Here, let's say we want to find out each staff&#39;s weekly number of rentals and the overall total number of rentals, along with the staff ID and the week number. We can reuse the CTE generated in the above example, as shown below:</p>
<pre><code class="lang-SQL hljs">WITH weekly_rentals AS(
    SELECT
        staff_id,
        DATE_PART(&#39;week&#39;, payment_date) AS week,
        COUNT(rental_id) AS rental_numbers
    FROM payment
    GROUP BY staff_id, week)

SELECT *, SUM(rental_numbers) OVER() AS total_rentals
FROM weekly_rentals
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102293/image1_zizhng.png" alt="Window Functions"></center>

<p>Having OVER in the code specifies that it's a window function. Here, we are saying the window function (SUM here) is applied to every row. We can also use the window function along with PARTITION BY to apply the window function at specified 'partitions'. Let&#39;s say that we now want to figure out the total number of rentals per week instead of the overall number of rentals (in addition to the other columns. Again, in this example below we are using the CTE generated <a target="_blank" href="https://docs.google.com/document/d/1J1UeJEvyURNO-NDlekfvmbSpoGyb9dDL_SyeWt25PXo/edit#heading=h.qr6vhyayaxfv">before</a>.</p>
<pre><code class="lang-SQL hljs">WITH weekly_rentals AS(
    SELECT
        staff_id,
        DATE_PART(&#39;week&#39;, payment_date) AS week,
        COUNT(rental_id) AS rental_numbers
    FROM payment
    GROUP BY staff_id, week)

SELECT *, SUM(rental_numbers) OVER(PARTITION BY week) AS total_weekly_rentals
FROM weekly_rentals
</code></pre><p>Which returns (only the first 10 rows shown):</p>
<center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/v1643102294/image13_dujtb3.png" alt="Window Functions 2"></center>

<p>The additional column contains the total weekly rental numbers instead of the running total.</p>
<p>We introduced only a couple of simple examples of window functions here that are important for being a data analyst or scientist in many companies. We encourage you to learn more about these commands using the links provided. You could also check out the <a target="_blank" href="https://www.datacamp.com/courses/intermediate-sql">Intermediate SQL course</a> to learn or brush up your SQL skills. Additionally, you can practice your skills by taking an open source database and trialling these different commands while thinking about relevant questions to answer.</p>


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