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>Coursera’s <a href="https://www.coursera.org/projects/pandas-python-library-beginners-data-science">Pandas Python Library for Beginners in Data Science</a> and <a href="https://www.coursera.org/projects/intermediate-pandas-python-library-data-science">Intermediate Pandas Python Library for Data Science</a> are great ways to get up to speed in pandas in a Jupyter Notebook environment.</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">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/">Flask</a>, <a href="https://www.djangoproject.com/">Django</a>, <a href="https://bottlepy.org/">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="http://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="http://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="http://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://medium.com/@SteelKiwiDev/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/internet-of-things-with-python-and-raspberry-pi/the-course-overview">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://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="http://datacamp.com/courses/cleaning-data-in-python">Cleaning Data in Python</a>.</em></p>


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<center>
<a target=“_blank” href="https://www.datacamp.com/learn/python/"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1583330891/python_1_b5jdxv.png" /></a>
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<p>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.
You will learn the following:</p>
<p><nav></p>
<ul>
<li><a href="#create">How to create a class</a></li>
<li><a href="#instantiate">Instantiating objects</a></li>
<li><a href="#attributes">Adding attributes to a class</a></li>
<li><a href="#methods">Defining methods within a class</a></li>
<li><a href="#arguments">Passing arguments to methods</a></li>
<li><a href="#finance">How OOP can be used in Python for finance</a>
</nav>
<div id="scoped-content">
<style type="text/css">:target:before { content:&quot;&quot;; display:block; height:150px; margin:-150px 0 0; } h3 {font-weight:normal; margin-top:.5em} h4 { font-weight:lighter }
</style>
<br></li>
</ul>
<h2 id="oop-introduction">OOP: Introduction</h2>
<p> Object-oriented programming has some advantages over other design patterns. Development is faster and cheaper, with better software maintainability. This, in turn, leads to higher-quality software, which is also extensible with new methods and attributes. The learning curve is, however, steeper. The concept may be too complex for beginners. Computationally, OOP software is slower, and uses more memory since more lines of code have to be written.</p>
<p>Object-oriented programming is based on the imperative programming paradigm, which uses statements to change a program&#39;s state. It focuses on describing how a program should operate. Examples of imperative programming languages are C, C++, Java, Go, Ruby and Python. This stands in contrast to declarative programming, which focuses on what the computer program should accomplish, without specifying how. Examples are database query languages like SQL and XQuery, where one only tells the computer what data to query from where, but now how to do it.</p>
<p>OOP uses the concept of objects and classes. A class can be thought of as a &#39;blueprint&#39; for objects. These can have their own attributes (characteristics they possess), and methods (actions they perform).</p>
<h3 id="oop-example">OOP Example</h3>
<p>An example of a class is the class <code>Dog</code>. Don&#39;t think of it as a specific dog, or your own dog. We&#39;re describing what a dog <em>is</em> and can <em>do</em>, in general. Dogs usually have a <code>name</code> and <code>age</code>; these are instance attributes. Dogs can also <code>bark</code>; this is a method.</p>
<p>When you talk about a specific dog, you would have an object in programming: an object is an instantiation of a class. This is the basic principle on which object-oriented programming is based. So my dog Ozzy, for example, belongs to the class <code>Dog</code>. His attributes are <code>name = &#39;Ozzy&#39;</code> and <code>age = &#39;2&#39;</code>. A different dog will have different attributes.</p>
<h2 id="oop-in-python">OOP in Python</h2>
<p>Python is a great programming language that supports OOP. You will use it to define a class with attributes and methods, which you will then call. Python offers a number of benefits compared to other programming languages like Java, C++ or R. It&#39;s a dynamic language, with high-level data types. This means that development happens much faster than with Java or C++. It does not require the programmer to declare types of variables and arguments. This also makes Python easier to understand and learn for beginners, its code being more readable and intuitive.</p>
<p>If you&#39;re new to Python, be sure to take a look at DataCamp&#39;s <a href="https://www.datacamp.com/courses/intro-to-python-for-data-science">Intro to Python for Data Science</a> course.</p>
<p><a id="create"></a></p>
<h3 id="how-to-create-a-class">How to create a class</h3>
<p>To define a class in Python, you can use the <code>class</code> keyword, followed by the class name and a colon. Inside the class, an <code>__init__</code> method has to be defined with <code>def</code>. This is the initializer that you can later use to instantiate objects. It&#39;s similar to a constructor in Java. <code>__init__</code> must always be present! It takes one argument: <code>self</code>, which refers to the object itself. Inside the method, the <code>pass</code> keyword is used as of now, because Python expects you to type something there. Remember to use correct indentation!</p>
<pre><code class="lang-python">class Dog:

    def __init__(self):
        pass
</code></pre>
<p><strong>Note</strong>: <code>self</code> in Python is equivalent to <code>this</code> in C++ or Java.</p>
<p>In this case, you have a (mostly empty) <code>Dog</code> class, but no object yet. Let&#39;s create one!
<a id="instantiate"></a></p>
<h3 id="instantiating-objects">Instantiating objects</h3>
<p>To instantiate an object, type the class name, followed by two brackets. You can assign this to a variable to keep track of the object.</p>
<pre><code class="lang-python">ozzy = Dog()
</code></pre>
<p>And print it:</p>
<pre><code class="lang-python">print(ozzy)
</code></pre>
<pre><code>&lt;__main__.Dog object at 0x111f47278&gt;
</code></pre><p><a id="attributes"></a></p>
<h3 id="adding-attributes-to-a-class">Adding attributes to a class</h3>
<p>After printing <code>ozzy</code>, it is clear that this object is a dog. But you haven&#39;t added any attributes yet. Let&#39;s give the <code>Dog</code> class a name and age, by rewriting it:</p>
<pre><code class="lang-python">class Dog:

    def __init__(self, name, age):  
        self.name = name
        self.age = age
</code></pre>
<p>You can see that the function now takes two arguments after <code>self</code>: <code>name</code> and <code>age</code>. These then get assigned to <code>self.name</code> and <code>self.age</code> respectively. You can now now create a new <code>ozzy</code> object, with a name and age:</p>
<pre><code class="lang-python">ozzy = Dog(&quot;Ozzy&quot;, 2)
</code></pre>
<p>To access an object&#39;s attributes in Python, you can use the dot notation. This is done by typing the name of the object, followed by a dot and the attribute&#39;s name.</p>
<pre><code class="lang-python">print(ozzy.name)

print(ozzy.age)
</code></pre>
<pre><code>Ozzy
2
</code></pre><p>This can also be combined in a more elaborate sentence:</p>
<pre><code class="lang-python">print(ozzy.name + &quot; is &quot; + str(ozzy.age) + &quot; year(s) old.&quot;)
</code></pre>
<pre><code>Ozzy is 2 year(s) old.
</code></pre><p>The <code>str()</code> function is used here to convert the <code>age</code> attribute, which is an integer, to a string, so you can use it in the <code>print()</code> function.
<a id="methods"></a></p>
<h3 id="define-methods-in-a-class">Define methods in a class</h3>
<p>Now that you have a<code>Dog</code> class, it does have a name and age which you can keep track of, but it doesn&#39;t actually do anything. This is where instance methods come in. You can rewrite the class to now include a <code>bark()</code> method. Notice how the <code>def</code> keyword is used again, as well as the <code>self</code> argument.</p>
<pre><code class="lang-python">class Dog:

    def __init__(self, name, age):  
        self.name = name
        self.age = age

    def bark(self):
        print(&quot;bark bark!&quot;)
</code></pre>
<p>The <code>bark</code> method can now be called using the dot notation, after instantiating a new <code>ozzy</code> object. The method should print &quot;bark bark!&quot; to the screen. Notice the parentheses (curly brackets) in <code>.bark()</code>. These are always used when calling a method. They&#39;re empty in this case, since the <code>bark()</code> method does not take any arguments.</p>
<pre><code class="lang-python">ozzy = Dog(&quot;Ozzy&quot;, 2)

ozzy.bark()
</code></pre>
<pre><code>bark bark!
</code></pre><p>Recall how you printed <code>ozzy</code> earlier? The code below now implements this functionality in the <code>Dog</code> class, with the <code>doginfo()</code> method. You then instantiate some objects with different properties, and call the method on them.</p>
<pre><code class="lang-python">class Dog:

    def __init__(self, name, age):  
        self.name = name
        self.age = age

    def bark(self):
        print(&quot;bark bark!&quot;)

    def doginfo(self):
        print(self.name + &quot; is &quot; + str(self.age) + &quot; year(s) old.&quot;)
</code></pre>
<pre><code class="lang-python">ozzy = Dog(&quot;Ozzy&quot;, 2)
skippy = Dog(&quot;Skippy&quot;, 12)
filou = Dog(&quot;Filou&quot;, 8)
</code></pre>
<pre><code class="lang-python">ozzy.doginfo()
skippy.doginfo()
filou.doginfo()
</code></pre>
<pre><code>Ozzy is 2 year(s) old.
Skippy is 12 year(s) old.
Filou is 8 year(s) old.
</code></pre><p>As you can see, you can call the <code>doginfo()</code> method on objects with the dot notation. The response now depends on which <code>Dog</code> object you are calling the method on.</p>
<p>Since dogs get older, it would be nice if you could adjust their age accordingly. Ozzy just turned 3, so let&#39;s change his age.</p>
<pre><code class="lang-python">ozzy.age = 3
</code></pre>
<pre><code class="lang-python">print(ozzy.age)
</code></pre>
<pre><code>3
</code></pre><p>It&#39;s as easy as assigning a new value to the attribute. You could also implement this as a <code>birthday()</code> method in the <code>Dog</code> class:</p>
<pre><code class="lang-python">class Dog:

    def __init__(self, name, age):  
        self.name = name
        self.age = age

    def bark(self):
        print(&quot;bark bark!&quot;)

    def doginfo(self):
        print(self.name + &quot; is &quot; + str(self.age) + &quot; year(s) old.&quot;)

    def birthday(self):
        self.age +=1
</code></pre>
<pre><code class="lang-python">ozzy = Dog(&quot;Ozzy&quot;, 2)
</code></pre>
<pre><code class="lang-python">print(ozzy.age)
</code></pre>
<pre><code>2
</code></pre><pre><code class="lang-python">ozzy.birthday()
</code></pre>
<pre><code class="lang-python">print(ozzy.age)
</code></pre>
<pre><code>3
</code></pre><p>Now, you don&#39;t need to manually change the dog&#39;s age. whenever it is its birthday, you can just call the <code>birthday()</code> method.
<a id="arguments"></a></p>
<h3 id="passing-arguments-to-methods">Passing arguments to methods</h3>
<p>You would like for our dogs to have a buddy. This should be optional, since not all dogs are as sociable. Take a look at the <code>setBuddy()</code> method below. It takes <code>self</code>, as per usual, and <code>buddy</code> as arguments. In this case, <code>buddy</code> will be another <code>Dog</code> object. Set the <code>self.buddy</code> attribute to <code>buddy</code>, and the <code>buddy.buddy</code> attribute to <code>self</code>. This means that the relationship is reciprocal; you are your buddy&#39;s buddy. In this case, Filou will be Ozzy&#39;s buddy, which means that Ozzy automatically becomes Filou&#39;s buddy. You could also set these attributes manually, instead of defining a method, but that would require more work (writing 2 lines of code instead of 1) every time you want to set a buddy. Notice that in Python, you don&#39;t need to specify of what type the argument is. If this were Java, it would be required.</p>
<pre><code class="lang-python">class Dog:

    def __init__(self, name, age):  
        self.name = name
        self.age = age

    def bark(self):
        print(&quot;bark bark!&quot;)

    def doginfo(self):
        print(self.name + &quot; is &quot; + str(self.age) + &quot; year(s) old.&quot;)

    def birthday(self):
        self.age +=1

    def setBuddy(self, buddy):
        self.buddy = buddy
        buddy.buddy = self
</code></pre>
<p>You can now call the method with the dot notation, and pass it another <code>Dog</code> object. In this case, Ozzy&#39;s buddy will be Filou:</p>
<pre><code class="lang-python">ozzy = Dog(&quot;Ozzy&quot;, 2)
filou = Dog(&quot;Filou&quot;, 8)

ozzy.setBuddy(filou)
</code></pre>
<p>If you now want to get some information about Ozzy&#39;s buddy, you can use the dot notation twice:. First, to refer to Ozzy&#39;s buddy, and a second time to refer to its attribute.</p>
<pre><code class="lang-python">print(ozzy.buddy.name)
print(ozzy.buddy.age)
</code></pre>
<pre><code>Filou
8
</code></pre><p>Notice how this can also be done for Filou.</p>
<pre><code class="lang-python">print(filou.buddy.name)
print(filou.buddy.age)
</code></pre>
<pre><code>Ozzy
2
</code></pre><p>The buddy&#39;s methods can also be called. The <code>self</code> argument that gets passed to <code>doginfo()</code> is now <code>ozzy.buddy</code>, which is <code>filou</code>.</p>
<pre><code class="lang-python">ozzy.buddy.doginfo()
</code></pre>
<pre><code>Filou is 8 year(s) old.
</code></pre><p><a id="finance"></a></p>
<h3 id="example-oop-in-python-for-finance">Example: OOP in Python for finance</h3>
<p>An example for where Object-Oriented programming in Python might come in handy, is our <a href="https://www.datacamp.com/community/tutorials/finance-python-trading">Python For Finance: Algorithmic Trading</a> tutorial. In it, Karlijn explains how to set up a trading strategy for a stock portfolio. The trading strategy is based on the moving average of a stock price. If <code>signals[&#39;short_mavg&#39;][short_window:] &gt; signals[&#39;long_mavg&#39;][short_window:]</code> is fulfilled, a signal is created. This signal is a prediction for the stock&#39;s future price change. In the code below, you&#39;ll see that there is first a initialisation, followed by the moving average calculation and signal generation. Since this is not object-oriented code, it&#39;s just one big chunk that gets executed at once. Notice that we&#39;re using <code>aapl</code> in the example, which is Apple&#39;s stock ticker. If you wanted to do this for a different stock, you would have to rewrite the code.</p>
<pre><code class="lang-python"># Initialize
short_window = 40
long_window = 100
signals = pd.DataFrame(index=aapl.index)
signals[&#39;signal&#39;] = 0.0

# Create short simple moving average over the short window
signals[&#39;short_mavg&#39;] = aapl[&#39;Close&#39;].rolling(window=short_window, min_periods=1, center=False).mean()

# Create long simple moving average over the long window
signals[&#39;long_mavg&#39;] = aapl[&#39;Close&#39;].rolling(window=long_window, min_periods=1, center=False).mean()

# Create signals
signals[&#39;signal&#39;][short_window:] = np.where(signals[&#39;short_mavg&#39;][short_window:] &gt; signals[&#39;long_mavg&#39;][short_window:], 1.0, 0.0)   

# Generate trading orders
signals[&#39;positions&#39;] = signals[&#39;signal&#39;].diff()

# Print `signals`
print(signals)
</code></pre>
<p>In an object-oriented approach, you only need to write the initialisation and signal generation code once. You can then create a new object for each stock you want to calculate a strategy on, and call the <code>generate_signals()</code> method on it. Notice that the OOP code is very similar to the code above, with the addition of <code>self</code>.</p>
<pre><code class="lang-Python">class MovingAverage():

    def __init__(self, symbol, bars, short_window, long_window):
        self.symbol = symbol
        self.bars = bars
        self.short_window = short_window
        self.long_window = long_window

    def generate_signals(self):
        signals = pd.DataFrame(index=self.bars.index)
        signals[&#39;signal&#39;] = 0.0

        signals[&#39;short_mavg&#39;] = bars[&#39;Close&#39;].rolling(window=self.short_window, min_periods=1, center=False).mean()
        signals[&#39;long_mavg&#39;] = bars[&#39;Close&#39;].rolling(window=self.long_window, min_periods=1, center=False).mean()

        signals[&#39;signal&#39;][self.short_window:] = np.where(signals[&#39;short_mavg&#39;][self.short_window:] &gt; signals[&#39;long_mavg&#39;][self.short_window:], 1.0, 0.0)   

        signals[&#39;positions&#39;] = signals[&#39;signal&#39;].diff()   

        return signals
</code></pre>
<p>You can now simply instantiate an object, with the parameters you want, and generate signals for it.</p>
<pre><code class="lang-Python">apple = MovingAverage(&#39;aapl&#39;, aapl, 40, 100)
print(apple.generate_signals())
</code></pre>
<p>Doing this for another stock becomes very easy. It&#39;s just a matter of instantiating a new object with a different stock symbol.</p>
<pre><code class="lang-python">microsoft = MovingAverage(&#39;msft&#39;, msft, 40, 100)
print(microsoft.generate_signals())
</code></pre>
<h3 id="congratulations-">Congratulations!</h3>
<p>You now know how to declare classes and methods, instantiate objects, set their attributes and call instance methods. These skills will come in handy during your future career as a data scientist. If you want to expand the key concepts that you need to further work with Python, be sure to check out our <a href="https://www.datacamp.com/courses/intermediate-python-for-data-science">Intermediate Python for Data Science</a> course.</p>
<p>With OOP, your code will grow in complexity as your program gets larger. You will have different classes, subclasses, objects, inheritance, instance methods, and more. You&#39;ll want to keep your code properly structured and readable. To do so, it is advised to follow design patterns. These are design principles that represent a set of guidelines to avoid bad design. They each represent a specific problem that often reoccurs in OOP, and describe the solution to that problem, which can then be used repeatedly. These OOP design patterns can be classified in several categories: creational patterns, structural patterns and behavioral patterns. An example of a creational pattern is the singleton, which should be used when you want to make sure that only one instance of a class can be created. An iterator, which is used to loop over all objects in a collection, is an example of a behavioral pattern. A great resource for design patterns is <a href="http://www.oodesign.com/">oodesign.com</a>. If you&#39;re more into books, I would recommend you to read <a href="https://www.amazon.com/Design-Patterns-Elements-Reusable-Object-Oriented/dp/0201633612">Design Patterns: Elements of Reusable Object-Oriented Software</a>.</p>


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Tackle the basics of Object-Oriented Programming (OOP) in Python: explore classes, objects, instance methods, attributes and much more!

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