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>


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

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

How to Learn Python

<p>Python is the most popular programming language today and is widely used across verticals from software and web development to game development, IoT and more. More importantly, it’s become the de facto programming language for data science and machine learning. This blog post will outline why and when to use Python, how to install it, basic Python syntax and functionality, an introduction to simple data analysis, and more.</p>
<h2 id="when-and-why-to-use-python">When and why to use Python</h2>
<p>Python is a highly versatile language with many use cases across verticals. Just like many programming languages, Python is open source and free to use, and contains a large library of tools and packages (more on packages later!) that simplify working with a variety of tasks. Here’s are some verticals where Python is useful:</p>
<h3 id="desktop-and-mobile-software-development">Desktop and Mobile Software Development</h3>
<p>Whether building simple software applications like calculators, or document editors and social networks. Python offers a plethora of tools to prototype, develop, and run desktop or mobile applications.</p>
<h3 id="web-development">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.</p>
<h3 id="game-development">Game Development</h3>
<p>This may come as a surprise, but games such as The Sims 4 and Civilization IV were built with Python. Many Python libraries exist that streamline prototyping and development of games.</p>
<h3 id="internet-of-things-iot-">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>
<h3 id="data-science-and-machine-learning">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. Python can be used across the data science workflow, from exploratory data analysis, data pre-processing, model building and deployment, and finally to results interpretation and communication. Since Python is used across the data science workflow, it is used by a variety of data roles from data analysts, data scientists, data engineers, machine learning engineers, and more.</p>
<h2 id="how-to-install-python">How to install Python</h2>
<p>Python can be installed in a variety of avenues, depending on if you have a Mac or Windows, or if you want to install it with Anaconda. Since Windows and Mac are installed similarly, the following instructions can apply to both operating systems. If you want a deeper look into installing Python for data science, <a href="https://www.datacamp.com/community/tutorials/python-developer-set-up">check out this article</a>.</p>
<h3 id="mac-and-windows">Mac and Windows</h3>
<p>Go to the Python website, which will send you to the downloads page as seen in the screenshot below. Choose Windows or Mac and click on the download button to access the installer package.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984299/python1_j4xkeo.png" /></center><br></p>
<h3 id="anaconda">Anaconda</h3>
<p>One of the more convenient ways to install Python for data science is by installing an <a href="https://www.anaconda.com/products/individual">Anaconda</a> distribution, which also installs all relevant data science packages. Follow the link above to install it on either Mac, Windows, or Linux. For a deep dive on installing Anaconda, check out these blog posts for <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-mac-os-x">MacOs</a> and <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-windows">Windows</a>.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617985265/DLanaconda_x9jrol.png" /></center><br></p>
<h2 id="how-to-get-started-with-python-for-data-science">How to get started with Python for data science</h2>
<p>In this section, we will cover how to open Python on Mac and Windows, how to launch the terminal, the basics of Python like print statements and operators, variables and assigning values, data types, packages and how to install them, as well as the popular data science library <code>pandas</code> and how to install it.</p>
<h3 id="1-launching-python">1. Launching Python</h3>
<h4 id="launching-terminal">Launching Terminal</h4>
<p>To launch Python, you will first need to launch your terminal. For both Mac and Windows, you can simply search it on your computer. On Mac, the easiest way is to do a Spotlight Search, which is the magnifying glass icon on the upper right corner of your desktop.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984299/python2_uasjil.png" /></center><br></p>
<p>Similarly, for Windows, you will search for the command prompt through the start menu, as described in a DataCamp article <a href="https://www.datacamp.com/community/tutorials/running-a-python-script">How to Run Python Scripts</a>. You can also launch the new Windows Terminal after installing and searching for it as mentioned on the Windows blog post <a href="https://devblogs.microsoft.com/commandline/introducing-windows-terminal/">Introducing Windows Terminal</a>. After you launch it, here&#39;s what you will see:</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984299/python3_pv1jvj.png" /></center><br></p>
<h4 id="how-to-open-python-on-mac-windows">How to open Python on Mac + Windows</h4>
<p>After Python installation is complete, you can launch your terminal by simply typing the word ‘python’. This will start up Python and show you the current version you have installed. The three arrows point to the right means you are ready to code in Python!</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984299/python4_gajrzb.png" /></center><br></p>
<h3 id="2-basic-python-syntax">2. Basic Python Syntax</h3>
<p>In the following syntax commands below, you will type the command and then hit <code>enter</code> or <code>return</code> on your keyboard to see the results.</p>
<h4 id="print-statements">Print statements</h4>
<p>Next, you can start your first command in Python. We will start off with the <code>print()</code> statement, which will be typing the word print, followed by parenthesis. The output will be typed within those same parentheses and enclosed by quotes.</p>
<pre><code class="lang-python">print(&quot;Hello, World&quot;)
</code></pre>
<pre><code>Hello, World
</code></pre><h4 id="operators">Operators</h4>
<p>Then, you can perform operations, like +, -, and *.  As you can see, the + adds the two numbers together, the - subtracts the second number from the first number, the * multiples both numbers together.</p>
<pre><code class="lang-python">5 + 5
</code></pre>
<pre><code>10
</code></pre><pre><code class="lang-python">5 - 5
</code></pre>
<pre><code>0
</code></pre><pre><code class="lang-python">5 * 5
</code></pre>
<pre><code>25
</code></pre><h4 id="booleans">Booleans</h4>
<p>The next command is to compare two values. In the case below, we will look at comparing the same two numbers, five and five. The command is asking if 5 is greater than 5, which of course, it is not, so the returned output is <code>False</code>. For booleans, we expect the output to be either true or false.</p>
<pre><code class="lang-python">5 &gt; 5
</code></pre>
<pre><code>False
</code></pre><h4 id="creating-variables-and-assigning-them">Creating variables and assigning them</h4>
<p>In this section, we see that you can assign a number to the name <code>variable_1</code>, and then return it by calling that same <code>variable_1</code> to get the value that you had assigned it:</p>
<pre><code class="lang-python">variable_1 = 100
</code></pre>
<pre><code class="lang-python">variable_1
</code></pre>
<pre><code class="lang-python">100
</code></pre>
<h4 id="lists">Lists</h4>
<pre><code class="lang-python">my_list = [&#39;potato&#39;, 3, 4.02]
</code></pre>
<pre><code class="lang-python">my_list[0]
</code></pre>
<pre><code>‘potato’
</code></pre><p>A list is a data structure in which values can be stored and accessed. The neat thing about lists is that they do not all need to be the same data type. Below, we can see that we have a str, int, and a float, respectively. In order to access the items in the list so that they appear in the output, you can type your list name followed by brackets with the place of the item you want. For example, we append [0] to our listname to return the 1st element in our list as seen below.</p>
<p>For a further exploration into Python lists, see the article <a href="https://www.datacamp.com/community/tutorials/python-list-index">Python List Index()</a>.</p>
<h4 id="data-types">Data types</h4>
<p>Python values can have a variety of forms, including ones like <code>int</code>, <code>float</code>, and <code>str</code>. For example, <code>int</code> represents integers, <code>float</code> represents decimal numbers, and <code>str</code> represents “strings”, or more generally text.</p>
<p>Using the <code>type()</code> function, we will be able to check the type of our values. In this case, we’re looking at three values, including 10, 10.06, and the word ‘tutorial’. The first is an integer, while the second is a float, and the last is a string. Below, we can see the command and its respected data type output.</p>
<pre><code class="lang-python">type(10)
</code></pre>
<pre><code>&lt;class &#39;int&#39;&gt;
</code></pre><pre><code class="lang-python">type(10.06)
</code></pre>
<pre><code>&lt;class &#39;float&#39;&gt;
</code></pre><pre><code class="lang-python">type(&#39;tutorial&#39;)
</code></pre>
<pre><code>&lt;class &#39;str&#39;&gt;
</code></pre><p>For a more in-depth look at data types and how to convert them, you can read the following blog, <a href="https://www.datacamp.com/community/tutorials/python-data-type-conversion">Python Data Type Conversion Tutorial</a>.</p>
<h4 id="packages-and-installation">Packages and installation</h4>
<p><a href="https://docs.python.org/3/installing/index.html">Packages</a> are powerful tools that have preserved functionalities. For example, <code>pandas</code> is a package that lets you work with tabular data in Python—more on that below. Think of them as apps you download on Python that provide a specific functionality. You will want to exit out of Python in your terminal by typing Ctrl + D, and then typing the following command below (for Mac users). Here we’re installing a sample package called SomePackage just to illustrate the installation.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617984300/python10_uwyf92.png" /></center><br></p>
<h4 id="installing-pandas-and-jupyter-notebook">Installing pandas and Jupyter Notebook</h4>
<p><a href="https://www.datacamp.com/community/tutorials/pandas">pandas</a> is perhaps the most used data science library in Python. It allows you to work with tabular data in Python and perform a variety of operations. In some sense, you can consider it as the Excel or spreadsheets of Python. The <a href="https://pandas.pydata.org/">developers of pandas</a> describe it as “a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.”</p>
<p>Jupyter notebook is a web application that interprets code, saves text, and displays visualizations. Both Jupyter and pandas are available to launch if you downloaded Anaconda, or you can install them by running the following commands in terminal:</p>
<pre><code class="lang-python">pip install pandas
</code></pre>
<pre><code class="lang-python">pip install notebooks
</code></pre>
<h3 id="3-basic-data-science-with-python">3. Basic data science with Python</h3>
<p>In this section, we’ll cover how to import a popular Python package while assigning it an alias, how to read in a CSV file, how to show your data in something called a DataFrame, and how to visualize a column by using a certain plotting function. We have already learned how to install pandas above, and because Jupyter Notebook is easy to use and works across several computer types, we will use that web application to display the following data science functions with pandas.</p>
<p>You can launch a Jupyter notebook by simply typing <code>jupyter notebook</code> in your terminal.</p>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617990202/jupyter_hello_world2_su37ik.png" /></center><br></p>
<h4 id="importing-packages-with-an-alias">Importing packages with an alias</h4>
<p>Before we begin working with pandas, we need to import it in our environment. Similar to launching an app after installing it, importing a package is simple. We can import pandas and assign an alias to it that is shorter than the original name so that it is easier to work with this package. We will assign pandas as the letters <code>pd</code> instead for shorthand notation—this change is purely optional.</p>
<pre><code class="lang-python">import pandas as pd
</code></pre>
<h4 id="importing-your-data-by-reading-in-a-csv-file">Importing your data by reading in a CSV file</h4>
<p>After you have imported pandas as <code>pd</code>, you can use <code>pd</code> to read in your data, which can be a CSV file. This function is to enclose the CSV file path in quotes, within the parentheses of <code>pd.read_csv()</code>. You can then store your CSV to a dataframe with the = operation. You will now be able to print out your dataframe, but you will only want to see the first rows instead of all of it. The data, in this case, is from past year’s stock performance with the fields date, open, high, low, close, volume, and name, all of which describe characteristics of daily popular stocks. However, it is important to note that you can use any CSV file here to illustrate importing data.</p>
<h4 id="dataframe-head-function">Dataframe head function</h4>
<p>Showing the first few rows of your data is important to check to see if you have imported the correct CSV, as well as seeing if data looks good in general. The way to do this method is by calling your dataframe and attaching the <code>.head()</code> method to the end of it. This function will return the first 5 rows of your data by default. If you want to show more, you can put that value in the parentheses of <code>.head()</code>.</p>
<pre><code class="lang-python">import pandas as pd
</code></pre>
<pre><code class="lang-python">stocks_df = pd.read.csv(&#39;/documents/data/stock_data.csv&#39;)
</code></pre>
<pre><code class="lang-python">stocks_df.head()
</code></pre>
<p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617986452/Screen_Shot_2021-04-09_at_12.40.35_PM_jtlhgz.png" /></center><br></p>
<h4 id="visualize-column-with-the-plot-function">Visualize column with the plot function</h4>
<p>The <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html">.plot()</a> method  is useful for displaying your dataframe’s columns. In this example, I am showing the x-axis as the “date” column, and the “close” column amount as the y-axis, visualized by a line chart.</p>
<pre><code class="lang-python">stocks_df.plot(x=&quot;date&quot;, y=&quot;close&quot;)
</code></pre>
<pre><code>&lt;AxesSubplot:xlabel=&#39;date&#39;&gt;
</code></pre><p><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1617986452/Screen_Shot_2021-04-09_at_12.40.20_PM_zkb0am.png" /></center><br></p>
<h3 id="4-suggestions-for-practical-applications-">4. Suggestions for practical applications:</h3>
<p>This overview of Python for data science has only just scratched the surface. There are plenty of practical applications that you can employ with Python in data science across several industries, including customer churn, sales forecasting, image classification, product classification, sentiment analysis, credit card fraud detection, and recommendation systems. DataCamp’s <a href="https://www.datacamp.com/community/tutorials/recommender-systems-python">Beginner Tutorial: Recommender Systems in Python</a> demonstrates how to successfully recommend movies to people based on a variety of factors with the use of machine learning. Another popular data science use case in the financial industry is credit card fraud detection, which is explained in <a href="https://www.datacamp.com/community/news/credit-card-fraud-detection-using-machine-learning-algorithm-8dyh3fefvrb">Credit Card Fraud Detection Using Machine Learning Algorithm</a>. As you can see, not only are there plenty of specific data science use cases, but there are also industry specific use cases.</p>
<p>For a more in-depth introduction to Python, check out DataCamp’s <a href="https://www.datacamp.com/courses/intro-to-python-for-data-science">Introduction to Python</a> course, or find out more about <a href="https://www.datacamp.com/community/blog/how-to-learn-python">learning Python on our blog</a>.</p>


https://www.datacamp.com/community/tutorials/python

Get a step-by-step guide on how to install Python and use it for basic data science functions.

Python Tutorial for Beginners | DataCamp

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