http://datacamp-community-prod.s3.amazonaws.com/29aa28bf-570a-4965-8f54-d6a541ae4e06

spaCy is a popular Natural Language Processing library with a concise API. This cheat sheet shows you how to load models, process text, and access linguistic annotations, all with a few handy objects and functions.

spaCy Cheat Sheet: Advanced NLP in Python

https://datacamp-community-prod.s3.amazonaws.com/50d31142-3de0-4159-89b9-18b718a728ef

A cheat sheet that covers several ways of getting data into Python: from flat files such as .txts and .csv to files native to other software, such as Excel, SAS, or Matlab, and relational databases such as SQLite & PostgreSQL.

Importing Data: Python Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Cheat+Sheets/Importing_Data_Python_Cheat_Sheet.pdf

https://www.datacamp.com/community/podcast/critical-thinking-in-data-science

<p>Debbie Berebichez, Chief Data Scientist at Metis, speaks with Hugo about what type of critical thinking skills are necessary in data science.</p>

Debbie Berebichez speaks with Hugo about the importance of critical thinking in data science.

Critical Thinking in Data Science

https://www.datacamp.com/community/blog/critical-thinking-in-data-science

https://www.datacamp.com/community/podcast/credibility-crisis-in-data-science

<p>Skipper Seabold, a Director of Data Science at Civis Analytics, discusses the current and looming credibility crisis in data science.</p>

Skipper Seabold, a Director of Data Science at Civis Analytics, discusses the current and looming credibility crisis in data science.

The Credibility Crisis in Data Science

https://www.datacamp.com/community/blog/credibility-crisis-in-data-science

https://www.datacamp.com/community/news/spacy-cheat-sheet-advanced-nlp-in-python-nryt9kwegmb

Check out the first official spaCy cheat sheet! A handy two-page reference to the most important concepts and features.

https://www.datacamp.com/community/news/the-credibility-crisis-in-data-science-with-skipper-seabold-6bbmgpwy1qm

The Credibility Crisis in Data Science (with Skipper Seabold)

https://www.datacamp.com/community/news/python-for-finance-algorithmic-trading-vrfjxb6qk2

This Python for Finance tutorial introduces you to the basics of algorithmic trading in Python

Python For Finance: Algorithmic Trading

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/blog/chris-murphy

Chris Murphy studied international business and worked at a Chinese e-commerce startup. He used DataCamp to land a role as a Data Analyst at UMass Medical School.

Learner Spotlight: How I Became a Data Analyst with DataCamp | DataCamp

Learner Spotlight: How I Became a Data Analyst with DataCamp

<p>In the midst of a global economic downturn and record unemployment last summer, we saw an opportunity to help people experiencing unprecedented hardships. We decided to help in the most direct way possible—by donating DataCamp subscriptions to nonprofit organizations that provide communities with free education, job training, and workforce development. Five months later, we’ve surpassed our initial goal of donating 10,000 licenses by partnering with over 35 nonprofits in 24 countries in every continent save for Antarctica. The success stories are pouring in:</p>
<blockquote>
<p>DataCamp has allowed more women to enter the field of technology, learn about data science, and practice their English. With this knowledge, many of us have attained jobs at technology companies. —Sharon Camacho, Co-founder at Data Science FEM</p>
</blockquote>
<p>&quot;Putting together this scholarship has been one of the most fulfilling initiatives for the employees here at DataCamp,” said Weston Stearns, DataCamp’s Head of Talent and Certification. “Our mission is to democratize data science and bring data fluency to the masses. We are getting significantly closer to accomplishing this mission by enabling free access for learners to gain the skills they need to advance their careers.&quot;</p>
<p>The scope of our initiative must evolve in tandem with the growing enthusiasm and need for data fluency. That’s why we’re formally announcing three exciting developments: We’ve officially named this initiative DataCamp Donates, we’ve increased our commitment to 25,000 subscriptions, and we’re expanding the program to scientific research nonprofits, public secondary schools, and social welfare agencies.</p>
<h2 id="the-giveaway-program-is-now-called-datacamp-donates-">The giveaway program is now called “DataCamp Donates”</h2>
<p>“DataCamp Donates” clearly describes exactly what we’re doing—donating thousands of Premium one-year subscriptions (each one normally valued at $399 USD) to nonprofits as well as features typically reserved for our business customers, including 24/7 customer support. This is all in addition to our long-running <a href="http://datacamp.com/groups/education">DataCamp for the Classroom</a> program, which provided free six-month subscriptions to over 170,000 students and professors in 2020 alone.</p>
<h2 id="we-ve-increased-our-commitment-to-25-000-subscriptions">We’ve increased our commitment to 25,000 subscriptions</h2>
<p>We’re matching the purchases of an additional 15,000 DataCamp learners—individuals like you who want to improve data skills while also helping others get a leg up. That brings our total number of committed subscriptions to 25,000. We’re confident we will achieve this goal by the first anniversary of DataCamp Donates in July 2021, if not sooner! We will achieve this by strengthening our preexisting partnerships and forging new ones in other parts of the world. Wherever there’s a need for free data science, analysis, and data engineering education, that’s where we want to be.</p>
<h2 id="datacamp-donates-is-now-available-to-more-types-of-qualified-organizations">DataCamp Donates is now available to more types of qualified organizations</h2>
<p>Since everyone stands to benefit from becoming data fluent, we’re expanding the scope of eligible partners for DataCamp Donates. DataCamp will now donate subscriptions to registered scientific research nonprofits, since research scientists are solving the great problems of our time, like climate change and COVID-19.</p>
<p>We are also seeking to partner with governmental social welfare agencies to support their unemployed citizens in great numbers. We hope to empower these communities with the best knowledge and tools to collect, visualize, and analyze their data for the benefit of their local communities and the world at large.</p>
<p>We&#39;ve long supported university education with free access to DataCamp for the Classroom. Now, we&#39;re expanding access to DataCamp Donates for public secondary schools. Any school administrator, teacher, and their students ages 12-18 are eligible to gain free Premium access. Read on to find out how to apply.</p>
<h2 id="become-a-datacamp-donates-partner">Become a DataCamp Donates Partner</h2>
<p>If you are an administrator or teacher at a nonprofit organization or public school, or an official at a governmental social welfare organization, follow these three easy steps to become a DataCamp Donates partner.</p>
<ul>
<li>First, send an email to <a href="mailto:donate@datacamp.com">donate@datacamp.com</a> that details your organization’s goals and how you intend to use the donated subscriptions. Our team reads every email and responds to the organizations that will help us make a significant impact.</li>
<li>Next, qualified organizations will be invited to complete an official application form.</li>
<li>If chosen, the final step is a meeting conducted on Zoom to finalize the terms of the partnership. We then donate the licenses to our new partner organization within a week.</li>
</ul>

<p><center><a href="mailto:donate@datacamp.com"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1611155669/DCD_oqgmld.png" /></a><br></p>
<h2 id="partners-of-datacamp-donates">Partners of DataCamp Donates</h2>
<p>DataCamp Donates would not be a success without you: our learners and our partners. Thank you for sending us your insight and encouragement, and for spreading the word about our initiative on your social networks. We could not have been able to donate so many subscriptions without individuals and organizations reaching out to us directly. Stay tuned to this blog for more posts spotlighting partners like <a href="https://www.datacamp.com/community/blog/data-science-nigeria">Data Science Nigeria</a> and <a href="https://www.datacamp.com/community/blog/datau">DataU</a> and inspiring success stories <a href="https://www.datacamp.com/community/blog/felix-ayoola">like this one</a>.</p>
<p>You can also help us spread the word and find more partners by reaching out to us on social media <strong><em>@DataCamp</em></strong> and use the hashtag <strong><em>#DataCampDonates</em></strong>.</p>
<p><a href="https://support.datacamp.com/hc/en-us/articles/360051596574">View our full list of partners</a>, including their websites, the reach of DataCamp Donates by country, and an FAQ about the specific terms of the program. A massive thanks to all of our partners for helping us achieve our goal of democratizing data science education.</p>
<p><center><a href="https://support.datacamp.com/hc/en-us/articles/360051596574#h_01EWE28C4T0FH74K12D84G5JS2"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1611588898/DCD_partners_logos_2021-1-25_dssadd.png" /></a><br></p>


https://www.datacamp.com/community/blog/datacamp-donates

Nonprofits, public schools, and social welfare organizations are now eligible to receive free hands-on data education through DataCamp Donates.

Donating 25,000 DataCamp Subscriptions to Organizations  Around the World

<h2>Conceptos básicos de Python</h2><p>Una introducción a los conceptos básicos de Python. Aprende como utilizar Python de forma interactiva y a tráves de una consola. Crea tus primeras variables y familiarícese con los tipos de datos básicos en Python.</p><h2>Listas de Python</h2><p>Aprende a almacenar, acceder y manipular datos en una lista: el primer paso hacia un trabajo eficiente con gran cantidad de datos.</p><h2>Funciones y paquetes</h2><p>Para aprovechar el código que los desarrolladores de Python han escrito, aprenderás sobre funciones, métodos y paquetes. ¡Esto te ayudará a reducir la cantidad de código que necesitas para resolver problemas!</p><h2>NumPy</h2><p>NumPy es un poderoso paquete de Python para hacer ciencia de datos eficientemente. Aprende como trabajar con arrays de NumPy, una alternativa más rápida y poderosa a las listas, y toma tus primeros pasos en la exploración de datos.</p>

https://www.datacamp.com/community/open-courses/introduccion-a-python

Aprenderás acerca de poderosas maneras de almacenar y manipular datos, así como también geniales herramientas de ciencia de datos para comenzar tus propios análisis. 

Introducción a Python

<h2>Introduction</h2><p>Introduction to data.world</p><h2>Working with datasets</h2><p>Working with data.world datasets</p><h2>Working with multiple datasets</h2><p>Working with multiple datasets</p><h2>Querying with data.world</h2><p>Querying with data.world</p><h2>Wrap up & next steps</h2><p>Setup your own data.world account and apply what you've learned and more!</p>

https://www.datacamp.com/community/open-courses/intro-to-dataworld-in-python

data.world allows data people to discover and share cool data. In this course, you'll learn to use the data.world Python SDK to pull data and metadata into your environment.

Intro to data.world in Python

<h2 id="what-is-a-jupyter-notebook-">What is a Jupyter Notebook?</h2>
<p>&#39;Jupyter&#39; is an acronym meaning Julia, Python, and R. These programming languages were the original use case for Jupyter. Now though, Jupyter Notebook supports many other languages (Source: <a href="https://medium.com/@blessedmarcel1/how-to-install-jupyter-notebook-on-mac-using-homebrew-528c39fd530f">How to install Jupyter Notebook on Mac using Homebrew</a>).</p>
<p>The main components of the whole environment are, on the one hand, the notebooks themselves and the application. On the other hand, you also have a notebook kernel and a notebook dashboard.</p>
<p>&quot;&#39;notebook&#39; denotes documents that contain both code and rich text elements, such as figures, links, equations, etc. Because of the mix of code and text elements, these documents are the ideal place to bring together an analysis description and its results. They also can be executed to perform the data analysis in real-time&quot; —<a href="https://medium.com/@blessedmarcel1/how-to-install-jupyter-notebook-on-mac-using-homebrew-528c39fd530f">How to install Jupyter Notebook on Mac using Homebrew</a>.</p>
<p>Let&#39;s look at these components in more detail.</p>
<h3 id="what-is-the-jupyter-notebook-app-">What is the Jupyter Notebook App?</h3>
<p>As a server-client application, the Jupyter Notebook App allows you to edit and run your notebooks via a web browser. The application can be executed on a PC without Internet access, or it can be installed on a remote server, where you can access it through the Internet.</p>
<h2 id="advantages-of-a-jupyter-notebook">Advantages of a Jupyter Notebook</h2>
<ul>
<li><p>Notebook has the ability to re-run individual code snippets, and it provides you the flexibility of modifying them before re-running.</p>
</li>
<li><p>You can deploy a Jupyter Notebook on a remote server and access it from your local web browser.</p>
</li>
<li><p>You can insert notes and documentation to your code in a Jupyter Notebook in various formats like markdown, latex, and HTML.</p>
</li>
<li><p>Headings in a Jupyter Notebook are also supported, which is very useful when you want to provide heading &amp; sub-heading to signify various important parts of your codebase. It helps improve the readers&#39; experience.</p>
</li>
<li><p>Use a Jupyter Notebook as cells.</p>
</li>
<li><p>Ability to share a Jupyter Notebook in various formats like <code>.py</code>, <code>.html</code>, <code>.md</code>, <code>.pdf</code>, etc.</p>
</li>
<li><p>For <em>machine learning</em> folks, Jupyter Notebook is very helpful since it allows for Data Visualization. Jupyter Notebook supports visualizations and enables you to render graphs and charts. These are generated from codes with the help of modules like Matplotlib, Plotly, or Bokeh.</p>
</li>
</ul>
<h2 id="how-to-install-the-notebook-using-pip-">How to Install the Notebook Using <code>pip</code></h2>
<p>If you don&#39;t want to install Anaconda, you just have to make sure that you have the latest version of <code>pip</code>. If you have installed Python, you will typically already have it.</p>
<p>What you do need to do is upgrade <code>pip</code> but before, that let&#39;s install and create a virtual environment:</p>
<pre><code class="lang-python"># On OS X or Linux
pip3 install virtualenv
</code></pre>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465233/jup1_woihqi.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>Now let&#39;s create a virtual environment <code>jupyter</code> and activate it!</p>
<pre><code class="lang-python">virtualenv jupyter
source jupyter/bin/activate
</code></pre>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465231/jup2_a5yndv.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>Let&#39;s upgrade <code>pip</code> now, but first, let&#39;s check the already installed <code>pip</code> version.</p>
<pre><code class="lang-python">pip3 --version
</code></pre>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465230/jup3_nnffd7.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>From the above output, you can see that we have <code>pip</code> version <code>20.2.1</code>.</p>
<p>Let&#39;s upgrade that:</p>
<pre><code class="lang-python"># On Windows
python -m pip install -U pip setuptools
# On OS X or Linux
pip3 install -U pip setuptools
</code></pre>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465231/jup4_qgtyl2.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>Once you have <code>pip</code>, you can just run the below command to install a Jupyter Notebook, which would take a while to install.</p>
<pre><code class="lang-python">pip3 install jupyter
</code></pre>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465232/jup5_rgihme.png" alt="Installing Jupyter Notebook">
</figure>
</center>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465232/jup6_cztm0d.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>Great! So you are all set to run and start using Jupyter Notebook but before that, let&#39;s also look at how you can install jupyter using Anaconda.</p>
<h2 id="how-to-install-jupyter-notebook-using-anaconda">How to Install Jupyter Notebook Using Anaconda</h2>
<p>One of the requirements here is Python, Python 3.3, or greater since Python 2.7 has reached the end of its life (EOL) on January 1st, 2020. The general recommendation is that you use the Anaconda distribution to install both Python and the Jupyter Notebook.</p>
<p>Jupyter Notebook in Anaconda comes pre-installed, meaning that you do not have to install it explicitly. All you need to install is Anaconda, and Jupyter Notebook would already be present inside it.</p>
<p>Anaconda&#39;s advantage is that you have access to over 720 packages that can easily be installed with Anaconda&#39;s conda, a package, dependency, and environment manager. You can follow the instructions for the installation of Anaconda here for <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-mac-os-x">Mac</a> or <a href="https://www.datacamp.com/community/tutorials/installing-anaconda-windows">Windows</a>.</p>
<p>Is something not clear? You can always read up on the Jupyter installation instructions <a href="https://jupyter.readthedocs.io/en/latest/install.html">here</a>.</p>
<p>If you need more information about installing packages in Python, you can go to <a href="https://packaging.python.org/installing/">this page</a>.</p>
<h2 id="getting-started-with-jupyter-notebook">Getting Started With Jupyter Notebook</h2>
<p>Now that you have learned all about what Jupyter Notebooks are, their benefits, and how to install them on your machines, let&#39;s put them to some practical use!</p>
<h3 id="launching-jupyter-notebook">Launching Jupyter Notebook</h3>
<p>To launch your Jupyter notebook, simply type the below command!</p>
<pre><code class="lang-python">jupyter notebook
</code></pre>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465232/jup7_hsg0fi.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>Once you do this, the Jupyter notebook should open up the below tab for you inside your web browser:</p>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465231/jup9_rv1uc2.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>Note that the Jupyter Notebook will open using the below URL:</p>
<p><a href="http://localhost:8888/tree">http://localhost:8888/tree</a></p>
<p>However, the port number <code>8888</code> might change if a process is using it or a second Jupyter Notebook session runs in the background.</p>
<p>In some cases, the Jupyter Notebook might not open up on its own, in which case you can simply copy one of the URLs that are generated when you run the command <code>jupyter notebook</code>, which in our case is:</p>
<p><a href="http://localhost:8889/?token=e41173caeda552cdcab1bcc775e2463ac510b8847f73604b">http://localhost:8889/?token=e41173caeda552cdcab1bcc775e2463ac510b8847f73604b</a></p>
<p>Now, let&#39;s open a Python3 Notebook inside Jupyter by clicking the <code>New</code> tab and select <code>Python3</code>:</p>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465231/jup10_mthrq0.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<p>And finally, we have our very first Python3 Jupyter Notebook:</p>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465232/jup11_pfmhbo.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<h3 id="adding-heading-and-notes-in-jupyter-notebook">Adding Heading and Notes in Jupyter Notebook</h3>
<p>In the drop-down menu shown below, you have four options:</p>
<ul>
<li><strong>Code</strong> – In the code cell, you write the Python code.</li>
</ul>
<ul>
<li><strong>Markdown</strong> – In this cell type, you can add headings, notes, documentation, etc.</li>
</ul>
<ul>
<li><strong>Raw NBConvert</strong> – This cell type can be used to render different code formats into HTML or LaTeX. This information is stored in the notebook metadata and converted appropriately.</li>
</ul>
<p>Let&#39;s look at the <code>markdown</code> example for adding headings and bullet points:</p>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465232/jup12_gki9jz.png" alt="Installing Jupyter Notebook">
</figure>
</center>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465233/jup13_njmhvx.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<h3 id="sharing-a-jupyter-notebook">Sharing a Jupyter Notebook</h3>
<p>This is one of the great and cool features of a Jupyter Notebook.</p>
<p>Go to the <code>Files</code> menu, and you’ll see a <code>Download As</code> option where you can download the Jupyter Notebook in almost nine different ways. The most commonly used is either a <code>.ipynb</code> or <code>.py</code> file:</p>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465232/jup14_v2gzvf.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<h3 id="installing-a-python-module-in-jupyter-notebook">Installing a Python Module in Jupyter Notebook</h3>
<p>Installing a Python module inside Jupyter Notebook is as easy as installing it from the terminal. All you need to do is use an <code>!</code>(exclamation) mark at the start of the command, as shown below:</p>
<center>
<figure>
<img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1603465233/jup15_prcdrd.png" alt="Installing Jupyter Notebook">
</figure>
</center>

<h3 id="writing-a-python-function-in-jupyter-notebook">Writing a Python Function in Jupyter Notebook</h3>
<p>Let&#39;s write a Python function that generates random integers between 0 and 100 five times, and each time the size of the vector increments by one.</p>
<p>First, let&#39;s import <code>numpy</code>.</p>
<pre><code class="lang-python">import numpy as np
</code></pre>
<p>Next, the <a href="https://www.datacamp.com/community/tutorials/numpy-random"><code>random</code> function</a> which takes two inputs, <code>low</code> and <code>size</code> of the array, and finally returns the <code>num</code>.</p>
<pre><code class="lang-python">def random(low, size):
    num = np.random.randint(low=low ,size=size)
    return num
</code></pre>
<pre><code class="lang-python">for i in range(5):
    print(random(100, i+1))
</code></pre>
<pre><code>[80]
[19 89]
[66 72 77]
[41 55 82 10]
[95 90 53 99 46]
</code></pre><h1 id="conclusion">Conclusion</h1>
<p>In this Jupyter Notebook tutorial, you were introduced to its advantages. You further learned how to install Jupyter Notebook using <code>pip</code> and Anaconda. Finally, we covered a few practical use-cases like how to make headings, write python code, and how to share a Jupyter Notebook.</p>
<p>To learn more about Jupyter Notebook, please check out this complete comprehensive tutorial <a href="https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook#WhatIs">Jupyter Notebook Tutorial: The Definitive Guide</a>.</p>


https://www.datacamp.com/community/tutorials/installing-jupyter-notebook

(Tutorial) Installing Jupyter Notebook

Installing Jupyter Notebook

<p>String formatting is also known as String interpolation. It is the process of inserting a custom string or variable in predefined text.</p>
<pre><code class="lang-python">custom_string = &quot;String formatting&quot;
print(f&quot;{custom_string} is a powerful technique&quot;)
</code></pre>
<pre><code>String formatting is a powerful technique
</code></pre><p>As a data scientist, you would use it for inserting a title in a graph, show a message or an error, or pass a statement to a function.</p>
<h2 id="methods-for-formatting">Methods for formatting</h2>
<ul>
<li>Positional formatting</li>
<li>Formatted string literals</li>
<li>Template method</li>
</ul>
<h3 id="string-format-method">String.Format Method</h3>
<p>We put placeholders defined by a pair of curly braces in a text. We call the string dot format method. Then, we pass the desired value into the method. The method replaces the placeholders using the values in the order of appearance replace by value:</p>
<pre><code>&#39;text{}&#39;.format(value)
</code></pre><h2 id="positional-formatting">Positional Formatting</h2>
<p>We define a string and insert two placeholders. We pass two strings to the method, which will be passed to get the following output:</p>
<pre><code class="lang-python">print(&quot;Machine learning provides {} the ability to learn {}&quot;.format(&quot;systems&quot;, &quot;automatically&quot;))
</code></pre>
<pre><code>Machine learning provides systems the ability to learn automatically
</code></pre><p>We can use variables for both the string and the values passed to the method. In the below example code, we define a string with placeholders and two other variables. We apply the format method to the string using the two defined variables. The method reads the string and replaces the placeholders with the given values.</p>
<pre><code class="lang-python">my_string = &quot;{} rely on {} datasets&quot;
method = &quot;Supervised algorithms&quot;
condition = &quot;labeled&quot;
</code></pre>
<pre><code class="lang-python">print(my_string.format(method, condition))
</code></pre>
<pre><code>Supervised algorithms rely on labeled datasets
</code></pre><h2 id="reordering-values">Reordering Values</h2>
<p>In the below example, you add index numbers into the placeholders to reorder values. This affects the order in which the method replaces the placeholders.</p>
<p>The method replaces them with the values in the given order.</p>
<pre><code class="lang-python">print(&quot;{} has a friend called {} and a sister called {}&quot;. format(&quot;Betty&quot;, &quot;Linda&quot;, &quot;Daisy&quot;))
</code></pre>
<pre><code>Betty has a friend called Linda and a sister called Daisy
</code></pre><p>If we add the index numbers, the replacement order changes accordingly.</p>
<pre><code class="lang-python">print(&quot;{2} has a friend called {0} and a sister called {1}&quot;. format(&quot;Betty&quot;, &quot;Linda&quot;, &quot;Daisy&quot;))
</code></pre>
<pre><code>Daisy has a friend called Betty and a sister called Linda
</code></pre><h2 id="name-placeholders">Name Placeholders</h2>
<p>We can also introduce keyword arguments that are called by their keyword name.</p>
<p>In the example code below, we inserted keywords in the placeholders. Then, we call these keywords in the format method. We then assign which variable will be passed for each of them, resulting in the following output.</p>
<pre><code class="lang-python">tool=&quot;Unsupervised algorithms&quot;
goal=&quot;patterns&quot;
print(&quot;{title} try to find {aim} in the dataset&quot;.format(title=tool, aim=goal))
</code></pre>
<pre><code>Unsupervised algorithms try to find patterns in the dataset
</code></pre><p>Let&#39;s examine this code below. We have defined a dictionary with keys: tool and goal.</p>
<pre><code class="lang-python">my_methods = {&quot;tool&quot;: &quot;Unsupervised algorithms&quot;, &quot;goal&quot;: &quot;patterns&quot;}
</code></pre>
<p>We want to insert their values in a string. Inside the placeholders, we can specify the value associated with the key tool of the variable data using bracket notation. Data is the dictionary specified in the method, and tool is the key present in that dictionary.</p>
<pre><code class="lang-python">print(&#39;{data[tool]} try to find {data[goal]} in the dataset&#39;.format(data=my_methods))
</code></pre>
<p>So, we get the desired output shown below. Be careful! You need to specify the index without using quotes.</p>
<pre><code>Unsupervised algorithms try to find patterns in the dataset
</code></pre><h2 id="format-specifier">Format Specifier</h2>
<p>We can also specify the format specifies inside curly braces. This defines how individual values are presented. Here, we’ll use the syntax index colon specifier. One of the most common format specifiers is float represented by <code>f</code>. In the code, we specify that the value passed with the index 0 will be a float.</p>
<pre><code class="lang-python">print(&quot;Only {0:f}% of the {1} produced worldwide is {2}!&quot;. format(0.5155675, &quot;data&quot;, &quot;analyzed&quot;))
</code></pre>
<pre><code>Only 0.515567% of the data produced worldwide is analyzed!
</code></pre><p>We could also add <code>.2f</code> indicating that we want the float to have two decimals, as seen in the resulting output.</p>
<pre><code class="lang-python">print(&quot;Only {0:.2f}% of the {1} produced worldwide is {2}!&quot;.format(0.5155675, &quot;data&quot;, &quot;analyzed&quot;))
</code></pre>
<pre><code>Only 0.52% of the data produced worldwide is analyzed!
</code></pre><h2 id="formatting-datetime-">Formatting <code>datetime</code></h2>
<p>Python has a module called <a href="https://www.datacamp.com/community/tutorials/python-datetime">datetime</a> that allows us to, for example, to get the time and date for today.</p>
<pre><code class="lang-python">from datetime import datetime
print(datetime.now())
</code></pre>
<pre><code>2020-08-08 06:28:42.715243
</code></pre><p>But since the format returned is very particular, you could use the format specifier such as <code>%y-%m-%d-%h-%m</code> to adjust the format to something more familiar to us!</p>
<pre><code class="lang-python">print(&quot;Today&#39;s date is {:%Y-%m-%d %H:%M}&quot;.format(datetime.now()))
</code></pre>
<pre><code>Today&#39;s date is 2020-08-08 06:29
</code></pre><h2 id="interactive-example">Interactive Example</h2>
<p>In the following example, you will assign the substrings going from the 4th to the 19th character, and from the 22nd to the 44th character of <code>wikipedia_article</code> to the variables <code>first_pos</code> and <code>second_pos</code>, respectively. Adjust the strings so they are lowercase. Finally, print the variables <code>first_pos</code> and <code>second_pos</code>.</p>
<pre><code class="lang-python"># Assign the substrings to the variables
first_pos = wikipedia_article[3:19].lower()
second_pos = wikipedia_article[21:44].lower()
</code></pre>
<p>When we run the above code, it produces the following result:</p>
<p><code>computer science
artificial intelligence</code></p>
<p><a href="https://campus.datacamp.com/courses/regular-expressions-in-python/formatting-strings?ex=2">Try it for yourself</a>.</p>
<p>To learn more about positional formatting, please see this video from our course, <a href="https://campus.datacamp.com/courses/regular-expressions-in-python/formatting-strings?ex=1">Regular Expressions in Python</a>.</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/1Qp_b3yS_YA" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

<p>This content is taken from DataCamp’s <a href="https://www.datacamp.com/courses/regular-expressions-in-python">Regular Expressions in Python</a> course by Maria Eugenia Inzaugarat.</p>


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