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

<p><center><a href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1601342438/nautilus_1_hpskar.png" /></a></center></p>
<p><center><a href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1601388673/L_D_banner_1_s7kjwd.png" /></a></center><br></p>
<h2 id="data-transformation-underpins-digital-transformation">Data transformation underpins digital transformation</h2>
<p>Over the past two decades, digital-first startups such as Uber, Amazon, Airbnb, and Stripe have disrupted vital industries such as transportation, commerce, travel, and banking. Organizations across all industries have recognized the need for digital transformation to compete in the new information economy. This is especially true of the COVID-19 economy, which has been accelerating the digitization of their processes and services (<a href="https://www.pwc.com/sk/en/current-press-releases/the-covid-19-crisis-has-accelerated-the-digital-transformation.html">PwC</a>). Despite massive digitalization investments, the painful truth is that approximately 70% of digital transformation initiatives fail to reach their stated goal (<a href="https://www.mckinsey.com/industries/retail/our-insights/the-how-of-transformation">McKinsey</a>).</p>
<p>While there may be many culprits for why digital transformation programs may fail, a key reason is not recognizing that having sustainable data transformation is a prerequisite for successful digital transformation. <a href="https://www.gartner.com/en/newsroom/press-releases/2019-10-23-gartner-says-traditional-data-and-analytics-strategies-cannot-satisfy-digital-business-demands">Gartner</a> finds that fewer than 50% of documented corporate strategies mention data analytics as a critical lever for delivering enterprise-wide value.</p>
<blockquote>
<p>Leaders need to look at data first to succeed in their digital initiatives, rather than treating them as an afterthought to help with ad hoc projects. —<a href="https://www.gartner.com/analyst/37069">Mike Rollings</a>, Research Vice President at Gartner</p>
</blockquote>
<p>There are many levers for <a href="https://www.datacamp.com/resources/webinars/scaling-data-science-at-your-organization-part-1">successful data transformation</a>—from investing in data infrastructure, to data tools, to improved processes and agile organizational structures. However, none of these levers would be effective at ushering in data transformation without investing in <a href="https://hbr.org/2020/05/digital-transformation-is-about-talent-not-technology">organization-wide data fluency skills</a>.</p>
<p>Many organizations have tried to cultivate data fluency skills by creating data science and analytics teams. But merely hiring data scientists isolates data science as a service center, and won’t usher in organization-wide data fluency. Moreover, as <a href="https://business.linkedin.com/content/dam/me/business/en-us/talent-solutions/emerging-jobs-report/Emerging_Jobs_Report_U.S._FINAL.pdf">demand for data scientists grows</a>, hiring one’s way out of addressing the data fluency skills gap is unsustainable.</p>
<p>In a data-driven organization, data science—and more broadly, data fluency—is an inclusive methodology for answering organizational questions where everyone is equipped to answer questions with data. The key differentiators between the disruptors and the incumbents is not technology-based but in their data-driven culture, the insights they draw from data while examining and iterating upon their services, and the data fluency skills they foster.</p>
<p>In short, the success of your digital transformation pivots on having the appropriate data fluency skills across the organization.</p>
<h2 id="addressing-the-data-fluency-skills-gap">Addressing the data fluency skills gap</h2>
<p>Indeed, many organizations have recognized the need to address their data fluency skill gaps. A <a href="https://www.mckinsey.com/business-functions/organization/our-insights/beyond-hiring-how-companies-are-reskilling-to-address-talent-gaps">McKinsey survey</a> of over a thousand businesses from various industries found that the most pressing skill gap to be addressed was data analytics—with 43% of respondents believing it to be the most urgent priority when it comes to upskilling. Similarly, <a href="https://www.pwc.com/gx/en/ceo-survey/2019/report/pwc-22nd-annual-global-ceo-survey.pdf">PwC’s 2019 annual CEO survey</a> found that 34% of CEOs believe skill gaps in data analytics are the most crucial threat for their organization.</p>
<p>Forward-thinking organizations are already pouring in investments to upskill their people to compete in the digital age. For example, <a href="https://corporate.marksandspencer.com/media/press-releases/2018/marks-and-spencer-partners-with-decoded-to-create-retail-data-academy">Marks &amp; Spencer</a> created a retail data academy to upskill over 1,000 employees. Amazon launched a <a href="https://aws.amazon.com/blogs/machine-learning/amazons-own-machine-learning-university-now-available-to-all-developers/">Machine Learning University</a> to equip their engineers with the skills needed to deploy machine learning at scale in their products and services. Airbnb developed its own <a href="https://medium.com/airbnb-engineering/how-airbnb-democratizes-data-science-with-data-university-3eccc71e073a">Data University</a> to provide every level of the organization with the skills to make data-driven decisions. <a href="https://hbr.org/2016/10/atts-talent-overhaul">AT&amp;T</a> embarked on a $1 billion, 10-year long project to upskill more than half of its 250,000 people workforce.</p>
<p>On aggregate, data fluency upskilling efforts are paying off. Another <a href="https://www.mckinsey.com/business-functions/organization/our-insights/beyond-hiring-how-companies-are-reskilling-to-address-talent-gaps">McKinsey study</a> found that 70% of organizations that invested in upskilling efforts are reporting positive business impacts that exceed the initial investment in upskilling. For example, 48% of organizations have reported moderate to significant positive effects on bottom-line growth due to upskilling—and 73% of organizations have reported moderate to substantial improvements in employee satisfaction. A <a href="https://www.datacamp.com/resources/whitepapers/what-300-l-and-d-leaders-have-learned-about-data-fluency">DataCamp survey of over 300 L&amp;D leaders</a> across industries found that companies that have invested in mature data fluency competencies are also outperforming their less data fluent counterparts in revenue growth, market share, profitability and more.</p>
<h2 id="the-challenges-of-l-d-when-building-data-fluency">The challenges of L&amp;D when building data fluency</h2>
<p>Unlike traditional learning &amp; development initiatives that often take the shape of one-off training for specific skills, data fluency is a methodology for answering business questions that organizations should hone over time. Learning journeys will vary depending on the level of interaction different individuals may have with data. For example, a marketing analyst who regularly works with Excel may need to learn R or Python to succeed at their job, while a manager or leader may only need to know how to make educated decisions using data. In short, there are no one-size-fits all when it comes to data fluency.</p>
<p>This is why a role-based, persona-driven learning journey is more effective at scaling data fluency training programs. Every persona has a different relationship with data and would need to acquire competencies in different tools, and grow different skills to thrive in the digital age.</p>
<p>At DataCamp, we have identified the different types of tools needed to succeed when working with data. More importantly, we have found 8 key data personas found in every data-driven organization alongside the different skills they need to possess, and the tools they most commonly use.</p>
<p>If you want to learn more about these data personas and how they could fit into your organization, download our white paper, <a href="https://www.datacamp.com/resources/whitepapers/l-and-d-guide-to-data-fluency">The L&amp;D Guide to Data Fluency</a>.</p>
<p><center><a href="https://www.datacamp.com/groups/business"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1601388673/L_D_banner_1_s7kjwd.png" /></a></center><br></p>


https://www.datacamp.com/community/blog/blog-data-upskilling

Scaling organization-wide data fluency training is not an easy task. Learn how to scale your data program and ensure the success of your digital transformation initiatives. 

Why Data Upskilling is the Backbone of Digital Transformation

<p>One of the main reasons behind the rapid innovation and advances in data science has been the proliferation of open-source code-centric tools. This has allowed data scientists and software engineers to build powerful abstractions that make it really easy to analyze data.</p>
<p>For example, R packages like dplyr and tidymodels allow users to manipulate data and build sophisticated machine learning models with a few lines of code. Similarly, Python packages like tensorflow and pytorch allow anyone to build a deep learning model with little effort.</p>
<p>While code-driven tools have served data work well, the future of data work will be much broader, and involve graphical user interfaces (GUIs) that will let a broader audience engage in data science using point-and-click tools. Note that this does not by any means imply the death of the coding data scientist. Similar to the industrial revolution, this move will allow coders to redirect their focus to solving more complex problems, and building interfaces to tackle problems, leaving the domain-specific work to the domain experts. This is already happening across different slices of data work: descriptive, predictive, and prescriptive analytics.</p>
<h2 id="descriptive-analytics-is-accessible-with-business-intelligence">Descriptive analytics is accessible with business intelligence</h2>
<p>Descriptive analytics has been made accessible to a wider audience by the drag-and-drop business intelligence (BI) tools that are everywhere now. Just look at last year’s $2.6 billion acquisition of Looker, a very popular BI tool, by none other than Google. Also last year, Salesforce completed the acquisition of Tableau, another very popular BI tool. Microsoft has invested in its own tool, Power BI. (DataCamp offers courses on both <a href="https://datacamp.com/courses/introduction-to-tableau">Tableau</a> and <a href="https://datacamp.com/courses/introduction-to-power-bi">Power BI</a>.)</p>
<p>Business intelligence tools make data discovery accessible for all skill levels—not just advanced analytics professionals. They are one of the simplest ways to work with data, providing the ability to collect data in one place, gain insight into what will move the needle, forecast outcomes, and much more.</p>
<h2 id="how-drag-and-drop-interfaces-reduce-the-barrier-to-entry-for-predictive-and-prescriptive-analytics">How drag-and-drop interfaces reduce the barrier to entry for predictive and prescriptive analytics</h2>
<p>The democratization of data work is evolving to also include predictive and prescriptive analytics. For example, <a href="https://www.alteryx.com/">Alteryx</a> is a $10 billion dollar company that provides a drag-and-drop data science platform that allows business analysts to manipulate data and build predictive models using a drag-and-drop interface.</p>
<p>Google is investing a huge amount in their AutoML tool, which makes “AI as simple as drag and drop.” We see that <a href="https://techcrunch.com/2019/05/02/microsoft-launches-a-drag-and-drop-machine-learning-tool-and-hosted-jupyter-notebooks/">Microsoft is also entering the space</a>:</p>
<p><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1600092285/aimodel_z4sx9l.png" /></a></center><br></p>
<p>Above is an example of a basic regression model with a data pipeline. You can see that the data flows from rectangle to rectangle. First data is ingested, then columns are selected, the data is cleaned, and the model splits and trains the data.</p>
<p>There are many potential use cases for drag-and-drop machine learning. This can be reframed as: what type of people in an organization would want to use ML tools that aren&#39;t coders? Perhaps your customer success team wants to model customer churn. They’ll be able to do this using automated, drag-and-drop tools to forgo writing new models and new code every time a new customer comes in. Similarly, your Chief People Officer and HR team will be using more and more automated machine learning models in hiring flows, including screening resumes (we’ll get to the potential perils of this in a minute). And your marketing team may be interested in using such tools for the marketing funnel, which changed with the advent of ML tools for programmatic ad buying. One other telling example is supply chain optimization, which can greatly reduce costs within an organization and is relatively low-hanging fruit.</p>
<p>This trend can also be observed in complex spaces like deep learning. For example, <a href="https://lobe.ai/about">Lobe</a> is a startup that makes deep learning accessible to all, allowing users to build models using a GUI. Lobe was acquired by Microsoft even before they came out of a beta, which is testament to the importance of GUI tools in shaping the future.</p>
<p>Another example is an <a href="https://ai.googleblog.com/2019/07/building-smily-human-centric-similar.html">image search engine</a> built by Google that lets medical doctors explore model predictions and fine-tune them further with human intelligence. Such tools can increase adoption with great effect by opening up the black box and supplementing ML models with human intelligence.</p>
<p>We&#39;re going to see more drag-and-drop interfaces that will reduce the barrier to entry. This will allow us all to think more about which types of tools we want to build in-house and which ones we want to source from vendors.</p>
<h2 id="algorithmic-marketplaces-allow-non-coders-to-purchase-models">Algorithmic marketplaces allow non-coders to purchase models</h2>
<p>We’ll see more and more marketplaces for these ML products. Booz Allen’s Modzy is a point-and-click marketplace where you can buy models that others have created to incorporate into your tech stack.</p>
<p>There are dangers to allowing non-coders and non-statisticians to use these tools. Namely, we must ensure that everyone knows of the inherent risks associated with these tools and how to mitigate them. Increasingly, product development advancements in point-and-click tools may focus on helping people recognize when they’re given biased results, and not to take them at face value.</p>
<h2 id="beware-of-the-danger-zone-of-drag-and-drop-ml">Beware of the danger zone of drag-and-drop ML</h2>
<p>An interesting educational game out of Mozilla Labs is Survival of the Best Fit, about hiring bias in AI. You get to play a CEO who attempts to scale the hiring process with an automated tool that causes hiring practices to go wrong.</p>
<h2 id="recap">Recap</h2>
<p>We&#39;ll see more data work done in GUIs, via drag-and-drop and point-and-click interfaces. The space is getting more and more competitive with key investments by big players like Google and Microsoft, with much more to come. And even if a lot of your data science work is currently done in-house, more data work is increasingly shifting to vendors—which means there needs to be widespread education of potential risks and consequences to adopting AI.</p>
<h2 id="call-to-action">Call to action</h2>
<p>Write down how much of the day-to-day work in your organization currently happens in code. You could write this in human hours or how much it actually costs your organization. Then calculate how much is happening in GUIs, point-and-click, and drag-and-drop interfaces. How do you see this changing over the next 24 months?</p>
<p>Find out more about building your data program for the future of work in <a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders">The Definitive Guide to Machine Learning for Business Leaders</a>.</p>
<p><center><a href="https://www.datacamp.com/resources/ebooks/definitive-guide-to-machine-learning-for-business-leaders"><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1596642506/eBook_CTA_banner_xwtyv2.png" /></a></center><br></p>


https://www.datacamp.com/community/blog/guis-and-the-future-of-work

We'll see more data work done in GUIs, via drag-and-drop and point-and-click interfaces.

GUIs and the Future of Work

<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

<p>Removing duplicates is an essential skill to get accurate counts because you often don&#39;t want to count the same thing multiple times. In Python, this could be accomplished by using the Pandas module, which has a method known as <code>drop_duplicates</code>.</p>
<p>Let&#39;s understand how to use it with the help of a few examples.</p>
<h2 id="dropping-duplicate-names">Dropping Duplicate Names</h2>
<p>Let&#39;s say you have a dataframe that contains vet visits, and the vet&#39;s office wants to know how many dogs of each breed have visited their office. However, there are dogs like Max and Stella, who have visited the vet more than once in your dataset. Hence, you cannot just count the number of each breed in the breed column.</p>
<pre><code class="lang-python">print(vet_visits)
</code></pre>
<pre><code>          date     name        breed  weight_kg
0   2018-09-02    Bella     Labrador      24.87
1   2019-06-07      Max     Labrador      28.35
2   2018-01-17   Stella    Chihuahua       1.51
3   2019-10-19     Lucy    Chow Chow      24.07
..         ...      ...          ...        ...
71  2018-01-20   Stella    Chihuahua       2.83
72  2019-06-07      Max    Chow Chow      24.01
73  2018-08-20     Lucy    Chow Chow      24.40
74  2019-04-22      Max     Labrador      28.54
</code></pre><p>To achieve this, you would remove rows that contain a dog name already listed earlier, or in other words, you will extract a dog with each name from the dataset once.</p>
<p>You would do this using the <code>drop_duplicates</code> method. It takes an argument <code>subset</code>, which is the column we want to find or duplicates based on - in this case, we want all the unique names.</p>
<pre><code class="lang-python">vet_visits.drop_duplicates(subset=&quot;name&quot;)
</code></pre>
<pre><code>          date     name        breed  weight_kg
0   2018-09-02    Bella     Labrador      24.87
1   2019-06-07      Max    Chow Chow      24.01
2   2019-03-19  Charlie       Poodle      24.95
3   2018-01-17   Stella    Chihuahua       1.51
4   2019-10-19     Lucy    Chow Chow      24.07
7   2019-03-30   Cooper    Schnauzer      16.91
10  2019-01-04   Bernie  St. Bernard      74.98
</code></pre><pre><code>(6  2019-06-07      Max     Labrador      28.35)
</code></pre><p>But, what if we have dogs with the same name?</p>
<h2 id="dropping-duplicate-pairs">Dropping Duplicate Pairs</h2>
<p>In that case, we need to consider more than just <code>name</code> when dropping duplicates. Since <code>Max</code> and <code>Max</code> are different breeds, we can drop the rows with pairs of names and breeds listed earlier in the dataset.</p>
<pre><code class="lang-python">unique_dogs = vet_visits.drop_duplicates(subset=[&quot;name&quot;, &quot;breed&quot;])
print(unique_dogs)
</code></pre>
<pre><code>          date     name        breed  weight_kg
0   2018-09-02    Bella     Labrador      24.87
1   2019-03-13      Max    Chow Chow      24.13
2   2019-03-19  Charlie       Poodle      24.95
3   2018-01-17   Stella    Chihuahua       1.51
4   2019-10-19     Lucy    Chow Chow      24.07
6   2019-06-07      Max     Labrador      28.35
7   2019-03-30   Cooper    Schnauzer      16.91
10  2019-01-04   Bernie  St. Bernard      74.98
</code></pre><p>To base our duplicate dropping on multiple columns, we can pass a list of column names to the subset argument, in this case, name and breed.</p>
<p>Now both Max&#39;s have been included.</p>
<h2 id="interactive-example">Interactive Example</h2>
<p>In this exercise, you&#39;ll create some new DataFrames using unique values from <code>sales</code>. <code>sales</code> is available, and <code>pandas</code> is imported as <code>pd</code>.</p>
<p>You will perform the following steps:</p>
<ul>
<li>First, you will remove rows of <code>sales</code> with duplicate pairs of <code>store</code> and <code>type</code> and save as <code>store_types</code> and print the head.</li>
<li>Then, you will remove rows of <code>sales</code> with duplicate pairs of <code>store</code> and <code>department</code> and save as <code>store_depts</code> and print the head.</li>
<li>Subset the rows that are holiday weeks, and drop the duplicate <code>dates</code>, saving as <code>holiday_dates</code>.</li>
<li>Finally, select the <code>date</code> column of <code>holiday_dates</code>, and print the <code>holiday_dates</code> dataframe.</li>
</ul>
<pre><code class="lang-python"># Drop duplicate store/type combinations
store_types = sales.drop_duplicates(subset=[&quot;store&quot;, &quot;type&quot;])
print(store_types.head())

# Drop duplicate store/department combinations
store_depts = sales.drop_duplicates(subset=[&quot;store&quot;, &quot;department&quot;])
print(store_depts.head())

# Subset the rows that are holiday weeks and drop duplicate dates
holiday_dates = sales[sales[&quot;is_holiday&quot;]].drop_duplicates(subset=&quot;date&quot;)

# Print date col of holiday_dates
print(holiday_dates[&quot;date&quot;])
</code></pre>
<p>When we run the above code, it produces the following result:</p>
<pre><code>      store type  department       date  weekly_sales  is_holiday  temperature_c  fuel_price_usd_per_l  unemployment
0         1    A           1 2010-02-05      24924.50       False          5.728                 0.679         8.106
901       2    A           1 2010-02-05      35034.06       False          4.550                 0.679         8.324
1798      4    A           1 2010-02-05      38724.42       False          6.533                 0.686         8.623
2699      6    A           1 2010-02-05      25619.00       False          4.683                 0.679         7.259
3593     10    B           1 2010-02-05      40212.84       False         12.411                 0.782         9.765
    store type  department       date  weekly_sales  is_holiday  temperature_c  fuel_price_usd_per_l  unemployment
0       1    A           1 2010-02-05      24924.50       False          5.728                 0.679         8.106
12      1    A           2 2010-02-05      50605.27       False          5.728                 0.679         8.106
24      1    A           3 2010-02-05      13740.12       False          5.728                 0.679         8.106
36      1    A           4 2010-02-05      39954.04       False          5.728                 0.679         8.106
48      1    A           5 2010-02-05      32229.38       False          5.728                 0.679         8.106
498    2010-09-10
691    2011-11-25
2315   2010-02-12
6735   2012-09-07
6810   2010-12-31
6815   2012-02-10
6820   2011-09-09
Name: date, dtype: datetime64[ns]
</code></pre><p><a href="https://campus.datacamp.com/courses/data-manipulation-with-pandas/aggregating-data-848344db-cbd9-4b34-a95f-949ce8af06c2?ex=7">Try it for yourself</a>.</p>
<p>To learn more about counting and aggregating data, please see this video from our course <a href="https://campus.datacamp.com/courses/data-manipulation-with-pandas/aggregating-data-848344db-cbd9-4b34-a95f-949ce8af06c2?ex=6">Data Manipulation with pandas</a>.</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/IbRokvcf6Ow?start=19" 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://learn.datacamp.com/courses/data-manipulation-with-pandas">Data Manipulation with pandas</a> course by Maggie Matsui and Richie Cotton.</p>


https://www.datacamp.com/community/tutorials/pandas-drop-duplicates

(Tutorial) Pandas Drop Duplicates

Pandas Drop Duplicates

<p>Finding interesting bits of data in a DataFrame is often easier if you change the rows&#39; order. You can sort the rows by passing a column name to <code>.sort_values()</code>.</p>
<p>In cases where rows have the same value (this is common if you sort on a categorical variable), you may wish to break the ties by sorting on another column. You can sort on multiple columns in this way by passing a list of column names.</p>
<br><center><img src="https://res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1601054651/37_fa9fh6.png" /></center>


<h2 id="modifying-the-order-of-columns">Modifying the Order of Columns</h2>
<p>You can change the rows&#39; order by sorting them so that the most interesting data is at the top of the dataframe.</p>
<p>For example, when we apply <code>sort_values()</code> on the <code>weight_kg</code> column of the dogs dataframe, we get the lightest dog at the top, Stella the Chihuahua, and the heaviest dog at the bottom, Bernie the Saint Bernard.</p>
<pre><code class="lang-python">dogs.sort_values(&quot;weight_kg&quot;)
</code></pre>
<pre><code>      name        breed  color  height_cm  weight_kg date_of_birth
5   Stella    Chihuahua    Tan         18          2    2015-04-20
3   Cooper    Schnauzer   Gray         49         17    2011-12-11
0    Bella     Labrador  Brown         56         24    2013-07-01
1  Charlie       Poodle  Black         43         24    2016-09-16
2     Lucy    Chow Chow  Brown         46         24    2014-08-25
4      Max     Labrador  Black         59         29    2017-01-20
6   Bernie  St. Bernard  White         77         74    2018-02-27
</code></pre><p>Setting the <code>ascending</code> argument to False will sort the data the other way round, from heaviest to lightest dog.</p>
<pre><code class="lang-python">dogs.sort_values(&quot;weight_kg&quot;, ascending=False)
</code></pre>
<pre><code>      name        breed  color  height_cm  weight_kg date_of_birth
6   Bernie  St. Bernard  White         77         74    2018-02-27
4      Max     Labrador  Black         59         29    2017-01-20
0    Bella     Labrador  Brown         56         24    2013-07-01
1  Charlie       Poodle  Black         43         24    2016-09-16
2     Lucy    Chow Chow  Brown         46         24    2014-08-25
3   Cooper    Schnauzer   Gray         49         17    2011-12-11
5   Stella    Chihuahua    Tan         18          2    2015-04-20
</code></pre><h2 id="sorting-by-multiple-variables">Sorting by Multiple Variables</h2>
<p>We can sort by multiple variables by passing a list of column names to <code>sort_values</code>. Here, we sort first by weight, then by height. Now, Charlie, Lucy, and Bella are ordered from shortest to tallest, even though they all weigh the same.</p>
<pre><code class="lang-python">dogs.sort_values([&quot;weight_kg&quot;, &quot;height_cm&quot;])
</code></pre>
<pre><code>      name        breed  color  height_cm  weight_kg date_of_birth
5   Stella    Chihuahua    Tan         18          2    2015-04-20
3   Cooper    Schnauzer   Gray         49         17    2011-12-11
1  Charlie       Poodle  Black         43         24    2016-09-16
2     Lucy    Chow Chow  Brown         46         24    2014-08-25
0    Bella     Labrador  Brown         56         24    2013-07-01
4      Max     Labrador  Black         59         29    2017-01-20
6   Bernie  St. Bernard  White         77         74    2018-02-27
</code></pre><p>To change the direction values are sorted in, pass a list to the ascending argument to specify which direction sorting should be done for each variable. Now, Charlie, Lucy, and Bella are ordered from tallest to shortest.</p>
<pre><code class="lang-python">dogs.sort_values([&quot;weight_kg&quot;, &quot;height_cm&quot;], ascending=[True, False])
</code></pre>
<pre><code>      name        breed  color  height_cm  weight_kg date_of_birth
5   Stella    Chihuahua    Tan         18          2    2015-04-20
3   Cooper    Schnauzer   Gray         49         17    2011-12-11
0    Bella     Labrador  Brown         56         24    2013-07-01
2     Lucy    Chow Chow  Brown         46         24    2014-08-25
1  Charlie       Poodle  Black         43         24    2016-09-16
4      Max     Labrador  Black         59         29    2017-01-20
6   Bernie  St. Bernard  White         77         74    2018-02-27
</code></pre><h2 id="interactive-example">Interactive Example</h2>
<p>In the following example, you will sort <code>homelessness</code> by the number of homeless individuals, from smallest to largest, and save this as <code>homelessness_ind</code>. Finally, you will print the head of the sorted DataFrame.</p>
<pre><code class="lang-python"># Sort homelessness by individuals
homelessness_ind = homelessness.sort_values(&quot;individuals&quot;)

# Print the top few rows
print(homelessness_ind.head())
</code></pre>
<p>When we run the above code, it produces the following result:</p>
<pre><code>                region         state  individuals  family_members  state_pop
50            Mountain       Wyoming        434.0           205.0     577601
34  West North Central  North Dakota        467.0            75.0     758080
7       South Atlantic      Delaware        708.0           374.0     965479
39         New England  Rhode Island        747.0           354.0    1058287
45         New England       Vermont        780.0           511.0     624358
</code></pre><p><a href="https://campus.datacamp.com/courses/data-manipulation-with-pandas/transforming-data?ex=5">Try it for yourself</a>.</p>
<p>To learn more about sorting and subsetting the data, please see this video from our course <a href="https://campus.datacamp.com/courses/data-manipulation-with-pandas/transforming-data?ex=4">Data Manipulation with pandas</a>.</p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/F0tQ1YG6BWQ" 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/data-manipulation-with-pandas">Data Manipulation with pandas</a> course by Maggie Matsui and Richie Cotton.</p>


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