Category: Python

These 5 Tech Skills Will Be In Demand In 2023

With changing technology available in 2023, having a list to show you the top five tech skills in demand maximizes your chances of landing a good job. If you want to stay on the cutting edge of technological changes in the job market, these skills are a must-have to give you an edge over other people applying for jobs.

A blog article about the top in-demand tech skills for jobs in the future. It briefly describes all five skills and how you can hone them to be more marketable.

The skills that will have the most significant demand in 2023 are more than computers – an organization’s management has a big say in the skill set that their employees should know, and these skills can change from year to year. Find out what you need to add to your resume if you want to apply for one of the hottest jobs on the market!

With technology advancing rapidly, the skills needed to succeed in those fields are likewise shifting. And what are those skills? Let’s find out!

What Will Be Future Jobs In 2023?

In 2023, the most in-demand jobs will likely be in artificial intelligence (AI), big data, and cloud computing. These three areas are experiencing the most rapid growth and are expected to continue for the foreseeable future.

AI is already being used in various ways, such as to create personal assistant applications, improve search engine results, and target online ads. The potential uses for AI are virtually limitless, and as its capabilities continue to increase, so will the number of businesses and industries adopting it.

Big data is another area with a lot of potentials. Companies are just beginning to scratch the surface of what they can do with all the data they collect. Currently, it is mainly used for marketing purposes. Still, it could be used to predict consumer behavior, improve product design, or identify new business opportunities.

Data Communicator/ Storyteller

As technology continues to evolve, so do the skills that employers are looking for in their employees. In the coming years, one of the most essential skills in demand is communicating data effectively.

With the ever-increasing amount of data collected and stored, it is becoming more and more difficult for businesses to make sense of it all. That’s where data communicators come in. Data communicators are experts at taking complex data sets and communicating them in a way that is easy to understand.

Not only do they need to be able to understand and interpret data, but they also need to be able to tell a story with it. The best data communicators can take data and turn it into an engaging story that can help organizations make better decisions.

If you have strong communication skills and are interested in working with data, then a career as a data communicator may be right for you!

Data Analyst: A data analyst analyzes, processes, and interprets data to find trends, patterns, and insights. Data analysts use their skills to help organizations make better decisions by providing them with actionable information.

Data storytellers use various communicative methods, such as written communication and visualizations, to convey insights. Tools like PowerBI, QlikView, MicroStrategy, Google Data Studio, and Tableau help them find the most effective and accurate ways of conveying information.

To be a successful data analyst, you must have strong analytical and problem-solving skills. You must also be able to effectively communicate your findings to others. See below some Data Communicator and Storyteller skills.

Data visualization: Data communicators and storytellers should be skilled in creating visualizations that clearly and effectively communicate data insights. This includes choosing the appropriate chart or graph type, using adequate labeling and formatting, and selecting an appropriate color scheme.

Writing: Writing clearly and concisely is essential for communicating data insights to a wide range of audiences. This includes explaining complex concepts in simple terms and using appropriate language for the audience.

Storytelling: Data communicators and storytellers should be skilled in using storytelling techniques to engage and inform their audience. This includes understanding how to structure a story, use compelling narratives to convey data insights, and use visual aids to support the story.

Presentation skills: Data communicators and storytellers should be skilled in presenting data insights effectively, whether in person or online. This includes understanding how to use visual aids, engage with the audience, and adapt the presentation to different audiences and contexts.

Data literacy: Understanding and interpreting data is essential for data communicators and storytellers. This includes understanding key concepts such as statistical significance and being able to critically evaluate data sources and methods.

If you are interested in a career that combines your love of numbers with your communication skills, then a career as a data analyst may be the perfect fit for you!

UX Design / Web Development

User experience (UX) design and the closely related field of user interface (UI) design will become increasingly valuable skills as businesses worldwide transform into tech companies. No matter your role on a team, you’re expected to know how to use technology. UX is what makes technology work for everyone, even when they don’t have coding knowledge. This becomes even more important in low-code/no-code environments, where businesses can build applications without hiring an engineer. Enterprises realize that good experiences lead to more engaged customers and employees. This isn’t just a trend that helps designers—it will help business owners retain their customers and make their employees happier going through their daily tasks.

The field of web development is constantly changing, with new technologies and trends always emerging. But some core skills will always be in demand. If you’re looking to get into web development, or move up in your career, make sure you have these skills:

1.HTML and CSS: These are the foundation languages of the web. Every website is built with HTML and CSS, so if you want to be a web developer, you need to know them inside out.

2.JavaScript: JavaScript is a programming language that helps make websites interactive. It’s used to add features like menus, forms, and animations.

3. Web Standards: Websites must be built using web standards to work correctly on all devices and browsers. This includes proper code structure and formatting, semantic markup, and ensuring your CSS is compatible with different browsers.

4. Responsive Design: With more people than ever accessing the internet on mobile devices, websites must be designed to be responsive – that is, they look good and work well on any screen size. This means using flexible layouts, media queries, and other techniques to ensure your site looks great on any device.

5. User Experience (UX): A good user experience is essential for any website or app. As a web developer, you must understand how users interact with websites and design your sites accordingly. This includes things

Cyber Security

Cyber security is one of the most in-demand tech skills of the future. With the increasing amount of data being stored and shared online, companies are looking for ways to protect their information from cyber attacks. As a result, the demand for cybersecurity professionals is expected to grow.

Information extracted from this article.

Cyber security specialists are responsible for developing and implementing security measures to protect computer networks and systems from unauthorized access or damage. They may also be required to monitor network activity for suspicious activity and respond to incidents when they occur.

Here are some Cybersecurity skills.

Network security: Involves protecting networks, devices, and data from unauthorized access or attacks. This includes understanding how to secure networks and devices, as well as how to detect and respond to security threats.

Security protocols: Cybersecurity professionals should be familiar with various security protocols, including encryption, access control, and authentication, to protect data and systems from cyber threats.

Risk assessment and management: Cybersecurity professionals need to be able to identify potential security risks and implement strategies to mitigate them. This includes understanding how to conduct risk assessments and develop risk management plans.

Security incident response: When a security incident occurs, it is important for cybersecurity professionals to respond quickly and effectively. This includes understanding how to identify the cause of an incident, contain it, and restore affected systems.

Compliance: Cybersecurity professionals must be familiar with relevant laws, regulations, and industry standards to ensure that their organization complies with all relevant requirements. This includes understanding data protection laws and industry-specific regulations.

To succeed in this field, you must have strong technical skills and be up-to-date on the latest security threats. You will also need to be able to think creatively to develop new solutions to address evolving security challenges.

Digital Marketing

Digital marketing is one of the most in-demand tech skills today. With the rise of online marketing and the growth of the digital economy, businesses are increasingly looking for candidates with strong digital marketing skills.

There are several reasons why digital marketing skills are in high demand. First, the growth of the internet and mobile devices has made it easier for businesses to reach their target audiences through digital channels. Second, as more businesses move into the online space, they need skilled marketers to help them navigate the complex world of digital marketing. Finally, as traditional advertising channels become less effective, businesses are turning to digital marketing to reach their customers and grow their business.

Many skills are essential for developing a solid foundation in digital marketing. Here are five key skills that can help you succeed in this field:

Data analysis and interpretation: Digital marketing relies heavily on data to guide strategy and measure the effectiveness of campaigns. Therefore, analyzing and interpreting data accurately is a crucial skill.

Content creation and management: Compelling, relevant content is crucial for attracting and retaining customers. This includes writing copy for websites and social media and creating visual content such as images and videos.

SEO: Search engine optimization (SEO) involves optimizing a website and its content to improve its ranking in search engine results pages. This includes researching and using relevant keywords and ensuring that a website is mobile-friendly and has fast loading times.

Advertising: Digital marketing includes advertising on platforms such as Google and social media. This includes understanding how to create and target ads and measuring their effectiveness.

Social media marketing: Social media is a powerful tool for connecting with customers and building brand awareness. Developing expertise in social media marketing involves understanding how to create and manage social media profiles and creating and sharing content that resonates with specific audiences.

If you’re looking to start or enhance your career in tech, developing solid digital marketing skills is a great place to start. Here are some tips to get you started:

  1. Familiarize yourself with different digital marketing channels.
  2. Learn how to create effective campaigns using different digital marketing tools.
  3. Understand how to measure and analyze your results to optimize your campaigns.
  4. Stay up-to-date on the latest trends and technologies in digital marketing.
  5. Get experience by working on projects for real businesses or organizations.

Artificial Intelligence

Artificial intelligence plays a crucial role in the skills I mentioned before, specifically the power to work alongside AI in a manner that is commonly described as “augmented working.” Data communicators have tools that suggest the most effective forms of visualization and storytelling to communicate their insights. Cyber security professionals can use AI to analyze network traffic and spot potential attacks before they cause damage. UX designers use AI-assisted user behavior analytics to determine which features and functionality should be emphasized electronically. Finally, digital marketers have many AI tools for predicting audience behavior and developing copy and content.

In recent years, there has been a lot of hype surrounding artificial intelligence (AI). And with good reason – AI has the potential to revolutionize several industries, from healthcare and finance to manufacturing and logistics.

But what does AI entail? And what skills do you need to get a job in this field?

Here’s a quick overview of AI, along with some of the most in-demand AI jobs and skills:

What is artificial intelligence?

At its core, artificial intelligence is all about using computers to simulate or carry out human tasks. This can involve anything from understanding natural language and recognizing objects to making decisions and planning actions.

There are different types of AI, but some of the most common are machine learning, deep learning, natural language processing, and computer vision.

AI jobs in demand

As AI continues gaining traction, the demand for AI-related jobs is rising. According to Indeed, job postings for AI roles have increased by 119% since 2015. And LinkedIn’s 2018 Emerging Jobs Report found that roles related to machine learning are among the fastest-growing jobs in the US.

Some of the most in-demand AI jobs include:

Data Scientist: A data scientist is a professional responsible for collecting, analyzing, and interpreting large amounts of data to identify trends and patterns. They use statistical methods, machine learning techniques, and domain knowledge to extract valuable insights from data and communicate their findings to stakeholders through reports, presentations, and visualizations.

Machine Learning Engineer: A machine learning engineer designs, builds and maintains machine learning systems. They work closely with data scientists to understand the requirements of a machine-learning project and use their programming skills to implement and deploy machine-learning models. They may also be responsible for evaluating these models’ performance and making necessary improvements.

Research Scientist: A research scientist is a professional who conducts research in a particular field, such as computer science, biology, or physics. They may work in academia, government, or industry and use a variety of methods, including experimentation, simulation, and data analysis, to advance the state of knowledge in their field.

Data Analyst: A data analyst is a professional responsible for collecting, processing, and analyzing data to support decision-making and strategic planning. They may use various tools and techniques, such as SQL, Excel, and statistical software, to manipulate and visualize data and communicate their findings through reports and visualizations.

Business Intelligence Analyst: A business intelligence analyst is a professional responsible for collecting, analyzing, and interpreting data to support business decision-making. They may use various tools and techniques, such as SQL, Excel, and business intelligence software, to extract and analyze data from various sources and present their findings to stakeholders through reports, dashboards, and visualizations.

Let’s see the Bernard Marr video on Youtube about these skills.

Video extract from this Forbes article.

Conclusion

As the world progresses, so too does the technology we use. It’s crucial to stay ahead of the curve and learn new skills that will be in demand in future years. The skills listed in this article will be in high demand in 2023, so start learning them now! Who knows, you might even be able to get a head start on your competition.

The tech industry is constantly evolving, so it’s essential to stay ahead of the curve. The skills listed in this article will be in high demand in 2023, so start learning them now! You might even be able to get a head start on your competition.

As technology rapidly evolves, keeping your skills up-to-date is essential to stay ahead. The five tech skills mentioned in this article will be in high demand in 2023, so if you don’t have them already, now is the time to start learning. With these skills under your belt, you’ll be well-positioned to take advantage of the many opportunities coming your way in the next few years. Do you have any of these tech skills? Are there other skills you think will be in high demand in 2023? Let us know in the comments below!

That’s it for today!

The Future of Legal Content Interpreting: How NLP Can Help?

Legal content is dense, complicated, and often less than straightforward. As such, it’s crucial to understand what you’re reading and make decisions based on that information. That’s where natural language processing (NLP) comes in.

What is NLP?

NLP, or natural language processing, is a field of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, mainly how to program computers to process and analyze large amounts of natural language data.

NLP interprets legal content through algorithms that identify relevant information from unstructured text. This process can extract key phrases, concepts, and entities from a document for further analysis. Additionally, NLP can generate summaries of legal documents or identify critical issues within a document.

Why Use NLP for Interpreting Legal Content?

There are many reasons to use natural language processing (NLP) when interpreting legal content. First, NLP can help you to identify the essential information in a document. This is especially helpful when dealing with long and complex legal documents. Second, NLP can help you understand the text’s meaning by extracting key concepts and ideas. This is extremely helpful in understanding the implications of a legal document. Finally, NLP can help you to identify relationships between different concepts in the text. This can help identify issues that may not be immediately apparent.
Overall, NLP can be a very helpful tool when interpreting legal content. It can help you to identify the most crucial information, understand the meaning of the text, and identify relationships between different concepts.

Applications of NLP in the Legal Field

There are many potential applications for NLP in the legal field. Here are a few examples:

  1. Automated contract analysis: NLP can automatically analyze contracts and identify critical provisions, such as parties, obligations, and termination clauses. This can save time and improve accuracy compared to manual contract review.
  2. Legal research: NLP can quickly search large volumes of legal documents (e.g., court opinions) for relevant information. This can save time and improve accuracy compared to traditional keyword search methods.
  3. Sentiment analysis of legal documents: NLP can be used to analyze the sentiment of legal documents, such as court opinions, to identify positive or negative feelings towards specific individuals or entities. This information could be helpful for lawyers when making strategic decisions about cases.
  4. Predictive analytics for litigation: NLP can predict the outcome of litigation based on past cases with similar facts and circumstances. This information could be helpful for lawyers when deciding whether to settle a claim or take it to trial.
  5. Automated document summarization: NLP can automatically summarize legal documents, such as court opinions, to save time and improve accuracy. This information could be helpful for lawyers who need a quick case overview.
  6. Entity extraction from legal documents: NLP can automatically extract entities, such as names of people and organizations, from legal documents. This information could be helpful for lawyers when they need to find information about specific individuals or entities quickly.

Let’s explore some real examples extracted from John Snow LABS.

What is John Snow LABS company?

John Snow Labs, an AI and NLP for a healthcare, legal, and finance company, provides state-of-the-art software, models, and data to help healthcare, legal, and life science organizations build, deploy, and operate AI projects. Click here to go to their LinkedIn page.

They have a model called “Spark NLP for Legal” to work on Legal documents. Let’s deeply in.

Introducing Spark NLP for Legal

What’s in the Spark NLP for Legal?

State-of-the-art software + pre-trained legal-specific models

One of the most common uses of NLP is Entity Recognition. Let’s try using a Portuguese document.

If you want yourself try this example, click here. Also, you can look at the Python code on Google Colab here.

Another exciting use of NLP technology is to extract relations between parties in an agreement. Look at the example below.

This model returns something like this to organize and save the insights.

Identified relations
Identified chunks

If you want yourself try this example, click here.

You can save this information with each document and use it to analyze and predict insights. This tool is so powerful and is available to work in multiple languages. In addition, you can look at other attractive models in John Snow LABS in healthcare and finance.

Conclusion

NLP is a powerful tool that can be used for various tasks, including the interpretation of legal content. In this article, we’ve looked at how NLP can be used to interpret legal documents and how it can be used to improve the accuracy of translations. We hope this has given you a better understanding of how NLP can be used in the legal industry and how it can benefit your business.
If you found this article helpful, please share it with your network! And if you have any questions or comments, please feel free to leave them below.

Let’s share more articles talking about Spark NLP For Legal:

Spark NLP For Legal 1.0.0: Over 300+ new state-of-the-art models in multiple languages!

Legal NLP 1.1.0 for Spark NLP has been released

Legal NLP 1.2.0 for Spark NLP has been released!

That’s it for today!

OpenAI Whisper – The Future of Conversational AI

OpenAI Whisper is a new artificial intelligence system that can achieves human level performance in speech recognition. This system was developed by OpenAI, an artificial intelligence research lab. The goal of this system is to improve the quality of speech-to-text systems. With a 1.6 billion parameters AI model that can transcribe and translate speech audio from 97 languages. Whisper was trained on 680,000 hours of audio data collected from the web and showed robust zero-shot performance on a wide range of automated speech recognition (ASR) tasks. This will benefit many applications, such as virtual assistants, smart speakers, and more.

This video can help you understand the benefits of the Whisper.

OpenAI introduced Whisper on September 21, 2022, in this article. This will accelerate the use of artificial intelligence in applications that need to make use of technology. Here are some examples:

You record in any language, and the API extracts the text.

Click on the image to open the app

In this example, the API extracts text from a YouTube video.

Click on the image to open the app

Let’s experiment using the OpenAI Whisper API in Python to extract the text from the YouTube video.

Python
# Author: Lawrence Teixeira
# Date: 02/11/2022

# Requirements to run this script:
#pip install git+https://github.com/openai/whisper.git
#pip install pytube

# import the necessary packages
import pytube as pt
import whisper

# download mp3 from youtube video (Indroductrion to Whisper: The speech recognition)
yt = pt.YouTube("https://www.youtube.com/watch?v=Bf6Z5bjlHcI")
stream = yt.streams.filter(only_audio=True)[0]
stream.download(filename="audio.mp3")

# load the model
model = whisper.load_model("medium")

# transcribe the audio file
result = model.transcribe("audio.mp3")

# print the text extracted from the video
print(result["text"])

Text extracted from the video “Introduction to Whisper: The speech recognition.”

“Whisper is an open source deep learning model for speech recognition that was released by Oppenai last week. Oppenai’s tests of Whisper show that it can do a good job of transcribing not just English audio, but also audio in a number of other languages. Developers and researchers who have worked with Whisper and seen what it can do are also impressed by it. But the release of Whisper may be just as important for what it tells us about how artificial intelligence AI research is changing, and what kinds of applications we can expect in the future. Whisper from Oppenai is open to all kinds of data. One of the most important things about Whisper is that it was trained with many different kinds of data. Whisper was trained on 680,000 hours of data from the web that was supervised by people who spoke different languages and did different tasks. A third of the training data is made up of audio examples that are not in English. Whisper can reliably transcribe English speech and perform at a state-of-the-art level with about 10 languages, an Oppenai representative told VentraBeat in written comments. It can also translate from those languages into English. Even though the lab’s analysis of languages other than English isn’t complete, people who have used it say it gives good results. Again, the AI research community has become more interested in different kinds of data. This year, Bloom was the first language model to work with 59 different languages. Meta is also working on a model that can translate between 200 different languages. By moving toward more data and language diversity, more people will be able to use and benefit from deep learning’s progress. Make your own test since Whisper is open source. Developers and users can choose to run it on their laptop, desktop workstation, mobile device, or a cloud server. OpenAI made Whisper in five different sizes. Each size traded accuracy for speed in a proportional way, with the smallest model being about 60 times faster than the largest. Developers who have used Whisper and seen what it can do are happy with it, and it can make cloud-based ASR services, which have been the main choice until now, less appealing. And Lobs expert Noah Giff told VentraBeat, At first glance, Whisper seems to be much more accurate than other SaaS products. Since it is free and can be programmed, it will probably be a very big problem for services that only do transcription. Whisper was released as an open source model that was already trained, and that anyone can download and run on any computer platform they want. In the past few months, commercial AI research labs have been moving in the direction of being more open to the public. You can make your own apps. There are already a number of ways to make it easier for people who don’t know how to set up and run machine learning models to use Whisper. One example is a project by journalist Peter Stern and GitHub engineer Christina Warren to make a free, secure, and easy to use transcription app for journalists based on Whisper. In the cloud, open source models like Whisper are making new things possible. Platforms like Hugging Face are used by developers to host Whisper and make it accessible through API calls. Jeff Bootyer, growth and product manager at Hugging Face, told VentraBeat, It takes a company 10 minutes to create their own transcription service powered by Whisper and start transcribing calls or audio content, even at a large scale. Hugging Face already has a number of services based on Whisper, such as an app that translates YouTube videos. Or, you can tweak existing apps to fit your needs. And fine-tuning, which is the process of taking a model that has already been trained and making it work best for a new application, is another benefit of open source models like Whisper. For example, Whisper can be tweaked to make ASR work better in a language that the current model doesn’t do as well with. Or, it can be tweaked to understand medical or technical terms better. Another interesting idea would be to fine-tune the model for tasks other than ASR, like verifying the speaker, finding sound events, and finding keywords. Hugging Face’s technical lead, Philip Schmidt, told VentraBeat that people have already told them that Whisper can be used as a plug-and-play service to get better results than before. When you put this together with fine-tuning the model, the performance will get even better. Fine-tuning for languages that were not well represented in the pre-training dataset can make a big difference in how well the system works.”

As you can see, the text is exactly what was spoken. Note that in this example, we use the intermediate model. Here are the models that we can use to increase the accuracy.

Available models and languages

There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and relative speed.

For English-only applications, the .en models tend to perform better, especially for the tiny.en and base.en models. We observed that the difference becomes less significant for the small.en and medium.en models.

Whisper’s performance varies widely depending on the language. The figure below shows a WER breakdown by languages of Fleur’s dataset using the large model. More WER and BLEU scores corresponding to the other models and datasets can be found in Appendix D of the paper.

The image is taken from the official Whisper documentation.

Conclusion: Although there is still some controversy around how well AI Whisper works, the concept behind it is something to think about. With more and more businesses moving towards automated marketing and customer service, AI Whisper could be a valuable tool for those looking to get ahead in the industry. Have you tried using AI Whisper or any other similar tools? Let us know in the comments!

Follow the official Whisper references:

Project link: https://openai.com/blog/whisper/
Code: https://github.com/openai/whisper

That’s it for today!

How to use Python in Google Colab integrated directly with Power BI to analyze patent data

This blog post will show you how to load and transform patent data and connect Power BI with Google Colab. Google Colab is a free cloud service that allows you to run Jupyter notebooks in the cloud. Jupyter notebooks are a great way to share your code and data analysis with others. Power BI is a business intelligence tool that allows you to visualize your data and create reports. Connecting Power BI with Google Colab allows you to easily share your data visualizations with others. Let’s get started!

What is a patent?

A patent is an exclusive right granted for an invention, which is a product or a process that provides, in general, a new way of doing something or offers a new technical solution to a problem. To get a patent, technical information about the invention must be disclosed to the public in a patent application.

What is WIPO?

WIPO is the global forum for intellectual property (IP) services, policy, information, and cooperation. WIPO’s activities include hosting forums to discuss and shape international IP rules and policies, providing global services that register and protect IP in different countries, resolving transboundary IP disputes, helping connect IP systems through uniform standards and infrastructure, and serving as a general reference database on all IP matters; this includes providing reports and statistics on the state of IP protection or innovation both globally and in specific countries.[7] WIPO also works with governments, nongovernmental organizations (NGOs), and individuals to utilize IP for socioeconomic development. If you need more information about WIPO, click here.

This video can demonstrate the Power BI functionality we will use today

Now, you understand what a patent is and what WIPO is. Let’s start our experiment!

First, we will load the patent data from WIPO. In this experiment, we will use the authority file from 2022.

Python
from powerbiclient import Report, models
from powerbiclient.authentication import DeviceCodeLoginAuthentication
import pandas as pd
from google.colab import drive
from google.colab import output
from urllib import request
import zipfile
import requests

# mount Google Drive
drive.mount('/content/gdrive')

file_url = "https://patentscope.wipo.int/search/static/authority/2022.zip"
	
r = requests.get(file_url, stream = True)

with open("/content/gdrive/My Drive/2022.zip", "wb") as file:
	for block in r.iter_content(chunk_size = 1024):
		if block:
			file.write(block)
   
compressed_file = zipfile.ZipFile('/content/gdrive/My Drive/2022.zip')

csv_file = compressed_file.open('2022.csv')

data = pd.read_csv(csv_file, delimiter=";", names=["Publication Number","Publication Date","Title","Kind Code","Application No","Classification","Applicant","Url"])

#Show the head data
data.head()

Now, we have the data let’s do some transformation to prepare to load in the Power BI report.

Python
# Transformations of the csv file dowloaded from wipo

#remove the two fisrt lines
data = data.iloc[1:]
data = data.iloc[1:]

#create a new column with the Classification name
data["Classification_Name"] = data["Classification"].str[:1]

#Modify this column with the classification description
data["Classification_Name"] = data["Classification_Name"].replace({
    'A': 'Human Necessities', 
    'B': 'Performing Operations and Transporting', 
    'C': 'Chemistry and Metallurgy', 
    'D': 'Textiles and Paper', 
    'E': 'Fixed Constructions', 
    'F': 'Mechanical Engineering', 
    'G': 'Physics', 
    'H': 'Electricity'
  }
)

#Show again the head data
data.head()

#Save the Excel file in google drive to share with the Power BI report.
data.to_excel("gdrive/MyDrive/datasets/Result_WIPO2022.xlsx")

After that, we will connect to Power BI and show the report inside Google Colab.

Python
# Import the DeviceCodeLoginAuthentication class to authenticate against Power BI and initiate the Micrsofot device authentication
device_auth = DeviceCodeLoginAuthentication()

group_id="YOU HAVE TO PUT HERE YOUR POWER BI GROUP ID OR WORKSPACE ID"
report_id="YOU HAVE TO PUT HERE YOUR POWER BI REPORT ID"

report = Report(group_id=group_id, report_id=report_id, auth=device_auth)
report.set_size(1024, 1600)
output.enable_custom_widget_manager()

# Show the power BI report with the wipo downloaded data.
report

Click here, to see this report in full-screen mode.

Follow here the Google Colab file with the Python code. If you want the Power BI report click here.

Conclusion

In this blog post, we showed you how to load data from external datasets, and transform and load in Power BI reports inside Google Colab. By following these steps, you can start using Google Colab and Power BI to analyze your data with Python and easily share it with others!

That’s it for today!