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.

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

file_url = ""
r = requests.get(file_url, stream = True)

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

csv_file ='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

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

# 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

#Save the Excel file in google drive to share with the Power BI report.

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

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


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

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

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.


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!

Twitter Sentiment Analysis using Open AI and Power BI

This article is an experiment that explains how to use an Open AI to predict the sentiment analysis and gender in recent tweets for a specific topic and show the result in a Power BI dashboard.

What is Open AI?

The Open AI model is trained on a dataset of 3.6 billion Tweets. The training process takes about 4 days on 8 GPUs. After training, the model can accurately predict the sentiment of a tweet with 85% accuracy. The model can also be fine-tuned to accurately predict the sentiment of tweets from a specific Twitter user with 90% accuracy.

How does it work?

You input some text as a prompt, and the API will return a text completion that attempts to match whatever instructions or context you gave it.

You can think of this as a very advanced autocomplete — the model processes your text prompt and tries to predict what’s most likely to come next.

This video explains better how Open AI works

In our case, we use the expression, “Decide whether a Tweet’s sentiment is positive, neutral, or negative. Tweet:“, to extract the sentiment, and, “Extract the gender and decide whether a name´s gender is male, female, or unknown. Name:“, to extract the gender from the user name.

How does the experiment work?

The Python script gets the recent tweets about a topic and analyzes the sentiment and the gender of the text of each tweet. After that, the result is saved in an Excel file. I don’t recommend it because it can get slow, but it’s possible to run the Python code directly from Power BI. Follow the instructions here.

Before executing the Python script, you must create an account in Twitter develop and Open AI to obtain the “BEARER_TOKEN” and the “OPEN AI KEY” respectively.

Follow below the Python code:

# Twitter sentiment analysis using Open AI and Power BI
# Author: Lawrence Teixeira
# Date: 2022-10-09

# Requirements
# pip install tweepy==4.0
# pip install openai

# Import the packages
import pandas as pd
import tweepy
import openai

# Connect to Twitter API

# create your client
client = tweepy.Client(bearer_token=MY_BEARER_TOKEN)

# Functions to extract sentiment and gender with Open AI API
# if you want to know more examples about how to use Open AI click [here](


def Generate_OpenAI_Sentiment(question_type, openai_response ):
    response = openai.Completion.create(
      prompt= question_type + ":/n/n" + format(openai_response) +"/n/n Sentiment:",
    return response['choices'] [0]['text']

def Generate_OpenAI_Gender(question_type, openai_response ):
    response = openai.Completion.create(
      prompt= question_type + ":/n/n" + format(openai_response),
    return response['choices'] [0]['text']

# Query search for tweets. Here your can put whatever you want.
# if you want to know more about que Twitter query parameters click [here](
query = "#UkraineWarNews lang:en"

# if wnat to your start and end time for fetching tweets
#start_time = "2022-10-07T00:00:00Z"
#end_time   = "2022-10-08T00:00:00Z"

# get tweets from the API
tweets = client.search_recent_tweets(query=query,
                                     tweet_fields = ["created_at", "text", "source"],
                                     user_fields = ["name", "username", "location", "verified", "description"],
                                     max_results = 100,

## Create a data frame to save the results
tweet_info_ls = []
# iterate over each tweet and corresponding user details
for tweet, user in zip(, tweets.includes['users']):
    tweet_info = {
        'created_at': tweet.created_at,
        'text': tweet.text,
        'source': tweet.source,
        'username': user.username,
        'location': user.location,
        'verified': user.verified,
        'description': user.description,
        'Sentiment': Generate_OpenAI_Sentiment("Decide whether a Tweet's sentiment is positive, neutral, or negative. Tweet", tweet.text ),
        'Gender': Generate_OpenAI_Gender("Extract the gender and decide whether a name´s gender is male, female, or unknown. Name", ),
        'Query': query.rsplit(' ', 2)[0]
# create dataframe from the extracted records
tweets_df = pd.DataFrame(tweet_info_ls)

# remove the timezone format
tweets_df['created_at'] = tweets_df['created_at'].dt.tz_localize(None)

# if your use Google Colab, save the result of a csv file in the Google Drive

# if your want to insert direct in Power BI

Once you execute this Python code and refresh the Power Bi report, you will see the analysis result. In my case, I chose UkraineWarNews. It’s interesting to see in the Power Bi dashboard, that 78% are negative tweets 16% of positive and 33% are male versus 5% female. You can interact with this report by clicking on the visuals.

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

Important: This experiment gets only the last 100 tweets to analyze, and gender is defined only by the spelling of the name and not by the sexual orientation of each individual.

You can download the Power BI report here, and, the version of the Python code in Google Colab here.

There are a lot of possibilities for using this solution in the real world. The Open AI has a lot of other examples like extracting keywords, text summarization, grammar correction, restaurant review creator, and much more. You can access all the examples here. If you have questions about the solution, feel free to comment in the box below.

That´s it for today.

Power BI Licensing Explained

Power BI is a data visualization and business intelligence tool from Microsoft. It allows users to connect to, visualize, and analyze data with greater speed, efficiency, and understanding. In order to use Power BI, you need to purchase a license. But what kind of license should you get? Read on to find out the different types of Power BI licensing and which one is right for you.

Power BI Desktop

Power BI Desktop is a free application that can be downloaded from the Microsoft website. It can be used by individuals or groups working together who want to create reports and visualizations based on their data. This version of Power BI is best for small businesses or teams who want to get started with data visualization and don’t need advanced features or collaboration tools.

Power BI Pro

Power BI Pro is a paid subscription that gives users access to additional features not available in Power BI Desktop. These features include sharing and collaboration tools, support for larger data sets, and more advanced data manipulation and visualization capabilities. Power BI Pro is best for small to medium businesses that need more than just the basics from their data visualization tool.

Power BI Premium

Power BI Premium is a scalable subscription plan that is designed for enterprise-level businesses. It provides all the features of Power BI Pro, plus the ability to host reports and visualizations on your dedicated server infrastructure. This makes it ideal for large businesses with complex data analysis requirements.

Premium Per User (PPU)

Premium Per User (PPU) is a new way to license premium features on a per-user basis and includes all Power BI Pro license capabilities, along with features like paginated reports, AI, and other capabilities that previously were only available with a Premium capacity. With a PPU license, you do not need a separate Power BI Pro license, as all Pro license capabilities are included in PPU.

Users must have a Premium Per User (PPU) license to access content in a Premium Per User (PPU) workspace or app. This requirement includes scenarios where users access the content through the XMLA endpoint, Analyze in Excel, Composite Models, and so on. You can grant access to users to the workspace who don’t have a PPU license, but they will receive a message stating they cannot access the content. They’ll then be prompted to get a trial license if they are eligible. If they aren’t eligible, they must be assigned a license by their Admin to gain access to the resource.

The following table describes who can see which kinds of content with PPU.

Premium Per User (PPU) works with Power BI embedded similarly to a Power BI Pro license. You can embed the content, and each user will need a PPU license to view it.

This video explains about the types of Power BI licenses


What type of Power BI license is right for you? If you’re an individual or small team just getting started with data visualization, Power BI Desktop will probably suffice. If you need more advanced features and collaboration tools, then you

Follow the Power BI licensing page:

Getting Started with Data Analytics in Power BI

If you’re new to Power BI, you might wonder how to get started with data analytics. After all, Power BI is a complex tool with many features and functionality. Don’t worry, though – in this blog post, we’ll walk you through the basics of data analytics in Power BI, so you can get started on your journey to becoming a Power BI pro!

Power BI is a business intelligence and data visualization tool from Microsoft. It allows users to connect to various data sources, analyze, and create interactive visualizations. Power BI is a powerful tool that can help businesses make better decisions by giving them insights into their data. However, it can be daunting for newcomers to learn how to use all of its features.

This Youtube channel can help you with everything about Power BI.

There are three main components to Power BI: data sources, reports, and dashboards. Data sources are the places where your data is stored, such as databases, spreadsheets, or text files. Reports are how you visualize your data, and dashboards are focal points for your most important metrics and visuals.

To get started with data analytics in Power BI, you’ll need to connect to a data source. You can do this by going to the Get Data button on the Home tab and selecting the type of data source that you want to connect to. Once connected to a data source, you can start exploring your data by creating reports. To do this, go to the Reports tab and select New Report. This will open up the Report Canvas, where you can drag and drop different fields from your data source onto the canvas to create charts and visuals.

How to connect to the data sources in Power BI

Once you’ve created some reports, you can add them to dashboards so that they’re always front-and-center when you open Power BI. To do this, go to the Dashboards tab and select New Dashboard. Then, click on the Add tile button and select Add report. Select the report you want to add from the list and click Add report again. Your report will now be added to the dashboard!


Now that you know the basics of data analytics in Power BI, it’s time to start exploring all of its features! Connecting to data sources, creating reports, and adding them to dashboards is just the beginning – there’s so much more than Power BI can do. Stay tuned for more blog posts about Power BI so that you can learn everything that this incredible tool has to offer!