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Connect your Jupyter Notebook to the BigQuery

Hi,

This is Jing, a data analyst with great passion for data science and big data technology. This blog gonna to show you how to connect your Jupyter Notebook to the BigQuery by using a service account.

Prerequisite

Before we start, I assume you already have Jupyter Notebook installed on you computer and have a project on the google cloud platform with data saved in BigQuery.

In this blog, I created a project called “helloworld”, with a dataset called “example_dataset” in the my BigQuery. I am going to show you how to get the data from BigQuery on the google cloud from my Jupyter Notebook on my local computer, MAC to be more specific.

Let’s begin

Step 1: Enable BigQuery API on the Google Cloud Platform Console

Search for the BigQuery API by using the search function on the Google Cloud Platform console and enable it. You should see something like this after you enabled it:

Step 2: Get authentication file for your Jupyter Notebook

Option 1: Create your own default application login in terminal if you already have Cloud SDK installed

Open you terminal -> type in the command below.

gcloud auth application-default login

Make sure application_default_credentials.json is created

ls ~/.config/gcloud/

Option 2: A service account key for the BigQuery in a JSON File. Recommended for scripts which will be used as a service later on. e.g. running on a virtual machine

The json file of service account key is used to authenticate your Jupyter Notebook on your local PC which will access the BigQuery later on.

Go to GoogleCloudPlatform -> API&Services -> Credentials

Get a service account by Create Credentials -> Service Account key

Select account -> new service account, give a name to your new service account, here we name it as “bq-jupyternotebook”. To limit the permission of this service account, we choose “BigQuery Admin” as the role for this service account. We want full access of BigQuery, but only to BigQuery, not other google products in this project. As for detailed information about service account, please refer to Service Accounts in Google Cloud

Click on “Create”, then you get the key for this service account in a json file. Put this json file in a folder which you created for your project. In this blog, we put it in a “key” folder under a folder called “helloworld” and also renamed it as “helloworld-key.json”.

Step 3 : Install the Google BigQuery API Client Libraries for Python on your computer

The google BigQuery api client python libraries includes the functions you need to connect your Jupyter Notebook to the BigQuery.

Open you terminal -> type in the command below. More information about Google BigQuery API client library

pip install google-cloud-bigquery

Step 4 : Set the environment variable GOOGLE_APPLICATION_CREDENTIALS for your python script

Set the environment variable called “GOOGLE_APPLICATION_CREDENTIALS” for pointing your Notebook to your service account key which you just created in Step 2. This variable lets Google knows where to find your authentication credentials. Include the following code in your Jupyer Notebook.

For the option 1 in Step 2

import os
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '~/.config/gcloud/application_default_credentials.json'

For the option 2 in the Step 2

import os
 os.environ['GOOGLE_APPLICATION_CREDENTIALS'] ='/Users/jinjing/Desktop/helloworld/key/helloworld-key.json'

Be careful with the path to the service account key json file, you need to specify the whole path. Look at the example:

Step 5 : Open your Jupyter Notebook, let’s get data from the BigQuery

Create a new Jupyter Notebook in your folder for your project, and look at the example code to see how it works. More examples for using Python Client for BigQuery For option 1, you need to specify which project you are querying for, e.g.

bigquery_client = bigquery.Client(project='prodounf-yew-217512')

Congratulations! We are done here!

Now you should have successfully connected your Jupyter Notebook to the BigQuery and gotten some data! Enjoy your data analysis and have fun!

This is not something amazing, but hopefully will make your work a little bit easier! This is Jing, thanks for reading my blog! Feel free to commend and ask questions! I will get back to you as much as possible.

Delete user with Google Analytics User Deletion API to be GDPR compliant

Hi,

This is Jing, a data analyst with great passion for data science and big data technology. The EU General Data Protection Regulation (GDPR) is really a big thing that happens in this year. As a customer, feel more secure about my personal information online thanks to GDPR. Though as a data analyst, working with e-commerce, GDPR leads to some problems to my daily work. This blog is gonna to show you how to use User Deletion API provided by Google Analytics to delete users’ personal information to be GDPR compliant simply in a Google Spread Sheet.

Delete Bulk of Users with User Deletion API

Prerequisite

Before we start, I assume you have an “Edit” level access to a Google Analytics view of a website. Besides, I also assume you are familiar with Google Analytics, for example, you know what is client ID, segment, event label, customer dimensions and etc.

Besides, you need to make sure your Google Cloud Platform is activated. For this project, it almost cost you like nothing. So, don’t worry about it.

Use Case : Delete a list of Segmented Users

To make a better explanation, I continue to use the use case I made up earlier in the blog Get Google Analytics data to your Jupyter Notebook by using Analytics Reporting API . Let’s imagine a scenario where for certain kind of reasons, some users’ phone numbers are saved in the GA Event Labels during their online sessions because of some tracking setups. In earlier blog, I have shown you how to find the list of Segmented Users. Now, I am gonna show you how to delete them at one click to avoid potential GDPR issues in a Spread sheet!

Let’s begin!

Step 0 : Get the list of users’ client ID you want to delete

Case 1: You already have a list of users’ client ID you want to delete. Then skip this step.
Case 2: You know what kind of users you want to delete and know how to create a segment to find them. Then refer to my another blog Get Google Analytics data to your Jupyter Notebook by using Analytics Reporting API. It may help you get the list little easier.

Step 1 : Create a Google Spread Sheet to use User Deletion API

Here is a template Google Spread Sheet I’ve already created to put your list of client IDs. Feel free to make a copy of it to build your owns on. If you are not interested in how to create this sheet, then Congratulations! We are done here.

Otherwise, please follow in steps:

Create a Google Spread Sheet
Tools -> Script Editor -> Edit project name, I named it as “user_deletion_API”

Modify the Google APPs script as below:

      
        function deleteUser() {
      
          var activeSpreadsheet = SpreadsheetApp.getActiveSpreadsheet();
      
          var sheet = activeSpreadsheet.getSheetByName('Main');
      
          var resultsheet = activeSpreadsheet.getSheetByName('Result');
      
          var userInputs = sheet.getRange("A3:B").getValues();
      
          var webPropertyId = String(userInputs[0][0]);
      
          var numClients = userInputs[0][1]
      
          for (var i = 0; i<numClients;i++){
      
            var typeOfId = String(userInputs[2+i][1]);
      
            var userId = String(userInputs[2+i][0]);
      
            try {
      
              var request = {
      
                "kind": "analytics#userDeletionRequest",
      
                "id": {
      
                  "type": typeOfId,
      
                  "userId": userId
      
                },
      
                "webPropertyId": webPropertyId
      
              };
      
            Analytics.UserDeletion.UserDeletionRequest.upsert(request);
      
            resultsheet.appendRow(["'" + userId, 'yes']); 
      
          }catch(error) {
      
            Browser.msgBox(error.message);
      
          }
      
        }
      
        }

view raw user_deletion.gs hosted with ❤ by GitHub

Go to Resources -> Advanced Google Services -> Enable Google Analytics API

Go to Resources -> Cloud Platform project -> currently associated project

Click on the project ID -> Google Cloud Platform -> APIs & Services -> Library

Search for Analytics API -> Enable it

Make it look nice
Rename current sheet as “Main” -> Create a new sheet “Result” -> Add a function to cell “B3”

Fill in your Google Analytics Property ID
Download a button you like from Google
Insert a image -> Assign script -> Type in the function name in Google APPS Script we created earlier. It calls “myFunction” in this case.

Finally it looks like below:

Click on “Start” Button, the scrip will run automatically
First time you run the script, it requires you to authenticate the script. Just follow the instruction from Google.
If it runs successfully, the result will show in “Result” sheet.

Step 2 : Put the list of users’ client ID in a Google Spread Sheet

Case 1: If you find it is convenient enough to manually cope paste the list to the Google Spread Sheet. Then Congratulations! You can begin use the shared template Google Spread Sheet to delete users!
Case 2: If you were in case 2 also in Step 0, then refer my another blog Connect your Jupyter Notebook to Google Spread Sheet.

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view raw Automate_User_Deletion.ipynb hosted with ❤ by GitHub

Here is a Jupyter Notebook template if you were in case 2 in both Step 0 and Step 2.

Find my complete code at GitHub

Congratulations! We are done here!

Now you should be able to delete a lot of users by one click! It might take some to setup all these things. But would you like to manually delete hundreds of users? Besides, what you have done here, you can easily reuse it next time!

Connect your Jupyter Notebook to Google Spread Sheet

Hi,

This is Jing, a data analyst with great passion for data science and big data technology. As a data analyst, working with Excel or Google Spread Sheet is sometimes inevitable, considering the reality that a lot your colleagues are more comfortable with seeing data or results in Excel or Google Spread Sheet. This blog gonna to show you how to connect your Jupyter Notebook to Google Spread Sheet to make your workflow more automatic.

Prerequisite

Before we start, I assume you already have Jupyter Notebook installed on you computer.

Let’s begin!

Step 0 : Create a project and Enable Google Drive API

Create a project for your application in Google Cloud Console if you do not have one. In this blog, I created a project called “user deletion”, which I used in another blog “Get Google Analytics data to your Jupyter Notebook by using Analytics Reporting API“

Go to APIs & Service -> Library, find the Google Drive API and enable it.

Step 1 : Create a Service Account for your Jupyter Notebook

Go to APIs & Services -> Credentials -> Create credentials -> Service account key -> New service account, named it as “Google_Sheet” -> Select role as “Service Account User” for this account -> select “JSON” -> click on “Create”. Download it, rename it as “service_account_GS.json” and put it in a folder you are comfortable with.

Step 2 : Create a Google Sheet and share it with Service Account E-mail

Open the “service_account_GS.json” you’ve just gotten. Find your “client_email”

Share the Google Sheet you just created with this “client_email”

Get the Sheet ID from the URL to the sheet

Step 3: Install the libraries

Open your terminal, type in the following command

pip install gspread oauth2client df2gspread

Step 4: Open your Jupyter Notebook

See the Notebook template here

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view raw Google_Drive.ipynb hosted with ❤ by GitHub

Now you should see the Google Sheet is updated with the data you uploaded!

Congratulations! We are done here!

Now you should have successfully connect your Jupyter Notebook to a Google Sheet! Find more information about Google Spread Sheet at df2gspread.

Get Google Analytics data to your Jupyter Notebook by using Analytics Reporting API

Hi,

This is Jing, a data analyst with great passion for data science and big data technology. This blog gonna to show you how to using Google Analytics Reporting API to get Google Analytics data to your Jupyter Notebook or save to Excel.

Prerequisite

Before we start, I assume you already have Jupyter Notebook installed on you computer and have access to a Google Analytics view of a website. Besides, I also assume you are familiar with Google Analytics, for example, you know what is client ID, session, event label, customer dimensions and etc.

Use Case : Get a list of segmented users’ client ID

To make a better explanation, I made up a use case which may sounds silly. Let’s imagine a scenario where for certain kind of reasons, some users’ phone numbers are saved in the GA Event Labels during their online sessions because of some tracking setups. We want to find out these users’ client ID and then later on we want to delete them to avoid potential GDPR issues.

In this blog, I will show you how to find these users and get a list of these users’ Client ID in your Jupyter Notebook. And then later on, I will show you how to delete these users in GA with once click in another blog.

In this use case, your Jupyter Notebook is an application, sending request to Google Analytics server. So, your Jupyter Notebook is also a client to Google, which also have a Client ID. This might be confusing with client id in Google Analytics. Sorry for this.

Let’s begin!

Step 0 : Create a project and Enable Google Analytics Reporting API

Create a project for your application in Google Cloud Console if you do not have one. In this blog, I created a project called “user deletion”.

Go to APIs & Service -> Library, find the Google Analytics Reporting API and enable it.

Step 1 : Create the client credential for your Jupyter Notebook for requesting Google Analytics

Go to APIs & Services -> Credentials -> Create credentials -> OAuth client ID

Select Other, name our client ID as “Jupyter_Notebook”, click on Create -> OK

Then you can find your client credentials on the Credentials page. By clicking on the download button, you can download your client credential as a json file. Rename it as “client_secret.json” and put it in a folder you are comfortable with.

Step 2: Install the client library for your Jupyter Notebook

If you have done this before, skip this. Otherwise, Open you terminal -> type in the command below. More information about Google API Client Libraries for Python.

sudo pip install --upgrade google-api-python-client

pip install --upgrade oauth2client

Step 3: Create a segment in Google Analytics to find the target users

Go to User Explorer -> Add Segment -> Conditions, write an Regex to find the users whose event labels have “phoneNumber =”. Then save this Segment as “phoneNumber”

Step 4: Clone the code from jing-jin-mc Github

https://github.com/jing-jin-mc/GA_reporting_API

In the repository, you have all the code you need. But there are some-places you need to modify to make this code work for you.

Step 5: Get the segment ID and Custom Dimension ID

Go to Query Explorer, authenticate ourselves and get the segment ID and Custom Dimension ID for the Client ID in Google Analytics. When we type in Client ID, we know Custom Dimension 42 (in our website) is set for Client ID.

Similar way to get the segment id for the segment we just created:

Step 6: Jupyter Notebook template for Getting data from Google Analytics

Now, open the Jupyter Notebook template and modify the code with the information you have gotten. For the first time to run the template, you will need to authorise your notebook in the web browser. Just follow the instruction from Google and go back to your Notebook , you will see it is done.

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view raw Get_ClientID_List.ipynb hosted with ❤ by GitHub

Congratulations! We are done here!

Now you should have successfully gotten Google Analytics data for this use case in your Jupyter Notebook! Besides, I will show you how to upload the data frame to a Google Sheet in another blog to avoid copy paste issues.