Forem: Alex Waiganjo

Python in Data Analytics

Alex Waiganjo — Mon, 11 May 2026 13:08:01 +0000

In today's fast digital world, data is shared, generated and updated at a very high rate due to the improved technology across the world. Organizations, businesses, persons all now becoming dependent on data in making decisions which require some expertise in interacting with the data effectively. Such expertise may include learning data analysis, data science and data engineering in an aim to extract, connect to existing data, transform it and visualize using specific tools and technologies, Python being one of them.

Python is a general purpose language used in Data Analytics, Machine Learning, Artificial Intelligence, Data Engineering and Automation. It is a beginner-friendly, concise, versatile high level interpreted programming language created by Guido van Rossum.

Why is Python the preferred language in Data Analytics:

It is easy to learn - It has a simple syntax which requires minimal effort to write programs as compared to other languages such as java.
Large ecosystem of libraries - Python provides 850+ of publicly accessible libraries used for data visualization, analysis, machine learning and more which enables data specialists to focus on the business use cases rather than spend time coming up with new ones.
Strong Community Support - Python has a strong community of developers and data professionals who share helpful information revolving around python through podcasts, tutorials, documentations, and open source projects.
Flexibility and Scalability - Python has tools which handle small and large datasets with ease. It also integrates easily with other tools used in connecting to databases, APIs, Data applications etc.
Integrates with Modern Data Tools - Over the years, developers in the python ecosystem have continued to develop and improve on the already existing tools such as Big Data Tools, Python itself, IDEs etc. Python has evolved and now can seamlessly integrate with modern tools such as:
- Apache tools (Kafka, Airflow, Spark, Luigi)
- MCPs (Model Context Protocols)
- Cloud Platforms such as AWS, Azure, GCP
- Databases eg Non SQL dbs(Cassandra, Mongo Db), Relational dbs(PostgreSQL).

Python Libraries used in Data Analysis include:

Pandas
Pandas plays a critical role in data analysis in that it enables:
- Data Loading and Integration: Pandas allow import/export and integrations of data in various formats such as CSV, Excel, JSON, and SQL Databases.
- Data Cleaning: It provides methodologies to handle missing values, removing duplicates and changing data types.
- Data Exploration: It provides ways to view summarized data through methods such as df.describe(), df.head(), df.info() to allow user to understand the data.
- Data Manipulation: It provides ways to filter out data based on specific rows and columns, perform aggregations etc
Matplotlib
Matplotlib is mostly used to create visual such as charts and graphs including line charts, bar graphs, pie charts, line/box plots, heatmaps and histograms.
NumPy
NumPy is used in performing statistical analysis, linear algebra and vectorized operations.

4.Scikit-learn
Scikit-learn is used for predictive analysis and machine learning methodologies supporting classification, regression clustering and evaluation techniques.

5.Seaborn
Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics such as plots, grids and maps.

How Python is used to clean, analyze and visualize data

Data Cleaning

Raw data is usually incomplete, inconsistent or in duplicates. Python uses pandas which helps clean such data before analysis through methods such as:

Removing duplicates
Filing missing values
Converting data types
Standardizing formats

Data Analysis

Using Pandas Python helps data analysts and analytics engineers uncover hidden insights from data through:

Statistical Analysis
Trend Analysis
Aggregation eg average, sum.
Correlation Analysis

Data Visualization

Visualizations help in laying out final data summarizations in graphs, charts, plots etc
Python uses Matplotlib in conveying such summarized visuals.

Real World Applications of Python in Data Analytics Include:

Python in Finance
Python uses pandas and scikit-learn in market prices and risk assessments which prevent heavy loses for hedge funds, stock exchange platforms and banks.
Python in Healthcare
Python is used to process massive datasets to identify markers for diseases like cancer, and brain diseases. Through the help of analysis doctors are able to identify and administer treatments early enough.
Python in Ecommerce
Python is used in predicting customer churn rates hence enabling businesses to come up with ways that encourage customers to continue shopping from their stores. Using Python, a logistic linear regression model can be of help in classifying customers based on their billing, support tickets and additional products/services purchase.
Python in Agriculture
Python is used in smart farming systems to analyze weather patterns, crop performance and soil data. IOT combined with Python can be integrated in ensuring high quality data pipelines are established for data delivery to data warehouses for cleaning and analysis.

Why should beginners learn Python?

Python is one of the best programming languages for beginners for a couple of reasons which are:

Beginner-friendly
Python has one of the easy to read and write syntax which is very enticing for starters. It has easy to understand concepts that anyone can grasp easily.
High demand in the current Job Market
In this AI and Data era, python stands out the most as it is used to build AI and Big Data Applications, do massive datasets analysis and predictions. Choosing Python would be a good decision.
Versatile Career Opportunities
Learning Python would enable one to switch to other python-related careers. For example learning python for Backend Software engineering using frameworks such as FastAPI and Flask, one would switch to become an Automation Engineer, AI Engineer, Data Engineer, Data Scientist, Data Analyst or even a Machine Learning Engineer. That is the beauty of learning Python.
Huge number of learning resources
There are a tone of learning and issue resolving platfrorms where beginners can learn python and post their python related questions for get help. Python communities are vibrant and always ready to help.

Conclusion

Python has become a dominant programming language in the data analysis filed due to its simplicity, flexibility, and vast ecosystem of libraries. It allows analysts to clean, process, analyze and visualize data efficiently while integrating with modern technologies. For beginners interested in technology, data analytics, engineering and data science learning Python is a valuable investment that can lead to impactful real world applications. Thank you for taking time to read this article, till next time!

Mastering Commonly Used SQL Joins: With Relatable Examples

Alex Waiganjo — Wed, 22 Apr 2026 20:03:00 +0000

Introduction

SQL is a defacto language when it comes to interacting with data in Data Storage containers such as relational databases, data warehouses, data lakes etc.

Whether you are professional such as a data engineer, analyst, scientist, analytics engineer or a motivated pal interested in tinkering with data, one needs to master SQL Joins.

For SQL joins to work, a relationship needs to be identified in order to join data from different tables. This is achieved using foreign and primary keys.

In this guide we'll look at how joins work using PostgreSQL as our database management system and DBeaver as our SQL Editor.

Primary vs Foreign Keys

A primary key is a specific column (or a combination of columns) that uniquely identifies every record in a database table.
A Foreign Key is a column in one table that points to the primary key of another table, creating a "link" between them.

The Example below cleary shows the primary and foreign keys in related tables.

Database name: Company

Tables:

  - Employees
  - Departments
  - Projects

Fig 1. Employees Table Columns

Columns Preview:

employee_id <-- Primary Key
name
department_id <-- Foreign Key
manager_id    <-- Foreign Key
salary

Fig 2. Departments Table Columns

Columns Preview:

department_id <-- Primary Key
department_name

Fig 3. Projects Table Columns

Columns Preview:

project_id <-- Primary Key
project_name
employee_id  <-- Foreign Key

SQL Joins Deep Dive

We have a variety of ways to compile data from table(s) using different types of joins. Examples Include:

1. INNER JOIN

It returns rows only when there is a match in both tables. If an employee isn't assigned to a project, they won't show up here.

Example:

select e.name,  p.project_name
from employees as e
inner join projects as p
on e.employee_id = p.employee_id;

Output:

NOTE: Use only when you want records that are complete ("Find all employees and their assigned projects").

2. LEFT JOIN

It returns all records from the left table, and the matched records from the right table. If there is no match, the result is NULL on the right side.

Example:

select e.name,  p.project_name
from employees as e
left join projects as p
on e.employee_id = p.employee_id;

Output:

3. RIGHT JOIN

It returns all records from the right-hand table and the matched records from the left-hand table. If there is no match for a record in the right table, the result will contain NULL values for the columns originating from the left table.

Example:

select  d.department_name, e.name
from departments as d
right join employees as e
on d.department_id = e.department_id;

Output:

4. FULL OUTER JOIN

It returns all records when there is a match in either left or right table records. It’s like a combination of Left and Right joins.

Example:

select e.name, d.department_name
from employees as e
full join departments as d
on e.department_id = d.department_id;

Output:

The above practical joins are the most commonly used when querying data, other joins include:

Cross Joins
Self Joins

Conclusion

Understanding Joins is more than just connecting tables, it’s about writing efficient, scalable queries. Whether you’re mapping organizational data, the goal is to have clean data retrieval with minimal overhead. Happy querying!

Practical SQL Concepts

Alex Waiganjo — Sun, 12 Apr 2026 22:29:12 +0000

Introduction

In today's world data driven world, data is becoming more valuable in that it can help organizations, persons, and companies in informed decision-making, enhanced customer experience and increased revenue & innovation among many other benefits. Using examples such as online stores, hospitals, digital entertainment spaces, digital learning websites and digital booking apps, SQL is widely used in making sure data is stored and turned into meaningful insights. SQL in this case is crucial tool in the data space and we'll be diving into it throughout this article.

What is SQL? SQL in full means Structured Query Language. It is a programming language used to manage, manipulate and retrieve data from data stores such as relational databases. It is mostly used by Data and Software professionals such as Software Engineers, Data engineers, Data Analysts, Data Scientists, Analytics Engineers, Database Administrators and Business Intelligence specialists.

What is a Relational Database This is a data storage tool used to host data in rows and columns in form of tables. A database organizes data in tables which are then organized stored inside a database as the main container. Examples of relational databases include PostgreSQL, MYSQL, MariaDB, SQLite, Oracle and Microsoft SQL Server.

Core Components of SQL

SQL is divided into several subcategories category wise. Please note that the core of these subdivisions is still SQL only that the components are ways to define, model and control data. Some of the components include:

Data Definition Language(DDL) - Used to define and modify database structures. Common commands include:
- CREATE - Creates databases and tables.
- ALTER - Modifies existing databases and tables.
- DROP - Deletes databases, tables etc
- TRUNCATE - Removes all data from a table.
Data Manipulation Language(DML) - Used to define and modify database structures. Common commands include:
- SELECT - Retrieves data from one or more tables.
- INSERT - Adds new records.
- UPDATE - Modifies existing records.
- DELETE - Removes records from a table.
Transaction Control Language(TCL) - Used in ensuring data integrity during transactions. Common commands include:
- COMMIT - Permanently saves changes.
- ROLLBACK - Undoes previous changes.

What is the difference between DDL and DML?

DDL changes the schema of the database and statements are auto-commited hence ussually permanent. DML is used to query and modify a database. Changes can be rolled-back if they are inside a transaction.

Practical Examples of using CREATE, INSERT, UPDATE and DELETE.

Using CREATE to make a students table

--Create Students Table
CREATE TABLE students (
  student_id INT PRIMARY KEY,
  first_name VARCHAR(50) NOT NULL,
  lastname VARCHAR(50) NOT NULL,
  gender VARCHAR(1),
  date_of_birth DATE,
  class VARCHAR(10),
  city VARCHAR(50)  
);

Using INSERT to feed data into the students table

-- Insert All 10 Exam Results into the Exam Results Table 
insert into students(student_id,first_name,last_name, gender,date_of_birth, class, city)
values
(1,'Amina','Wanjiku','F','2008-03-12','Form 3','Nairobi'),
(2,'Brian','Ochieng','M','2007-07-25','Form 4','Mombasa'),
(3,'Cynthia','Mutua','F','2008-11-05','Form 3','Kisumu'),
(4,'David','Kamau','M','2007-02-18','Form 4','Nairobi'),
(5,'Esther','Akinyi','F','2009-06-30','Form 2','Nakuru'),
(6,'Felix','Otieno','M','2009-09-14','Form 2','Eldoret'),
(7,'Grace','Mwangi','F','2008-01-22','Form 3','Nairobi'),
(8,'Hassan','Abdi','M','2007-04-09','Form 4','Mombasa'),
(9,'Ivy','Chebet','F','2009-12-01','Form 2','Nakuru'),
(10,'James','Kariuki','M','2008-08-17','Form 3','Nairobi');

Using UPDATE to modify the students table

-- UPDATE marks from 49 to 59
update  students
set city = 'Nairobi'
where student_id =5;

Using DELETE to remove data in the exam results table

-- DELETE Exam Result with id =9
delete from exam_results
where result_id =9;

Using the WHERE keyword

WHERE is used in filtering out specific data. eg writing a query to find a student whose student id number is 209 would be written as:

select student_name from students where student_id = 209;

WHERE is normally accompanied with other keywords such as

LIKE - Used to return records starting or ending with specific letters

-- Get all Students whose First name starts with letter A or E
select * from students
where first_name like 'A%' or  first_name like 'E%';

BETWEEN - Used to filter records that fall in certain ranges. Eg Dates, Prices, Marks etc

-- Get all Exam Results of marks between 50 and 80
select * from exam_results
where marks between 50 and 80;

IN - Used to filter records that have multiple required values for a specific column. eg

-- Get all Students who live in Nairobi, Mombasa or Kisumu
select * from students
where city IN('Nairobi','Mombasa','Kisumu');

Using the CASE WHEN keyword

Case When is used as to add the IF-THEN-ELSE logic to queries. It evaluates a list of conditions and returns a result when the first condition is met. See the sql example below.

-- Use Case-When to classify marks in Categories
select * from exam_results;
select *,
case 
    when marks >=80 then 'Distinction'
    when marks >=60 then 'Merit'
    when marks >=40 then 'Pass'
    when marks <40 then 'Fail'
end as performance
from exam_results;

Conclussion

SQL is an interesting language to tinker aroud with, especially while working with relatable data. Learning to insert, modify, delete and query data has been a fullfiling experience and I will continue to learn complex SQL concepts as I enjoy my journey to becoming a skilled Data Engineer. If you would like to learn more SQL Advanced concepts, feel free to follow me as I will be dropping another article soon. Happy coding!

How to Embed a Power BI dashboard in a Web Page

Alex Waiganjo — Sun, 05 Apr 2026 18:34:50 +0000

Introduction

This a comprehensive guide on how to publish and display a Power BI dashboard in a web page.

Here are a couple of terms that will be mentioned in this article, feel free to skim through them.

Power BI is a Microsoft business intelligence platform that transforms data from multiple sources into interactive, visually rich insights for analysis and decision-making.

Power BI dashboard is a single-page, interactive canvas that provides a high-level view of key business metrics and insights, consolidating data from multiple sources into visual tiles.

Web Page is a document, commonly written in HTML (hypertext markup language) viewed in an Internet browser and can be accessed by entering a URL (uniform resource locator) address into a browser's address bar. It may contain text, graphics, and hyperlinks to other web pages and files.

Steps to Follow:

Download Power BI App
Login to your Power BI account.
Load some data in the app.
Create your Workspace in the Power BI Service.
Publish your dashboard into your workspace.
Embed and view the dashboard in the web page.

1. Download Power BI App

Use this link to get a local copy of the analytics app on your device. Power BI desktop app

2. Login to your Power BI Account.

Navigate to the top right corner of your Power BI Desktop.

Go to sign in, login with your work/organization email. Using your personal email may not be accepted, use any work/org emails. Interact with the features in the app just to get a glimpse of how to navigate and use most components. The app itself is straight forward, in case of any struggles using it, feel free to learn the tool in depth using Microsoft Learn Resources

3. Load Some data in the Power BI App

Get data from your preferred source, load/transform and connect it to Power BI. Create a simple visualization e.g., one that includes a bar chart, pie chart or a line graph with clear descriptions of the data visualized. Here is simple dashboard of what I mean.

4. Create your workspace in the Power BI Workspace.

To create the workspace, you'll need to have logged into Power BI desktop, remember to use your work/org email. Go to top right corner, click your username, and click "Power BI Service". Login and head over to Power BI Service. Here you will view existing workspaces from other people and get to create/edit your personal workspace(s) as well. See the current step in the image below.

5. Publish your dashboard into your workspace.

Use the dashboard you created earlier for this, go to publish(top center position) feature in your Power BI app. Click publish, save your changes and choose the personal workspace you created.

Now the dashboard and data will be uploaded into your preferred workspace.

Navigate to your workspace and locate the dashboard, click the dashboard and view it.

6. Embed and view the dashboard in the web page.

Now our dashboard and data is in the Power BI service and it can be viewed as well. We now want to view it in a web page.

Click > file(top left corner), go to embed report, then website or portal. You'll see a pop up with some options to customize your embedded link.

Copy the iframe element(HTML Visual embedding element). Create or use an existing html page. Feel free to use this html template.

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Embeding a Power BI Dashboard in a Web Page</title>
    <style>
        p{
            text-align: center;
        }
    </style>
</head>
<body>
    <p>Embeding a Power BI Dashboard in a Web Page</p>
    <!-- Copy the iframe element content here -->

</body>
</html>

Save the html file. Remember to save the file using a .html extension. Locate your file and open it in a browser or open the file from your code editor using live server.

Here is the final output from the browser. Login in to view the dashboard.

We have successfully gone through the 6 steps and our dashboard can be viewed from a web page. In case of any issues or inquiries, feel free to drop them in the comments section below. Till next time, keep pushing beyond your limits!