The Shift from Excel to Python in Modern Data Analytics
Excel has been a firm favourite for working data analyst professionals for many years and for good reason. Its wide capabilities and ease of use have made it critical in all manners of business, education, finance and research. With over 800 million active users, it’s still the default for sharing reports and analyzing data in many organizations.
But the data world is evolving.
Enter Python. This programming language has gained traction over recent years. One report states that the demand for Python skills, as a requisite in job postings, has increased by 27.6 percent in the last year and shows no signs of slowing down. Initially built as a way to write scripts that ‘automate the boring stuff’, Python has become a leader in web development, data analysis and infrastructure management.
In this blog, we explore why data analyst are shifting from Excel to Python, why the transition matters, and how professionals can future-proof their data toolkit in a rapidly changing landscape.
Overview of Data Analytics
Data analytics is the practice of examining raw data to uncover meaningful insights and draw conclusions. By identifying patterns and trends within datasets, data analytics supports informed decision-making and enhances understanding of the information at hand.
Steps in the Data Analytics Process:
Step 1: Define Data Grouping Criteria
The first step involves deciding how the data will be categorized. This could be based on factors such as age, gender, income level, or population. Data may be either numerical (quantitative) or categorical (qualitative).
Step 2: Data Collection
Data is gathered from various sources, including digital platforms, computer systems, internal staff, and external community sources. Reliable data collection is key to accurate analysis.
Step 3: Data Organization
Once collected, the data must be structured and arranged for easy examination. This is typically done using spreadsheets or specialized statistical software tools that facilitate further analysis.
Step 4: Data Cleaning
Before analysis, the dataset undergoes a cleaning process to correct any inconsistencies, errors, or missing values. This step ensures that the information is accurate and complete, reducing the risk of flawed conclusions.
Performing Data Analysis in Excel
Microsoft Excel has long been a cornerstone in the world of data analysis. Its ability to handle, transform, and visualize data makes it a go-to tool for data analyst professionals.
From tracking sales performance to analyzing financial statements, Excel empowers users to generate insights and make data-driven decisions efficiently. Even after more than three decades in the tech landscape, Excel continues to remain relevant—especially in sectors like finance, where managing and presenting large datasets is critical.
Challenges and Limitations of Excel
Despite its widespread use and robust capabilities, Excel does have certain drawbacks that have become more pronounced in today’s data-heavy environment:
Handling Large Volumes of Data
As organizations scale and accumulate massive datasets, managing numerous spreadsheets becomes cumbersome. The lack of scalability in Excel can lead to inefficiencies and complications in complex analytical workflows.
Prone to Human Error
Manually entering formulas or copying data across cells increases the risk of syntax mistakes. These small errors can cascade into significant issues, especially when dealing with critical business metrics.
Security Concerns
Storing sensitive business information in spreadsheets can pose security risks. Without proper protection protocols, Excel files are vulnerable to unauthorized access, misuse, and cyber threats.
Welcome, Python
First introduced in 1991, Python has evolved into one of the most widely used programming languages in the world. While Excel and Python serve different core purposes, Python has gained immense traction, especially in the data analysis and data science communities, for its flexibility and advanced capabilities. Many professionals now view Python as a superior tool for data analysis due to its speed, scalability, and efficiency.
With many companies seeking candidates skilled in quantitative roles, Python proficiency—even at a basic level—is increasingly seen as a must-have skill. According to the Popularity of Programming Language index, Python currently holds the top spot, with an 11.4% growth rate over the past five years and a 28% share in overall popularity—surpassing Java by 10%.
Why Choose Python Over Excel?
Python’s versatility and cross-functional nature make it ideal for data analysts handling large or complex datasets.
Readable & Collaborative: Python’s clear, intuitive syntax makes it easy to write, share, and maintain code—ideal for teamwork and stakeholder communication.
Easy to Learn: With a gentle learning curve and simple syntax, Python is beginner-friendly, even for those without a programming background.
Rich Library Ecosystem: Libraries like NumPy, Pandas, Matplotlib, and Scikit-learn streamline data analysis, saving time and boosting efficiency.
Strong Community Support: Python’s global user base offers extensive documentation, forums, and tutorials—making help easy to find when needed.
Powerful Visualizations: Libraries such as Seaborn and Plotly let you create detailed, interactive charts to clearly communicate insights.
Advanced Analytics: From pattern recognition to forecasting, Python enables deeper analysis and better decision-making.
Excel vs. Python: A Side-by-Side Comparison for Data Analysts
How to Transition from Excel to Python?
If you're looking to grow your data analyst skills, Python is a smart next step. It's powerful, flexible, and great for handling large or complex data.
1. Set Up Python Install Anaconda, which comes with everything you need—like Jupyter Notebook and key libraries (Pandas, NumPy, etc.).
2. Learn Key Libraries
Pandas – For cleaning and organizing data
NumPy – For fast math operations
Matplotlib – For creating charts and graphs
Scikit-learn – For simple machine learning tasks
3. Practice Regularly Work with sample datasets to clean data, create visuals, and apply basic analysis. This builds real-world data analyst skills fast.
Final Words
Excel is a great starting point—especially for small-scale data tasks or quick insights. But Python is a must-have skill for data analysts, and now is the time to learn. It offers a more scalable, powerful, and efficient solution for modern data analysts. Mastering both tools will not only expand your capabilities but also future-proof your data analytics career.