Python vs Excel: Which is Better for Data Analysis? (2025 Guide)
Overview: Many data analysts rely on Microsoft Excel for quick, familiar spreadsheets and charts, but Python has been gaining traction as a powerful analysis tool. Excel boasts an estimated 800 million users worldwide, making it a staple in finance, marketing, and reporting. At the same time, Python is now the world’s most popular programming language (28% usage share) and is used by companies like Google, Facebook, and Netflix. In this blog, we’ll compare the strengths and limitations of each. We’ll examine real-world scenarios where one tool shines over the other, and we’ll help you decide which fits your data analysis needs – or how to use both effectively.
Excel for Data Analysis: Ubiquitous and Accessible
Excel has been a favorite among professionals for decades. It’s installed on over a billion PCs, with about 800 million active users globally. Most organizations have Excel, so sharing workbooks is straightforward. Its point-and-click interface – with features like PivotTables, formulas, and charts – makes it easy for anyone to get started. For example, creating a summary pivot table or chart in Excel takes a few clicks, whereas doing the same in Python would require coding. Excel is often the “one-stop shop” for quick data tasks: you can store data, run calculations, and build graphs all in one place. This integrated environment is convenient for ad hoc reports and collaborations, especially when team members aren’t programmers.
Despite its strengths, Excel has some notable limitations for large or complex analyses:
Data Volume: Excel sheets are limited to 1,048,576 rows by 16,384 columns. Once data grows beyond these limits, or even approaches them, Excel can become slow or unwieldy. Large datasets may force analysts to split data across multiple files, causing fragmentation.
Manual Errors: Human errors are common when editing many cells or formulas. Copy-paste mistakes or missed cell references can introduce hidden bugs. As Omics Diary notes, “drag and drop” operations in Excel don’t scale well, and combining filters with formulas can be cumbersome. By contrast, coding logic in Python (with Pandas) can apply filters and formulas programmatically, reducing manual slip-ups.
Security & Auditing: Sensitive data in spreadsheets can be mismanaged or lost, especially if version history isn’t tracked. Complex workbooks with hidden cells or macros also raise security flags. Larger teams may worry about governance and reproducibility with Excel files.
In short, Excel excels at small-to-medium sized projects where rapid setup and human-friendly interfaces matter. It remains “highly in demand” in many industries, and it’s an essential skill for any analyst. However, when analyses require more automation, collaboration through code, or handling huge data, Excel’s GUI model can slow you down.
Python for Data Analysis: Power and Popularity
Python is a full-fledged programming language, and it’s become a cornerstone of modern data analysis. With around 8+ million users (growing daily) and backing from giants like Google and Microsoft, Python offers unmatched flexibility. Unlike Excel, Python is free and open source – there is no per-seat license cost (unlike Excel’s ~$150/license). This open nature means thousands of contributors constantly improve Python and its libraries. A few statistics underscore Python’s rise:
Massive Ecosystem: The Python Package Index (PyPI) hosts over 200,000 packages. In data analysis alone, tools like Pandas (for tabular data), NumPy (numerical computing), Matplotlib/Seaborn/Altair (visualizations), and machine learning libraries (TensorFlow, Scikit-Learn, PyTorch) give you ready-made functions for nearly any task. For example, Pandas provides
dataframe.pivot_table()andgroupby()methods that mirror Excel’s PivotTables and SUBTOTAL functions – but can be scripted and repeated at scale.Handling Big Data: Python can manage much larger datasets. Libraries like Pandas and Dask allow processing of millions of rows (limited mainly by memory), and even integrate with big data frameworks like PySpark for distributed computing. Tasks that would crash or hang Excel often run smoothly in Python. Cambridge Spark notes that Python “can handle much larger volumes of data” and is faster than Excel for pipelines and complex algorithms.
Automation & Reproducibility: In Python, you write scripts to clean, analyze, and visualize data. These scripts can be version-controlled, reused, and scheduled. Instead of repeating tedious manual steps (like updating multiple sheets), you can run a Python program that does it in seconds. This boosts productivity. For example, you can automate Excel report generation by scripting with openpyxl or XlsxWriter, or skip Excel entirely by generating charts directly with Matplotlib.
Community Support: With ~8.2 million developers, Python has a vibrant global community. There are countless tutorials, forums, and conferences (like PyCon) to help you solve problems. Even advanced techniques (e.g. natural language processing, deep learning) have rich documentation. As Cambridge Spark points out, academia and industry share code openly, so the latest analysis methods are often available to you free, immediately.
Why Python can be more powerful: As one expert notes, “with Python, you can do so much more because of its general-purpose nature” – it covers the entire data analytics pipeline from raw data gathering (via APIs or web scraping) to machine learning and beyond. And importantly, Python code is text-based, so you can apply rigorous code reviews and testing. In short, Python is built for scalability, complexity, and innovation in ways Excel isn’t.
Python vs Excel: Key Differences
To summarize the contrasts:
Cost: Python is free (open-source). Excel requires a paid license (or Microsoft 365 subscription), which adds cost for businesses.
Data Limits: Excel maxes out at ~1 million rows. Python’s DataFrames (with Pandas) have no fixed limit. Analytics can scale from thousands to billions of records, depending on hardware.
Error-Proneness: Manual spreadsheets are easy to break. Python’s code approach means you define logic once and run it repeatably, reducing human error.
Automation: Python shines at automating repetitive tasks (e.g. daily report updates, merging files). Excel has macros for automation, but these require VBA skills and can be fragile.
Libraries & Analytics: Python has specialized libraries for statistics, machine learning and complex visuals. Excel has built-in charts and the new Data Types/Dynamic Arrays, but adding a new capability usually depends on Microsoft updates. Python can use bleeding-edge research immediately.
Collaboration: Excel is easy to share (everyone can open an .xlsx), and tools like SharePoint or Google Sheets support co-editing. Python is code, so collaboration happens via version control (Git) or notebooks. Analysts comfortable with code can collaborate via platforms like GitHub or shared Jupyter notebooks. Recent developments like Python in Excel aim to blend these worlds (see below).
Limitations of Excel
Data Volume & Performance: Excel slows with large data. Long formulas and multiple linked sheets can hang or crash the app.
Manual Work: Copying, pasting, and adjusting formulas manually is tedious. There’s no undo for certain batch edits. Errors can propagate unnoticed.
Limited Analytics: Excel lacks built-in advanced analytics (no native machine learning, no easy handling of JSON/SQL). You often have to export data or use add-ins.
Cost and Licensing: Businesses must manage licenses, and not all advanced features (like Power Pivot, Power Query) are available in every version.
Strengths of Python
Scalability: Handles bigger data seamlessly. Python is built for production workloads.
Rich Ecosystem: Over 150,000 Python packages are available (e.g. Pandas, NumPy, SciPy, Scikit-Learn) for almost any analysis need.
Speed (once set up): Well-written Python code can process tasks faster than Excel, especially for repetitive data tasks and complex calculations.
Automation: Easily automate ETL (extract-transform-load) pipelines. For example, use Python scripts to read databases, clean data, and push results back.
Advanced Analytics: Straightforward integration with machine learning frameworks. You can prototype models in Python notebooks and then scale them in production.
Use Cases: When to Choose Python vs Excel
There’s no one-size-fits-all answer – the best tool depends on the task at hand. Consider these scenarios:
When Excel is Best:
You have a small-to-medium dataset and need quick analysis or prototyping.
You want to pivot and chart data with a GUI (e.g. sales spreadsheets, budget forecasts).
Your colleagues expect an Excel file and may not have Python skills.
You need easy collaboration with non-technical stakeholders (Excel/Google Sheets are universal).
Example: Summarizing this month’s sales data in a pivot table and chart.
When Python is Best:
You’re working with large or multiple datasets (e.g. logs, big CSVs, databases).
You need to automate repetitive processes (data cleaning, report generation, emailing results).
You want to apply statistical models or machine learning.
You’re integrating data from web APIs, JSON files, or other non-tabular sources.
You need reproducible analysis (scripts/notebooks can be version-controlled).
Example: Cleaning and merging datasets from different systems, or scraping web data and running a regression analysis.
Even within a project, it’s common to use both tools. For instance, an analyst might prototype an analysis in Excel to explore data, then switch to Python for scaling and final reporting. The new Python in Excel feature (rolling out in 2024) even allows writing Python code directly in an Excel workbook, giving you the best of both worlds.
"Python in Excel combines Python’s powerful data analysis libraries with Excel’s familiar features... use Python plots and libraries, then refine insights with Excel’s formulas and charts." techcommunity.microsoft.com
This integration (currently in preview) means you can, for example, call =PYTHON() in a cell to run Pandas code on your Excel data, then feed results back into formulas or PivotTables. It streamlines workflows: you can use Pandas for a heavy transformation, and then chart with Excel’s interface. As Microsoft explains, this “uninterrupted workflow” requires no extra setup – Python runs in the cloud via an Anaconda distribution including Pandas, Matplotlib, Seaborn, and more.
Example Workflow: Download data from an API into Excel, call a Python script to clean and analyze it, and then use Excel’s charting to present results – all in the same workbook.
Data Cleaning and Preparation
Whether in Excel or Python, thorough data cleaning is essential. Many analysts spend most of their time fixing data issues (missing values, outliers, formatting) before analysis. Checklists and templates can help ensure no step is missed.
Softcraft Studio’s Data Cleaning Checklist template guides users through every step of prepping data (compatible with Excel, SQL, or Python). It outlines 30+ tasks across seven stages – from initial setup to final validation – and even includes examples (outlier detection, null imputation, etc.). This kind of structured approach is useful regardless of tool. You might fill in the checklist while cleaning data in Excel, or adapt it to a Python script workflow. The key is to have a repeatable process: downloadable guides or templates (like Softcraft Studio’s) ensure consistent quality.
Visualizations and Reporting
Visualization is another area where tool choice matters. Excel makes charts and dashboards easily: bar charts, line graphs, sparklines, even simple mapping. Its “one-click” charts are intuitive for quick insights. Tools like Power Query and Power Pivot extend Excel’s capabilities for dashboards.
Python, however, has advanced plotting libraries (Matplotlib, Seaborn, Plotly, Altair) that can create nearly any chart, often with more customization and interactivity. For example, plotting a multi-layered heatmap or an interactive web-based chart is more straightforward in Python. codecademy.com notes that tools like NumPy and Matplotlib can generate “elegant and interactive data visualizations” that go beyond Excel’s static graphs. If you need reusable or interactive charts (e.g. for a web app or report), Python has the edge.
Excel visual tools: Built-in bar/line/area charts, slicers, and conditional formatting. Great for quick dashboards and presentations, and understood by most stakeholders.
Python visual tools: Libraries like Matplotlib/Seaborn for static plots, Plotly/Bokeh for interactive web plots. Python can also generate dashboards via frameworks (Dash, Streamlit) for a more professional data product.
Both paths are valid: sometimes you might do initial data exploration with quick Excel charts, then recreate final visuals with Python for consistency and polish.
Automation and Advanced Analytics
Excel can automate certain tasks with macros (VBA) and new Office Scripts, but writing macros is a specialty. Python’s automation is more robust: scheduling scripts, integrating with other systems, and batch processing. If your data work involves any programming logic or repeated execution, Python can save significant time.
Furthermore, for predictive analytics and machine learning, Python is virtually indispensable. Implementing even simple ML models (like linear regression or clustering) in Excel requires complex add-ins or manual workarounds. In Python, libraries like Scikit-Learn or TensorFlow make this straightforward. As one industry expert notes, “It’s possible for an Excel super-user to incorporate predictions, but it’s much more straightforward with Python” due to its mature ML ecosystem. Even advanced analytics can be prototyped in Jupyter notebooks, shared among teams, and scaled on cloud servers.
Career Impact and Growth
Learning both tools can boost your career. Excel proficiency is expected in almost any analyst role today. Python skills, however, can be a differentiator. Cambridge Spark reports that data roles requiring Python command far higher salaries than those requiring only Excel skills. In the UK market, jobs needing Python skills pay on average £67,500, versus about £37,500 for Excel-heavy roles. That’s nearly double, reflecting the premium on modern coding skills. In fact, many data analyst job postings now list Python (often specifically Pandas) as a desirable skill.
Aside from salary, Python fluency “future-proofs” your career. The demand for Python is growing (its prevalence in job ads rose ~28% year-over-year), and it opens doors to roles in data science, engineering, and AI. Meanwhile, maintaining solid Excel skills ensures you can handle any data that lands on your desk. Softcraft Studio recommends that analysts build skills across tools: for instance, it offers micro-courses on both dashboards (Power BI/Excel) and coding workflows. Indeed, our recent micro-course “Excel vs. Python: Which Tool Is Best for Data Analysts?” dives into exactly this question, helping analysts choose the right path and grow their toolset.
Python in Excel: The Best of Both Worlds
Excitingly, the gap between Excel and Python is narrowing. Microsoft’s Python in Excel feature (currently in preview) embeds Python directly in the spreadsheet grid. This means you can keep using Excel’s familiar UI and formulas, but leverage Python’s libraries right beside them. For example, you could import a messy CSV into Excel, write a short Python script to clean the data (using Pandas), and then use an Excel PivotTable on the result – without leaving the workbook.
This integration is designed to maintain enterprise-grade security and collaboration: coworkers without Python enabled can still refresh and see the updated results. In short, Python in Excel promises “uninterrupted workflow” where “Python’s powerful data analysis and visualization libraries [are combined] with Excel’s features”. This trend suggests that rather than competing, Python and Excel are becoming complementary – making it easier for analysts to have the best of both.
Choosing Your Tool and Next Steps
Which should YOU use? It depends on your project:
If speed and ease-of-use are paramount (and the task is relatively simple), Excel may be the fastest way. It’s great for quick ad-hoc analysis, building shared spreadsheets, and visual exploration.
If you face large-scale data, need to repeat work regularly, or require complex analysis (like statistical models or data from APIs), lean on Python. It will save time in the long run and unlock capabilities beyond Excel’s reach.
For many analysts, the answer is “both”. You might start in Excel for familiarity, then transition to Python for scaling up or vice versa.
Conclusion
In the end, both Python and Excel have important roles in a data analyst’s toolkit. Excel offers instant familiarity and ease for small data tasks, while Python offers power, automation, and scalability for more advanced work. As an early-career or mid-career analyst, strengthening skills in both tools is a smart career move.
👉 Ready to dive in? Download Softcraft’s Data Cleaning Checklist template to standardize your data prep (works with Excel or Python), and watch our YouTube video “Excel vs. Python: Which Tool Is Best for Data Analysts?”. These resources, along with our blog library, will help you sharpen your analysis workflow and master the right tools for any project.
Citations & Resources:
Python vs Excel for Data Analysis: https://www.cambridgespark.com/blog/python-vs-excel
Python vs Excel: Which One to Use for Data Analysis: https://www.codecademy.com/resources/blog/python-vs-excel-for-data-science/
Python vs Microsoft Excel: Choosing the Right Option for Data Analysis | by Kuan Rong Chan, Ph.D. | Omics Diary | Medium: https://medium.com/omics-diary/is-learning-python-worth-the-effort-or-is-microsoft-excel-adequate-f8e49eac74fd
Introducing Python in Excel: The Best of Both Worlds for Data Analysis and Visualization | Microsoft Community Hub: https://techcommunity.microsoft.com/blog/microsoft_365blog/introducing-python-in-excel-the-best-of-both-worlds-for-data-analysis-and-visual/3905482
Data Cleaning Checklist — Softcraft Studio: https://www.softcraftstudio.net/templates-for-analysts/p/data-cleaning-checklist
Data Analyst Hub: Templates, Blogs & Micro-Courses: https://www.softcraftstudio.net/data-analysis-hub
Excel vs. Python: Which Should You Use for Data Analysis? — Softcraft Studio: https://www.softcraftstudio.net/microcourses/v/excel-vs-python-which-should-you-use-for-data-analysis
