No, Python is not replacing Excel; they serve different purposes and can work well together. Excel is user-friendly for simple tasks, while Python is powerful for complex analysis and automation.
Why is Python better than Excel for data analytics?
Key takeaways:
Python handles large-scale data effortlessly with tools like Dask and supports automation for tasks like updating datasets, generating reports, and creating dashboards, saving time and effort compared to Excel.
Python’s clear, reusable code ensures reproducibility and facilitates collaboration through tools like Git, unit testing, and documentation, making it ideal for both team projects and automated workflows.
With a wide range of libraries, Python offers advanced analytics, machine learning, and visualization capabilities that surpass Excel. It enables users to perform complex computations and create interactive, professional-grade visualizations.
Python integrates seamlessly with multiple data sources such as APIs, databases, and cloud storage, providing unmatched flexibility and scalability compared to Excel’s limited options.
Learning Python opens broader career opportunities, as its skills are transferable to other programming languages and widely valued in data analysis and software development.
As an open-source tool, Python is cost-effective and complements Excel for simple tasks, making the two a powerful combination for data analytics workflows.
In today’s data-driven world, businesses and individuals rely more heavily on data analytics to make informed decisions. From simple reports to complex machine learning models, the right tools can make a world of difference in extracting insights. While many have historically turned to Excel for its accessibility, the rapid growth of data and the need for more sophisticated analysis have pushed data professionals to explore more powerful tools like Python. With the right toolset, data analytics becomes not just easier but far more impactful.
Python vs. Excel for data analytics
There is no doubt that Excel has been an incredibly important tool for companies and still has a place in the toolkit of every data analyst and scientist, but for most of your work, you might want to upgrade to Python.
Why choose Python for data analytics over Excel
Python offers a whole suite of features that make analyzing data more efficient and effective. Here are some key reasons why Python is the better choice for data analytics:
Scale and automation
Reproducibility
Transferable skills
Advanced capabilities
1. Scale and automation
Excel is great when you want to do quick, ad-hoc analysis with small data, but once you want to move to a larger scale, it doesn’t work. Excel can support data up to 1,048,576 rows by 16,384 columns. On the other hand, Python can scale to the size of your memory and also has many tools that support out-of-memory computations.
For example, the Dask library allows you to scale your computations to run on a cluster of machines, not just your laptop. In fact, if you are familiar with pandas, it’s almost exactly the same code to read in a CSV:
# Import the Dask Python libraryimport dask.dataframe as dd# Load the data with Dask instead of Pandas.df = dd.read_csv(<file_path>)
In just a single line of code, you are now able to read in data larger than your computer’s memory—this is not possible in Excel.
Some other differences:
Python can scale when it comes to multiple data sources. While Excel is both the data store and the computation engine, Python is completely
data-agnostic. If you can find a way to read your data into Python, you can use it. It is very easy to automate updates in Python. Since you can connect Python directly to any data source, it is easy to schedule a job that will re-pull your data with any updates, run your calculations, and even create a report or dynamic dashboard, saving you tons of time. Excel, on the other hand, requires far too much manual labor and cannot automate updates.
import pandas as pdimport matplotlib.pyplot as plt# Read the dataset from the CSV filedf = pd.read_csv('sample_data.csv', parse_dates=['Date'], index_col='Date')# Filter data for the year 2015columns = ['High', 'Low']plt.plot(df.loc['2015', columns])# Add legend to the plotplt.legend(['Highest Price', 'Lowest Price'])plt.show()
2. Reproducibility
Reproducibility means that any analytics or visualizations you create should be easy and straightforward enough for someone else to reproduce. Not only does someone need to be able to re-run your process and end up with the same result, but they should also be able to walk through your steps to ensure accuracy. This concept is extremely important once you begin relying on automation. Automation is amazing when it works correctly, but when it doesn’t, automated reports can be a nightmare.
Reproducibility with Excel is very challenging. Excel calculations in cells are all but impossible to check at any type of scale. Data types are extremely confusing because what you see isn’t always what is represented in the raw data. While VBA does make reproducibility slightly better, at that point, you’re much better off investing in learning Python.
Take a look at this Excel document:
Summing data in Excel
You know the sum column should be the sum of A and B, but how do you confirm that? You could check one of the formulas and see that it is, in fact, the sum, but since every cell can have its own formula, how do we know they are all correct? If you were not paying attention, you might have missed that row 6 was incorrect.
However in Python, your sum would look like this:
a = [1,2,3,4]b = [5,6,7,8]sum_data = []for i in range(len(a)):sum_data.append(a[i] + b[i])print(sum_data)
This code is clear and it is easy to confirm that the sum is always calculated correctly.
With Python, you get all the tools developed to improve reproducibility and collaboration for software engineers. Python is also superior for data connectivity, which allows us to analyze data in the cloud and repeat a process instantly. Git, unit testing, documentation, and code formatting standards are all prevalent in the Python community.
With Python3, you can even add static typing to make your code clearer. All of these tools make it easier to ensure that your code is written well and correctly, this way, the next time you look at your code, or someone else picks it up, it’s easy to reproduce and understand.
3. Transferable skills
f you know Excel, you know Excel—but the skills you learn, while useful, aren't transferable to other tools or applications.
Python is much closer to other programming languages than Excel, which makes it much easier to pick up other languages you may encounter along the way. When you learn Python, you open far more doors than you could with just Excel. As an example, Python and R are often used collectively for data analysis and visualization tasks.
Plus, the demand for Python is incredibly high, and it is a popular programming language among professional software developers.
4. Advanced capabilities
Excel has a lot of built-in formulas, but it pales in comparison to Python’s capabilities. Not only does Python offer hundreds of libraries to make advanced statistics and analytics easier, but it can also take your visualizations to another level. Tools like Matplotlib, Plotly, Streamlit, and seaborn allow you to create beautiful visualizations of data, interactive dashboards, and plots.
Numpy and SciPy have amazing support for scientific computations, linear algebra, and vectorized calculations. And scikit-learn lets you train machine learning algorithms from decision trees to gradient-boosting machines.
Comparison of Excel and Python for Data Analysis
Aspect | Excel | Python |
Scale | Limited to 1M rows. | Scales beyond memory with tools like Dask. |
Automation | Requires manual updates. | Easily automated for updates and reports. |
Reproducibility | Hard to trace and verify formulas. | Clear, reusable code with version control and testing. |
Transferable Skills | Excel skills are not transferable. | Python skills transfer to other programming languages. |
Advanced Capabilities | Basic formulas and visualizations. | Libraries for advanced analytics and visualization. |
In conclusion, Python’s advantages for data analytics over Excel are clear and compelling. Its scalability, automation capabilities, reproducibility, transferable skills, and advanced features make it an invaluable tool for analysts and developers alike. As the demand for Python continues to grow, numerous resources and projects can help you enhance your skills and apply Python to real-world data challenges.
If you’re looking to dive deeper into Python projects, consider exploring this exciting project "Stock Market Data Visualization Using Python." This project explores the techniques to visualize stock market trends and insights with basic Python libraries.
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