Chapter 1: Transitioning from Excel to Python

Gone are the days when Excel was the only tool for data management. With the advent of Python spreadsheets, users can harness the power of coding while enjoying the familiar interface of Excel.

As someone who heavily relied on Excel during my university years, I understand the limitations it posed. Back then, coding was a foreign concept to me, and Excel was my go-to tool for tasks like data cleaning, creating pivot tables, and generating charts. However, when faced with massive datasets or intricate calculations, Excel often slowed down, or worse, crashed entirely.

I’ve lost count of how many times those frustrating moments interrupted my workflow or forced me to redo tasks due to the constraints of macros and the complexity of VBA. Fortunately, learning Python has liberated me from those issues. Yet, I recognize that not everyone can invest the time to master Python, leading many to stick with Excel.

This dilemma is now resolved with the introduction of Python spreadsheets, which combine the power of Python with the user-friendliness of Excel. Below, I will highlight some of the top Python spreadsheet tools and explain why they surpass Excel.

If you're short on time, consider checking out my video instead.

Chapter 2: Top Python Spreadsheet Tools

Spreadsheet #1: Mito

Mito is a groundbreaking spreadsheet application designed for Python. In essence, it allows users to manipulate data as they would in Excel, while automatically generating the corresponding Python code for each action performed.

To get started with Mito, ensure you have Python and either Jupyter Notebook or Jupyter Lab installed. Then, install Mito with the following commands in your terminal or command prompt:

python -m pip install mitoinstaller

python -m mitoinstaller install

Once installed, Mito enables you to execute familiar Excel tasks such as importing files, creating new columns, generating pivot tables, and more.

Reason #1: Handling Large Datasets

Excel has a row limitation of approximately 1 million entries. If you try to load a larger file, you may find yourself unable to view all the data. Conversely, Python can manage datasets that far exceed this limit, constrained only by your computer's capabilities.

To read a file with Mito, first, create a Mito spreadsheet using the following code:

import mitosheet

mitosheet.sheet()

Next, you can import a CSV file. For this demonstration, you can download a dataset containing school scores. After downloading, simply click the “+” or “import” button.

In Defense of Python's Speed

While Python may be perceived as slow, its advantages are undeniable, particularly for automation and data manipulation.

Reason #2: Enhanced Automation

In Excel, you can create basic macros to automate repetitive tasks. With Mito, you can achieve the same effect, but with the added flexibility of Python's extensive library of free resources, enabling you to send reports via email, share files on WhatsApp, and utilize Google Sheets as a rudimentary database.

For instance, to create a new column in Mito, simply click on the “Add Col” button and rename it. Mito will automatically generate the relevant code.

To calculate the average score across different subjects, you only need to enter the formula directly in the desired cell.

Creating pivot tables is equally straightforward—just click the “Pivot” button and select the desired parameters.

Visualizations like bar charts can be effortlessly generated by selecting the chart type from Mito’s interface.

Chapter 3: Advanced Computation with Python

Reason #3: Complex Computations Made Easy

While Excel may falter under memory-intensive calculations, Python excels. For instance, using another library called bamboolib, we can handle computations on large datasets without crashing.

To install bamboolib, run the following command:

pip install --upgrade bamboolib --user

Next, you can read a CSV or Excel file using:

import bamboolib as bam

bam

Afterwards, creating new columns, pivot tables, and even pie charts becomes a seamless process.

By following these steps, you can confirm that Python handles complex computations smoothly, even with extensive data.

For those embarking on a journey into Data Science with Python, feel free to join my mailing list for a FREE Python for Data Science Cheat Sheet, shared with over 10,000 subscribers.

Conclusion

In summary, Python spreadsheets like Mito and bamboolib provide a powerful and user-friendly alternative to Excel, especially for handling larger datasets and more complex computations. Embrace this new era of data management and unlock the full potential of your data with Python.