Excel vs Python for Data Analysis: Which is Better?

Excel vs Python Data Analysis

A Complete Guide for Transitioning from Excel to Python: Benefits + Uses

Data analysis and Excel are two words that usually go together. But, now, over time Organizations are transitioning to Python as they continue to work with large data sets. Python is considered a worthy replacement chiefly for the benefits that it offers. So, here the blog walks you through Excel vs Python and why many choose to transition from Excel to Json Python.

This transition is mainly justified for the below reasons: 

  • Execution of complex calculations and algorithms by Python 
  • Excel includes a customized language called VBA, which is complicated to master and execute, which makes Python easier to learn
  • Users can enjoy various benefits, such as an open-source coding platform, many volunteer contributors, and free libraries.

Why is it Python Vs Excel?

The most asked question now is Excel vs Python for data analysis. Which is better? Why choose Python Vs Excel?  What we need to first understand is that Excel, though a very powerful tool, still has its limitations. That’s why we use Python, as it is a more effective and efficient choice over Excel

1) With Python Data Manipulation and Importing is Very Powerful 

Unlike Excel, Python can read all types of data- both structured and unstructured. This means that the data manipulation tasks such as sub setting, merging, and recording data are all so much easier with Python. Meanwhile, trying to merge and clean several large datasets in Excel for analysis is a difficult and time-consuming process. But Python makes this process both simpler and faster. 

2) Automation is much easier 

Excel is frustrating when it comes to automating a process or for running the same analysis multiple times. To make it faster, it is better to use Python. For instance,

In Excel, running the same analyses on new sales data each week would require opening a new file and re-entering formulas and other elements each week. In Python, you could write a script that imports new data every week and runs the same analysis, outputting the results in whatever format you like.

3) Makes it easier to find and fix the errors 

Finding out what went wrong, when you have made an error in Excel can be challenging. However, if you make an error in a coding language, you would get an error message that explains what went wrong. And also, there would be comments explaining each line of the code. This makes it easier to revert back and re-check each step, seeking mistakes. 

4) Open-source accessibility makes it convenient  

Even if Excel is great, as it is owned by Microsoft, you will have to depend on Microsoft for any updates or feature support. However, Python, unlike Excel, is free and open source, which means that any of the developers including you can create the package and functionality to boost the ease of use. 

Tools to Make Transition from Excel to Python Easy 

Here are some tools that make the transition from Excel to Python. Check below: 

1. Mito: A spreadsheet that generates Python. How does it work? 

You call it into your Python environment and every edit that you make on the spreadsheet will generate an equivalent Python. 

How to install Mito? 

You need to run these commands in your terminal: 

python -m pip install mitoinstaller

python -m mitoinstaller install

python -m jupyter lab

2. Lux: 

Python has better visualization which makes it a better choice for many over Excel. Python is able to access more dynamic charts using larger data sets than Excel is able to access. Also, Python is better because it requires very little coding. You can just pass the dataset and Lux will recommend all the charts for the data, which you can select from. Different distribution relationships will be recommended for columns in the data frame by Lux and the user can just click on the one they want to use.

Now, to install Lux in the Python environment, just run the command below: 

**pip install lux-api**

3. Pandas Profiling 

It allows user-friendly Exploratory Data Analysis. You can also easily explore summary statistics with Pandas Profiling and find Null Values, and see correlations etc., in a visual environment. 

Now to install the Pandas Profiling you can run the below command: 

import sys

!{sys.executable} -m pip install -U pandas-profiling[notebook]

!jupyter nbextension enable –py widgetsnbextension

Alternatively, for those who do not want Python as their primary data analysis tool but want to reap its benefits, there are also tools such as 

XL Wings: Run the command pip install xlwing to import the package. 

It is an open-source Python package that enables the automation of spreadsheet work with Python, which also creates Python macros, to replace the VBA. 

Openpyxl: With this Python package, you can easily read and write Excel files. Creating an Excel workbook from scratch, defining cell values, merging cells, inserting images, etc, can also be carried out easily and quickly with Openpyxl. 

And you may want to read up about Loops in Python to get a better understanding of the Python language. 


Meanwhile, there are also some great resources for Excel users who transition to Python to use. These include the Practical Business Python website, the YouTube Python tutorial channel by Derrick Sherill, and the site Python-excel.org. These tools and the benefits of Python make it easier for people to replace many functions they may have done in the past using Excel.  

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