Excel spreadsheets have been the standard in the business world, allowing people to leverage spreadsheets for everything from accounting to managing schedules. As one of the world’s most popular software programs, Excel is used in all facets of a company, from human resources to Finance. However, there comes a time when one may look to grow beyond Excel and foray into the world of databases and coding-based solutions.
Businesses love using spreadsheets to organize data. However, over time, companies can amass hundreds to thousands of spreadsheets, making it difficult to unify data for potential company growth. Spreadsheets can be stored in multiple directories and across many computers, which results in data that is hard to manage. However, storing data through a database is the next logical step in the company’s growth, and learning Python is an excellent way for employees to leverage that data.
When working with several spreadsheets, there can often be a lot of memory overhead in loading many instances of the spreadsheets. By transitioning into Python, one can load the relevant information of hundreds to thousands of spreadsheets at once, with minimal overhead, and can perform calculations that would usually take hours in minutes or even seconds.
For the past few years, Python has become the fastest growing programming language and is wildly popular in the fields of data analysis and data science. As a programming language, Python is very concise and expressive compared to a more traditional object-oriented language like C++ or Java. Because of this, Python is very easy to learn and code can be written in much fewer lines. The “secret sauce” in what makes Python the select language to learn are its ever-growing and robust libraries, which greatly improves the scope and functionality of Python and can better utilize the data to satisfy a number of business needs.
The Python Pandas library makes the transition from Excel a breeze! With Pandas, you can import data through multiple sources, such as databases to .csv files, and manage the data in similar ways to an Excel spreadsheet. Python thinks of data in lists and dictionaries, but Pandas speaks a more familiar language - rows and columns. All of your favorite Excel functionality has an analog in Python, from basic functions such as sum and average to a lookup() function that can perform your Excel HLOOKUP and VLOOKUP functions. The Pandas pivot() function has the same functionality as Pivot Tables. Pandas also has the read_excel() and to_excel() functions that can read and output excel workbooks at both a sheet and workbook level so that the company can continue to use their Excel but harness the full power of Python and Pandas.
For those that frequently use charts and graphs with Excel, Matplotlib is the perfect Python library. Matplotlib was initially designed as a way to simulate all the charting and graphing functionality of the popular statistics software MATLAB, and because of this, it offers not only bar and line graphs, but also extends to 3D graphs, heat maps, and geographical models.
Finally, a key to having access to better data is knowing how to share the data, whether it is to members of a team or key stakeholders in a business. Most of the time code is compartmentalized into separate files, which require a program or working through a computer terminal to access. Python solves this technical gap by the usage of Jupyter Notebooks. With Jupyter Notebooks, one can import data, write and debug code, display results and visualizations, and even present information as slides. For this reason, many in the scientific community, as well as the business community, are leaving traditional statistical software and using Jupyter Notebooks for their data visualization and analysis solution.
Excel and the spreadsheet format have their place in smaller scales. But as we face an ever-increasing storm of data, sometimes rows and cells aren’t enough. Python, with its libraries and notebooks, is the next step in navigating through that storm.
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