Is Python The New MS Excel In Data Science?
Advanced Excel skills are still in high demand, especially in the financial sector. But, in the data science industry, Python is fast picking up as a competing tool against Excel. While there can be no grounds for comparing the two, in this article we set out the many overlapping areas that make Python a much-favoured tool over Excel, especially for data scientists. The Evergreen Excel Excel has evolved over the last few years, making it completely different compared to what it was a decade ago. It boasts of more user-friendly features and more effective functionalities for all sorts of businesses. It has advantages such as flexibility along with providing a simple and powerful computational model. The reactive model of Excel allows calculations in real-time. Despite being one of the favourites, many consultants, IT experts and risk managers have pointed out for decades how fragile spreadsheets are and they are working to overcome few challenges such as: Growing Data: With the growing amount of data, there is a need to re-think how businesses keep and access this data, make it more automated and error-free. Experts believe that Excel is being misused as a temporary quick-fix which has never matured into a permanent, enterprise-grade solution. Spreadsheets Are Becoming Big And Complicated: Enterprises, small and large alike use Excel sheet. Each organisation may have millions of spreadsheets which makes it difficult to analyse huge volumes of data. While it is hard to imagine corporates without spreadsheets, it has its own set of challenges. Syntax Errors: Many times, Excel is considered notorious for copying and pasting data or specific cell ranges. There can be many errors while putting formulas. Security Issues And Risks: With millions of spreadsheets scattered across the organisation, the risk managers have to be highly cautious about the kind of information that is stored in these sheets so that they are not misused. Many experts question if Excel is the right place to hold mission-critical data? There are several IT security policies that need to be addressed. Therefore, experts are trying to build better tool than Excel or trying to modify it to offer better options. Hence Comes Python We have assiduously covered how Python is becoming one of the most preferred tools for data scientists. Python has different functionality when compared to Excel but can prove to be much more powerful when it comes to data analysis. While Python needs coding skills, it has been looked upon as a prerequisite for many quantitative roles. Companies are looking to hire new roles with Python skills with at least beginner-level proficiency. Though it involves coding, many experts believe that it is picking up fast an alternative for Excel. For instance, Excel user can sum up numbers in a column by using =SUM or sum up cells which meet specific criteria by using =IF or =IFS statement. All this can be done in Python as well, and a lot of other functionalities that Excel boasts. Now you might think then why do you need to do this in Python if you might as well do it in Excel. Here are a few advantages that Python may have over Excel: Easy To Read: For many business users and data scientists, it may be quite difficult to read someone else’s spreadsheet. It becomes even more difficult for a person who has never used Excel in their life ever. If data analysts and data scientists use Python, it will allow for an easy preparation, analysis and visualisation in Python. If someone has left working on it at a certain point, someone else can easily pick it up as it involves universally defined coding. Python can extract data from Excel, cleanse data and perform a calculation or visualise data in a much effective manner. Better Data Analysis: It goes without saying that Python is much more effective in huge data analysis and forms a basic requirement to have in many data science teams. It is better in bringing automation, and can easily overcome mundane tasks. Python Vs Excel: Who Wins? That is a difficult question. While Excel in its current form may not be fit for all-purpose, it definitely remains one of the favourites. Excel calls for better ways to analyse data, as mentioned above, and managers will have to get better with performing data analysis — which would be possible only by writing codes. It will soon become important to see new data analysis tools that will emerge more powerful, convenient and easier than Excel, and it calls for a preference inclining towards Python. Python is gaining popularity given that it is simple, straightforward, open source, can holistically embed with the technology stack, is extremely versatile, and is a very polyvalent language. Given that it has free and affordable resources, it makes it easy to get started. Python can be effectively used throughout the entire workflow, allowing programmers to code more quickly, using fewer steps than other scripting languages. It is considered highly useful even by the non-developers in all facet of project discussion, and serves better in collaborating internally for a project, build prototypes, understand programmatic workflow for clients, estimate the complexity of the project, and more. With a wide range of content available on learning Python, it can get really easy to get started with it.
Advanced Excel skills are still in high demand, especially in the financial sector. But, in the data science industry, Python is fast picking up as a competing tool against Excel. While there can be no grounds for comparing the two, in this article we set out the many overlapping areas that make Python a much-favoured […]
While there can be no grounds for comparing the two, in this article we set out the many overlapping areas that make Python a much-favoured tool over Excel.
["excel", "Python", "Python for Data Science"]
["data science", "excel", "Go", "programming_languages:R", "AI", "RAG", "Python", "automation", "programming_languages:Python", "Python for Data Science", "GAN", "R"]
["AI", "data science", "RAG", "Python", "R", "Go", "GAN", "automation", "programming_languages:Python", "programming_languages:R"]
Srishti currently works as Associate Editor at Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures.