Transitioning from Excel to Python
Many companies are now transitioning from Excel to Python, a high-level, general-purpose programming language created by Dutch programmer, Guido van Rossum. A growing number of software developers today consider Python a worthy replacement tool for Excel, due to the benefits the former can offer.
Excel is a common tool for data analysis, and it is commonly used to carry out analytical operations in the financial industry. However, Excel tends to be more complex since it requires the application of VBAs. VBAs are complex to operate, and they make Excel difficult to work with when dealing with multiple operations during data analysis.
Python, as a programming language, offers various benefits compared to Excel. It is an open-source programming language, with numerous contributors who volunteer to provide regular updates to the code and improve its functionality. On the contrary, Excel is a paid software that only provides program updates to those who bought the application, thus limiting its use. Python also comes with a wide variety of preinstalled libraries, which saves time for developers who would otherwise be required to create projects from scratch.
- The transition from Excel to Python can be justified due to the capability of the latter in executing complex calculations and algorithms.
- Python is easier to learn and master, unlike Excel which includes a personalized language known as VBA that is complex to master and execute.
- Transitioning from Excel to Python enables users to enjoy various benefits, such as an open-source coding platform, many volunteer contributors, and free libraries.
A good data analysis software should be able to integrate with other analytical and non-analytical software. Python fits well into this description since it integrates well with other programs. Users can import and export different types of file formats into Python.
For example, Python is compatible with SQL syntax and can even run it within its framework to extract data and tables to its environment. The Python environment is also efficient in automating tasks such as importing data and writing analyzed data to Excel or CSV functions for data analysis.
Transitioning from Excel to Python can be justified from a functional integration point of view. First, Python is user-friendly, and both beginners and experienced analysts can use the language with ease. Excel uses VBA language, which is a personalized platform that uses macros to automate tasks for data analysis.
The use of macros to automate tasks is more complex than the automation of tasks in the Python environment. Also, the fact that Python can be easily integrated with other programs makes it more suitable for data analysis.
Data analysis code can be stored as scripts for reuse and further manipulation. Python code is reproducible and compatible, which makes it suitable for further manipulation by other contributors who are running independent projects. Unlike the VBA language used in Excel, data analysis using Python is cleaner and provides better version control.
Better still is Python’s consistency and accuracy in the execution of code. Other users can replicate the original code and still experience a smooth execution at the same level as the original code. The ability to reproduce code makes Python more efficient than Excel since users can bypass the initial coding process and start with an already functioning framework.
Scalability and Efficiency
Data scientists prefer Python over Excel due to its ability to handle large data sets, as well as incorporate machine learning and modeling. When handling large amounts of data, Excel takes longer to finish calculations compared to Python. When data is loaded onto the two programs simultaneously, Excel will lag behind Python since it not built to handle large amounts of data.
Also, Excel takes longer to import data created in other analytical software. It can be even slower when the amount of data being imported into the spreadsheet is enormous. Python bridges the gap since it is a more efficient tool in importing and exporting data in different formats, making it ideal for data scraping. Compared to Excel, Python is better placed for handling data pipelines, automating tasks, and performing complex calculations. Moreover, it comes with a wide pool of manipulation tools and libraries.
Python vs. Excel in Organizations
Python is considered a more efficient data analysis tool for complex calculations and large volumes of data. However, Excel is still more popular overall than Python, and it is used by a large number of people in financial analysis.
While Excel is not ideal for handling large volumes of data, it is a more convenient tool for organizations with small volumes of data that require simple calculations. Python, on the other hand, is more efficient than Excel when the organization handles large volumes of data that require automation to produce results within a short period.
CFI offers the Financial Modeling & Valuation Analyst (FMVA)™ certification program for those looking to take their careers to the next level. To keep learning and advancing your career, the following CFI resources will be helpful: