{"id":78,"date":"2023-07-04T15:45:00","date_gmt":"2023-07-04T13:45:00","guid":{"rendered":"https:\/\/beebole.com\/blog\/?p=43"},"modified":"2025-09-18T10:57:52","modified_gmt":"2025-09-18T08:57:52","slug":"python-for-finance-examples","status":"publish","type":"post","link":"https:\/\/beebole.com\/blog\/python-for-finance-examples","title":{"rendered":"Python for finance: Unlocking the power of data analysis"},"content":{"rendered":"\n

Are you exploring the intriguing world of python for finance<\/strong>? Whether you aspire to work in the financial industry, or wish to advance your existing career, mastering Python is a key step forward. In today’s fast-paced financial arena, Python programming is a crucial skill that top institutions seek in their professionals. Dive into this article to discover the four major reasons behind Python’s rising popularity in the financial industry, and how it can propel your career to new heights.<\/p>\n\n\n\n

Universal languages & how Python programming for finance fits in<\/h2>\n\n\n\n

There are not too many universal languages. When it comes to spoken and written languages, English can be considered universal. If you are not a native speaker, English often is the first foreign language that you learn. And there are good reasons to do so. First, most of the published texts, such as news articles or books, are written in English. Secondly, you can get by with English basically around the world. Mathematics is a universal symbolic language that is also understood and used around the world and across many domains.<\/p>\n\n\n\n

A deeper look at Python<\/h3>\n\n\n\n

When it comes to programming languages, Python<\/a> has reached a similar status in finance and other domains. Python is used in basically every area of the financial industry, be it for financial data science, machine learning, credit ratings, trading, asset management, pricing, risk, or more administrative tasks. In general, the reach of Python is indeed universal in that it is used around the world, across basically all domains, and for basically any task that requires some form of programming and data processing.<\/p>\n\n\n\n\n \n \n <\/svg>\n\n \n \n <\/svg>\n\n

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Rancho BioSciences isn\u2019t just tracking hours\u2014they\u2019re engineering data-driven foresight. By combining Beebole\u2019s real-time time tracking with Python, their team builds custom dashboards and burndown charts that make forecasting sharper and decision-making faster.<\/p>\n

With Beebole, they\u2019re able to:
\ud83d\ude80 Transform time and budget data into actionable Python models
\ud83d\ude80 Align 130+ simultaneous projects under one reliable source of truth
\ud83d\ude80 Forecast with confidence instead of chasing outdated reports
\ud83d\ude80 Empower leadership with clarity backed by real numbers<\/p>\n

This is what forecasting looks like when data and finance finally click.<\/p>\n <\/div>\n\n \n Read the case study\n \n \n \n <\/svg>\n <\/a>\n <\/div>\n<\/div>\n\n\n

Against this background, basically all financial institutions expect from their new hires and existing staff at least a certain level of Python programming. For specific roles, such as quantitative developers or researchers, Python skills are usually required these days. For other roles, such as in trading or risk management financial institutions offer Python training programs to reflect its increasing importance. Therefore, it for sure pays off to learn Python early on and gain relevant experience in using it for financial use cases.<\/p>\n\n\n\n

Without a doubt, there are still several other programming languages in use in finance. For example, C++, as a compiled programming language, is still very popular when code execution speed and performance are of the essence. On the other hand, domain-specific languages, such as R for statistical applications, are also widely used.<\/p>\n\n\n\n

Why is Python so huge in finance?<\/h2>\n\n\n\n

But what are the major reasons why Python has become basically omnipresent in finance and has in many instances replaced other, more focused, programming languages? Some of the basic characteristics of Python are shared by several other languages as well, such as being open-source, interpreted, high-level, and dynamically typed. This alone cannot explain its rise in finance.<\/p>\n\n\n\n

In what follows, four arguments are discussed for why Python has become so dominant in finance. The first two are presented in the form of what you could call the power packages in Python: NumPy and pandas. The second two are related to important aspects in finance: code performance and efficient input-output (IO) operations. But first a simple use case illustrating the power of Python for financial data analysis.<\/p>\n\n\n\n

Is Python for finance controllers and analysts better than Excel?<\/h2>\n\n\n\n

A financial controller within an organization can absolutely work with both Microsoft Excel and Python at the same time. Both tools have their unique strengths and can be used complementarily.<\/p>\n\n\n\n

Excel is widely used in the business world for simpler data analysis<\/a>, financial modeling, budget vs actuals<\/a>, and quick calculations. It offers an intuitive interface and allows for the easy manipulation of data, making it accessible for those without a coding background. Finance professionals who use Excel might also be interested in learning about using Microsoft Fabric<\/a>.<\/p>\n\n\n\n

On the other hand, Python is a powerful programming language favored by analysts and data scientists for more complex tasks<\/strong>. It’s excellent for large-scale data analysis, automating repetitive tasks, building sophisticated models, and conducting advanced statistical analysis. Python also has the ability to read and write Excel files, which can be useful for integrating the two tools<\/strong>.<\/p>\n\n\n\n

A financial analyst might use Excel for quick and simple analyses, data visualization, or when sharing data with others in a user-friendly format. Meanwhile, Python would come into play for more advanced analysis or when dealing with large datasets that Excel cannot efficiently handle. In other words, Python and Excel can be used in tandem, leveraging the strengths of each depending on the task at hand.<\/p>\n\n\n\n

How do finance professionals use Python? A simple use case<\/h2>\n\n\n\n

The returns of the share prices of stocks in financial markets are important measures for many different financial applications. In this context, consider a relatively simple question: \u201cHow did the Apple Stock perform in 2021 compared to the S&P 500 stock index?\u201d.<\/p>\n\n\n\n

Working with traditional tools used in finance, such as Microsoft Excel, is often a tedious experience with regard to answering such questions. An analyst would need to download the data, for example, in the form of a CSV (comma-separated value) file containing the time series of prices for the financial instruments of interest. The analyst then needs to import the data into an Excel spreadsheet, which usually involves some configuration and several formatting and layout-related steps. <\/p>\n\n\n\n

In addition, such data sets often comprise thousands of rows\u2009\u2014\u2009something not efficient to process within a spreadsheet since it involves constant up- and down-scrolling. Once the data is ready, the analyst needs to find the relevant dates in the spreadsheet, that is the 31. December of 2020 and 2021, and pick the prices at these dates for Apple and the S&P 500. Based on these prices, the returns for 2021 can then be calculated and compared.<\/p>\n\n\n\n

Where Python packages come in<\/h3>\n\n\n\n

On the other hand, an analyst who is proficient in using Python can answer such questions\u2009\u2014\u2009and even more complex ones\u2009\u2014\u2009in a much more efficient manner. Leveraging powerful Python packages such as pandas (see section Pandas Package<\/a>) boils down the whole analysis to three relatively simple lines of code:<\/p>\n\n\n\n

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  1. Importing the pandas package <\/li>\n\n\n\n
  2. Retrieving the CSV data file <\/li>\n\n\n\n
  3. Calculating the net returns<\/li>\n<\/ol>\n\n\n\n

    Executing the three lines of the following Python code shows that Apple has outperformed the S&P 500 stock index in 2021 by about 7 percentage points.<\/p>\n\n\n\n

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