How to Use Python Pandas with Excel

Within the data science industry, there are multiple methods of collecting and storing data, with some tools and techniques viewed as more advanced than others. Data analysts have traditionally used spreadsheet software to analyze data, but this type of software was created to handle more structured data from a business or clients. So, the past few years have seen the rapid growth of data science tools for collecting big data with different data types and volume. While these tools have larger storage capacities and faster processing speeds, they also require data scientists to have a deeper knowledge of programming languages and libraries. 

In order to keep up with these changes in the industry, many data scientists are learning to use programming languages and libraries with more traditional tools. Most notably, by combining Python with data science libraries like Pandas, data scientists and analysts can clean, automate, and visualize data from Microsoft Excel. This process is especially useful when working in industries that utilize real-world data as well as financial and/or business analysis. Any Data Scientist or analyst can benefit from learning more about how to combine their knowledge of Python and Excel through more statistics-focused data science libraries like Pandas.

Why Data Scientists Use Pandas with Excel

Python is an open-source programming language, so learning Python not only includes instruction in the language itself, but also the many data science libraries that support it. These libraries and packages are primarily used to order, analyze, and visualize complex datasets, but they can also be used to work with software from different companies and ecosystems. This is why many data scientists who are familiar with Python also have some knowledge of the Pandas library. Like many of Python’s data science libraries, Pandas is most well-known for its data analytics capabilities, as well as unique features like DataFrames. 

In addition to these functions, the Pandas Python library can also be used to work with spreadsheet software, like Microsoft Excel, making it a popular tool for analyzing large amounts of current and historical numerical data in the business and finance world. This combination of tools is essential for data science professionals analyzing real-world datasets and generating statistics based on financial or corporate data. It is also fundamental knowledge for data analysts exploring spreadsheet data and using automation and machine learning. Even outside of these sectors, there are countless reasons for data scientists to use the Python Pandas library to manipulate data from Microsoft Excel.

How to Combine Python, Pandas, and Excel

Following are a few methods that data scientists use to combine their knowledge and expertise in Python and Pandas with Excel spreadsheet data. First, Python can be paired with Pandas to read and write data from Excel. Depending on the interface being used, data scientists and analysts can import the Pandas library into their environment of choice to work with the data in Pandas or another one of Python’s data science libraries. By importing the Pandas library into a terminal or interface, data scientists can also explore, manipulate, and clean Excel spreadsheet data.

Exploratory Data Analysis

Many users pair the Pandas library with Excel to perform an exploratory data analysis with Python script. After reading the Excel data through DataFrames or another function within Pandas, data scientists can explore the dataset using methods such as shape and tail. While the shape method returns the number of rows and columns in the DataFrame, the tail method can be used to describe the specific records included within the DataFrame. The describe method can even be used to view the descriptive statistics for a dataset.

Data Manipulation and Mathematical Equations

One of the other main purposes of using Pandas with Excel is manipulating the dataset through mathematical functions and data analysis. So, when working with Excel and Pandas, many of the same formulas that are used in Microsoft Excel are also available through Pandas, making it easy to create formulas and calculations using the numerical data in your dataset. This could be as simple as subtracting one column from another or adding the values of certain rows together, which is a form of data manipulation that is especially useful in accounting, time-keeping, and other forms of numerical data analysis. 

Data Cleaning and Organization

Many of the methods for cleaning and organizing data with Python, Pandas, and Excel are very similar to the methods that you use when working in Microsoft Excel. For example, you can use the sort_values function to organize your data similarly to how it is organized within an Excel spreadsheet. The Pandas library even includes Excel’s Pivot Table function so that users can index data using the same operators that they have learned through Excel. Then, once you are done cleaning the dataset, Pandas can be used to export your changes to Excel format so that you can share your files with other analysts working in Microsoft Excel. 

Want to Learn More About Python and Excel?

Learning Python is one of many ways to supercharge your training in Excel. By drawing on Python’s data science libraries, Microsoft Excel users can incorporate more advanced methods of data analysis and visualization into the spreadsheet software. Data science students and professionals interested in learning more about combining Python and Excel can attend any of Noble Desktop’s Python classes and bootcamps or Excel classes and workshops.

Specifically, the Python for Data Science Bootcamp focuses on teaching students how to work with real-world examples and data science libraries. Then, data analysts who want to further advance their skills with Microsoft Excel can take the Excel Bootcamp, which includes a series of workshops that take students from the fundamentals of Excel to more advanced tools. By incorporating instruction around working with pivot tables and data cleaning techniques, pairing the Excel Bootcamp with Noble Desktop’s Python Bootcamps creates new opportunities for data scientists looking to expand their analytics skills.