Transitioning from Excel to Python and Pandas: A Guide to Python for Spreadsheet Users

Do you regularly use spreadsheets in Excel or Google Sheets? If you’re looking for something more powerful, using Python can give you more tools and flexibility for handling data. However, the transition from Excel to Python might be intimidating for some.

In this blog, we’ll give you the information you need to make the jump from using Excel to using Python and Pandas. Read on to learn how to unlock the potential of these tools.

Spreadsheets are great, but they have limitations

There’s a reason that Excel and Google Sheets have been staple data tools for years. They’re intuitive, accessible and most users can jump right in and start using them. But even if you master Excel and Google Sheets, you will run into limitations. Python provides much more complex analysis, speed, flexibility and more. If you find yourself getting frustrated with the limitations of Excel and Google Sheets, then it’s time for you to consider moving to Python and Pandas.

The benefits of moving from Excel or Google Sheets to Python + Pandas

Let’s dive a little deeper into some of the main benefits of Python and Pandas and why they are more powerful than using Excel and Google Sheets. Those who make the switch to Python + Pandas will notice benefits like:

• Flexibility — Excel and Google Sheets are best used for small data sets and simple calculations. Python can handle more complex and sophisticated tasks and also give you access to thousands of libraries and tools suited to a range of different tasks and projects.
• Scalability — Excel and Google Sheets tend to slow down and even crash when data sets are too large. Python and Pandas allow you to easily scale up when you’re working with bigger data sets. This ensures you can analyze and manipulate multiple large data sets without issue.
• Easier automation — Python has automation capabilities to take care of both simple and complex tasks for you. You can create routines to generate reports, update data and much more. The automation capabilities of Excel and Google Sheets are comparatively limited.
• Reproducibility — This is a key feature in Python that makes collaboration, research and analysis much simpler. You can run code scripts over and over again to always reproduce consistent results.
• Improved performance and efficiency — Python and Pandas can easily outperform spreadsheets in Excel and Google Sheets. Python’s intuitive programming and Pandas’ ability to process data efficiently allows you to work with data and analyze it at lightning-fast speeds.
  

In short, making the transition from Excel or Google Sheets to Python and Pandas is well worth the benefits. Now that we understand why it’s worth making the switch let’s dive into some key concepts

Key concepts for transitioning from spreadsheets to Python

Making the transition from using spreadsheets like Excel or Google Sheets to working with Python and Pandas can seem daunting. However, with the right guidance and a willingness to learn, it is a move that can greatly enhance your data processing and analysis capabilities. Here are four key concepts that will help make your transition to Python smoother.

Data types and structures in Python

Data types and structures are important concepts to learn in Python. As an object-oriented programming language, understanding the basic data types and structures will be essential to making the switch from spreadsheets. A data structure defines how data is stored and organized, which allows it to be accessed and analyzed efficiently.

A key difference between spreadsheet software and Python is how data structures and types are handled. Spreadsheet cells can only contain certain data types, such as numbers, text and formulas. Python, on the other hand, offers flexible options. Python data types include strings, integers, lists, floats, stacks, tuples, dictionaries and more.

Another good object to understand for data analysis is the Pandas DataFrame. This is a two-dimensional table that stores data in rows and columns, similar to a spreadsheet. This is one of the most commonly used objects in Pandas, and it’s incredibly useful for data manipulation and analysis.

Data manipulation with Pandas

Speaking of Pandas, this Python library is something that you’ll want to familiarize yourself with when switching from spreadsheets. Pandas is built for data manipulation and analysis, and learning to use it effectively will greatly improve your Python data skills.

Pandas lets you easily read and write data, group data, filter data and much more. As mentioned, a commonly used object in Pandas is DataFrame, which is a table of data with rows and columns. Think of the spreadsheets that you’re used to but much more powerful and versatile.

Pandas also includes a wide range of other functions for working with data, including reshaping, pivoting and aggregating data.

Overall, Pandas is a powerful tool for data manipulation that can help you move beyond the limitations of spreadsheets and unlock new insights in your data. With some practice and dedication, you’ll be well on your way to becoming a Python and Pandas power user.

Conditional statements and loops

Conditional statements and loops are also essential programming concepts in Python. Essentially, they let you control the flow of data and perform operations based on certain conditions. It’s good to have a foundational understanding of how to use if/elif/else statements. For loops and while loops are also helpful concepts to learn. For loops you can iterate through data lists and arrays and while loops allow you to repeat a task until the defined condition is met.

In short, using conditional statements and loops in Python can simplify complex data manipulation tasks that may have been difficult or time-consuming to perform in a spreadsheet. Becoming proficient in conditional statements and loops in Python will allow you to harness even more of Python’s data analysis capabilities.

Functions and modules

Finally, it’s a good idea to become familiar with functions and modules when you’re transitioning from spreadsheets to Python. Let’s define both of these concepts briefly.

A function is a block of code that performs a specific task and returns a value. They can easily be reused throughout a program. Those who use spreadsheets will be familiar with built-in functions like SUM or AVERAGE. Python allows you to create your own functions that can perform more complex or complicated calculations or operations.

Modules allow you to organize Python code. These modules can contain code that you can easily import into your own scripts.

Overall, functions and modules are powerful tools for data analysis and can help you unlock new capabilities as you transition from spreadsheets to Python. Once you become comfortable creating and using functions and modules, you'll be able to perform more complex data analysis and even automate repetitive tasks.

An easy example: Converting a spreadsheet to Python with Mito

Mito is an incredibly useful tool for making the transition from spreadsheets to Python. Mito renders data in a spreadsheet format and automatically generates the corollary code in Python. This allows Excel and Google Sheets users to use a familiar format while learning Python.

Let’s do a quick step-by-step walk-through for converting a spreadsheet to Python using Mito.

Preparing your Python environment

First, you will need to install Mito. You will need Python 3.6 or above to get started. Open a terminal, command prompt or Anaconda Prompt. Then, download the Mito sheet package.

This will install Mito for JupyterLab >3.0. Once installation is complete, you’re ready to import your data from Excel.

Importing Excel data with Pandas and Mito

Simply follow these steps to important Excel data with Pandas and Mito:

Click on the Import button in the Mito toolbar.

• Select the Import Files option that appears in the drop-down.
• Use the file browser to navigate to the directory, and XLSX file you want to import.
• Click on the file, and then click the Import button. This will open a configuration screen.
• Configure which tabs you want to import from the Excel file.
• Set any additional configuration necessary for your import:
• Has Header Row: Select “Yes” if Mito should set the first non-skipped row as the column headers. Select “No” if Mito should generate column headers
• Number of Rows to Skip: The number of rows at the top of the file to skip when reading data into the data frame.
• Decimal Separator: The character used to separate the decimal places in numbers.
• Click Import Selected Sheets. For each tab you selected to import, a tab will appear in the Mito sheet with this data.
  

That’s all there is to it. Your Excel data will be in a Mito sheet and ready for you to manipulate and analyze.

Analyzing data

Instead of having to write Pandas code to manipulate and analyze data, Mito will let you explore your data frames directly in your Mito sheet within your notebook. This makes the learning curve for switching from spreadsheets much simpler for Google Sheets and Excel users.

Creating visualizations

Mito makes it easy to graph your data as well. Using the Plotly Express open-source graphing library, you can quickly create interactive and customizable graphs from your data sets. When you create a graph, Mito will also generate the equivalent Python code automatically.

Simply select your data source and then choose your graph type. With Mito, you can make visualizations such as line graphs, bar graphs, scatter plots, histograms, density heat maps and so much more.

Tips for a successful switch from Excel to Python

Transitioning from Excel to Python may seem intimidating at first, but you’ll be off to a great start after you understand the core concepts. Here are some general tips to help you make the transition successfully:

Practice with real data you regularly use in Excel

A great way to learn and understand Python is by using real data that you use regularly. Importing your own data will help you see how Python can apply to your specific work. Simply convert your spreadsheet data into a readable format and load it into a Jupyter Notebook. You can then use Pandas to practice manipulating and analyzing your data.

Take your time exploring your data and try to apply some of the concepts we talked about earlier. Don’t be discouraged or afraid to make mistakes and experiment. It will likely take you some time to get comfortable in a Python environment, but being thorough will ensure you fully understand the concepts and syntax.

Use a spreadsheet GUI in Jupyter to visualize your dataframe

If you are new to Python, creating charts from scratch can be a daunting task. Using a spreadsheet GUI in a Jupyter Notebook can be a great workaround. Jupyter is an interactive application in Python that makes it easy to create visualizations with a spreadsheet-like interface while also performing data manipulation tasks using Pandas.

Using a spreadsheet GUI in Jupyter can make the transition from Excel to Python and Pandas much easier and less intimidating. It allows you to work with your data in a familiar interface and gradually become comfortable with Python and Pandas at your own pace.

Keep Excel open as you engage with your data set in Python

You don’t have to leave Excel behind right away when transitioning to Python and Pandas. You can keep your Excel data set open as a reference point as you learn to manipulate data in Python. For example, using Excel to spot-check your data after importing it to Python can help you spot any errors or inconsistencies that may have occurred due to formatting.

Overall, the transition from Excel to Python and Pandas goes a lot smoother by keeping Excel open as you work on your data set. Don’t be afraid to use both tools together as you’re taking this programming journey!

AI and code-generator tools have come a long way

AI and code-generator tools are now widely used and reliable ways to write code. Mito, for instance, can automatically generate the equivalent Python code when you make changes to a spreadsheet. As you’re learning to harness Python for your data analytics purpose, you can look at the code Mito generates and start to understand how it relates to what you're doing in your spreadsheet. This is a great way for you to learn when you’re just getting started.

Engage and collaborate with the Python community

Finally, don’t forget that there is a huge, active Python community with a ton of free resources for beginners. There are numerous forums where you can find experts and beginners alike who can offer you encouragement and solutions to obstacles that you might run into. You may even find like-minded programmers to collaborate with on your learning endeavor.

Mito makes the move from spreadsheets to Python a lot easier

In short, while the move from spreadsheets to Python can be an intimidating one, taking the right steps can make the process smooth and enjoyable. Once you see how much more in-depth your data analysis capabilities are with Python, you’ll never want to go back to Excel and Google Sheets.

To help you along the way, don’t forget to try Mito. Our Python-based spreadsheet app will make the transition much easier as you explore and edit data in Python as you would in Excel or Google Sheets. Thanks to automated spreadsheet workflows in Mito, you can generate Pandas code in real-time and learn as you go. Ready to get started? Install Mito today and try it out!