Format / Suppress Scientific Notation from Pandas Aggregation Results

How to Format / Suppress Scientific Notation from Pandas Aggregation Results 📊🔢

Have you ever encountered a situation where you perform a groupby operation in pandas and end up with numbers displayed in scientific notation? 🧐

Fear not! In this blog post, we will tackle this common issue and provide easy solutions to suppress scientific notation from your pandas aggregation results. 💪✨

The Problem 🤔

Let's say you have performed a groupby operation in pandas on a DataFrame called df1, and you want to display the sum of a specific column, let's call it data1. However, when you print the result, you see something like this:

dept
value1       1.192433e+08
value2       1.293066e+08
value3       1.077142e+08

The numbers are displayed in scientific notation (e.g., 1.192433e+08). While scientific notation is great for representing very large or small numbers, it might not be the best choice if you prefer a more human-readable format. 😯

The Solution 💡

Luckily, pandas provides us with simple ways to format the numbers and suppress scientific notation. Here are two easy solutions you can try:

1. Using the apply method and lambda function:

   df1.groupby('dept')['data1'].sum().apply(lambda x: "{:.2f}".format(x))

   This solution applies a lambda function to each value in the resulting series. The lambda function formats the number with two decimal places ("{:.2f}") and returns it as a string.

2. Using the map method and formatting options:

   df1.groupby('dept')['data1'].sum().map("{:,.2f}".format)

   In this solution, we use the map method to apply a specific string formatting option to each value. The "{:,.2f}" format specifies that we want two decimal places and comma separators for thousands.

Let's see it in action! 🚀👀

import pandas as pd

# Groupby operation
result = df1.groupby('dept')['data1'].sum()

# Formatting using apply and lambda
formatted_result_1 = result.apply(lambda x: "{:.2f}".format(x))
print(formatted_result_1)

# Formatting using map
formatted_result_2 = result.map("{:,.2f}".format)
print(formatted_result_2)

By using either of these solutions, you should get the desired output without the scientific notation:

dept
value1       119,243,300.00
value2       129,306,600.00
value3       107,714,200.00

Much better, right? Now you have clean and readable numbers to work with! 🎉

An Extra Tip: Adding Decimals 🌟

If you also want to add a specific number of decimal places to your formatted results, you can modify the formatting string accordingly.

For example, to display three decimal places, you would change {:.2f} to {:.3f} in the apply or map methods.

# Formatting with three decimal places using apply and lambda
formatted_result_3 = result.apply(lambda x: "{:.3f}".format(x))
print(formatted_result_3)

# Formatting with three decimal places using map
formatted_result_4 = result.map("{:,.3f}".format)
print(formatted_result_4)

Your Turn! 📝✨

Now that you know how to format and suppress scientific notation from your pandas aggregation results, go ahead and try it yourself! Apply these solutions to your own code and see the clean and readable numbers in action. 😉

Don't forget to share your experience and any other tips you may have in the comments below. Let's grow our pandas formatting knowledge together! 🌱💬

Happy coding! 💻🔢

[Image: pandas logo]

Note: Remember to import the necessary pandas library before running the code examples.