Hi Reader!

In case you missed last week's announcement, **I'll be sharing a new data science tip with you every Tuesday!**

## 👉 Tip #2: How to use f-strings in Python

Python introduced f-strings back in version 3.6 (six years ago!), but I've only recently realized how useful they can be.

Let's start with some simple examples of how they're commonly used, and then I'll end with a real-world example (using pandas).

### Substituting objects:

To make an f-string, you simply put an "f" in front of a string. By putting the "name" and "age" objects inside of curly braces, those objects are automatically substituted into the string.

### Calling methods and functions:

Strings have an upper() method, and so I was able to call that method on the "role" string from within the f-string.

### Evaluating expressions:

You can evaluate an expression (a math expression, in this case) within an f-string.

### Formatting numbers:

This looks much nicer, right? The colon begins the format specification, and the ".1%" means "format as a percentage with 1 digit after the decimal point."

### Further reading:

🔗 Guide to f-strings (written by my pal Trey Hunner)

🔗 f-string cheat sheet (also by Trey)

### Real-world example using pandas:

Recently, I was analyzing the survey data submitted by 500+ Data School subscribers. I asked each person about their **level of experience** with 11 different data science topics/tools, plus their **level of interest** in improving those skills this year.

Thus I had 22 columns of data, with names like **“python_experience”**, **“python_interest”**, **“pandas_experience”**, **“pandas_interest”**, etc.

Each **“experience”** column was coded from 0 (None) to 3 (Advanced), and each **“interest”** column was coded from 0 (Not interested) to 2 (Definitely interested).

Among other things, I wanted to know the **mean level of interest in each topic**, as well as the **mean level of interest in each topic by experience level**.

Here's what I did to answer those questions:

(The “cats” list actually had 11 categories, so the loop allowed me to examine all of the categories at once.)

Notice how I used f-strings:

🧵 Because of the naming convention, I could access the DataFrame columns using **df[f'{cat}_interest']** and **df[f'{cat}_experience']**

🧵 I capitalized the category using **f'{cat.upper()}'** to help it stand out

🧵 I formatted the mean interest to 2 decimal places using **f'{mean_interest:.2f}'**

*How helpful was today’s tip?*

**Do you have a favorite use for f-strings?** Click reply and let me know!

See you next Tuesday!

- Kevin

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