How to Split Strings and Integers in a Pandas Series using Python

Learn how to easily split movie titles and years from a single string in a Pandas DataFrame, ensuring the year is converted to an integer type.
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Splitting Strings and Integers in a Pandas Series using Python

When working with data in Python using the Pandas library, you may encounter situations where you have combined information in a single column that needs to be separated. One common example is having a movie title and its release year combined in a single string, like "Toy Story (1995)". This can be problematic, especially when you need the year as an integer for analysis. In this guide, we will explore how to effectively split such strings and ensure the year is converted to the correct data type.

The Problem

Imagine you have a DataFrame that contains a column named "Movie", which includes strings formatted like "Title (Year)". Your goal is to extract the movie title and the year into separate columns. Moreover, you want the year to be stored as an integer, not as a string or object type.

For instance, given the string "Toy Story (1995)", your aim would be to:

Create a new column "Title" with the value "Toy Story"

Create another column "Year" with the integer value 1995

The Solution

Let’s break down the solution into clear, manageable steps.

Step 1: Splitting the String

Here's how to do it:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Cleaning the Year Column

After splitting the string, the "Year" column will still contain a closing parenthesis ) which means it remains a string. We need to eliminate this character to convert it to an integer.

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Converting to Integer

The final step is to convert the "Year" column from a string to an integer type. This allows for numerically accurate analysis later on.

Here’s how it’s done:

[[See Video to Reveal this Text or Code Snippet]]

Final Code Example

Combining all steps, your code would look something like this:

[[See Video to Reveal this Text or Code Snippet]]

Expected Output

When you run the code above, you should see that your DataFrame now has the "Title" and "Year" columns populated correctly:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By following these steps, you can effectively split strings containing movie titles and years into separate columns within a Pandas DataFrame, while also ensuring that the year is formatted as an integer. This method allows for better data manipulation and analysis in Python.

Feel free to implement this solution in your data projects, and happy coding!