How to Convert a DataFrame Column to Integer in Pandas

Hey there, data wizards! Working with Pandas DataFrames is super fun, but sometimes, you need to change the data type of a column—especially when dealing with numbers stored as strings or floats. If you’ve ever wondered how to convert a column to an integer in Pandas, you’re in the right place! Let’s explore some easy ways to do it.

Convert a DataFrame Column to Integer in Pandas

 

1. Using .astype(int) (The Quickest Way)

If your column contains only numeric values, converting it to integers is super easy with .astype(int).

import pandas as pd

# Sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': ['25', '30', '35']}  # Age is stored as strings
df = pd.DataFrame(data)

# Converting Age column to integer
df['Age'] = df['Age'].astype(int)

print(df.dtypes)  # Check data types

Output:

Now, the ‘Age’ column is an integer!

2. Using pd.to_numeric() (For Safety!)

If your column has mixed data (like numbers and text), .astype(int) will throw an error. Instead, use pd.to_numeric() to handle errors gracefully.

# Sample DataFrame with mixed data
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': ['25', 'N/A', '35']}  # One non-numeric value
df = pd.DataFrame(data)

# Convert column to integer, replacing errors
df['Age'] = pd.to_numeric(df['Age'], errors='coerce').fillna(0).astype(int)

print(df)

Output:

Setting errors='coerce' replaces invalid values with NaN, and .fillna(0) replaces them with 0 before conversion. No more errors!

3. Converting Float Columns to Integer

If your column has decimals, converting directly to int will remove the decimal part.

# Sample DataFrame with float values
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Height': [5.8, 6.1, 5.5]}
df = pd.DataFrame(data)

# Convert Height column to integer
df['Height'] = df['Height'].astype(int)

print(df)

Output:

The decimals are removed, so be careful when converting!

4. Changing Multiple Columns to Integer

What if you need to convert more than one column at once? Use .astype() on multiple columns!

# Sample DataFrame
data = {'Age': ['25', '30', '35'], 'Salary': ['50000', '60000', '70000']}
df = pd.DataFrame(data)

# Convert multiple columns to integer
df = df.astype({'Age': int, 'Salary': int})

print(df.dtypes)

Output:

Super easy, right?

Conclusion:

And there you have it! Whether you’re working with numeric strings, mixed data, or floats, these methods will help you convert DataFrame columns to integers smoothly.

Now, go ahead and transform your data like a pro! If you found this helpful, share it with your fellow data enthusiasts. Happy coding!

Got any cool Pandas tricks? Drop them in the comments below!