dfのiterrowsの使い方

2024年6月8日

iterrows is a method in pandas that allows you to iterate over DataFrame rows as (index, Series) pairs. However, it’s important to note that using iterrows can be less efficient than other methods for large DataFrames, so it’s recommended to use it with caution, especially in performance-critical code.

Here’s a simple example of how to use iterrows:

python
import pandas as pd

# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 22],
        'City': ['New York', 'San Francisco', 'Los Angeles']}

df = pd.DataFrame(data)

# Iterate over rows using iterrows
for index, row in df.iterrows():
    print(f"Index: {index}, Name: {row['Name']}, Age: {row['Age']}, City: {row['City']}")

Output:

yaml
Index: 0, Name: Alice, Age: 25, City: New York
Index: 1, Name: Bob, Age: 30, City: San Francisco
Index: 2, Name: Charlie, Age: 22, City: Los Angeles

In each iteration, index represents the row index, and row is a pandas Series containing the data for that row.

Keep in mind that using vectorized operations in pandas is generally more efficient than iterating over rows with iterrows, so it’s a good practice to explore alternatives like using apply, vectorized operations, or the agg function depending on the specific task you’re trying to accomplish.