How Do I Efficiently Iterate Over Rows in a Pandas DataFrame?

Iterating Over Rows in Pandas DataFrames

When working with data in Pandas, one common task is iterating over the rows of a DataFrame. This allows you to access each row's elements individually.

How to Iterate Using iterrows()

The preferred method for iterating over rows is to use the DataFrame.iterrows() method. This method yields a tuple for each row, containing both the index and the row as a Series.

df = pd.DataFrame({'c1': [10, 11, 12], 'c2': [100, 110, 120]})

for index, row in df.iterrows():
    print(row['c1'], row['c2'])

This will output:

10 100
11 110
12 120

How the row Object Works

The row object is a Pandas Series that represents the row's data. You can access its elements by their column names or by their index.

Alternatives to iterrows()

There are alternative methods that you can use to iterate over rows, but they are generally less efficient.

• DataFrame.itertuples() yields a namedtuple instead of a Series.
• DataFrame.T.iteritems() iterates over columns instead of rows.

Performance Considerations

Iterating over rows in a DataFrame can be computationally expensive. If performance is a concern, consider using vectorized solutions or writing inner loops with Cython or NumPy.

The above is the detailed content of How Do I Efficiently Iterate Over Rows in a Pandas DataFrame?. For more information, please follow other related articles on the PHP Chinese website!