If you want to iterate over the rows in a DataFrame in Pandas, there are a few different ways that you can do it. In this post, we will discuss some of the best tips and tricks for iterating over rows in a DataFrame. We will also show you how to use the iterrows() function to make iterating over your data much easier.
Let’s get started!
If you have a DataFrame with a lot of data, it can be helpful to iterate over the rows. This can help you to avoid loading all of the data into memory at once. Additionally, iterating over the rows can help you to process your data one row at a time, which can be helpful if you are working with large files.
Iterate Over Rows in a DataFrame in Pandas
One way to iterate over the rows in a DataFrame is to use the iterrows() function. This function will return a tuple for each row in the DataFrame, where the first element is the index and the second element is the row itself. You can then use this tuple to access the data in each row.
For example:
#python with wholeblogs for index, row in df.iterrows(): print(index, row['column_name'])
This code will iterate over the rows in the DataFrame and print the index and the value of the column_name column for each row.
Another way to iterate over the rows in a DataFrame is to use the itertuples() function. This function will return a namedtuple for each row in the DataFrame. You can then use this namedtuple to access the data in each row.
For example:
#python with wholeblogs for row in df.itertuples(): print(row.index, row.column_name)
This code will iterate over the rows in the DataFrame and print the index and the value of the column_name column for each row.
You can also use the apply() function to iterate over the rows in a DataFrame. This function will apply a function to each row in the DataFrame and return a new Series.
For example:
#python with wholeblogs def my_function(row): print(row.index, row['column_name']) df.apply(my_function, axis=0) print(my_function(2))
This code will iterate over the rows in the DataFrame and print the index and the value of the column_name column for each row.
As you can see, there are a few different ways that you can iterate over the rows in a DataFrame in Pandas. Which method you use will depend on your specific needs. However, all of these methods can be helpful when working with large data sets.
Do you have any tips or tricks for iterating over rows in a DataFrame? Let us know in the comments!
Read More: Iterating over dictionaries using ‘for’ loops: A detailed guide