topic Loop through Dataframe in Python in Data Engineering

Loop through Dataframe in Python

FernandoBenedet — Wed, 10 Jun 2020 01:08:04 GMT

Hello,

Imagine you have a dataframe with cols: A, B, C. I want to add a column D based on some calculations of columns B and C of the previous record of the df. Which is the best way of doing this? I am trying to avoid looping through the df. I am using python.

Thanks.

Fernando.

Re: Loop through Dataframe in Python

ColbyCarrillo — Wed, 10 Jun 2020 01:52:29 GMT

I would probably use a window function within pyspark.

Link to Databricks blog: https://databricks.com/blog/2015/07/15/introducing-window-functions-in-spark-sql.html

Another option is to use lag or lead columns to help you capture the data in the relative position you desire.

You can find theme here in the SQL functions list: https://docs.databricks.com/spark/latest/spark-sql/language-manual/functions.html

Re: Loop through Dataframe in Python

quincybatten — Mon, 03 May 2021 06:25:39 GMT

Iterating through pandas dataFrame objects is generally slow. Pandas Iteration beats the whole purpose of using DataFrame. It is an anti-pattern and is something you should only do when you have exhausted every other option. It is better look for a List Comprehensions , vectorized solution or DataFrame.apply() method.

Pandas DataFrame loop using list comprehension:

result = [(x, y,z) for x, y,z in zip(df['Name'], df['Promoted'],df['Grade'])]