I have a dataframe that shows details about employees and the site they are at and the positions they have held. The dataframe has columns for Site Id, Employee ID, and StartDate (plus a lot more fields). I have this sorted by Site and Employee ID ASC and then EffectiveDate DESC (latest record is first)

I have a multiindex Pandas DataFrame and I’m using groupby to extract the rows containing the first appearances of the first index.
After this operation, however, the output column values does not always correspond to the original values.
Here is a simple example to reproduce this behaviour:

This is a continuation of a question I asked previously in How to efficiently select the top N columns by grouping for each row of a pandas DataFrame?. My needs have evolved as I work with my dataset — thanks again to everyone who has helped me so far. Here is the problem as it currently stands:

I would like to impute some missing values in a pandas data frame using .bfill() or .ffill(). I want to use .groupby() first. The problem is that .bfill() and .ffill() depend on previous or next rows and I am using .groupby() on multiple columns.

I have a very large dataset called bin_df.

I’m curious about a weird behavior I got while using Pandas.

I have a pandas Dataframe that looks similar to this:

I have the following dataframe:

I have a puzzling situation with pandas groupby objects. I’m in a situation where I have a dataset with ids, features, and targets for training a machine learning model. In some cases, there are groups of features with differing target values, and since that doesn’t make sense, I would like to compute the mean of target values within those groups.

In this dataframe I want to create a column ‘desired_output’ that takes the first value of ‘lumpsum’ from the ‘index’ of each ‘ID’.