I’m trying to pass a user defined function pct to Pandas agg method, and it works if I only pass that function but it doesn’t when I use the dictionary format for defining the functions. Does anyone know why? returns as expected But returns the following error: Answer There is string ‘pct’, need variable pct – lambda function by removing
Tag: pandas-groupby
Pandas: add column with progressive count of elements meeting a condition
Given the following dataframe df: I want to add another column that counts, progressively, the elements with df[‘B’]=’yes’: How can I do this? Answer You can use numpy.where with cumsum of boolean mask: Another solution is count boolean mask created by filtering and then add 0 values by reindex: Performance (in real data should be different, best check it first):
How to use groupby to concatenate strings in python pandas? [duplicate]
This question already has answers here: Concatenate strings from several rows using Pandas groupby (8 answers) Closed 6 months ago. I currently have dataframe at the top. Is there a way to use a groupby function to get another dataframe to group the data and concatenate the words into the format like further below using python pandas? Thanks [ Answer
How to loop over grouped Pandas dataframe?
DataFrame: Code: I’m trying to just loop over the aggregated data, but I get the error: ValueError: too many values to unpack @EdChum, here’s the expected output: The output is not the problem, I wish to loop over every group. Answer df.groupby(‘l_customer_id_i’).agg(lambda x: ‘,’.join(x)) does already return a dataframe, so you cannot loop over the groups anymore. In general: df.groupby(…)