How can I identify which column(s) in my DataFrame contain a specific string 'foo'?
Sample DataFrame:
>>> import pandas as pd >>> df = pd.DataFrame({'A':[10,20,42], 'B':['foo','bar','blah'],'C':[3,4,5], 'D':['some','foo','thing']}) I want to find B and D here.
I can search for numbers:
If I'm looking for a number (e.g. 42) instead of a string, I can generate a boolean mask like this:
>>> ~(df.where(df==42)).isnull().all() A True B False C False D False dtype: bool but not strings:
>>> ~(df.where(df=='foo')).isnull().all() TypeError: Could not compare ['foo'] with block values I don't want to iterate over each column and row if possible (my actual data is much larger than this example). It feels like there should be a simple and efficient way.
How can I do this?
