dropping rows from dataframe based on a “not in” condition
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
I am running a basic script that loops over a nested dictionary, grabs data from each record, and appends it to a Pandas DataFrame. The data looks something like this:
I want to remove the stop words from my column “tweets”. How do I iterative over each row and each item?
I would like to know if there is a function to change specific column names but without selecting a specific name or without changing all of them.
I’m having trouble getting the pandas dataframe.to_csv(...) output quoting strings right.
I want to make a pivot table from the following dataframe with columns sales, rep. The pivot table shows sales but no rep. When I tried with only rep, I got the error DataError: No numeric types to aggregate. How to fix this such that I see both the numeric field sales and the field(string) rep
For example, I have the following table:
I’ve got a DataFrame who’s index is just datetime.time and there’s no method in DataFrame.Index and datetime.time to shift the time. datetime.time has replace but that’ll only work on individual items of the Series?
In Python generally, membership of a hashable collection is best tested via set. We know this because the use of hashing gives us O(1) lookup complexity versus O(n) for list or np.ndarray.
I am starting to render plots with matplotlib as I learn both python and this interesting plotting library. I need help with a custom plot for a problem I am working on. May be there is an inbuilt function already for this.