I have to create a function which would split provided dataframe into chunks of needed size. For instance if dataframe contains 1111 rows, I want to be able to specify chunk size of 400 rows, and get three smaller dataframes with sizes of 400, 400 and 311. Is there a convenience function to do the job? What would be the best way to store and iterate over sliced dataframe?
The operation pandas.DataFrame.lookup is “Deprecated since version 1.2.0”, and has since invalidated a lot of previous answers.
I have a DataFrame with a MultiIndex created after some grouping:
I’ve got a dataframe with the following information:
I have a column Date_Time that I wish to groupby date time without creating a new column. Is this possible the current code I have does not work.
If you must store a column of lists, ensure not to copy the same reference multiple times.
This question is motivated by an answer to a question on improving performance when performing comparisons with DatetimeIndex in pandas.
This is probably easy, but I have the following data:
I noticed Pandas now has support for Sparse Matrices and Arrays. Currently, I create DataFrame()s like this:
I have a pandas DataFrame, df_test. It contains a column ‘size’ which represents size in bytes. I’ve calculated KB, MB, and GB using the following code: