How to iterate over consecutive chunks of Pandas dataframe efficiently

Question

I have a large dataframe (several million rows).

I want to be able to do a groupby operation on it, but just grouping by arbitrary consecutive (preferably equal-sized) subsets of rows, rather than using any particular property of the individual rows to decide which group they go to.

The use case: I want to apply a function to each row via a parallel map in IPython. It doesn't matter which rows go to which back-end engine, as the function calculates a result based on one row at a time. (Conceptually at least; in reality it's vectorized.)

I've come up with something like this:

# Generate a number from 0-9 for each row, indicating which tenth of the DF it belongs to
max_idx = dataframe.index.max()
tenths = ((10 * dataframe.index) / (1 + max_idx)).astype(np.uint32)

# Use this value to perform a groupby, yielding 10 consecutive chunks
groups = [g[1] for g in dataframe.groupby(tenths)]

# Process chunks in parallel
results = dview.map_sync(my_function, groups)

But this seems very long-winded, and doesn't guarantee equal sized chunks. Especially if the index is sparse or non-integer or whatever.

Any suggestions for a better way?

Thanks!

Answer 1

Use numpy's array_split():

import numpy as np
import pandas as pd

data = pd.DataFrame(np.random.rand(10, 3))
for chunk in np.array_split(data, 5):
  assert len(chunk) == len(data) / 5