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Jul 20, 2018
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Sparse get dummies perf #21997

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PERF
TomAugspurger Jul 20, 2018
8fda4fb
Whatsnew
TomAugspurger Jul 20, 2018
234e9b2
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TomAugspurger Jul 20, 2018
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TomAugspurger Jul 20, 2018
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18 changes: 18 additions & 0 deletions asv_bench/benchmarks/reshape.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,9 @@
import string
from itertools import product

import numpy as np
from pandas import DataFrame, MultiIndex, date_range, melt, wide_to_long
import pandas as pd

from .pandas_vb_common import setup # noqa

Expand Down Expand Up @@ -132,3 +134,19 @@ def setup(self):

def time_pivot_table(self):
self.df.pivot_table(index='key1', columns=['key2', 'key3'])


class GetDummies(object):
goal_time = 0.2

def setup(self):
categories = list(string.ascii_letters[:12])
s = pd.Series(np.random.choice(categories, size=1_000_000),
dtype=pd.api.types.CategoricalDtype(categories))
self.s = s

def time_get_dummies_1d(self):
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Small nit: you can param over sparce=False/True

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I have a slight preference for leaving them separate, since they're such distinct code paths and it's a tad easier to run just sparse with this layout. Happy to change if you feel strongly about this.

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Sounds good, no strong preference to use params then.

pd.get_dummies(self.s, sparse=False)

def time_get_dummies_1d_sparse(self):
pd.get_dummies(self.s, sparse=True)
2 changes: 1 addition & 1 deletion doc/source/whatsnew/v0.24.0.txt
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Expand Up @@ -343,7 +343,7 @@ Performance Improvements
- Improved performance of :meth:`HDFStore.groups` (and dependent functions like
:meth:`~HDFStore.keys`. (i.e. ``x in store`` checks are much faster)
(:issue:`21372`)
-
- Improved the performance of :func:`pandas.get_dummies` with ``sparse=True`` (:issue:`21997`)

.. _whatsnew_0240.docs:

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9 changes: 5 additions & 4 deletions pandas/core/reshape/reshape.py
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Expand Up @@ -940,10 +940,11 @@ def get_empty_Frame(data, sparse):
sparse_series = {}
N = len(data)
sp_indices = [[] for _ in range(len(dummy_cols))]
for ndx, code in enumerate(codes):
if code == -1:
# Blank entries if not dummy_na and code == -1, #GH4446
continue
mask = codes != -1
codes = codes[mask]
n_idx = np.arange(N)[mask]

for ndx, code in zip(n_idx, codes):
sp_indices[code].append(ndx)

if drop_first:
Expand Down