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BUG/PERF: SeriesGroupBy.value_counts sorting bug and categorical performance #50548

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Merged
merged 10 commits into from
Jan 11, 2023
Merged

BUG/PERF: SeriesGroupBy.value_counts sorting bug and categorical performance #50548

merged 10 commits into from
Jan 11, 2023

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rhshadrach
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This takes a bit of a perf hit with SeriesGroupBy.value_counts; I think this is because the current implementation always sorts the groupers (which is also fixed here).

pandas/pandas/core/groupby/generic.py

Lines 711 to 715 in 0a4ba64

# group boundaries are where group ids change
idchanges = 1 + np.nonzero(ids[1:] != ids[:-1])[0]
idx = np.r_[0, idchanges]
if not len(ids):
idx = idchanges

Also get a good perf improvement with categorical.

Perf code 
import numpy as np
import pandas as pd
import time

size = 1000
col1_possible_values = ["".join(np.random.choice(list("ABCDEFGHIJKLMNOPRSTUVWXYZ"), 20)) for _ in range(700000)]
col2_possible_values = ["".join(np.random.choice(list("ABCDEFGHIJKLMNOPRSTUVWXYZ"), 10)) for _ in range(860)]
col1_values = np.random.choice(col1_possible_values, size=size, replace=True)
col2_values = np.random.choice(col2_possible_values, size=size, replace=True)
col3_values = np.random.choice(col2_possible_values, size=size, replace=True)
df = pd.DataFrame(zip(col1_values, col2_values, col3_values), columns=["col1", "col2", "col3"])

print('DataFrameGroupBy - object')
%timeit df.groupby("col1").value_counts()

print('SeriesGroupBy - object')
%timeit df.groupby("col1")["col2"].value_counts()

df['col2'] = df['col2'].astype('category')

print('DataFrameGroupBy - category')
%timeit df.groupby("col1", observed=True)[["col2"]].value_counts()

print('SeriesGroupBy - category')
%timeit df.groupby("col1", observed=True)["col2"].value_counts()

# main
DataFrameGroupBy - object
4.09 ms ± 97.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
SeriesGroupBy - object
1.32 ms ± 7 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
DataFrameGroupBy - category
86.1 ms ± 611 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
SeriesGroupBy - category
654 ms ± 7.04 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

# This PR
DataFrameGroupBy - object
4.1 ms ± 53.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
SeriesGroupBy - object
2 ms ± 17.5 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
DataFrameGroupBy - category
84.7 ms ± 576 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
SeriesGroupBy - category
83.1 ms ± 565 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

@rhshadrach rhshadrach added Bug Groupby Performance Memory or execution speed performance Algos Non-arithmetic algos: value_counts, factorize, sorting, isin, clip, shift, diff labels Jan 4, 2023
@rhshadrach rhshadrach added this to the 2.0 milestone Jan 4, 2023
mroeschke
mroeschke reviewed Jan 4, 2023
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Looks fairly good. Just a merge conflict

mroeschke
mroeschke approved these changes Jan 9, 2023
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LGTM after the merge conflicts are resolved

@rhshadrach
Merge branch 'main' of https://github.com/pandas-dev/pandas into grou…
e1d3ccf 
…pby_value_counts_sort

# Conflicts:
#	doc/source/whatsnew/v2.0.0.rst
#	pandas/tests/groupby/test_value_counts.py
@rhshadrach
Merge branch 'groupby_value_counts_sort' of https://github.com/rhshad…
334f2ab 
…rach/pandas into groupby_value_counts_sort

# Conflicts:
#	doc/source/whatsnew/v2.0.0.rst
@rhshadrach
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Merging to avoid any further whatsnew conflicts

@rhshadrach rhshadrach merged commit 4f42ecb into pandas-dev:main Jan 11, 2023
@rhshadrach rhshadrach deleted the groupby_value_counts_sort branch January 11, 2023 02:22
@rhshadrach rhshadrach mentioned this pull request Jan 21, 2023
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@mroeschke mroeschke mroeschke approved these changes

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Algos Non-arithmetic algos: value_counts, factorize, sorting, isin, clip, shift, diff Bug Groupby Performance Memory or execution speed performance
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