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Description:

pecking 0.2.2

:hatching_chick: pecking identifies the set of lowest-ranked groups and set of highest-ranked groups in a dataset using nonparametric statistical tests.

Free software: MIT license
Repository: https://github.com/mmore500/pecking
Documentation: https://github.com/mmore500/pecking/blob/master/README.md

Install
python3 -m pip install pecking
Example Usage
>>> import pecking
>>> samples = [[1, 2, 3, 4, 5], [2, 3, 4, 4, 4], [8, 9, 7, 6, 4]]
>>> labels = ['Group 1', 'Group 2', 'Group 3']
>>> pecking.skim_highest(samples, labels)
['Group 1']

import functools
from matplotlib import pyplot as plt
import pecking
import seaborn as sns

g = peckplot(
sns.load_dataset("titanic"),
score="age",
x="who",
y="age",
hue="class",
col="survived",
legend_kws=dict(prop={"size": 8}, bbox_to_anchor=(0.88, 0.5)),
skimmers=(
functools.partial(
skim_highest, alpha=0.05, min_obs=8, nan_policy="omit"
),
functools.partial(
skim_lowest, alpha=0.05, min_obs=8, nan_policy="omit"
),
),
skim_labels=["Oldest", "Youngest"],
palette=sns.color_palette("tab10")[:3],
)
assert g is not None
g.map_dataframe(
sns.stripplot,
x="who",
y="age",
hue="class",
s=2,
color="black",
dodge=True,
jitter=0.3,
)

plt.show()

API
See function docstrings for full parameter and return value descriptions.
pecking.skim_lowest/pecking.skim_highest
Direct interface to the underlying statistical tests.
def skim_highest(
samples: typing.Sequence[typing.Sequence[float]],
labels: typing.Optional[typing.Sequence[typing.Union[str, int]]] = None,
alpha: float = 0.05,
) -> typing.List[typing.Union[str, int]]:
"""Identify the set of highest-ranked groups that are statistically
indistinguishable amongst themselves based on a Kruskal-Wallis H-test
followed by multiple Mann-Whitney U-tests."""

def skim_highest(
samples: typing.Sequence[typing.Sequence[float]],
labels: typing.Optional[typing.Sequence[typing.Union[str, int]]] = None,
alpha: float = 0.05,
) -> typing.List[typing.Union[str, int]]:
"""Identify the set of lowest-ranked groups that are statistically
indistinguishable amongst themselves based on a Kruskal-Wallis H-test
followed by multiple Mann-Whitney U-tests."""

pecking.mask_skimmed_rows
Tidy-data interface to calculate the results of skim_lowest/skim_highest among row groups in a DataFrame.
def mask_skimmed_rows(
data: pd.DataFrame,
score: str,
groupby_inner: typing.Union[typing.Sequence[str], str],
groupby_outer: typing.Union[typing.Sequence[str], str] = tuple(),
skimmer: typing.Callable = skim_highest,
**kwargs: dict,
) -> pd.Series:
"""Create a boolean mask for a DataFrame, identifying rows within
significantly outstanding groups.

This function applies a two-level grouping to the input DataFrame: an outer
grouping ('groupby_outer') followed by an inner grouping ('groupby_inner').
For each inner group, it uses a 'skimmer' function to determine which rows
are part of significantly outstanding groups based on a specified 'score'
column. Only inner groups within the same outer group are compared.

Rows identified as members of significantly outstanding inner groups are
marked True in the returned Series, while all others are marked False."""

pecking.peckplot
Wraps seaborn.catplot to add hatched backgrounds behind the best and worst groups within the each row/col facet.
(Comparison scope/pooling can be controlled with *_group parameters.)
def peckplot(
data: pd.DataFrame,
score: str,
x: typing.Optional[str] = None,
y: typing.Optional[str] = None,
hue: typing.Optional[str] = None,
col: typing.Optional[str] = None,
row: typing.Optional[str] = None,
x_group: typing.Literal["inner", "outer", "ignore"] = "inner",
y_group: typing.Literal["inner", "outer", "ignore"] = "inner",
hue_group: typing.Literal["inner", "outer", "ignore"] = "inner",
col_group: typing.Literal["inner", "outer", "ignore"] = "outer",
row_group: typing.Literal["inner", "outer", "ignore"] = "outer",
skimmers: typing.Sequence[typing.Callable] = (
functools.partial(skim_highest, alpha=0.05),
functools.partial(skim_lowest, alpha=0.05),
),
skim_hatches: typing.Sequence[str] = ("*", "O.", "xx", "++"),
skim_labels: typing.Sequence[str] = ("Best", "Worst"),
skim_title: typing.Optional[str] = "Rank",
orient: typing.Literal["v", "h"] = "v",
**kwargs: dict,
) -> sns.FacetGrid:
"""Boxplot the distribution of a score across various categories,
highlighting the best (and/or worst) performing groups.

Uses nonparametric `skim_highest`/`skim_lowest` to distinguish the sets of
groups with statistically indistinguishable highest/lowest scores. Uses
`backstrip`'s `backplot` to add hatched backgrounds behind the best and
worst groups."""

Citing
If pecking contributes to a scientific publication, please cite it as

Matthew Andres Moreno. (2024). mmore500/pecking. Zenodo. https://doi.org/10.5281/zenodo.10701185

@software{moreno2024pecking,
author = {Matthew Andres Moreno},
title = {mmore500/pecking},
month = feb,
year = 2024,
publisher = {Zenodo},
doi = {10.5281/zenodo.10701185},
url = {https://doi.org/10.5281/zenodo.10701185}
}

Consider also citing matplotlib, seaborn, and SciPy.
And don't forget to leave a star on GitHub!