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scipy-yli
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In auto_descriptives, autodetect ordinal variables based on category dtype

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RunasSudo 2022-12-03 22:19:24 +11:00
parent 5633a191f1
commit 0fa261498a
Signed by: RunasSudo
GPG Key ID: 7234E476BF21C61A
1 changed files with 37 additions and 36 deletions
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73
yli/descriptives.py
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@ -19,7 +19,7 @@ import pandas as pd
from .config import config
from .utils import check_nan
def auto_descriptives(df, cols, *, ordinal_range=[], ordinal_iqr=[]):
def auto_descriptives(df, cols, *, ordinal_range=[]):
"""
Automatically compute descriptive summary statistics
@ -27,7 +27,7 @@ def auto_descriptives(df, cols, *, ordinal_range=[], ordinal_iqr=[]):
* For a categorical variable – Counts of values
* For a continuous variable – Mean and standard deviation
* For an ordinal variable – Median and range or IQR
* For an ordinal variable – Median and IQR (default) or range
There is no *nan_policy* argument. *nan* values are omitted from summary statistics for each variable, and the count of *nan* values is reported.
@ -35,10 +35,8 @@ def auto_descriptives(df, cols, *, ordinal_range=[], ordinal_iqr=[]):
:type df: DataFrame
:param cols: Columns in *df* for the variables to summarise
:type cols: List[str]
:param ordinal_range: Columns in *df* to treat as ordinal, and report median and range
:param ordinal_range: Columns of ordinal variables in *df* to report median and range (rather than IQR)
:type ordinal_range: List[str]
:param ordinal_iqr: Columns in *df* to treat as ordinal, and report median and IQR
:type ordinal_iqr: List[str]
:rtype: :class:`yli.descriptives.AutoDescriptivesResult`
"""
@ -49,7 +47,31 @@ def auto_descriptives(df, cols, *, ordinal_range=[], ordinal_iqr=[]):
for col in cols:
data_cleaned = df[col].dropna()
if data_cleaned.dtype in ('bool', 'boolean', 'category', 'object'):
if data_cleaned.dtype == 'category' and data_cleaned.cat.ordered and data_cleaned.cat.categories.dtype in ('float64', 'int64', 'Float64', 'Int64'):
# Ordinal numeric data
data_cleaned = data_cleaned.astype('float64')
if col in ordinal_range:
# Report range
result_labels.append((
'{}, median (range)'.format(col),
'{}, median (range)'.format(col),
))
result_data.append((
'{:.2f} ({:.2f}–{:.2f})'.format(data_cleaned.median(), data_cleaned.min(), data_cleaned.max()),
len(df) - len(data_cleaned)
))
else:
# Report IQR
result_labels.append((
'{}, median (IQR)'.format(col),
'{}, median (IQR)'.format(col),
))
result_data.append((
'{:.2f} ({:.2f}–{:.2f})'.format(data_cleaned.median(), data_cleaned.quantile(0.25), data_cleaned.quantile(0.75)),
len(df) - len(data_cleaned)
))
elif data_cleaned.dtype in ('bool', 'boolean', 'category', 'object'):
# Categorical data
# FIXME: Sort order
values = sorted(data_cleaned.unique())
@ -64,36 +86,15 @@ def auto_descriptives(df, cols, *, ordinal_range=[], ordinal_iqr=[]):
len(df) - len(data_cleaned)
))
elif data_cleaned.dtype in ('float64', 'int64', 'Float64', 'Int64'):
if col in ordinal_range:
# Ordinal data (report range)
result_labels.append((
'{}, median (range)'.format(col),
'{}, median (range)'.format(col),
))
result_data.append((
'{:.2f} ({:.2f}–{:.2f})'.format(data_cleaned.median(), data_cleaned.min(), data_cleaned.max()),
len(df) - len(data_cleaned)
))
elif col in ordinal_iqr:
# Ordinal data (report IQR)
result_labels.append((
'{}, median (IQR)'.format(col),
'{}, median (IQR)'.format(col),
))
result_data.append((
'{:.2f} ({:.2f}–{:.2f})'.format(data_cleaned.median(), data_cleaned.quantile(0.25), data_cleaned.quantile(0.75)),
len(df) - len(data_cleaned)
))
else:
# Continuous data
result_labels.append((
'{}, μ (SD)'.format(col),
'{}, <i>μ</i> (SD)'.format(col),
))
result_data.append((
'{:.2f} ({:.2f})'.format(data_cleaned.mean(), data_cleaned.std()),
len(df) - len(data_cleaned)
))
# Continuous data
result_labels.append((
'{}, μ (SD)'.format(col),
'{}, <i>μ</i> (SD)'.format(col),
))
result_data.append((
'{:.2f} ({:.2f})'.format(data_cleaned.mean(), data_cleaned.std()),
len(df) - len(data_cleaned)
))
else:
raise Exception('Unsupported dtype for auto_descriptives, {}'.format(df[col].dtype))