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Merge pull request #154 from tompollard/tp/typing_pyright
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Ignore typing errors for now. Remove the "type: ignore" comments later!
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@tompollard
tompollard committed May 1, 2023
2 parents 85419ea + 0e1846e commit e550f5e
Showing 1 changed file with 41 additions and 41 deletions.
82 changes: 41 additions & 41 deletions tableone/tableone.py
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Expand Up @@ -2,7 +2,7 @@
The tableone package is used for creating "Table 1" summary statistics for
research papers.
"""
from typing import Optional, Tuple, Union
from typing import Optional, Union
import warnings

import numpy as np
Expand Down Expand Up @@ -286,11 +286,11 @@ def __init__(self, data: pd.DataFrame,

# if columns are not specified, use all columns
if not columns:
columns = data.columns.values
columns = data.columns.values # type: ignore

# check that the columns exist in the dataframe
if not set(columns).issubset(data.columns):
notfound = list(set(columns) - set(data.columns))
if not set(columns).issubset(data.columns): # type: ignore
notfound = list(set(columns) - set(data.columns)) # type: ignore
raise InputError("""Columns not found in
dataset: {}""".format(notfound))

Expand Down Expand Up @@ -320,26 +320,26 @@ def __init__(self, data: pd.DataFrame,

# if input df has ordered categorical variables, get the order.
order_cats = [x for x in data.select_dtypes("category")
if data[x].dtype.ordered]
if data[x].dtype.ordered] # type: ignore
if any(order_cats):
d_order_cats = {v: data[v].cat.categories for v in order_cats}
d_order_cats = {k: ["{}".format(v) for v in d_order_cats[k]]
for k in d_order_cats}

# combine the orders. custom order takes precedence.
if order_cats and order:
new = {**order, **d_order_cats}
new = {**order, **d_order_cats} # type: ignore
for k in order:
new[k] = order[k] + [x for x in new[k] if x not in order[k]]
order = new
elif order_cats:
order = d_order_cats
order = d_order_cats # type: ignore

if pval and not groupby:
raise InputError("If pval=True then groupby must be specified.")

self._columns = list(columns)
self._continuous = [c for c in columns
self._columns = list(columns) # type: ignore
self._continuous = [c for c in columns # type: ignore
if c not in categorical + [groupby]]
self._categorical = categorical
self._nonnormal = nonnormal
Expand Down Expand Up @@ -367,7 +367,7 @@ def __init__(self, data: pd.DataFrame,
'P-Value (adjusted)', 'SMD', 'Overall']

if self._groupby:
self._groupbylvls = sorted(data.groupby(groupby).groups.keys())
self._groupbylvls = sorted(data.groupby(groupby).groups.keys()) # type: ignore

# reorder the groupby levels if order is provided
if self._order and self._groupby in self._order:
Expand Down Expand Up @@ -635,10 +635,10 @@ def _cont_smd(self, data1=None, data2=None, mean1=None, mean2=None,
# raise InputError('n1 and n2 must both be provided.')

# cohens_d
smd = (mean2 - mean1) / np.sqrt((sd1 ** 2 + sd2 ** 2) / 2)
smd = (mean2 - mean1) / np.sqrt((sd1 ** 2 + sd2 ** 2) / 2) # type: ignore

# standard error
v_d = ((n1+n2) / (n1*n2)) + ((smd ** 2) / (2*(n1+n2)))
v_d = ((n1+n2) / (n1*n2)) + ((smd ** 2) / (2*(n1+n2))) # type: ignore
se = np.sqrt(v_d)

if unbiased:
Expand All @@ -647,7 +647,7 @@ def _cont_smd(self, data1=None, data2=None, mean1=None, mean2=None,
# Introduction to Meta-Analysis. Michael Borenstein,
# L. V. Hedges, J. P. T. Higgins and H. R. Rothstein
# Wiley (2011). Chapter 4. Effect Sizes Based on Means.
j = 1 - (3/(4*(n1+n2-2)-1))
j = 1 - (3/(4*(n1+n2-2)-1)) # type: ignore
smd = j * smd
v_g = (j ** 2) * v_d
se = np.sqrt(v_g)
Expand Down Expand Up @@ -697,11 +697,11 @@ def _cat_smd(self, prop1=None, prop2=None, n1=None, n2=None,
lst_cov = []
for p in [prop1, prop2]:
variance = p * (1 - p)
covariance = - np.outer(p, p)
covariance = - np.outer(p, p) # type: ignore
covariance[np.diag_indices_from(covariance)] = variance
lst_cov.append(covariance)

mean_diff = np.asarray(prop2 - prop1).reshape((1, -1))
mean_diff = np.asarray(prop2 - prop1).reshape((1, -1)) # type: ignore
mean_cov = (lst_cov[0] + lst_cov[1])/2

# TODO: add steps to deal with nulls
Expand All @@ -717,7 +717,7 @@ def _cat_smd(self, prop1=None, prop2=None, n1=None, n2=None,
smd = np.nan

# standard error
v_d = ((n1+n2) / (n1*n2)) + ((smd ** 2) / (2*(n1+n2)))
v_d = ((n1+n2) / (n1*n2)) + ((smd ** 2) / (2*(n1+n2))) # type: ignore
se = np.sqrt(v_d)

if unbiased:
Expand All @@ -726,7 +726,7 @@ def _cat_smd(self, prop1=None, prop2=None, n1=None, n2=None,
# Introduction to Meta-Analysis. Michael Borenstein,
# L. V. Hedges, J. P. T. Higgins and H. R. Rothstein
# Wiley (2011). Chapter 4. Effect Sizes Based on Means.
j = 1 - (3/(4*(n1+n2-2)-1))
j = 1 - (3/(4*(n1+n2-2)-1)) # type: ignore
smd = j * smd
v_g = (j ** 2) * v_d
se = np.sqrt(v_g)
Expand Down Expand Up @@ -851,25 +851,25 @@ def _t1_summary(self, x: pd.Series) -> str:
f = "{{:.{}f}} [{{:.{}f}},{{:.{}f}}]".format(n, n, n)
if self._min_max and x.name in self._min_max:
return f.format(
np.nanmedian(x.values), np.nanmin(x.values),
np.nanmax(x.values),
np.nanmedian(x.values), np.nanmin(x.values), # type: ignore
np.nanmax(x.values), # type: ignore
)
else:
return f.format(
np.nanmedian(x.values),
np.nanpercentile(x.values, 25),
np.nanpercentile(x.values, 75),
np.nanmedian(x.values), # type: ignore
np.nanpercentile(x.values, 25), # type: ignore
np.nanpercentile(x.values, 75), # type: ignore
)
else:
if self._min_max and x.name in self._min_max:
f = "{{:.{}f}} [{{:.{}f}},{{:.{}f}}]".format(n, n, n)
return f.format(
np.nanmean(x.values), np.nanmin(x.values),
np.nanmax(x.values),
np.nanmean(x.values), np.nanmin(x.values), # type: ignore
np.nanmax(x.values), # type: ignore
)
else:
f = '{{:.{}f}} ({{:.{}f}})'.format(n, n)
return f.format(np.nanmean(x.values), self._std(x))
return f.format(np.nanmean(x.values), self._std(x)) # type: ignore

def _create_cont_describe(self,
data: pd.DataFrame,
Expand Down Expand Up @@ -929,17 +929,17 @@ def _create_cont_describe(self,
aggfunc=aggfuncs)
else:
# if no groupby, just add single group column
df_cont = cont_data.apply(aggfuncs).T
df_cont = cont_data.apply(aggfuncs).T # type: ignore
df_cont.columns.name = 'Overall'
df_cont.columns = pd.MultiIndex.from_product([df_cont.columns,
['Overall']])

df_cont.index = df_cont.index.rename('variable')

# remove prefix underscore from column names (e.g. _std -> std)
agg_rename = df_cont.columns.levels[0]
agg_rename = df_cont.columns.levels[0] # type: ignore
agg_rename = [x[1:] if x[0] == '_' else x for x in agg_rename]
df_cont.columns = df_cont.columns.set_levels(agg_rename, level=0)
df_cont.columns = df_cont.columns.set_levels(agg_rename, level=0) # type: ignore

return df_cont

Expand All @@ -949,7 +949,7 @@ def _format_cat(self, row, col) -> str:
"""
var = row.name[0]
if var in self._decimals:
n = self._decimals[var]
n = self._decimals[var] # type: ignore
else:
n = 1
f = '{{:.{}f}}'.format(n)
Expand Down Expand Up @@ -979,7 +979,7 @@ def _create_cat_describe(self, data: pd.DataFrame,

cat_slice = data[self._categorical].copy()

for g in groupbylvls:
for g in groupbylvls: # type: ignore
if groupby:
df = cat_slice.loc[data[groupby] == g, self._categorical]
else:
Expand Down Expand Up @@ -1119,14 +1119,14 @@ def _create_htest_table(self, data: pd.DataFrame) -> pd.DataFrame:
grouped_data = cross_tab.T.to_dict('list')

# minimum number of observations across all levels
df.loc[v, 'min_observed'] = min_observed
df.loc[v, 'min_observed'] = min_observed # type: ignore

# compute pvalues
(df.loc[v, 'P-Value'],
df.loc[v, 'Test']) = self._p_test(v, grouped_data,
df.loc[v, 'Test']) = self._p_test(v, grouped_data, # type: ignore
is_continuous,
is_categorical, is_normal,
min_observed, catlevels)
min_observed, catlevels) # type: ignore

return df

Expand Down Expand Up @@ -1185,7 +1185,7 @@ def _create_smd_table(self, data: pd.DataFrame) -> pd.DataFrame:
n1=self.cat_describe.loc[[v]]['freq'][p[0]].sum(),
n2=self.cat_describe.loc[[v]]['freq'][p[1]].sum(),
unbiased=False)
df[colname.format(p[0], p[1])].loc[v] = smd
df[colname.format(p[0], p[1])].loc[v] = smd # type: ignore
except AttributeError:
pass

Expand Down Expand Up @@ -1308,7 +1308,7 @@ def _create_cont_table(self, data, overall) -> pd.DataFrame:

# add an empty value column, for joining with cat table
table['value'] = ''
table = table.set_index([table.index, 'value'])
table = table.set_index([table.index, 'value']) # type: ignore

# add pval column
if self._pval and self._pval_adjust:
Expand Down Expand Up @@ -1390,7 +1390,7 @@ def _create_tableone(self, data):
table = self.cat_table

# ensure column headers are strings before reindexing
table = table.reset_index().set_index(['variable', 'value'])
table = table.reset_index().set_index(['variable', 'value']) # type: ignore
table.columns = table.columns.values.astype(str)

# sort the table rows
Expand Down Expand Up @@ -1445,7 +1445,7 @@ def _create_tableone(self, data):
try:
all_var = table.loc[k].index.unique(level='value')
except KeyError:
if k not in self._groupby:
if k not in self._groupby: # type: ignore
warnings.warn("Order variable not found: {}".format(k))
continue

Expand Down Expand Up @@ -1524,7 +1524,7 @@ def _create_tableone(self, data):
table.loc['n', '{}'.format(g)] = ct

# only display data in first level row
dupe_mask = table.groupby(level=[0]).cumcount().ne(0)
dupe_mask = table.groupby(level=[0]).cumcount().ne(0) # type: ignore
dupe_columns = ['Missing']
optional_columns = ['P-Value', 'P-Value (adjusted)', 'Test']
if self._smd:
Expand Down Expand Up @@ -1561,13 +1561,13 @@ def _create_tableone(self, data):

# ensure the order of columns is consistent
if self._groupby and self._order and (self._groupby in self._order):
header = ['{}'.format(v) for v in table.columns.levels[1].values]
header = ['{}'.format(v) for v in table.columns.levels[1].values] # type: ignore
cols = self._order[self._groupby] + ['{}'.format(v)
for v in header
if v not in
self._order[self._groupby]]
elif self._groupby:
cols = ['{}'.format(v) for v in table.columns.levels[1].values]
cols = ['{}'.format(v) for v in table.columns.levels[1].values] # type: ignore
else:
cols = ['{}'.format(v) for v in table.columns.values]

Expand All @@ -1588,7 +1588,7 @@ def _create_tableone(self, data):
table = table.reindex(cols, axis=1)

try:
if 'Missing' in self._alt_labels or 'Overall' in self._alt_labels:
if 'Missing' in self._alt_labels or 'Overall' in self._alt_labels: # type: ignore
table = table.rename(columns=self._alt_labels)
except TypeError:
pass
Expand Down

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