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making column checking robust to passing pandas index #61

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making column checking robust to passing pandas index #61

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b2425ae
making column checking robust to passing pandas index
alistairewj Apr 20, 2018
ac7345b
explicitly convert categorical/columns to list if not recognized
alistairewj Apr 20, 2018
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12 changes: 9 additions & 3 deletions tableone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,8 +84,10 @@ def __init__(self, data, columns=None, categorical=None, groupby=None,
nonnormal = [nonnormal]

# if columns not specified, use all columns
if not columns:
if type(columns) == type(None):
columns = data.columns.get_values()
elif 'pandas.core.indexes' in str(type(columns)):
columns = columns.get_values()

# check that the columns exist in the dataframe
if not set(columns).issubset(data.columns):
Expand All @@ -98,7 +100,11 @@ def __init__(self, data, columns=None, categorical=None, groupby=None,
raise InputError('Input contains duplicate columns: {}'.format(dups))

# if categorical not specified, try to identify categorical
if not categorical and type(categorical) != list:
if type(columns) == type(None):
categorical = self._detect_categorical_columns(data[columns])
elif 'pandas.core.indexes' in str(type(categorical)):
categorical = categorical.get_values()
elif type(categorical) != list:
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If someone specifies a single categorical variable as a string (e.g. categorical = 'ICU'), then I think we now ignore their selection and generate the list ourselves. Might be good to catch the string too?

categorical = self._detect_categorical_columns(data[columns])

if pval and not groupby:
Expand Down Expand Up @@ -267,7 +273,7 @@ def _normaltest(self,x):
Compute test for normal distribution.

Null hypothesis: x comes from a normal distribution
p < alpha suggests the null hypothesis can be rejected.
p < alpha suggests the null hypothesis can be rejected.
"""
stat,p = stats.normaltest(x.values, nan_policy='omit')
return p
Expand Down