Open tompollard opened 6 years ago
It would be good to support multilevel groupby arguments. e.g. groupby = ['icu_type','gender'].
groupby = ['icu_type','gender']
e.g.: achieve something similar to:
from tableone import TableOne import pandas as pd url="https://raw.githubusercontent.com/tompollard/tableone/master/data/pn2012_demo.csv" data=pd.read_csv(url) df = TableOne(data) pd.concat([df.tableone,df.tableone], axis=1)
Notes:
# columns to be summarized columns = ['Age', 'SysABP', 'Height', 'Weight', 'ICU', 'death'] # columns containing categorical variables categorical = ['ICU', 'death'] # non-normal variables nonnormal = ['Age'] # alternative labels labels={'LOS': 'Length of stay', 'death': 'mortality'} groupby = ['death'] second = 'ICU' tdict = {} for d in data[second].unique(): print(d) tdict[d] = TableOne(data[data[second] == d], columns, categorical, groupby, nonnormal) pd.concat([tdict['SICU'].tableone,tdict['CSRU'].tableone,tdict['MICU'].tableone],axis=1) pd.concat([tdict[x].tableone for x in data[second].unique()],axis=1)
It would be good to support multilevel groupby arguments. e.g.
groupby = ['icu_type','gender']
.e.g.: achieve something similar to:
Notes: