def df_to_table(df, incl_vars, categorical, nonnormal, groupvar, pval, labels,
order, missing):
df_cols = list(df)
# this is the list of columns for which Table 1 is generated
col_list = [df_col for df_col in incl_vars]
my_table = TableOne(df,
columns=col_list,
groupby=groupvar,
nonnormal=nonnormal,
categorical=categorical,
pval=pval,
missing=missing,
label_suffix=True,
rename=labels,
order=order)
my_table_html = my_table.to_html(
classes=["table", "table-dark", 'table-sm'])
return my_table_html
data = pd.read_csv('./data/20210602-134857-completed/dataset.csv')
# ------------------------
# Create tableone
# ------------------------
# Columns
columns = ['age', 'gender', 'haematocrit_percent', 'plt']
# Categorical
categorical = ['gender']
# Groupby
groupby = ['cvs_hos_split']
#
mytable = TableOne(data, columns, categorical, groupby)
########################################################
# Show
#
mytable.tableone
########################################################
# Show (HTML)
#
# Html
html = mytable.to_html()
# show
print(html)
MYCATEGORICAL = set()
for name, mytype in zip(data.dtypes.index, data.dtypes.values):
if mytype == object:
MYCATEGORICAL.add(name)
for c in categorical:
MYCATEGORICAL.add(c)
MYCATEGORICAL = list(MYCATEGORICAL) if len(MYCATEGORICAL) > 0 else None
# create grouped_table with p values
# API: https://tableone.readthedocs.io/en/latest/tableone.html
# Example: https://github.com/tompollard/tableone/blob/master/tableone.ipynb
grouped_table = TableOne(data,
categorical=MYCATEGORICAL,
groupby=group,
nonnormal=nonnormal,
label_suffix=True,
pval=pval)
print(grouped_table)
if outfile:
if outFMT == 'csv':
grouped_table.to_csv(outfile)
elif outFMT == 'latex':
grouped_table.to_latex(outfile)
elif outFMT == 'html':
grouped_table.to_html(outfile)
sys.stdout.flush()
sys.stdout.close()
sys.stderr.flush()
sys.stderr.close()
def data_dash2(request, doc_id ):
doc=get_object_or_404(Document, id=doc_id)
#########################################################################
WARN_MESSAGE=dataCheck(doc_id)
if WARN_MESSAGE[2] == 'EMPTY' :
df = pd.read_csv('figures/static/tmpdata/'+doc.domain+'.csv',encoding="iso-8859-1")
df_raw = df
df = df.rename(columns=lambda x: x.upper())
##########################Data for Pie plot#############################
df_a=df.groupby(['RACE']).count()
bar_all=df_a['USUBJID'].values.tolist()
##########################Data for Bar Chart#############################
df_f=df[(df.SEX == "F")].groupby(['RACE']).count()
df_m=df[(df.SEX == "M")].groupby(['RACE']).count()
bar_male=df_m['USUBJID'].values.tolist()
bar_female=df_f['USUBJID'].values.tolist()
df['RACE']=df['RACE'].str.split(" ", n = 1, expand = True)
bar_labels = mark_safe(df.RACE.unique().tolist())
###Height and Weight are available then we calculate BMI
if WARN_MESSAGE[1] == '' :
df['BMIBL'] = df[WARN_MESSAGE[4]] / ((df[WARN_MESSAGE[3]]/100)**2)
WV={}
age_f0 = []
age_m0 = []
bgcolor_arm = ['rgba(46, 204, 113, 0.4)','rgba(252, 185, 65, 0.4)','rgba(129, 207, 224, 0.4)','rgba(255, 99, 132, 0.4)','yellow']
bocolor_arm = ['rgba(46, 204, 113, 1)','rgba(252, 185, 65, 1)','rgba(129, 207, 224, 1)','rgba(255, 99, 132, 1)','rgba(245, 230, 83, 1)']
if "ARMCD" not in list(df) :
df['ARMCD'] = df['ARM']
armcd=df.ARMCD.unique().tolist()
arml=df.ARM.unique().tolist()
for num,val in enumerate(armcd, start=1):
age_f0.append(df[(df.SEX == "F") & (df.ARMCD == val)]['AGE'].values.tolist() )
age_m0.append(df[(df.SEX == "M") & (df.ARMCD == val)]['AGE'].values.tolist() )
if num == 1 :
df.loc[df['ARMCD'] == val, 'ARM'] = mark_safe('<span class="label label-success">' + arml[num-1] + '</span>')
if num == 2 :
df.loc[df['ARMCD'] == val, 'ARM'] = mark_safe('<span class="label label-warning">' + arml[num-1] + '</span>')
if num == 3 :
df.loc[df['ARMCD'] == val, 'ARM'] = mark_safe('<span class="label label-info">' + arml[num-1] + '</span>')
if num == 4 :
df.loc[df['ARMCD'] == val, 'ARM'] = mark_safe('<span class="label label-danger">' + arml[num-1] + '</span>')
###Height and Weight are available
if WARN_MESSAGE[1] == '' :
dftmp=df[(df['ARMCD'] == val)]
dftmp['BMIBL']=dftmp['BMIBL']/3
WTMP = dftmp[[WARN_MESSAGE[3],WARN_MESSAGE[4],'BMIBL']].to_json(orient='records').replace('"','').replace(WARN_MESSAGE[3],"y").replace('BMIBL',"r").replace(WARN_MESSAGE[4],"x")
WTMP_ =mark_safe( "label: ['"+ val +"'],data:" + WTMP + ",backgroundColor: '"+bgcolor_arm[num-1]+"',borderColor: '"+bocolor_arm[num-1] +"', borderWidth: 1" )
WV['line_dict_{}'.format(num)] = WTMP_
age_f=age_f0
age_m=age_m0
box_labels = mark_safe(armcd)
listing=df[["USUBJID", "SITEID", "ARM", "AGE", "SEX"] ].values.tolist()
###Demographic tableone
columns = ['AGE','SEX','RACE','ARM']
categorical = ['SEX','RACE']
groupby = ['ARM']
mytable = TableOne(df, columns, categorical, groupby,isnull=False,)
mytablej = mark_safe(mytable.to_html(classes='stripe row-border order-column').replace('<','<').replace('>','>').replace('<table border="1" class="dataframe stripe row-border order-column">','<table class="table no-margin">'))
if WARN_MESSAGE[0] == '' :
FLAG_ = flag_var(doc_id)
flag1=FLAG_[0]
flag2=FLAG_[1]
c_flag1= df_f=df[(df[flag1] == "Y")]['USUBJID'].count()
c_flag2= df_f=df[(df[flag2] == "Y")]['USUBJID'].count()
p_flag1=100*c_flag1/df['USUBJID'].count()
p_flag2=100*c_flag2/df['USUBJID'].count()
return render(request, 'figures/data_dash2.html', { 'document':doc,'bar_all':bar_all,'bar_male':bar_male, 'bar_female':bar_female, 'bar_labels':bar_labels,'box_labels':box_labels,'age_f':age_f,'age_m':age_m,'listing':listing[:10] ,'WARN_MESSAGE':WARN_MESSAGE,'FLAG1':flag1,'FLAG2':flag2,'C_FLAG1':c_flag1,'C_FLAG2':c_flag2,'P_FLAG1':p_flag1,'P_FLAG2':p_flag2,'WV':WV,'mytable' : mytablej} )
else :
return render(request, 'figures/CDISC_ERROR.html', { 'document':doc, 'CDISC_ERROR' :WARN_MESSAGE[2] } )