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report.py
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report.py
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import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
data = pd.read_csv("output/input_paired.csv")
data_symptom = data[["colorectal_symptom_1_date_symp_date", "colorectal_symptom_2_date_symp_date", "colorectal_symptom_3_date_symp_date", "colorectal_symptom_4_date_symp_date", "colorectal_symptom_5_date_symp_date", "colorectal_symptom_6_date_symp_date"]]
data_referral = data[["colorectal_symptom_1_date_ref_date", "colorectal_symptom_2_date_ref_date", "colorectal_symptom_3_date_ref_date", "colorectal_symptom_4_date_ref_date", "colorectal_symptom_5_date_ref_date", "colorectal_symptom_6_date_ref_date"]]
data_diagnosis = data[["colorectal_symptom_1_date_diag_date", "colorectal_symptom_2_date_diag_date", "colorectal_symptom_3_date_diag_date", "colorectal_symptom_4_date_diag_date", "colorectal_symptom_5_date_diag_date", "colorectal_symptom_6_date_diag_date"]]
num_symptoms = data_symptom.count(axis=1)
plt.hist(num_symptoms.values.tolist(), bins=6)
plt.savefig('output/colorectal_cancer.jpg')
plt.clf()
data_symptom['colorectal_symptom_1_date_symp_date'] = pd.to_datetime(data_symptom['colorectal_symptom_1_date_symp_date'])
data_symptom['colorectal_symptom_2_date_symp_date'] = pd.to_datetime(data_symptom['colorectal_symptom_2_date_symp_date'])
data_symptom['colorectal_symptom_3_date_symp_date'] = pd.to_datetime(data_symptom['colorectal_symptom_3_date_symp_date'])
data_symptom['colorectal_symptom_4_date_symp_date'] = pd.to_datetime(data_symptom['colorectal_symptom_4_date_symp_date'])
data_symptom['colorectal_symptom_5_date_symp_date'] = pd.to_datetime(data_symptom['colorectal_symptom_5_date_symp_date'])
data_symptom['colorectal_symptom_6_date_symp_date'] = pd.to_datetime(data_symptom['colorectal_symptom_6_date_symp_date'])
data_month_year_1 = data_symptom['colorectal_symptom_1_date_symp_date'].dt.to_period('M')
data_month_year_2 = data_symptom['colorectal_symptom_2_date_symp_date'].dt.to_period('M')
data_month_year_3 = data_symptom['colorectal_symptom_3_date_symp_date'].dt.to_period('M')
data_month_year_4 = data_symptom['colorectal_symptom_4_date_symp_date'].dt.to_period('M')
data_month_year_5 = data_symptom['colorectal_symptom_5_date_symp_date'].dt.to_period('M')
data_month_year_6 = data_symptom['colorectal_symptom_6_date_symp_date'].dt.to_period('M')
data_month_year = pd.concat([data_month_year_1, data_month_year_2, data_month_year_3, data_month_year_4, data_month_year_5, data_month_year_6], axis=1)
counts = data_month_year.stack().value_counts()
counts = counts.sort_index()
ax = counts.plot.bar()
plt.savefig('output/colorectal_cancer_2.jpg')
plt.clf()
data_null = data.notnull()
def condition1(s):
if s['colorectal_symptom_1_date_symp_date'] & s['colorectal_symptom_1_date_ref_date']:
return 1
else:
return 0
def condition2(s):
if s['colorectal_symptom_2_date_symp_date'] & s['colorectal_symptom_2_date_ref_date']:
return 1
else:
return 0
def condition3(s):
if s['colorectal_symptom_3_date_symp_date'] & s['colorectal_symptom_3_date_ref_date']:
return 1
else:
return 0
def condition4(s):
if s['colorectal_symptom_4_date_symp_date'] & s['colorectal_symptom_4_date_ref_date']:
return 1
else:
return 0
def condition5(s):
if s['colorectal_symptom_5_date_symp_date'] & s['colorectal_symptom_5_date_ref_date']:
return 1
else:
return 0
def condition6(s):
if s['colorectal_symptom_6_date_symp_date'] & s['colorectal_symptom_6_date_ref_date']:
return 1
else:
return 0
data['colorectal_conversion_1'] = data_null.apply(condition1, axis=1)
data['colorectal_conversion_2'] = data_null.apply(condition2, axis=1)
data['colorectal_conversion_3'] = data_null.apply(condition3, axis=1)
data['colorectal_conversion_4'] = data_null.apply(condition4, axis=1)
data['colorectal_conversion_5'] = data_null.apply(condition5, axis=1)
data['colorectal_conversion_6'] = data_null.apply(condition6, axis=1)
data_1 = data[['colorectal_symptom_1_date_symp_date', 'colorectal_conversion_1']]
data_1 = data_1.rename(columns={"colorectal_symptom_1_date_symp_date":"symptom_date", "colorectal_conversion_1":"conversion"})
data_1['symptom_date'] = pd.to_datetime(data_1['symptom_date'])
data_1['symptom_date'] = data_1['symptom_date'].dt.to_period('M')
data_2 = data[['colorectal_symptom_2_date_symp_date', 'colorectal_conversion_2']]
data_2 = data_2.rename(columns={"colorectal_symptom_2_date_symp_date":"symptom_date", "colorectal_conversion_2":"conversion"})
data_2['symptom_date'] = pd.to_datetime(data_2['symptom_date'])
data_2['symptom_date'] = data_2['symptom_date'].dt.to_period('M')
data_3 = data[['colorectal_symptom_3_date_symp_date', 'colorectal_conversion_3']]
data_3 = data_3.rename(columns={"colorectal_symptom_3_date_symp_date":"symptom_date", "colorectal_conversion_3":"conversion"})
data_3['symptom_date'] = pd.to_datetime(data_3['symptom_date'])
data_3['symptom_date'] = data_3['symptom_date'].dt.to_period('M')
data_4 = data[['colorectal_symptom_4_date_symp_date', 'colorectal_conversion_4']]
data_4 = data_4.rename(columns={"colorectal_symptom_4_date_symp_date":"symptom_date", "colorectal_conversion_4":"conversion"})
data_4['symptom_date'] = pd.to_datetime(data_4['symptom_date'])
data_4['symptom_date'] = data_4['symptom_date'].dt.to_period('M')
data_5 = data[['colorectal_symptom_5_date_symp_date', 'colorectal_conversion_5']]
data_5 = data_5.rename(columns={"colorectal_symptom_5_date_symp_date":"symptom_date", "colorectal_conversion_5":"conversion"})
data_5['symptom_date'] = pd.to_datetime(data_5['symptom_date'])
data_5['symptom_date'] = data_5['symptom_date'].dt.to_period('M')
data_6 = data[['colorectal_symptom_6_date_symp_date', 'colorectal_conversion_6']]
data_6 = data_6.rename(columns={"colorectal_symptom_6_date_symp_date":"symptom_date", "colorectal_conversion_6":"conversion"})
data_6['symptom_date'] = pd.to_datetime(data_6['symptom_date'])
data_6['symptom_date'] = data_6['symptom_date'].dt.to_period('M')
data_symptom_conversion = pd.concat([data_1, data_2, data_3, data_4, data_5, data_6])
unique_dates = data_symptom_conversion['symptom_date'].unique()
unique_dates = sorted(unique_dates)
dict = {}
for item in unique_dates:
df = data_symptom_conversion.loc[data_symptom_conversion['symptom_date'] == item]
con = df['conversion'].mean()
dict.update({item: con})
plt.bar(range(len(dict)), list(dict.values()), tick_label=list(dict.keys()))
plt.savefig('output/colorectal_cancer_5.jpg')
plt.clf()
data_null = data.notnull()
def condition1(s):
if s['colorectal_symptom_1_date_ref_date'] & s['colorectal_symptom_1_date_diag_date']:
return 1
else:
return 0
def condition2(s):
if s['colorectal_symptom_2_date_ref_date'] & s['colorectal_symptom_2_date_diag_date']:
return 1
else:
return 0
def condition3(s):
if s['colorectal_symptom_3_date_ref_date'] & s['colorectal_symptom_3_date_diag_date']:
return 1
else:
return 0
def condition4(s):
if s['colorectal_symptom_4_date_ref_date'] & s['colorectal_symptom_4_date_diag_date']:
return 1
else:
return 0
def condition5(s):
if s['colorectal_symptom_5_date_ref_date'] & s['colorectal_symptom_5_date_diag_date']:
return 1
else:
return 0
def condition6(s):
if s['colorectal_symptom_6_date_ref_date'] & s['colorectal_symptom_6_date_diag_date']:
return 1
else:
return 0
data['colorectal_conversion_1'] = data_null.apply(condition1, axis=1)
data['colorectal_conversion_2'] = data_null.apply(condition2, axis=1)
data['colorectal_conversion_3'] = data_null.apply(condition3, axis=1)
data['colorectal_conversion_4'] = data_null.apply(condition4, axis=1)
data['colorectal_conversion_5'] = data_null.apply(condition5, axis=1)
data['colorectal_conversion_6'] = data_null.apply(condition6, axis=1)
data_1 = data[['colorectal_symptom_1_date_symp_date', 'colorectal_conversion_1']]
data_1 = data_1.rename(columns={"colorectal_symptom_1_date_symp_date":"symptom_date", "colorectal_conversion_1":"conversion"})
data_1['symptom_date'] = pd.to_datetime(data_1['symptom_date'])
data_1['symptom_date'] = data_1['symptom_date'].dt.to_period('M')
data_2 = data[['colorectal_symptom_2_date_symp_date', 'colorectal_conversion_2']]
data_2 = data_2.rename(columns={"colorectal_symptom_2_date_symp_date":"symptom_date", "colorectal_conversion_2":"conversion"})
data_2['symptom_date'] = pd.to_datetime(data_2['symptom_date'])
data_2['symptom_date'] = data_2['symptom_date'].dt.to_period('M')
data_3 = data[['colorectal_symptom_3_date_symp_date', 'colorectal_conversion_3']]
data_3 = data_3.rename(columns={"colorectal_symptom_3_date_symp_date":"symptom_date", "colorectal_conversion_3":"conversion"})
data_3['symptom_date'] = pd.to_datetime(data_3['symptom_date'])
data_3['symptom_date'] = data_3['symptom_date'].dt.to_period('M')
data_4 = data[['colorectal_symptom_4_date_symp_date', 'colorectal_conversion_4']]
data_4 = data_4.rename(columns={"colorectal_symptom_4_date_symp_date":"symptom_date", "colorectal_conversion_4":"conversion"})
data_4['symptom_date'] = pd.to_datetime(data_4['symptom_date'])
data_4['symptom_date'] = data_4['symptom_date'].dt.to_period('M')
data_5 = data[['colorectal_symptom_5_date_symp_date', 'colorectal_conversion_5']]
data_5 = data_5.rename(columns={"colorectal_symptom_5_date_symp_date":"symptom_date", "colorectal_conversion_5":"conversion"})
data_5['symptom_date'] = pd.to_datetime(data_5['symptom_date'])
data_5['symptom_date'] = data_5['symptom_date'].dt.to_period('M')
data_6 = data[['colorectal_symptom_6_date_symp_date', 'colorectal_conversion_6']]
data_6 = data_6.rename(columns={"colorectal_symptom_6_date_symp_date":"symptom_date", "colorectal_conversion_6":"conversion"})
data_6['symptom_date'] = pd.to_datetime(data_6['symptom_date'])
data_6['symptom_date'] = data_6['symptom_date'].dt.to_period('M')
data_referral_conversion = pd.concat([data_1, data_2, data_3, data_4, data_5, data_6])
unique_dates = data_referral_conversion['symptom_date'].unique()
unique_dates = sorted(unique_dates)
dict = {}
for item in unique_dates:
df = data_referral_conversion.loc[data_referral_conversion['symptom_date'] == item]
con = df['conversion'].mean()
dict.update({item: con})
plt.bar(range(len(dict)), list(dict.values()), tick_label=list(dict.keys()))
plt.savefig('output/colorectal_cancer_6.jpg')