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report_2wwcolorectal.py
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report_2wwcolorectal.py
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import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
data = pd.read_csv("output/input_2wwcolorectal.csv")
data_null = data.notnull()
def condition1(s):
if s['colorectal_referral_date'] & s['colorectal_diagnosis_date']:
return 1
else:
return 0
data['colorectal_conversion_1'] = data_null.apply(condition1, axis=1)
data_referral_conversion = data[['colorectal_referral_date', 'colorectal_conversion_1']]
data_referral_conversion = data_referral_conversion.rename(columns={"colorectal_referral_date":"referral_date", "colorectal_conversion_1":"conversion"})
data_referral_conversion['referral_date'] = pd.to_datetime(data_referral_conversion['referral_date'])
data_referral_conversion['referral_date'] = data_referral_conversion['referral_date'].dt.to_period('M')
unique_dates = data_referral_conversion['referral_date'].unique()
unique_dates = sorted(unique_dates)
dict = {}
for item in unique_dates:
df = data_referral_conversion.loc[data_referral_conversion['referral_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_10.jpg')
plt.clf()
data_null = data.notnull()
def condition2(s):
if s['colorectal_referral_date'] & s['fit_date']:
return 1
else:
return 0
data['colorectal_fit_rate'] = data_null.apply(condition2, axis=1)
data_fit_rate = data[['colorectal_referral_date', 'colorectal_fit_rate']]
data_fit_rate = data_fit_rate.rename(columns={"colorectal_referral_date":"referral_date", "colorectal_fit_rate":"fit_rate"})
data_fit_rate['referral_date'] = pd.to_datetime(data_fit_rate['referral_date'])
data_fit_rate['referral_date'] = data_fit_rate['referral_date'].dt.to_period('M')
unique_dates = data_fit_rate['referral_date'].unique()
unique_dates = sorted(unique_dates)
dict = {}
for item in unique_dates:
df = data_fit_rate.loc[data_fit_rate['referral_date'] == item]
con = df['fit_rate'].mean()
dict.update({item: con})
plt.bar(range(len(dict)), list(dict.values()), tick_label=list(dict.keys()))
plt.savefig('output/colorectal_cancer_11.jpg')
plt.clf()
data_null = data.notnull()
def condition3(s):
if s['colorectal_referral_date'] & s['anaemia_symptom_date']:
return 1
else:
return 0
data['colorectal_anaemia_rate'] = data_null.apply(condition3, axis=1)
data_anaemia_rate = data[['colorectal_referral_date', 'colorectal_anaemia_rate']]
data_anaemia_rate = data_anaemia_rate.rename(columns={"colorectal_referral_date":"referral_date", "colorectal_anaemia_rate":"anaemia_rate"})
data_anaemia_rate['referral_date'] = pd.to_datetime(data_anaemia_rate['referral_date'])
data_anaemia_rate['referral_date'] = data_anaemia_rate['referral_date'].dt.to_period('M')
unique_dates = data_anaemia_rate['referral_date'].unique()
unique_dates = sorted(unique_dates)
dict = {}
for item in unique_dates:
df = data_anaemia_rate.loc[data_anaemia_rate['referral_date'] == item]
con = df['anaemia_rate'].mean()
dict.update({item: con})
plt.bar(range(len(dict)), list(dict.values()), tick_label=list(dict.keys()))
plt.savefig('output/colorectal_cancer_12.jpg')
plt.clf()
data_null = data.notnull()
def condition4(s):
if s['colorectal_referral_date'] & s['cibh_symptom_date']:
return 1
else:
return 0
data['colorectal_cibh_rate'] = data_null.apply(condition4, axis=1)
data_cibh_rate = data[['colorectal_referral_date', 'colorectal_cibh_rate']]
data_cibh_rate = data_cibh_rate.rename(columns={"colorectal_referral_date":"referral_date", "colorectal_cibh_rate":"cibh_rate"})
data_cibh_rate['referral_date'] = pd.to_datetime(data_cibh_rate['referral_date'])
data_cibh_rate['referral_date'] = data_cibh_rate['referral_date'].dt.to_period('M')
unique_dates = data_cibh_rate['referral_date'].unique()
unique_dates = sorted(unique_dates)
dict = {}
for item in unique_dates:
df = data_cibh_rate.loc[data_cibh_rate['referral_date'] == item]
con = df['cibh_rate'].mean()
dict.update({item: con})
plt.bar(range(len(dict)), list(dict.values()), tick_label=list(dict.keys()))
plt.savefig('output/colorectal_cancer_13.jpg')
plt.clf()
data_null = data.notnull()
def condition5(s):
if s['colorectal_referral_date'] & s['prbleeding_symptom_date']:
return 1
else:
return 0
data['colorectal_prbleeding_rate'] = data_null.apply(condition5, axis=1)
data_prbleeding_rate = data[['colorectal_referral_date', 'colorectal_prbleeding_rate']]
data_prbleeding_rate = data_prbleeding_rate.rename(columns={"colorectal_referral_date":"referral_date", "colorectal_prbleeding_rate":"prbleeding_rate"})
data_prbleeding_rate['referral_date'] = pd.to_datetime(data_prbleeding_rate['referral_date'])
data_prbleeding_rate['referral_date'] = data_prbleeding_rate['referral_date'].dt.to_period('M')
unique_dates = data_prbleeding_rate['referral_date'].unique()
unique_dates = sorted(unique_dates)
dict = {}
for item in unique_dates:
df = data_prbleeding_rate.loc[data_prbleeding_rate['referral_date'] == item]
con = df['prbleeding_rate'].mean()
dict.update({item: con})
plt.bar(range(len(dict)), list(dict.values()), tick_label=list(dict.keys()))
plt.savefig('output/colorectal_cancer_14.jpg')
plt.clf()
data_null = data.notnull()
def condition6(s):
if s['colorectal_referral_date'] & s['wl_symptom_date']:
return 1
else:
return 0
data['colorectal_wl_rate'] = data_null.apply(condition6, axis=1)
data_wl_rate = data[['colorectal_referral_date', 'colorectal_wl_rate']]
data_wl_rate = data_wl_rate.rename(columns={"colorectal_referral_date":"referral_date", "colorectal_wl_rate":"wl_rate"})
data_wl_rate['referral_date'] = pd.to_datetime(data_wl_rate['referral_date'])
data_wl_rate['referral_date'] = data_wl_rate['referral_date'].dt.to_period('M')
unique_dates = data_wl_rate['referral_date'].unique()
unique_dates = sorted(unique_dates)
dict = {}
for item in unique_dates:
df = data_wl_rate.loc[data_wl_rate['referral_date'] == item]
con = df['wl_rate'].mean()
dict.update({item: con})
plt.bar(range(len(dict)), list(dict.values()), tick_label=list(dict.keys()))
plt.savefig('output/colorectal_cancer_15.jpg')