bruno,melissa_final project.py

'''This code uses 2014 colon cancer cases from the SEER dataset and aims to create variables
 to score cases on likelihood of receiving neoadjuvant care'''

#Importing and viewing various aspects of dataframe
#Importing pandas for data wrangling
from typing import Any, Union
import pandas as pd


# Importing the Neoadjuvant variables for colon cancer cases
from pandas import DataFrame
from pandas.io.parsers import TextFileReader

neocolon: Union[Union[TextFileReader, DataFrame], Any]= pd.read_csv('/Users/mbruno2/Documents/neo_colon.csv')

#Viewing head/tail/shape  of the dataframe
print(neocolon.head())
print(neocolon.tail())

#Pulling description of dataframe
print(neocolon.describe())
neocolon.shape

#Creating list of variable names
for col in neocolon.columns:
    print(col)


#Counting number of observations
total_rows= neocolon.count()
    print(total_rows)

#Looking at participants <18 years old for special characteristics
neocolon.rename(columns={'Age recode with <1 year olds':'Age'}, inplace=True)
print(neocolon[neocolon['Age']<18])













#Exploratory data analysis (EDA)
#Importing matplotlib for data visualization
import matplotlib.pyplot as plt

#Creating variables for outcome of interest
sys_surg_seq=neocolon['RX Summ--Systemic Surg Seq']
rad_surg_seq=neocolon['Radiation sequence with surgery']

#Histogram for Systemic-Surgery Sequence
x_ticks_seq= [0,2,3,4,5,6,7,9]


plt.hist(sys_surg_seq, bins=8)
plt.xlabel('NAACCR code')
plt.ylabel('Number of observations')
plt.title('Distribution of cases for Systemic Surgery Sequence')
plt.xticks(x_ticks_seq)
plt.show()

#Histogram for Radiation-Surgery Sequence
plt.hist(rad_surg_seq, bins=8)
plt.xlabel('NAACCR code')
plt.ylabel('Number of observations')
plt.title('Distribution of cases for Radiation Surgery Sequence')
plt.xticks(x_ticks_seq)
plt.show()





'''In this next section we will be creating new neoadjuvant variables for future use in an algorithm
The following coding categories will be used for the new variables:
0= no neoadjuvant
1= unlikely neoadjuvant
2= possible neoadjuvant
3= likely neoadjuvant
4= unindicative of neoadjuvant
9= unknown'''




#Looking at the unique NAACCR codes for Chemotherapy
#Creating list from chemotherapy column
chemo_list=list(neocolon['Chemotherapy'])


def unique(list1):
    '''This function prints out the unique codes or values for list1'''
    # insert the list to the set
    list_set = set(list1)
    # convert the set to the list
    unique_list = (list(list_set))
    for x in unique_list:
        print(x)

print('The NAACCR codes for Chemotherapy are:')
unique(chemo_list)


#Creating new chemotherapy variable for use in algorithm
neocolon.loc[neocolon.Chemotherapy == 0,  'neo_chemotherapy'] = 0
neocolon.loc[neocolon.Chemotherapy == 1,  'neo_chemotherapy'] = 2
neocolon.loc[neocolon.Chemotherapy == 2,  'neo_chemotherapy'] = 2
neocolon.loc[neocolon.Chemotherapy == 3,  'neo_chemotherapy'] = 1
neocolon.loc[neocolon.Chemotherapy == 82, 'neo_chemotherapy'] = 1
neocolon.loc[neocolon.Chemotherapy == 85, 'neo_chemotherapy'] = 3
neocolon.loc[neocolon.Chemotherapy == 86, 'neo_chemotherapy'] = 4
neocolon.loc[neocolon.Chemotherapy == 87, 'neo_chemotherapy'] = 4
neocolon.loc[neocolon.Chemotherapy == 88, 'neo_chemotherapy'] = 2
neocolon.loc[neocolon.Chemotherapy == 99, 'neo_chemotherapy'] = 9

print(neocolon[['Chemotherapy','neo_chemotherapy']])
neo_chemotherapy=neocolon['neo_chemotherapy']
neo_chemotherapy.astype(int)



#Creating histogram of new neo_chemotherapy variable categories
x_ticks=[0, 1, 2, 3, 4, 9]
x_labs= ['no', 'unl', 'pos', 'lik', 'uni', 'unk']

plt.hist(neo_chemotherapy, bins=8)
plt.xlabel('Neoadjuvant category')
plt.ylabel('Number of observations')
plt.xticks(x_ticks,x_labs)
plt.title('Distribution of cases for neoadjuvant chemotherapy categories')
plt.show()








#Looking at the unique NAACCR codes for Immunotherapy
immuno_list=list(neocolon['Immunotherapy'])

print('The NAACCR codes for Immunotherapy are:')
unique()

#Creating new immunotherapy variable for use in algorithm
neocolon.loc[neocolon.Immunotherapy == 0,  'neo_immunotherapy'] = 0
neocolon.loc[neocolon.Immunotherapy == 1,  'neo_immunotherapy'] = 2
neocolon.loc[neocolon.Immunotherapy == 82, 'neo_immunotherapy'] = 1
neocolon.loc[neocolon.Immunotherapy == 85, 'neo_immunotherapy'] = 3
neocolon.loc[neocolon.Immunotherapy == 86, 'neo_immunotherapy'] = 4
neocolon.loc[neocolon.Immunotherapy == 87, 'neo_immunotherapy'] = 4
neocolon.loc[neocolon.Immunotherapy == 88, 'neo_immunotherapy'] = 2
neocolon.loc[neocolon.Immunotherapy == 99, 'neo_immunotherapy'] = 9

print(neocolon[['Immunotherapy','neo_immunotherapy']])
neo_immunotherapy=neocolon['neo_immunotherapy']
neo_immunotherapy.astype(int)

#Creating histogram of new neo_immunotherapy variable categories
x_ticks=[0, 1, 2, 3, 4, 9]
x_labs= ['no', 'unl', 'pos', 'lik', 'uni', 'unk']

plt.hist(neo_immunotherapy, bins=8)
plt.xlabel('Neoadjuvant category')
plt.ylabel('Number of observations')
plt.xticks(x_ticks,x_labs)
plt.title('Distribution of cases for neoadjuvant immunotherapy categories')
plt.show()







#Summing chemotherapy and immunotherapy variables
#importing numpy
import numpy as np

def neo_cat_sum(neo_var1, neo_var2):
    '''This function changes the columns of neo_var1 & neo_var2 into
    numpy arrays that can be summed across rows'''
    #Creating new dataframe with neo_var1 and neo_var2 as columns
    neoadj_sum = neocolon.loc[:, [neo_var1, neo_var2]]
    #Changing new dataframe into numpy array
    neoadj_sum_array = np.array(neoadj_sum)
    #Summing across the rows of the dataframe and printing the totals as an array
    total = np.sum(neoadj_sum_array, axis=1)
    print(total)
    return total

#Adding total as column in neocolon dataframe
neo_tot= neo_cat_sum('neo_chemotherapy','neo_immunotherapy')
neocolon['neo_tot']=neo_tot

neocolon['neo_chemotherapy']=neo_chemotherapy.astype(int)
neocolon['neo_immunotherapy']=neo_immunotherapy.astype(int)
print(neocolon[['neo_chemotherapy', 'neo_immunotherapy', 'neo_tot']])





#Test code for a function to create neoadj variables
#Lists for translating NAACCR codes into neoadjuvant codes
no_neo = [0]
unl_neo= [3, 82]
pos_neo= [88, 1, 2]
lik_neo= [85]
uni_neo= [86, 87]
unk_neo= [99]


def translate_to_neo(og_var):
    '''This function translates the original NAACCR variable coding into the new
    neoadjuvant variable codings and returns a list with the new codings'''
    #Creating empty list for new neoadjuvant variable codes
    new_neo_var=[]
    #Creating new variable codes from above lists of neoadjuvant codes
    for code in og_var:
        if code in no_neo:
            new_neo_var.append(0)
        if code in unl_neo:
            new_neo_var.append(1)
        if code in pos_neo:
            new_neo_var.append(2)
        if code in lik_neo:
            new_neo_var.append(3)
        if code in uni_neo:
            new_neo_var.append(4)
        if code in unk_neo:
            new_neo_var.append(9)
    return new_neo_var

chemotherapy_neo=translate_to_neo(neocolon["Chemotherapy"])

neocolon['chemotherapy_neo']=chemotherapy_neo
print(neocolon[['neo_chemotherapy', 'chemotherapy_neo']])

























neo_TEST_chemo=[]
for code in neocolon["Chemotherapy"]:
    if code in no_neo:
        neo_TEST_chemo.append(0)
    if code in unl_neo:
        neo_TEST_chemo.append(1)
    if code in pos_neo:
        neo_TEST_chemo.append(2)
    if code in lik_neo:
        neo_TEST_chemo.append(3)
    if code in uni_neo:
        neo_TEST_chemo.append(4)
    if code in unk_neo:
        neo_TEST_chemo.append(9)


print(neo_TEST_chemo)
len(neo_TEST_chemo)

neocolon['neo_TEST_chemo']=neo_TEST_chemo

print(neocolon[['neo_chemotherapy', 'neo_TEST_chemo']])