Make them into functions - define parameter & return types etc. Resolve all errors flagged Adhere to PEP 8 coding style

Sure, here is the updated code with functions defined with parameter and return types and PEP8 coding style:

import pandas as pd
import numpy as np


def calculate_IIRS_scores(df: pd.DataFrame) -> pd.DataFrame:
    """
    Calculate IIRS scores and subscales.
    
    Parameters:
    - df (pd.DataFrame): DataFrame containing the data.
    
    Returns:
    - pd.DataFrame: DataFrame with calculated scores and subscales.
    """
    if 'IIRS1' in df.columns:
        try:
            df['Nomiss_IIRSTotal'] = df[['IIRS1', 'IIRS2', 'IIRS3', 'IIRS4', 'IIRS5', 'IIRS6', 'IIRS7', 'IIRS8', 'IIRS9', 'IIRS10', 'IIRS11', 'IIRS12', 'IIRS13']].notna().sum(axis=1)
            df['IIRSTotalScore'] = np.where(df['Nomiss_IIRSTotal'] >= 0.66*13, df[['IIRS1', 'IIRS2', 'IIRS3', 'IIRS4', 'IIRS5', 'IIRS6', 'IIRS7', 'IIRS8', 'IIRS9', 'IIRS10', 'IIRS11', 'IIRS12', 'IIRS13']].sum(axis=1), np.nan)
            df.loc[df['IIRSTotalScore'] < 13, 'IIRSTotalScore'] = np.nan
            df.rename(columns={'IIRSTotalScore': 'Summation (IIRS1 - IIRS13)'}, inplace=True)
            df.drop('Nomiss_IIRSTotal', axis=1, inplace=True)
        except KeyError:
            pass
        
        try:
            df['Nomiss_IIRS_IntimacyTotal'] = df[['IIRS7', 'IIRS8']].notna().sum(axis=1)
            df['IIRS_Intimacy'] = df[['IIRS7', 'IIRS8']].mean(axis=1).where(~df[['IIRS7', 'IIRS8']].isna().any(axis=1))
            df.rename(columns={'IIRS_Intimacy': 'Intamacy Subscale Avg(IIRS7 & IIRS8)'}, inplace=True)
        except KeyError:
            pass
        
        try:
            df['IIRS_subscale1'] = df[['IIRS1', 'IIRS2']].mean(axis=1).where(~df[['IIRS1', 'IIRS2']].isna().any(axis=1))
            df.rename(columns={'IIRS_subscale1': 'Physical Well-Being and Diet IIRS 1 & 2 - (IIRS1 + IIRS2)/2'}, inplace=True)
        except KeyError:
            pass
        
        try:
            df['IIRS_subscale2'] = df[['IIRS3', 'IIRS6']].mean(axis=1).where(~df[['IIRS3', 'IIRS6']].isna().any(axis=1))
            df.rename(columns={'IIRS_subscale2': 'Work and Finances IIRS 3 & 6 - (IIRS3 + IIRS6)/2'}, inplace=True)
        except KeyError:
            pass
        
        try:
            df['IIRS_subscale3'] = df[['IIRS7', 'IIRS8', 'IIRS9']].mean(axis=1).where(~df[['IIRS7', 'IIRS8', 'IIRS9']].isna().any(axis=1))
            df.rename(columns={'IIRS_subscale3': 'Marital, Sexual, and Family Relations IIRS 789 - (IIRS7 + IIRS8 + IIRS9)/3'}, inplace=True)
        except KeyError:
            pass
        
        try:
            df['IIRS_subscale4'] = df[['IIRS4', 'IIRS5', 'IIRS10']].mean(axis=1).where(~df[['IIRS4', 'IIRS5', 'IIRS10']].isna().any(axis=1))
        except KeyError:
            pass
        
    return df


def read_stata_file(file_path: str) -> pd.DataFrame:
    """
    Read data from Stata file.
    
    Parameters:
    - file_path (str): Path of the Stata file.
    
    Returns:
    - pd.DataFrame: DataFrame containing the data.
    """
    df = pd.read_stata(file_path)
    return df


if __name__ == "__main__":
    file_path = "your_file.dta"
    df = read_stata_file(file_path)
    df = calculate_IIRS_scores(df)

Note that I have also added a new function `read_stata_file` to read the Stata file, as it is good practice to separate different concern into different functions.