mithunkamat

data = pd.read_csv(mappings['Filepath'])

# lowercase and strip whitespace from all column headers
data.rename({col: col.strip().lower() for col in data.columns},
            axis=1, inplace=True)

# we must also do the same for inv_mappings
# also add snake_case formatting to internal columns (optional)
inv_mappings = {
    mappings[key].strip().lower():

mithunkamat

empty = 0  # initialize our empty cell count

while empty < 2:
    # increment the row number (which begins at the row we found 'Internal'
    row += 1
    # assign the value we find in the 'Internal' column to internal
    internal = ws[f'B{row}'].value
    
    if internal is None:
        empty += 1  # if we get blank row, increment empty counter

mithunkamat

# define function to search a given column in an openpyxl worksheet object
def search_col(sheet, column, value, limit=100):
  
  # loop through each row upto the limit, beginning from 1
    for row in range(1, limit+1):
      
        if sheet[f"column{row}"].value == value:
            # if we find the value we are searching for, return col + row
            return (col, row)

mithunkamat

# first we set the path to Excel file
path = r".\documents\tool_setup.xlsx"

# open the file as a workbook object
wb = openpyxl.load_workbook(path, data_only=True)

# find the index of 'Mapping' sheet
idx = [i for i, name in wb.sheetnames if name == 'Mapping'][0]

# set the mapping sheet as active

mithunkamat

import openpyxl

# loan the workbook object using 'sheet.xlsx'
wb = openpyxl.load_workbook("sheet.xlsx", data_only=True)

# create a worksheet object with the active sheet
ws = wb.active

# get the value in cell 'E4'
value = ws['E4'].value

mithunkamat

mappings = {'loan identifier': 'loan_id',
            'amt': 'amount',
             ...
            'init fees': 'initial_fees'}

data.rename(mappings, axis=1, inplace=True)

# now, for the remainder of the code we use the data's Python given column names, 'loan_id', 'loan_amount', etc

mithunkamat

import os
from email.mime.text import MIMEText
from email.mime.image import MIMEImage
from email.mime.application import MIMEApplication
from email.mime.multipart import MIMEMultipart

def message(subject="Python Notification", text="", img=None, attachment=None):
    # build message contents
    msg = MIMEMultipart()
    msg['Subject'] = subject  # add in the subject

mithunkamat

end = datetime.datetime.now()  # get the ending datetime

# get the total runtime in hours:minutes:seconds
hours, rem = divmod((end - start).seconds, 3600)
mins, secs = divmod(rem, 60)
runtime = '{:02d}:{:02d}:{:02d}'.format(hours, mins, secs)

# now built our message
notify.msg(
    subject="Cashflow Model Completion",

mithunkamat

import os
import notify
from data import Sql  # see https://jamescalam.github.io/pysqlplus/lib/data/sql.html

dt = Sql('database123', 'server001')  # setup the connection to SQL Server

for i, file in enumerate(os.listdir('../data/new')):
    dt.push_raw(f'../data/new/{file}')  # push a file to SQL Server

# once the upload is complete, send a notification

mithunkamat

import notify

START = datetime.now()  # this line would be placed before model training begins
MODELNAME = "Synthwave GAN"  # giving us our model name
NOTIFY = 100  # so we send an update notification every 100 epochs

# for each epoch e, we would include the following code
if e % notify_epoch == 0 and e != 0:
    # here we create the email body message
    txt = (f"{MODELNAME} update as of "