Data-Analysis-Jupyter/weighter/run_weighter.py

# pandas and numpy for data manipulation
import pandas as pd
import numpy as np

# fbprophet for additive models
import fbprophet

# gspread for Google Sheets access
import gspread

# slacker for interacting with Slack
from slacker import Slacker

# oauth2client for authorizing access to Google Sheets
from oauth2client.service_account import ServiceAccountCredentials

# os for deleting images
import os

# matplotlib for plotting
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib

# import weighter
from weighter import Weighter

if __name__ == "__main__":

    # google sheets access
    scope = ["https://spreadsheets.google.com/feeds"]

    # Use local stored credentials in json file
    # make sure to first share the sheet with the email in the json file
    credentials = ServiceAccountCredentials.from_json_keyfile_name(
        "C:/Users/Will Koehrsen/Desktop/weighter-2038ffb4e5a6.json", scope
    )

    # Authorize access
    gc = gspread.authorize(credentials)

    # Slack api key is stored as text file
    with open("C:/Users/Will Koehrsen/Desktop/slack_api.txt", "r") as f:
        slack_api_key = f.read()

    slack = Slacker(slack_api_key)

    # Open the sheet, need to share the sheet with email specified in json file
    gsheet = gc.open("Auto Weight Challenge").sheet1

    # List of lists with each row in the sheet as a list
    weight_lists = gsheet.get_all_values()

    # Headers are the first list
    # Pop returns the element (list in this case) and removes it from the list
    headers = weight_lists.pop(0)

    # Convert list of lists to a dataframe with specified column header
    weights = pd.DataFrame(weight_lists, columns=headers)

    # Record column should be a boolean
    weights["Record"] = weights["Record"].astype(bool)

    # Name column is a string
    weights["Name"] = weights["Name"].astype(str)

    # Convert dates to datetime, then set as index, then set the time zone
    weights["Date"] = pd.to_datetime(weights["Date"], unit="s")
    weights = weights.set_index("Date", drop=True).tz_localize(tz="US/Eastern")

    # Drop any extra entries
    weights = weights.drop("NaT")

    # If there are new entries create the weighter object
    if len(weights) > np.count_nonzero(weights["Record"]):
        # Initialize with dataframe of weights, google sheet, and slack object
        weighter = Weighter(weights, gsheet, slack)
        weighter.process_entries()
        print("Success")