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Another quick solution I have found here (it is applicable for Windows Powershell only) is like this:
Here is a source code for a simple inventory management system using Google Apps Script: function checkInventory() { var sheet = SpreadsheetApp.getActiveSheet(); var data = sheet.getDataRange().getValues(); var message = "Inventory:\n"; for (var i = 1; i < data.length; i++) { var item = data[i][0]; var quantity = data[i][1]; message += item + ": " + quantity + "\n"; } Browser.msgBox(message); } function updateInventory(item, amount) { var sheet = SpreadsheetApp.getActiveSheet(); var data = sheet.getDataRange().getValues(); for (var i = 1; i < data.length; i++) { if (data[i][0] == item) { var newQuantity = parseInt(data[i][1]) + amount; sheet.getRange(i+1, 2).setValue(newQuantity); break; } } } function submitForm(e) { var i...
How to Connect Android TV to Your PC Using ADB Normally, we use a USB cable to connect a PC with an Android. Since this is not feasible with Android TVs, manufacturers allow you to set up ADB wirelessly . To do that: On your Android TV, go to Settings > Device Preferences > About > Status and note down the IP address . Open the command prompt on your PC and enter the command adb connect <the IP address> . tiny adb on desktop enter ip address You will get a prompt on your Android TV asking you to authorize a connection to the computer. Tap on OK . To check if you have successfully established the ADB connection to your Android TV, enter the command adb devices and see if the device shows up under List of devices attached . Source: https://www.makeuseof.com/how-to-use-adb-on-android-tv/
This code loads the sales data from a CSV file, sets the date as the index, and aggregates the sales by day. It then splits the data into training and testing sets, fits a SARIMA model to the training data, and forecasts the sales for the next 30 days. Finally, it plots the actual and forecasted sales, along with the confidence intervals. Note that you may need to adjust the model parameters (order and seasonal_order) depending on your data. import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.statespace.sarimax import SARIMAX # Load sales data sales_data = pd.read_csv('sales_data.csv', parse_dates=['date']) # Set date as index and aggregate sales by day sales_data.set_index('date', inplace=True) sales_data = sales_data.resample('D').sum() # Split data into training and testing sets train_data = sales_data.iloc[:-30] test_data = sales_data.iloc[-30:] # Fit SARIMA model to training data model = SARIMAX(train_data, o...
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