Table of contents — Statistical functions Averages and measures of central location Measures of Spread Statistical distributions Discrete distributions Continuous distributions Law of Large Numbers Central Limit Theorem Statistical functions Averages and measures of central location Mean The mean is the sum of all values divided by the number of values. import statistics as st x = [4,5,1,2,7,2,6,9,3,7,2] print('Mean:', st.mean(x))

Table of contents — Functions Classes How to create a class Attributes in class Methods in class The init method Functions How to create a function def Car(n): print('My car is Toyota!'+n) Car('health ') My car is Toyota! def Car(carname): print('My car is ' + carname + '!') Car('Ford') My car is Ford! Write a Car function with price as…

The Google Cloud SDK provides a set of commands for working with data stored in Cloud Storage. This notebook introduces several gsutil commands for interacting with Cloud Storage. Note that shell commands in a notebook must be prepended with a !. List available commands The gsutil command can be used to perform a…

This tutorial shows how to get started with the Cloud Storage Python client library. Create a storage bucket Buckets are the basic containers that hold your data. Everything that you store in Cloud Storage must be contained in a bucket. You can use buckets to organize your data and control access to your data. …

The following notebook trains an XGBoost model locally and deploys the resulting model file to GCP using the ML Engine Online Prediction API. # Copyright 2019 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with…

This notebook demonstrates how to use AI Platform to train a simple classification model using scikit-learn, and then deploy the model to get predictions. You train the model to predict a person’s income level based on the Census Income data set. Before you jump in, let’s cover some of the…

BigQuery is a petabyte-scale analytics data warehouse that you can use to run SQL queries over vast amounts of data in near realtime. Data visualization tools can help you make sense of your BigQuery data and help you analyze the data interactively. You can use visualization tools to help you…

BigQuery ML enables users to create and execute machine learning models in BigQuery using SQL queries. The goal is to democratize machine learning by enabling SQL practitioners to build models using their existing tools and to increase development speed by eliminating the need for data movement. In this tutorial, you…

Jupyter magics are notebook-specific shortcuts that allow you to run commands with minimal syntax. Jupyter notebooks come with many built-in commands. The BigQuery client library, google-cloud-bigquery, provides a cell magic, %%bigquery. The %%bigquery magic runs a SQL query and returns the results as a pandas DataFrame. Run a query on a public dataset The following example queries…