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data_simple = [0.1, 0.2, 0.3, 0.7, 0.8, 0.9]'

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function fizzer()
  for i in 1:100
    if i % 15 == 0
      x = "FizzBuzz"
    elseif i % 5 == 0
      x = "Buzz"
    elseif i % 3 == 0
      x = "Fizz"
    else
      x = i

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SELECT *
FROM UNNEST(GENERATE_ARRAY(1, 15)) AS num

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WITH
  numbers AS (
  -- this is the same as before, but wrapped in a with statement
  SELECT
    *
  FROM
    UNNEST(GENERATE_ARRAY(1, 100)) AS num )

SELECT
  num,

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function fib(n) {
  var numbers = [0, 1] // initialise the array
  while (n > 1) {
    var new_num = numbers[0] + numbers[1];
    numbers.shift(); // drop the first element
    numbers.push(new_num); // add the new element to the end
    n -= 1;
  }
  return numbers[n];
}

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CREATE TEMP FUNCTION fibonacci(n INT64)
RETURNS INT64
LANGUAGE js AS
"""
  var numbers = [0, 1]
  while (n > 1) {
    var new_num = numbers[0] + numbers[1];
    numbers.shift();
    numbers.push(new_num);
    n -= 1;

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CREATE TEMP FUNCTION fibonacci(n INT64)
RETURNS INT64
LANGUAGE js AS
"""
  var numbers = [0, 1]
  while (n > 1) {
    var new_num = numbers[0] + numbers[1];
    numbers.shift();
    numbers.push(new_num);
    n -= 1;

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CREATE TEMP FUNCTION fibonacci(n INT64)
RETURNS INT64
LANGUAGE js AS
"""
  var numbers = [0, 1]
  while (n > 1) {
    var new_num = (numbers[0] + numbers[1]) % 1000000;
    numbers.shift();
    numbers.push(new_num);
    n -= 1;

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export LOCATION=europe-west2

bq --location=$LOCATION mk --dataset \
    --default_table_expiration 7200 \
    --description "Tables for different file types" \
    files

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import pandas as pd
from sklearn import datasets

# load a toy dataset
data = datasets.load_boston()
boston_df = pd.DataFrame(data["data"], columns=data["feature_names"])

# save as CSV
boston_df.to_csv("boston.csv", index=False)