def main():
noaa_goes = BigQueryHelper(active_project="bigquery-public-data",
dataset_name="noaa_goes16")
noaa_goes.list_tables()
noaa_goes.table_schema('abi_l1b_radiance')
print(noaa_goes.head("abi_l1b_radiance", num_rows=10))
query = """
SELECT dataset_name, platform_id, scene_id FROM `bigquery-public-data.noaa_goes16.abi_l1b_radiance` WHERE geospatial_westbound_longitude<120 and geospatial_eastbound_longitude>75 and geospatial_northbound_latitude<50 and geospatial_southbound_latitude>30
"""
print("Query size in GB is %f " % noaa_goes.estimate_query_size(query))
class TestBQHelper(unittest.TestCase):
def setUp(self):
self.my_bq = BigQueryHelper("bigquery-public-data", "openaq")
self.query = "SELECT location FROM `bigquery-public-data.openaq.global_air_quality`"
# Query randomized so it won't hit the cache across multiple test runs
self.randomizable_query = """
SELECT value FROM `bigquery-public-data.openaq.global_air_quality`
WHERE value = {0}"""
def test_list_tables(self):
self.assertEqual(self.my_bq.list_tables(), ['global_air_quality'])
def test_list_schema(self):
self.assertEqual(len(self.my_bq.table_schema('global_air_quality')),
11)
def test_estimate_query_size(self):
self.assertIsInstance(self.my_bq.estimate_query_size(self.query),
float)
def test_query_to_pandas(self):
self.assertIsInstance(self.my_bq.query_to_pandas(self.query),
DataFrame)
def test_query_safe_passes(self):
self.assertIsInstance(self.my_bq.query_to_pandas_safe(self.query),
DataFrame)
def test_query_safe_fails(self):
# Different query must be used for this test to ensure we don't hit the
# cache and end up passing by testing a query that would use zero bytes.
fail_query = self.randomizable_query.format(random())
self.assertIsNone(self.my_bq.query_to_pandas_safe(fail_query, 10**-10))
def test_head(self):
self.assertIsInstance(self.my_bq.head('global_air_quality'), DataFrame)
def test_useage_tracker(self):
self.my_bq.query_to_pandas(self.randomizable_query.format(random()))
self.assertNotEqual(self.my_bq.total_gb_used_net_cache, 0)
def test_bad_query_raises_right_error(self):
with self.assertRaises(BadRequest):
self.my_bq.query_to_pandas("Not a valid query")
def test_list_nested_schema(self):
nested_helper = BigQueryHelper("bigquery-public-data", "github_repos")
self.assertEqual(len(nested_helper.table_schema('commits')), 33)
import pandas as pd
import bq_helper
from bq_helper import BigQueryHelper
# https://www.kaggle.com/sohier/introduction-to-the-bq-helper-package
medicare = bq_helper.BigQueryHelper(active_project="bigquery-public-data",
dataset_name="cms_medicare")
bq_assistant = BigQueryHelper("bigquery-public-data", "cms_medicare")
bq_assistant.list_tables()
bq_assistant.head("inpatient_charges_2015", num_rows=15)
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly as py
import plotly.graph_objs as go
init_notebook_mode(connected=True)
plt.rcParams['figure.figsize'] = (12, 5)
from google.cloud import bigquery
from bq_helper import BigQueryHelper
client = bigquery.Client()
bq_assistant = BigQueryHelper("bigquery-public-data", "bitcoin_blockchain")
def satoshi_to_bitcoin(satoshi):
return float(float(satoshi) / float(100000000))
test_data = bq_assistant.head("transactions")
bq = DataSaver(bq_assistant)
def Create_Bar_plotly(list_of_tuples, items_to_show=40, title=""):
#list_of_tuples=list_of_tuples[:items_to_show]
data = [
go.Bar(x=[val[0] for val in list_of_tuples],
y=[val[1] for val in list_of_tuples])
]
layout = go.Layout(
title=title,
xaxis=dict(autotick=False, tickangle=290),
)
fig = go.Figure(data=data, layout=layout)
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from google.cloud import bigquery
import bq_helper
from bq_helper import BigQueryHelper
stackOverflow = bq_helper.BigQueryHelper(active_project="bigquery-public-data",
dataset_name="stackoverflow")
bq_assistant = BigQueryHelper("bigquery-public-data", "stackoverflow")
bq_assistant.list_tables()
tables = [
'badges', 'comments', 'post_history', 'post_links', 'posts_answers',
'posts_moderator_nomination', 'posts_orphaned_tag_wiki',
'posts_privilege_wiki', 'posts_questions', 'posts_tag_wiki',
'posts_tag_wiki_excerpt', 'posts_wiki_placeholder', 'stackoverflow_posts',
'tags', 'users', 'votes'
]
for t in tables:
tag = bq_assistant.head(t, num_rows=5000)
tag.to_csv('stackoverflow_' + t + '.csv')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
from google.cloud import bigquery
from bq_helper import BigQueryHelper
bq_assist = BigQueryHelper(active_project='bigquery-public-data',
dataset_name='epa_historical_air_quality')
bq_assist.list_tables()
bq_assist.head('temperature_daily_summary')
query = """ SELECT EXTRACT (YEAR FROM date_local) AS Year,
AVG ((arithmetic_mean - 32.0)/ 1.80) AS avg_temp_celcius
FROM `bigquery-public-data.epa_historical_air_quality.temperature_daily_summary`
GROUP BY Year
Order BY Year
"""
avg_temp = bq_assist.query_to_pandas_safe(query, max_gb_scanned=10)
query = """ SELECT EXTRACT (YEAR FROM date_local) AS Year,
AVG (arithmetic_mean) AS avg_co
FROM `bigquery-public-data.epa_historical_air_quality.co_daily_summary`
GROUP BY Year
Order BY Year
"""
avg_co = bq_assist.query_to_pandas_safe(query, max_gb_scanned=10)
