bqdf 0.0.7

Ergonomic wrapper for pandas_gbq that simplifies loading BigQuery data into DataFrames

bqdf

Usage

Installation

Install latest from the GitHub repository:

$ pip install git+https://github.com/motdam/bqdf.git

or from conda

$ conda install -c motdam bqdf

or from pypi

$ pip install bqdf

Documentation

Documentation can be found hosted on this GitHub repository’s pages. Additionally you can find package manager specific guidelines on conda and pypi respectively.

How to use

This lib provides convenience functions for streamlining the interface of the pandas-gbq library to perform CRUD operations in BigQuery more quickly

import pandas_gbq
import pandas as pd

top_terms_query = """
-- todays top 10 search terms in England
SELECT refresh_date, rank, term, score, percent_gain / 100 as percent_gain, country_name, week
FROM `bigquery-public-data.google_trends.international_top_rising_terms` 
WHERE country_name = 'United Kingdom'
  and refresh_date = current_date - 1
  and region_name = 'England'
order by refresh_date desc, week desc, rank
limit 5
"""

Reading a BigQuery table

df = read(top_terms_query, project_id='bq-sandbox-motdam')
df.head()

To recreate the above with the original library you would need the below boiler plate to inspect the results and convert columns into pandas friendly dtypes.

df = pandas_gbq.read_gbq(top_terms_query, project_id='bq-sandbox-motdam')
df = df.astype({
    'percent_gain':'Float64'
})
df['week'] = pd.to_datetime(df['week'])
df['refresh_date'] = pd.to_datetime(df['refresh_date'])
print(df.info())
df.head()

Writing a df to BigQuery

The rest to function is unchanged beyond removing the redundant _gbq suffix. We can write our df back into BigQuery using hte to function.

# Write the dataframe to a temporary table
to(df, 'bq-sandbox-motdam.temporary.top_10_eng_search_terms', if_exists='replace')

Executing SQL in BigQuery

The ex fucntion enables non df based CRUD operations within the same api which can be useful for creating feature processing pipelines.

project = 'bq-sandbox-motdam'

def create_top_terms(period, days):
    return f"""
    CREATE OR REPLACE TABLE `{project}.temporary.top_terms_{period}` AS
    WITH ranked AS (
      SELECT region_name, term, COUNT(*) as appearances, AVG(rank) as avg_rank,
        ROW_NUMBER() OVER (PARTITION BY region_name ORDER BY COUNT(*) DESC, AVG(rank)) as rn
      FROM `bigquery-public-data.google_trends.international_top_rising_terms`
      WHERE country_name = 'United Kingdom'
        AND region_name IN ('England', 'Scotland', 'Wales', 'Northern Ireland')
        AND refresh_date BETWEEN CURRENT_DATE() - {days} AND CURRENT_DATE()
        AND rank <= 100
      GROUP BY region_name, term
    )
    SELECT region_name, term as top_term_{period}
    FROM ranked WHERE rn = 1
    """

ex(create_top_terms('today', 1), project_id=project)
ex(create_top_terms('week', 8), project_id=project)
ex(create_top_terms('month', 31), project_id=project)
ex(create_top_terms('year', 366), project_id=project)

final_query = f"""
SELECT t.region_name, t.top_term_today, w.top_term_week, m.top_term_month, y.top_term_year
FROM `{project}.temporary.top_terms_today` as t
JOIN `{project}.temporary.top_terms_week` as w ON t.region_name = w.region_name
JOIN `{project}.temporary.top_terms_month` as m ON t.region_name = m.region_name
JOIN `{project}.temporary.top_terms_year` as y ON t.region_name = y.region_name
ORDER BY t.region_name
"""

read(final_query, project_id=project)

Developer Guide

If you are new to using nbdev here are some useful pointers to get you started.

Install bqdf in Development mode

# make sure bqdf package is installed in development mode
$ pip install -e .

# make changes under nbs/ directory
# ...

# compile to have changes apply to bqdf
$ nbdev_prepare