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Python Tools for BigQuery

Project description

Build Status PyPI version

Python Tools for BigQuery

Why?

For data collection and data exploration, we like to work with BigQuery. But we have not found a python library, to easily handle recurring tasks like adding new data (of potentially inconsistent schema) and schema migrations. So we took a couple of our solutions for those tasks and put them into this library.

What?

bqtools provides a light-weight solution to explicit schema management with python-native types (unlike pandas dtype) and some convenient type checking, inference and conversions. Table-objects created by bqtools can be read from BigQuery, stored locally, read from a local file and written to BigQuery. Table schemas can be changed and data can be added or modified.

Install

pip install --upgrade bqtools

Examples:

Create basic tables

from fourtytwo import bqtools

schema = [
    {'name': 'number', 'field_type': 'INTEGER'},
    {'name': 'text', 'field_type': 'STRING'},
    {'name': 'struct', 'field_type':'RECORD', 'mode':'REPEATED', 
        'fields': [
            {'name':'integer', 'field_type':'INTEGER'},
            {'name':'text', 'field_type':'STRING'}
        ]
    }
]
# valid BigQuery types see: 
# https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types
# geo and array are currently not/not fully supported

# data = columns of lists
table = bqtools.BQTable(
    schema=schema, 
    data=[[1, 2, 3, 4], ['a', 'b', 'c', 'd']]
)

# data = rows of dicts
table = bqtools.BQTable(
    schema=schema, 
    data=[
        {'number': 1, 'text': 'a'}, 
        {'number': 2, 'text': 'b'},
        ...
    ]
)

View data

print(table.data)       # list of all columns
print(table.rows(n=10)) # list of first n rows

# convert to pandas.DataFrame
df = table.to_df()               
# warning: pandas dtypes may be inconsistent 
# with BigQuery Schema field_types

Append data

rows = [{'number': 5, 'text': 'e'}]
table.append(rows)

row = [[6, 'f']]
table.append(rows)

Load table from BigQuery

# requires environment variable GOOGLE_APPLICATION_CREDENTIALS 
# or parameter credentials='path-to-credentials.json'
table = bqtools.read_bq(
    table_ref='project_id.dataset_id.new_table_id', 
    limit=10,           # limit query rows
    schema_only=False   # set True to only add data
)

Modify table schema

# change column order and field_type
new_schema = [
    {'name': 'text', 'field_type': 'STRING'},
    {'name': 'number', 'field_type': 'FLOAT'},
]
table.schema(new_schema)

# change column names
table.rename(columns={'number': 'decimal'})

Write table to BigQuery

# requires environment variable GOOGLE_APPLICATION_CREDENTIALS
# or parameter credentials='path-to-credentials.json'
table.to_bq(table_ref, mode='append')

Persist tables locally

# write to local file (compressed binary format)
table.save('local_table.bqt')

# load from local file
table = bqtools.load('local_table.bqt')

Project details


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