Google

Google Cloud services allow you to upload and manipulate Tables as spreadsheets (via GoogleSheets) or query them as SQL database tables (via GoogleBigQuery). You can also upload/store/download them as binary objects (via GoogleCloudStorage). Google also offers an API for civic information using GoogleCivic and admin information using the Google Workspace Admin SDK.

For all of these services you will need to enable the APIs for your Google Cloud account and obtain authentication tokens or other credentials to access them from your scripts. If you are the administrator of your Google Cloud account, you can do both of these at Google Cloud Console APIs and Services. The connectors below have more specific information about how to authenticate.

Google Admin

Overview

The GoogleAdmin class allows you to get information about groups and members in Google Admin.

In order to instantiate the class, you must pass Google service account credentials as a dictionary, or store the credentials as a JSON string in the GOOGLE_APPLICATION_CREDENTIALS environment variable. You must also provide an email address for domain-wide delegation.

Quickstart

To instantiate the GoogleAdmin class, you can either pass the constructor a dict containing your Google service account credentials or define the environment variable GOOGLE_APPLICATION_CREDENTIALS to contain a JSON encoding of the dict.

from parsons import GoogleAdmin

# First approach: Use API credentials via environmental variables
admin = GoogleAdmin(None, 'fakeemail@fakedomain.com')

# Second approach: Pass API credentials as argument
credential_filename = 'google_application_credentials.json'
credentials = json.load(open(credential_filename))
sheets = GoogleSheets(credentials, 'fakeemail@fakedomain.com')

You can then get information about groups and members using instance methods:

members = admin.get_all_group_members('group_key')
groups = admin.get_all_groups(domain='fakedomain.com')

API

class parsons.google.google_admin.GoogleAdmin(app_creds=None, sub=None)[source]

A connector for Google Admin.

Args:
app_creds: str

A credentials json string or a path to a json file. Not required if GOOGLE_APPLICATION_CREDENTIALS env variable set.

sub: str

An email address that this service account will act on behalf of (via domain-wide delegation)

Returns:

GoogleAdmin Class

get_aliases(group_key, params=None)[source]

Get aliases for a group. Google Admin API Documentation

Args:
group_key: str

The Google group id

params: dict

A dictionary of fields for the GET request

Returns:

Table Class

get_all_group_members(group_key, params=None)[source]

Get all members in a group. Google Admin API Documentation

Args:
group_key: str

The Google group id

params: dict

A dictionary of fields for the GET request

Returns:

Table Class

get_all_groups(params=None)[source]

Get all groups in a domain or account. Google Admin API Documentation Args:

params: dict

A dictionary of fields for the GET request.

Returns:

Table Class

BigQuery

Overview

Google BigQuery is a cloud data warehouse solution. Data is stored in tables, and users can query using SQL. BigQuery uses datasets as top level containers for tables, and datasets are themselves contained within Google Cloud projects.

Quickstart

To instantiate the GoogleBigQuery class, you can pass the constructor a string containing either the name of the Google service account credentials file or a JSON string encoding those credentials. Alternatively, you can set the environment variable GOOGLE_APPLICATION_CREDENTIALS to be either of those strings and call the constructor without that argument.

from parsons import GoogleBigQuery

# Set as environment variable so we don't have to pass it in. May either
# be the file name or a JSON encoding of the credentials.
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'google_credentials_file.json'

bigquery = GoogleBigQuery()

Alternatively, you can pass the credentials in as an argument. In the example below, we also specify the project.

# Project in which we're working
project = 'parsons-test'
bigquery = GoogleBigQuery(
   app_creds='google_credentials_file.json',
   project=project
)

We can now upload/query data.

dataset = 'parsons_dataset'
table = 'parsons_table'

# Table name should be project.dataset.table, or dataset.table, if
# working with the default project
table_name = f"`{project}.{dataset}.{table}`"

# Must be pre-existing bucket. Create via GoogleCloudStorage() or
# at https://console.cloud.google.com/storage/create-bucket. May be
# omitted if the name of the bucket is specified in environment
# variable GCS_TEMP_BUCKET.
gcs_temp_bucket = 'parsons_bucket'

# Create dataset if it doesn't already exist
bigquery.client.create_dataset(dataset=dataset, exists_ok=True)

parsons_table = Table([{'name':'Bob', 'party':'D'},
                       {'name':'Jane', 'party':'D'},
                       {'name':'Sue', 'party':'R'},
                       {'name':'Bill', 'party':'I'}])

# Copy table in to create new BigQuery table
bigquery.copy(
   table_obj=parsons_table,
   table_name=table_name,
   tmp_gcs_bucket=gcs_temp_bucket
)

# Select from project.dataset.table
bigquery.query(f'select name from {table_name} where party = "D"')

# Query with parameters
bigquery.query(
   f"select name from {table_name} where party = %s",
   parameters=["D"]
)

# Delete the table when we're done
bigquery.client.delete_table(table=table_name)

API

class parsons.google.google_bigquery.GoogleBigQuery(app_creds: Optional[Union[str, dict, Credentials]] = None, project=None, location=None, client_options: dict = {'scopes': ['https://www.googleapis.com/auth/drive', 'https://www.googleapis.com/auth/bigquery', 'https://www.googleapis.com/auth/cloud-platform']})[source]

Class for querying BigQuery table and returning the data as Parsons tables.

This class requires application credentials in the form of a json. It can be passed in the following ways:

  • Set an environmental variable named GOOGLE_APPLICATION_CREDENTIALS with the local path to the credentials json.

    Example: GOOGLE_APPLICATION_CREDENTALS='path/to/creds.json'

  • Pass in the path to the credentials using the app_creds argument.

  • Pass in a json string using the app_creds argument.

Args:
app_creds: str

A credentials json string or a path to a json file. Not required if GOOGLE_APPLICATION_CREDENTIALS env variable set.

project: str

The project which the client is acting on behalf of. If not passed then will use the default inferred environment.

location: str

Default geographic location for tables

client_options: dict

A dictionary containing any requested client options. Defaults to the required scopes for making API calls against External tables stored in Google Drive. Can be set to None if these permissions are not desired

property client

Get the Google BigQuery client to use for making queries.

Returns:

google.cloud.bigquery.client.Client

connection()[source]

Generate a BigQuery connection. The connection is set up as a python “context manager”, so it will be closed automatically when the connection goes out of scope. Note that the BigQuery API uses jobs to run database operations and, as such, simply has a no-op for a “commit” function.

If you would like to manage transactions, please use multi-statement queries as [outlined here](https://cloud.google.com/bigquery/docs/transactions) or utilize the query_with_transaction method on this class.

When using the connection, make sure to put it in a with block (necessary for any context manager): with bq.connection() as conn:

Returns:

Google BigQuery connection object

query(sql: str, parameters: Optional[Union[list, dict]] = None, return_values: bool = True) Optional[Table][source]

Run a BigQuery query and return the results as a Parsons table.

To include python variables in your query, it is recommended to pass them as parameters, following the BigQuery style where parameters are prefixed with @`s. Using the ``parameters` argument ensures that values are escaped properly, and avoids SQL injection attacks.

Parameter Examples

name = "Beatrice O'Brady"
sql = 'SELECT * FROM my_table WHERE name = %s'
rs.query(sql, parameters=[name])
name = "Beatrice O'Brady"
sql = "SELECT * FROM my_table WHERE name = %(name)s"
rs.query(sql, parameters={'name': name})
Args:
sql: str

A valid BigTable statement

parameters: dict

A dictionary of query parameters for BigQuery.

Returns:
Parsons Table

See Parsons Table for output options.

query_with_connection(sql, connection, parameters=None, commit=True, return_values: bool = True)[source]

Execute a query against the BigQuery database, with an existing connection. Useful for batching queries together. Will return None if the query returns zero rows.

Args:
sql: str

A valid SQL statement

connection: obj

A connection object obtained from redshift.connection()

parameters: list

A list of python variables to be converted into SQL values in your query

commit: boolean

Must be true. BigQuery

Returns:
Parsons Table

See Parsons Table for output options.

copy_from_gcs(gcs_blob_uri: str, table_name: str, if_exists: str = 'fail', max_errors: int = 0, data_type: str = 'csv', csv_delimiter: str = ',', ignoreheader: int = 1, nullas: Optional[str] = None, allow_quoted_newlines: bool = True, allow_jagged_rows: bool = True, quote: Optional[str] = None, schema: Optional[List[dict]] = None, job_config: Optional[LoadJobConfig] = None, force_unzip_blobs: bool = False, compression_type: str = 'gzip', new_file_extension: str = 'csv', template_table: Optional[str] = None, **load_kwargs)[source]

Copy a csv saved in Google Cloud Storage into Google BigQuery.

Args:
gcs_blob_uri: str

The GoogleCloudStorage URI referencing the file to be copied.

table_name: str

The table name to load the data into. Will be used to generate load schema if no custom schema or template table are supplied and the if_exists is set to “truncate” or “append”.

if_exists: str

If the table already exists, either fail, append, drop or truncate the table. This maps to write_disposition in the LoadJobConfig class.

max_errors: int

The maximum number of rows that can error and be skipped before the job fails. This maps to max_bad_records in the LoadJobConfig class.

data_type: str

Denotes whether target file is a JSON or CSV

csv_delimiter: str

Character used to separate values in the target file

ignoreheader: int

Treats the specified number_rows as a file header and doesn’t load them

nullas: str

Loads fields that match null_string as NULL, where null_string can be any string

allow_quoted_newlines: bool

If True, detects quoted new line characters within a CSV field and does not interpret the quoted new line character as a row boundary

allow_jagged_rows: bool

Allow missing trailing optional columns (CSV only).

quote: str

The value that is used to quote data sections in a CSV file. BigQuery converts the string to ISO-8859-1 encoding, and then uses the first byte of the encoded string to split the data in its raw, binary state.

schema: list

BigQuery expects a list of dictionaries in the following format ``` schema = [

{“name”: “column_name”, “type”: STRING}, {“name”: “another_column_name”, “type”: INT}

job_config: object

A LoadJobConfig object to provide to the underlying call to load_table_from_uri on the BigQuery client. The function will create its own if not provided. Note if there are any conflicts between the job_config and other parameters, the job_config values are preferred.

force_unzip_blobs: bool

If True, target blobs will be unzipped before being loaded to BigQuery.

compression_type: str

Accepts zip or gzip values to differentially unzip a compressed blob in cloud storage.

new_file_extension: str

Provides a file extension if a blob is decompressed and rewritten to cloud storage.

template_table: str

Table name to be used as the load schema. Load operation wil use the same columns and data types as the template table.

**load_kwargs: kwargs

Other arguments to pass to the underlying load_table_from_uri call on the BigQuery client.

copy_large_compressed_file_from_gcs(gcs_blob_uri: str, table_name: str, if_exists: str = 'fail', max_errors: int = 0, data_type: str = 'csv', csv_delimiter: str = ',', ignoreheader: int = 1, nullas: Optional[str] = None, allow_quoted_newlines: bool = True, allow_jagged_rows: bool = True, quote: Optional[str] = None, schema: Optional[List[dict]] = None, job_config: Optional[LoadJobConfig] = None, compression_type: str = 'gzip', new_file_extension: str = 'csv', template_table: Optional[str] = None, **load_kwargs)[source]

Copy a compressed CSV file that exceeds the maximum size in Google Cloud Storage into Google BigQuery.

Args:
gcs_blob_uri: str

The GoogleCloudStorage URI referencing the file to be copied.

table_name: str

The table name to load the data into. Will be used to generate load schema if no custom schema or template table are supplied and the if_exists is set to “truncate” or “append”.

if_exists: str

If the table already exists, either fail, append, drop or truncate the table. This maps to write_disposition in the LoadJobConfig class.

max_errors: int

The maximum number of rows that can error and be skipped before the job fails. This maps to max_bad_records in the LoadJobConfig class.

data_type: str

Denotes whether target file is a JSON or CSV

csv_delimiter: str

Character used to separate values in the target file

ignoreheader: int

Treats the specified number_rows as a file header and doesn’t load them

nullas: str

Loads fields that match null_string as NULL, where null_string can be any string

allow_quoted_newlines: bool

If True, detects quoted new line characters within a CSV field and does not interpret the quoted new line character as a row boundary

allow_jagged_rows: bool

Allow missing trailing optional columns (CSV only).

quote: str

The value that is used to quote data sections in a CSV file. BigQuery converts the string to ISO-8859-1 encoding, and then uses the first byte of the encoded string to split the data in its raw, binary state.

schema: list

BigQuery expects a list of dictionaries in the following format ``` schema = [

{“name”: “column_name”, “type”: STRING}, {“name”: “another_column_name”, “type”: INT}

job_config: object

A LoadJobConfig object to provide to the underlying call to load_table_from_uri on the BigQuery client. The function will create its own if not provided. Note if there are any conflicts between the job_config and other parameters, the job_config values are preferred.

compression_type: str

Accepts zip or gzip values to differentially unzip a compressed blob in cloud storage.

new_file_extension: str

Provides a file extension if a blob is decompressed and rewritten to cloud storage.

template_table: str

Table name to be used as the load schema. Load operation wil use the same columns and data types as the template table.

**load_kwargs: kwargs

Other arguments to pass to the underlying load_table_from_uri call on the BigQuery client.

copy_s3(table_name, bucket, key, if_exists: str = 'fail', max_errors: int = 0, data_type: str = 'csv', csv_delimiter: str = ',', ignoreheader: int = 1, nullas: Optional[str] = None, aws_access_key_id: Optional[str] = None, aws_secret_access_key: Optional[str] = None, gcs_client: Optional[GoogleCloudStorage] = None, tmp_gcs_bucket: Optional[str] = None, template_table: Optional[str] = None, job_config: Optional[LoadJobConfig] = None, **load_kwargs)[source]

Copy a file from s3 to BigQuery.

Args:
table_name: str

The table name and schema (tmc.cool_table) to point the file.

bucket: str

The s3 bucket where the file or manifest is located.

key: str

The key of the file or manifest in the s3 bucket.

if_exists: str

If the table already exists, either fail, append, drop or truncate the table.

max_errors: int

The maximum number of rows that can error and be skipped before the job fails.

data_type: str

The data type of the file. Only csv supported currently.

csv_delimiter: str

The delimiter of the csv. Only relevant if data_type is csv.

ignoreheader: int

The number of header rows to skip. Ignored if data_type is json.

nullas: str

Loads fields that match string as NULL

aws_access_key_id:

An AWS access key granted to the bucket where the file is located. Not required if keys are stored as environmental variables.

aws_secret_access_key:

An AWS secret access key granted to the bucket where the file is located. Not required if keys are stored as environmental variables.

gcs_client: object

The GoogleCloudStorage Connector to use for loading data into Google Cloud Storage.

tmp_gcs_bucket: str

The name of the Google Cloud Storage bucket to use to stage the data to load into BigQuery. Required if GCS_TEMP_BUCKET is not specified.

template_table: str

Table name to be used as the load schema. Load operation wil use the same columns and data types as the template table.

job_config: object

A LoadJobConfig object to provide to the underlying call to load_table_from_uri on the BigQuery client. The function will create its own if not provided. Note if there are any conflicts between the job_config and other parameters, the job_config values are preferred.

Returns
Parsons Table or None

See Parsons Table for output options.

copy(tbl: Table, table_name: str, if_exists: str = 'fail', max_errors: int = 0, tmp_gcs_bucket: Optional[str] = None, gcs_client: Optional[GoogleCloudStorage] = None, job_config: Optional[LoadJobConfig] = None, template_table: Optional[str] = None, ignoreheader: int = 1, nullas: Optional[str] = None, allow_quoted_newlines: bool = True, allow_jagged_rows: bool = True, quote: Optional[str] = None, schema: Optional[List[dict]] = None, **load_kwargs)[source]

Copy a Parsons Table into Google BigQuery via Google Cloud Storage.

Args:
tbl: obj

The Parsons Table to copy into BigQuery.

table_name: str

The table name to load the data into. Will be used to generate load schema if no custom schema or template table are supplied and if_exists is set to “truncate” or “append”.

if_exists: str

If the table already exists, either fail, append, drop or truncate the table.

max_errors: int

The maximum number of rows that can error and be skipped before the job fails.

tmp_gcs_bucket: str

The name of the Google Cloud Storage bucket to use to stage the data to load into BigQuery. Required if GCS_TEMP_BUCKET is not specified.

gcs_client: object

The GoogleCloudStorage Connector to use for loading data into Google Cloud Storage.

job_config: object

A LoadJobConfig object to provide to the underlying call to load_table_from_uri on the BigQuery client. The function will create its own if not provided.

template_table: str

Table name to be used as the load schema. Load operation wil use the same columns and data types as the template table.

**load_kwargs: kwargs

Arguments to pass to the underlying load_table_from_uri call on the BigQuery client.

duplicate_table(source_table, destination_table, if_exists='fail', drop_source_table=False)[source]

Create a copy of an existing table (or subset of rows) in a new table.

Args:
source_table: str

Name of existing schema and table (e.g. myschema.oldtable)

destination_table: str

Name of destination schema and table (e.g. myschema.newtable)

if_exists: str

If the table already exists, either fail, replace, or ignore the operation.

drop_source_table: boolean

Drop the source table

upsert(table_obj, target_table, primary_key, distinct_check=True, cleanup_temp_table=True, from_s3=False, **copy_args)[source]

Preform an upsert on an existing table. An upsert is a function in which rows in a table are updated and inserted at the same time.

Args:
table_obj: obj

A Parsons table object

target_table: str

The schema and table name to upsert

primary_key: str or list

The primary key column(s) of the target table

distinct_check: boolean

Check if the primary key column is distinct. Raise error if not.

cleanup_temp_table: boolean

A temp table is dropped by default on cleanup. You can set to False for debugging.

from_s3: boolean

Instead of specifying a table_obj (set the first argument to None), set this to True and include copy_s3() arguments to upsert a pre-existing s3 file into the target_table

**copy_args: kwargs

See copy() for options.

delete_table(table_name)[source]

Delete a BigQuery table.

Args:
table_name: str

The name of the table to delete.

table_exists(table_name: str) bool[source]

Check whether or not the Google BigQuery table exists in the specified dataset.

Args:
table_name: str

The name of the BigQuery table to check for

Returns:
bool

True if the table exists in the specified dataset, false otherwise

get_tables(schema, table_name: Optional[str] = None)[source]

List the tables in a schema including metadata.

Args:
schema: str

Filter by a schema

table_name: str

Filter by a table name

Returns:
Parsons Table

See Parsons Table for output options.

get_views(schema, view: Optional[str] = None)[source]

List views.

Args:
schema: str

Filter by a schema

view: str

Filter by a table name

Returns:
Parsons Table

See Parsons Table for output options.

get_columns(schema: str, table_name: str)[source]

Gets the column names (and other column metadata) for a table. If you need just the column names run get_columns_list(), as it is faster.

Args:
schema: str

The schema name

table_name: str

The table name

Returns:

A dictionary mapping column name to a dictionary with extra info. The keys of the dictionary are ordered just liked the columns in the table. The extra info is a dict with format

get_columns_list(schema: str, table_name: str) list[source]

Gets the column names for a table.

Args:
schema: str

The schema name

table_name: str

The table name

Returns:

A list of column names

get_row_count(schema: str, table_name: str) int[source]

Gets the row count for a BigQuery materialization.

Caution: This method uses SELECT COUNT(*) which can be expensive for large tables, especially those with many columns. This is because BigQuery scans all table data to perform the count, even though only the row count is returned.

Args:
schema: str

The schema name

table_name: str

The table name

Returns:

Row count of the target table

Cloud Storage

Overview

Google Cloud Storage is a cloud file storage system. It uses buckets in which to store arbitrary files referred to as blobs. You may use this connector to upload Parsons tables as blobs, download them to files, and list available blobs.

To use the GoogleCloudStorage class, you will need Google service account credentials. If you are the administrator of your Google Cloud account, you can generate them in the Google Cloud Console APIs and Services.

Quickstart

To instantiate the GoogleBigQuery class, you can pass the constructor a string containing either the name of your Google service account credentials file or a JSON string encoding those credentials. Alternatively, you can set the environment variable GOOGLE_APPLICATION_CREDENTIALS to be either of those strings and call the constructor without that argument.

from parsons import GoogleCloudStorage

# Set as environment variable so we don't have to pass it in. May either
# be the file name or a JSON encoding of the credentials.
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'google_credentials_file.json'

gcs = GoogleCloudStorage()

Alternatively, you can pass the credentials in as an argument. In the example below, we also specify the project.

credentials_filename = 'google_credentials_file.json'
project = 'parsons-test'    # Project in which we're working
gcs = GoogleCloudStorage(app_creds=credentials_filename, project=project)

Now we can create buckets, upload blobs to them and and list/retrieve the available blobs.

gcs.create_bucket('parsons_bucket')
gcs.list_buckets()

gcs.upload_table(bucket='parsons_bucket', table=parsons_table, blob_name='parsons_blob')
gcs.get_blob(bucket_name='parsons_bucket', blob_name='parsons_blob')

API

class parsons.google.google_cloud_storage.GoogleCloudStorage(app_creds=None, project=None)[source]

This class requires application credentials in the form of a json. It can be passed in the following ways:

  • Set an environmental variable named GOOGLE_APPLICATION_CREDENTIALS with the local path to the credentials json.

    Example: GOOGLE_APPLICATION_CREDENTALS='path/to/creds.json'

  • Pass in the path to the credentials using the app_creds argument.

  • Pass in a json string using the app_creds argument.

Args:
app_creds: str

A credentials json string or a path to a json file. Not required if GOOGLE_APPLICATION_CREDENTIALS env variable set.

project: str

The project which the client is acting on behalf of. If not passed then will use the default inferred environment.

Returns:

GoogleCloudStorage Class

client

Access all methods of google.cloud package

list_buckets()[source]

Returns a list of buckets

Returns:

List of buckets

bucket_exists(bucket_name)[source]

Verify that a bucket exists

Args:
bucket_name: str

The name of the bucket

Returns:

boolean

get_bucket(bucket_name)[source]

Returns a bucket object

Args:
bucket_name: str

The name of bucket

Returns:

GoogleCloud Storage bucket

create_bucket(bucket_name)[source]

Create a bucket.

Args:
bucket_name: str

A globally unique name for the bucket.

Returns:

None

delete_bucket(bucket_name, delete_blobs=False)[source]

Delete a bucket. Will fail if not empty unless delete_blobs argument is set to True.

Args:
bucket_name: str

The name of the bucket

delete_blobs: boolean

Delete blobs in the bucket, if it is not empty

Returns:

None

list_blobs(bucket_name, max_results=None, prefix=None, match_glob=None, include_file_details=False)[source]

List all of the blobs in a bucket

Args:
bucket_name: str

The name of the bucket

max_results: int

Maximum number of blobs to return

prefix: str

A prefix to filter files

match_glob: str

Filters files based on glob string. NOTE that the match_glob parameter runs on the full blob URI, include a preceding wildcard value to account for nested files (/ for one level, */ for n levels)

include_file_details: bool

If True, returns a list of Blob objects with accessible metadata. For documentation of attributes associated with Blob objects see https://cloud.google.com/python/docs/reference/storage/latest/google.cloud.storage.blob.Blob

Returns:

A list of blob names (or Blob objects if include_file_details is invoked)

blob_exists(bucket_name, blob_name)[source]

Verify that a blob exists in the specified bucket

Args:
bucket_name: str

The bucket name

blob_name: str

The name of the blob

Returns:

boolean

get_blob(bucket_name, blob_name)[source]

Get a blob object

Args:
bucket_name: str

A bucket name

blob_name: str

A blob name

Returns:

A Google Storage blob object

put_blob(bucket_name, blob_name, local_path, **kwargs)[source]

Puts a blob (aka file) in a bucket

Args:
bucket_name:

The name of the bucket to store the blob

blob_name:

The name of blob to be stored in the bucket

local_path: str

The local path of the file to upload

Returns:

None

download_blob(bucket_name, blob_name, local_path=None)[source]

Gets a blob from a bucket

Args:
bucket_name: str

The name of the bucket

blob_name: str

The name of the blob

local_path: str

The local path where the file will be downloaded. If not specified, a temporary file will be created and returned, and that file will be removed automatically when the script is done running.

Returns:
str

The path of the downloaded file

delete_blob(bucket_name, blob_name)[source]

Delete a blob

Args:
bucket_name: str

The bucket name

blob_name: str

The blob name

Returns:

None

upload_table(table, bucket_name, blob_name, data_type='csv', default_acl=None)[source]

Load the data from a Parsons table into a blob.

Args:
table: obj

A Parsons Table

bucket_name: str

The name of the bucket to upload the data into.

blob_name: str

The name of the blob to upload the data into.

data_type: str

The file format to use when writing the data. One of: csv or json

default_acl:

ACL desired for newly uploaded table

Returns:

String representation of file URI in GCS

get_url(bucket_name, blob_name, expires_in=60)[source]

Generates a presigned url for a blob

Args:
bucket_name: str

The name of the bucket

blob_name: str

The name of the blob

expires_in: int

Minutes until the url expires

Returns:
url:

A link to download the object

copy_bucket_to_gcs(gcs_sink_bucket: str, source: str, source_bucket: str, destination_path: str = '', source_path: str = '', aws_access_key_id: Optional[str] = None, aws_secret_access_key: Optional[str] = None)[source]

Creates a one-time transfer job from Amazon S3 to Google Cloud Storage. Copies all blobs within the bucket unless a key or prefix is passed.

Args:
gcs_sink_bucket (str):

Destination for the data transfer (located in GCS)

source (str):

File storge vendor [gcs or s3]

source_bucket (str):

Source bucket name

source_path (str):

Path in the source system pointing to the relevant keys / files to sync. Must end in a ‘/’

aws_access_key_id (str):

Access key to authenticate storage transfer

aws_secret_access_key (str):

Secret key to authenticate storage transfer

format_uri(bucket: str, name: str)[source]

Represent a GCS URI as a string

Args:
bucket: str

GCS bucket name

name: str

Filename in bucket

Returns:

String represetnation of URI

split_uri(gcs_uri: str)[source]

Split a GCS URI into a bucket and blob name

Args:
gcs_uri: str

GCS URI

Returns:

Tuple of strings with bucket_name and blob_name

unzip_blob(bucket_name: str, blob_name: str, compression_type: str = 'gzip', new_filename: Optional[str] = None, new_file_extension: Optional[str] = None) str[source]

Downloads and decompresses a blob. The decompressed blob is re-uploaded with the same filename if no new_filename parameter is provided.

Args:
bucket_name: str

GCS bucket name

blob_name: str

Blob name in GCS bucket

compression_type: str

Either zip or gzip

new_filename: str

If provided, replaces the existing blob name when the decompressed file is uploaded

new_file_extension: str

If provided, replaces the file extension when the decompressed file is uploaded

Returns:

String representation of decompressed GCS URI

Civic

Overview

Google Civic is an API which provides helpful information about elections. In order to access Google Civic you must create a Google Developer Key in their API console. In order to use Google Civic, you must enable this specific end point.

The Google Civic API utilizes the Voting Information Project to collect key civic information such as personalized ballots and polling location information.

Quickstart

To instantiate the GoogleCivic class, you can pass the constructor a string containing the Google Civic API key you’ve generated for your project, or set the environment variable GOOGLE_CIVIC_API_KEY to that value.

from parsons import GoogleCivic

# Set as environment variable so we don't have to pass it in. May either
# be the file name or a JSON encoding of the credentials.
os.environ['GOOGLE_CIVIC_API_KEY'] = 'AIzaSyAOVZVeL-snv3vNDUdw6QSiCvZRXk1xM'

google_civic = GoogleCivic()

Alternatively, you can pass the credentials in as an argument. In the example below, we also specify the project.

google_civic = GoogleCivic(api_key='AIzaSyAOVZVeL-snv3vNDUdw6QSiCvZRXk1xM')

Now you can retrieve election information

elections = google_civic.get_elections()

address = '1600 Pennsylvania Avenue, Washington DC'
election_id = '7000'  # General Election
google_civic.get_polling_location(election_id=election_id, address=address)

You can also retrieve represntative information such as offices, officals, etc.

address = '1600 Pennsylvania Avenue, Washington DC'
representatives = google_civic.get_representatives_by_address(address=address)

API

class parsons.google.google_civic.GoogleCivic(api_key=None)[source]
Args:
api_keystr

A valid Google api key. Not required if GOOGLE_CIVIC_API_KEY env variable set.

Returns:

class

get_elections()[source]

Get a collection of information about elections and voter information.

Returns:
Parsons Table

See Parsons Table for output options.

get_polling_location(election_id, address)[source]

Get polling location information for a given address.

Args:
election_id: int

A valid election id. Election ids can be found by running the get_elections() method.

address: str

A valid US address in a single string.

Returns:
Parsons Table

See Parsons Table for output options.

get_polling_locations(election_id, table, address_field='address')[source]

Get polling location information for a table of addresses.

Args:
election_id: int

A valid election id. Election ids can be found by running the get_elections() method.

address: str

A valid US address in a single string.

address_field: str

The name of the column where the address is stored.

Returns:
Parsons Table

See Parsons Table for output options.

get_representative_info_by_address(address: str, include_offices=True, levels=None, roles=None)[source]

Get representative information for a given address. This method returns the raw JSON response from the Google Civic API. It is a complex response that is not easily parsed into a table. Here is the information on how to parse the response: https://developers.google.com/civic-information/docs/v2/representatives/representativeInfoByAddress

Args:
address: str

A valid US address in a single string.

include_offices: bool

Whether to return information about offices and officials. If false, only the top-level district information will be returned. (Default: True)

levels: list of str

A list of office levels to filter by. Only offices that serve at least one of these levels will be returned. Divisions that don’t contain a matching office will not be returned.

Acceptable values are: “administrativeArea1” “administrativeArea2” “country” “international” “locality” “regional” “special” “subLocality1” “subLocality2”

roles: list of str

A list of office roles to filter by. Only offices fulfilling one of these roles will be returned. Divisions that don’t contain a matching office will not be returned.

Acceptable values are: “deputyHeadOfGovernment” “executiveCouncil” “governmentOfficer” “headOfGovernment” “headOfState” “highestCourtJudge” “judge” “legislatorLowerBody” “legislatorUpperBody” “schoolBoard” “specialPurposeOfficer”

Returns:
Parsons Table

See Parsons Table for output options.

Google Sheets

Overview

The GoogleSheets class allows you to interact with Google service account spreadsheets, called “Google Sheets.” You can create, modify, read, format, share and delete sheets with this connector.

In order to instantiate the class, you must pass Google service account credentials as a dictionary, or store the credentials as a JSON file locally and pass the path to the file as a string in the GOOGLE_DRIVE_CREDENTIALS environment variable. You can follow these steps:

  • Go to the Google Developer Console and make sure the “Google Drive API” and the “Google Sheets API” are both enabled.

  • Go to the credentials page via the lefthand sidebar. On the credentials page, click “create credentials”.

  • Choose the “Service Account” option and fill out the form provided. This should generate your credentials.

  • Select your newly created Service Account on the credentials main page.

  • select “keys”, then “add key”, then “create new key”. Pick the key type JSON. The credentials should start to automatically download.

You can now copy and paste the data from the key into your script or (recommended) save it locally as a JSON file.

Quickstart

To instantiate the GoogleSheets class, you can either pass the constructor a dict containing your Google service account credentials or define the environment variable GOOGLE_DRIVE_CREDENTIALS to contain a path to the JSON file containing the dict.

from parsons import GoogleSheets

# First approach: Use API credentials via environmental variables
sheets = GoogleSheets()

# Second approach: Pass API credentials as argument
credential_filename = 'google_drive_service_credentials.json'
credentials = json.load(open(credential_filename))
sheets = GoogleSheets(google_keyfile_dict=credentials)

You can then create/modify/retrieve documents using instance methods:

sheet_id = sheets.create_spreadsheet('Voter Cell Phones')
sheets.append_to_sheet(sheet_id, people_with_cell_phones)
parsons_table = sheets.get_worksheet(sheet_id)

You may also want to share the document with your service or user account.

API

class parsons.google.google_sheets.GoogleSheets(google_keyfile_dict=None, subject=None)[source]

A connector for Google Sheets, handling data import and export.

Args:
google_keyfile_dict: dict

A dictionary of Google Drive API credentials, parsed from JSON provided by the Google Developer Console. Required if env variable GOOGLE_DRIVE_CREDENTIALS is not populated.

subject: string

In order to use account impersonation, pass in the email address of the account to be impersonated as a string.

list_worksheets(spreadsheet_id)[source]

Return a list of worksheets in the spreadsheet.

Args:
spreadsheet_id: str

The ID of the spreadsheet (Tip: Get this from the spreadsheet URL)

Returns:
list

A List of worksheets order by their index

get_worksheet_index(spreadsheet_id, title)[source]

Get the first sheet in a Google spreadsheet with the given title. The title is case sensitive and the index begins with 0.

Args:
spreadsheet_id: str

The ID of the spreadsheet (Tip: Get this from the spreadsheet URL)

title: str

The sheet title

Returns:
str

The sheet index

get_worksheet(spreadsheet_id, worksheet=0)[source]

Create a parsons table from a sheet in a Google spreadsheet, given the sheet index.

Args:
spreadsheet_id: str

The ID of the spreadsheet (Tip: Get this from the spreadsheet URL)

worksheet: str or int

The index or the title of the worksheet. The index begins with 0.

Returns:
Parsons Table

See Parsons Table for output options.

share_spreadsheet(spreadsheet_id, sharee, share_type='user', role='reader', notify=True, notify_message=None, with_link=False)[source]

Share a spreadsheet with a user, group of users, domain and/or the public.

Args:
spreadsheet_id: str

The ID of the spreadsheet (Tip: Get this from the spreadsheet URL)

sharee: str

User or group e-mail address, domain name to share the spreadsheet with. To share publicly, set sharee value to None.

share_type: str

The sharee type. Allowed values are: user, group, domain, anyone.

role: str

The primary role for this user. Allowed values are: owner, writer, reader.

notify: boolean

Whether to send an email to the target user/domain.

email_message: str

The email to be sent if notify kwarg set to True.

with_link: boolean

Whether a link is required for this permission.

get_spreadsheet_permissions(spreadsheet_id)[source]

List the permissioned users and groups for a spreadsheet.

Args:
spreadsheet_id: str

The ID of the spreadsheet (Tip: Get this from the spreadsheet URL)

Returns:
Parsons Table

See Parsons Table for output options.

create_spreadsheet(title, editor_email=None, folder_id=None)[source]

Creates a new Google spreadsheet. Optionally shares the new doc with the given email address. Optionally creates the sheet in a specified folder.

Args:
title: str

The human-readable title of the new spreadsheet

editor_email: str (optional)

Email address which should be given permissions on this spreadsheet. Tip: You may want to share this file with the service account.

folder_id: str (optional)

ID of the Google folder where the spreadsheet should be created. Tip: Get this from the folder URL. Anyone shared on the folder will have access to the spreadsheet.

Returns:
str

The spreadsheet ID

delete_spreadsheet(spreadsheet_id)[source]

Deletes a Google spreadsheet.

Args:
spreadsheet_id: str

The ID of the spreadsheet (Tip: Get this from the spreadsheet URL)

add_sheet(spreadsheet_id, title=None, rows=100, cols=25)[source]

Adds a sheet to a Google spreadsheet.

Args:
spreadsheet_id: str

The ID of the spreadsheet (Tip: Get this from the spreadsheet URL)

rows: int

Number of rows

cols

Number of cols

Returns:
str

The sheet index

append_to_sheet(spreadsheet_id, table, worksheet=0, user_entered_value=False, **kwargs)[source]

Append data from a Parsons table to a Google sheet. Note that the table’s columns are ignored, as we’ll be keeping whatever header row already exists in the Google sheet.

Args:
spreadsheet_id: str

The ID of the spreadsheet (Tip: Get this from the spreadsheet URL)

table: obj

Parsons table

worksheet: str or int

The index or the title of the worksheet. The index begins with 0.

user_entered_value: bool (optional)

If True, will submit cell values as entered (required for entering formulas). Otherwise, values will be entered as strings or numbers only.

paste_data_in_sheet(spreadsheet_id, table, worksheet=0, header=True, startrow=0, startcol=0)[source]

Pastes data from a Parsons table to a Google sheet. Note that this may overwrite presently existing data. This function is useful for adding data to a subsection if an existing sheet that will have other existing data - contrast to overwrite_sheet (which will fully replace any existing data) and append_to_sheet (which sticks the data only after all other existing data).

Args:
spreadsheet_id: str

The ID of the spreadsheet (Tip: Get this from the spreadsheet URL).

table: obj

Parsons table

worksheet: str or int

The index or the title of the worksheet. The index begins with 0.

header: bool

Whether or not the header row gets pasted with the data.

startrow: int

Starting row position of pasted data. Counts from 0.

startcol: int

Starting column position of pasted data. Counts from 0.

overwrite_sheet(spreadsheet_id, table, worksheet=0, user_entered_value=False, **kwargs)[source]

Replace the data in a Google sheet with a Parsons table, using the table’s columns as the first row.

Args:
spreadsheet_id: str

The ID of the spreadsheet (Tip: Get this from the spreadsheet URL)

table: obj

Parsons table

worksheet: str or int

The index or the title of the worksheet. The index begins with 0.

user_entered_value: bool (optional)

If True, will submit cell values as entered (required for entering formulas). Otherwise, values will be entered as strings or numbers only.

format_cells(spreadsheet_id, range, cell_format, worksheet=0)[source]

Format the cells of a worksheet.

Args:
spreadsheet_id: str

The ID of the spreadsheet (Tip: Get this from the spreadsheet URL)

range: str

The cell range to format. E.g. "A2" or "A2:B100"

cell_format: dict

The formatting to apply to the range. Full options are specified in the GoogleSheets API documentation.

worksheet: str or int

The index or the title of the worksheet. The index begins with 0.

Examples

# Set 'A4' cell's text format to bold
gs.format_cells(sheet_id, "A4", {"textFormat": {"bold": True}}, worksheet=0)

# Color the background of 'A2:B2' cell range yellow,
# change horizontal alignment, text color and font size
gs.format_cells.format(sheet_id, "A2:B2", {
    "backgroundColor": {
        "red": 0.0,
        "green": 0.0,
        "blue": 0.0
        },
    "horizontalAlignment": "CENTER",
    "textFormat": {
        "foregroundColor": {
            "red": 1.0,
            "green": 1.0,
            "blue": 0.0
            },
            "fontSize": 12,
            "bold": True
            }
        }, worksheet=0)