moovai 0.0.9

MOOVAI TOOLBOX

This repository contains reusable code to expedite development. To use repository, ensure you are using Python 3.6.

Installation:

To use this package, install using pip:

pip install moovai

Google Cloud:

This folder contains code to handle GCP resources. Prior to using the methods here, you need to be authenticated to GCP. If you are using a service account, and have the key JSON file:

• On Linux or MacOS

export GOOGLE_APPLICATION_CREDENTIALS="[PATH]"

• On Windows:

set GOOGLE_APPLICATION_CREDENTIALS=[PATH]

cloud-storage

Before using the code here, first make sure that you have a bucket on GCS. If you don't, create one.

upload_to_gcs:

Uploads a local file to a folder on google cloud storage. The file is then DELETED locally.

Parameters:

• file: REQUIRED: [STRING] Local file path to upload to google cloud storage.
• bucket_name: REQUIRED: [STRING] Name of your bucket.
• folder: REQUIRED: [STRING] Folder path to where you want your file to be uploaded to on GCS.

returns: GCS URI where the file was uploaded

Sample usage:

from moovai.google_cloud import cloud_storage

file = "/path/to/my/file.txt"
bucket_name = "my_bucket"
folder = "Folder/Subfolder/"

cloud_storage.upload_to_gcs(file, bucket_name, folder)
# returns "gs://my_bucket/Folder/Subfolder/file.txt"

download_file_gcs:

Downloads file from google cloud storage to local disk.

Parameters:

• gcs_uri: REQUIRED. [STRING] URI of file located on google cloud storage to be downloaded

returns: None. GCS file is downloaded locally.

Sample usage:

from moovai.google_cloud import cloud_storage

gcs_uri = "gs://my_bucket/Folder/Subfolder/my_file.txt"

cloud_storage.download_file_gcs(gcs_uri)
# file "my_file.txt" downloaded locally

bigquery

get_schema_from_json:

Takes a schema.json file and converts it into a schema file compatible with BigQuery.

Parameters:

• schema_path: REQUIRED. [STRING] Path to your_schema.json file.

returns: schema to plug into BigQuery upload job.

Sample usage:

from moovai.google_cloud import bigquery

schema_file = "/path/to/my/schema.json"
bigquery.get_schema_from_json(schema_file)

get_schema_from_csv:

Takes a csv file containing your data and extracts the schema from your file. It is recommended to simply use BigQuery's auto-detect schema feature. Use this as a last resort.

Parameters:

• csv_file_path: REQUIRED. [STRING] Path to csv file.

returns: schema to plug into BigQuery upload job.

Sample usage:

from moovai.google_cloud import bigquery

csv_file = "/path/to/my/file.csv"
bigquery.get_schema_from_csv(csv_file)

upload_local_file_to_bq:

Uploads local file to BigQuery. schema_path and schema are optional arguments. They are exclusive of one another, provide only one if you want to.

Parameters:

• file: REQUIRED. [STRING] Path to local CSV file to upload
• dataset_id: REQUIRED. [STRING] Name of your BigQuery dataset.
• table_id: REQUIRED. [STRING] Name of your BigQuery table to query.
• schema_path: OPTIONAL. [STRING] Path to schema.json file.
• schema: OPTIONAL. [STRING] schema compatible with BigQuery.
• overwrite: OPTIONAL. [BOOL] Defaults to False. if set to True, BigQuery will overwrite table, else, it will append new data to table.

returns: None.

Sample usage:

from moovai.google_cloud import bigquery

file = "/path/to/my_file.csv"
dataset_id = "my_dataset_id"
table_id = "my_table_id"
bigquery.upload_local_file_to_bq(file, dataset_id, table_id)

upload_gcs_file_to_bq:

Uploads file from cloud storage to BigQuery. schema_path and schema are optional arguments. They are exclusive of one another, provide only one if you want to.

Parameters:

• gcs_uri: REQUIRED. [STRING] Path to CSV file located on cloud storage to upload to BigQuery.
• dataset_id: REQUIRED. [STRING] Name of your BigQuery dataset.
• table_id: REQUIRED. [STRING] Name of your BigQuery table to query.
• schema_path: OPTIONAL. [STRING] Path to schema.json file.
• schema: OPTIONAL. [STRING] schema compatible with BigQuery.
• overwrite: OPTIONAL. [BOOL] Defaults to False. if set to True, BigQuery will overwrite table, else, it will append new data to table.

returns: None.

Sample usage:

from moovai.google_cloud import bigquery

gcs_uri = "gs://my_bucket/Path/To/my_file.csv"
dataset_id = "my_dataset_id"
table_id = "my_table_id"
bigquery.upload_local_file_to_bq(gcs_uri, dataset_id, table_id)

generate_sql_query:

Generates a SQL query string to use to query a BigQuery table.

Parameters:

• project: REQUIRED. [STRING] Project ID
• dataset_id: REQUIRED. [STRING] Name of your BigQuery dataset.
• table_id: REQUIRED. [STRING] Name of your BigQuery table to query.
• columns: OPTIONAL. [ARRAY] List of column names you want to select.
• conditions: OPTIONAL. [ARRAY] List of conditions to satisfy.

returns: STRING. Standard SQL query string.

Sample usage:

from moovai.google_cloud import bigquery

project = "my_project_id"
dataset_id = "my_dataset_id"
table_id = "my_table_id"
bigquery.generate_sql_query(project, dataset_id, table_id)
#returns "SELECT * FROM `project.dataset_id.table_id`" (return everything from table)

columns = ["col1", "col2"]
conditions = ["Date >= TIMESTAMP('2019-05-01')", "col3 < 3"]

bigquery.generate_sql_query(project, dataset_id, table_id, columns=columns, conditions=conditions)
#returns "SELECT col1, col2 FROM `project.dataset_id.table_id` WHERE Date >= TIMESTAMP('2019-05-01') AND col3 < 3" (returns col1 and col2 that meet the specified conditions)

get_data_from_bq:

Takes a SQL query string (Standard SQL) and returns pandas dataframe

Parameters:

• sql_query: REQUIRED. [STRING] string representing the query you want to make.

returns: pandas Dataframe containg the result of your query.

Sample usage:

from moovai.google_cloud import bigquery

sql_query = "SELECT * FROM `my_project.my_dataset.my_table`"
bigquery.get_data_from_bq(sql_query)