| Name | shipyard-bigquery JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | None |
| Summary | A local client for connecting to and working with Google BigQuery |
| upload_time | 2024-03-21 19:55:15 |
| maintainer | None |
| docs_url | None |
| author | wrp801 |
| requires_python | <4.0,>=3.9 |
| license | Apache 2.0 |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# shipyard-bigquery
The `shipyard-bigquery` Python package is a local client for connecting to BigQuery to perform common operations such as loading and unloading data.
## Installation
```bash
python3 -m pip install shipyard-bigquery
```
## Usage
To initialize the client, provide the service account (in JSON format) as a string:
```python
import os
from shipyard_bigquery import BigQueryClient
service_account = os.getenv('GOOGLE_APPLICATION_CREDENTIALS') # in this case, service account is stored as an environment variable
client = BigQueryClient(service_account)
client.connect()
```
#### Upload a file to a table in BigQuery
The upload method allows for the loading of a local CSV file to BigQuery and supports two different types of upload: `overwrite` and `append`.
```python
client.upload(file = "my_file.csv", dataset = "my_dataset", table = "my_table", upload_type = 'overwrite')
```
Additionally, a the schema of the file to be loaded can be decalred ahead of time, otherwise they will be infered
```python
schema = '[{"name": "string_col", "type": "string"}, {"name": "char_col", "type": "string"}, {"name": "int_col", "type": "Int64"}, {"name": "float_col", "type": "Float64"}, {"name": "bool_col", "type": "Bool"}, {"name": "date_col", "type": "Date"},{"name": "datetime_col", "type": "Datetime"}]'
client.upload(file = "my_file.csv", dataset = "my_dataset", table = "my_table", upload_type = 'overwrite',schema = schema)
```
#### Fetch Query Results
The fetch method allows for the downloading of a SQL query to a pandas dataframe
```python
df = client.fetch('select * from my_table limit 10')
```
#### Execute Remote Queries
The execute method allows for you to execute queries without return values in BigQuery. Common operations would be dropping tables, deleting tables, or altering tables
```python
client.execute_query('drop table if exists my_table')
```
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"description": "# shipyard-bigquery\n\nThe `shipyard-bigquery` Python package is a local client for connecting to BigQuery to perform common operations such as loading and unloading data.\n\n## Installation \n\n```bash\npython3 -m pip install shipyard-bigquery\n```\n\n## Usage \n\nTo initialize the client, provide the service account (in JSON format) as a string:\n```python\nimport os\nfrom shipyard_bigquery import BigQueryClient \nservice_account = os.getenv('GOOGLE_APPLICATION_CREDENTIALS') # in this case, service account is stored as an environment variable\nclient = BigQueryClient(service_account)\nclient.connect()\n```\n\n#### Upload a file to a table in BigQuery\n\n\nThe upload method allows for the loading of a local CSV file to BigQuery and supports two different types of upload: `overwrite` and `append`. \n```python\nclient.upload(file = \"my_file.csv\", dataset = \"my_dataset\", table = \"my_table\", upload_type = 'overwrite')\n```\n\nAdditionally, a the schema of the file to be loaded can be decalred ahead of time, otherwise they will be infered\n```python \nschema = '[{\"name\": \"string_col\", \"type\": \"string\"}, {\"name\": \"char_col\", \"type\": \"string\"}, {\"name\": \"int_col\", \"type\": \"Int64\"}, {\"name\": \"float_col\", \"type\": \"Float64\"}, {\"name\": \"bool_col\", \"type\": \"Bool\"}, {\"name\": \"date_col\", \"type\": \"Date\"},{\"name\": \"datetime_col\", \"type\": \"Datetime\"}]'\nclient.upload(file = \"my_file.csv\", dataset = \"my_dataset\", table = \"my_table\", upload_type = 'overwrite',schema = schema)\n```\n\n#### Fetch Query Results\nThe fetch method allows for the downloading of a SQL query to a pandas dataframe\n```python \ndf = client.fetch('select * from my_table limit 10')\n```\n\n#### Execute Remote Queries\nThe execute method allows for you to execute queries without return values in BigQuery. Common operations would be dropping tables, deleting tables, or altering tables\n\n```python \nclient.execute_query('drop table if exists my_table')\n```\n\n\n\n\n",
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