redivis 0.2.2

Redivis python client library

redivis-python

Redivis client library for python!

Connect data on Redivis to the Python scientific stack, and script your data management tasks.

Beta disclaimer

This library is currently in beta testing. Many of the interfaces are nearing stability, but there may still be backwards-incompatible changes in the future.

Please report any issues or feature requests in the issue tracker.

Comprehensive documentation for this package is available at https://apidocs.redivis.com/client-libraries/redivis-python

Installation

pip install redivis

Authentication

If you are using this library within a Redivis notebook, you'll automatically be authenticated; you can ignore this section.

In order to authenticate with your Redivis account, you must first generate an API Token.

This API token must then be set as the REDIVIS_API_TOKEN environment variable before running your script. You can set the variable for your current session by running the following in your terminal:

export REDIVIS_API_TOKEN=YOUR_TOKEN

Interface

import redivis

query = redivis.query("""
    SELECT * FROM demo.cms_2014_medicare_data.home_health_agencies 
    LIMIT 10
""")

df = query.to_dataframe();

When referencing datasets, projects, and tables on Redivis, you should be familiar with the resource reference syntax.

Full reference for this package is available at https://apidocs.redivis.com/client-libraries/redivis-python/reference

Examples

Querying data

Execute a query

import redivis

# Perform a query on the Demo CMS Medicare data. Table at https://redivis.com/demo/datasets/1754/tables
query = redivis.query("""
    SELECT * FROM demo.cms_2014_medicare_data.home_health_agencies 
    WHERE state = 'CA'
""")

for row in query.list_rows():
    print(row['agency_name'])

# We can also use data frames
df = query.to_dataframe();
print(df)

Execute a scoped query

import redivis

# Perform a query on the Demo CMS Medicare data. Table at https://redivis.com/demo/datasets/1754/tablesd

# We don't need to include fully-qualified table names if we scope our query to the appropriate dataset or project

query = (
    redivis
    .organization("Demo")
    .dataset("CMS 2014 Medicare Data")
    .query("""
        SELECT provider_name, average_total_payments 
        FROM nursing_facilities
        INNER JOIN outpatient_charges USING (provider_id)
        WHERE state = 'CA'
    """)
)

for row in query.list_rows():
    print(row.agency_name)

# We can also use data frames
df = query.to_dataframe();
print(df)

Reading table data

table = (
    redivis
    .organization("Demo")
    .dataset("CMS 2014 Medicare Data")
    .table("Hospice providers")

# We can specify an optional limit argument to only load some of the records
for row in table.list_rows(limit=100):
    print(row)

df = table.to_dataframe(limit=100)
print(df)

Uploading data

Create a new dataset

import redivis

dataset = redivis.user("your-username").dataset("some dataset")

# Could also create a dataset under an organization:
# dataset = redivis.organization("Demo organization").dataset("some dataset")


# public_access_level can be one of ('none', 'overview', 'metadata', 'sample', 'data')
dataset.create(public_access_level="overview")

Create a table and upload data

import redivis

dataset = redivis.user("your-username").dataset("some dataset")

# Create a table on the dataset. Datasets may have multiple tables
# Merge strategy (append or replace) will affect whether future updates
#    are appended to or replace the existing table. This can also be modified at a later point.

table = (
    dataset
    .table("Table name")
    .create(description="Some description", upload_merge_strategy="replace")
)

# Upload a file to the table. You can upload multiple files to any table, and they'll be appended together

with open("path/to/file", "rb") as f:
    table.create_upload(name="tiny.csv", type="delimited", data=f)

Release a new version

import redivis

dataset = redivis.organization("Demo").dataset("some dataset")
dataset.release()

Update an existing dataset

import redivis

dataset = redivis.user("your-username").dataset("some dataset")

# dataset.create_next_version will throw an error if a "next" version already exists,
# unless the ignore_if_exists argument is provided
dataset = dataset.create_next_version(ignore_if_exists=True)

# Upload new data to your table
with open("tiny.csv", "rb") as f:
    dataset.table("Table name").upload(name="tiny.csv", type="delimited", data=f)

dataset.release()

Contributing

Please report any issues or feature requests in the issue tracker — your feedback is much appreciated!