Open Data Blend for Python

Open Data Blend for Python is the fastest way to get data from the Open Data Blend Dataset API. It is a lightweight, easy-to-use extract and load (EL) tool.

The get_data function will only download a data file if the requested version does not already exist in the local cache. It also saves a copy of the dataset metadata (datapackage.json) for future use. You can learn about how we version our datasets in the Open Data Blend Docs.

After downloading the data and metadata files, get_data returns an object called Output which contains the local paths of the files. From there, you can load the data in Pandas, Koalas, or something similar to begin your analysis or feature engineering.

Installation

Install the latest version of opendatablend from PyPI:

pip install opendatablend

Usage Examples

The following examples require the pandas and pyarrow packages to be installed:

pip install pandas
pip install pyarrow

Making Public API Requests

Note: Public API requests are limited per month.

Get The Data

import opendatablend as odb
import pandas as pd

dataset_path = 'https://packages.opendatablend.io/v1/open-data-blend-road-safety/datapackage.json'

# Specify the resource name of the data file. In this example, the 'date' data file will be requested in .parquet format.
resoure_name = 'date-parquet'

# Get the data and store the output object
output = odb.get_data(dataset_path, resource_name)

# Print the file locations
print(output.data_file_name)
print(output.metadata_file_name)

Use The Data

# Read a subset of the columns into a dataframe
df_date = pd.read_parquet(output.data_file_name, columns=['drv_date_key', 'drv_date', 'drv_month_name', 'drv_month_number', 'drv_quarter_name', 'drv_quarter_number', 'drv_year'])

# Check the contents of the dataframe
df_date

Making Authenticated API Requests

Get The Data

import opendatablend as odb
import pandas as pd

dataset_path = 'https://packages.opendatablend.io/v1/open-data-blend-road-safety/datapackage.json'
access_key = '<ACCESS_KEY_HERE>'

# Specify the resource name of the data file. In this example, the 'date' data file will be requested in .parquet format.
resoure_name = 'date-parquet'

# Get the data and store the output object
output = odb.get_data(dataset_path, resource_name, access_key=access_key)

# Print the file locations
print(output.data_file_name)
print(output.metadata_file_name)

Use The Data

# Read a subset of the columns into a dataframe
df_date = pd.read_parquet(output.data_file_name, columns=['drv_date_key', 'drv_date', 'drv_month_name', 'drv_month_number', 'drv_quarter_name', 'drv_quarter_number', 'drv_year'])

# Check the contents of the dataframe
df_date

Additional Examples

For more in-depth examples, see the examples folder.