Link Search Menu Expand Document

GET checks into CSV files

Last modified on 11-Jun-24

This example Python script uses Pandas and the Soda Cloud API to retrieve information from your Soda Cloud account and prepare two CSV files:

  • one file contains information about all the datasets in your Soda Cloud account
  • one file contains a list of all the checks in your Soda Cloud account, including a column attributes you may have added to checks

✖️    Requires Soda Core Scientific (included in a Soda Agent)
✖️    Supported in Soda Core
✔️    Supported in Soda Cloud + Soda Library
✔️    Supported in Soda Cloud + Soda Agent


  • You have set up a Soda Cloud account and have connected to a data source via Soda Library or a Soda-hosted or self-hosted Soda Agent.
  • You have prepared at least one data quality check and have run at least one Soda scan in Soda Cloud.
  • You have Admin privileges in your Soda Cloud account which gives you permission to access to the account and all its checks and datasets via the API.

Set up and run

  1. Best practice dictates that you use a virtual environment to execute the script. Use the following commands to create and activate a venv, then upgrade pip and install pandas.
    python -m venv .sodaapi 
    source .sodaapi/bin/activate 
    pip install --upgrade pip  
    pip install requests pandas


    python -m venv .sodaapi
    source .sodaapi\Scripts\activate
    python.exe -m pip install --upgrade pip
    python.exe -m pip install requests pandas
  2. Create a new Python file called, then copy the script below into the file.
  3. In the # Soda Cloud account section of the file, replace the placeholder values with your own Soda Cloud URL and API key values. See Generate API keys for instructions on how to create new keys.
  4. If you wish, change the value of file_type in the script to json if you prefer that type of output over CSV.
  5. Run the script! It saves the new files to the same directory as your file.


    Starting collection of all datasets/checks...
    Fetching all datasets on page: 0
    The following csv file was generated successfully: all_datasets
    Fetching all checks on page 0
    The following csv file was generated successfully: all_checks

API to CSV script

import requests
import time
import sys
import pandas as pd
from datetime import datetime

# Soda Cloud account 

soda_cloud_url = '' # (US Customers) or (EU Customers)
soda_apikey = 'XXX' # Soda Cloud API key ID 
soda_apikey_secret = 'XXX' # Soda Cloud API key secret

# The file names and file type to which you want to save the output (CSV or JSON)

datasets_filename = 'all_datasets'
checks_filename = 'all_checks'
file_type = 'csv'

# ------------------------------------------------------------------------------

if file_type not in ['csv', 'json']:
        print("You must input the correct file_type: csv or json. You input "+file_type)

print("Starting collection of all datasets/checks...")

datasets = []
checks = []

response_datasets = requests.get(
        soda_cloud_url + '/api/v1/datasets?page=0', 
        auth=(soda_apikey , soda_apikey_secret)

if response_datasets.status_code == 401 or response_datasets.status_code == 403:
    print("Unauthorized or Forbidden access. Please check your API keys and/or permissions in Soda.")

# Collect all datasets

if response_datasets.status_code == 200:
    dataset_pages = response_datasets.json().get('totalPages')
    i = 0
    while i < dataset_pages:
        dq_datasets = requests.get(
        soda_cloud_url + '/api/v1/datasets?page='+str(i), 
        auth=(soda_apikey , soda_apikey_secret))
        if dq_datasets.status_code == 200:
            print("Fetching all datasets on page: "+str(i))
            list = dq_datasets.json().get("content")
            i += 1
        elif dq_datasets.status_code == 429:
            print("API Rate Limit reached when fetching datasets on page: " +str(i)+ ". Pausing for 30 seconds.")
            # Retry fetching the same page
            print("Error fetching datasets on page "+str(i)+". Status code:", dq_datasets.status_code)
    print("Error fetching initial datasets. Status code:", response_datasets.status_code)

df_datasets = pd.DataFrame(datasets)
current_time ='%Y-%m-%d %I:%M:%S %p')
df_datasets.insert(0, 'record_created', current_time)

# Create datasets file

if file_type == 'csv':
    df_datasets.to_csv(datasets_filename +'.csv', index=False)
if file_type == 'json':
    df_datasets.to_json(datasets_filename +'.json', orient='records', date_format='iso', lines=False)

print(f"The following {file_type} file was generated successfully: {datasets_filename}")

# Fetch all checks

response_checks = requests.get(
    soda_cloud_url + '/api/v1/checks?size=100', 
    auth=(soda_apikey , soda_apikey_secret))

if response_checks.status_code == 200:
    check_pages = response_checks.json().get('totalPages')
    i = 0
    while i < check_pages:
        dq_checks = requests.get(
            soda_cloud_url + '/api/v1/checks?size=100&page='+str(i), 
            auth=(soda_apikey , soda_apikey_secret))
        if dq_checks.status_code == 200:
            print("Fetching all checks on page "+str(i))
            check_list = dq_checks.json().get("content")
            i += 1 
        elif dq_checks.status_code == 429:
            print("API Rate Limit reached when fetching checks on page: " +str(i)+ ". Pausing for 30 seconds.")
            # Retry fetching the same page
            print("Error fetching checks on page "+str(i)+". Status code:", dq_checks.status_code)
    print("Error fetching initial checks. Status code:", response_checks.status_code)

df_checks = pd.DataFrame(checks)

# Clean up dataframe

df_checks.insert(0, 'record_created', current_time)
df_checks['dataset_id'] = df_checks['datasets'].apply(lambda x: x[0]['id'] if x else None)
df_checks['dataset_name'] = df_checks['datasets'].apply(lambda x: x[0]['name'] if x else None)
df_checks['dataset_url'] = df_checks['datasets'].apply(lambda x: x[0]['cloudUrl'] if x else None)
df_checks['lastCheckResultValue'] = df_checks['lastCheckResultValue'].apply(lambda x: x.get('value') if isinstance(x, dict) and 'value' in x else (x.get('valueLabel') if isinstance(x, dict) and 'valueLabel' in x else x))
df_checks['attributes'] = df_checks['attributes'].fillna({})
df_checks['check_owner'] = df_checks['owner'].apply(lambda x: x.get('firstName', '') + ' ' + x.get('lastName', '') if isinstance(x, dict) else '')
df_checks['owner_email'] = df_checks['owner'].apply(lambda x: x.get('email', '') if isinstance(x, dict) else '')

# Rename columns

df_checks.rename(columns={'id': 'check_id'}, inplace=True)
df_checks.rename(columns={'name': 'check_name'}, inplace=True)
df_checks.rename(columns={'evaluationStatus': 'check_status'}, inplace=True)
df_checks.rename(columns={'definition': 'check_definition'}, inplace=True)
df_checks.rename(columns={'cloudUrl': 'check_url'}, inplace=True)

# Add attribute names as separate columns with attribute values

for index, row in df_checks.iterrows():
    attributes_dict = row['attributes']
    for key in attributes_dict:
        column_name = key
        column_value = attributes_dict[key][index, column_name] = column_value

# Drop original columns

df_checks = df_checks.drop(columns=['attributes'])
df_checks = df_checks.drop(columns=['datasets'])
df_checks = df_checks.drop(columns=['owner'])

# Create checks file

if file_type == 'json':
    df_checks.to_json(checks_filename +'.json', orient='records', date_format='iso', lines=False)

if file_type == 'csv':
    df_checks.to_csv(checks_filename +'.csv', index=False)

print(f"The following {file_type} file was generated successfully: {checks_filename}")

Go further

Was this documentation helpful?

What could we do to improve this page?

Documentation always applies to the latest version of Soda products
Last modified on 11-Jun-24