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Quick start
Large CSV data export
For large datasets that take a long time to export via the UI, Trellis provides an API export feature, allowing you to access the full processed dataset efficiently. You can also use this functionality as a foundation to integrate Trellis with your data store.
In this step, we are defining a function to get all the rows in the transformation table as a Json object.
Python
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def get_all_transform_results(transform_id, limit=250):
base_url = "https://api.runtrellis.com/v1/transforms/{}/results"
url = base_url.format(transform_id)
headers = {
"Authorization": YOUR_API_KEY,
"Content-Type": "application/json"
}
offset = 0
all_results = []
while True:
print("Processing batch", offset)
payload = {}
querystring = {"limit":limit,"offset":offset}
response = requests.post(url, json=payload, headers=headers, params=querystring)
if response.status_code == 200:
data = response.json()
results = data.get('data', [])
all_results.extend(results)
# Check if we've received fewer results than the limit
if len(results) < limit:
break
offset += limit
else:
print(f"Error: {response.status_code}")
print(response.text)
break
return all_results, data.get('metadata', {})
In this section, we’re mapping the Json object to the data frame format which matches the table view in the UI.
Python
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import pandas as pd
def process_results_to_csv(results, metadata):
# Create op_id to name mapping
op_mapping = {
col['id']: col['name']
for col in metadata['column_definitions']
}
# Convert results to DataFrame
df = pd.DataFrame(results)
# Rename columns using mapping
renamed_columns = {
col: op_mapping.get(col, col)
for col in df.columns
}
df = df.rename(columns=renamed_columns)
return df
Python
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# Usage
from datetime import datetime
YOUR_TRANSFORMATION_ID = "YOUR_TRANSFORMATION_ID"
YOUR_API_KEY = "YOUR_API_KEY"
results, metadata = get_all_transform_results(YOUR_TRANSFORMATION_ID)
print(f"Total results retrieved: {len(results)}")
# Convert to CSV
df = process_results_to_csv(results, metadata)
current_date = datetime.now().strftime('%Y_%m_%d')
df.to_csv(f"trellis_transform_{transform_id}_results_{current_date}.csv", index=False)print(f"Results saved to trellis_transform_results.csv")
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Responses are generated using AI and may contain mistakes.