Zoho Expense Python API Docs | dltHub

Zoho Expense is a REST API for managing business expense tracking, reports, receipts, users, currencies, categories, projects and related resources. The REST API base URL is https://www.zohoapis.com/expense/v1 and OAuth 2.0 access tokens (Zoho‑oauthtoken) required in Authorization header..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Zoho Expense data in under 10 minutes.

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What data can I load from Zoho Expense?

Here are some of the endpoints you can load from Zoho Expense:

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How do I authenticate with the Zoho Expense API?

Zoho Expense uses OAuth2. Obtain access and refresh tokens via Zoho's OAuth flow and send requests with header Authorization: "Zoho‑oauthtoken {access_token}". Include X-com-zoho-expense-organizationid header (organization ID) with requests.

1. Get your credentials

1. Sign in to the Zoho API Console (https://api-console.zoho.com/).
2. Create a new client for Zoho Expense and note the Client ID and Client Secret.
3. Add required scopes (e.g., ZohoExpense.orgsettings.READ, ZohoExpense.expense.READ).
4. Use the generated authorization URL to obtain a grant code from the user.
5. Exchange the grant code for access and refresh tokens at Zoho's OAuth token endpoint.
6. Store the tokens and the organization ID for use in API calls.

2. Add them to .dlt/secrets.toml

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[sources.zoho_expense_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
access_token = "your_access_token"
refresh_token = "your_refresh_token"
organization_id = "your_organization_id"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Zoho Expense API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python zoho_expense_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline zoho_expense_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset zoho_expense_data
The duckdb destination used duckdb:/zoho_expense.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline zoho_expense_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads expensereports and reports/expensedetails from the Zoho Expense API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def zoho_expense_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.zohoapis.com/expense/v1",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "expensereports", "endpoint": {"path": "expensereports", "data_selector": "expensereports"}},
            {"name": "expenses", "endpoint": {"path": "reports/expensedetails", "data_selector": "expenses"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="zoho_expense_pipeline",
        destination="duckdb",
        dataset_name="zoho_expense_data",
    )
    load_info = pipeline.run(zoho_expense_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("zoho_expense_pipeline").dataset()
sessions_df = data.expensereports.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM zoho_expense_data.expensereports LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("zoho_expense_pipeline").dataset()
data.expensereports.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Zoho Expense data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

Ensure Authorization header is set to: Authorization: Zoho‑oauthtoken {access_token} and X‑com‑zoho‑expense‑organizationid header contains a valid organization_id. Expired tokens return 401; use the refresh token flow to obtain a new access token.

Rate limits and throttling

Zoho Expense enforces 100 requests/minute per organization and daily limits per plan (see docs). Exceeding limits returns HTTP 429 with JSON like { "code": 45, "message": "The API call for this organization has exceeded the maximum call rate limit..." } or code 44 for per‑minute blocks.

Pagination

Most list endpoints accept page and per_page query parameters (default page=1, per_page=200). Use these to iterate pages; responses include resource arrays under the documented data selector key.

Common error responses

API error responses are JSON with code and message fields. Examples:

• 429: { "code": 45, "message": "The API call for this organization has exceeded the maximum call rate limit of 1000." }
• 429: { "code": 44, "message": "For security reasons your account has been blocked as you have exceeded the maximum number of requests per minute..." }
• 401: invalid/expired token responses; check Authorization header.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack