> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tryprofound.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Headline score and the daily series, together

> Fetch both the period score and the daily line for one asset, and understand why they're different calls.

A real dashboard tile shows two numbers: the **headline period score** (one
big number) and the **daily series** (the line). These come from **two
separate calls** to the same endpoint. This recipe runs both side-by-side
and explains how each is extracted and why you can't derive one from the
other.

<Warning>
  **Don't average daily rows to derive a period score** — they're computed
  differently. See [Conventions & gotchas](/cookbook/setup/conventions#don%E2%80%99t-average-daily-rows-to-get-a-period-score).
</Warning>

## How this example works

1. **Headline call** has no `date` dimension. The API returns a single
   traffic-weighted score for the whole window.
2. **Series call** adds `date` to dimensions and sets `date_interval`. The
   API returns one row per day, each containing that day's unweighted rate.
3. **Don't average the daily values to compute the headline.** Days with
   more prompts count more in the headline; the daily mean treats every day
   equally. The two are computed differently on purpose.

<RequestExample>
  ```python Python theme={null}
  import os
  from profound import Profound

  client = Profound(api_key=os.environ["PROFOUND_API_KEY"])

  # What to fetch — replace with your own values.
  CATEGORY_NAME = "<your-category-name>"
  ASSET_NAME    = "<your-asset-name>"
  START_DATE    = "2026-05-05"
  END_DATE      = "2026-05-12"   # exclusive — returns data through 2026-05-11


  def get_headline_score(category_id, asset_name, start_date, end_date):
      """Traffic-weighted period score (no date dim)."""
      res = client.reports.visibility(
          category_id=category_id,
          start_date=start_date,
          end_date=end_date,
          metrics=["visibility_score"],
          filters=[{"field": "asset_name", "operator": "is", "value": asset_name}],
      )
      order = res.info.query["metrics"]
      return res.data[0].metrics[order.index("visibility_score")]


  def get_daily_series(category_id, asset_name, start_date, end_date):
      """One (date, score) point per day — for the line chart."""
      res = client.reports.visibility(
          category_id=category_id,
          start_date=start_date,
          end_date=end_date,
          metrics=["visibility_score"],
          dimensions=["date"],
          date_interval="day",
          filters=[{"field": "asset_name", "operator": "is", "value": asset_name}],
      )
      m_order = res.info.query["metrics"]
      d_order = res.info.query["dimensions"]
      i_score = m_order.index("visibility_score")
      i_date  = d_order.index("date")
      points = [
          (row.dimensions[i_date], row.metrics[i_score])
          for row in res.data
      ]
      return sorted(points, key=lambda p: p[0])


  # Helpers — translate human-readable names into the IDs the report API needs.

  def find_category_id(name):
      """Return the UUID of the category whose name matches (case-insensitive)."""
      for c in client.organizations.categories.list():
          if c.name.lower() == name.lower():
              return c.id
      raise ValueError(f"No category named {name!r}")


  def find_asset_name(category_id, name):
      """Return the canonical asset name (case-insensitive) inside the category."""
      for a in client.organizations.categories.assets(category_id):
          if a.name.lower() == name.lower():
              return a.name
      raise ValueError(f"No asset named {name!r} in this category")


  # Resolve names → IDs, then run both calls.
  category_id = find_category_id(CATEGORY_NAME)
  asset_name  = find_asset_name(category_id, ASSET_NAME)

  headline_score = get_headline_score(category_id, asset_name, START_DATE, END_DATE)
  daily          = get_daily_series(category_id, asset_name, START_DATE, END_DATE)

  print(f"Headline: {headline_score:.1%}\n")
  print("Daily:")
  for date, score in daily:
      print(f"  {date}  {score:.1%}")
  ```

  ```bash curl theme={null}
  export PROFOUND_API_KEY=your_api_key_here

  # 1) Headline (no date dimension)
  curl -X POST "https://api.tryprofound.com/v1/reports/visibility" \
    -H "X-API-Key: $PROFOUND_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "category_id": "your-category-uuid",
      "start_date": "2026-05-05",
      "end_date": "2026-05-12",
      "metrics": ["visibility_score"],
      "filters": [{"field": "asset_name", "operator": "is", "value": "<your-asset-name>"}]
    }'

  # 2) Daily series (add date dimension + date_interval)
  curl -X POST "https://api.tryprofound.com/v1/reports/visibility" \
    -H "X-API-Key: $PROFOUND_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "category_id": "your-category-uuid",
      "start_date": "2026-05-05",
      "end_date": "2026-05-12",
      "metrics": ["visibility_score"],
      "dimensions": ["date"],
      "date_interval": "day",
      "filters": [{"field": "asset_name", "operator": "is", "value": "<your-asset-name>"}]
    }'
  ```
</RequestExample>

## How the two responses differ

|                 | Headline call                      | Series call                |
| --------------- | ---------------------------------- | -------------------------- |
| `dimensions`    | `[]`                               | `["date"]`                 |
| `date_interval` | not sent                           | `"day"` (or week/month)    |
| Rows returned   | 1                                  | N (one per bucket)         |
| Score per row   | Traffic-weighted across the window | Unweighted within that day |
| Used for        | The big KPI number                 | The line on the chart      |

## Why you can't average the daily values

The headline weights days by their prompt volume — a day with 10,000
prompts moves the headline more than a day with 100. Averaging daily rates
treats those two days as equal. For most assets the gap is 1-3 percentage
points; for assets with bursty traffic it can be more. Always use the
headline call for the headline.