## Get time series distribution of HTTP authentication requests by dimension.

`radar.leaked_credentials.timeseries_groups_v2(Literal["COMPROMISED", "BOT_CLASS"]dimension, LeakedCredentialTimeseriesGroupsV2Params**kwargs)  -> LeakedCredentialTimeseriesGroupsV2Response`

**get** `/radar/leaked_credential_checks/timeseries_groups/{dimension}`

Retrieves the distribution of HTTP authentication requests, grouped by the specified dimension over time.

### Parameters

- `dimension: Literal["COMPROMISED", "BOT_CLASS"]`

  Specifies the attribute by which to group the results.

  - `"COMPROMISED"`

  - `"BOT_CLASS"`

- `agg_interval: Optional[Literal["15m", "1h", "1d", "1w"]]`

  Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://developers.cloudflare.com/radar/concepts/aggregation-intervals/).

  - `"15m"`

  - `"1h"`

  - `"1d"`

  - `"1w"`

- `asn: Optional[SequenceNotStr[str]]`

  Filters results by Autonomous System. Specify one or more Autonomous System Numbers (ASNs) as a comma-separated list. Prefix with `-` to exclude ASNs from results. For example, `-174, 3356` excludes results from AS174, but includes results from AS3356.

- `bot_class: Optional[List[Literal["LIKELY_AUTOMATED", "LIKELY_HUMAN"]]]`

  Filters results by bot class. Refer to [Bot classes](https://developers.cloudflare.com/radar/concepts/bot-classes/).

  - `"LIKELY_AUTOMATED"`

  - `"LIKELY_HUMAN"`

- `check_result: Optional[List[Literal["CLEAN", "USERNAME_LEAKED", "USERNAME_PASSWORD_SIMILAR", 2 more]]]`

  Filters results by leaked credential check result.

  - `"CLEAN"`

  - `"USERNAME_LEAKED"`

  - `"USERNAME_PASSWORD_SIMILAR"`

  - `"USERNAME_AND_PASSWORD_LEAKED"`

  - `"PASSWORD_LEAKED"`

- `compromised: Optional[List[Literal["CLEAN", "COMPROMISED"]]]`

  Filters results by compromised credential status (clean vs. compromised).

  - `"CLEAN"`

  - `"COMPROMISED"`

- `continent: Optional[SequenceNotStr[str]]`

  Filters results by continent. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude continents from results. For example, `-EU,NA` excludes results from EU, but includes results from NA.

- `date_end: Optional[SequenceNotStr[Union[str, datetime]]]`

  End of the date range (inclusive).

- `date_range: Optional[SequenceNotStr[str]]`

  Filters results by date range. For example, use `7d` and `7dcontrol` to compare this week with the previous week. Use this parameter or set specific start and end dates (`dateStart` and `dateEnd` parameters).

- `date_start: Optional[SequenceNotStr[Union[str, datetime]]]`

  Start of the date range.

- `format: Optional[Literal["JSON", "CSV"]]`

  Format in which results will be returned.

  - `"JSON"`

  - `"CSV"`

- `limit_per_group: Optional[int]`

  Limits the number of objects per group to the top items within the specified time range. When item count exceeds the limit, extra items appear grouped under an "other" category.

- `location: Optional[SequenceNotStr[str]]`

  Filters results by location. Specify a comma-separated list of alpha-2 codes. Prefix with `-` to exclude locations from results. For example, `-US,PT` excludes results from the US, but includes results from PT.

- `name: Optional[SequenceNotStr[str]]`

  Array of names used to label the series in the response.

- `normalization: Optional[Literal["PERCENTAGE_CHANGE", "MIN0_MAX"]]`

  Normalization method applied to the results. Refer to [Normalization methods](https://developers.cloudflare.com/radar/concepts/normalization/).

  - `"PERCENTAGE_CHANGE"`

  - `"MIN0_MAX"`

### Returns

- `class LeakedCredentialTimeseriesGroupsV2Response: …`

  - `meta: Meta`

    Metadata for the results.

    - `agg_interval: Literal["FIFTEEN_MINUTES", "ONE_HOUR", "ONE_DAY", 2 more]`

      Aggregation interval of the results (e.g., in 15 minutes or 1 hour intervals). Refer to [Aggregation intervals](https://developers.cloudflare.com/radar/concepts/aggregation-intervals/).

      - `"FIFTEEN_MINUTES"`

      - `"ONE_HOUR"`

      - `"ONE_DAY"`

      - `"ONE_WEEK"`

      - `"ONE_MONTH"`

    - `confidence_info: MetaConfidenceInfo`

      - `annotations: List[MetaConfidenceInfoAnnotation]`

        - `data_source: Literal["ALL", "AI_BOTS", "AI_GATEWAY", 22 more]`

          Data source for annotations.

          - `"ALL"`

          - `"AI_BOTS"`

          - `"AI_GATEWAY"`

          - `"BGP"`

          - `"BOTS"`

          - `"CONNECTION_ANOMALY"`

          - `"CT"`

          - `"DNS"`

          - `"DNS_MAGNITUDE"`

          - `"DNS_AS112"`

          - `"DOS"`

          - `"EMAIL_ROUTING"`

          - `"EMAIL_SECURITY"`

          - `"FW"`

          - `"FW_PG"`

          - `"HTTP"`

          - `"HTTP_CONTROL"`

          - `"HTTP_CRAWLER_REFERER"`

          - `"HTTP_ORIGINS"`

          - `"IQI"`

          - `"LEAKED_CREDENTIALS"`

          - `"NET"`

          - `"ROBOTS_TXT"`

          - `"SPEED"`

          - `"WORKERS_AI"`

        - `description: str`

        - `end_date: datetime`

        - `event_type: Literal["EVENT", "GENERAL", "OUTAGE", 3 more]`

          Event type for annotations.

          - `"EVENT"`

          - `"GENERAL"`

          - `"OUTAGE"`

          - `"PARTIAL_PROJECTION"`

          - `"PIPELINE"`

          - `"TRAFFIC_ANOMALY"`

        - `is_instantaneous: bool`

          Whether event is a single point in time or a time range.

        - `linked_url: str`

        - `start_date: datetime`

      - `level: int`

        Provides an indication of how much confidence Cloudflare has in the data.

    - `date_range: List[MetaDateRange]`

      - `end_time: datetime`

        Adjusted end of date range.

      - `start_time: datetime`

        Adjusted start of date range.

    - `last_updated: datetime`

      Timestamp of the last dataset update.

    - `normalization: Literal["PERCENTAGE", "MIN0_MAX", "MIN_MAX", 5 more]`

      Normalization method applied to the results. Refer to [Normalization methods](https://developers.cloudflare.com/radar/concepts/normalization/).

      - `"PERCENTAGE"`

      - `"MIN0_MAX"`

      - `"MIN_MAX"`

      - `"RAW_VALUES"`

      - `"PERCENTAGE_CHANGE"`

      - `"ROLLING_AVERAGE"`

      - `"OVERLAPPED_PERCENTAGE"`

      - `"RATIO"`

    - `units: List[MetaUnit]`

      Measurement units for the results.

      - `name: str`

      - `value: str`

  - `serie_0: Serie0`

    - `timestamps: List[datetime]`

### Example

```python
import os
from cloudflare import Cloudflare

client = Cloudflare(
    api_token=os.environ.get("CLOUDFLARE_API_TOKEN"),  # This is the default and can be omitted
)
response = client.radar.leaked_credentials.timeseries_groups_v2(
    dimension="COMPROMISED",
)
print(response.meta)
```

#### Response

```json
{
  "result": {
    "meta": {
      "aggInterval": "FIFTEEN_MINUTES",
      "confidenceInfo": {
        "annotations": [
          {
            "dataSource": "ALL",
            "description": "Cable cut in Tonga",
            "endDate": "2019-12-27T18:11:19.117Z",
            "eventType": "EVENT",
            "isInstantaneous": true,
            "linkedUrl": "https://example.com",
            "startDate": "2019-12-27T18:11:19.117Z"
          }
        ],
        "level": 0
      },
      "dateRange": [
        {
          "endTime": "2022-09-17T10:22:57.555Z",
          "startTime": "2022-09-16T10:22:57.555Z"
        }
      ],
      "lastUpdated": "2019-12-27T18:11:19.117Z",
      "normalization": "PERCENTAGE",
      "units": [
        {
          "name": "*",
          "value": "requests"
        }
      ]
    },
    "serie_0": {
      "timestamps": [
        "2023-08-08T10:15:00Z"
      ]
    }
  },
  "success": true
}
```