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articles/cognitive-services/Anomaly-Detector/includes/quickstarts/anomaly-detector-client-library-python.md

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### Understanding your results
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In our code above, we call the Anomaly Detector API twice. The first call checks for trend change points across our sample data series with the [`detect_change_point`](/python/api/azure-ai-anomalydetector/azure.ai.anomalydetector.operations.anomalydetectorclientoperationsmixin?view=azure-python-preview#azure-ai-anomalydetector-operations-anomalydetectorclientoperationsmixin-detect-change-point&preserve-view=true) method. This call returns a [`ChangePointDetectResponse`](/python/api/azure-ai-anomalydetector/azure.ai.anomalydetector.models.changepointdetectresponse?view=azure-python-preview&preserve-view=true) that we stored in a variable we named `change_point_request`. We then iterate through the response's `is_change_point` list, and print the index of any values with a boolean of `true`.
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In our code above, we call the Anomaly Detector API twice. The first call checks for trend change points across our sample data series with the `detect_change_point` method. This call returns a `ChangePointDetectResponse` that we stored in a variable we named `change_point_request`. We then iterate through the response's `is_change_point` list, and print the index of any values with a boolean of `true`.
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The second call checks the entire sample data series for anomalies using the [`detect_entire_series`](/python/api/azure-ai-anomalydetector/azure.ai.anomalydetector.operations.anomalydetectorclientoperationsmixin?view=azure-python-preview#azure-ai-anomalydetector-operations-anomalydetectorclientoperationsmixin-detect-entire-series&preserve-view=true) method. This call returns a [`EntireDetectResponse`](/python/api/azure-ai-anomalydetector/azure.ai.anomalydetector.models.entiredetectresponse?view=azure-python-preview&preserve-view=true) that we stored in a variable we named `anomaly_response`. We iterate through the response's `is_anomaly` list, and print the index of any values with a boolean of `true`. Alternatively, we could have used the [`detect_last_point`](/python/api/azure-ai-anomalydetector/azure.ai.anomalydetector.operations.anomalydetectorclientoperationsmixin?view=azure-python-preview#azure-ai-anomalydetector-operations-anomalydetectorclientoperationsmixin-detect-last-point&preserve-view=true) method, which is more appropriate for detecting anomalies in real-time data. To learn more, consult the [best practices guide](../../concepts/anomaly-detection-best-practices.md).
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The second call checks the entire sample data series for anomalies using the `detect_entire_series` method. This call returns a `EntireDetectResponse` that we stored in a variable we named `anomaly_response`. We iterate through the response's `is_anomaly` list, and print the index of any values with a boolean of `true`. Alternatively, we could have used the `detect_last_point` method, which is more appropriate for detecting anomalies in real-time data. To learn more, consult the [best practices guide](../../concepts/anomaly-detection-best-practices.md).
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## Visualize results
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