> ## Documentation Index > Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt > Use this file to discover all available pages before exploring further. # Schema [View Source on Github](https://github.com/Arize-ai/client_python/blob/576b5e62ac11124dd27201bc3db7587c46d9a371/arize/utils/types.py#L199) Arize class to organize and map column names containing model data within your Pandas dataframe to Arize. Import and initialize Arize Schema from `arize.utils.types` ```python theme={null} from arize.utils.types import Schema ``` ```python theme={null} class Schema( prediction_id_column_name: Optional[str] = None feature_column_names: Optional[Union[List[str], TypedColumns]] = None tag_column_names: Optional[Union[List[str], TypedColumns]] = None timestamp_column_name: Optional[str] = None prediction_label_column_name: Optional[str] = None prediction_score_column_name: Optional[str] = None actual_label_column_name: Optional[str] = None actual_score_column_name: Optional[str] = None shap_values_column_names: Optional[Dict[str, str]] = None embedding_feature_column_names: Optional[Dict[str, EmbeddingColumnNames]] = None # type:ignore prediction_group_id_column_name: Optional[str] = None rank_column_name: Optional[str] = None attributions_column_name: Optional[str] = None relevance_score_column_name: Optional[str] = None relevance_labels_column_name: Optional[str] = None object_detection_prediction_column_names: Optional[ObjectDetectionColumnNames] = None object_detection_actual_column_names: Optional[ObjectDetectionColumnNames] = None prompt_column_names: Optional[Union[str, EmbeddingColumnNames]] = None response_column_names: Optional[Union[str, EmbeddingColumnNames]] = None prompt_template_column_names: Optional[PromptTemplateColumnNames] = None llm_config_column_names: Optional[LLMConfigColumnNames] = None llm_run_metadata_column_names: Optional[LLMRunMetadataColumnNames] = None retrieved_document_ids_column_name: Optional[List[str]] = None multi_class_threshold_scores_column_name: Optional[str] = None ) ``` | Parameter | Data Type | Expected Type In Column | Description | | ------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `prediction_id_column_name` | str | Contents must be a string limited to 128 characters | (Optional) A unique string to identify a prediction event. **Required** to match a prediction to [delayed actuals](/ax/machine-learning/machine-learning/concepts-ml/how-to-send-delayed-actuals) or feature importances in Arize. If the column is not provided, Arize will generate a random prediction id. | | `feature_column_names` | List\[str] or TypedColumns | Feature values can be int, float, string, list of strings | (Optional) Column names for features. If TypedColumns is used, the columns will be cast to the provided types prior to logging. | | `embedding_feature_column_names` | [Dict\[str, EmbeddingColumnNames\]](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/embeddingcolumnnames) | Learn more [here](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/embeddingcolumnnames) | (Optional) Dictionary mapping embedding display names to `EmbeddingColumnNames` objects | | `timestamp_column_name` | str | The content of this column must be int Unix Timestamps in seconds | (Optional) Column name for timestamps | | `prediction_label_column_name` | str | The content of this column must be convertible to string | (Optional) Column name for categorical prediction values | | `prediction_score_column_name` | str | The content of this column must be int/float.For [**Multi-Class**](/ax/machine-learning/machine-learning/use-cases-ml/multiclass-classification) models, content of this column must be a dictionary, mapping class name to int/float prediction scores. | (Optional Column name for numeric prediction values | | `actual_label_column_name` | str | The content of this column must be convertible to string | (Optional) Column name for categorical ground truth values | | `actual_score_column_name` | str | The content of this column must be int/float.For [**Multi-Class**](/ax/machine-learning/machine-learning/use-cases-ml/multiclass-classification) models, content of this column must be a dictionary, mapping class name to int/float actual scores. | (Optional) Column name for numeric ground truth | | `tag_column_names` | List\[str] or TypedColumns | Tag values can be int, float, string. LImited to 1k values | (Optional) Column names for tags. If TypedColumns is used, the columns will be cast to the provided types prior to logging. | | `shap_values_column_names` | Dict\[str,str] | The content of this column must be int/float | (Optional) dict of k-v pairs where k is the *feature\_colname* and v is the *corresponding shap\_val\_col\_name.* For example, your dataframe contains features columns`feat1, feat2, feat3,...`and corresponding shap value columns `feat1_shap, feat2_shap, feat3_shap,...` You want to set **shap\_values\_column\_names =** `{"feat1": "feat1shap", "feat2": "feat2_shap:", "feat3": "feat3_shap"}` | | `prediction_group_id_column_name` | str | The content of this column must be string and is limited to 128 characters | (Required\*) Column name for ranking groups or lists in ranking models \*for ranking models only | | `rank_column_name` | str | The content of this column must be integer between 1-100 | (Required\*) Column name for rank of each element on the its group or list \*for ranking models only | | `relevance_score_column_name` | str | The content of this column must be int/float | (Required\*) Column name for ranking model type numeric ground truth values \*for ranking models only | | `relevance_labels_column_name` | str | The content of this column must be a string | (Required\*) Column name for ranking model type categorical ground truth values \*for ranking models only | | `object_detection_prediction_column_names` | [ObjectDetectionColumnNames](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/objectdetectioncolumnnames) | Learn more [here](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/objectdetectioncolumnnames) | ObjectDetectionColumnNames object containing information defining the predicted bounding boxes' coordinates, categories, and scores. | | `object_detection_actual_column_names` | [ObjectDetectionColumnNames](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/objectdetectioncolumnnames) | Learn more [here](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/objectdetectioncolumnnames) | ObjectDetectionColumnNames object containing information defining the actula bounding boxes' coordinates, categories, and scores. | | `prompt_column_names` | [EmbeddingColumnNames](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/embeddingcolumnnames) | Learn more [here](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/embeddingcolumnnames) | EmbeddingColumnNames object containing the embedding vector data (required) and raw text (optional) for the input text your model acts on | | `response_column_names` | [EmbeddingColumnNames](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/embeddingcolumnnames) | Learn more [here](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/embeddingcolumnnames) | EmbeddingColumnNames object containing the embedding vector data (required) and raw text (optional) for the text your model generates | | `prompt_template_column_names` | [PromptTemplateColumnNames](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/prompttemplatecolumnnames) | Learn more [here](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/prompttemplatecolumnnames) | PromptTemplateColumnNames object containing the prompt template and prompt template version, both optional | | `llm_config_column_names` | [LLMConfigColumnNames](https://arize.com/docs/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize.pandas/llmconfigcolumnnames) | Learn more [here](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/llmconfigcolumnnames) | LLMConfigColumnNames object containing the LLM model name (optional) and its hyper-parameters (optional) used at inference time | | `llm_run_metadata_column_names` | [LLMRunMetadataColumnNames](https://arize.com/docs/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize.pandas/llmrunmetadatacolumnnames) | Learn more [here](/ax/machine-learning/machine-learning/api-reference-ml/python-sdk/arize-pandas/llmrunmetadatacolumnnames) | LLMRunMetadata object containing metadata about the LLM inference, i.e., token counts and response latency | | `retrieved_document_ids_column_name` | str | The contents of this column must be list of entries convertible to strings | Column name for retrieved document ids | | `multi_class_threshold_scores_column_name` | str | Contents of this column must be a dictionary mapping string class names to float scores.Learn more [here](/ax/machine-learning/machine-learning/use-cases-ml/multiclass-classification) | (Optional) Column name used only for Multi-Label Multi-Class models and determines the minimum prediction value for a class to be considered a positive prediction. | ### Code Example | prediction id | feature\_1 | feature\_2 | tag\_1 | tag\_2 | prediction\_ts | prediction\_label | actual\_label | embedding | | ------------- | ---------- | ---------- | ------ | ------ | -------------- | ----------------- | ------------- | ------------------------ | | 1fcd50f4689 | ca | \[ca, ak] | female | 25 | 1637538845 | No Claims | No Claims | \[1.27346, -0.2138, ...] | ```python theme={null} schema = Schema( prediction_id_column_name="prediction id", feature_column_names=["feature_1", "feature_2"], tag_column_names=TypedColumns( to_str=["tag_1", "tag_2"], ), timestamp_column_name="prediction_ts", prediction_label_column_name="prediction_label", prediction_score_column_name="prediction_score", actual_label_column_name="actual_label", actual_score_column_name="actual_score", shap_values_column_names=shap_values_column_names=dict(zip("feature_1", shap_cols)), embedding_feature_column_names=EmbeddingColumnNames( vector_column_name="embedding", ), prediction_group_id_column_name="group_example_name", rank_column_name="example_rank", relevance_score_column_name="relevance_score", relevance_labels_column_name="actual_relevancy", ) ```