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Use Example Datasets

Quickly explore Phoenix with concrete examples
Phoenix ships with a collection of examples so you can quickly try out the app on concrete use-cases. This guide shows you how to download, inspect, and launch the app with example datasets.

View Available Datasets

To see a list of datasets available for download, run
This displays the docstring for the phoenix.load_example function, which contain a list of datasets available for download.

Download Your Dataset of Choice

Choose the name of a dataset to download and pass it as an argument to phoenix.load_example. For example, run the following to download production and training data for our demo sentiment classification model:
datasets = px.load_example("sentiment_classification_language_drift")
px.load_example returns your downloaded data in the form of an ExampleDatasets instance. After running the code above, you should see the following in your cell output.
ExampleDatasets(primary=<Dataset "sentiment_classification_language_drift_primary">, reference=<Dataset "sentiment_classification_language_drift_reference">)

Inspect Your Datasets

Next, inspect the name, dataframe, and schema that define your primary dataset. First, run
prim_ds = datasets.primary
to see the name of the dataset in your cell output:
Next, run
to see your dataset's schema in the cell output:
Schema(prediction_id_column_name='prediction_id', timestamp_column_name='prediction_ts', feature_column_names=['reviewer_age', 'reviewer_gender', 'product_category', 'language'], tag_column_names=None, prediction_label_column_name='pred_label', prediction_score_column_name=None, actual_label_column_name='label', actual_score_column_name=None, embedding_feature_column_names={'text_embedding': EmbeddingColumnNames(vector_column_name='text_vector', raw_data_column_name='text', link_to_data_column_name=None)}, excluded_column_names=None)
Last, run
to get an overview of your dataset's underlying dataframe:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 33411 entries, 2022-05-01 07:00:16+00:00 to 2022-06-01 07:00:16+00:00
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 prediction_ts 33411 non-null datetime64[ns, UTC]
1 reviewer_age 33411 non-null int16
2 reviewer_gender 33411 non-null object
3 product_category 33411 non-null object
4 language 33411 non-null object
5 text 33411 non-null object
6 text_vector 33411 non-null object
7 label 33411 non-null object
8 pred_label 33411 non-null object
9 prediction_id 0 non-null object
dtypes: datetime64[ns, UTC](1), int16(1), object(8)
memory usage: 2.6+ MB

Launch the App

Launch Phoenix with
px.launch_app(datasets.primary, datasets.reference)
Follow the instructions in the cell output to open the Phoenix UI in your notebook or in a separate browser tab.

View Available Traces

Phoenix supports LLM application Traces and has examples that you can take a look at as well.\
# Load up the LlamaIndex RAG example