featuretools.EntitySet.add_dataframe¶
- EntitySet.add_dataframe(dataframe, dataframe_name=None, index=None, logical_types=None, semantic_tags=None, make_index=False, time_index=None, secondary_time_index=None, already_sorted=False)[source]¶
Add a DataFrame to the EntitySet with Woodwork typing information.
- Parameters
dataframe (pandas.DataFrame) – Dataframe containing the data.
dataframe_name (str, optional) – Unique name to associate with this dataframe. Must be provided if Woodwork is not initialized on the input DataFrame.
index (str, optional) – Name of the column used to index the dataframe. Must be unique. If None, take the first column.
logical_types (dict[str -> Woodwork.LogicalTypes/str, optional]) – Keys are column names and values are logical types. Will be inferred if not specified.
semantic_tags (dict[str -> str/set], optional) – Keys are column names and values are semantic tags.
make_index (bool, optional) – If True, assume index does not exist as a column in dataframe, and create a new column of that name using integers. Otherwise, assume index exists.
time_index (str, optional) – Name of the column containing time data. Type must be numeric or datetime in nature.
secondary_time_index (dict[str -> list[str]]) – Name of column containing time data to be used as a secondary time index mapped to a list of the columns in the dataframe associated with that secondary time index.
already_sorted (bool, optional) – If True, assumes that input dataframe is already sorted by time. Defaults to False.
Notes
Will infer logical types from the data.
Example
In [1]: import featuretools as ft In [2]: import pandas as pd In [3]: transactions_df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6], ...: "session_id": [1, 2, 1, 3, 4, 5], ...: "amount": [100.40, 20.63, 33.32, 13.12, 67.22, 1.00], ...: "transaction_time": pd.date_range(start="10:00", periods=6, freq="10s"), ...: "fraud": [True, False, True, False, True, True]}) ...: In [4]: es = ft.EntitySet("example") In [5]: es.add_dataframe(dataframe_name="transactions", ...: index="id", ...: time_index="transaction_time", ...: dataframe=transactions_df) ...: Out[5]: Entityset: example DataFrames: transactions [Rows: 6, Columns: 5] Relationships: No relationships In [6]: es["transactions"] Out[6]: id session_id amount transaction_time fraud 1 1 1 100.40 2022-02-17 10:00:00 True 2 2 2 20.63 2022-02-17 10:00:10 False 3 3 1 33.32 2022-02-17 10:00:20 True 4 4 3 13.12 2022-02-17 10:00:30 False 5 5 4 67.22 2022-02-17 10:00:40 True 6 6 5 1.00 2022-02-17 10:00:50 True