# featuretools.primitives.ExpandingMin#

class featuretools.primitives.ExpandingMin(gap=1, min_periods=1)[source]#

Computes the expanding minimum of events over a given window.

Description:

Given a list of datetimes, returns an expanding minimum starting at the row gap rows away from the current row. An expanding primitive calculates the value of a primitive for a given time with all the data available up to the corresponding point in time.

Input datetimes should be monotonic.

Parameters:
• gap (int, optional) – Specifies a gap backwards from each instance before the usable data begins. Corresponds to number of rows. Defaults to 1.

• min_periods (int, optional) – Minimum number of observations required for performing calculations over the window. Defaults to 1.

Examples

```>>> import pandas as pd
>>> expanding_min = ExpandingMin()
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> expanding_min(times, [5, 4, 3, 2, 1]).tolist()
[nan, 5.0, 4.0, 3.0, 2.0]
```

We can also control the gap before the expanding calculation.

```>>> import pandas as pd
>>> expanding_min = ExpandingMin(gap=0)
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> expanding_min(times, [5, 4, 3, 2, 1]).tolist()
[5.0, 4.0, 3.0, 2.0, 1.0]
```

We can also control the minimum number of periods required for the rolling calculation.

```>>> import pandas as pd
>>> expanding_min = ExpandingMin(min_periods=3)
>>> times = pd.date_range(start='2019-01-01', freq='1min', periods=5)
>>> expanding_min(times, [5, 4, 3, 2, 1]).tolist()
[nan, nan, nan, 3.0, 2.0]
```
__init__(gap=1, min_periods=1)[source]#

Methods

 `__init__`([gap, min_periods]) `flatten_nested_input_types`(input_types) Flattens nested column schema inputs into a single list. `generate_name`(base_feature_names) `generate_names`(base_feature_names) `get_args_string`() `get_arguments`() `get_description`(input_column_descriptions[, ...]) `get_filepath`(filename) `get_function`()

Attributes

 `base_of` `base_of_exclude` `commutative` `compatibility` Additional compatible libraries `default_value` Default value this feature returns if no data found. `description_template` `input_types` woodwork.ColumnSchema types of inputs `max_stack_depth` `name` Name of the primitive `number_output_features` Number of columns in feature matrix associated with this feature `return_type` ColumnSchema type of return `series_library` `stack_on` `stack_on_exclude` `stack_on_self` `uses_calc_time` `uses_full_dataframe`