Documentation

aggregate_numerical_features()

Parameters:

df: Pandas DataFrame, Default = None
Dataset to join with.

attr: String, Default = None
Attribute to aggregate on.

Returns:

agg_df: Pandas DataFrame, Default = None
The aggregated dataframe with the aggregated values mean, max, min, median, count, sum for each unique attribute.

Example:

aggregate_categorical_features()

Parameters:

df: Pandas DataFrame, Default = None
Dataset to join with.

attr: String, Default = None
Attribute to aggregate on.

Returns:

agg_df: Pandas DataFrame, Default = None
The aggregated dataframe with the aggregated values mean, max, min, median, count, sum for each unique attribute.

Example:

check_missing_data()

Parameters:

df: Pandas DataFrame, Default = None
Dataset to check

Returns:

missing_df: Pandas DataFrame, Default = None
Dataframe with two columns, number of missing data in each feature and percentage missing per feature.

Example:

Install

To install, download the featureit.py file, place it in your project folder and import the functions to your python project. The Pandas library needs to be installed.

import featureit as fi

agg_df = fi.aggregate_numerical_features(df, attr)