Skoot is a lightweight python library of machine learning transformer classes that interact with scikit-learn and pandas. Its objective is to expedite data munging and pre-processing tasks that can tend to take up so much of data science practitioners' time. See the documentation for more info.
Note that skoot is the preferred alternative to the now deprecated skutil library
Two minutes to model-readiness
Real world data is nasty. Most data scientists spend the majority of their time tackling data cleansing tasks. With skoot, we can automate away so much of the bespoke hacking solutions that consume data scientists' time.
In this example, we'll examine a common dataset (the adult dataset from the UCI machine learning repo) that requires significant pre-processing.
from skoot.datasets import load_adult_df from skoot.feature_selection import FeatureFilter from skoot.decomposition import SelectivePCA from skoot.preprocessing import DummyEncoder from skoot.utils.dataframe import get_numeric_columns from skoot.utils.dataframe import get_categorical_columns from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # load the dataset with the skoot-native loader & split it adult = load_adult_df(tgt_name="target") y = adult.pop("target") X_train, X_test, y_train, y_test = train_test_split( adult, y, random_state=42, test_size=0.2) # get numeric and categorical feature names num_cols = get_numeric_columns(X_train).columns obj_cols = get_categorical_columns(X_train).columns # remove the education-num from the num_cols since we're going to remove it num_cols = num_cols[~(num_cols == "education-num")] # build a pipeline pipe = Pipeline([ # drop out the ordinal level that's otherwise equal to "education" ("dropper", FeatureFilter(cols=["education-num"])), # decompose the numeric features with PCA ("pca", SelectivePCA(cols=num_cols)), # dummy encode the categorical features ("dummy", DummyEncoder(cols=obj_cols, handle_unknown="ignore")), # and a simple classifier class ("clf", RandomForestClassifier(n_estimators=100, random_state=42)) ]) pipe.fit(X_train, y_train) # produce predictions preds = pipe.predict(X_test) print("Test accuracy: %.3f" % accuracy_score(y_test, preds))
For more tutorials, check out the documentation.