Skoot: Accelerate your data science workflow

Travis status Appveyor status Coverage gluten free

Skoot’s aim is to expedite and automate away many of the common pain points data scientists experience as they work through their exploratory data analysis & data cleansing/preparation stages. It does so by wrapping and augmenting useful functions and transformers in scikit-learn, adapting them for use with Pandas, as well as by providing its own custom transformer classes to solve common problems that typically demand bespoke solutions.


Example: 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, and show how Skoot enables us to quickly clean up data and prepare it for modeling.


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))

Voíla! The entire pre-processing and modeling process is achievable in a single pipeline.


If you have a common data preparation or transformation task you feel could be written into a transformer, please consider Contributing to Skoot!


Quick refs, indices and tables

Helpful quickstart sections:

To search for specific sections or class documentation, visit the index.