datacleaner can drop NaNs, do imputation with "the mode (for categorical variables) or median (for continuous variables) on a column-by-column basis", and encode "non-numerical variables (e.g., categorical variables with strings) with numerical equivalents" with Pandas DataFrames and scikit-learn.
sklearn-pandas "[maps] DataFrame columns to transformations, which are later recombined into features", and provides "A couple of special transformers that work well with pandas inputs: CategoricalImputer and FunctionTransformer"
> Featuretools is a python library for automated feature engineering. [using DFS: Deep Feature Synthesis]
auto-sklearn does feature selection (with e.g. PCA) in a "preprocessing" step; as well as "One-Hot encoding of categorical features, imputation of missing values and the normalization of features or samples"
auto_ml uses "Deep Learning [with Keras and TensorFlow] to learn features for us, and Gradient Boosting [with XGBoost] to turn those features into accurate predictions"