Scikit & Pandas

Question 1

sklearn.preprocessing.MinMaxScaler

Answer 1

Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.

Question 2

sklearn.preprocessing.StandardScaler

Answer 2

Standardize features by removing the mean and scaling to unit variance

Question 3

sklearn.preprocessing.normalize

Answer 3

Scale input vectors individually to unit norm (vector length).

Question 4

sklearn.preprocessing.LabelEncoder

Answer 4

Encode target labels with value between 0 and n_classes-1. This transformer should be used to encode target values, i.e. y, and not the input X

Question 5

sklearn.preprocessing.OneHotEncoder

Answer 5

Encode categorical features as a one-hot numeric array.

Question 6

sklearn.preprocessing.binarize

Answer 6

Boolean thresholding of array-like or scipy.sparse matrix.

Question 7

pandas.melt

Answer 7

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name=’value’, col_level=None, ignore_index=True)