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diff --git a/sklearn_pandas/dataframe_mapper.py b/sklearn_pandas/dataframe_mapper.py
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+++ b/sklearn_pandas/dataframe_mapper.py
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+import sys
+import pandas as pd
+import numpy as np
+from scipy import sparse
+from sklearn.base import BaseEstimator, TransformerMixin
+
+from .cross_validation import DataWrapper
+from .pipeline import make_transformer_pipeline
+
+# load in the correct stringtype: str for py3, basestring for py2
+string_types = str if sys.version_info >= (3, 0) else basestring
+
+
+def _handle_feature(fea):
+    """
+    Convert 1-dimensional arrays to 2-dimensional column vectors.
+    """
+    if len(fea.shape) == 1:
+        fea = np.array([fea]).T
+
+    return fea
+
+
+def _build_transformer(transformers):
+    if isinstance(transformers, list):
+        transformers = make_transformer_pipeline(*transformers)
+    return transformers
+
+
+class DataFrameMapper(BaseEstimator, TransformerMixin):
+    """
+    Map Pandas data frame column subsets to their own
+    sklearn transformation.
+    """
+
+    def __init__(self, features, sparse=False):
+        """
+        Params:
+
+        features    a list of pairs. The first element is the pandas column
+                    selector. This can be a string (for one column) or a list
+                    of strings. The second element is an object that supports
+                    sklearn's transform interface, or a list of such objects.
+        sparse      will return sparse matrix if set True and any of the
+                    extracted features is sparse. Defaults to False.
+        """
+        if isinstance(features, list):
+            features = [(columns, _build_transformer(transformers))
+                        for (columns, transformers) in features]
+        self.features = features
+        self.sparse = sparse
+
+    def __setstate__(self, state):
+        # compatibility shim for pickles created with sklearn-pandas<1.0.0
+        self.features = [(columns, _build_transformer(transformers))
+                         for (columns, transformers) in state['features']]
+        self.sparse = state.get('sparse', False)
+
+    def _get_col_subset(self, X, cols):
+        """
+        Get a subset of columns from the given table X.
+
+        X       a Pandas dataframe; the table to select columns from
+        cols    a string or list of strings representing the columns
+                to select
+
+        Returns a numpy array with the data from the selected columns
+        """
+        return_vector = False
+        if isinstance(cols, string_types):
+            return_vector = True
+            cols = [cols]
+
+        if isinstance(X, list):
+            X = [x[cols] for x in X]
+            X = pd.DataFrame(X)
+
+        elif isinstance(X, DataWrapper):
+            # if it's a datawrapper, unwrap it
+            X = X.df
+
+        if return_vector:
+            t = X[cols[0]].values
+        else:
+            t = X[cols].values
+
+        return t
+
+    def fit(self, X, y=None):
+        """
+        Fit a transformation from the pipeline
+
+        X       the data to fit
+        """
+        for columns, transformers in self.features:
+            if transformers is not None:
+                transformers.fit(self._get_col_subset(X, columns))
+        return self
+
+    def transform(self, X):
+        """
+        Transform the given data. Assumes that fit has already been called.
+
+        X       the data to transform
+        """
+        extracted = []
+        for columns, transformers in self.features:
+            # columns could be a string or list of
+            # strings; we don't care because pandas
+            # will handle either.
+            Xt = self._get_col_subset(X, columns)
+            if transformers is not None:
+                Xt = transformers.transform(Xt)
+            extracted.append(_handle_feature(Xt))
+
+        # combine the feature outputs into one array.
+        # at this point we lose track of which features
+        # were created from which input columns, so it's
+        # assumed that that doesn't matter to the model.
+
+        # If any of the extracted features is sparse, combine sparsely.
+        # Otherwise, combine as normal arrays.
+        if any(sparse.issparse(fea) for fea in extracted):
+            stacked = sparse.hstack(extracted).tocsr()
+            # return a sparse matrix only if the mapper was initialized
+            # with sparse=True
+            if not self.sparse:
+                stacked = stacked.toarray()
+        else:
+            stacked = np.hstack(extracted)
+
+        return stacked