python DMatrix now accepts pandas DataFrame

python-package/xgboost/core.py

@@ -138,6 +138,28 @@ def c_array(ctype, values):
     return (ctype * len(values))(*values)
 
 
+def _maybe_from_pandas(data, feature_names, feature_types):
+    """ Extract internal data from pd.DataFrame """
+    try:
+        import pandas as pd
+    except ImportError:
+        return data, feature_names, feature_types
+
+    if not isinstance(data, pd.DataFrame):
+        return data, feature_names, feature_types
+
+    dtypes = data.dtypes
+    if not all(dtype.name in ('int64', 'float64', 'bool') for dtype in dtypes):
+        raise ValueError('DataFrame.dtypes must be int, float or bool')
+
+    if feature_names is None:
+        feature_names = data.columns.tolist()
+    if feature_types is None:
+        mapper = {'int64': 'int', 'float64': 'q', 'bool': 'i'}
+        feature_types = [mapper[dtype.name] for dtype in dtypes]
+    data = data.values.astype('float')
+    return data, feature_names, feature_types
+
 class DMatrix(object):
     """Data Matrix used in XGBoost.
 
@@ -157,7 +179,7 @@ class DMatrix(object):
 
         Parameters
         ----------
-        data : string/numpy array/scipy.sparse
+        data : string/numpy array/scipy.sparse/pd.DataFrame
             Data source of DMatrix.
             When data is string type, it represents the path libsvm format txt file,
             or binary file that xgboost can read from.
@@ -178,6 +200,13 @@ class DMatrix(object):
         if data is None:
             self.handle = None
             return
+
+        klass = getattr(getattr(data, '__class__', None), '__name__', None)
+        if klass == 'DataFrame':
+            # once check class name to avoid unnecessary pandas import
+            data, feature_names, feature_types = _maybe_from_pandas(data, feature_names,
+                                                                    feature_types)
+
         if isinstance(data, STRING_TYPES):
             self.handle = ctypes.c_void_p()
             _check_call(_LIB.XGDMatrixCreateFromFile(c_str(data),

scripts/travis_script.sh

@@ -64,7 +64,7 @@ if [ ${TASK} == "python-package" -o ${TASK} == "python-package3" ]; then
         conda create -n myenv python=2.7
     fi
     source activate myenv
-    conda install numpy scipy matplotlib nose
+    conda install numpy scipy pandas matplotlib nose
     python -m pip install graphviz
 
     make all CXX=${CXX} || exit -1

tests/python/test_basic.py

@@ -97,6 +97,27 @@ class TestBasic(unittest.TestCase):
             dm = xgb.DMatrix(dummy, feature_names=list('abcde'))
             self.assertRaises(ValueError, bst.predict, dm)
 
+    def test_pandas(self):
+        import pandas as pd
+        df = pd.DataFrame([[1, 2., True], [2, 3., False]], columns=['a', 'b', 'c'])
+        dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
+        assert dm.feature_names == ['a', 'b', 'c']
+        assert dm.feature_types == ['int', 'q', 'i']
+        assert dm.num_row() == 2
+        assert dm.num_col() == 3
+
+        # overwrite feature_names and feature_types
+        dm = xgb.DMatrix(df, label=pd.Series([1, 2]),
+                         feature_names=['x', 'y', 'z'], feature_types=['q', 'q', 'q'])
+        assert dm.feature_names == ['x', 'y', 'z']
+        assert dm.feature_types == ['q', 'q', 'q']
+        assert dm.num_row() == 2
+        assert dm.num_col() == 3
+
+        # incorrect dtypes
+        df = pd.DataFrame([[1, 2., 'x'], [2, 3., 'y']], columns=['a', 'b', 'c'])
+        self.assertRaises(ValueError, xgb.DMatrix, df)
+
     def test_load_file_invalid(self):
 
         self.assertRaises(ValueError, xgb.Booster,