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Address #3933: document limitation of DMLC CSV parser + recommend Pandas (#3934)

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Philip Hyunsu Cho 2018-11-23 04:13:36 -08:00 committed by GitHub
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doc/python/python_intro.rst
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@ -48,9 +48,15 @@ The data is stored in a :py:class:`DMatrix <xgboost.DMatrix>` object.
dtrain = xgb.DMatrix('train.csv?format=csv&label_column=0') dtrain = xgb.DMatrix('train.csv?format=csv&label_column=0')
dtest = xgb.DMatrix('test.csv?format=csv&label_column=0') dtest = xgb.DMatrix('test.csv?format=csv&label_column=0')
(Note that XGBoost does not support categorical features; if your data contains .. note:: Categorical features not supported
Note that XGBoost does not support categorical features; if your data contains
categorical features, load it as a NumPy array first and then perform categorical features, load it as a NumPy array first and then perform
`one-hot encoding <http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html>`_.) `one-hot encoding <http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html>`_.
.. note:: Use Pandas to load CSV files with headers
Currently, the DMLC data parser cannot parse CSV files with headers. Use Pandas (see below) to read CSV files with headers.
* To load a NumPy array into :py:class:`DMatrix <xgboost.DMatrix>`: * To load a NumPy array into :py:class:`DMatrix <xgboost.DMatrix>`: