fix warning; fix doc bug (#232)

.github/ISSUE_TEMPLATE.md

-[](For bugs and unexpected issues, please provide the following information, so that we could reproduce in our system)
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+
+For bugs and unexpected issues, please provide following information, so that we could reproduce on our system.
 
 ## Environment info
 Operating System:
 CPU:
+C++/Python/R version:
 
 ## Error Message:
 
-
 ## Reproducible examples
 
 ## Steps to reproduce

docs/Python-intro.md

@@ -78,13 +78,13 @@ LightGBM can use categorical features as input directly. It doesn't need to cove
 
 #### Weights can be set when needed:
 ```python
-w = np.random.rand(500, 1)
+w = np.random.rand(500, )
 train_data = lgb.Dataset(data, label=label, weight=w)
 ```
 or
 ```python
 train_data = lgb.Dataset(data, label=label)
-w = np.random.rand(500, 1)
+w = np.random.rand(500, )
 train_data.set_weight(w)
 ```
 

python-package/lightgbm/basic.py

@@ -222,7 +222,7 @@ PANDAS_DTYPE_MAPPER = {'int8': 'int', 'int16': 'int', 'int32': 'int',
 def _data_from_pandas(data, feature_name, categorical_feature, pandas_categorical):
     if isinstance(data, DataFrame):
         if feature_name == 'auto' or feature_name is None:
-            data.rename(columns=str, inplace=True)
+            data = data.rename(columns=str)
         cat_cols = data.select_dtypes(include=['category']).columns
         if pandas_categorical is None:  # train dataset
             pandas_categorical = [list(data[col].cat.categories) for col in cat_cols]
@@ -241,7 +241,7 @@ def _data_from_pandas(data, feature_name, categorical_feature, pandas_categorica
             if categorical_feature == 'auto':
                 categorical_feature = list(cat_cols)
             else:
-                categorical_feature += list(cat_cols)
+                categorical_feature = list(categorical_feature) + list(cat_cols)
         if feature_name == 'auto':
             feature_name = list(data.columns)
         data_dtypes = data.dtypes