DOC: update the pandas.errors.DtypeWarning docstring

pandas/errors/__init__.py

@@ -38,9 +38,60 @@ class ParserError(ValueError):
 
 class DtypeWarning(Warning):
     """
-    Warning that is raised for a dtype incompatibility. This
-    can happen whenever `pd.read_csv` encounters non-
-    uniform dtypes in a column(s) of a given CSV file.
+    Warning raised when reading different dtypes in a column from a file.
+
+    Raised for a dtype incompatibility. This can happen whenever `read_csv`
+    or `read_table` encounter non-uniform dtypes in a column(s) of a given
+    CSV file.
+
+    See Also
+    --------
+    pandas.read_csv : Read CSV (comma-separated) file into a DataFrame.
+    pandas.read_table : Read general delimited file into a DataFrame.
+
+    Notes
+    -----
+    This warning is issued when dealing with larger files because the dtype
+    checking happens per chunk read.
+
+    Despite the warning, the CSV file is read with mixed types in a single
+    column which will be an object type. See the examples below to better
+    understand this issue.
+
+    Examples
+    --------
+    This example creates and reads a large CSV file with a column that contains
+    `int` and `str`.
+
+    >>> df = pd.DataFrame({'a': (['1'] * 100000 + ['X'] * 100000 +
+    ...                          ['1'] * 100000),
+    ...                    'b': ['b'] * 300000})
+    >>> df.to_csv('test.csv', index=False)
+    >>> df2 = pd.read_csv('test.csv')
+
+        DtypeWarning: Columns (0) have mixed types
+
+    Important to notice that ``df2`` will contain both `str` and `int` for the
+    same input, '1'.
+
+    >>> df2.iloc[262140, 0]
+    '1'
+    >>> type(df2.iloc[262140, 0])
+    <class 'str'>
+    >>> df2.iloc[262150, 0]
+    1
+    >>> type(df2.iloc[262150, 0])
+    <class 'int'>
+
+    One way to solve this issue is using the `dtype` parameter in the
+    `read_csv` and `read_table` functions to explicit the conversion:
+
+    >>> df2 = pd.read_csv('test.csv', sep=',', dtype={'a': str})
+
+    No warning was issued.
+
+    >>> import os
+    >>> os.remove('test.csv')
     """