diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst
index 7db3b63fd8f08..2ae1a127fb218 100644
--- a/doc/source/visualization.rst
+++ b/doc/source/visualization.rst
@@ -1389,7 +1389,7 @@ Here is an example of one way to easily plot group means with standard deviation
    # Plot
    fig, ax = plt.subplots()
    @savefig errorbar_example.png
-   means.plot.bar(yerr=errors, ax=ax)
+   means.plot.bar(yerr=errors, ax=ax, capsize=4)
 
 .. ipython:: python
    :suppress:
diff --git a/pandas/io/sql.py b/pandas/io/sql.py
index 9c6d01d236c57..dbe9dc7123e96 100644
--- a/pandas/io/sql.py
+++ b/pandas/io/sql.py
@@ -109,7 +109,10 @@ def _handle_date_column(col, utc=None, format=None):
               issubclass(col.dtype.type, np.integer)):
             # parse dates as timestamp
             format = 's' if format is None else format
-            return to_datetime(col, errors='coerce', unit=format, utc=utc)
+            if '%' in format:
+                return to_datetime(col, errors='coerce', format=format, utc=utc)
+            else:
+                return to_datetime(col, errors='coerce', unit=format, utc=utc)
         elif is_datetime64tz_dtype(col):
             # coerce to UTC timezone
             # GH11216