diff --git a/pandas/io/pytables.py b/pandas/io/pytables.py
index 64a05c87e0f80..3c328939955d9 100644
--- a/pandas/io/pytables.py
+++ b/pandas/io/pytables.py
@@ -1169,9 +1169,16 @@ def put(
             This parameter is currently not accepted.
         complevel : int, 0-9, default None
             Specifies a compression level for data.
-            A value of 0 or None disables compression.
-        min_itemsize : int, dict, or None
-            Dict of columns that specify minimum str sizes.
+            A value of 0 or None disables compression.        
+        min_itemsize : int, dict of str: int, or None, default None
+            Minimum size in bytes for string columns when format = 'table'. 
+            int - Apply the same minimum size to all string columns, 
+            dict - Map column names to their minimum sizes or, 
+            None - use the default the sizing
+            Important: This specifies the byte length after encoding, not the 
+            character count. For multi-byte characters, calculate the required
+            size using the encoded byte length.
+            See examples below for use.
         nan_rep : str
             Str to use as str nan representation.
         data_columns : list of columns or True, default None
@@ -1203,6 +1210,10 @@ def put(
         >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
         >>> store = pd.HDFStore("store.h5", "w")  # doctest: +SKIP
         >>> store.put("data", df)  # doctest: +SKIP
+
+        >>> ASCII 'hello' = 5 bytes
+        >>> UTF-8 '香' = 3 bytes (though only 1 character)
+        >>> To find byte length: len(string.encode('utf-8'))
         """
         if format is None:
             format = get_option("io.hdf.default_format") or "fixed"
@@ -1329,9 +1340,16 @@ def append(
             Specifies a compression level for data.
             A value of 0 or None disables compression.
         columns : default None
-            This parameter is currently not accepted, try data_columns.
-        min_itemsize : int, dict, or None
-            Dict of columns that specify minimum str sizes.
+            This parameter is currently not accepted, try data_columns.        
+        min_itemsize : int, dict of str: int, or None, default None
+            Minimum size in bytes for string columns when format = 'table'. 
+            int - Apply the same minimum size to all string columns, 
+            dict - Map column names to their minimum sizes or, 
+            None - use the default the sizing
+            Important: This specifies the byte length after encoding, not the 
+            character count. For multi-byte characters, calculate the required
+            size using the encoded byte length.
+            See examples below for use.
         nan_rep : str
             Str to use as str nan representation.
         chunksize : int or None
@@ -1364,6 +1382,10 @@ def append(
         Does *not* check if data being appended overlaps with existing
         data in the table, so be careful
 
+        When appending to an existing table, the min_itemsize parameter has no effect
+        as column sizes are already fixed. Set min_itemsize when initially creating
+        the table with put() or the first append() call.
+
         Examples
         --------
         >>> df1 = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"])
@@ -1377,6 +1399,10 @@ def append(
         1  3  4
         0  5  6
         1  7  8
+
+        >>> ASCII 'hello' = 5 bytes
+        >>> UTF-8 '香' = 3 bytes (though only 1 character)
+        >>> To find byte length: len(string.encode('utf-8'))
         """
         if columns is not None:
             raise TypeError(