DOC: Adding examples for dtype_backend="pyarrow" for read_json() (pandas-dev#52813)

Author: Dharni0607

doc/source/user_guide/io.rst

@@ -998,7 +998,7 @@ pass  ``format='mixed'``
 
 .. ipython:: python
 
-   data = io.StringIO("date\n12 Jan 2000\n2000-01-13\n")
+   data = StringIO("date\n12 Jan 2000\n2000-01-13\n")
    df = pd.read_csv(data)
    df['date'] = pd.to_datetime(df['date'], format='mixed')
    df
@@ -1007,7 +1007,7 @@ or, if your datetime formats are all ISO8601 (possibly not identically-formatted
 
 .. ipython:: python
 
-   data = io.StringIO("date\n2020-01-01\n2020-01-01 03:00\n")
+   data = StringIO("date\n2020-01-01\n2020-01-01 03:00\n")
    df = pd.read_csv(data)
    df['date'] = pd.to_datetime(df['date'], format='ISO8601')
    df
@@ -2167,6 +2167,19 @@ Dates written in nanoseconds need to be read back in nanoseconds:
    dfju = pd.read_json(json, date_unit="ns")
    dfju
 
+By setting the ``dtype_backend`` argument you can control the default dtypes used for the resulting DataFrame.
+
+.. ipython:: python
+
+    data = (
+     '{"a":{"0":1,"1":3},"b":{"0":2.5,"1":4.5},"c":{"0":true,"1":false},"d":{"0":"a","1":"b"},'
+     '"e":{"0":null,"1":6.0},"f":{"0":null,"1":7.5},"g":{"0":null,"1":true},"h":{"0":null,"1":"a"},'
+     '"i":{"0":"12-31-2019","1":"12-31-2019"},"j":{"0":null,"1":null}}'
+    )
+    df = pd.read_json(StringIO(data), dtype_backend="pyarrow")
+    df
+    df.dtypes
+
 .. _io.json_normalize:
 
 Normalization