Fix double {{ examples

Author: mroeschke

pandas/core/base.py

@@ -714,14 +714,13 @@ def argmax(
         Consider dataset containing cereal calories
 
         >>> s = pd.Series(
-        ...     {
-        ...         {
-        ...             "Corn Flakes": 100.0,
-        ...             "Almond Delight": 110.0,
-        ...             "Cinnamon Toast Crunch": 120.0,
-        ...             "Cocoa Puff": 110.0,
-        ...         }
-        ...     }
+        ...     [100.0, 110.0, 120.0, 110.0],
+        ...     index=[
+        ...         "Corn Flakes",
+        ...         "Almond Delight",
+        ...         "Cinnamon Toast Crunch",
+        ...         "Cocoa Puff",
+        ...     ],
         ... )
         >>> s
         Corn Flakes              100.0

pandas/core/frame.py

@@ -2828,12 +2828,7 @@ def to_stata(
         Examples
         --------
         >>> df = pd.DataFrame(
-        ...     {
-        ...         {
-        ...             "animal": ["falcon", "parrot", "falcon", "parrot"],
-        ...             "speed": [350, 18, 361, 15],
-        ...         }
-        ...     }
+        ...     [["falcon", 350], ["parrot", 18]], columns=["animal", "parrot"]
         ... )
         >>> df.to_stata("animals.dta")  # doctest: +SKIP
         """
@@ -3510,13 +3505,8 @@ def to_xml(
         Examples
         --------
         >>> df = pd.DataFrame(
-        ...     {
-        ...         {
-        ...             "shape": ["square", "circle", "triangle"],
-        ...             "degrees": [360, 360, 180],
-        ...             "sides": [4, np.nan, 3],
-        ...         }
-        ...     }
+        ...     [["square", 360, 4], ["circle", 360, np.nan], ["triangle", 180, 3]],
+        ...     columns=["shape", "degrees", "sides"],
         ... )
 
         >>> df.to_xml()  # doctest: +SKIP

pandas/core/generic.py

@@ -3933,13 +3933,8 @@ def to_csv(
         Create 'out.csv' containing 'df' without indices
 
         >>> df = pd.DataFrame(
-        ...     {
-        ...         {
-        ...             "name": ["Raphael", "Donatello"],
-        ...             "mask": ["red", "purple"],
-        ...             "weapon": ["sai", "bo staff"],
-        ...         }
-        ...     }
+        ...     [["Raphael", "red", "sai"], ["Donatello", "purple", "bo staff"]],
+        ...     columns=["name", "mask", "weapon"],
         ... )
         >>> df.to_csv("out.csv", index=False)  # doctest: +SKIP
 
@@ -5457,13 +5452,10 @@ def reindex(
         Create a dataframe with some fictional data.
 
         >>> index = ["Firefox", "Chrome", "Safari", "IE10", "Konqueror"]
+        >>> columns = ["http_status", "response_time"]
         >>> df = pd.DataFrame(
-        ...     {
-        ...         {
-        ...             "http_status": [200, 200, 404, 404, 301],
-        ...             "response_time": [0.04, 0.02, 0.07, 0.08, 1.0],
-        ...         }
-        ...     },
+        ...     [[200, 0.04], [200, 0.02], [404, 0.07], [404, 0.08], [301, 1.0]],
+        ...     columns=columns,
         ...     index=index,
         ... )
         >>> df
@@ -9678,13 +9670,8 @@ def resample(
         For DataFrame objects, the keyword `on` can be used to specify the
         column instead of the index for resampling.
 
-        >>> d = {
-        ...     {
-        ...         "price": [10, 11, 9, 13, 14, 18, 17, 19],
-        ...         "volume": [50, 60, 40, 100, 50, 100, 40, 50],
-        ...     }
-        ... }
-        >>> df = pd.DataFrame(d)
+        >>> df = pd.DataFrame([10, 11, 9, 13, 14, 18, 17, 19], columns=["price"])
+        >>> df["volume"] = [50, 60, 40, 100, 50, 100, 40, 50]
         >>> df["week_starting"] = pd.date_range("01/01/2018", periods=8, freq="W")
         >>> df
            price  volume week_starting
@@ -9706,14 +9693,11 @@ def resample(
         specify on which level the resampling needs to take place.
 
         >>> days = pd.date_range("1/1/2000", periods=4, freq="D")
-        >>> d2 = {
-        ...     {
-        ...         "price": [10, 11, 9, 13, 14, 18, 17, 19],
-        ...         "volume": [50, 60, 40, 100, 50, 100, 40, 50],
-        ...     }
-        ... }
         >>> df2 = pd.DataFrame(
-        ...     d2, index=pd.MultiIndex.from_product([days, ["morning", "afternoon"]])
+        ...     [[10, 50],
+        ...      [11, 60], [9, 40], [13, 100], [14, 50], [18, 100], [17, 40], [19, 50]],
+        ...     columns=["price", "volume"]
+        ...     index=pd.MultiIndex.from_product([days, ["morning", "afternoon"]])
         ... )
         >>> df2
                               price  volume

pandas/io/xml.py

@@ -916,9 +916,7 @@ def read_xml(
         Note: if XML document uses default namespace denoted as
         `xmlns='<URI>'` without a prefix, you must assign any temporary
         namespace prefix such as 'doc' to the URI in order to parse
-        underlying nodes and/or attributes. For example, ::
-
-            namespaces = {{"doc": "https://example.com"}}
+        underlying nodes and/or attributes.
 
     elems_only : bool, optional, default False
         Parse only the child elements at the specified ``xpath``. By default,
@@ -987,9 +985,7 @@ def read_xml(
         and unlike ``xpath``, descendants do not need to relate to each other but can
         exist any where in document under the repeating element. This memory-
         efficient method should be used for very large XML files (500MB, 1GB, or 5GB+).
-        For example, ::
-
-            iterparse = {{"row_element": ["child_elem", "attr", "grandchild_elem"]}}
+        For example, ``{{"row_element": ["child_elem", "attr", "grandchild_elem"]}}``.
 
         .. versionadded:: 1.5.0