DOC: use black to fix code style in doc #36777 (#36824)

Author: PrayagS

doc/source/development/extending.rst

@@ -50,8 +50,9 @@ decorate a class, providing the name of attribute to add. The class's
 
 Now users can access your methods using the ``geo`` namespace:
 
-      >>> ds = pd.DataFrame({'longitude': np.linspace(0, 10),
-      ...                    'latitude': np.linspace(0, 20)})
+      >>> ds = pd.Dataframe(
+      ...     {"longitude": np.linspace(0, 10), "latitude": np.linspace(0, 20)}
+      ... )
       >>> ds.geo.center
       (5.0, 10.0)
       >>> ds.geo.plot()
@@ -499,4 +500,4 @@ registers the default "matplotlib" backend as follows.
 
 
 More information on how to implement a third-party plotting backend can be found at
-https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/__init__.py#L1.
+https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/__init__.py#L1.

doc/source/user_guide/duplicates.rst

@@ -29,8 +29,8 @@ duplicates present. The output can't be determined, and so pandas raises.
 .. ipython:: python
    :okexcept:
 
-   s1 = pd.Series([0, 1, 2], index=['a', 'b', 'b'])
-   s1.reindex(['a', 'b', 'c'])
+   s1 = pd.Series([0, 1, 2], index=["a", "b", "b"])
+   s1.reindex(["a", "b", "c"])
 
 Other methods, like indexing, can give very surprising results. Typically
 indexing with a scalar will *reduce dimensionality*. Slicing a ``DataFrame``
@@ -39,30 +39,30 @@ return a scalar. But with duplicates, this isn't the case.
 
 .. ipython:: python
 
-   df1 = pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=['A', 'A', 'B'])
+   df1 = pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=["A", "A", "B"])
    df1
 
 We have duplicates in the columns. If we slice ``'B'``, we get back a ``Series``
 
 .. ipython:: python
 
-   df1['B']  # a series
+   df1["B"]  # a series
 
 But slicing ``'A'`` returns a ``DataFrame``
 
 
 .. ipython:: python
 
-   df1['A']  # a DataFrame
+   df1["A"]  # a DataFrame
 
 This applies to row labels as well
 
 .. ipython:: python
 
-   df2 = pd.DataFrame({"A": [0, 1, 2]}, index=['a', 'a', 'b'])
+   df2 = pd.DataFrame({"A": [0, 1, 2]}, index=["a", "a", "b"])
    df2
-   df2.loc['b', 'A']  # a scalar
-   df2.loc['a', 'A']  # a Series
+   df2.loc["b", "A"]  # a scalar
+   df2.loc["a", "A"]  # a Series
 
 Duplicate Label Detection
 ~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -121,29 +121,24 @@ will be raised.
 .. ipython:: python
    :okexcept:
 
-   pd.Series(
-       [0, 1, 2],
-       index=['a', 'b', 'b']
-   ).set_flags(allows_duplicate_labels=False)
+   pd.Series([0, 1, 2], index=["a", "b", "b"]).set_flags(allows_duplicate_labels=False)
 
 This applies to both row and column labels for a :class:`DataFrame`
 
 .. ipython:: python
    :okexcept:
 
-   pd.DataFrame(
-       [[0, 1, 2], [3, 4, 5]], columns=["A", "B", "C"],
-   ).set_flags(allows_duplicate_labels=False)
+   pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=["A", "B", "C"],).set_flags(
+       allows_duplicate_labels=False
+   )
 
 This attribute can be checked or set with :attr:`~DataFrame.flags.allows_duplicate_labels`,
 which indicates whether that object can have duplicate labels.
 
 .. ipython:: python
 
-   df = (
-       pd.DataFrame({"A": [0, 1, 2, 3]},
-                    index=['x', 'y', 'X', 'Y'])
-         .set_flags(allows_duplicate_labels=False)
+   df = pd.DataFrame({"A": [0, 1, 2, 3]}, index=["x", "y", "X", "Y"]).set_flags(
+       allows_duplicate_labels=False
    )
    df
    df.flags.allows_duplicate_labels
@@ -198,7 +193,7 @@ operations.
 .. ipython:: python
    :okexcept:
 
-   s1 = pd.Series(0, index=['a', 'b']).set_flags(allows_duplicate_labels=False)
+   s1 = pd.Series(0, index=["a", "b"]).set_flags(allows_duplicate_labels=False)
    s1
    s1.head().rename({"a": "b"})
 

doc/source/user_guide/gotchas.rst

@@ -21,12 +21,19 @@ when calling :meth:`~DataFrame.info`:
 
 .. ipython:: python
 
-    dtypes = ['int64', 'float64', 'datetime64[ns]', 'timedelta64[ns]',
-              'complex128', 'object', 'bool']
+    dtypes = [
+        "int64",
+        "float64",
+        "datetime64[ns]",
+        "timedelta64[ns]",
+        "complex128",
+        "object",
+        "bool",
+    ]
     n = 5000
     data = {t: np.random.randint(100, size=n).astype(t) for t in dtypes}
     df = pd.DataFrame(data)
-    df['categorical'] = df['object'].astype('category')
+    df["categorical"] = df["object"].astype("category")
 
     df.info()
 
@@ -40,7 +47,7 @@ as it can be expensive to do this deeper introspection.
 
 .. ipython:: python
 
-   df.info(memory_usage='deep')
+   df.info(memory_usage="deep")
 
 By default the display option is set to ``True`` but can be explicitly
 overridden by passing the ``memory_usage`` argument when invoking ``df.info()``.
@@ -155,7 +162,7 @@ index, not membership among the values.
 
 .. ipython:: python
 
-    s = pd.Series(range(5), index=list('abcde'))
+    s = pd.Series(range(5), index=list("abcde"))
     2 in s
     'b' in s
 
@@ -206,11 +213,11 @@ arrays. For example:
 
 .. ipython:: python
 
-   s = pd.Series([1, 2, 3, 4, 5], index=list('abcde'))
+   s = pd.Series([1, 2, 3, 4, 5], index=list("abcde"))
    s
    s.dtype
 
-   s2 = s.reindex(['a', 'b', 'c', 'f', 'u'])
+   s2 = s.reindex(["a", "b", "c", "f", "u"])
    s2
    s2.dtype
 
@@ -227,12 +234,11 @@ the nullable-integer extension dtypes provided by pandas
 
 .. ipython:: python
 
-   s_int = pd.Series([1, 2, 3, 4, 5], index=list('abcde'),
-                     dtype=pd.Int64Dtype())
+   s_int = pd.Series([1, 2, 3, 4, 5], index=list("abcde"), dtype=pd.Int64Dtype())
    s_int
    s_int.dtype
 
-   s2_int = s_int.reindex(['a', 'b', 'c', 'f', 'u'])
+   s2_int = s_int.reindex(["a", "b", "c", "f", "u"])
    s2_int
    s2_int.dtype
 
@@ -334,7 +340,7 @@ constructors using something similar to the following:
 
 .. ipython:: python
 
-   x = np.array(list(range(10)), '>i4')  # big endian
+   x = np.array(list(range(10)), ">i4")  # big endian
    newx = x.byteswap().newbyteorder()  # force native byteorder
    s = pd.Series(newx)