more code formatting

Author: erfannariman

doc/source/user_guide/10min.rst

@@ -43,12 +43,16 @@ Creating a :class:`DataFrame` by passing a dict of objects that can be converted
 
 .. ipython:: python
 
-   df2 = pd.DataFrame({'A': 1.,
-                       'B': pd.Timestamp('20130102'),
-                       'C': pd.Series(1, index=list(range(4)), dtype='float32'),
-                       'D': np.array([3] * 4, dtype='int32'),
-                       'E': pd.Categorical(["test", "train", "test", "train"]),
-                       'F': 'foo'})
+   df2 = pd.DataFrame(
+       {
+           "A": 1.0,
+           "B": pd.Timestamp("20130102"),
+           "C": pd.Series(1, index=list(range(4)), dtype="float32"),
+           "D": np.array([3] * 4, dtype="int32"),
+           "E": pd.Categorical(["test", "train", "test", "train"]),
+           "F": "foo",
+       }
+   )
    df2
 
 The columns of the resulting :class:`DataFrame` have different
@@ -512,12 +516,14 @@ See the :ref:`Grouping section <groupby>`.
 
 .. ipython:: python
 
-   df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
-                            'foo', 'bar', 'foo', 'foo'],
-                      'B': ['one', 'one', 'two', 'three',
-                            'two', 'two', 'one', 'three'],
-                      'C': np.random.randn(8),
-                      'D': np.random.randn(8)})
+   df = pd.DataFrame(
+       {
+           "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+           "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+           "C": np.random.randn(8),
+           "D": np.random.randn(8),
+       }
+   )
    df
 
 Grouping and then applying the :meth:`~pandas.core.groupby.GroupBy.sum` function to the resulting
@@ -545,10 +551,14 @@ Stack
 
 .. ipython:: python
 
-   tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
-                        'foo', 'foo', 'qux', 'qux'],
-                       ['one', 'two', 'one', 'two',
-                        'one', 'two', 'one', 'two']]))
+   tuples = list(
+       zip(
+           *[
+               ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
+               ["one", "two", "one", "two", "one", "two", "one", "two"],
+           ]
+       )
+   )
    index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
    df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
    df2 = df[:4]
@@ -578,11 +588,15 @@ See the section on :ref:`Pivot Tables <reshaping.pivot>`.
 
 .. ipython:: python
 
-   df = pd.DataFrame({'A': ['one', 'one', 'two', 'three'] * 3,
-                      'B': ['A', 'B', 'C'] * 4,
-                      'C': ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
-                      'D': np.random.randn(12),
-                      'E': np.random.randn(12)})
+   df = pd.DataFrame(
+       {
+           "A": ["one", "one", "two", "three"] * 3,
+           "B": ["A", "B", "C"] * 4,
+           "C": ["foo", "foo", "foo", "bar", "bar", "bar"] * 2,
+           "D": np.random.randn(12),
+           "E": np.random.randn(12),
+       }
+   )
    df
 
 We can produce pivot tables from this data very easily:
@@ -653,8 +667,10 @@ pandas can include categorical data in a :class:`DataFrame`. For full docs, see
 
 .. ipython:: python
 
-    df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6],
-                       "raw_grade": ['a', 'b', 'b', 'a', 'a', 'e']})
+   df = pd.DataFrame(
+       {"id": [1, 2, 3, 4, 5, 6], "raw_grade": ["a", "b", "b", "a", "a", "e"]}
+   )
+
 
 Convert the raw grades to a categorical data type.
 
@@ -674,8 +690,9 @@ Reorder the categories and simultaneously add the missing categories (methods un
 
 .. ipython:: python
 
-    df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium",
-                                                  "good", "very good"])
+    df["grade"] = df["grade"].cat.set_categories(
+        ["very bad", "bad", "medium","good", "very good"]
+    )
     df["grade"]
 
 Sorting is per order in the categories, not lexical order.
@@ -705,8 +722,7 @@ We use the standard convention for referencing the matplotlib API:
 
 .. ipython:: python
 
-   ts = pd.Series(np.random.randn(1000),
-                  index=pd.date_range('1/1/2000', periods=1000))
+   ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
    ts = ts.cumsum()
 
    @savefig series_plot_basic.png
@@ -717,8 +733,10 @@ of the columns with labels:
 
 .. ipython:: python
 
-   df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
-                     columns=['A', 'B', 'C', 'D'])
+   df = pd.DataFrame(
+       np.random.randn(1000, 4), index=ts.index, columns=["A", "B", "C", "D"]
+   )
+
    df = df.cumsum()
 
    plt.figure()

doc/source/user_guide/sparse.rst

@@ -303,24 +303,28 @@ The method requires a ``MultiIndex`` with two or more levels.
 .. ipython:: python
 
    s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
-   s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),
-                                        (1, 2, 'a', 1),
-                                        (1, 1, 'b', 0),
-                                        (1, 1, 'b', 1),
-                                        (2, 1, 'b', 0),
-                                        (2, 1, 'b', 1)],
-                                       names=['A', 'B', 'C', 'D'])
-   s
+   s.index = pd.MultiIndex.from_tuples(
+       [
+           (1, 2, "a", 0),
+           (1, 2, "a", 1),
+           (1, 1, "b", 0),
+           (1, 1, "b", 1),
+           (2, 1, "b", 0),
+           (2, 1, "b", 1),
+       ],
+       names=["A", "B", "C", "D"],
+   )
    ss = s.astype('Sparse')
    ss
 
 In the example below, we transform the ``Series`` to a sparse representation of a 2-d array by specifying that the first and second ``MultiIndex`` levels define labels for the rows and the third and fourth levels define labels for the columns. We also specify that the column and row labels should be sorted in the final sparse representation.
 
 .. ipython:: python
 
-   A, rows, columns = ss.sparse.to_coo(row_levels=['A', 'B'],
-                                       column_levels=['C', 'D'],
-                                       sort_labels=True)
+   A, rows, columns = ss.sparse.to_coo(
+       row_levels=["A", "B"], column_levels=["C", "D"], sort_labels=True
+   )
+
 
    A
    A.todense()
@@ -331,9 +335,9 @@ Specifying different row and column labels (and not sorting them) yields a diffe
 
 .. ipython:: python
 
-   A, rows, columns = ss.sparse.to_coo(row_levels=['A', 'B', 'C'],
-                                       column_levels=['D'],
-                                       sort_labels=False)
+   A, rows, columns = ss.sparse.to_coo(
+       row_levels=["A", "B", "C"], column_levels=["D"], sort_labels=False
+   )
 
    A
    A.todense()
@@ -345,8 +349,7 @@ A convenience method :meth:`Series.sparse.from_coo` is implemented for creating
 .. ipython:: python
 
    from scipy import sparse
-   A = sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])),
-                         shape=(3, 4))
+   A = sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4))
    A
    A.todense()