DOC: Fix code block line length (#36773)

Author: dsaxton

doc/source/getting_started/intro_tutorials/06_calculate_statistics.rst

@@ -123,7 +123,10 @@ aggregating statistics for given columns can be defined using the
 .. ipython:: python
 
     titanic.agg(
-        {"Age": ["min", "max", "median", "skew"], "Fare": ["min", "max", "median", "mean"]}
+        {
+            "Age": ["min", "max", "median", "skew"],
+            "Fare": ["min", "max", "median", "mean"],
+        }
     )
 
 .. raw:: html

doc/source/user_guide/advanced.rst

@@ -304,7 +304,8 @@ whereas a tuple of lists refer to several values within a level:
 .. ipython:: python
 
    s = pd.Series(
-       [1, 2, 3, 4, 5, 6], index=pd.MultiIndex.from_product([["A", "B"], ["c", "d", "e"]])
+       [1, 2, 3, 4, 5, 6],
+       index=pd.MultiIndex.from_product([["A", "B"], ["c", "d", "e"]]),
    )
    s.loc[[("A", "c"), ("B", "d")]]  # list of tuples
    s.loc[(["A", "B"], ["c", "d"])]  # tuple of lists
@@ -819,7 +820,9 @@ values **not** in the categories, similarly to how you can reindex **any** panda
 
 .. ipython:: python
 
-   df3 = pd.DataFrame({"A": np.arange(3), "B": pd.Series(list("abc")).astype("category")})
+   df3 = pd.DataFrame(
+       {"A": np.arange(3), "B": pd.Series(list("abc")).astype("category")}
+   )
    df3 = df3.set_index("B")
    df3
 
@@ -934,7 +937,9 @@ example, be millisecond offsets.
                np.random.randn(5, 2), index=np.arange(5) * 250.0, columns=list("AB")
            ),
            pd.DataFrame(
-               np.random.randn(6, 2), index=np.arange(4, 10) * 250.1, columns=list("AB")
+               np.random.randn(6, 2),
+               index=np.arange(4, 10) * 250.1,
+               columns=list("AB"),
            ),
        ]
    )

doc/source/user_guide/basics.rst

@@ -464,7 +464,10 @@ which we illustrate:
        {"A": [1.0, np.nan, 3.0, 5.0, np.nan], "B": [np.nan, 2.0, 3.0, np.nan, 6.0]}
    )
    df2 = pd.DataFrame(
-       {"A": [5.0, 2.0, 4.0, np.nan, 3.0, 7.0], "B": [np.nan, np.nan, 3.0, 4.0, 6.0, 8.0]}
+       {
+           "A": [5.0, 2.0, 4.0, np.nan, 3.0, 7.0],
+           "B": [np.nan, np.nan, 3.0, 4.0, 6.0, 8.0],
+       }
    )
    df1
    df2
@@ -712,7 +715,10 @@ Similarly, you can get the most frequently occurring value(s), i.e. the mode, of
     s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7])
     s5.mode()
     df5 = pd.DataFrame(
-        {"A": np.random.randint(0, 7, size=50), "B": np.random.randint(-10, 15, size=50)}
+        {
+            "A": np.random.randint(0, 7, size=50),
+            "B": np.random.randint(-10, 15, size=50),
+        }
     )
     df5.mode()
 
@@ -1192,7 +1198,9 @@ to :ref:`merging/joining functionality <merging>`:
 
 .. ipython:: python
 
-   s = pd.Series(["six", "seven", "six", "seven", "six"], index=["a", "b", "c", "d", "e"])
+   s = pd.Series(
+       ["six", "seven", "six", "seven", "six"], index=["a", "b", "c", "d", "e"]
+   )
    t = pd.Series({"six": 6.0, "seven": 7.0})
    s
    s.map(t)
@@ -1494,7 +1502,9 @@ labels).
 
    df = pd.DataFrame(
        {"x": [1, 2, 3, 4, 5, 6], "y": [10, 20, 30, 40, 50, 60]},
-       index=pd.MultiIndex.from_product([["a", "b", "c"], [1, 2]], names=["let", "num"]),
+       index=pd.MultiIndex.from_product(
+           [["a", "b", "c"], [1, 2]], names=["let", "num"]
+       ),
    )
    df
    df.rename_axis(index={"let": "abc"})
@@ -1803,7 +1813,9 @@ used to sort a pandas object by its index levels.
        }
    )
 
-   unsorted_df = df.reindex(index=["a", "d", "c", "b"], columns=["three", "two", "one"])
+   unsorted_df = df.reindex(
+       index=["a", "d", "c", "b"], columns=["three", "two", "one"]
+   )
    unsorted_df
 
    # DataFrame
@@ -1849,7 +1861,9 @@ to use to determine the sorted order.
 
 .. ipython:: python
 
-   df1 = pd.DataFrame({"one": [2, 1, 1, 1], "two": [1, 3, 2, 4], "three": [5, 4, 3, 2]})
+   df1 = pd.DataFrame(
+       {"one": [2, 1, 1, 1], "two": [1, 3, 2, 4], "three": [5, 4, 3, 2]}
+   )
    df1.sort_values(by="two")
 
 The ``by`` parameter can take a list of column names, e.g.:
@@ -1994,7 +2008,9 @@ all levels to ``by``.
 
 .. ipython:: python
 
-   df1.columns = pd.MultiIndex.from_tuples([("a", "one"), ("a", "two"), ("b", "three")])
+   df1.columns = pd.MultiIndex.from_tuples(
+       [("a", "one"), ("a", "two"), ("b", "three")]
+   )
    df1.sort_values(by=("a", "two"))
 
 
@@ -2245,7 +2261,11 @@ to the correct type.
      import datetime
 
      df = pd.DataFrame(
-         [[1, 2], ["a", "b"], [datetime.datetime(2016, 3, 2), datetime.datetime(2016, 3, 2)]]
+         [
+             [1, 2],
+             ["a", "b"],
+             [datetime.datetime(2016, 3, 2), datetime.datetime(2016, 3, 2)],
+         ]
      )
      df = df.T
      df

doc/source/user_guide/categorical.rst

@@ -513,7 +513,11 @@ The ordering of the categorical is determined by the ``categories`` of that colu
 
    dfs = pd.DataFrame(
        {
-           "A": pd.Categorical(list("bbeebbaa"), categories=["e", "a", "b"], ordered=True),
+           "A": pd.Categorical(
+               list("bbeebbaa"),
+               categories=["e", "a", "b"],
+               ordered=True,
+           ),
            "B": [1, 2, 1, 2, 2, 1, 2, 1],
        }
    )
@@ -642,7 +646,13 @@ Groupby will also show "unused" categories:
     df.groupby("cats").mean()
 
     cats2 = pd.Categorical(["a", "a", "b", "b"], categories=["a", "b", "c"])
-    df2 = pd.DataFrame({"cats": cats2, "B": ["c", "d", "c", "d"], "values": [1, 2, 3, 4]})
+    df2 = pd.DataFrame(
+        {
+            "cats": cats2,
+            "B": ["c", "d", "c", "d"],
+            "values": [1, 2, 3, 4],
+        }
+    )
     df2.groupby(["cats", "B"]).mean()
 
 
@@ -1115,7 +1125,11 @@ You can use ``fillna`` to handle missing values before applying a function.
 .. ipython:: python
 
     df = pd.DataFrame(
-        {"a": [1, 2, 3, 4], "b": ["a", "b", "c", "d"], "cats": pd.Categorical([1, 2, 3, 2])}
+        {
+            "a": [1, 2, 3, 4],
+            "b": ["a", "b", "c", "d"],
+            "cats": pd.Categorical([1, 2, 3, 2]),
+        }
     )
     df.apply(lambda row: type(row["cats"]), axis=1)
     df.apply(lambda col: col.dtype, axis=0)

doc/source/user_guide/computation.rst

@@ -787,7 +787,11 @@ can even be omitted:
 
 .. ipython:: python
 
-   covs = df[["B", "C", "D"]].rolling(window=50).cov(df[["A", "B", "C"]], pairwise=True)
+   covs = (
+       df[["B", "C", "D"]]
+       .rolling(window=50)
+       .cov(df[["A", "B", "C"]], pairwise=True)
+   )
    covs.loc["2002-09-22":]
 
 .. ipython:: python

doc/source/user_guide/cookbook.rst

@@ -266,7 +266,9 @@ New columns
 
 .. ipython:: python
 
-   df = pd.DataFrame({"AAA": [1, 1, 1, 2, 2, 2, 3, 3], "BBB": [2, 1, 3, 4, 5, 1, 2, 3]})
+   df = pd.DataFrame(
+       {"AAA": [1, 1, 1, 2, 2, 2, 3, 3], "BBB": [2, 1, 3, 4, 5, 1, 2, 3]}
+   )
    df
 
 Method 1 : idxmin() to get the index of the minimums
@@ -327,7 +329,9 @@ Arithmetic
 
 .. ipython:: python
 
-   cols = pd.MultiIndex.from_tuples([(x, y) for x in ["A", "B", "C"] for y in ["O", "I"]])
+   cols = pd.MultiIndex.from_tuples(
+       [(x, y) for x in ["A", "B", "C"] for y in ["O", "I"]]
+   )
    df = pd.DataFrame(np.random.randn(2, 6), index=["n", "m"], columns=cols)
    df
    df = df.div(df["C"], level=1)
@@ -566,7 +570,9 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to
 
 .. ipython:: python
 
-   df = pd.DataFrame({"Color": "Red Red Red Blue".split(), "Value": [100, 150, 50, 50]})
+   df = pd.DataFrame(
+       {"Color": "Red Red Red Blue".split(), "Value": [100, 150, 50, 50]}
+   )
    df
    df["Counts"] = df.groupby(["Color"]).transform(len)
    df
@@ -648,7 +654,10 @@ Create a list of dataframes, split using a delineation based on logic included i
    dfs = list(
        zip(
            *df.groupby(
-               (1 * (df["Case"] == "B")).cumsum().rolling(window=3, min_periods=1).median()
+               (1 * (df["Case"] == "B"))
+               .cumsum()
+               .rolling(window=3, min_periods=1)
+               .median()
            )
        )
    )[-1]
@@ -740,7 +749,18 @@ The :ref:`Pivot <reshaping.pivot>` docs.
                "yes",
            ],
            "Passed": ["yes" if x > 50 else "no" for x in grades],
-           "Employed": [True, True, True, False, False, False, False, True, True, False],
+           "Employed": [
+               True,
+               True,
+               True,
+               False,
+               False,
+               False,
+               False,
+               True,
+               True,
+               False,
+           ],
            "Grade": grades,
        }
    )
@@ -791,7 +811,9 @@ Apply
        return pd.Series(aList)
 
 
-   df_orgz = pd.concat({ind: row.apply(SeriesFromSubList) for ind, row in df.iterrows()})
+   df_orgz = pd.concat(
+       {ind: row.apply(SeriesFromSubList) for ind, row in df.iterrows()}
+   )
    df_orgz
 
 `Rolling apply with a DataFrame returning a Series
@@ -1162,7 +1184,12 @@ Option 1: pass rows explicitly to skip rows
     from io import StringIO
 
     pd.read_csv(
-        StringIO(data), sep=";", skiprows=[11, 12], index_col=0, parse_dates=True, header=10
+        StringIO(data),
+        sep=";",
+        skiprows=[11, 12],
+        index_col=0,
+        parse_dates=True,
+        header=10,
     )
 
 Option 2: read column names and then data

doc/source/user_guide/groupby.rst

@@ -267,7 +267,9 @@ the length of the ``groups`` dict, so it is largely just a convenience:
    height = np.random.normal(60, 10, size=n)
    time = pd.date_range("1/1/2000", periods=n)
    gender = np.random.choice(["male", "female"], size=n)
-   df = pd.DataFrame({"height": height, "weight": weight, "gender": gender}, index=time)
+   df = pd.DataFrame(
+       {"height": height, "weight": weight, "gender": gender}, index=time
+   )
 
 .. ipython:: python
 
@@ -767,7 +769,10 @@ For example, suppose we wished to standardize the data within each group:
    ts.head()
    ts.tail()
 
-   transformed = ts.groupby(lambda x: x.year).transform(lambda x: (x - x.mean()) / x.std())
+   transformed = ts.groupby(lambda x: x.year).transform(
+       lambda x: (x - x.mean()) / x.std()
+   )
+
 
 We would expect the result to now have mean 0 and standard deviation 1 within
 each group, which we can easily check: