DOC: Remove multiple blank lines in ipython directives (#41400)

Author: rhshadrach

doc/source/user_guide/basics.rst

@@ -1184,11 +1184,9 @@ a single value and returning a single value. For example:
 
    df4
 
-
    def f(x):
        return len(str(x))
 
-
    df4["one"].map(f)
    df4.applymap(f)
 

doc/source/user_guide/cookbook.rst

@@ -494,15 +494,12 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to
 
    S = pd.Series([i / 100.0 for i in range(1, 11)])
 
-
    def cum_ret(x, y):
        return x * (1 + y)
 
-
    def red(x):
        return functools.reduce(cum_ret, x, 1.0)
 
-
    S.expanding().apply(red, raw=True)
 
 
@@ -514,12 +511,10 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to
    df = pd.DataFrame({"A": [1, 1, 2, 2], "B": [1, -1, 1, 2]})
    gb = df.groupby("A")
 
-
    def replace(g):
        mask = g < 0
        return g.where(mask, g[~mask].mean())
 
-
    gb.transform(replace)
 
 `Sort groups by aggregated data
@@ -551,13 +546,11 @@ Unlike agg, apply's callable is passed a sub-DataFrame which gives you access to
    rng = pd.date_range(start="2014-10-07", periods=10, freq="2min")
    ts = pd.Series(data=list(range(10)), index=rng)
 
-
    def MyCust(x):
        if len(x) > 2:
            return x[1] * 1.234
        return pd.NaT
 
-
    mhc = {"Mean": np.mean, "Max": np.max, "Custom": MyCust}
    ts.resample("5min").apply(mhc)
    ts
@@ -803,11 +796,9 @@ Apply
        index=["I", "II", "III"],
    )
 
-
    def SeriesFromSubList(aList):
        return pd.Series(aList)
 
-
    df_orgz = pd.concat(
        {ind: row.apply(SeriesFromSubList) for ind, row in df.iterrows()}
    )
@@ -827,12 +818,10 @@ Rolling Apply to multiple columns where function calculates a Series before a Sc
    )
    df
 
-
    def gm(df, const):
        v = ((((df["A"] + df["B"]) + 1).cumprod()) - 1) * const
        return v.iloc[-1]
 
-
    s = pd.Series(
        {
            df.index[i]: gm(df.iloc[i: min(i + 51, len(df) - 1)], 5)
@@ -859,11 +848,9 @@ Rolling Apply to multiple columns where function returns a Scalar (Volume Weight
    )
    df
 
-
    def vwap(bars):
        return (bars.Close * bars.Volume).sum() / bars.Volume.sum()
 
-
    window = 5
    s = pd.concat(
        [

doc/source/user_guide/groupby.rst

@@ -1617,12 +1617,10 @@ column index name will be used as the name of the inserted column:
        }
    )
 
-
    def compute_metrics(x):
        result = {"b_sum": x["b"].sum(), "c_mean": x["c"].mean()}
        return pd.Series(result, name="metrics")
 
-
    result = df.groupby("a").apply(compute_metrics)
 
    result

doc/source/user_guide/io.rst

@@ -4648,11 +4648,9 @@ chunks.
 
    store.append("dfeq", dfeq, data_columns=["number"])
 
-
    def chunks(l, n):
        return [l[i: i + n] for i in range(0, len(l), n)]
 
-
    evens = [2, 4, 6, 8, 10]
    coordinates = store.select_as_coordinates("dfeq", "number=evens")
    for c in chunks(coordinates, 2):

doc/source/user_guide/merging.rst

@@ -1578,4 +1578,5 @@ to ``True``.
 You may also keep all the original values even if they are equal.
 
 .. ipython:: python
+
    df.compare(df2, keep_shape=True, keep_equal=True)

doc/source/user_guide/reshaping.rst

@@ -18,7 +18,6 @@ Reshaping by pivoting DataFrame objects
 
    import pandas._testing as tm
 
-
    def unpivot(frame):
        N, K = frame.shape
        data = {
@@ -29,7 +28,6 @@ Reshaping by pivoting DataFrame objects
        columns = ["date", "variable", "value"]
        return pd.DataFrame(data, columns=columns)
 
-
    df = unpivot(tm.makeTimeDataFrame(3))
 
 Data is often stored in so-called "stacked" or "record" format:

doc/source/user_guide/sparse.rst

@@ -325,7 +325,6 @@ In the example below, we transform the ``Series`` to a sparse representation of
        row_levels=["A", "B"], column_levels=["C", "D"], sort_labels=True
    )
 
-
    A
    A.todense()
    rows

doc/source/user_guide/text.rst

@@ -297,24 +297,19 @@ positional argument (a regex object) and return a string.
    # Reverse every lowercase alphabetic word
    pat = r"[a-z]+"
 
-
    def repl(m):
        return m.group(0)[::-1]
 
-
    pd.Series(["foo 123", "bar baz", np.nan], dtype="string").str.replace(
        pat, repl, regex=True
    )
 
-
    # Using regex groups
    pat = r"(?P<one>\w+) (?P<two>\w+) (?P<three>\w+)"
 
-
    def repl(m):
        return m.group("two").swapcase()
 
-
    pd.Series(["Foo Bar Baz", np.nan], dtype="string").str.replace(
        pat, repl, regex=True
    )

doc/source/user_guide/timeseries.rst

@@ -1422,7 +1422,6 @@ An example of how holidays and holiday calendars are defined:
         MO,
     )
 
-
     class ExampleCalendar(AbstractHolidayCalendar):
         rules = [
             USMemorialDay,
@@ -1435,7 +1434,6 @@ An example of how holidays and holiday calendars are defined:
             ),
         ]
 
-
     cal = ExampleCalendar()
     cal.holidays(datetime.datetime(2012, 1, 1), datetime.datetime(2012, 12, 31))
 
@@ -1707,13 +1705,11 @@ We can instead only resample those groups where we have points as follows:
     from functools import partial
     from pandas.tseries.frequencies import to_offset
 
-
     def round(t, freq):
         # round a Timestamp to a specified freq
         freq = to_offset(freq)
         return pd.Timestamp((t.value // freq.delta.value) * freq.delta.value)
 
-
     ts.groupby(partial(round, freq="3T")).sum()
 
 .. _timeseries.aggregate:
@@ -2255,11 +2251,9 @@ To convert from an ``int64`` based YYYYMMDD representation.
    s = pd.Series([20121231, 20141130, 99991231])
    s
 
-
    def conv(x):
        return pd.Period(year=x // 10000, month=x // 100 % 100, day=x % 100, freq="D")
 
-
    s.apply(conv)
    s.apply(conv)[2]
 

doc/source/user_guide/window.rst

@@ -212,7 +212,6 @@ from present information back to past information. This allows the rolling windo
 
    df
 
-
 .. _window.custom_rolling_window:
 
 Custom window rolling
@@ -294,13 +293,12 @@ conditions. In these cases it can be useful to perform forward-looking rolling w
 This :func:`BaseIndexer <pandas.api.indexers.BaseIndexer>` subclass implements a closed fixed-width
 forward-looking rolling window, and we can use it as follows:
 
-.. ipython:: ipython
+.. ipython:: python
 
    from pandas.api.indexers import FixedForwardWindowIndexer
    indexer = FixedForwardWindowIndexer(window_size=2)
    df.rolling(indexer, min_periods=1).sum()
 
-
 .. _window.rolling_apply:
 
 Rolling apply
@@ -319,7 +317,6 @@ the windows are cast as :class:`Series` objects (``raw=False``) or ndarray objec
    s = pd.Series(range(10))
    s.rolling(window=4).apply(mad, raw=True)
 
-
 .. _window.numba_engine:
 
 Numba engine