DOC: Supress setups less in user guide (#54086)

* DOC: Supress setups less in user guide

* Fix error

* Formatting

Author: mroeschke

doc/source/user_guide/advanced.rst

@@ -470,11 +470,6 @@ Compare the above with the result using ``drop_level=True`` (the default value).
 
    df.xs("one", level="second", axis=1, drop_level=True)
 
-.. ipython:: python
-   :suppress:
-
-   df = df.T
-
 .. _advanced.advanced_reindex:
 
 Advanced reindexing and alignment

doc/source/user_guide/basics.rst

@@ -220,11 +220,6 @@ either match on the *index* or *columns* via the **axis** keyword:
    df.sub(column, axis="index")
    df.sub(column, axis=0)
 
-.. ipython:: python
-   :suppress:
-
-   df_orig = df
-
 Furthermore you can align a level of a MultiIndexed DataFrame with a Series.
 
 .. ipython:: python
@@ -272,13 +267,9 @@ case the result will be NaN (you can later replace NaN with some other value
 using ``fillna`` if you wish).
 
 .. ipython:: python
-   :suppress:
 
    df2 = df.copy()
    df2["three"]["a"] = 1.0
-
-.. ipython:: python
-
    df
    df2
    df + df2
@@ -936,17 +927,13 @@ Another useful feature is the ability to pass Series methods to carry out some
 Series operation on each column or row:
 
 .. ipython:: python
-   :suppress:
 
    tsdf = pd.DataFrame(
        np.random.randn(10, 3),
        columns=["A", "B", "C"],
        index=pd.date_range("1/1/2000", periods=10),
    )
    tsdf.iloc[3:7] = np.nan
-
-.. ipython:: python
-
    tsdf
    tsdf.apply(pd.Series.interpolate)
 
@@ -1170,13 +1157,9 @@ another array or value), the methods :meth:`~DataFrame.map` on DataFrame
 and analogously :meth:`~Series.map` on Series accept any Python function taking
 a single value and returning a single value. For example:
 
-.. ipython:: python
-   :suppress:
-
-   df4 = df_orig.copy()
-
 .. ipython:: python
 
+   df4 = df.copy()
    df4
 
    def f(x):
@@ -1280,14 +1263,9 @@ is a common enough operation that the :meth:`~DataFrame.reindex_like` method is
 available to make this simpler:
 
 .. ipython:: python
-   :suppress:
 
    df2 = df.reindex(["a", "b", "c"], columns=["one", "two"])
    df3 = df2 - df2.mean()
-
-
-.. ipython:: python
-
    df2
    df3
    df.reindex_like(df2)

doc/source/user_guide/groupby.rst

@@ -271,7 +271,6 @@ the length of the ``groups`` dict, so it is largely just a convenience:
 ``GroupBy`` will tab complete column names (and other attributes):
 
 .. ipython:: python
-   :suppress:
 
    n = 10
    weight = np.random.normal(166, 20, size=n)
@@ -281,9 +280,6 @@ the length of the ``groups`` dict, so it is largely just a convenience:
    df = pd.DataFrame(
        {"height": height, "weight": weight, "gender": gender}, index=time
    )
-
-.. ipython:: python
-
    df
    gb = df.groupby("gender")
 
@@ -334,19 +330,14 @@ number:
 Grouping with multiple levels is supported.
 
 .. ipython:: python
-   :suppress:
 
    arrays = [
        ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
        ["doo", "doo", "bee", "bee", "bop", "bop", "bop", "bop"],
        ["one", "two", "one", "two", "one", "two", "one", "two"],
    ]
-   tuples = list(zip(*arrays))
-   index = pd.MultiIndex.from_tuples(tuples, names=["first", "second", "third"])
+   index = pd.MultiIndex.from_arrays(arrays, names=["first", "second", "third"])
    s = pd.Series(np.random.randn(8), index=index)
-
-.. ipython:: python
-
    s
    s.groupby(level=["first", "second"]).sum()
 
@@ -963,17 +954,13 @@ match the shape of the input array.
 Another common data transform is to replace missing data with the group mean.
 
 .. ipython:: python
-   :suppress:
 
    cols = ["A", "B", "C"]
    values = np.random.randn(1000, 3)
    values[np.random.randint(0, 1000, 100), 0] = np.nan
    values[np.random.randint(0, 1000, 50), 1] = np.nan
    values[np.random.randint(0, 1000, 200), 2] = np.nan
    data_df = pd.DataFrame(values, columns=cols)
-
-.. ipython:: python
-
    data_df
 
    countries = np.array(["US", "UK", "GR", "JP"])

doc/source/user_guide/indexing.rst

@@ -1029,14 +1029,10 @@ input data shape. ``where`` is used under the hood as the implementation.
 The code below is equivalent to ``df.where(df < 0)``.
 
 .. ipython:: python
-   :suppress:
 
    dates = pd.date_range('1/1/2000', periods=8)
    df = pd.DataFrame(np.random.randn(8, 4),
                      index=dates, columns=['A', 'B', 'C', 'D'])
-
-.. ipython:: python
-
    df[df < 0]
 
 In addition, ``where`` takes an optional ``other`` argument for replacement of
@@ -1431,7 +1427,6 @@ This plot was created using a ``DataFrame`` with 3 columns each containing
 floating point values generated using ``numpy.random.randn()``.
 
 .. ipython:: python
-   :suppress:
 
    df = pd.DataFrame(np.random.randn(8, 4),
                      index=dates, columns=['A', 'B', 'C', 'D'])
@@ -1694,15 +1689,11 @@ DataFrame has a :meth:`~DataFrame.set_index` method which takes a column name
 To create a new, re-indexed DataFrame:
 
 .. ipython:: python
-   :suppress:
 
    data = pd.DataFrame({'a': ['bar', 'bar', 'foo', 'foo'],
                         'b': ['one', 'two', 'one', 'two'],
                         'c': ['z', 'y', 'x', 'w'],
                         'd': [1., 2., 3, 4]})
-
-.. ipython:: python
-
    data
    indexed1 = data.set_index('c')
    indexed1
@@ -1812,11 +1803,6 @@ But it turns out that assigning to the product of chained indexing has
 inherently unpredictable results. To see this, think about how the Python
 interpreter executes this code:
 
-.. ipython:: python
-    :suppress:
-
-    value = None
-
 .. code-block:: python
 
    dfmi.loc[:, ('one', 'second')] = value

doc/source/user_guide/io.rst

@@ -704,20 +704,16 @@ Comments
 Sometimes comments or meta data may be included in a file:
 
 .. ipython:: python
-   :suppress:
 
    data = (
        "ID,level,category\n"
        "Patient1,123000,x # really unpleasant\n"
        "Patient2,23000,y # wouldn't take his medicine\n"
        "Patient3,1234018,z # awesome"
    )
-
    with open("tmp.csv", "w") as fh:
        fh.write(data)
 
-.. ipython:: python
-
    print(open("tmp.csv").read())
 
 By default, the parser includes the comments in the output:

doc/source/user_guide/missing_data.rst

@@ -142,14 +142,10 @@ Missing values propagate naturally through arithmetic operations between pandas
 objects.
 
 .. ipython:: python
-   :suppress:
 
    df = df2.loc[:, ["one", "two", "three"]]
    a = df2.loc[df2.index[:5], ["one", "two"]].ffill()
    b = df2.loc[df2.index[:5], ["one", "two", "three"]]
-
-.. ipython:: python
-
    a
    b
    a + b
@@ -247,12 +243,8 @@ If we only want consecutive gaps filled up to a certain number of data points,
 we can use the ``limit`` keyword:
 
 .. ipython:: python
-   :suppress:
 
    df.iloc[2:4, :] = np.nan
-
-.. ipython:: python
-
    df
    df.ffill(limit=1)
 
@@ -308,13 +300,9 @@ You may wish to simply exclude labels from a data set which refer to missing
 data. To do this, use :meth:`~DataFrame.dropna`:
 
 .. ipython:: python
-   :suppress:
 
    df["two"] = df["two"].fillna(0)
    df["three"] = df["three"].fillna(0)
-
-.. ipython:: python
-
    df
    df.dropna(axis=0)
    df.dropna(axis=1)
@@ -333,7 +321,6 @@ Both Series and DataFrame objects have :meth:`~DataFrame.interpolate`
 that, by default, performs linear interpolation at missing data points.
 
 .. ipython:: python
-   :suppress:
 
    np.random.seed(123456)
    idx = pd.date_range("1/1/2000", periods=100, freq="BM")
@@ -343,8 +330,6 @@ that, by default, performs linear interpolation at missing data points.
    ts[60:80] = np.nan
    ts = ts.cumsum()
 
-.. ipython:: python
-
    ts
    ts.count()
    @savefig series_before_interpolate.png
@@ -361,26 +346,19 @@ that, by default, performs linear interpolation at missing data points.
 Index aware interpolation is available via the ``method`` keyword:
 
 .. ipython:: python
-   :suppress:
 
    ts2 = ts.iloc[[0, 1, 30, 60, 99]]
-
-.. ipython:: python
-
    ts2
    ts2.interpolate()
    ts2.interpolate(method="time")
 
 For a floating-point index, use ``method='values'``:
 
 .. ipython:: python
-   :suppress:
 
    idx = [0.0, 1.0, 10.0]
    ser = pd.Series([0.0, np.nan, 10.0], idx)
 
-.. ipython:: python
-
    ser
    ser.interpolate()
    ser.interpolate(method="values")

doc/source/user_guide/visualization.rst

@@ -42,12 +42,9 @@ The ``plot`` method on Series and DataFrame is just a simple wrapper around
 :meth:`plt.plot() <matplotlib.axes.Axes.plot>`:
 
 .. ipython:: python
-   :suppress:
 
    np.random.seed(123456)
 
-.. ipython:: python
-
    ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
    ts = ts.cumsum()
 
@@ -1468,7 +1465,6 @@ otherwise you will see a warning.
 Another option is passing an ``ax`` argument to :meth:`Series.plot` to plot on a particular axis:
 
 .. ipython:: python
-   :suppress:
 
    np.random.seed(123456)
    ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
@@ -1583,12 +1579,8 @@ Plotting tables
 Plotting with matplotlib table is now supported in  :meth:`DataFrame.plot` and :meth:`Series.plot` with a ``table`` keyword. The ``table`` keyword can accept ``bool``, :class:`DataFrame` or :class:`Series`. The simple way to draw a table is to specify ``table=True``. Data will be transposed to meet matplotlib's default layout.
 
 .. ipython:: python
-   :suppress:
 
    np.random.seed(123456)
-
-.. ipython:: python
-
    fig, ax = plt.subplots(1, 1, figsize=(7, 6.5))
    df = pd.DataFrame(np.random.rand(5, 3), columns=["a", "b", "c"])
    ax.xaxis.tick_top()  # Display x-axis ticks on top.
@@ -1663,12 +1655,8 @@ colormaps will produce lines that are not easily visible.
 To use the cubehelix colormap, we can pass ``colormap='cubehelix'``.
 
 .. ipython:: python
-   :suppress:
 
    np.random.seed(123456)
-
-.. ipython:: python
-
    df = pd.DataFrame(np.random.randn(1000, 10), index=ts.index)
    df = df.cumsum()
 
@@ -1701,12 +1689,8 @@ Alternatively, we can pass the colormap itself:
 Colormaps can also be used other plot types, like bar charts:
 
 .. ipython:: python
-   :suppress:
 
    np.random.seed(123456)
-
-.. ipython:: python
-
    dd = pd.DataFrame(np.random.randn(10, 10)).map(abs)
    dd = dd.cumsum()
 
@@ -1764,12 +1748,8 @@ level of refinement you would get when plotting via pandas, it can be faster
 when plotting a large number of points.
 
 .. ipython:: python
-   :suppress:
 
    np.random.seed(123456)
-
-.. ipython:: python
-
    price = pd.Series(
        np.random.randn(150).cumsum(),
        index=pd.date_range("2000-1-1", periods=150, freq="B"),