DOC/BLD: Fix a bunch of doc build warnings that weren't being previously caught

doc/source/dsintro.rst

@@ -482,19 +482,23 @@ column-wise:
 .. ipython:: python
 
    index = date_range('1/1/2000', periods=8)
-   df = DataFrame(randn(8, 3), index=index,
-                  columns=['A', 'B', 'C'])
+   df = DataFrame(randn(8, 3), index=index, columns=list('ABC'))
    df
    type(df['A'])
    df - df['A']
 
-Technical purity aside, this case is so common in practice that supporting the
-special case is preferable to the alternative of forcing the user to transpose
-and do column-based alignment like so:
+.. warning::
 
-.. ipython:: python
+   .. code-block:: python
+
+      df - df['A']
+
+   is now deprecated and will be removed in a future release. The preferred way
+   to replicate this behavior is
+
+   .. code-block:: python
 
-   (df.T - df['A']).T
+      df.sub(df['A'], axis=0)
 
 For explicit control over the matching and broadcasting behavior, see the
 section on :ref:`flexible binary operations <basics.binop>`.

doc/source/timeseries.rst

@@ -930,89 +930,103 @@ They can be both positive and negative.
 
 .. ipython:: python
 
-    from datetime import datetime, timedelta
-    s  = Series(date_range('2012-1-1', periods=3, freq='D'))
-    td = Series([ timedelta(days=i) for i in range(3) ])
-    df = DataFrame(dict(A = s, B = td))
-    df
-    df['C'] = df['A'] + df['B']
-    df
-    df.dtypes
-
-    s - s.max()
-    s - datetime(2011,1,1,3,5)
-    s + timedelta(minutes=5)
+   from datetime import datetime, timedelta
+   s  = Series(date_range('2012-1-1', periods=3, freq='D'))
+   td = Series([ timedelta(days=i) for i in range(3) ])
+   df = DataFrame(dict(A = s, B = td))
+   df
+   df['C'] = df['A'] + df['B']
+   df
+   df.dtypes
+
+   s - s.max()
+   s - datetime(2011,1,1,3,5)
+   s + timedelta(minutes=5)
 
 Getting scalar results from a ``timedelta64[ns]`` series
 
+.. ipython:: python
+   :suppress:
+
+   from distutils.version import LooseVersion
+
 .. ipython:: python
 
    y = s - s[0]
    y
-   y.apply(lambda x: x.item().total_seconds())
-   y.apply(lambda x: x.item().days)
-
-.. note:: 
 
-   These operations are different in numpy 1.6.2 and in numpy >= 1.7. The ``timedelta64[ns]`` scalar
-   type in 1.6.2 is much like a ``datetime.timedelta``, while in 1.7 it is a nanosecond based integer.
-   A future version of pandas will make this transparent.
+   if LooseVersion(np.__version__) <= '1.6.2':
+       y.apply(lambda x: x.item().total_seconds())
+       y.apply(lambda x: x.item().days)
+   else:
+       y.apply(lambda x: x / np.timedelta64(1, 's'))
+       y.apply(lambda x: x / np.timedelta64(1, 'D'))
+
+.. note::
 
-   These are the equivalent operation to above in numpy >= 1.7
+   As you can see from the conditional statement above, these operations are
+   different in numpy 1.6.2 and in numpy >= 1.7. The ``timedelta64[ns]`` scalar
+   type in 1.6.2 is much like a ``datetime.timedelta``, while in 1.7 it is a
+   nanosecond based integer.  A future version of pandas will make this
+   transparent.
 
-   ``y.apply(lambda x: x.item()/np.timedelta64(1,'s'))``
+.. note::
 
-   ``y.apply(lambda x: x.item()/np.timedelta64(1,'D'))``
+   In numpy >= 1.7 dividing a ``timedelta64`` array by another ``timedelta64``
+   array will yield an array with dtype ``np.float64``.
 
 Series of timedeltas with ``NaT`` values are supported
 
 .. ipython:: python
 
-    y = s - s.shift()
-    y
+   y = s - s.shift()
+   y
+
 The can be set to ``NaT`` using ``np.nan`` analagously to datetimes
 
 .. ipython:: python
 
-    y[1] = np.nan
-    y
+   y[1] = np.nan
+   y
 
 Operands can also appear in a reversed order (a singluar object operated with a Series)
 
 .. ipython:: python
 
-    s.max() - s
-    datetime(2011,1,1,3,5) - s
-    timedelta(minutes=5) + s
+   s.max() - s
+   datetime(2011,1,1,3,5) - s
+   timedelta(minutes=5) + s
 
 Some timedelta numeric like operations are supported.
 
 .. ipython:: python
 
-    td - timedelta(minutes=5,seconds=5,microseconds=5)
+   td - timedelta(minutes=5, seconds=5, microseconds=5)
 
 ``min, max`` and the corresponding ``idxmin, idxmax`` operations are support on frames
 
 .. ipython:: python
 
-    df = DataFrame(dict(A = s - Timestamp('20120101')-timedelta(minutes=5,seconds=5),
-                        B = s - Series(date_range('2012-1-2', periods=3, freq='D'))))
-    df
+   A = s - Timestamp('20120101') - timedelta(minutes=5, seconds=5)
+   B = s - Series(date_range('2012-1-2', periods=3, freq='D'))
 
+   df = DataFrame(dict(A=A, B=B))
+   df
 
-    df.min()
-    df.min(axis=1)
+   df.min()
+   df.min(axis=1)
 
-    df.idxmin()
-    df.idxmax()
+   df.idxmin()
+   df.idxmax()
 
-``min, max`` operations are support on series, these return a single element ``timedelta64[ns]`` Series (this avoids
-having to deal with numpy timedelta64 issues). ``idxmin, idxmax`` are supported as well.
+``min, max`` operations are support on series, these return a single element
+``timedelta64[ns]`` Series (this avoids having to deal with numpy timedelta64
+issues). ``idxmin, idxmax`` are supported as well.
 
 .. ipython:: python
 
-    df.min().max()
-    df.min(axis=1).min()
+   df.min().max()
+   df.min(axis=1).min()
 
-    df.min().idxmax()
-    df.min(axis=1).idxmin()
+   df.min().idxmax()
+   df.min(axis=1).idxmin()

doc/source/v0.10.1.txt

@@ -69,7 +69,7 @@ Retrieving unique values in an indexable or data column.
 
    import warnings
    with warnings.catch_warnings():
-       warnings.simplefilter('ignore', category=DeprecationWarning)
+       warnings.simplefilter('ignore', category=UserWarning)
        store.unique('df','index')
        store.unique('df','string')
 

doc/source/visualization.rst

@@ -5,14 +5,14 @@
    :suppress:
 
    import numpy as np
+   from numpy.random import randn, rand, randint
    np.random.seed(123456)
-   from pandas import *
+   from pandas import DataFrame, Series, date_range, options
    import pandas.util.testing as tm
-   randn = np.random.randn
    np.set_printoptions(precision=4, suppress=True)
    import matplotlib.pyplot as plt
    plt.close('all')
-   options.display.mpl_style='default'
+   options.display.mpl_style = 'default'
 
 ************************
 Plotting with matplotlib
@@ -60,8 +60,7 @@ On DataFrame, ``plot`` is a convenience to plot all of the columns with labels:
 
 .. ipython:: python
 
-   df = DataFrame(randn(1000, 4), index=ts.index,
-                  columns=['A', 'B', 'C', 'D'])
+   df = DataFrame(randn(1000, 4), index=ts.index, columns=list('ABCD'))
    df = df.cumsum()
 
    @savefig frame_plot_basic.png width=6in
@@ -101,7 +100,7 @@ You can plot one column versus another using the `x` and `y` keywords in
 
    plt.figure()
 
-   df3 = DataFrame(np.random.randn(1000, 2), columns=['B', 'C']).cumsum()
+   df3 = DataFrame(randn(1000, 2), columns=['B', 'C']).cumsum()
    df3['A'] = Series(range(len(df)))
 
    @savefig df_plot_xy.png width=6in
@@ -169,7 +168,7 @@ Here is the default behavior, notice how the x-axis tick labelling is performed:
    df.A.plot()
 
 
-Using the ``x_compat`` parameter, you can suppress this bevahior:
+Using the ``x_compat`` parameter, you can suppress this behavior:
 
 .. ipython:: python
 
@@ -200,6 +199,15 @@ Targeting different subplots
 
 You can pass an ``ax`` argument to ``Series.plot`` to plot on a particular axis:
 
+.. ipython:: python
+   :suppress:
+
+   ts = Series(randn(1000), index=date_range('1/1/2000', periods=1000))
+   ts = ts.cumsum()
+
+   df = DataFrame(randn(1000, 4), index=ts.index, columns=list('ABCD'))
+   df = df.cumsum()
+
 .. ipython:: python
 
    fig, axes = plt.subplots(nrows=2, ncols=2)
@@ -210,6 +218,7 @@ You can pass an ``ax`` argument to ``Series.plot`` to plot on a particular axis:
    @savefig series_plot_multi.png width=6in
    df['D'].plot(ax=axes[1,1]); axes[1,1].set_title('D')
 
+
 .. _visualization.other:
 
 Other plotting features
@@ -239,7 +248,7 @@ bar plot:
 
 .. ipython:: python
 
-   df2 = DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
+   df2 = DataFrame(rand(10, 4), columns=['a', 'b', 'c', 'd'])
 
    @savefig bar_plot_multi_ex.png width=5in
    df2.plot(kind='bar');
@@ -298,10 +307,10 @@ New since 0.10.0, the ``by`` keyword can be specified to plot grouped histograms
 
 .. ipython:: python
 
-   data = Series(np.random.randn(1000))
+   data = Series(randn(1000))
 
    @savefig grouped_hist.png width=6in
-   data.hist(by=np.random.randint(0, 4, 1000))
+   data.hist(by=randint(0, 4, 1000))
 
 
 .. _visualization.box:
@@ -317,7 +326,7 @@ a uniform random variable on [0,1).
 
 .. ipython:: python
 
-   df = DataFrame(np.random.rand(10,5))
+   df = DataFrame(rand(10,5))
    plt.figure();
 
    @savefig box_plot_ex.png width=6in
@@ -328,7 +337,7 @@ groupings.  For instance,
 
 .. ipython:: python
 
-   df = DataFrame(np.random.rand(10,2), columns=['Col1', 'Col2'] )
+   df = DataFrame(rand(10,2), columns=['Col1', 'Col2'] )
    df['X'] = Series(['A','A','A','A','A','B','B','B','B','B'])
 
    plt.figure();
@@ -341,7 +350,7 @@ columns:
 
 .. ipython:: python
 
-   df = DataFrame(np.random.rand(10,3), columns=['Col1', 'Col2', 'Col3'])
+   df = DataFrame(rand(10,3), columns=['Col1', 'Col2', 'Col3'])
    df['X'] = Series(['A','A','A','A','A','B','B','B','B','B'])
    df['Y'] = Series(['A','B','A','B','A','B','A','B','A','B'])
 
@@ -361,7 +370,7 @@ Scatter plot matrix
 .. ipython:: python
 
    from pandas.tools.plotting import scatter_matrix
-   df = DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd'])
+   df = DataFrame(randn(1000, 4), columns=['a', 'b', 'c', 'd'])
 
    @savefig scatter_matrix_kde.png width=6in
    scatter_matrix(df, alpha=0.2, figsize=(6, 6), diagonal='kde')
@@ -378,7 +387,7 @@ setting `kind='kde'`:
 
 .. ipython:: python
 
-   ser = Series(np.random.randn(1000))
+   ser = Series(randn(1000))
 
    @savefig kde_plot.png width=6in
    ser.plot(kind='kde')
@@ -444,7 +453,7 @@ implies that the underlying data are not random.
 
    plt.figure()
 
-   data = Series(0.1 * np.random.random(1000) +
+   data = Series(0.1 * rand(1000) +
       0.9 * np.sin(np.linspace(-99 * np.pi, 99 * np.pi, num=1000)))
 
    @savefig lag_plot.png width=6in
@@ -467,7 +476,7 @@ confidence band.
 
    plt.figure()
 
-   data = Series(0.7 * np.random.random(1000) +
+   data = Series(0.7 * rand(1000) +
       0.3 * np.sin(np.linspace(-9 * np.pi, 9 * np.pi, num=1000)))
 
    @savefig autocorrelation_plot.png width=6in
@@ -488,7 +497,7 @@ are what constitutes the bootstrap plot.
 
    from pandas.tools.plotting import bootstrap_plot
 
-   data = Series(np.random.random(1000))
+   data = Series(rand(1000))
 
    @savefig bootstrap_plot.png width=6in
    bootstrap_plot(data, size=50, samples=500, color='grey')