Remove plt.figure()

calling plt.figure() doesn't change outputted graphs.
Therefore it's not needed.

Signed-off-by: Fabian Haase <[email protected]>

Author: FHaase

doc/source/visualization.rst

@@ -79,7 +79,6 @@ On DataFrame, :meth:`~DataFrame.plot` is a convenience to plot all of the column
    df = df.cumsum()
 
    @savefig frame_plot_basic.png
-   plt.figure()
    df.plot()
 
 You can plot one column versus another using the `x` and `y` keywords in
@@ -89,7 +88,6 @@ You can plot one column versus another using the `x` and `y` keywords in
    :suppress:
 
    plt.close('all')
-   plt.figure()
    np.random.seed(123456)
 
 .. ipython:: python
@@ -132,8 +130,6 @@ For example, a bar plot can be created the following way:
 
 .. ipython:: python
 
-   plt.figure()
-
    @savefig bar_plot_ex.png
    df.iloc[5].plot(kind='bar')
 
@@ -178,8 +174,6 @@ For labeled, non-time series data, you may wish to produce a bar plot:
 
 .. ipython:: python
 
-   plt.figure()
-
    @savefig bar_plot_ex.png
    df.iloc[5].plot.bar(); plt.axhline(0, color='k')
 
@@ -190,7 +184,6 @@ bar plot:
    :suppress:
 
    plt.close('all')
-   plt.figure()
    np.random.seed(123456)
 
 .. ipython:: python
@@ -206,7 +199,6 @@ To produce a stacked bar plot, pass ``stacked=True``:
    :suppress:
 
    plt.close('all')
-   plt.figure()
 
 .. ipython:: python
 
@@ -219,7 +211,6 @@ To get horizontal bar plots, use the ``barh`` method:
    :suppress:
 
    plt.close('all')
-   plt.figure()
 
 .. ipython:: python
 
@@ -238,8 +229,6 @@ Histograms can be drawn by using the :meth:`DataFrame.plot.hist` and :meth:`Seri
    df4 = pd.DataFrame({'a': np.random.randn(1000) + 1, 'b': np.random.randn(1000),
                        'c': np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])
 
-   plt.figure()
-
    @savefig hist_new.png
    df4.plot.hist(alpha=0.5)
 
@@ -254,8 +243,6 @@ using the ``bins`` keyword.
 
 .. ipython:: python
 
-   plt.figure()
-
    @savefig hist_new_stacked.png
    df4.plot.hist(stacked=True, bins=20)
 
@@ -270,8 +257,6 @@ horizontal and cumulative histograms can be drawn by
 
 .. ipython:: python
 
-   plt.figure()
-
    @savefig hist_new_kwargs.png
    df4['a'].plot.hist(orientation='horizontal', cumulative=True)
 
@@ -288,8 +273,6 @@ The existing interface ``DataFrame.hist`` to plot histogram still can be used.
 
 .. ipython:: python
 
-   plt.figure()
-
    @savefig hist_plot_ex.png
    df['A'].diff().hist()
 
@@ -303,8 +286,6 @@ subplots:
 
 .. ipython:: python
 
-   plt.figure()
-
    @savefig frame_hist_ex.png
    df.diff().hist(color='k', alpha=0.5, bins=50)
 
@@ -315,7 +296,6 @@ The ``by`` keyword can be specified to plot grouped histograms:
    :suppress:
 
    plt.close('all')
-   plt.figure()
    np.random.seed(123456)
 
 .. ipython:: python
@@ -402,7 +382,6 @@ The existing interface ``DataFrame.boxplot`` to plot boxplot still can be used.
    :okwarning:
 
    df = pd.DataFrame(np.random.rand(10, 5))
-   plt.figure()
 
    @savefig box_plot_ex.png
    bp = df.boxplot()
@@ -422,8 +401,6 @@ groupings.  For instance,
    df = pd.DataFrame(np.random.rand(10, 2), columns=['Col1', 'Col2'])
    df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'])
 
-   plt.figure()
-
    @savefig box_plot_ex2.png
    bp = df.boxplot(by='X')
 
@@ -443,8 +420,6 @@ columns:
    df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'])
    df['Y'] = pd.Series(['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B'])
 
-   plt.figure()
-
    @savefig box_plot_ex3.png
    bp = df.boxplot(column=['Col1', 'Col2'], by=['X', 'Y'])
 
@@ -524,7 +499,6 @@ When input data contains `NaN`, it will be automatically filled by 0. If you wan
    :suppress:
 
    np.random.seed(123456)
-   plt.figure()
 
 .. ipython:: python
 
@@ -539,7 +513,6 @@ To produce an unstacked plot, pass ``stacked=False``. Alpha value is set to 0.5
    :suppress:
 
    plt.close('all')
-   plt.figure()
 
 .. ipython:: python
 
@@ -560,7 +533,6 @@ These can be specified by the ``x`` and ``y`` keywords.
 
    np.random.seed(123456)
    plt.close('all')
-   plt.figure()
 
 .. ipython:: python
 
@@ -626,7 +598,6 @@ too dense to plot each point individually.
 .. ipython:: python
    :suppress:
 
-   plt.figure()
    np.random.seed(123456)
 
 .. ipython:: python
@@ -654,7 +625,6 @@ given by column ``z``. The bins are aggregated with NumPy's ``max`` function.
    :suppress:
 
    plt.close('all')
-   plt.figure()
    np.random.seed(123456)
 
 .. ipython:: python
@@ -688,7 +658,6 @@ A ``ValueError`` will be raised if there are any negative values in your data.
    :suppress:
 
    np.random.seed(123456)
-   plt.figure()
 
 .. ipython:: python
 
@@ -718,7 +687,6 @@ drawn in each pie plots by default; specify ``legend=False`` to hide it.
    :suppress:
 
    np.random.seed(123456)
-   plt.figure()
 
 .. ipython:: python
 
@@ -744,12 +712,6 @@ If you want to hide wedge labels, specify ``labels=None``.
 If ``fontsize`` is specified, the value will be applied to wedge labels.
 Also, other keywords supported by :func:`matplotlib.pyplot.pie` can be used.
 
-
-.. ipython:: python
-   :suppress:
-
-   plt.figure()
-
 .. ipython:: python
 
    @savefig series_pie_plot_options.png
@@ -762,7 +724,6 @@ If you pass values whose sum total is less than 1.0, matplotlib draws a semicirc
    :suppress:
 
    plt.close('all')
-   plt.figure()
 
 .. ipython:: python
 
@@ -860,7 +821,6 @@ You can create density plots using the :meth:`Series.plot.kde` and :meth:`DataFr
 .. ipython:: python
    :suppress:
 
-   plt.figure()
    np.random.seed(123456)
 
 .. ipython:: python
@@ -895,8 +855,6 @@ of the same class will usually be closer together and form larger structures.
 
    data = pd.read_csv('data/iris.data')
 
-   plt.figure()
-
    @savefig andrews_curves.png
    andrews_curves(data, 'Name')
 
@@ -919,8 +877,6 @@ represents one data point. Points that tend to cluster will appear closer togeth
 
    data = pd.read_csv('data/iris.data')
 
-   plt.figure()
-
    @savefig parallel_coordinates.png
    parallel_coordinates(data, 'Name')
 
@@ -949,8 +905,6 @@ be passed, and when ``lag=1`` the plot is essentially ``data[:-1]`` vs.
 
    from pandas.plotting import lag_plot
 
-   plt.figure()
-
    spacing = np.linspace(-99 * np.pi, 99 * np.pi, num=1000)
    data = pd.Series(0.1 * np.random.rand(1000) + 0.9 * np.sin(spacing))
 
@@ -986,8 +940,6 @@ autocorrelation plots.
 
    from pandas.plotting import autocorrelation_plot
 
-   plt.figure()
-
    spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
    data = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
 
@@ -1055,8 +1007,6 @@ for more information.
 
    data = pd.read_csv('data/iris.data')
 
-   plt.figure()
-
    @savefig radviz.png
    radviz(data, 'Name')
 
@@ -1091,7 +1041,7 @@ layout and formatting of the returned plot:
 .. ipython:: python
 
    @savefig series_plot_basic2.png
-   plt.figure(); ts.plot(style='k--', label='Series')
+   ts.plot(style='k--', label='Series')
 
 .. ipython:: python
    :suppress:
@@ -1138,7 +1088,6 @@ You may pass ``logy`` to get a log-scale Y axis.
 .. ipython:: python
    :suppress:
 
-   plt.figure()
    np.random.seed(123456)
 
 .. ipython:: python
@@ -1162,11 +1111,6 @@ Plotting on a Secondary Y-axis
 
 To plot data on a secondary y-axis, use the ``secondary_y`` keyword:
 
-.. ipython:: python
-   :suppress:
-
-   plt.figure()
-
 .. ipython:: python
 
    df.A.plot()
@@ -1184,7 +1128,6 @@ keyword:
 
 .. ipython:: python
 
-   plt.figure()
    ax = df.plot(secondary_y=['A', 'B'])
    ax.set_ylabel('CD scale')
    @savefig frame_plot_secondary_y.png
@@ -1201,8 +1144,6 @@ with "(right)" in the legend. To turn off the automatic marking, use the
 
 .. ipython:: python
 
-   plt.figure()
-
    @savefig frame_plot_secondary_y_no_right.png
    df.plot(secondary_y=['A', 'B'], mark_right=False)
 
@@ -1223,8 +1164,6 @@ Here is the default behavior, notice how the x-axis tick labeling is performed:
 
 .. ipython:: python
 
-   plt.figure()
-
    @savefig ser_plot_suppress.png
    df.A.plot()
 
@@ -1237,8 +1176,6 @@ Using the ``x_compat`` parameter, you can suppress this behavior:
 
 .. ipython:: python
 
-   plt.figure()
-
    @savefig ser_plot_suppress_parm.png
    df.A.plot(x_compat=True)
 
@@ -1252,8 +1189,6 @@ in ``pandas.plotting.plot_params`` can be used in a `with statement`:
 
 .. ipython:: python
 
-   plt.figure()
-
    @savefig ser_plot_suppress_context.png
    with pd.plotting.plot_params.use('x_compat', True):
        df.A.plot(color='r')
@@ -1531,8 +1466,6 @@ To use the cubehelix colormap, we can pass ``colormap='cubehelix'``.
    df = pd.DataFrame(np.random.randn(1000, 10), index=ts.index)
    df = df.cumsum()
 
-   plt.figure()
-
    @savefig cubehelix.png
    df.plot(colormap='cubehelix')
 
@@ -1547,8 +1480,6 @@ Alternatively, we can pass the colormap itself:
 
    from matplotlib import cm
 
-   plt.figure()
-
    @savefig cubehelix_cm.png
    df.plot(colormap=cm.cubehelix)
 
@@ -1569,8 +1500,6 @@ Colormaps can also be used other plot types, like bar charts:
    dd = pd.DataFrame(np.random.randn(10, 10)).applymap(abs)
    dd = dd.cumsum()
 
-   plt.figure()
-
    @savefig greens.png
    dd.plot.bar(colormap='Greens')
 
@@ -1583,8 +1512,6 @@ Parallel coordinates charts:
 
 .. ipython:: python
 
-   plt.figure()
-
    @savefig parallel_gist_rainbow.png
    parallel_coordinates(data, 'Name', colormap='gist_rainbow')
 
@@ -1597,8 +1524,6 @@ Andrews curves charts:
 
 .. ipython:: python
 
-   plt.figure()
-
    @savefig andrews_curve_winter.png
    andrews_curves(data, 'Name', colormap='winter')
 
@@ -1634,8 +1559,6 @@ when plotting a large number of points.
    ma = price.rolling(20).mean()
    mstd = price.rolling(20).std()
 
-   plt.figure()
-
    plt.plot(price.index, price, 'k')
    plt.plot(ma.index, ma, 'b')
    @savefig bollinger.png