Review datapythonista for file timeseries.rst

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

Author: FHaase

doc/source/visualization.rst

@@ -2,20 +2,15 @@
 .. _visualization:
 
 .. ipython:: python
-   :flake8-add-ignore: E702, E703
+   :flake8-add-ignore: E702, # multiple statements on one line. Needed for @savefig
    :suppress:
 
    import numpy as np
    import pandas as pd
 
-   import matplotlib  # noqa: F401
-   # matplotlib.style.use('default')
-   import matplotlib.pyplot as plt
-
    np.random.seed(123456)
    np.set_printoptions(precision=4, suppress=True)
    pd.options.display.max_rows = 15
-   plt.close('all')
 
 
 *************
@@ -25,11 +20,14 @@ Visualization
 We use the standard convention for referencing the matplotlib API:
 
 .. ipython:: python
-   :flake8-group: None
-   :flake8-set-ignore: F401
 
    import matplotlib.pyplot as plt
 
+.. ipython:: python
+   :suppress:
+
+   plt.close('all')
+
 We provide the basics in pandas to easily create decent looking plots.
 See the :ref:`ecosystem <ecosystem.visualization>` section for visualization
 libraries that go beyond the basics documented here.
@@ -81,7 +79,8 @@ 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();
+   plt.figure()
+   df.plot()
 
 You can plot one column versus another using the `x` and `y` keywords in
 :meth:`~DataFrame.plot`:
@@ -133,21 +132,22 @@ For example, a bar plot can be created the following way:
 
 .. ipython:: python
 
-   plt.figure();
+   plt.figure()
 
    @savefig bar_plot_ex.png
-   df.iloc[5].plot(kind='bar');
+   df.iloc[5].plot(kind='bar')
 
 You can also create these other plots using the methods ``DataFrame.plot.<kind>`` instead of providing the ``kind`` keyword argument. This makes it easier to discover plot methods and the specific arguments they use:
 
 .. ipython::
     :flake8-group: None
-    :flake8-add-ignore: E999, E225, F821
+    :flake8-add-ignore: E999, E225, F821,  # E999 breaks linting for complete block
     :verbatim:
 
-    In [14]: df = pd.DataFrame()
+    In [0]: df = pd.DataFrame()
 
-    In [15]: df.plot.<TAB>
+    In [1]: df.plot.<TAB>
+    Out[1]:
     df.plot.area     df.plot.barh     df.plot.density  df.plot.hist     df.plot.line     df.plot.scatter
     df.plot.bar      df.plot.box      df.plot.hexbin   df.plot.kde      df.plot.pie
 
@@ -178,7 +178,7 @@ For labeled, non-time series data, you may wish to produce a bar plot:
 
 .. ipython:: python
 
-   plt.figure();
+   plt.figure()
 
    @savefig bar_plot_ex.png
    df.iloc[5].plot.bar(); plt.axhline(0, color='k')
@@ -198,7 +198,7 @@ bar plot:
    df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
 
    @savefig bar_plot_multi_ex.png
-   df2.plot.bar();
+   df2.plot.bar()
 
 To produce a stacked bar plot, pass ``stacked=True``:
 
@@ -211,7 +211,7 @@ To produce a stacked bar plot, pass ``stacked=True``:
 .. ipython:: python
 
    @savefig bar_plot_stacked_ex.png
-   df2.plot.bar(stacked=True);
+   df2.plot.bar(stacked=True)
 
 To get horizontal bar plots, use the ``barh`` method:
 
@@ -224,7 +224,7 @@ To get horizontal bar plots, use the ``barh`` method:
 .. ipython:: python
 
    @savefig barh_plot_stacked_ex.png
-   df2.plot.barh(stacked=True);
+   df2.plot.barh(stacked=True)
 
 .. _visualization.hist:
 
@@ -238,7 +238,7 @@ 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();
+   plt.figure()
 
    @savefig hist_new.png
    df4.plot.hist(alpha=0.5)
@@ -254,7 +254,7 @@ using the ``bins`` keyword.
 
 .. ipython:: python
 
-   plt.figure();
+   plt.figure()
 
    @savefig hist_new_stacked.png
    df4.plot.hist(stacked=True, bins=20)
@@ -270,7 +270,7 @@ horizontal and cumulative histograms can be drawn by
 
 .. ipython:: python
 
-   plt.figure();
+   plt.figure()
 
    @savefig hist_new_kwargs.png
    df4['a'].plot.hist(orientation='horizontal', cumulative=True)
@@ -288,7 +288,7 @@ The existing interface ``DataFrame.hist`` to plot histogram still can be used.
 
 .. ipython:: python
 
-   plt.figure();
+   plt.figure()
 
    @savefig hist_plot_ex.png
    df['A'].diff().hist()
@@ -402,7 +402,7 @@ The existing interface ``DataFrame.boxplot`` to plot boxplot still can be used.
    :okwarning:
 
    df = pd.DataFrame(np.random.rand(10, 5))
-   plt.figure();
+   plt.figure()
 
    @savefig box_plot_ex.png
    bp = df.boxplot()
@@ -422,7 +422,7 @@ 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();
+   plt.figure()
 
    @savefig box_plot_ex2.png
    bp = df.boxplot(by='X')
@@ -443,7 +443,7 @@ 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();
+   plt.figure()
 
    @savefig box_plot_ex3.png
    bp = df.boxplot(column=['Col1', 'Col2'], by=['X', 'Y'])
@@ -531,7 +531,7 @@ When input data contains `NaN`, it will be automatically filled by 0. If you wan
    df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
 
    @savefig area_plot_stacked.png
-   df.plot.area();
+   df.plot.area()
 
 To produce an unstacked plot, pass ``stacked=False``. Alpha value is set to 0.5 unless otherwise specified:
 
@@ -544,7 +544,7 @@ To produce an unstacked plot, pass ``stacked=False``. Alpha value is set to 0.5
 .. ipython:: python
 
    @savefig area_plot_unstacked.png
-   df.plot.area(stacked=False);
+   df.plot.area(stacked=False)
 
 .. _visualization.scatter:
 
@@ -567,16 +567,16 @@ These can be specified by the ``x`` and ``y`` keywords.
    df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])
 
    @savefig scatter_plot.png
-   df.plot.scatter(x='a', y='b');
+   df.plot.scatter(x='a', y='b')
 
 To plot multiple column groups in a single axes, repeat ``plot`` method specifying target ``ax``.
 It is recommended to specify ``color`` and ``label`` keywords to distinguish each groups.
 
 .. ipython:: python
 
-   ax = df.plot.scatter(x='a', y='b', color='DarkBlue', label='Group 1');
+   ax = df.plot.scatter(x='a', y='b', color='DarkBlue', label='Group 1')
    @savefig scatter_plot_repeated.png
-   df.plot.scatter(x='c', y='d', color='DarkGreen', label='Group 2', ax=ax);
+   df.plot.scatter(x='c', y='d', color='DarkGreen', label='Group 2', ax=ax)
 
 .. ipython:: python
    :suppress:
@@ -589,7 +589,7 @@ each point:
 .. ipython:: python
 
    @savefig scatter_plot_colored.png
-   df.plot.scatter(x='a', y='b', c='c', s=50);
+   df.plot.scatter(x='a', y='b', c='c', s=50)
 
 
 .. ipython:: python
@@ -604,7 +604,7 @@ bubble chart using a column of the ``DataFrame`` as the bubble size.
 .. ipython:: python
 
    @savefig scatter_plot_bubble.png
-   df.plot.scatter(x='a', y='b', s=df['c'] * 200);
+   df.plot.scatter(x='a', y='b', s=df['c'] * 200)
 
 .. ipython:: python
    :suppress:
@@ -1091,7 +1091,7 @@ layout and formatting of the returned plot:
 .. ipython:: python
 
    @savefig series_plot_basic2.png
-   plt.figure(); ts.plot(style='k--', label='Series');
+   plt.figure(); ts.plot(style='k--', label='Series')
 
 .. ipython:: python
    :suppress:
@@ -1286,7 +1286,7 @@ with the ``subplots`` keyword:
 .. ipython:: python
 
    @savefig frame_plot_subplots.png
-   df.plot(subplots=True, figsize=(6, 6));
+   df.plot(subplots=True, figsize=(6, 6))
 
 .. ipython:: python
    :suppress:
@@ -1309,7 +1309,7 @@ or columns needed, given the other.
 .. ipython:: python
 
    @savefig frame_plot_subplots_layout.png
-   df.plot(subplots=True, layout=(2, 3), figsize=(6, 6), sharex=False);
+   df.plot(subplots=True, layout=(2, 3), figsize=(6, 6), sharex=False)
 
 .. ipython:: python
    :suppress:
@@ -1320,7 +1320,7 @@ The above example is identical to using:
 
 .. ipython:: python
 
-   df.plot(subplots=True, layout=(2, -1), figsize=(6, 6), sharex=False);
+   df.plot(subplots=True, layout=(2, -1), figsize=(6, 6), sharex=False)
 
 .. ipython:: python
    :suppress:
@@ -1340,15 +1340,15 @@ otherwise you will see a warning.
 
 .. ipython:: python
 
-   fig, axes = plt.subplots(4, 4, figsize=(6, 6));
-   plt.subplots_adjust(wspace=0.5, hspace=0.5);
+   fig, axes = plt.subplots(4, 4, figsize=(6, 6))
+   plt.subplots_adjust(wspace=0.5, hspace=0.5)
    target1 = [axes[0][0], axes[1][1], axes[2][2], axes[3][3]]
    target2 = [axes[3][0], axes[2][1], axes[1][2], axes[0][3]]
 
-   df.plot(subplots=True, ax=target1, legend=False, sharex=False, sharey=False);
+   df.plot(subplots=True, ax=target1, legend=False, sharex=False, sharey=False)
    @savefig frame_plot_subplots_multi_ax.png
    (-df).plot(subplots=True, ax=target2, legend=False,
-              sharex=False, sharey=False);
+              sharex=False, sharey=False)
 
 .. ipython:: python
    :suppress:
@@ -1377,12 +1377,12 @@ Another option is passing an ``ax`` argument to :meth:`Series.plot` to plot on a
 .. ipython:: python
 
    fig, axes = plt.subplots(nrows=2, ncols=2)
-   df['A'].plot(ax=axes[0, 0]); axes[0, 0].set_title('A');
-   df['B'].plot(ax=axes[0, 1]); axes[0, 1].set_title('B');
-   df['C'].plot(ax=axes[1, 0]); axes[1, 0].set_title('C');
+   df['A'].plot(ax=axes[0, 0]); axes[0, 0].set_title('A')
+   df['B'].plot(ax=axes[0, 1]); axes[0, 1].set_title('B')
+   df['C'].plot(ax=axes[1, 0]); axes[1, 0].set_title('C')
 
    @savefig series_plot_multi.png
-   df['D'].plot(ax=axes[1, 1]); axes[1, 1].set_title('D');
+   df['D'].plot(ax=axes[1, 1]); axes[1, 1].set_title('D')
 
 .. ipython:: python
    :suppress: