Fix PEP-8 issues in visualization.rst (#23899)

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

Author: FHaase

doc/source/visualization.rst

@@ -6,13 +6,11 @@
 
    import numpy as np
    import pandas as pd
+
    np.random.seed(123456)
    np.set_printoptions(precision=4, suppress=True)
    pd.options.display.max_rows = 15
-   import matplotlib
-   # matplotlib.style.use('default')
-   import matplotlib.pyplot as plt
-   plt.close('all')
+
 
 *************
 Visualization
@@ -50,7 +48,8 @@ The ``plot`` method on Series and DataFrame is just a simple wrapper around
 
 .. ipython:: python
 
-   ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
+   ts = pd.Series(np.random.randn(1000),
+                  index=pd.date_range('1/1/2000', periods=1000))
    ts = ts.cumsum()
 
    @savefig series_plot_basic.png
@@ -69,11 +68,13 @@ On DataFrame, :meth:`~DataFrame.plot` is a convenience to plot all of the column
 
 .. ipython:: python
 
-   df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
+   df = pd.DataFrame(np.random.randn(1000, 4),
+                     index=ts.index, columns=list('ABCD'))
    df = df.cumsum()
 
+   plt.figure();
    @savefig frame_plot_basic.png
-   plt.figure(); df.plot();
+   df.plot();
 
 You can plot one column versus another using the `x` and `y` keywords in
 :meth:`~DataFrame.plot`:
@@ -355,8 +356,8 @@ more complicated colorization, you can get each drawn artists by passing
 
 .. ipython:: python
 
-   color = dict(boxes='DarkGreen', whiskers='DarkOrange',
-                medians='DarkBlue', caps='Gray')
+   color = {'boxes': 'DarkGreen', 'whiskers': 'DarkOrange',
+            'medians': 'DarkBlue', 'caps': 'Gray'}
 
    @savefig box_new_colorize.png
    df.plot.box(color=color, sym='r+')
@@ -391,7 +392,7 @@ The existing interface ``DataFrame.boxplot`` to plot boxplot still can be used.
 .. ipython:: python
    :okwarning:
 
-   df = pd.DataFrame(np.random.rand(10,5))
+   df = pd.DataFrame(np.random.rand(10, 5))
    plt.figure();
 
    @savefig box_plot_ex.png
@@ -409,8 +410,8 @@ groupings.  For instance,
 .. ipython:: python
    :okwarning:
 
-   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'])
+   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();
 
@@ -429,14 +430,14 @@ columns:
 .. ipython:: python
    :okwarning:
 
-   df = pd.DataFrame(np.random.rand(10,3), columns=['Col1', 'Col2', 'Col3'])
-   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'])
+   df = pd.DataFrame(np.random.rand(10, 3), columns=['Col1', 'Col2', 'Col3'])
+   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'])
+   bp = df.boxplot(column=['Col1', 'Col2'], by=['X', 'Y'])
 
 .. ipython:: python
    :suppress:
@@ -594,7 +595,7 @@ 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:
@@ -654,8 +655,7 @@ given by column ``z``. The bins are aggregated with NumPy's ``max`` function.
    df['z'] = np.random.uniform(0, 3, 1000)
 
    @savefig hexbin_plot_agg.png
-   df.plot.hexbin(x='a', y='b', C='z', reduce_C_function=np.max,
-           gridsize=25)
+   df.plot.hexbin(x='a', y='b', C='z', reduce_C_function=np.max, gridsize=25)
 
 .. ipython:: python
    :suppress:
@@ -682,7 +682,8 @@ A ``ValueError`` will be raised if there are any negative values in your data.
 
 .. ipython:: python
 
-   series = pd.Series(3 * np.random.rand(4), index=['a', 'b', 'c', 'd'], name='series')
+   series = pd.Series(3 * np.random.rand(4),
+                      index=['a', 'b', 'c', 'd'], name='series')
 
    @savefig series_pie_plot.png
    series.plot.pie(figsize=(6, 6))
@@ -711,7 +712,8 @@ drawn in each pie plots by default; specify ``legend=False`` to hide it.
 
 .. ipython:: python
 
-   df = pd.DataFrame(3 * np.random.rand(4, 2), index=['a', 'b', 'c', 'd'], columns=['x', 'y'])
+   df = pd.DataFrame(3 * np.random.rand(4, 2),
+                     index=['a', 'b', 'c', 'd'], columns=['x', 'y'])
 
    @savefig df_pie_plot.png
    df.plot.pie(subplots=True, figsize=(8, 4))
@@ -939,8 +941,8 @@ be passed, and when ``lag=1`` the plot is essentially ``data[:-1]`` vs.
 
    plt.figure()
 
-   data = pd.Series(0.1 * np.random.rand(1000) +
-       0.9 * np.sin(np.linspace(-99 * np.pi, 99 * np.pi, num=1000)))
+   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))
 
    @savefig lag_plot.png
    lag_plot(data)
@@ -976,8 +978,8 @@ autocorrelation plots.
 
    plt.figure()
 
-   data = pd.Series(0.7 * np.random.rand(1000) +
-      0.3 * np.sin(np.linspace(-9 * np.pi, 9 * np.pi, num=1000)))
+   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))
 
    @savefig autocorrelation_plot.png
    autocorrelation_plot(data)
@@ -1078,8 +1080,9 @@ layout and formatting of the returned plot:
 
 .. ipython:: python
 
+   plt.figure();
    @savefig series_plot_basic2.png
-   plt.figure(); ts.plot(style='k--', label='Series');
+   ts.plot(style='k--', label='Series');
 
 .. ipython:: python
    :suppress:
@@ -1106,7 +1109,8 @@ shown by default.
 
 .. ipython:: python
 
-   df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
+   df = pd.DataFrame(np.random.randn(1000, 4),
+                     index=ts.index, columns=list('ABCD'))
    df = df.cumsum()
 
    @savefig frame_plot_basic_noleg.png
@@ -1130,7 +1134,8 @@ You may pass ``logy`` to get a log-scale Y axis.
 
 .. ipython:: python
 
-   ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
+   ts = pd.Series(np.random.randn(1000),
+                  index=pd.date_range('1/1/2000', periods=1000))
    ts = np.exp(ts.cumsum())
 
    @savefig series_plot_logy.png
@@ -1326,14 +1331,15 @@ 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);
    @savefig frame_plot_subplots_multi_ax.png
-   (-df).plot(subplots=True, ax=target2, legend=False, sharex=False, sharey=False);
+   (-df).plot(subplots=True, ax=target2, legend=False,
+              sharex=False, sharey=False);
 
 .. ipython:: python
    :suppress:
@@ -1346,10 +1352,12 @@ Another option is passing an ``ax`` argument to :meth:`Series.plot` to plot on a
    :suppress:
 
    np.random.seed(123456)
-   ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
+   ts = pd.Series(np.random.randn(1000),
+                  index=pd.date_range('1/1/2000', periods=1000))
    ts = ts.cumsum()
 
-   df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
+   df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
+                     columns=list('ABCD'))
    df = df.cumsum()
 
 .. ipython:: python
@@ -1360,12 +1368,15 @@ 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');
+   df['D'].plot(ax=axes[1, 1]);
    @savefig series_plot_multi.png
-   df['D'].plot(ax=axes[1,1]); axes[1,1].set_title('D');
+   axes[1, 1].set_title('D');
 
 .. ipython:: python
    :suppress:
@@ -1392,10 +1403,16 @@ Here is an example of one way to easily plot group means with standard deviation
 .. ipython:: python
 
    # Generate the data
-   ix3 = pd.MultiIndex.from_arrays([['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b'], ['foo', 'foo', 'bar', 'bar', 'foo', 'foo', 'bar', 'bar']], names=['letter', 'word'])
-   df3 = pd.DataFrame({'data1': [3, 2, 4, 3, 2, 4, 3, 2], 'data2': [6, 5, 7, 5, 4, 5, 6, 5]}, index=ix3)
+   ix3 = pd.MultiIndex.from_arrays([
+       ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b'],
+       ['foo', 'foo', 'bar', 'bar', 'foo', 'foo', 'bar', 'bar']],
+       names=['letter', 'word'])
+
+   df3 = pd.DataFrame({'data1': [3, 2, 4, 3, 2, 4, 3, 2],
+                       'data2': [6, 5, 7, 5, 4, 5, 6, 5]}, index=ix3)
 
-   # Group by index labels and take the means and standard deviations for each group
+   # Group by index labels and take the means and standard deviations
+   # for each group
    gp3 = df3.groupby(level=('letter', 'word'))
    means = gp3.mean()
    errors = gp3.std()
@@ -1616,7 +1633,8 @@ when plotting a large number of points.
    plt.plot(price.index, price, 'k')
    plt.plot(ma.index, ma, 'b')
    @savefig bollinger.png
-   plt.fill_between(mstd.index, ma-2*mstd, ma+2*mstd, color='b', alpha=0.2)
+   plt.fill_between(mstd.index, ma - 2 * mstd, ma + 2 * mstd,
+                    color='b', alpha=0.2)
 
 .. ipython:: python
    :suppress: