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DOC: Fix PEP-8 issues in visualization.rst #23899

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DOC: Fix PEP-8 issues in visualization.rst #23899

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102 changes: 60 additions & 42 deletions doc/source/visualization.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand All @@ -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`:
Expand Down Expand Up @@ -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+')
Expand Down Expand Up @@ -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
Expand All @@ -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();

Expand All @@ -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:
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand All @@ -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))
Expand Down Expand Up @@ -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))
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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:
Expand All @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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:
Expand All @@ -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
Expand All @@ -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:
Expand All @@ -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()
Expand Down Expand Up @@ -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:
Expand Down