第十一章 可视化

Visualization with Seaborn

• Seaborn is a Python data visualization library based on matplotlib.

• It provides a high-level interface for drawing attractive and informative statistical graphics.

• it integrates with the functionality provided by Pandas DataFrames.

%matplotlib inline

import numpy as np; np.random.seed(22)
import seaborn as sns; 
import pylab as plt

To be fair, the Matplotlib team is addressing this:

• it has recently added the plt.style tools,

• is starting to handle Pandas data more seamlessly.

Matplotlib Styles

plt.style.available

['seaborn-dark',
 'seaborn-darkgrid',
 'seaborn-ticks',
 'fivethirtyeight',
 'seaborn-whitegrid',
 'classic',
 '_classic_test',
 'fast',
 'seaborn-talk',
 'seaborn-dark-palette',
 'seaborn-bright',
 'seaborn-pastel',
 'grayscale',
 'seaborn-notebook',
 'ggplot',
 'seaborn-colorblind',
 'seaborn-muted',
 'seaborn',
 'Solarize_Light2',
 'seaborn-paper',
 'bmh',
 'tableau-colorblind10',
 'seaborn-white',
 'dark_background',
 'seaborn-poster',
 'seaborn-deep']

The basic way to switch to a stylesheet is to call

plt.style.use('stylename')

But keep in mind that this will change the style for the rest of the session! Alternatively, you can use the style context manager, which sets a style temporarily:

with plt.style.context('stylename'):
    make_a_plot()

x = np.linspace(0, 10, 1000)
plt.plot(x, np.sin(x));

plt.style.use('fivethirtyeight')
x = np.linspace(0, 10, 1000)
plt.plot(x, np.sin(x));

Seaborn Datasets

sns.get_dataset_names()

/Users/datalab/anaconda3/lib/python3.7/site-packages/seaborn/utils.py:376: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system ("lxml"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.

The code that caused this warning is on line 376 of the file /Users/datalab/anaconda3/lib/python3.7/site-packages/seaborn/utils.py. To get rid of this warning, pass the additional argument 'features="lxml"' to the BeautifulSoup constructor.

  gh_list = BeautifulSoup(http)

['anscombe',
 'attention',
 'brain_networks',
 'car_crashes',
 'diamonds',
 'dots',
 'exercise',
 'flights',
 'fmri',
 'gammas',
 'iris',
 'mpg',
 'planets',
 'tips',
 'titanic']

lineplot

fmri = sns.load_dataset("fmri")

fmri.head()

	subject	timepoint	event	region	signal
0	s13	18.0	stim	parietal	-0.017552
1	s5	14.0	stim	parietal	-0.080883
2	s12	18.0	stim	parietal	-0.081033
3	s11	18.0	stim	parietal	-0.046134
4	s10	18.0	stim	parietal	-0.037970

ax = sns.lineplot(x="timepoint", y="signal", err_style="band",data=fmri)

ax = sns.lineplot(x="timepoint", y="signal", err_style="bars",data=fmri)

ax = sns.lineplot(x="timepoint", y="signal", ci=95, color="m",data=fmri)
ax = sns.lineplot(x="timepoint", y="signal", ci=68, color="b",data=fmri)

ax = sns.lineplot(x="timepoint", y="signal", ci='sd', color="m",data=fmri)

ax = sns.lineplot(x="timepoint", y="signal", estimator=np.median, data=fmri)

#ax = sns.tsplot(data=data, err_style="boot_traces", n_boot=500)
ax = sns.lineplot(x="timepoint", y="signal", err_style="band", n_boot=500, data=fmri)

http://seaborn.pydata.org/generated/seaborn.barplot.html#seaborn.barplot

Bar Plot

import seaborn as sns; 
sns.set(color_codes=True)
tips = sns.load_dataset("tips")
ax = sns.barplot(x="day", y="total_bill", data=tips)

ax = sns.barplot(x="day", y="total_bill", hue="sex", data=tips)

Clustermap

Discovering structure in heatmap data

http://seaborn.pydata.org/examples/structured_heatmap.html

df = sns.load_dataset("titanic")
df.corr()

	survived	pclass	age	sibsp	parch	fare	adult_male	alone
survived	1.000000	-0.338481	-0.077221	-0.035322	0.081629	0.257307	-0.557080	-0.203367
pclass	-0.338481	1.000000	-0.369226	0.083081	0.018443	-0.549500	0.094035	0.135207
age	-0.077221	-0.369226	1.000000	-0.308247	-0.189119	0.096067	0.280328	0.198270
sibsp	-0.035322	0.083081	-0.308247	1.000000	0.414838	0.159651	-0.253586	-0.584471
parch	0.081629	0.018443	-0.189119	0.414838	1.000000	0.216225	-0.349943	-0.583398
fare	0.257307	-0.549500	0.096067	0.159651	0.216225	1.000000	-0.182024	-0.271832
adult_male	-0.557080	0.094035	0.280328	-0.253586	-0.349943	-0.182024	1.000000	0.404744
alone	-0.203367	0.135207	0.198270	-0.584471	-0.583398	-0.271832	0.404744	1.000000

# Draw the full plot
ax = sns.clustermap(df.corr(), center=0, cmap="vlag",
               #row_colors=network_colors, col_colors=network_colors,
               linewidths=.75, figsize=(5, 5))