seaborn

来源：互联网发布：网络大神小说排行榜编辑：程序博客网时间：2024/05/24 06:06

%matplotlib inline

import pandas as pdimport numpy as npimport seaborn as snsfrom sklearn import preprocessingimport matplotlib.pyplot as pltnp.random.seed(sum(map(ord, "aesthetics")))def sinplot(flip=1):    x=np.linspace(0,14,100)    for i in range(1,7):        plt.plot(x,np.sin(x+i*.5)*(7-i)*flip)sinplot()

![png](output_1_0.png)

sns.set_style('whitegrid')data=np.random.normal(size=(20,6))+np.arange(6)/2sns.boxplot(data=data)

stock=pd.read_csv('sample.csv',index_col=0)ohcl=stock.ix[:15,:4][::-1].Tsns.boxplot(ohcl)

# set_style()

sns.set_style("dark")sns.boxplot(ohcl)

sns.set_style("ticks",{'xtick.direction': u'out'})sns.boxplot(ohcl)sns.despine()

![png](output_6_0.png)

sns.violinplot(ohcl,palette="deep")sns.despine(offset=10,trim=True,left=True)

![png](output_7_0.png)

sns.axes_style()

{‘axes.axisbelow’: True, ‘axes.edgecolor’: ‘.15’, ‘axes.facecolor’: ‘white’, ‘axes.grid’: False, ‘axes.labelcolor’: ‘.15’, ‘axes.linewidth’: 1.25, ‘figure.facecolor’: ‘white’, ‘font.family’: [u’sans-serif’], ‘font.sans-serif’: [u’Arial’, u’Liberation Sans’, u’Bitstream Vera Sans’, u’sans-serif’], ‘grid.color’: ‘.8’, ‘grid.linestyle’: u’-‘, ‘image.cmap’: u’Greys’, ‘legend.frameon’: False, ‘legend.numpoints’: 1, ‘legend.scatterpoints’: 1, ‘lines.solid_capstyle’: u’round’, ‘text.color’: ‘.15’, ‘xtick.color’: ‘.15’, ‘xtick.direction’: u’out’, ‘xtick.major.size’: 6.0, ‘xtick.minor.size’: 3.0, ‘ytick.color’: ‘.15’, ‘ytick.direction’: u’out’, ‘ytick.major.size’: 6.0, ‘ytick.minor.size’: 3.0}

sns.set_context('talk')   #notebook ,poster,talksns.boxplot(ohcl)

sns.set_style('darkgrid')sns.regplot(x=stock.volume,y=stock.ma10,logx=True,x_estimator=np.mean)

sns.lmplot(x='ma5',y='ma20',data=stock,aspect=.5)sns.lmplot(x='ma5',y='ma20',data=stock,aspect=.5)

sns.jointplot(x='ma10',y='price_change',data=stock,kind='reg')#{ "scatter" | "reg" | "resid" | "kde" | "hex" }, optional

sns.jointplot(x='ma10',y='price_change',data=stock,kind='kde')

sns.jointplot(x='ma10',y='price_change',data=stock,kind='resid')

sns.jointplot(x='ma10',y='price_change',data=stock,kind='hex')

sns.jointplot(x='ma10',y='price_change',data=stock,kind='scatter')

titanic = sns.load_dataset("titanic")tips = sns.load_dataset("tips")iris = sns.load_dataset("iris")tips

total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 5 25.29 4.71 Male No Sun Dinner 4 6 8.77 2.00 Male No Sun Dinner 2 7 26.88 3.12 Male No Sun Dinner 4 8 15.04 1.96 Male No Sun Dinner 2 9 14.78 3.23 Male No Sun Dinner 2 10 10.27 1.71 Male No Sun Dinner 2 11 35.26 5.00 Female No Sun Dinner 4 12 15.42 1.57 Male No Sun Dinner 2 13 18.43 3.00 Male No Sun Dinner 4 14 14.83 3.02 Female No Sun Dinner 2 15 21.58 3.92 Male No Sun Dinner 2 16 10.33 1.67 Female No Sun Dinner 3 17 16.29 3.71 Male No Sun Dinner 3 18 16.97 3.50 Female No Sun Dinner 3 19 20.65 3.35 Male No Sat Dinner 3 20 17.92 4.08 Male No Sat Dinner 2 21 20.29 2.75 Female No Sat Dinner 2 22 15.77 2.23 Female No Sat Dinner 2 23 39.42 7.58 Male No Sat Dinner 4 24 19.82 3.18 Male No Sat Dinner 2 25 17.81 2.34 Male No Sat Dinner 4 26 13.37 2.00 Male No Sat Dinner 2 27 12.69 2.00 Male No Sat Dinner 2 28 21.70 4.30 Male No Sat Dinner 2 29 19.65 3.00 Female No Sat Dinner 2 … … … … … … … … 214 28.17 6.50 Female Yes Sat Dinner 3 215 12.90 1.10 Female Yes Sat Dinner 2 216 28.15 3.00 Male Yes Sat Dinner 5 217 11.59 1.50 Male Yes Sat Dinner 2 218 7.74 1.44 Male Yes Sat Dinner 2 219 30.14 3.09 Female Yes Sat Dinner 4 220 12.16 2.20 Male Yes Fri Lunch 2 221 13.42 3.48 Female Yes Fri Lunch 2 222 8.58 1.92 Male Yes Fri Lunch 1 223 15.98 3.00 Female No Fri Lunch 3 224 13.42 1.58 Male Yes Fri Lunch 2 225 16.27 2.50 Female Yes Fri Lunch 2 226 10.09 2.00 Female Yes Fri Lunch 2 227 20.45 3.00 Male No Sat Dinner 4 228 13.28 2.72 Male No Sat Dinner 2 229 22.12 2.88 Female Yes Sat Dinner 2 230 24.01 2.00 Male Yes Sat Dinner 4 231 15.69 3.00 Male Yes Sat Dinner 3 232 11.61 3.39 Male No Sat Dinner 2 233 10.77 1.47 Male No Sat Dinner 2 234 15.53 3.00 Male Yes Sat Dinner 2 235 10.07 1.25 Male No Sat Dinner 2 236 12.60 1.00 Male Yes Sat Dinner 2 237 32.83 1.17 Male Yes Sat Dinner 2 238 35.83 4.67 Female No Sat Dinner 3 239 29.03 5.92 Male No Sat Dinner 3 240 27.18 2.00 Female Yes Sat Dinner 2 241 22.67 2.00 Male Yes Sat Dinner 2 242 17.82 1.75 Male No Sat Dinner 2 243 18.78 3.00 Female No Thur Dinner 2

244 rows × 7 columns

sns.stripplot(x='day',y='total_bill',data=tips)

<matplotlib.axes._subplots.AxesSubplot at 0x42fce940>

png

sns.stripplot(x='day',y='total_bill',data=tips,jitter=True)

<matplotlib.axes._subplots.AxesSubplot at 0x430546a0>

png

sns.swarmplot(x='day',y='total_bill',data=tips)

<matplotlib.axes._subplots.AxesSubplot at 0x4235d860>

png

sns.swarmplot(x='day',y='total_bill',hue='sex',data=tips)

<matplotlib.axes._subplots.AxesSubplot at 0x43503b00>

png

sns.swarmplot(x='size',y='total_bill',data=tips)

<matplotlib.axes._subplots.AxesSubplot at 0x435c7d30>

png

sns.swarmplot(x='total_bill',y='day',hue='time',data=tips)

<matplotlib.axes._subplots.AxesSubplot at 0x3e010828>

png

sns.boxplot(x='day',y='total_bill',data=tips)

<matplotlib.axes._subplots.AxesSubplot at 0x3e1b2240>

png

hs_stock=pd.read_csv('hs_stock.csv',index_col=0)sns.boxplot(x='code',y='ma10',data=hs_stock)

<matplotlib.axes._subplots.AxesSubplot at 0x517754e0>

png

sns.violinplot(x="total_bill", y="day", hue="time", data=tips);

png

sns.violinplot(x="total_bill", y="day", hue="time", data=tips,               bw=.1, scale="count", scale_hue=False);

png

sns.violinplot(x="day", y="total_bill", hue="sex", data=tips, split=True);

png

sns.violinplot(x="day", y="total_bill", data=tips, inner=None)sns.swarmplot(x="day", y="total_bill", data=tips, color="w", alpha=.5);

png

sns.barplot(x="sex", y="survived", hue="class", data=titanic);

png

sns.countplot(x="deck", data=titanic, palette="Greens_d");

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sns.factorplot(x="day", y="total_bill", hue="smoker", data=tips);

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sns.factorplot(x="day", y="total_bill", hue="smoker", data=tips, kind="bar");

png

sns.factorplot(x="day", y="total_bill", hue="smoker",               col="time", data=tips, kind="swarm");

png

sns.factorplot(x="time", y="total_bill", hue="smoker",               col="day", data=tips, kind="box", size=4, aspect=.5);

png

g = sns.PairGrid(tips,                 x_vars=["smoker", "time", "sex"],                 y_vars=["total_bill", "tip"],                 aspect=.75, size=3.5)g.map(sns.violinplot, palette="pastel");

png

ohls=hs_stock.ix[:3000,:]g=sns.FacetGrid(ohls,col='code')g.map(sns.boxplot,'open',"ma5")g.add_legend()

<seaborn.axisgrid.FacetGrid at 0x660e4f28>

png

gk=sns.PairGrid(ohls.ix[:,:9])gk.map(plt.scatter)

<seaborn.axisgrid.PairGrid at 0xaa438828>

png

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