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seabornBoot.py
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seabornBoot.py
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import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
sns.set(style="darkgrid")
tips = sns.load_dataset('tips')
tips.head()
tips.tail()
#relplot
sns.relplot(x='total_bill',y='tip',data=tips)
sns.relplot(x='total_bill',y='tip',data=tips,hue='smoker')
sns.relplot(x='total_bill',y='tip',data=tips,hue='smoker',style="time")
sns.relplot(x='total_bill',y='tip',hue='size',data=tips)
sns.relplot(x="total_bill",y='tip',hue='size',data=tips,palette='ch:r=-0.8,l=0.95')
sns.relplot(x='total_bill',y='tip',size='size',data=tips,sizes=(15,200))
from numpy.random import randn
df = pd.DataFrame(dict(time=np.arange(500),value=randn(500).cumsum()))
df.head()
sns.relplot(x='time',y='value',kind='line',data=df,sort=True)
df = pd.DataFrame(randn(500,2).cumsum(axis=0),columns=['time','value'])
df.head()
sns.relplot(x='time',y='value',kind='line',data=df,sort=False)
sns.relplot(x='timepoint',y='signal',kind='line',data=fmri)
sns.relplot(x='timepoint',y='signal',kind='line',data=fmri,ci=False)
sns.relplot(x='timepoint',y='signal',estimator=None,kind='line',data=fmri)
sns.relplot(x='timepoint',y='signal',kind='line',hue='event',data=fmri)
sns.relplot(x='timepoint',y='signal',hue='region',style='event',kind='line',data=fmri)
sns.relplot(x='timepoint',y='signal',hue='region',style='event',kind='line',data=fmri,markers=True,dashes=False)
sns.relplot(x='timepoint',y='signal',hue='region',units='subject',estimator=None,kind='line',data=fmri.query("event=='stim'"))
sns.relplot(x='time',y='firing_rate',data=dots,kind='line')
sns.relplot(x='time',y='firing_rate',data=dots,hue='coherence',kind='line',style='choice')
palette = sns.cubehelix_palette(light=0.8,n_colors=6)
sns.relplot(x='time',y='firing_rate',data=dots,hue='coherence',kind='line',style='choice',palette=palette)
sns.relplot(x='time',y='firing_rate',size='coherence',style='choice',kind='line',data=dots,sizes=(1,5))
sns.relplot(x='time',y='firing_rate',size='coherence',style='choice',hue='coherence',kind='line',data=dots,sizes=(1,5))
df = pd.DataFrame(dict(time=pd.date_range('2019-06-02',periods=500),value=randn(500).cumsum()))
g = sns.relplot(x='time',y='value',kind='line',data=df)
g.fig.autofmt_xdate()
sns.relplot(x='total_bill',y='tip',hue='smoker',col='time',data=tips)
sns.relplot(x='total_bill',y='tip',hue='smoker',col='size',data=tips)
sns.relplot(x='total_bill',y='tip',hue='smoker',row='size',data=tips)
sns.relplot(x='timepoint',y='signal',hue='subject',col='region',row='event',height=3,kind='line',estimator=None,data=fmri)
sns.relplot(x='total_bill',y='tip',hue='smoker',col='size',data=tips,col_wrap=3,height=3)
# lineplot
fmri.head()
sns.lineplot(x='timepoint',y='signal',data=fmri)
sns.lineplot(x='timepoint',y='signal',hue='event',data=fmri)
sns.lineplot(x='timepoint',y='signal',hue='region',data=fmri)
sns.lineplot(x='timepoint',y='signal',style='event',hue='region',data=fmri)
sns.lineplot(x='timepoint',y='signal',style='event',hue='region',data=fmri,markers=True,ci=68,err_style='bars')
sns.lineplot(x='timepoint',y='signal',hue='event',units='subject',estimator=None,lw=1,data=fmri.query("region=='frontal'"))
sns.lineplot(x='time',y='firing_rate',hue='coherence',style='choice',data=dots)
# scatterplot
sns.scatterplot(x='total_bill',y='tip',data=tips,hue='smoker',size='size',style='time')
iris = sns.load_dataset('iris')
iris.head()
sns.scatterplot(x='sepal_length',y='petal_length',data=iris)
sns.scatterplot(x=iris['sepal_length'],y=iris['petal_length'])
# Categorical Data Plot
# catplot
titanic = sns.load_dataset('titanic')
titanic.head()
sns.catplot(x='day',y='total_bill',data=tips)
sns.catplot(x='day',y='total_bill',data=tips,jitter=False)
sns.catplot(x='day',y='total_bill',data=tips,kind='swarm')
sns.catplot(x='day',y='total_bill',data=tips,kind='swarm',hue='sex')
sns.catplot(x='smoker',y='tip',data=tips)
sns.catplot(x='smoker',y='tip',data=tips,order=['No','Yes'])
# boxplot
sns.catplot(x='day',y='total_bill',kind='box',data=tips)
sns.catplot(x='day',y='total_bill',hue='time',kind='box',data=tips)
sns.catplot(x='day',y='total_bill',hue='sex',kind='box',data=tips)
sns.catplot(x='day',y='total_bill',hue='sex',kind='box',dodge=False,data=tips,)
sns.catplot(x='color',y='price',kind='boxen',data=diamonds.sort_values('color'))
sns.catplot(x='day',y='total_bill',hue='sex',kind='boxen',dodge=False,data=tips,)
# violin
sns.catplot(x='total_bill',y='day',hue='time',kind='violin',data=tips)
sns.catplot(x='total_bill',y='day',hue='sex',kind='violin',data=tips)
sns.catplot(x='total_bill',y='day',hue='sex',kind='violin',data=tips,split=True)
sns.catplot(x='total_bill',y='day',hue='sex',kind='violin',data=tips,split=True,inner='stick')
g = sns.catplot(x='day',y='total_bill',kind='violin',inner=None,data=tips)
sns.swarmplot(x='day',y='total_bill',color='k',size=3,data=tips,ax= g.ax)
# barplot
sns.catplot(x='sex',y='survived',hue='class',kind='bar',data=titanic)
sns.catplot(x='deck',kind='count',palette='ch:0.25',data=titanic)
sns.catplot(x='deck',kind='count',palette='ch:0.25',data=titanic,hue='class')
# pointplot
sns.catplot(x='sex',y='survived',hue='class',kind='point',data=titanic)