seaborn tutorial

 # visualizing statistical relations

In [1]:

#relplot for ststtistical realation
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd 

In [16]:

a = sns.load_dataset("flights")
sns.relplot(x="passengers", y= "month", data=a)

Out[16]:

<seaborn.axisgrid.FacetGrid at 0x1ea533d01f0>

In [17]:

a = sns.load_dataset("flights")
sns.relplot(x="passengers",hue="year", y= "month", data=a)

Out[17]:

<seaborn.axisgrid.FacetGrid at 0x1ea53d35400>

In [20]:

b=sns.load_dataset("tips")
sns.relplot(x="time", y="tip",data=b,kind="line")

Out[20]:

<seaborn.axisgrid.FacetGrid at 0x1ea53dae940>

ploting catorigal data¶

In [21]:

sns.catplot(x="day",y="total_bill",data=b)

Out[21]:

<seaborn.axisgrid.FacetGrid at 0x1ea53dddc40>

In [24]:

sns.catplot(x="day", y="total_bill",data=b,kind="boxen")

Out[24]:

<seaborn.axisgrid.FacetGrid at 0x1ea53eaf7f0>

In [26]:

from scipy import stats

In [28]:

c=np.random.normal(loc=5,size =200,scale=5)

In [30]:

sns.distplot(c)

Out[30]:

<matplotlib.axes._subplots.AxesSubplot at 0x1ea53f66820>

multiplot grid¶

In [ ]:

In [33]:

a=sns.load_dataset("iris")
b= sns.FacetGrid(a,col="species")
b.map(plt.hist,"sepal_length")

Out[33]:

<seaborn.axisgrid.FacetGrid at 0x1ea53fff100>

In [35]:

a=sns.load_dataset("flights")
b= sns.PairGrid(a)
b.map(plt.scatter)

Out[35]:

<seaborn.axisgrid.PairGrid at 0x1ea54117580>

In [ ]:

plot-asthetic¶

In [36]:

sns.set(style="darkgrid")
a=sns.load_dataset("flights")
b= sns.PairGrid(a)
b.map(plt.scatter)

Out[36]:

<seaborn.axisgrid.PairGrid at 0x1ea540e4730>

In [42]:

sns.set(style="white",color_codes =True)
a= sns.load_dataset("tips")
sns.boxplot(x="day",y="total_bill" ,data=a)

Out[42]:

<matplotlib.axes._subplots.AxesSubplot at 0x1ea5530d7f0>

In [44]:

sns.set(style="white",color_codes =True)
a= sns.load_dataset("tips")
sns.boxplot(x="day",y="total_bill" ,data=a)
sns.despine(offset=10,trim=True)

In [46]:

c=sns.color_palette()
sns.palplot(c)

In [ ]: