Learning Seaborn

Seaborn is a great python library for data viz, but I don't know it well. Here I follow the tutorial provided on the seaborn docs website:

http://stanford.edu/~mwaskom/software/seaborn/tutorial/aesthetics.html



In [1]:

    
%matplotlib inline



In [28]:

    
import numpy as np
import matplotlib as mpl
from matplotlib import pyplot as plt
import time



In [35]:

    
# np.random.seed(sum(map(ord, "aesthetics")))
rng = np.random.RandomState(seed=1069) # same as the code above, which a pretentious way of setting seed = 1069

First we make a plot example.



In [12]:

    
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)

When we call it, matplotlib plots things with its defaults. Note that seaborn hasn't been import yet in any code thus far.



In [20]:

    
sinplot(1)

To get seaborn styling, merely import the package:



In [21]:

    
import seaborn as sns



In [23]:

    
sinplot()

from the docs:

"There are five preset seaborn themes: darkgrid, whitegrid, dark, white, and ticks"



In [32]:

    
for style in ('darkgrid', 'whitegrid', 'dark', 'white', 'ticks'):
    sns.set_style(style)
    sinplot()
    plt.show()



In [60]:

    
rng = np.random.RandomState(1)
data = rng.normal(size=(20, 6)) + np.arange(6) / 2
sns.set_style('whitegrid')
sns.boxplot(data)









    Out[60]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f8ec3c1e090>

We can also use despine() to remove axes bars on the bottom, left, right and/or top of the plot area. This call must come after the plots have been generated.



In [64]:

    
sns.set_style('darkgrid')
sinplot()
sns.despine(right = False, left = True)