Seaborn offers:

Beautiful themes
Nice color palettes
Plotting univariate and multivariate distributions
Plotting regression results with error bars
Time series visualizations
Abstractions for grid of plots

Seaborn could be used with bokeh



In [1]:

    
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline



In [2]:

    
np.random.seed(sum(map(ord, "aesthetics")))



In [3]:

    
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)



In [4]:

    
sinplot()



In [5]:

    
import seaborn as sns
sinplot()

Controlling figure properties:

axes_style()
set_style()
plotting_context()
set_context()

Basic seaborn themes:

darkgird
whitegrid
dark
white
ticks



In [6]:

    
sns.set_style("whitegrid")
data = np.random.normal(size=(20, 6)) 
sns.boxplot(data=data);



In [7]:

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



In [8]:

    
sns.set_style('dark')
sinplot()



In [11]:

    
sns.set_style("white")
sinplot()



In [12]:

    
sns.set_style("ticks")
sinplot()



In [13]:

    
sinplot()
sns.despine()

`sns.violinplot()`



In [14]:

    
f, ax = plt.subplots()
sns.violinplot(data=data)
sns.despine(offset=10);



In [15]:

    
f, ax = plt.subplots()
sns.violinplot(data=data)
sns.despine(offset=10, trim=True);

`sns.boxplot()`

sns.set_style("whitegrid") sns.boxplot(data=data, palette="deep") sns.despine(left=True)

Temporarily setting figure style



In [9]:

    
with sns.axes_style("darkgrid"):
    plt.subplot(211)
    sinplot()
plt.subplot(212)
sinplot(-1)

Customizing Seaborn styles



In [19]:

    
sns.axes_style()









    Out[19]:





{'axes.axisbelow': True,
 'axes.edgecolor': '.8',
 'axes.facecolor': 'white',
 'axes.grid': True,
 'axes.labelcolor': '.15',
 'axes.linewidth': 1.0,
 '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': 0.0,
 'xtick.minor.size': 0.0,
 'ytick.color': '.15',
 'ytick.direction': u'out',
 'ytick.major.size': 0.0,
 'ytick.minor.size': 0.0}



In [10]:

    
sns.set_style("darkgrid", {"axes.facecolor": ".9"})
sinplot()



In [11]:

    
sns.set_style("darkgrid", {"axes.facecolor": ".96"})
sinplot()



In [12]:

    
# reset default parameters
sns.set()
sinplot()

Use `set_context()` to change context

The following context are available

'paper'
'notebook'
'talk'
'poster'



In [13]:

    
sns.set_context("paper")
plt.figure(figsize=(8, 6))
sinplot()



In [14]:

    
sns.set_context("talk")
plt.figure(figsize=(8, 6))
sinplot()



In [28]:

    
sns.set_context("talk")
plt.figure(figsize=(8, 6))
sinplot()



In [32]:

    
sns.set_context("notebook", font_scale=1.5, rc={"lines.linewidth": 2.5})
sinplot()

`color_palette()`



In [35]:

    
sns.set(rc={"figure.figsize": (6, 6)})

np.random.seed(sum(map(ord, "palettes")))









    



<built-in function ord>



In [39]:

    
print np.random.seed()









    



None



In [40]:

    
current_palette = sns.color_palette()
sns.palplot(current_palette)

Available standard color palettes deep, muted, pastel, bright, dark, and colorblind.

hls is good



In [41]:

    
sns.palplot(sns.color_palette("hls", 8))



In [49]:

    
sns.palplot(sns.hls_palette(8, l=.3, s=.8))
sns.palplot(sns.hls_palette(8, l=.7, s=.8))
sns.palplot(sns.hls_palette(8, l=.3, s=.3))
sns.palplot(sns.hls_palette(8, l=.7, s=.3))
sns.palplot(sns.hls_palette(8, l=.6, s=.9))

Even perception `husl`



In [52]:

    
sns.palplot(sns.color_palette("husl", 8))



In [53]:

    
sns.palplot(sns.color_palette("Paired"))



In [54]:

    
sns.palplot(sns.color_palette("Set2", 10))

`choose_colorbrewer_palette()`

This is great!



In [58]:

    
# data_type : {‘sequential’, ‘diverging’, ‘qualitative’}
sns.choose_colorbrewer_palette('qualitative', 0)









    Out[58]:





[(0.8941176470588235, 0.10196078431372557, 0.10980392156862737),
 (0.21568627450980393, 0.4941176470588236, 0.7215686274509804),
 (0.3019607843137256, 0.6862745098039216, 0.29019607843137263),
 (0.5960784313725492, 0.3058823529411765, 0.6392156862745098),
 (1.0, 0.4980392156862745, 0.0),
 (0.9999999999999998, 1.0, 0.19999999999999996),
 (0.6509803921568629, 0.33725490196078434, 0.1568627450980391),
 (0.9686274509803922, 0.5058823529411766, 0.7490196078431374),
 (0.6, 0.6, 0.6)]



In [59]:

    
flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"]
sns.palplot(sns.color_palette(flatui))

xkcd ran a crowdsourced effort to name random
This produced a set of 954 named colors, which you can now reference in seaborn using the xkcd_rgb dictionary:



In [60]:

    
plt.plot([0, 1], [0, 1], sns.xkcd_rgb["pale red"], lw=3)
plt.plot([0, 1], [0, 2], sns.xkcd_rgb["medium green"], lw=3)
plt.plot([0, 1], [0, 3], sns.xkcd_rgb["denim blue"], lw=3);



In [61]:

    
colors = ["windows blue", "amber", "greyish", "faded green", "dusty purple"]
sns.palplot(sns.xkcd_palette(colors))

sequential color palattes



In [65]:

    
sns.palplot(sns.color_palette("Blues"))
sns.palplot(sns.color_palette("BuGn_r"))
sns.palplot(sns.color_palette("GnBu_r"))
sns.palplot(sns.color_palette("GnBu_d"))

Coilors appropriate for black and white printing

`cubehelix`



In [66]:

    
sns.palplot(sns.color_palette("cubehelix", 8))



In [67]:

    
sns.palplot(sns.cubehelix_palette(8))
sns.palplot(sns.cubehelix_palette(8, start=.5, rot=-.75))
sns.palplot(sns.cubehelix_palette(8, start=2, rot=0, dark=0, light=.95, reverse=True))



In [69]:

    
x, y = np.random.multivariate_normal([0, 0], [[1, -.5], [-.5, 1]], size=300).T
cmap = sns.cubehelix_palette(light=1, as_cmap=True)
sns.kdeplot(x, y, cmap=cmap, shade=True)









    Out[69]:





<matplotlib.axes._subplots.AxesSubplot at 0x1068f4210>



In [72]:

    
x, y = np.random.multivariate_normal([0, 0], [[1, -.5], [-.5, 1]], size=300).T
cmap = sns.cubehelix_palette(light=1, as_cmap=True)
sns.kdeplot(x, y, cmap=cmap)









    Out[72]:





<matplotlib.axes._subplots.AxesSubplot at 0x103adba50>



In [73]:

    
sns.palplot(sns.light_palette("green"))
sns.palplot(sns.dark_palette("purple"))
sns.palplot(sns.light_palette("navy", reverse=True))



In [74]:

    
pal = sns.dark_palette("palegreen", as_cmap=True)
sns.kdeplot(x, y, cmap=pal);



In [75]:

    
sns.palplot(sns.light_palette((210, 90, 60), input="husl"))
sns.palplot(sns.dark_palette("muted purple", input="xkcd"))

`diverging` when high and low values are important



In [77]:

    
sns.palplot(sns.color_palette("BrBG", 7))
sns.palplot(sns.color_palette("RdBu_r", 7))
sns.palplot(sns.color_palette("coolwarm", 7))



In [78]:

    
sns.palplot(sns.diverging_palette(220, 20, n=7))
sns.palplot(sns.diverging_palette(145, 280, s=85, l=25, n=7))
sns.palplot(sns.diverging_palette(10, 220, sep=80, n=7))
sns.palplot(sns.diverging_palette(255, 133, l=60, n=7, center="dark"))



In [79]:

    
sns.set_palette("husl")
sinplot()



In [81]:

    
with sns.color_palette("PuBuGn_d"):
    sinplot()



In [82]:

    
sinplot()



In [83]:

    
print np.random.seed(sum(map(ord, "distributions")))









    



None



In [87]:

    
print sum(map(ord, "distributions"))



In [88]:

    
print map(ord, "distributions")









    



[100, 105, 115, 116, 114, 105, 98, 117, 116, 105, 111, 110, 115]



In [91]:

    
print ord('d')



In [92]:

    
help(ord)









    



Help on built-in function ord in module __builtin__:

ord(...)
    ord(c) -> integer
    
    Return the integer ordinal of a one-character string.



In [ ]:

Seaborn offers:

Seaborn could be used with bokeh

Controlling figure properties:

Basic seaborn themes:

sns.violinplot()

sns.boxplot()