Usual stuff to import and set

In [1]:
import seaborn as sns
import pandas as pd

%matplotlib inline
%config InlineBackend.figure_format = 'svg'
sns.set_context("notebook", font_scale=1.25)
sns.set_style("ticks", {"": "Liberation Sans"})

Seaborn - Statistical Data Visualization

  • If you're a matplotlib pro, you probably know how to do everything that seaborn does. Only the fact that matplotlib is just so insanely detailed and low level with myriad of options, it always is difficult to appreciate how powerful matplotlib really is
  • Behold seaborn, a Python package for high level statistical plotting. Before coming across seaborn, I always went back to ggplot2 for my extensive plotting needs but no more R or ggplot2 as I have found my safe haven
  • I cannot do justice to seaborn by writing about its functions and plots here. The documentation is just done so beautifully, you can't miss anything
  • Remember xkcd color survey, yes they are baked in seaborn to choose from

How am I planning to cover seaborn?

  • There are two major plot types that seaborn does (1. FacetGrid 2. Matplotlib axes). Not every seaborn object returned is a matplotlib axes object but it's mostly grid of multiple matplotlib axes.
  • Seaborn does one major FacetGrid type plot for every plot type where it has the option to choose the kind of plot. I'll be covering these major FacetGrid type plots which will encompass different plots inside them.

Major plot types

Again, this section is from API reference. I've listed subplots based on what I am going to cover today.

Distribution plots


  • This is scatter matrix visualization made very easy.


Some parameters:

  • kind: {'scatter', 'reg'}
  • diag_kind: {'hist', 'kde'}
  • hue : Select column to apply color by

In [2]:
iris = pd.read_csv("iris.csv", index_col=0)

sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa

In [3]:
g = sns.pairplot(iris, hue="species", diag_kind="kde")


  • Scatterplot on steriods. Provides variants like hexagonal bins, density estimates apart from scatter. Provides linear regression plots with annotation. Mix and match ;)

sns.jointplot(data=df, x="", y="")

Some parameters:

  • kind : { “scatter” | “reg” | “resid” | “kde” | “hex” }, optional
    • This is the one you change if you want a different plot

In [4]:
g = sns.jointplot(data=iris, x="sepal_length", y="sepal_width")

In [5]:
from scipy.stats import spearmanr
g = sns.jointplot(data=iris, x="sepal_length", y="sepal_width",
                 kind="kde", stat_func=spearmanr)

Regression plots


This is a grid plot to fit regression across subsets of dataset

sns.lmplot(data=df, x="", y="")

Some parameters:

  • col - Separately plot forming different columns of plot based on this column
  • row - Separately plot forming different rows of plot based on this column
  • fit_reg - Default True, If you don't want regression, add False here

In [6]:
sepal_lmplot = sns.lmplot(data=iris, fit_reg=False, x="sepal_length", y="sepal_width", 
           col="species", scatter_kws={'s': 25}, size=3.05).set_xticklabels(rotation=90)

In [7]:
petal_lmplot = sns.lmplot(data=iris, fit_reg=False, x="petal_length", y="petal_width", 
           col="species", scatter_kws={'s': 25}, size=3.05).set_xticklabels(rotation=90)

Categorical plots


sns.factorplot(data=df, x="", y="")

Really powerful and versatile.

Some parameters:

  • kind : {point, bar, count, box, violin, strip}

In [8]:
planets = pd.read_csv("planets.csv", index_col=0)

method number orbital_period mass distance year
0 Radial Velocity 1 269.300 7.10 77.40 2006
1 Radial Velocity 1 874.774 2.21 56.95 2008
2 Radial Velocity 1 763.000 2.60 19.84 2011
3 Radial Velocity 1 326.030 19.40 110.62 2007
4 Radial Velocity 1 516.220 10.50 119.47 2009

In [9]:
planets_factorplot = sns.factorplot(kind="strip", jitter=True, 
    data=planets[planets.method=="Radial Velocity"].sort_values(by="year"),
    col="method", x="year", y="orbital_period", color=sns.xkcd_rgb["warm blue"], 
    size=5, aspect=1.8)
_ = planets_factorplot.set_xticklabels(rotation=90).set(ylim=0, yscale="log")

In [10]:
planets_factorplot = sns.factorplot(kind="box",  
    data=planets[planets.method=="Radial Velocity"].sort_values(by="year"),
    col="method", x="year", y="orbital_period", color=sns.xkcd_rgb["green apple"], 
    size=5, aspect=1.8)
_ = planets_factorplot.set_xticklabels(rotation=90).set(ylim=0, yscale="log")

Matrix plots


Heatmap with clustering. Damn easy and beautiful

Some parameters:

  • z_score: optional, Calculate z_score if specified.
  • {row,col}_cluster : bool, optional. Whether to cluster by rows and columns. Enabled by default
  • {row,col}_colors : list-like, optional. Useful to add colors to columns or rows and check whether stuff is clustering according to condition and stuff

In [11]:
tpm_filtered = pd.read_table("Heatmap.tsv", sep="\t", index_col=0)

In [12]:
# Classic green and red colors for heatmap
cmap = sns.diverging_palette(133, 10, n=13, center="dark", as_cmap=True)

# Providing column colors. Awesome xkcd colors integration in seaborn
col_colors = [sns.xkcd_rgb["amber"]]*4 + [sns.xkcd_rgb["windows blue"]]*4

# Clustermap function
clustermap = sns.clustermap(tpm_filtered.sample(30), cmap=cmap, z_score=0,
                         figsize=(6,9), col_colors=col_colors)

# Just to set x axis ticks rotation to 0, by default it is 90
ax = clustermap.ax_heatmap
labels = ax.get_yticklabels()
_ = ax.set_yticklabels(labels, rotation=0)