Seaborn - grids and customization¶

In [4]:

import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
iris= sns.load_dataset('iris')
iris.head()

import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline iris= sns.load_dataset('iris') iris.head()

Out[4]:

	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

Pairgrids¶

Pairgrid is similar to pairplot, except, it returns back an empty grid that you can fill up with desired plots later. Refresher on pariplot

In [6]:

grd = sns.PairGrid(data=iris)

#then you can assign what you want plotted for diagonal, above diagonal, below diagonal.
# when mapping, pass just function pointers, dont call the function itself.
grd.map_diag(sns.distplot)
grd.map_upper(plt.scatter)
grd.map_lower(sns.kdeplot)

grd = sns.PairGrid(data=iris) #then you can assign what you want plotted for diagonal, above diagonal, below diagonal. # when mapping, pass just function pointers, dont call the function itself. grd.map_diag(sns.distplot) grd.map_upper(plt.scatter) grd.map_lower(sns.kdeplot)

Out[6]:

<seaborn.axisgrid.PairGrid at 0x11ea36f98>

lmplot() for scatter and regression per category¶

Sometimes, you need to do a joinplot() but split it by some categorical column. You can custom build it using FacetGrid shown in next section. However, seaborn provides a convenience function called lmplot(). Note: In previous pages, you created lmplots() for just 2 columns without any category.

In [330]:

pgrid = sns.lmplot(x='min_season', y='min_pressure_merged', 
                   col='any_basin', # the column by which you need to split - needs to be categorical
                   data=set1, 
                   col_wrap=3, # number of columns per row
                   sharex=False, sharey=False, # will repeat ticks, coords for each plot
                   line_kws={'color':'green'} # symbol for regression line
                  )

pgrid = sns.lmplot(x='min_season', y='min_pressure_merged', col='any_basin', # the column by which you need to split - needs to be categorical data=set1, col_wrap=3, # number of columns per row sharex=False, sharey=False, # will repeat ticks, coords for each plot line_kws={'color':'green'} # symbol for regression line )