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 )

FacetGrid¶

During EDA, you want to find the distribution of data by sub-categories, sub-conditions. You can do so by building FacetGrids. As it means, you get a grid for every facet of the data.

In [7]:

#load tips data
tips = sns.load_dataset('tips')
tips.head()

#load tips data tips = sns.load_dataset('tips') tips.head()

Out[7]:

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

Suppose we want to visualize total_bill by time of day and wheather or not it was a smoker. You need to filter data out then make dist plots. YOu can do all of that in 1 step with FacetGrids.

In [12]:

#for each unique value in `time` you get a row and 
# each unique value in `smoker` you get a col
fg = sns.FacetGrid(data=tips, row='time', col='smoker')

#now map a plot for each of the grid
fg.map(sns.distplot, 'total_bill')

#for each unique value in `time` you get a row and # each unique value in `smoker` you get a col fg = sns.FacetGrid(data=tips, row='time', col='smoker') #now map a plot for each of the grid fg.map(sns.distplot, 'total_bill')

Out[12]:

<seaborn.axisgrid.FacetGrid at 0x11db6aa90>

Customizing grids¶

If you dont like the pale blue background of seaborn plots, you can modify that with set_style.

Note: Using set_style() will control the appearance for the entire notebook and all future plots

In [16]:

sns.set_style(style='ticks') #ticks, white, dark, darkgrid, whitegrid
#redraw the facet grid from above
fg = sns.FacetGrid(data=tips, row='time', col='smoker')

#now map a plot for each of the grid
fg.map(sns.distplot, 'total_bill')

sns.set_style(style='ticks') #ticks, white, dark, darkgrid, whitegrid #redraw the facet grid from above fg = sns.FacetGrid(data=tips, row='time', col='smoker') #now map a plot for each of the grid fg.map(sns.distplot, 'total_bill')

Out[16]:

<seaborn.axisgrid.FacetGrid at 0x11db832b0>

Fig and font size¶

You can use matplotlib figsize but have to specify that as a context as well.

In [24]:

plt.figure(figsize=(5,5)) #generate a fig, sns will piggyback this with the plot
sns.distplot(tips['total_bill'])

plt.figure(figsize=(5,5)) #generate a fig, sns will piggyback this with the plot sns.distplot(tips['total_bill'])

Out[24]:

<matplotlib.axes._subplots.AxesSubplot at 0x1204cd828>

Using seaborn context¶

You can use the set_context() to pick sizing templates

In [31]:

sns.set_context(context='poster', font_scale=0.8)
# valid contexts = paper, notebook, talk, poster - 
# with notebook being 1:1 and paper being smaller and poster being largest

#draw the facet grid
fg = sns.FacetGrid(data=tips, row='smoker', col='time')
fg.map(sns.distplot, 'total_bill')

sns.set_context(context='poster', font_scale=0.8) # valid contexts = paper, notebook, talk, poster - # with notebook being 1:1 and paper being smaller and poster being largest #draw the facet grid fg = sns.FacetGrid(data=tips, row='smoker', col='time') fg.map(sns.distplot, 'total_bill')

Out[31]:

<seaborn.axisgrid.FacetGrid at 0x120fccd68>

Another way to set the size is to access the fig handle direclty

In [35]:

#draw the facet grid
fg = sns.FacetGrid(data=tips, row='smoker', col='time')
#set the size
fg.fig.set_size_inches(w=10, h=10)
#plot the fig
fg.map(sns.distplot, 'total_bill')

#draw the facet grid fg = sns.FacetGrid(data=tips, row='smoker', col='time') #set the size fg.fig.set_size_inches(w=10, h=10) #plot the fig fg.map(sns.distplot, 'total_bill')

Out[35]:

<seaborn.axisgrid.FacetGrid at 0x122589ef0>