Grids

Grids are general types of plots that allow you to map plot types to rows and columns of a grid, this helps you create similar plots separated by features.



In [22]:

    
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline



In [27]:

    
iris = sns.load_dataset('iris')



In [28]:

    
iris.head()









    Out[28]:






  
    
      
      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

PairGrid

Pairgrid is a subplot grid for plotting pairwise relationships in a dataset.



In [25]:

    
# Just the Grid
sns.PairGrid(iris)









    Out[25]:





<seaborn.axisgrid.PairGrid at 0x11e39bb38>



In [26]:

    
# Then you map to the grid
g = sns.PairGrid(iris)
g.map(plt.scatter)









    Out[26]:





<seaborn.axisgrid.PairGrid at 0x11f431208>



In [30]:

    
# Map to upper,lower, and diagonal
g = sns.PairGrid(iris)
g.map_diag(plt.hist)
g.map_upper(plt.scatter)
g.map_lower(sns.kdeplot)









    Out[30]:





<seaborn.axisgrid.PairGrid at 0x121944128>

pairplot

pairplot is a simpler version of PairGrid (you'll use quite often)



In [31]:

    
sns.pairplot(iris)









    Out[31]:





<seaborn.axisgrid.PairGrid at 0x12024b5c0>



In [33]:

    
sns.pairplot(iris,hue='species',palette='rainbow')









    Out[33]:





<seaborn.axisgrid.PairGrid at 0x12633f0f0>

FacetGrid is the general way to create grids of plots based off of a feature:



In [34]:

    
tips = sns.load_dataset('tips')



In [35]:

    
tips.head()









    Out[35]:






  
    
      
      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



In [36]:

    
# Just the Grid
g = sns.FacetGrid(tips, col="time", row="smoker")



In [37]:

    
g = sns.FacetGrid(tips, col="time",  row="smoker")
g = g.map(plt.hist, "total_bill")



In [42]:

    
g = sns.FacetGrid(tips, col="time",  row="smoker",hue='sex')
# Notice hwo the arguments come after plt.scatter call
g = g.map(plt.scatter, "total_bill", "tip").add_legend()

JointGrid

JointGrid is the general version for jointplot() type grids, for a quick example:



In [43]:

    
g = sns.JointGrid(x="total_bill", y="tip", data=tips)



In [45]:

    
g = sns.JointGrid(x="total_bill", y="tip", data=tips)
g = g.plot(sns.regplot, sns.distplot)









    



/Users/marci/anaconda/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j

Reference the documentation as necessary for grid types, but most of the time you'll just use the easier plots discussed earlier.

Grids

PairGrid

pairplot

Facet Grid

JointGrid

Great Job!

	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

	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