

In [1]:

    
%matplotlib inline
from ggplot import *

Basics

To make a scatter plot in ggplot, the first thing you'll need is a dataset. ggplot likes pandas DataFrames the best, so it is highly recommended that you load your data into pandas before starting to use ggplot.

We'll use ggplot's built-in mtcars dataset for this set of examples.



In [2]:

    
mtcars.head()









    Out[2]:






  
    
      
      name
      mpg
      cyl
      disp
      hp
      drat
      wt
      qsec
      vs
      am
      gear
      carb
    
  
  
    
      0
      Mazda RX4
      21.0
      6
      160.0
      110
      3.90
      2.620
      16.46
      0
      1
      4
      4
    
    
      1
      Mazda RX4 Wag
      21.0
      6
      160.0
      110
      3.90
      2.875
      17.02
      0
      1
      4
      4
    
    
      2
      Datsun 710
      22.8
      4
      108.0
      93
      3.85
      2.320
      18.61
      1
      1
      4
      1
    
    
      3
      Hornet 4 Drive
      21.4
      6
      258.0
      110
      3.08
      3.215
      19.44
      1
      0
      3
      1
    
    
      4
      Hornet Sportabout
      18.7
      8
      360.0
      175
      3.15
      3.440
      17.02
      0
      0
      3
      2

Simple X/Y Plot

The simplest plot you can make is an x/y plot. To do this in ggplot, first create a base layer using the ggplot function. Pass in your data and aesthetics that map columns in your DataFrame to x and y. In this case, we're going to look at the relationship between car weight (wt) and miles per gallon (mpg), so we'll set the x value of our aesthetics to wt and the y value to mpg.

Once the aesthetics are defined, we just need to add a geom_point layer to our plot.



In [3]:

    
p = ggplot(mtcars, aes(x='wt', y='mpg'))
p + geom_point()









    












    Out[3]:





<ggplot: (285176705)>

Controlling other aesthetics

In addition to the x and y variables, you can also control the shape, size, color, alpha (see throughness), and other "aesthetics" of your scatterplot. To do this, just include definitions for the aesthetic you'd like to control. For instance, let's set the color of each point in our graph to the acceleration (qsec) of each car.



In [4]:

    
p = ggplot(mtcars, aes(x='wt', y='mpg', color='qsec')) + geom_point()
print(p)









    












    



<ggplot: (285320953)>

Discrete Color

Since qsec is a continuous variable you probably noticed that you got that nice, graduated legend on the right side of the last plot that indicated the value of each color. If you were to use a discrete variable such as name for the color, watch what happens.

As you can see, each name is assigned its own color and displayed in the legend. This can get a bit unruly (especially if you're plotting a discrete value with hundreds of possible values) so be careful.



In [5]:

    
p = ggplot(mtcars, aes(x='wt', y='mpg', color='name')) + geom_point()
p









    












    Out[5]:





<ggplot: (285460973)>

You can also use the ggplot shorthand factor to discretize continuous variables. For example, let's change cyl from a numerical to a categorical variable.



In [6]:

    
ggplot(mtcars, aes(x='wt', y='mpg', color='factor(cyl)')) + geom_point()









    












    Out[6]:





<ggplot: (285133253)>

Pro Tip

Keep in mind that some aesthetics only work for continuous data, some only work for discrete data, and some work for both.

For more on which aesthetics are best in certain situations, read the aesthetics API documentation.



In [ ]:

	name	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
0	Mazda RX4	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4
1	Mazda RX4 Wag	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4
2	Datsun 710	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1
3	Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1
4	Hornet Sportabout	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2