

In [1]:

    
%matplotlib inline
from ggplot import *
import pandas as pd
import numpy as np

Logarithmic Scales

ggplot allows you to adjust both the x and y axis to use a logarithmic scale. The scale_x_log or scale_y_log can be added to any plot. You can also adjust the type of logarithm that is used by providing a base parameter (i.e. scale_x_log(base=2) for natural log) to the function. If not specified log base 10 will be used.



In [2]:

    
diamonds.head()



In [3]:

    
ggplot(diamonds, aes(x='carat', y='price')) + geom_point()









    












    Out[3]:





<ggplot: (285111113)>



In [4]:

    
ggplot(diamonds, aes(x='carat', y='price')) + \
    geom_point() + \
    scale_y_log()









    












    Out[4]:





<ggplot: (273694445)>



In [ ]:



In [5]:

    
df = pd.DataFrame(dict(
        x=np.arange(1, 1000)
    ))
df['y'] = np.log(df.x)
df.head()



In [6]:

    
ggplot(df, aes(x='x', y='y')) + geom_point()









    












    Out[6]:





<ggplot: (285247637)>



In [7]:

    
ggplot(df, aes(x='x', y='y')) + geom_point() + scale_x_log()









    












    Out[7]:





<ggplot: (285024853)>

Reversing Axes

If you find yourself in the position where you need to reverse an axis, you can do so using scale_x_reverse or scale_y_reverse.



In [8]:

    
ggplot(mtcars, aes(x='mpg')) + geom_histogram() + scale_x_reverse()









    












    Out[8]:





<ggplot: (285218201)>



In [9]:

    
ggplot(mtcars, aes(x='mpg')) + geom_histogram() + scale_y_reverse()









    












    Out[9]:





<ggplot: (290502653)>

Changing Coordinate Systems

You can switch between different coordinate systems using the coord_* family of layers. Just be careful that you're using the correct aesthetics! The available coordinate systems are:

coord_equal
coord_flip
coord_polar
coord_cartesian (this is the default, so you never explicitly invoke it)

`coord_equal`

coord_equal will make the x and y axes use the same scale. This is handy if you're comparing 2 variables together, or want a square-looking plot.



In [10]:

    
ggplot(aes(x='beef', y='pork'), data=meat) + \
    geom_point() + \
    coord_equal()









    












    Out[10]:





<ggplot: (290784017)>



In [11]:

    
ggplot(aes(x='beef', y='pork'), data=meat) + \
    geom_point() + \
    geom_abline(slope=1, intercept=0, color='teal') + \
    coord_equal()









    












    Out[11]:





<ggplot: (290926537)>

`coord_flip`

coord_flip will make the x axis the y axis and vice-versa. So taking the plot we just made and flipping it would look like this.



In [12]:

    
# sadly, this doesn't appear to work
ggplot(aes(x='beef', y='pork'), data=meat) + \
    geom_point() + \
    coord_flip()









    












    Out[12]:





<ggplot: (290926389)>

`coord_polar`

coord_polar uses a polar coordinate system instead of cartesian.



In [13]:

    
df = pd.DataFrame({"x": np.arange(100)})
df['y'] = df.x * 10
df['z'] = ["a" if x%2==0 else "b" for x in df.x]

# polar coords
p = ggplot(df, aes(x='x', y='y')) + geom_point() + coord_polar()
print p









    












    



<ggplot: (291101857)>



In [14]:

    
ggplot(df, aes(x='x', y='y')) + geom_point() + geom_line() + coord_polar()









    












    Out[14]:





<ggplot: (291107933)>



In [ ]:

	carat	cut	color	clarity	depth	table	price	x	y	z
0	0.23	Ideal	E	SI2	61.5	55.0	326	3.95	3.98	2.43
1	0.21	Premium	E	SI1	59.8	61.0	326	3.89	3.84	2.31
2	0.23	Good	E	VS1	56.9	65.0	327	4.05	4.07	2.31
3	0.29	Premium	I	VS2	62.4	58.0	334	4.20	4.23	2.63
4	0.31	Good	J	SI2	63.3	58.0	335	4.34	4.35	2.75

	x	y
0	1	0.000000
1	2	0.693147
2	3	1.098612
3	4	1.386294
4	5	1.609438