Accessing axis or figure in python ggplot

Question

python ggplot is great, but still new, and I find the need to fallback on traditional matplotlib techniques to modify my plots. But I'm not sure how to either pass an axis instance to ggplot, or get one back from it.

So let's say I build a plot like so:

import ggplot as gp

(explicit import)

p = gp.ggplot(gp.aes(x='basesalary', y='compensation'), data = df)
p + gp.geom_histogram(binwidth = 10000)      

No problems so far. But now let's say I want the y-axis in log scale. I'd like to be able to do this:

plt.gca().set_yscale('log')

Unfortunately, plt.gca() doesn't access the axis created by ggplot. I end up with two figures: the histogram from ggplot in linear scale, and an empty figure with a log-scale y axis.

I've tried a few variations with both gca() and gcf() without success.

Answer 1

There might have been some changes since 2013 when this question was asked. The way to produce a matplotlib figure from a ggplot is

g.make()

after that, figure and axes can be obtained via

fig = plt.gcf()
ax = plt.gca()

or, if there are more axes, axes = fig.axes.

Then, additional features can be added in matplotlib, like also shown in this question's answer.

Finally the plot can be saved using the usual savefig command.

Complete example:

import ggplot as gp
import matplotlib.pyplot as plt

# produce ggplot
g = gp.ggplot(gp.aes(x='carat', y='price'), data=gp.diamonds)
g = g + gp.geom_point()
g = g + gp.ylab(' ')+ gp.xlab(' ')
# Make
g.make()

# obtain figure from ggplot
fig = plt.gcf()
ax = plt.gca()
# adjust some of the ggplot axes' parameters
ax.set_title("ggplot plot")
ax.set_xlabel("Some x label")
plt.savefig(__file__+".png")
plt.show()

Answer 2

[This is outdated with current ggpy]

There is now a scale_y_log(). If you want to do something in matplotlib, you can get the current figure/axis with

g = ggplot(...)
fig = g.draw()
#or
g.draw() # or print(g)
fig = plt.gcf() 
ax = plt.gca()

Your version fails because ggplots draws the plot on print(g) in the ggplot.__repr__() method (which calls ggplot.draw()), so there is simple no matplotlib figure right after constructing the ggplot object but only after print (or g.draw()). g.draw() also returns the figure, so you don't need to use plt.gcf()

Answer 3

Did you try:

p = gp.ggplot(gp.aes(x='basesalary', y='compensation'), data = df)
p + gp.geom_histogram(binwidth = 10000) + gp.scale_y_log()

Not sure if it works just like that though, just guessing from looking at the code...