ggplot2 syntax in python. Actually wrapper around Wickham's ggplot2 in R
Particularly good if you have preprocessed CSVs or Postgres data to render. Passable support for simple data in python lists, dictionaries, and panda DataFrame objects
pygg allows you to use ggplot2 syntax nearly verbatim in Python, and execute the ggplot program in R. Since this is just a wrapper and passes all arguments to the R backend, it is almost completely API compatible.
For a nearly exhaustive list of supported ggplot2 functions, see bin/make_ggplot2_functions.R.
Setup
- install R
# on osx
brew install R
# on unix e.g., ubuntu
sudo apt-get install R- install R packages (run the following in the R shell)
install.packages("ggplot2")
install.packages("RPostgreSQL") # optionalInstall
pip install pyggCommand line usage
runpygg.py --help
runpygg.py -c "ggplot('diamonds', aes('carat', 'price')) + geom_point()" -o test.pdf
runpygg.py -c "ggplot('diamonds', aes('carat', 'price')) + geom_point()" -csv foo.csv
For Python usage, see tests/example.py
from pygg import *
# Example using diamonds dataset (comes with ggplot2)
p = ggplot('diamonds', aes('carat', y='price'))
g = geom_point() + facet_wrap(None, "color")
ggsave("test1.pdf", p+g, data=None)The library performs a simple syntactic translation from python ggplot objects to R code. Because of this, there are some quirks regarding datasets and how we deal with strings.
In R, ggplot directly references the data frame object present in the runtime
(e.g., ggplot(<datasetname>, aes(...)). However, the python
objects being plotted are not directly available in the R runtime.
pygg provides two ways of loading datasets from Python into R.
The primary way is to explicitly pass the data object to ggsave using its data keyword argument.
ggsave then converts the data object to a suitable CSV file, writes it to a temp file,
and loads it into the data variable in R for use with the ggplot2 functions
For example (notice that the string "data" is passed to ggplot()):
df = pandas.DataFrame(...)
p = ggplot("data", aes(...)) + geom_point()
ggsave("out.pdf", p, data=df)
In addition, we provide several convenience functions that generate the appropriate R code for common python dataset formats:
- csv file: if you have a CSV file already, provide the filename to data
p = ggplot("data", aes(...)) + geom_point()
ggsave("out.pdf", p, data="file.csv")
# or more explicitly, pass a wrapped object that represents the csv file:
ggsave("out.pdf", p, data=data_py("file.csv"))
- python object: if your data is a python object in columnar (
{x: [1,2], y: [3,4]}) or row ([{x:1,y:3}, {x:2,y:4}]) format
p = ggplot("data", aes(...)) + geom_point()
ggsave("out.pdf", p, data={'x': [1,2], 'y': [3,4]})
- pandas dataframe: if your data is a
pandasdata frame object already you can just provide the dataframe df directly to data
p = ggplot("data", aes(...)) + geom_point()
ggsave("out.pdf", p, data=df)
- PostgresQL: if your data is stored in a postgres database
p = ggplot("data", aes(...)) + geom_point()
ggsave("out.pdf", p, data=data_sql('DBNAME', 'SELECT * FROM ...')
- existing R datasets: can you refer to any R dataframe object using the
first argument to
ggplot()
p = ggplot('diamonds', aes(...)) + geom_point()
ggsave("out.pdf", p, data=None)
By default, the library directly prints a python string argument into the R code string. For example the following python code to set the x axis label would generate incorrect R code:
# incorrect python code
scales_x_continuous(name="special label")
# incorrect generated R code
scales_x_continuous(name=special label)
# correct python code
scales_x_continuous(name="'special label'")
# correct generated R code
scales_x_continuous(name='special label')
# less convenient but more explicit alternative syntax
scales_x_continuous(name=pygg.esc('special label'))
You'll need to explicitly wrap these types of strings (intended as R strings) in a layer of quotes. For convenience, we automatically provide wrapping for common functions:
# "filename.pdf" is wrapped
ggsave("filename.pdf", p)
It feels silly to pass a dummy "data" string to ggplot() and then pass the object to
ggsave. We have extended the ggplot() call so it recognizes non string python data objects
and uses the data object by default during the ggsave call:
df = pandas.DataFrame(...)
p = ggplot(df, aes(...)) + geom_point()
ggsave("out.pdf", p)
p = ggplot(dict(x=[0,1], y=[3,4]), aes(x='x', y='y')) + geom_point()
ggsave("out.pdf", p)
Note that unlike ggsave, it is not smart enough to distinguish string arguments that
are R variable names and file names. Thus, the following will likely lead to an error because it
assumes the R variable data.csv exists in the environment when in reality it's the name of a csv file
to be loaded:
p = ggplot("data.csv", aes(x='x', y='y')) + geom_point()
ggsave("out.pdf", p)
Simply wrap the filename with a data_py() call:
p = ggplot(data_py("data.csv"), aes(x='x', y='y')) + geom_point()
ggsave("out.pdf", p)
axis_labels() is a shortcut for setting the x and y axis titles and scale types.
The following names the x axis "Dataset Size (MB)"and sets it to log scale,
names the y axis "Latency (sec)"and is by default continuous scale, and
sets the breaks for the x axis to [0, 10, 100, 5000]:
p = ggplot(...)
p += axis_labels("Dataset Size (MB)",
"Latency (sec)",
"log10",
xkwargs=dict(breaks=[0, 10, 100, 5000]))
Alternatives
-
yhat's ggplot: yhat's port of ggplot is really awesome. It runs everything natively in python, works with numpy data structures, and renders using matplotlib.
pyggexists partly due to personal preference, and partly because the R version of ggplot2 is more mature, and its layout algorithms are really really good. -
pyggplot: Pyggplot does not adhere strictly to R's ggplot syntax but pythonifies it, making it harder to transpose ggplot2 examples. Also pyggplot requires rpy2.
-
plotnine: another implementation of ggplot2 in Python