pandas-ply: functional data manipulation for pandas
pandas-ply is a thin layer which makes it easier to manipulate data with pandas. In particular, it provides elegant, functional, chainable syntax in cases where pandas would require mutation, saved intermediate values, or other awkward constructions. In this way, it aims to move pandas closer to the "grammar of data manipulation" provided by the dplyr package for R.
For example, take the dplyr code below:
flights %>% group_by(year, month, day) %>% summarise( arr = mean(arr_delay, na.rm = TRUE), dep = mean(dep_delay, na.rm = TRUE) ) %>% filter(arr > 30 & dep > 30)
The most common way to express this in pandas is probably:
grouped_flights = flights.groupby(['year', 'month', 'day']) output = pd.DataFrame() output['arr'] = grouped_flights.arr_delay.mean() output['dep'] = grouped_flights.dep_delay.mean() filtered_output = output[(output.arr > 30) & (output.dep > 30)]
pandas-ply lets you instead write:
(flights .groupby(['year', 'month', 'day']) .ply_select( arr = X.arr_delay.mean(), dep = X.dep_delay.mean()) .ply_where(X.arr > 30, X.dep > 30))
In our opinion, this pandas-ply code is cleaner, more expressive, more readable, more concise, and less error-prone than the original pandas code.
Explanatory notes on the pandas-ply code sample above:
- pandas-ply's methods (like
ply_whereabove) are attached directly to pandas objects and can be used immediately, without any wrapping or redirection. They start with a
ply_prefix to distinguish them from built-in pandas methods.
- pandas-ply's methods are named for (and modelled after) SQL's operators. (But keep in mind that these operators will not always appear in the same order as they do in a SQL statement:
SELECT a FROM b WHERE c GROUP BY dprobably maps to
- pandas-ply includes a simple system for building "symbolic expressions" to provide as arguments to its methods.
Xabove is an instance of
ply.symbolic.Symbol. Operations on this symbol produce larger compound symbolic expressions. When
pandas-plyreceives a symbolic expression as an argument, it converts it into a function. So, for instance,
X.arr > 30in the above code could have instead been provided as
lambda x: x.arr > 30. Use of symbolic expressions allows the
lambda x:to be left off, resulting in less cluttered code.
pandas-ply is new, and in an experimental stage of its development. The API is not yet stable. Expect the unexpected.
(Pull requests are welcome. Feel free to contact us at firstname.lastname@example.org.)
Install pandas-ply with:
$ pip install pandas-ply
Typical use of pandas-ply starts with:
import pandas as pd from pandas_ply import install_ply, X, sym_call install_ply(pd)
install_ply, all pandas objects have pandas-ply's methods attached.
Full API reference is available at http://pythonhosted.org/pandas-ply/.
groupbyto support symbolic expressions?
applyto support symbolic expressions?
pandasobjects to extend chainability?
- Version of
ply_selectwhich supports later computed columns relying on earlier computed columns?
- Version of
ply_selectwhich supports careful column ordering?
- Better handling of indices?
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