pandas-ply 0.1.1

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.arr_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_select and ply_where above) 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 d probably maps to b.ply_where(c).groupby(d).ply_select(a).)

• pandas-ply includes a simple system for building “symbolic expressions” to provide as arguments to its methods. X above is an instance of ply.symbolic.Symbol. Operations on this symbol produce larger compound symbolic expressions. When pandas-ply receives a symbolic expression as an argument, it converts it into a function. So, for instance, X.arr > 30 in 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.

$ pip install pandas-ply

import pandas as pd
from ply import install_ply, X, sym_call

install_ply(pd)