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 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)
After calling install_ply, all pandas objects have pandas-ply’s methods attached.
Full API reference is available at http://pythonhosted.org/pandas-ply/.
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