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_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.
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 email@example.com.)
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/.
- Extend pandas’ native groupby to support symbolic expressions?
- Extend pandas’ native apply to support symbolic expressions?
- Add .ply_call to pandas objects to extend chainability?
- Version of ply_select which supports later computed columns relying on earlier computed columns?
- Version of ply_select which supports careful column ordering?
- Better handling of indices?
Copyright 2015 Coursera Inc.
Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.