rapidsai/cudf

[FEA] Series and DataFrame mean absolute deviation

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#3,676 opened on Dec 30, 2019

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Description

For API compatibility and supporting exploratory analysis, we should support Series and DataFrame mean absolute deviation. See the pandas mean absolute deviation for more information.

This can be implemented for Python Series and DataFrame as a stopgap as Series.mad and then leverage _apply_support_method for DataFrame.mad. It's not 10x faster, but it gets the job done well.

import cudf
import numpy as np
​
def mad(self):
    # mad formula
    n = len(self)
    m = self.mean()
    mad = ((self - m).abs() / n).sum()
    return mad
​
​# 1 million rows
s = cudf.Series(np.random.normal(10,5,1_000_000))
ps = s.to_pandas()
​
%time mp = ps.mad()
%time mg = mad(s)
print(mp)
print(mg)
CPU times: user 31.9 ms, sys: 0 ns, total: 31.9 ms
Wall time: 32 ms
CPU times: user 0 ns, sys: 7.24 ms, total: 7.24 ms
Wall time: 23.2 ms
3.990811998439671
3.990811998439673

Contributor guide

Research direction
Study the pandas `mad` method, implement `Series.mad` using the formula `(self self.mean()).abs().sum() / len(self)`, then add `DataFrame.mad` via ` apply support method`.
Tech stack
python
Domain
data
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonGit
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
85