What is a simple framework for time testing pandas and numpy related solutions

Motivation

I answer a few questions here and there. I'm often testing the how fast mine and other peoples solutions are.

Question

Is there a simple framework to get a fairly comprehensive view into how fast various solutions are?

Example

Consider the dataframe df

df = pd.DataFrame({
        'Group': list('QLCKPXNLNTIXAWYMWACA'),
        'Value': [29, 52, 71, 51, 45, 76, 68, 60, 92, 95,
                  99, 27, 77, 54, 39, 23, 84, 37, 99, 87]
    })

I want to sum up the Value column grouped by distinct values in Group. I have three methods for doing it.

import pandas as pd
import numpy as np
from numba import njit


def sum_pd(df):
    return df.groupby('Group').Value.sum()

def sum_fc(df):
    f, u = pd.factorize(df.Group.values)
    v = df.Value.values
    return pd.Series(np.bincount(f, weights=v).astype(int), pd.Index(u, name='Group'), name='Value').sort_index()

@njit
def wbcnt(b, w, k):
    bins = np.arange(k)
    bins = bins * 0
    for i in range(len(b)):
        bins[b[i]] += w[i]
    return bins

def sum_nb(df):
    b, u = pd.factorize(df.Group.values)
    w = df.Value.values
    bins = wbcnt(b, w, u.size)
    return pd.Series(bins, pd.Index(u, name='Group'), name='Value').sort_index()

Are they the same?

print(sum_pd(df).equals(sum_nb(df)))
print(sum_pd(df).equals(sum_fc(df)))

True
True

How fast are they?

%timeit sum_pd(df)
%timeit sum_fc(df)
%timeit sum_nb(df)

1000 loops, best of 3: 536 µs per loop
1000 loops, best of 3: 324 µs per loop
1000 loops, best of 3: 300 µs per loop

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