Prototype for a DataLogger object based on pandas DataFrame for the DEAP framework. This object would take care of statistics computation and bookeeping along the evolution.

datalogger.py

from collections import defaultdict
from pandas import DataFrame, concat
from sys import stdout

from numpy import random
from matplotlib import pyplot as plt

class DataLogger(object):
    def __init__(self, key=None):
        self.key = key
        self.data = []
        self.table = None
        self.header = True

    def update(self, seq, **kargs):
        if self.key is not None:
            seq = map(self.key, seq)
        stats = DataFrame(seq).describe()
        stats = stats.drop('count').T
        for i, (name, value) in enumerate(kargs.items()):
            stats.insert(i, name, (value,) * len(stats))

        self.data.append(stats)
        self.table = concat(self.data, keys=range(len(self.data)), names=['gen', 'obj'])
        self.ndim = len(stats)

    def log(self):
        tail = self.table.tail(self.ndim)
        string = tail.to_string().split("\n")
        if self.header:
            print "\n".join(string[0:2])
            self.header = False
        print "\n".join(string[2:])
            

if __name__ == "__main__":

    logger = DataLogger()
    NGEN = 10
    SIZE = 20

    for i in range(NGEN):
        fits = random.randn(SIZE, 2)
        logger.update(fits, eval=len(fits))
        logger.log()
    
    # We group the results by objective number:
    grouped = logger.table.groupby(level=1)
    
    # Then we can simply plot the data
    mean_obj1 = grouped.get_group(0)['mean'][:, 0]
    mean_obj1.plot()
    plt.show()