A call center contacted you because they have several mounths of call history. To better plan their activity, they would like to accurately predict the amount of call they should expect.
The data they provide is gathered in the data/CallCenter directory. As you see you have one listing per day of the week. The data are formated as follows:
You are hired to give them the probability distribution function of the number of call they expect every day.
You should be able to parametrize it. Also, because they are not statisticien, they want a clear anwser of the 5-95% and 18-64% intervals for the number of call expected every half-hour for each day of the week.
In [1]:
# == Basic import == #
# No annoying warnings
import warnings
warnings.filterwarnings('ignore')
# plot within the notebook
%matplotlib inline
In [30]:
## 1 Read the files
import numpy as np
In [155]:
import pandas
In [158]:
p = pandas.read_csv("data/CallCenter/%ss.csv"%"Monday", sep=" ")
In [168]:
d_ = p.to_dict()
In [169]:
d_
Out[169]:
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In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [31]:
dayoftheweek = ["Monday","Tuesday","Wednesday", "Thursday","Friday"]
In [139]:
def get_weekday_data(day):
""" Read the call center file and returns an dictionary"""
if day not in dayoftheweek:
raise ValueError("%d is not a day or the week or has the wrong format. Week day: ",+ ", ".join(dayoftheweek))
data = [l[2:].split() for l in open("data/CallCenter/%ss.csv"%day).read().splitlines() if len(l.split())>4 and l[0] !="#"]
return np.asarray(data[2:], dtype="float")
In [140]:
monday = get_weekday_data("Monday").T
In [141]:
import matplotlib.pyplot as mpl
In [144]:
mpl.hist(monday[0], histtype="step")
Out[144]:
(array([ 3., 17., 13., 24., 10., 22., 9., 1., 0., 1.]),
array([ 2. , 3.6, 5.2, 6.8, 8.4, 10. , 11.6, 13.2, 14.8,
16.4, 18. ]),
<a list of 1 Patch objects>)
In [146]:
ERROR: TypeError: _parse_args() takes at least 2 arguments (1 given) [scipy.stats._distn_infrastructure]
ERROR:astropy:TypeError: _parse_args() takes at least 2 arguments (1 given)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-146-7f6ea98c3714> in <module>()
----> 1 p = stats.poisson()
//anaconda/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.pyc in __call__(self, *args, **kwds)
767
768 def __call__(self, *args, **kwds):
--> 769 return self.freeze(*args, **kwds)
770 __call__.__doc__ = freeze.__doc__
771
//anaconda/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.pyc in freeze(self, *args, **kwds)
764
765 """
--> 766 return rv_frozen(self, *args, **kwds)
767
768 def __call__(self, *args, **kwds):
//anaconda/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.pyc in __init__(self, dist, *args, **kwds)
432
433 # a, b may be set in _argcheck, depending on *args, **kwds. Ouch.
--> 434 shapes, _, _ = self.dist._parse_args(*args, **kwds)
435 self.dist._argcheck(*shapes)
436 self.a, self.b = self.dist.a, self.dist.b
TypeError: _parse_args() takes at least 2 arguments (1 given)
In [ ]:
In [147]:
from scipy import stats
In [148]:
p = stats.poisson(4)
In [153]:
x = np.linspace(0,10,11)
print x
[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]
In [154]:
mpl.plot(x, p.cdf(x))
Out[154]:
[<matplotlib.lines.Line2D at 0x10ffe33d0>]
In [ ]:
Content source: HUDataScience/StatisticalMethods2016
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