On 04/18/2013 04:07 PM, Neil Cerutti wrote:
On 2013-04-18, Wayne Werner <wa...@waynewerner.com> wrote:
On Wed, 17 Apr 2013, someone wrote:
....
Go to line 214, and take a look-see at what you find. My guess is it will
be something like:
def rule_code():
raise NotImplementedError()
Which is terribly unhelpful.
It most likely means that the program is instantiating an
abstract base class when it should be using one of its subclasses
instead, e.g., BusinessDay, MonthEnd, MonthBegin,
BusinessMonthEnd, etc.
http://pandas.pydata.org/pandas-docs/dev/timeseries.html
Hi Neil and Wayne,
Thank you very much for your suggestions... I now found out something:
In the function:
def convertListPairToTimeSeries(dList, cList):
...
...
# create the timeseries
ts = pandas.Series(cListL, index=indx)
# fill in missing days
#ts = ts.asfreq(pandas.datetools.DateOffset())
return ts
I had to out-comment the last line before the return-statement (not sure
what that line is supposed to do, in the first case)...
Now the program runs, but no plot is seen. Then I found out that I had
to add:
import matplotlib.pyplot as plt
in the top of the program and add the following in the bottom of the
program:
plt.show()
Final program:
==================
#!/usr/bin/python
import pandas
import datetime
import numpy
import ipdb
import matplotlib.pyplot as plt
datesList = [datetime.date(2011,12,1), \
datetime.date(2011,12,2), \
datetime.date(2011,12,3), \
datetime.date(2011,12,10)]
countsList = numpy.random.randn(len(datesList))
startData = datetime.datetime(2011,12,3)
endData = datetime.datetime(2011,12,8)
def convertListPairToTimeSeries(dList, cList):
# my dateList had date objects, so convert back to datetime objects
dListDT = [datetime.datetime.combine(x, datetime.time()) for x in
dList]
# found that NaN didn't work if the cList contained int data
cListL = [float(x) for x in cList]
# create the index from the datestimes list
indx = pandas.Index(dListDT)
# create the timeseries
ts = pandas.Series(cListL, index=indx)
# fill in missing days
#ts = ts.asfreq(pandas.datetools.DateOffset())
return ts
print "\nOriginal datesList list:\n", datesList
tSeries = convertListPairToTimeSeries(datesList, countsList)
print "\nPandas timeseries:\n", tSeries
# use slicing to change length of data
tSeriesSlice = tSeries.ix[startData:endData]
print "\nPandas timeseries sliced between", startData.date(), \
"and", endData.date(), ":\n", tSeriesSlice
# use truncate instead of slicing to change length of data
tSeriesTruncate = tSeries.truncate(before=startData, after=endData)
print "\nPandas timeseries truncated between", startData.date(), \
"and", endData.date(), ":\n", tSeriesTruncate
# my data had lots of gaps that were actually 0 values, not missing data
# So I used this to fix the NaN outside the known outage
startOutage = datetime.datetime(2011,12,7)
endOutage = datetime.datetime(2011,12,8)
tsFilled = tSeries.fillna(0)
# set the known outage values back to NAN
tsFilled.ix[startOutage:endOutage] = numpy.NAN
print "\nPandas timeseries NaN reset to 0 outside known outage between", \
startOutage.date(), "and", endOutage.date(), ":\n", tsFilled
print "\nPandas series.tail(1) and series.head(1) are handy for " +\
"checking ends of list:\n", tsFilled.head(1), tsFilled.tail(1)
print
tsFilled.plot()
plt.show()
==================
This seem to work, although I don't fully understand it, as I'm pretty
new to pandas...
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