# Day 6

## Jun 13, 2017 · 884 words

Today I’ll be implementing the time_filter function that I outlined yesterday. Since then, I made some slight adjustments to the signature that you can see in the final version below.

## Packing and Unpacking

First thing I had to look up was how to handle optional parameters. It seems that you can use an asterisk * before an argument to get a Tuple of optional positional parameters, while a double asterisk ** is for a Dictionary of optional keyword parameters.

This is a subset of the larger topic of packing and unpacking in Python. When used in a parameter field, the * and ** operations pack arguments into a Tuple or Dictionary, respectively. Outside of that context, they perform the inverse operation, unpacking a Tuple or Dictionary into its contained arguments.

## Dates and Times

This proves useful with my time_filter function which has a number or optional parameters, some of which are Tuples themselves which simply need to be unpacked.

For example, the line:

data.between_time(*timerange)


will unpack the start and end times contained in the timerange parameter and pass them as arguments to the between_time function.

I also learned about the slice() function which turns its arguments into a slice to use for indexing. In combination with the * operator, I can say

data[slice(*daterange)]


to unpack the start and end dates, turn them into a slice, and use that slice to state the index bounds.

To handle the possible list of entries for each of these parameters, I can check if type(daterange[0]) is tuple and in that case do some more lambda calculus to perform the operation on all the subranges provided, then concatenate the results. I learned that it is important to then perform the sort_index function on the concatenated result since it does not automatically reorder the entries by their timestamp.

For the daysofweek parameter, I can use Python’s list comprehension feature to generate a boolean array of whether each day’s datetime.dayofweek attribute is contained in the list provided by the user:

data[[day in opt_kwds['daysofweek'] for day in data.index.weekday]]


## Inclusion / Exclusion

The exclusions parameter is a little tricky, mainly from my minimal familiarity with Timestamp and DateTimeIndex objects. Normally you would use the pandas.DataFrame.drop() function to remove objects based on their index, but it requires the exact index labels. The partial string indexing that lets you easily index all entries from a certain day don’t apply here. One solution is to use partial string indexing to get a series of entries from the given days, retrieve the indexes of those days, and pass that list of indexes to the drop() function:

The other annoying part is that partial string indexing, unlike exact string indexing, doesn’t let you pass in a list of items to select. So I’ll have to iterate through the exclusion dates and build up a cumulative list of indexes. Not a big deal but I wish there was a built in way to do this.

Instead of doing this with a loop, I can use the map() function and lambda keyword to apply my action over each date. The list of indexes that .index returns is an ndarray object, part of the numpy library. As such, I have to use the numpy.concatenate function as described in the documentation.

to_drop = np.concatenate(list (map(lambda x: out[x].index,
opt_kwds['exclusions'])))
out = out.drop(to_drop)


Adding elements is a simpler process so I will just use a loop to add the desired entries in the inclusions list:

for date in opt_kwds['inclusions']:
out = out + data[date]


## Final Product

In my examples I operated on the data directly but really I’m keeping a running out DataFrame variable that tracks all of my changes.

This is the function in its current state, subject to change as I learn more efficient ways to use Pandas and NumPy.

"""
time_filter: filters data by properties like date and time

ARGS:
data : DataFrame or Series with DateTimeIndex
*timerange: Tuple with start and end time strings as 'HH:MM'
or list of such tuples
*daterange: Tuple with start and end dates as 'YYYY-MM-DD'
or list of such tuples.
Enter None to set to min or max date
*exclusions: List of dates to be excluded as 'YYYY-MM-DD'
*inclusions: List of dates to be explicity included as 'YYYY-MM-DD'
This will override the daterange property
*daysofweek: List of integers for days to be included
0 = Mon, 6 = Sun

starred parameters are optional
ranges are all inclusive
"""

def time_filter(data, **opt_kwds):

out = data

if ('exclusions' in opt_kwds):
to_drop = np.concatenate(list (map(lambda x: out[x].index,
opt_kwds['exclusions'])))
out = out.drop(to_drop)
if ('timerange' in opt_kwds):
timerange = opt_kwds['timerange']
if type(timerange[0]) is tuple:
out = pd.concat(list(map(
lambda subrange: out.between_time(*subrange),
timerange))).sort_index()
else:
out = out.between_time(*timerange)
if ('daterange' in opt_kwds):
daterange = opt_kwds['daterange']
if type(daterange[0]) is tuple:
out = pd.concat(list(map(
lambda subrange: out[slice(*subrange)],
daterange))).sort_index()
else:
out = out[slice(*daterange)]
if ('daysofweek' in opt_kwds):
out = out[[day in opt_kwds['daysofweek'] for day in out.index.weekday]]
if ('inclusions' in opt_kwds):
for date in opt_kwds['inclusions']:
out = out + data[date]
return out


As an example, if I want entries from March and April 2016 between 7:40AM and 2:20PM on Mon Wed Fri, I can do so with

time_filter(df_energy,daterange=('2016-03','2016-04'),timerange=('7:40','14:20'),daysofweek=(0,2,4))


As I write this I remember I will want to add another parameter for month selection, since the daterange argument only lets you select months from a specified year.