Frame timeseries_from_observations

---

timeseries_from_observations(self, date_time_index, timestamp_column, key_column, value_column)

Returns a frame that has the observations formatted as a time series.

Parameters:

date_time_index : list

DateTimeIndex to conform all series to.

timestamp_column : unicode

The name of the column telling when the observation occurred.

key_column : unicode

The name of the column that contains which string key the observation belongs to.

value_column : unicode

The name of the column that contains the observed value.

Returns:

: Frame

Uses the specified timestamp, key, and value columns and the date/time

index provided to format the observations as a time series. The time series frame will have columns for the key and a vector of the observed values that correspond to the date/time index.

Examples

In this example, we will use a frame of observations of resting heart rate for three individuals over three days. The data is accessed from Frame object called my_frame:

>>> my_frame.inspect( my_frame.row_count )
[#]  name     date                      resting_heart_rate
==========================================================
[0]  Edward   2016-01-01T12:00:00.000Z                62.0
[1]  Stanley  2016-01-01T12:00:00.000Z                57.0
[2]  Edward   2016-01-02T12:00:00.000Z                63.0
[3]  Sarah    2016-01-02T12:00:00.000Z                64.0
[4]  Stanley  2016-01-02T12:00:00.000Z                57.0
[5]  Edward   2016-01-03T12:00:00.000Z                62.0
[6]  Sarah    2016-01-03T12:00:00.000Z                64.0
[7]  Stanley  2016-01-03T12:00:00.000Z                56.0

We then need to create an array that contains the date/time index, which will be used when creating the time series. Since our data is for three days, our date/time index will just contain those three dates:

>>> datetimeindex = ["2016-01-01T12:00:00.000Z","2016-01-02T12:00:00.000Z","2016-01-03T12:00:00.000Z"]

Then we can create our time series frame by specifying our date/time index along with the name of our timestamp column (in this example, it’s

“date”), key column (in this example, it’s “name”), and value column (in

this example, it’s “resting_heart_rate”).

>>> ts = my_frame.timeseries_from_observations(datetimeindex, "date", "name", "resting_heart_rate")
[===Job Progress===]

Take a look at the resulting time series frame schema and contents:

>>> ts.schema
[(u'name', <type 'unicode'>), (u'resting_heart_rate', vector(3))]

>>> ts.inspect()
[#]  name     resting_heart_rate
================================
[0]  Stanley  [57.0, 57.0, 56.0]
[1]  Edward   [62.0, 63.0, 62.0]
[2]  Sarah    [None, 64.0, 64.0]