I’ve taken my Series and coerced it to a datetime column of dtype=datetime64[ns] (though only need day resolution…not sure how to change).
import pandas as pd
df = pd.read_csv(‘somefile.csv’)
column = df[‘date’]
column = pd.to_datetime(column, coerce=True)
but plotting doesn’t work:
ipdb> column.plot(kind=’hist’)
*** TypeError: ufunc add canno….
I have a pandas dataframe with the following columns:
data = {‘Date’: [’01-06-2013′, ’02-06-2013′, ’02-06-2013′, ’02-06-2013′, ’02-06-2013′, ’03-06-2013′, ’03-06-2013′, ’03-06-2013′, ’03-06-2013′, ’04-06-2013′],
‘Time’: [’23:00:00′, ’01:00:00′, ’21:00:00′, ’22:00:00′, ’23:00:00′, ’01:00:00′, ’21:00:00′, ’22:00:00′, ’23:00:00′, ’01:00:00′]}….
There seems to be no function that simply calculates the moving average on numpy/scipy, leading to convoluted solutions.
My question is two-fold:
What’s the easiest way to (correctly) implement a moving average with numpy?
Since this seems non-trivial and error prone, is there a good reason not to have the batteries included in this case?
….