pandas groupby and filling in missing frequencies - pandas

Question

I have a dataset of events each of which occurred on a specific day. Using Pandas I have been able to aggregate these into a count of events per month using the groupby function, and then plot a graph with Matplotlib. However, in the original dataset some months do not have any events and so there is no count of events in such a month. Such months do not therefore appear on the graph, but I would like to include then somehow with their zero count
bpm2 = df2_yr1.groupby(['month_year', 'month'])['event_no'].count()
which produces
month_year month
2016-01 January 9
2016-02 February 7
2016-04 April 1
2016-06 June 4
2016-07 July 1
2016-08 August 3
2016-09 September 2
2016-10 October 5
2016-11 November 17
2016-12 December 3
I have been trying to find a way of filling missing months in the dataframe generated by the groupby function with a 'count' value of 0 for, in this example, March and May..
Can anyone offer some advice on how this might be achieved. I have been trying to carry out FFill on the month column but with little success and can't work out how to add in a corresponding zero value for the missing months

Answer 1

First of all, if bpm2 = df2_yr1.groupby(['month_year', 'month'])['event_no'].count() is your code, then it is a series. So, let's change it to a dataframe with bpm2 = df2_yr1.groupby(['month_year', 'month'])['event_no'].count().reset_index(). Now, into the problem.
Change to date format and use pd.Grouper and change back to string format. Also add back the month column and change the formatting of the event_no column:
bpm2 = df2_yr1.groupby(['month_year', 'month'])['event_no'].count().reset_index()
bpm2['month_year'] = bpm2['month_year'].astype(str)
bpm2['month_year'] = pd.to_datetime(bpm2['month_year'])
bpm2 = bpm2.groupby([pd.Grouper(key='month_year', freq='1M')])['event_no'].first().fillna(0).astype(int).reset_index()
bpm2['month'] = bpm2['month_year'].dt.strftime('%B')
bpm2['month_year'] = bpm2['month_year'].dt.strftime('%Y-%m')
bpm2
output:
month_year event_no month
0 2016-01 9 January
1 2016-02 7 February
2 2016-03 0 March
3 2016-04 1 April
4 2016-05 0 May
5 2016-06 4 June
6 2016-07 1 July
7 2016-08 3 August
8 2016-09 2 September
9 2016-10 5 October
10 2016-11 17 November
11 2016-12 3 December