Pandas: Add data for missing months
I have monthly customer sales data that looks something like this: multiple customers and different monthly periods and spend:
customer_id month_year sales
0 12 2012-05 2.58
1 12 2011-07 33.14
2 12 2011-11 182.06
3 12 2012-03 155.32
4 12 2012-01 71.24
As you can see, there are many months missing for each client. I would like to add additional lines for each customer, with sales = 0.0, for all months in the month_year range.
Can anyone advise on a better way to do this?
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1 answer
Something like that; note that the padding of the customer_id is undefined (as you are probably in a group or whatever).
You may need reset_index
at the end (if desired)
In [130]: df2 = df.set_index('month_year')
In [131]: df2 = df2.sort_index()
In [132]: df2
Out[132]:
customer_id sales
month_year
2011-07 12 33.14
2011-11 12 182.06
2012-01 12 71.24
2012-03 12 155.32
2012-05 12 2.58
In [133]: df2.reindex(pd.period_range(df2.index[0],df2.index[-1],freq='M'))
Out[133]:
customer_id sales
2011-07 12 33.14
2011-08 NaN NaN
2011-09 NaN NaN
2011-10 NaN NaN
2011-11 12 182.06
2011-12 NaN NaN
2012-01 12 71.24
2012-02 NaN NaN
2012-03 12 155.32
2012-04 NaN NaN
2012-05 12 2.58
In [135]: df2['customer_id'] = 12
In [136]: df2.fillna(0.0)
Out[136]:
customer_id sales
2011-07 12 33.14
2011-08 12 0.00
2011-09 12 0.00
2011-10 12 0.00
2011-11 12 182.06
2011-12 12 0.00
2012-01 12 71.24
2012-02 12 0.00
2012-03 12 155.32
2012-04 12 0.00
2012-05 12 2.58
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