I have a dataframe like so:
Column A Column B Date Value
1 A 1 2011-01-01 10
2 B 1 2011-01-01 10
3 A 2 2011-01-01 10
4 B 2 2011-01-01 10
5 A 1 2011-01-02 10
6 B 1 2011-01-02 10
7 A 2 2011-01-02 10
8 B 2 2011-01-02 10
9 A 1 2011-01-03 10
10 B 1 2011-01-03 10
11 B 2 2011-01-03 10
I want to find missing dates for every value of A and B (in this case, it would be A, date: 2011-01-03), and insert NaN there. I tried the reindex function:
df.sort_values(['Column A','Column B'],ascending = [True,True], inplace = True)
df.index = range(1,len(df)+1)
dates = pd.date_range('2011-01-01','2011-01-03')
df = df.reindex(dates, fill_value = None)
print df
But it gives me NaN in every column. Does anyone have any suggestions as to how I can flag these missing values?
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