Pandas Compare - How to compare 2 date columns in 2 separate dataframes - pandas

Question

I have once csv with missing dates, I have created a new df of that same date range, without the missing dates. I want to compare the two csvs and place an NaN wherever there are blank dates in the original csv:
Example:
DateTime Measurement Dates
0 2016-10-09 00:00:00 1021.9 2016-10-09
1 2016-10-11 00:00:00 1019.9 2016-10-10
2 2016-10-12 00:00:00 1015.8 2016-10-11
3 2016-10-13 00:00:00 1013.2 2016-10-12
4 2016-10-14 00:00:00 1005.9 2016-10-13
so I want the new table to be:
DateTime Measurement Dates
0 2016-10-09 00:00:00 1021.9 2016-10-09
1 Nan 00:00:00 Nan 2016-10-10
2 2016-10-11 00:00:00 1015.8 2016-10-11
3 2016-10-12 00:00:00 1013.2 2016-10-12
4 2016-10-13 00:00:00 1005.9 2016-10-13
And then I will remove the DateTime column so the final df is a complete list of dates with the missing measurements.
The code I have used thus far:
new_dates = pandas.date_range(start = '2016-10-09 00:00:00', end = '2017-10-09 00:00:00')
merged = pandas.merge(measurements, updated_dates,left_index=True, right_index=True)

Answer 1

If I understand your correctly you want to resample your DateTime column to a daily frequency and fill the gaps with NaN:
# Use this line if your DateTime column is not datetime type yet
# df['DateTime'] = pd.to_datetime(df['DateTime'])
dates = pd.date_range(df['DateTime'].min(), df['DateTime'].max(), freq='D')
df = df.set_index('DateTime').reindex(dates).reset_index()
Output
index Measurement
0 2016-10-09 1021.9
1 2016-10-10 NaN
2 2016-10-11 1019.9
3 2016-10-12 1015.8
4 2016-10-13 1013.2
5 2016-10-14 1005.9
If you have unique dates, you can use resample as well. If your dates are not unique it would aggregate them and take the mean of two dates:
df.set_index('DateTime').resample('D').mean()
Output
DateTime Measurement
0 2016-10-09 1021.9
1 2016-10-10 NaN
2 2016-10-11 1019.9
3 2016-10-12 1015.8
4 2016-10-13 1013.2
5 2016-10-14 1005.9