Python:Fill in missing datetime values in dataframe and fill forward?

dataframe, pandas, python

Solution

You can use `resample` with `ffill`:

print (df.dtypes)
timestamp     object
value        float64
dtype: object

df['timestamp'] = pd.to_datetime(df['timestamp'])

print (df.dtypes)
timestamp    datetime64[ns]
value               float64
dtype: object

df = df.set_index('timestamp').resample('S').ffill()
print (df)
                     value
timestamp                 
2013-01-01 00:00:00    2.1
2013-01-01 00:00:01    2.1
2013-01-01 00:00:02    2.1
2013-01-01 00:00:03    3.7
2013-01-01 00:00:04    3.7
2013-01-01 00:00:05    2.4

df = df.set_index('timestamp').resample('S').ffill().reset_index()
print (df)
            timestamp  value
0 2013-01-01 00:00:00    2.1
1 2013-01-01 00:00:01    2.1
2 2013-01-01 00:00:02    2.1
3 2013-01-01 00:00:03    3.7
4 2013-01-01 00:00:04    3.7
5 2013-01-01 00:00:05    2.4

Problem

Let's say I have a dataframe as: ``` | timestamp | value | | ------------------- | ----- | | 01/01/2013 00:00:00 | 2.1 | | 01/01/2013 00:00:03 | 3.7 | | 01/01/2013 00:00:05 | 2.4 | ``` I'd like to have the dataframe as: ``` | timestamp | value | | ------------------- | ----- | | 01/01/2013 00:00:00 | 2.1 | | 01/01/2013 00:00:01 | 2.1 | | 01/01/2013 00:00:02 | 2.1 | | 01/01/2013 00:00:03 | 3.7 | | 01/01/2013 00:00:04 | 3.7 | | 01/01/2013 00:00:05 | 2.4 | ``` How do I go about this?

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