Shift time series with missing dates in Pandas

In [588]: df = pd.DataFrame({ 'date':[2000,2001,2003,2004,2005,2007],                              'value':[5,10,8,72,12,13] })In [589]: df['previous_value'] = df.value.shift()[ df.date == df.date.shift() + 1 ]In [590]: dfOut[590]:    date  value  previous_value0  2000      5             NaN1  2001     10               52  2003      8             NaN3  2004     72               84  2005     12              725  2007     13             NaN

Also see here for a time series approach using resample(): Using shift() with unevenly spaced data

Your example doesn't look like real time series data with timestamps. Let's take another example with the missing date 2020-01-03:

df = pd.DataFrame({"val": [10, 20, 30, 40, 50]},                  index=pd.date_range("2020-01-01", "2020-01-05"))df.drop(pd.Timestamp('2020-01-03'), inplace=True)            val2020-01-01   102020-01-02   202020-01-04   402020-01-05   50

To shift by one day you can set the freq parameter to 'D':

df.shift(1, freq='D')

Output:

            val2020-01-02   102020-01-03   202020-01-05   402020-01-06   50

To combine original data with the shifted one you can merge both tables:

df.merge(df.shift(1, freq='D'),         left_index=True,         right_index=True,         how='left',         suffixes=('', '_previous'))

Output:

            val  val_previous2020-01-01   10           NaN2020-01-02   20          10.02020-01-04   40           NaN2020-01-05   50          40.0

Other offset aliases you can find here