Extract Date Ranges where data is missing-CodePudding

Given dummy data frame as below:

 ----------------- ----------------- ------- 
|   Start_Date    |    End_Date     | Price |
 ----------------- ----------------- ------- 
| 01/01/2021 0:00 | 01/01/2021 0:59 |    10 |
| 01/01/2021 0:01 | 01/01/2021 0:01 |    20 |
| 01/01/2021 0:02 | 01/01/2021 0:02 |    24 |
| 01/01/2021 0:03 | 01/01/2021 0:03 |    23 |
| 01/01/2021 0:07 | 01/01/2021 0:07 |    34 |
| 01/01/2021 0:08 | 01/01/2021 0:08 |    37 |
| 01/01/2021 0:10 | 01/01/2021 0:10 |    21 |
| 01/01/2021 0:12 | 01/01/2021 0:12 |    22 |
| 01/01/2021 0:14 | 01/01/2021 0:14 |    56 |
 ----------------- ----------------- -------

The above data frame can be generated using code below:

data = {'Start_Date':['2021-01-01 00:00:00', '2021-01-01 00:01:00', '2021-01-01 00:02:00', '2021-01-01 00:03:00', '2021-01-01 00:07:00',
                      '2021-01-01 00:08:00', '2021-01-01 00:10:00', '2021-01-01 00:12:00', '2021-01-01 00:14:00'],
        'End_Date':['2021-01-01 00:59:00', '2021-01-01 00:01:59', '2021-01-01 00:02:59', '2021-01-01 00:03:59', '2021-01-01 00:07:59',
                      '2021-01-01 00:08:59', '2021-01-01 00:10:59', '2021-01-01 00:12:59', '2021-01-01 00:14:59'],
        'Avg_Price':[10, 20, 24, 23, 34, 37, 21, 22, 56]}
df1 = pd.DataFrame(data)
df1['Start_Date'] = pd.to_datetime(df1['Start_Date'])
df1['End_Date'] = pd.to_datetime(df1['End_Date'])

It can be seen that there are date ranges where data is missing. Missing ranges can be seen in data frame below:

 --------------------- --------------------- ------- 
|     Start_Date      |      End_Date       | Price |
 --------------------- --------------------- ------- 
| 2021-01-01 00:00:00 | 2021-01-01 00:59:00 | 10    |
| 2021-01-01 00:01:00 | 2021-01-01 00:01:59 | 20    |
| 2021-01-01 00:02:00 | 2021-01-01 00:02:59 | 24    |
| 2021-01-01 00:03:00 | 2021-01-01 00:03:59 | 23    |
| 2021-01-01 00:04:00 | NaT                 | NaN   |
| 2021-01-01 00:05:00 | NaT                 | NaN   |
| 2021-01-01 00:06:00 | NaT                 | NaN   |
| 2021-01-01 00:07:00 | 2021-01-01 00:07:59 | 34    |
| 2021-01-01 00:08:00 | 2021-01-01 00:08:59 | 37    |
| 2021-01-01 00:09:00 | NaT                 | NaN   |
| 2021-01-01 00:10:00 | 2021-01-01 00:10:59 | 21    |
| 2021-01-01 00:11:00 | NaT                 | NaN   |
| 2021-01-01 00:12:00 | 2021-01-01 00:12:59 | 22    |
| 2021-01-01 00:13:00 | NaT                 | NaN   |
| 2021-01-01 00:14:00 | 2021-01-01 00:14:59 | 56    |
 --------------------- --------------------- -------

Above can be generated using code below:

df2 = pd.DataFrame(index=pd.date_range('2021-01-01 00:00:00', '2021-01-01 00:14:00', freq='min'))
df2 = df2.join(df1.set_index('Start_Date'))

I want list of lists to get dates ranges where data is missing.

Expected Output

result = [['2021-01-01 00:04:00','2021-01-01 00:06:00'], ['2021-01-01 00:09:00','2021-01-01 00:09:00'],
['2021-01-01 00:11:00', '2021-01-01 00:11:00'], ['2021-01-01 00:13:00','2021-01-01 00:13:00']]

What would be elegant way to achieve the desired output?

CodePudding user response：

Idea is create groups by consecutive NaNs by Series.notna with Series.cumsum, filter only NaNs by invert by ~ and aggregate by GroupBy.agg with min and max, last convert to strings and then to nested lists:

m = df2['End_Date'].notna()

L = (df2.index.to_series()
        .groupby(m.cumsum()[~m])
        .agg(['min','max'])
        .astype(str)
        .to_numpy()
        .tolist())
print (L)
[['2021-01-01 00:04:00', '2021-01-01 00:06:00'], 
 ['2021-01-01 00:09:00', '2021-01-01 00:09:00'],
 ['2021-01-01 00:11:00', '2021-01-01 00:11:00'], 
 ['2021-01-01 00:13:00', '2021-01-01 00:13:00']]

CodePudding user response：

A combination of null checking, combined with np.split and a list comprehension works on the sample data shared :


boolean = df2.End_Date.isna().cumsum()
# get position count for each null end
val = boolean[boolean.gt(0) & boolean.duplicated()].array.unique()

nulls = df2.index[df2.End_Date.isna()]


[[ent[0], ent[-1]] 
 for ent in np.split(nulls, val) 
if ent.size > 0]
 
[[Timestamp('2021-01-01 00:04:00'), Timestamp('2021-01-01 00:06:00')],
 [Timestamp('2021-01-01 00:09:00'), Timestamp('2021-01-01 00:09:00')],
 [Timestamp('2021-01-01 00:11:00'), Timestamp('2021-01-01 00:11:00')],
 [Timestamp('2021-01-01 00:13:00'), Timestamp('2021-01-01 00:13:00')]]


# if you prefer string form :
[[str(ent[0]), str(ent[-1])] 
 for ent in np.split(nulls, val) 
if ent.size > 0]
 
[['2021-01-01 00:04:00', '2021-01-01 00:06:00'],
 ['2021-01-01 00:09:00', '2021-01-01 00:09:00'],
 ['2021-01-01 00:11:00', '2021-01-01 00:11:00'],
 ['2021-01-01 00:13:00', '2021-01-01 00:13:00']]