interpolating consecutive numbers-CodePudding

Based on below Pandas DataFrame, there are time(secound) & heartrate values. Although 'time' should be consecutive numbers, we see some missing values. (1,2,4,5,7,9,10 etc) In that case, should I use pandas.DataFrame.interpolate to get desired result? or any other great idea to achieve it? Pls do note the original data are coming from API. Tried to look for any answers on the web, but no luck...

Original:

    time heartrate
    0   97
    3   105
    6   105
    8   111
    11  111
    13  114
    16  115

Desired output:

time    heartrate
0   97
1   100
2   103
3   105
4   105
5   105
6   105
7   109
8   111
9   111
10  111
11  111
12  113
13  114
14  114
15  114
16  115

CodePudding user response：

Define time as index then reindex with pd.RangeIndex to get continuous values and interpolate heartrate:

idx = pd.RangeIndex(df.time.min(), df.time.max() 1, name='time')

out = df.set_index('time').reindex(idx)['heartrate'] \
        .interpolate(method='linear') \
        .pipe(np.ceil) \
        .reset_index()

Output:

>>> out
    time  heartrate
0      0       97.0
1      1      100.0
2      2      103.0
3      3      105.0
4      4      105.0
5      5      105.0
6      6      105.0
7      7      108.0
8      8      111.0
9      9      111.0
10    10      111.0
11    11      111.0
12    12      113.0
13    13      114.0
14    14      115.0
15    15      115.0
16    16      115.0

The result is imprecise. You have to adjust the interpolate method to get the desired result but the principle is the same.

CodePudding user response：

You can reindex and interpolate, and use np.ceil to ceil the numbers:

(np.ceil(df.set_index('time')
           .reindex(range(df['time'].max() 1))
           .interpolate())
   .reset_index()
)

output:

    time  heartrate
0      0       97.0
1      1      100.0
2      2      103.0
3      3      105.0
4      4      105.0
5      5      105.0
6      6      105.0
7      7      108.0
8      8      111.0
9      9      111.0
10    10      111.0
11    11      111.0
12    12      113.0
13    13      114.0
14    14      115.0
15    15      115.0
16    16      115.0