Is there a way to calculate between two value of NaN value?
Question:
i’m having a trouble to code into finding in between values in pandas dataframe.
the dataframe:
value
30
NaN
NaN
25
NaN
20
NaN
NaN
NaN
NaN
15
…
the formula is like this:
value before nan - ((value before nan - value after nan)/div by no. of nan in between the values)
example of expected value should be like this:
30 - (30-25)/2 = 27.5
27.5 - (27.5-25)/1 = 25
so the expected dataframe will look like this:
value
expected value
30
30
NaN
27.5
NaN
25
25
25
NaN
20
20
20
NaN
18.75
NaN
17.5
NaN
16.25
NaN
15
15
15
…
…
Answers:
IIUC, you can generalize your formula into two parts:
- Any
nan right before a non-nan is just same as that number
- {value-before-nan} – ({value-before-nan} – {value-after-nan})/1 = {value-after-nan}
- Rest of
nan are linear interpolation.
So you can use bfill with interpolate:
df.bfill(limit=1).interpolate()
Output:
value
0 30.00
1 27.50
2 25.00
3 25.00
4 20.00
5 20.00
6 18.75
7 17.50
8 16.25
9 15.00
10 15.00
i’m having a trouble to code into finding in between values in pandas dataframe.
the dataframe:
| value |
|---|
| 30 |
| NaN |
| NaN |
| 25 |
| NaN |
| 20 |
| NaN |
| NaN |
| NaN |
| NaN |
| 15 |
| … |
the formula is like this:
value before nan - ((value before nan - value after nan)/div by no. of nan in between the values)
example of expected value should be like this:
30 - (30-25)/2 = 27.5
27.5 - (27.5-25)/1 = 25
so the expected dataframe will look like this:
| value | expected value |
|---|---|
| 30 | 30 |
| NaN | 27.5 |
| NaN | 25 |
| 25 | 25 |
| NaN | 20 |
| 20 | 20 |
| NaN | 18.75 |
| NaN | 17.5 |
| NaN | 16.25 |
| NaN | 15 |
| 15 | 15 |
| … | … |
IIUC, you can generalize your formula into two parts:
- Any
nanright before a non-nan is just same as that number
- {value-before-nan} – ({value-before-nan} – {value-after-nan})/1 = {value-after-nan}
- Rest of
nanare linear interpolation.
So you can use bfill with interpolate:
df.bfill(limit=1).interpolate()
Output:
value
0 30.00
1 27.50
2 25.00
3 25.00
4 20.00
5 20.00
6 18.75
7 17.50
8 16.25
9 15.00
10 15.00