Special functions
Last updated
Last updated
Syntax | Example |
---|---|
To better understand this function, let's suppose you have a set of variables that you want to sum up together.
_
The variables A, B, C, and D don't have data in all of the timestamps (as shown in the table below), so the fill missing() function is used to fill in the gaps and do the calculation. The following examples are based on this table:
Date | A | B | C | D | Output |
---|---|---|---|---|---|
By default, the fill_missing() expression makes the first_fill
parameter as "ffill"
, meaning that the function will fill the gaps forward, starting at a point where it has enough data to fill the gaps for all the involved time series. The result would be as follows:
Date | A | B | C | D | Output |
---|---|---|---|---|---|
On the other hand, it's also possible to make the first_fill
a "bfill"
. The gaps would be filled in the following manner:
Date | A | B | C | D | Output |
---|---|---|---|---|---|
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Date | A | B | C | D | Output |
---|---|---|---|---|---|
Syntax | Example | Output |
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Syntax | Example | Output |
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Syntax | Example | Output |
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Sintax | Example | Output |
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5
0
4
5
14
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3
2
3
4
12
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0
4
0
2
6
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4
3
5
2
12
6
where(condition, operation if true, operation if false)
Step Function:
Assuming a variable
Interval Function:
Assuming a variable
diff(<variable>,<steps>)
Assuming a variable
[3, 1]
shift(<variable>,<step> )
Assuming a variable
This allows you to retrieve the previous value of the variable by shifting the series to the left.
Assuming a variable
Shifts the series to the right
fill_missing (expression, <first_fill>, <last_fill>, <fill_value>)
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5
2
4
5
16
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3
2
3
4
12
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4
4
5
2
15
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4
3
5
5
-
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5
2
4
5
-
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3
2
3
4
12
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3
4
3
2
12
07/12/2020 00:00:01
4
3
5
2
14
07/12/2020 00:00:04
5
2
4
5
16
07/12/2020 00:00:03
3
2
3
4
12
07/12/2020 00:00:02
4
4
5
2
15
07/12/2020 00:00:01
4
3
5
2
14