How to add a column in a pandas dataframe with values that repeat?

Question

If I have a pandas dataframe that looks like this:

     X [ m ]  Y [ m ]  Z [ m ]
0         1     1      0.0
1         2     0.5    0.1
2         3     2      0.3
3         4     1      0.4
4         5     3      0.5 
5         1     4      0.6
6         2     1.5    0.8
7         3     6      1.0
8         4     3      1.2
9         5     4      1.5
...

How would I add a fourth column (Z_2) that would look like this (only include values that are a 0.5 jump):

     X [ m ]  Y [ m ]  Z [ m ]  Z_2 [ m ]
0         1     1      0.0      0.0
1         2     0.5    0.1      0.0
2         3     2      0.3      0.5
3         4     1      0.4      0.5
4         5     3      0.5      0.5
5         1     4      0.6      0.5
6         2     1.5    0.8      1.0 
7         3     6      1.0      1.0
8         4     3      1.2      1.0
9         5     4      1.5      1.5 
...

Is Z always increasing? Can it contain negative numbers? If so do you want -1.3 to go to -1.0 (round towards zero) or -1.5 (round down)? — Dan
– Dan, Commented Aug 20, 2020 at 15:37

cyrilb38 · Accepted Answer · 2020-08-20 15:39:29Z

2

You can try :

df["Z_2"] = (df["Z"] // 0.5) * 0.5

If you're talking about only 0.5 "jump"in the Z column.

answered Aug 20, 2020 at 15:39

cyrilb38

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nick · Accepted Answer · 2020-08-20 15:33:18Z

0

You could try something like:

df["Z_2"] = (df["Z"] % .5 == 0).cumsum() * .5

Here the df["Z"] % .5 == 0 finds the values in df["Z"] that are on the .5 boundary, the cumsum will only add 1 when a True is encountered.

answered Aug 20, 2020 at 15:33

nick

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2 Comments

Dan Over a year ago

This will only work if Z is monotonically increasing and it never skips a multiple of 0.5 right? If Z had [... 0.4, 0.6...] then wouldn't 0.6 still get rounded to 0 in this case? Granted the OP is quite ambiguous on this point.

nick Over a year ago

Yep that’s a valid point. I suppose I had assumed the values are sorted. If they’re unsorted your solution seems like the best one

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