Python - Running Average If number is great than 0

Question

I have a column in my dataframe comprised of numbers. Id like to have another column in the dataframe that takes a running average of the values greater than 0 that i can ideally do in numpy without iteration. (data is huge)

Vals    Output
-350    
1000    1000
1300    1150
1600    1300
1100    1250
1000    1200
450     1075
1900    1192.857143
-2000   1192.857143
-3150   1192.857143
1000    1168.75
-900    1168.75
800     1127.777778
8550    1870

Code:

list =[-350,    1000,   1300,   1600,   1100,   1000,   450,
    1900,   -2000,  -3150,  1000,   -900,   800,    8550]
    df = pd.DataFrame(data = list)

Side note, don't shadow built-ins, use L instead of list. — jpp
– jpp, Commented Aug 6, 2018 at 17:22

user3483203 · Accepted Answer · 2018-08-06 17:36:33Z

5

Option 1
expanding and mean

df.assign(out=df.loc[df.Vals.gt(0)].Vals.expanding().mean()).ffill()

If you have other columns in your DataFrame that have NaN values, this method will ffill those too, so if that is a concern, you may want to consider using something like this:

df['Out'] = df.loc[df.Vals.gt(0)].Vals.expanding().mean()
df['Out'] = df.Out.ffill()

Which will only fill in the Out column.

Option 2
mask:

df.assign(Out=df.mask(df.Vals.lt(0)).Vals.expanding().mean())

Both of these result in:

    Vals          Out
0   -350          NaN
1   1000  1000.000000
2   1300  1150.000000
3   1600  1300.000000
4   1100  1250.000000
5   1000  1200.000000
6    450  1075.000000
7   1900  1192.857143
8  -2000  1192.857143
9  -3150  1192.857143
10  1000  1168.750000
11  -900  1168.750000
12   800  1127.777778
13  8550  1870.000000

edited Aug 6, 2018 at 17:36

answered Aug 6, 2018 at 17:17

user3483203

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1 Comment

user3483203 Over a year ago

@novawaly I personally think my second solution (the mask) is more straightforward, so I would recommend using that.

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