I want to calculate the mean of columns a,b,c,d of the dataframe BUT if one of four values in each dataframe row differs more then 20% from this mean (of the four values), the mean has to be set to NaN.
Calculation of the mean of 4 columns is easy, but I'm stuck at defining the condition 'if mean*0.8 <= one of the values in the data row <= mean*1,2 then mean == NaN.
In the example, one or more of the values in ID:5 en ID:87 don't fit in the interval and therefore the mean is set to NaN. (NaN-values in the initial dataframe are ignored when calculating the mean and when applying the 20%-condition to the calculated mean)
So I'm trying to calculate the mean only for the data rows with no 'outliers'.
Initial df:
ID a b c d
2 31 32 31 31
5 33 52 159 2
7 51 NaN 52 51
87 30 52 421 2
90 10 11 10 11
102 41 42 NaN 42
Desired df:
ID a b c d mean
2 31 32 31 31 31.25
5 33 52 159 2 NaN
7 51 NaN 52 51 51.33
87 30 52 421 2 NaN
90 10 11 10 11 10.50
102 41 42 NaN 42 41.67
Code:
import pandas as pd
import numpy as np
df = pd.DataFrame({"ID": [2,5,7,87,90,102],
"a": [31,33,51,30,10,41],
"b": [32,52,np.nan,52,11,42],
"c": [31,159,52,421,10,np.nan],
"d": [31,2,51,2,11,42]})
print(df)
a = df.loc[:, ['a','b','c','d']]
df['mean'] = (a.iloc[:,0:]).mean(1)
print(df)
b = df.mean.values[:,None]*0.8 < a.values[:,:] < df.mean.values[:,None]*1.2
print(b)
...
IIUC:
# extract related information
s = df.iloc[:,1:]
# calculate mean
mean = s.mean(1)
# where condition is violated
mask = s.lt(mean*.8, axis=0) | s.gt(mean*1.2, axis=0)
# mask where mask is True on any row
df['mean'] = mean.mask(mask.any(1))
Output:
ID a b c d mean
0 2 31 32.0 31.0 31 31.250000
1 5 33 52.0 159.0 2 NaN
2 7 51 NaN 52.0 51 51.333333
3 87 30 52.0 421.0 2 NaN
4 90 10 11.0 10.0 11 10.500000
5 102 41 42.0 NaN 42 41.666667
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