I see you've already imported numpy because you're using np.linspace in the code. You are probably confusing numpy's abs, which will happily work on lists and arrays, with __builtin__.abs, which only works for scalars.

Change this:

abs(wf[0:100])

To this:

np.abs(wf[0:100])

I don't recommend you use apply here

Use GroupBy.idxmax + DataFrame.replace

max_df = df.abs().groupby(np.arange(len(df)) // n).idxmax().replace(df)
print(max_df)

Output

           A          B          C          D
0  95.483784  72.261024  90.289008 -99.557204
1  92.303663 -92.933734  98.741863 -73.221129
2 -94.858459  91.925163  90.394739  94.129047
3  85.727608  96.168424  69.747412  74.943672

print(df)
 # df = pd.read_clipboard() # read

            A          B          C          D
0  -49.710250 -44.714960  90.289008  78.021054
1   15.779849  72.261024 -80.564941 -99.557204
2  -25.535893  44.418568  -3.654055  -4.656012
3   -1.792691  52.828214 -24.383645  54.337480
4   95.483784 -33.604944  60.210426 -68.157859
5   85.614669 -88.756766  14.634241 -73.221129
6   -1.461207  41.078068  98.741863   0.152652
7   92.303663 -77.230608  63.205845 -45.439176
8   36.255957 -92.933734  -8.668916  24.251590
9   15.387012 -17.044411 -84.098159  53.797730
10 -15.277586  91.925163  90.394739  94.129047
11 -94.858459  19.069534  62.672051  10.852176
12 -48.550836  59.084142 -22.185758 -58.797477
13 -16.430060 -26.718411 -23.169127  90.198812
14  14.495206  14.054623 -59.593696  35.043442
15  21.148221  16.673029  42.788121 -23.932640
16  74.617433 -53.114081  69.747412  74.943672
17  85.727608  96.168424  41.474511   7.672894
18  -9.282754  -0.151546  -8.765613 -26.973899
19 -39.272002  85.819052  21.355006  67.018427

The problem is that Groupby.apply apply the function to each daframe, then max receives a dataframearg... We could check it with:

def absmax(x):
    print('This is a DataFrame arg: \n', x)
    return x.apply(lambda y: max(y, key=abs))

df_max = df.groupby(np.arange(len(df))//n).apply(absmax)

This is a DataFrame arg: 
            A          B          C          D
0  23.080753  89.918599 -62.273324   0.674636
1 -61.096176  68.840583  20.359616 -82.110220
2 -86.942716  97.269852  57.320944  84.340152
3  36.632979  53.376849  95.817563  39.398515
4 -36.960907 -12.796490 -79.833804  32.708664
This is a DataFrame arg: 
            A          B          C          D
5  24.360600 -49.486819 -65.995965 -93.884078
6 -46.623600 -57.999896 -86.946372  -0.250644
7 -35.103092  61.971385  86.165203 -32.619381
8  50.155064 -38.999355  98.856747 -56.195841
9 -92.646512  94.217864 -86.628196 -55.859978
This is a DataFrame arg: 
             A          B          C          D
10 -55.846413  34.281246 -90.523268  71.148029
11  22.753896 -33.659637  74.225409  24.498337
12 -52.384172  16.169118 -10.788839 -99.874961
13  49.235215 -74.372901  11.509361 -43.676953
14  67.255287 -84.477123  12.725054 -85.892184
This is a DataFrame arg: 
             A          B          C          D
15  72.522972 -13.079824  48.973703 -87.913843
16 -64.110924 -81.324560   7.067080  97.073997
17  87.319482  76.021534  80.780322 -90.320084
18 -84.848110  70.732111  34.160013  99.269365
19 -17.924337  12.191496  46.020178  30.532568

So I'm assuming here that fp(x) or f(x) are functions that don't return anything.

Take a look at this,

def fp(x):
    print(x)

print(fp(10))

You might think that the output of this would be 10 but you get this,

10
None

Take a look at this function,

def fp():
    pass
print(fp())

Output:

None

Functions by default return a None type. So in your case when you are doing abs(fp(x)) the problem seems to be that you are doing abs() on None that's why you get the error.

Look at this,

def fp():
        pass
abs(fp())

Output:

TypeError: bad operand type for abs(): 'NoneType' python error

So add a return statement to the function and you can see the error will be gone

def fp(x):
    return x
some_value = abs(fp(-10.5))
print(some_value)

Output:

10.5

Now the error is gone.

Looking at the docs docs, it seems that you mistook inputs for outputs. residue takes a and b and returns r, p and k.

Third parameter (tol) should be a number but you are passing a list.

You might be using wrong method. Take a look at scipy.signal.invres, that should be what you are looking for.