You can use clip and align on the first axis:

df.clip(df.quantile(0.05), df.quantile(0.95), axis=1)
Out: 
          0         1
0  0.734355  0.594992
1 -0.706864  0.597601
2  0.295606  0.972196
3  0.474539  1.241788
4  0.238838  0.684790
5 -0.659094  0.451718
6  0.675360 -0.884488
7  0.713914  0.135179
8 -0.435309 -0.344975
9  0.990799 -0.392945

You can pass arrays to np.clip, so it is possible to have different limits across the z dimension of mx:

import numpy as np

# Create random mx
x, y, z = 10, 11, 12
mx = np.random.random((x, y, z))

# Calculate the percentiles across the x and y dimension
perc01 = np.percentile(mx, 1, axis=(0, 1), keepdims=True)
perc99 = np.percentile(mx, 99, axis=(0, 1), keepdims=True)

# Clip array with different limits across the z dimension
filtered_mx = np.clip(mx, a_min=perc01, a_max=perc99)