Fit data with a lognormal function via Maximum Likelihood estimators

Could someone help me in fitting the data collapse_fractions with a lognormal function, which has median and standard deviation derived via the maximum likelihood method?

I tried scipy.stats.lognormal.fit(data), but I did not obtain the data I retrieved with Excel. The excel file can be downloaded: https://stacks.stanford.edu/file/druid:sw589ts9300/p_collapse_from_msa.xlsx

Also, any reference is really welcomed.

import numpy as np

intensity_measure_vector = np.array([[0.2, 0.3, 0.4, 0.6, 0.7, 0.8, 0.9, 1]])    
no_analyses = 40    
no_collapses = np.array([[0, 0, 0, 4, 6, 13, 12, 16]])    
collapse_fractions = np.array(no_collapses/no_analyses)

print(collapse_fractions)
# array([[0.   , 0.   , 0.   , 0.1  , 0.15 , 0.325, 0.3  , 0.4  ]])

collapse_fractions.shape
# (1, 8)

import matplotlib.pyplot as plt
plt.scatter(intensity_measure_vector, collapse_fractions)

from Fit data with a lognormal function via Maximum Likelihood estimators