It clips the histogram by minimum frequency

clip_histogram.py

import numpy as np
import matplotlib.pyplot as plt

def clip_histogram(data, get_indices=False, **hist_kwargs):
    n, bns = np.histogram(data, **hist_kwargs)
    bin_pairs = [(bns[i], bns[i+1]) for i in range(len(bns)-1)]  
    new_data = []
    indices = []
    for (i, (bnl, bnu)) in enumerate(bin_pairs):
        bin_width = bnu - bnl
        ith_bin_values = []
        for (idx, v) in enumerate(data):
            if (bnl < v < bnu) and (len(ith_bin_values) < min(n)):
                ith_bin_values.append(v)
                indices.append(idx)
        #
        new_data += ith_bin_values
    #
    return (new_data, indices) if get_indices else new_data

N = 1000
a = np.random.randn(N)
hist_kwargs = {"bins": 24, "range": (0.0, 1.0)}
a_clipped = clip_vals(a, **hist_kwargs)
plt.figure(figsize=(20, 10))
plt.subplot(1, 2, 1)
plt.hist(a, **hist_kwargs)
plt.subplot(1, 2, 2)
plt.hist(a_clipped, **hist_kwargs)
plt.show()