import numpy as np
import matplotlib.pyplot as plt

data = np.loadtxt('random_timestamp.txt')

print(data)

# Choose how many bins you want here
num_bins = 20

# Use the histogram function to bin the data
counts, bin_edges = np.histogram(data, bins=num_bins, normed=True)

# Now find the cdf
cdf = np.cumsum(counts)

# And finally plot the cdf
plt.plot(bin_edges[1:], cdf)

plt.show()

[1.37969832e+09 1.37969826e+09 1.37969682e+09 1.37969814e+09
 1.37969802e+09 1.37969856e+09 1.37969664e+09 1.37969676e+09
 1.37969940e+09 1.37969976e+09 1.37969850e+09]


/Users/rajacsp/anaconda3/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:9: VisibleDeprecationWarning: Passing `normed=True` on non-uniform bins has always been broken, and computes neither the probability density function nor the probability mass function. The result is only correct if the bins are uniform, when density=True will produce the same result anyway. The argument will be removed in a future version of numpy.
  if __name__ == '__main__':