[Python] How to plot a Probability Density Function in Python?

I want to plot a Probability Density Function for the offsets from a log file. Here is the code:

timestamps = []
sequences = []

log_Name = 'test_rtt_25-01-17_13-07-41_values5_rate50.log'
log_Path = "/home/ubuntu/results-25-01-09-docker/"
true_Path = log_Path + log_Name
with open(true_Path, "r") as f:
for line in f:
if not line.startswith('#'):
time_part, seq_part = line.strip().split('(')
base, offset = time_part.split('+')

timestamps.append(float(offset))

seq = int(seq_part[:-1])
sequences.append(seq)


The code upon reads the data from the log file and then saves the offsets and sequences in 'timestamps' and 'sequences'.

Here is an example for 'timestamps' and 'sequences'.

[0.001009023, 0.001055868, 0.000992934, 0.001148472, 0.001086814, 0.001110649, 0.001066759, 0.00126167, 0.001231778, 0.000944345]

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]


As you can see there are 10 offsets and 10 sequences. Each offset has its number, for example: 0.001009023 is number 1. I want to plot a Probability Density Function, and i tried this:

source = {'seqs': sequences, 'times': timestamps}
df = pd.DataFrame(source)
df.sort_values(by = ['times'], inplace=True)
df_mean = np.mean(df['times'])
df_std = np.std(df['times'])
pdf = stats.norm.pdf(df['times'], df_mean, df_std)
plt.plot(df['times'], pdf)
plt.xlabel('Offsets') # Label for the x-axis
plt.savefig('/home/ubuntu/')


But the output y-axis is really strange, it looks like this:

Working complete code with input and imports:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import scipy.stats as stats

timestamps = [0.001009023, 0.001055868, 0.000992934, 0.001148472, 0.001086814, 0.001110649, 0.001066759, 0.00126167, 0.001231778, 0.000944345]

sequences = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

source = {'seqs': sequences, 'times': timestamps}

df = pd.DataFrame(source)

df.sort_values(by = ['times'], inplace=True)

df_mean = np.mean(df['times'])

df_std = np.std(df['times'])

pdf = stats.norm.pdf(df['times'], df_mean, df_std)

plt.plot(df['times'], pdf)

plt.xlabel('Offsets') # Label for the x-axis

plt.savefig('fig.png')

plt.show()


Output:



I have no idea why the probability is much bigger than 1, it should be smaller than 1. Anyone knows where i did wrong?

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