Gaussian curve stays almost unchanged when outliers are removed

So I have a program that plots the data (126 values) I have as a bar chart and fits a Gaussian curve to it:

from matplotlib.pyplot import *
from numpy import *
from scipy.stats import norm
f=open("data.txt")
items=[]
for i in range(0, 500000, 20000):
    items.append(0)
arr=f.readlines()
for i in range(len(arr)):
    arr[i] = int(arr[i])
for i in range(len(arr)):
    for j in range(0, 500000, 20000):
        if arr[i] < j:
            items[j//20000] += 1
            break
yticks(range(32), fontsize = 5)
xticks(list(range(len(items))), list(range(0, 500, 20)), fontsize = 5)
xlabel("Стоимость, тыс. руб")
ylabel("Количество квартир")
bar(list(range(25)), items, 1, edgecolor = "black")
t_mean = mean(items)
t_variance = var(items)
t_sigma = t_variance ** 0.5
t_x = linspace(-5, 25,50)
plot(t_x,norm.pdf(t_x,t_mean,t_sigma) *500)
show()

Here's the result:

Then I remove 6 biggest values from the dataset, and here's the new result:

You can see that the outliers are gone from the diagram, but the Gaussian curve is almost unchanged (if you open these graphs side by side and look REALLY closely you can notice the difference, but otherwise the curves look exactly the same). Is this normal?

`data.txt`

Solution

I'm sorry to say this, but I think you should start over. Your code is impossible to understand (for me). I recommend starting from a working example using norm.pdf, such as the first answer to this question, and modify the code to fit your problem. You are using a lot of numbers that to a person that did not write your code seem to come from nowhere, especially because we do not have your data. For larger programs this becomes impossible to debug.

I am also pretty sure that you what you are currently doing: taking the mean and standard deviation of your items is not what you should be doing.

Edit: Using the data you provided I made a short example of how you could create a standard distribution out of it:

import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt


arr = np.loadtxt("data.txt")

x_lin = np.linspace(0, 5e5, 1000)
standard_distr = norm.pdf(x_lin, np.mean(arr), np.std(arr))
plt.hist(arr, bins=30)
plt.plot(x_lin, standard_distr/np.max(standard_distr)*25)
plt.show()