Having some problem to understand the x_bin in regplot of Seaborn

I used the seaborn.regplot to plot data, but not quite understand how the error bar in regplot was calculated. I have compared the results with the mean and standard deviation derived from mannual calculation. Here is my testing script.

import numpy as np
import pandas as pd
import seaborn as sn


def get_data_XYE(p):
    x_list = []
    lower_list = []
    upper_list = []
    for line in p.lines:
        x_list.append(line.get_xdata()[0])
        lower_list.append(line.get_ydata()[0])
        upper_list.append(line.get_ydata()[1])
    y = 0.5 * (np.asarray(lower_list) + np.asarray(upper_list))
    y_error = np.asarray(upper_list) - y
    x = np.asarray(x_list)
    return x, y, y_error

x = [37.3448,36.6026,42.7795,34.7072,75.4027,226.2615,192.7984,140.8045,242.9952,458.451,640.6542,726.1024,231.7347,107.5605,200.2254,190.0006,314.1349,146.8131,152.4497,175.9096,284.9926,116.9681,118.2953,312.3787,815.8389,458.0146,409.5797,595.5373,188.9955,15.7716,36.1839,244.8689,57.4579,94.8717,112.2237,87.0687,72.79,22.3457,24.1728,29.505,80.8765,252.7454,280.6002,252.9573,348.246,112.705,98.7545,317.0541,300.9573,402.8411,406.6884,56.1286,30.1385,32.9909,497.556,19.3606,20.8409,95.2324,108.6074,15.7753,54.5511,45.5623,64.564,101.1934,81.8459,88.286,58.2642,56.1225,51.2943,38.0649,63.5882,63.6847,120.495,102.4097,49.3255,111.3309,171.6028,58.9526,28.7698,144.6884,180.0661,116.6028,146.2594,199.8702,128.9378,423.2363,119.8537,124.6508,518.8625,306.3023,79.5213,121.0309,116.9346,170.8863,930.361,48.9983,55.039,47.1092,72.0548,75.4045,103.521,83.4134,142.3253,146.6215,121.4467,101.4252,68.4812,291.4275,143.9475,142.647,78.9826,47.094,204.2196,89.0208,82.792,27.1346,142.4764,83.7874,67.3216,112.9531,138.2549,133.3446,86.2659,45.3464,56.1604,43.5882,54.3623,86.296,115.7272,96.5498,111.8081,36.1756,40.2947,34.2532,89.1452,53.9062,36.458,113.9297,176.9962,77.3125,77.8891,64.807,64.1515,127.7242,119.6876,976.2324,322.8454,434.2883,168.6923,250.0284,234.7329,131.0793,152.335,118.8838,243.1772,24.1776,168.6327,170.7541,167.8444,75.9315,110.1045,113.4417,60.5464,66.8956,79.7606,71.6659,72.5251,77.513,207.8019,21.8592,35.2787,169.7698,146.5012,412.9934,248.0708,318.5489,104.1278,184.7592,108.0581,175.2646,169.7698,340.3732,570.3396,23.9853,69.0405,66.7391,67.9435,294.6085,68.0537,77.6344,433.2713,104.3178,229.4615,187.8587,78.1399,121.4737,122.5451,384.5935,38.5232,117.6835,50.3308,318.2513,103.6695,20.7181,321.9601,510.3248,13.4754,16.1188,44.8082,37.7291,733.4587,446.6241,21.1822,287.9603,327.2367,274.1109,195.4713,158.2114,64.4537,26.9857,172.8503]
y  = [37,40,30,29,24,23,27,12,21,20,29,28,27,32,23,29,28,22,28,23,24,29,32,18,22,12,12,14,29,31,34,31,22,40,25,36,27,27,29,35,33,25,25,27,27,19,35,26,18,24,25,37,52,47,34,39,40,48,41,44,35,36,53,46,38,44,23,26,26,28,27,21,25,21,20,27,35,24,46,34,22,30,30,30,31,26,25,28,21,31,24,27,33,21,31,33,29,33,32,21,25,22,39,31,34,26,23,18,20,18,34,25,20,12,23,25,21,21,25,31,17,27,28,29,25,24,25,21,24,27,23,22,23,22,22,26,22,19,26,35,33,35,29,26,26,30,22,32,33,33,28,32,26,29,36,37,37,28,24,30,25,20,29,24,33,35,30,32,31,33,40,35,37,24,34,29,27,24,36,26,26,26,27,27,20,17,28,34,18,20,20,18,19,23,20,22,25,32,44,41,39,41,40,44,36,42,31,32,26,29,23,29,29,28,31,22,29,24,28,28,25]
xbreaks = [13.4754, 27.1346,  43.5882,  58.9526,  72.79,  89.1452,  110.1045,  131.0793,  158.2114,  180.0661,  207.8019,  234.7329,  252.9573,  300.9573,  327.2367,  348.246,  412.9934,  434.2883,  458.451,  518.8625,  595.5373,  640.6542,  733.4587,  815.8389,  930.361,  976.2324]
df = pd.DataFrame([x,y]).T
df.columns = ['x','y']

# Check the bin average and std using agge
bins = pd.cut(df.x,xbreaks,right=False)
t = df[['x','y']].groupby(bins).agg({"x": "mean", "y": ["mean","std"]})
t.reset_index(inplace=True)
t.columns = ['range_cut','x_avg_cut','y_avg_cut','y_std_cut']
t.index.name ='id'

# Get the bin average from 
g = sns.regplot(x='x',y='y',data=df,fit_reg=False,x_bins=xbreaks,seed=seed)
xye = pd.DataFrame(get_data_XYE(g)).T
xye.columns = ['x_regplot','y_regplot','e_regplot']
xye.index.name = 'id'

t2 = xye.merge(t,on='id',how='left')
t2

You can see the y and e from the two ways are different. I understand that the default x_ci or x_estimator may afect the result of regplot, but I still can not the these values in excel by removing some lowest and/or highest values in each bin.

Answer

In seaborn.regplot, the x_bins are the center of each bin, and the original x values are assigned to the nearest bin value. Whereas in pandas.cut, the breaks define the bin edges.

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Answer