Problem in combining bar plot and line plot

I have dataframe like this:

df_meshX_min_select = pd.DataFrame({
'Number of Elements'  : [5674, 8810,13366,19751,36491],
'Time (a)'            : [42.14, 51.14, 55.64, 55.14, 56.64],
'Different Result(Temperature)' : [0.083849, 0.057309, 0.055333, 0.060516, 0.035343]})

and I tried to combine bar plot (number of elements Vs Different result) and line plot (Number of elements Vs Time) in the same figure, but I found the following problem like this:

it seems that x_value doesn't match when combining 2 plots, but if you see the data frame, the x value is exactly the same value.

My expectation is combining these 2 plots into 1 figure:

and this is the code that I made:

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

df_meshX_min_select = pd.DataFrame({
    'Number of Elements'  : [5674, 8810,13366,19751,36491],
    'Time (a)'            : [42.14, 51.14, 55.64, 55.14, 56.64],
    'Different Result(Temperature)' : [0.083849, 0.057309, 0.055333, 0.060516, 0.035343]})

x1= df_meshX_min_select["Number of Elements"]
t1= df_meshX_min_select["Time (a)"]
T1= df_meshX_min_select["Different Result(Temperature)"]

#Create combo chart
fig, ax1 = plt.subplots(figsize=(10,6))
color = 'tab:green'
#bar plot creation
ax1.set_title('Mesh Analysis', fontsize=16)
ax1.set_xlabel('Number of elements', fontsize=16)
ax1.set_ylabel('Different Result(Temperature)', fontsize=16)
ax1 = sns.barplot(x='Number of Elements', y='Different Result(Temperature)', data = df_meshX_min_select)
ax1.tick_params(axis='y')

#specify we want to share the same x-axis
ax2 = ax1.twinx()
color = 'tab:red'
#line plot creation
ax2.set_ylabel('Time (a)', fontsize=16)
ax2 = sns.lineplot(x='Number of Elements', y='Time (a)', data = df_meshX_min_select, sort=False, color=color, ax=ax2)
ax2.tick_params(axis='y', color=color)
#show plot


plt.show()

Seaborn and pandas use a categorical x-axis for bar plots (internally numbered 0,1,2,...) and floating-point numbers for a line plot. Note that your x-values aren't evenly spaced, so either the bars would have weird distances between them, or wouldn't align with the x-values from the line plot.

Here is a solution using standard matplotlib to combine both graphs.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

df_meshx_min_select = pd.DataFrame({
    'number of elements': [5674, 8810, 13366, 19751, 36491],
    'time (a)': [42.14, 51.14, 55.64, 55.14, 56.64],
    'different result(temperature)': [0.083849, 0.057309, 0.055333, 0.060516, 0.035343]})
x1 = df_meshx_min_select["number of elements"]
t1 = df_meshx_min_select["time (a)"]
d1 = df_meshx_min_select["different result(temperature)"]

fig, ax1 = plt.subplots(figsize=(10, 6))
color = 'limegreen'
ax1.set_title('mesh analysis', fontsize=16)
ax1.set_xlabel('number of elements', fontsize=16)
ax1.set_ylabel('different result(temperature)', fontsize=16, color=color)
ax1.bar(x1, height=d1, width=2000, color=color)
ax1.tick_params(axis='y', colors=color)

ax2 = ax1.twinx()  # share the x-axis, new y-axis
color = 'crimson'
ax2.set_ylabel('time (a)', fontsize=16, color=color)
ax2.plot(x1, t1, color=color)
ax2.tick_params(axis='y', colors=color)

plt.show()