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Large Datsample: failing to better visualize data point using matplotlib plot

The size of the data samples is very large, making it difficult to visualise the data points using a matplotlib plot.
Sample Code:
import matplotlib.pyplot as plt
plt.plot(myList_timestamps, myList_fitnessValues)
plt.xlabel('Timestamps (seconds)')
#plt.xticks(range(1, 51)
#plt.xticks(range(1, 53, 5))
plt.ylabel('WATT - MSU Fitness Values')
plt.title('Evolutionary Optimization - Execution Time')
plt.show()
Output:
I have 9113 candidates solutions as data samples to plot against 9113 data samples as fitness values. How should I plot this large data using python to better visualize the data?
Data Sample:
myList_timestamps = [[0.06160092353820801,
0.07070684432983398,
0.0794517993927002,
0.08730483055114746,
0.09506797790527344,
0.10278487205505371,
0.11050796508789062,
0.11819696426391602,
0.12598776817321777,
0.13364410400390625,
0.1412339210510254,
0.14882898330688477,
0.15642499923706055,
0.16405892372131348,
0.171644926071167,
0.17924880981445312,
0.1868269443511963,
0.1943988800048828,
0.2020108699798584,
0.21060776710510254,
0.219498872756958,
0.22813701629638672,
0.23638296127319336,
0.24529194831848145,
0.25347185134887695,
0.26166296005249023,
0.2696189880371094,
0.2773740291595459,
0.2849307060241699,
0.2925240993499756,
0.30014586448669434,
0.3077728748321533,
0.31533288955688477,
0.32283592224121094,
0.3303370475769043,
0.3378570079803467,
0.34534668922424316,
0.352841854095459,
0.36031174659729004,
0.3678579330444336,
0.3753628730773926,
0.3828439712524414,
0.3903648853302002,
0.39795589447021484,
0.40547990798950195,
0.412992000579834,
0.42046594619750977,
0.42803382873535156,
0.435579776763916,
0.44308996200561523,
0.450577974319458,
0.45802807807922363,
0.4655318260192871,
0.4730229377746582,
0.48052191734313965,
0.488048791885376,
0.49558186531066895,
0.5031087398529053,
0.5106048583984375,
0.5181560516357422,
0.525662899017334,
0.5331556797027588,
0.5406389236450195,
0.5481007099151611,
0.5555720329284668,
0.563060998916626,
0.5705769062042236,
0.578115701675415,
0.5856177806854248,
0.5931298732757568,
0.6006178855895996,
0.6081528663635254,
0.6156718730926514,
0.6231448650360107,
0.6306188106536865,
0.6381428241729736,
0.6456358432769775,
0.6531088352203369,
0.6606647968292236,
0.6682088375091553,
0.6756858825683594,
0.6832168102264404,
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myList_fitnessValues = [1.177397872785327,
1.1838368070851042,
1.198426283830517,
1.1971495165606483,
1.1300637485336795,
1.2187992912454821,
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How should I plot this large data using python to better visualize the data?

A simple and common method to get a better overview about this kind of data is to calculate the moving average (mainly it to see if there are any trends). See this post for different approaches to calculate the moving average in python.
Note 1: You need to decide how much you want to smooth the data by setting a parameter (called N here). You might have to play around a little with N to see which value best suits your data.
Note 2: By calculating the moving average the length of you data changes (it gets N-1 shorter). So the timestamp values must also be shortened.
Using the sample data from the question and choosing N=51:
import numpy as np
N = 51
myList_fitnessValues_ma = np.convolve(myList_fitnessValues, np.ones(N)/N, mode='valid')
myList_timestamps_ma = myList_timestamps[int((N-1)/2):-int((N-1)/2)]
plt.plot(myList_timestamps_ma, myList_fitnessValues_ma)
plt.xlabel('Timestamps (seconds)')
plt.ylabel('WATT - MSU Fitness Values (moving average)')
plt.title('Evolutionary Optimization - Execution Time')
plt.show()

Plot standard deviation with only positive values

I wanted to ask about a problem that I can't find a solution to, I'm quite new to python and programming.
I have my code where I calculate different statistical measurements and in the fourth graph axes [1:1] I am trying to represent the standard deviation of my variable (accumulated daily rainfall in mm) but I have a problem and that is that the standard deviation represents values upwards and below average. Precipitation cannot have negative values and I wanted to know if it is possible to put some kind of filter so that only values equal to or greater than 0 are plotted.
Here I leave my example code and the data that I use
import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
import seaborn as sns
from matplotlib import pyplot as plt
SALIDAS = 'C:/Users/ferfo/Desktop/'
datos = pd.read_excel('C:/Users/ferfo/Desktop/Distribuciones/prueba.xlsx')
datos1 = pd.read_excel('C:/Users/ferfo/Desktop/Distribuciones/lineas.xlsx')
sns.set_style('darkgrid')
fig, axes =plt.subplots(2,2, figsize=(10,6))
sns.ecdfplot(ax=axes[0,0], data=datos)
sns.histplot(ax=axes[0,1], data=datos, fill = True, common_norm=False, alpha=0.2, linewidth=2, element="step")
sns.lineplot(ax=axes[1,0], data=datos1, markers=True, dashes=False,)
sns.barplot(ax=axes[1,1], data=datos, ci = "sd", capsize=0.1, )
axes[0,0].set_ylabel("Probabilidad")
axes[0,0].set_xlabel("mm/día")
axes[0,0].set_ylim(0, 1.1)
axes[0,0].set_yticks([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.1])
axes[0,0].set_xticks([0,50,100,150,200,250,300,350])
axes[0,1].set_xticks([0,50,100,150,200,250,300,350])
axes[0,1].set_yticks([0,50,100,150,200,250,300,350,400,450,500])
axes[0,1].set_ylabel("Frecuencia")
axes[0,1].set_xlabel("mm/día")
axes[0,1].get_legend().remove()
axes[1,0].set_yticks([0,50,100,150,200,250,300])
axes[1,0].set_xlabel("Meses")
axes[1,0].set_ylabel("mm")
axes[1,0].get_legend().remove()
axes[1,0].set_xticks([1,3,5,8,11])
#axes[1,1].set_yticks([-10,30])
axes[1,1].set_xlabel("Producto")
#axes[1,1].set_ylabel("mm/día")
fig.suptitle('lomitas, 2001-2020', fontsize=20)
plt.show()
fig.savefig(SALIDAS + 'graficos', dpi=600, bbox_inches='tight')
prueba.txt
This is my data: https://drive.google.com/file/d/1TSwulKNFerHMvv5Mdhc_m1zqwRac0lYj/view?usp=sharing
The first 1000 rows (that will fit on SO), out of 1831.
Observed data,Imerg data,Persiann Data
33.0,12.70423317,65.75
12.0,13.56632233,31.32
4.0,21.09570122,43.52
1.0,9.74461746,5.77
17.3,1.820376158,6.55
18.0,5.2507658,61.730003
2.0,8.476202965,14.250001
40.0,3.271785736,8.710001
1.0,9.8995018,25.009998
24.0,8.377342225,22.08
1.0,13.10612583,13.53
3.0,7.375349045,14.24
7.0,41.91541291,3.44
68.0,26.54439736,57.81
15.0,4.023840905,2.65
2.0,6.984012125,14.709999
50.0,13.24643517,2.61
35.0,2.314537525,62.61
22.0,4.787216664,47.95
23.0,6.563237665,80.09
41.0,26.61835861,2.73
52.5,87.12622835,35.05
21.0,20.77411652,4.5
5.0,39.9513588,2.77
21.3,34.07968521,46.68
4.0,4.571947575,2.94
21.0,7.785022735,11.190001
7.0,24.5557766,6.7700005
2.0,14.00565529,2.15
14.0,6.835758685,3.9299998
6.0,13.60206604,1.5
1.0,31.71725464,19.919998
7.0,3.962635517,33.16
20.2,1.291003466,12.959999
20.0,55.3718796,1.03
10.0,1.06314838,150.06
2.0,1.467782021,8.41
5.8,4.719767094,7.9300003
2.0,2.669240952,2.72
10.0,17.57000542,17.4
1.0,1.548810959,2.03
8.0,1.151179791,1.37
3.0,20.55989838,3.85
9.0,2.467414141,1.9200001
2.0,1.306825042,2.07
8.0,1.14,2.69
2.0,1.127427101,14.9800005
18.0,1.399605274,2.4500003
10.0,6.10573721,2.1100001
8.0,2.593387604,4.76
16.0,1.714526534,5.64
20.0,7.021852015,1.24
49.0,1.874579191,1.54
3.3,1.057072401,2.88
16.0,5.83480644,3.24
8.0,3.455219269,5.8599997
8.0,1.475891114,2.72
28.0,6.827443125,8.690001
13.0,18.33798981,2.26
4.0,6.57049513,4.49
2.0,31.0540371,1.25
35.0,53.7753334,1.3299999
11.0,21.80572129,2.3899999
6.5,34.6747551,3.56
33.0,1.931889653,2.33
13.0,12.64225388,3.62
5.0,8.975834845,2.33
37.0,1.71813643,15.01
8.0,4.632123471,5.06
16.0,20.17470742,1.51
11.0,27.08102036,3.59
2.0,2.386453629,4.99
1.0,1.059182048,1.5600001
21.0,26.59081078,4.0099998
2.0,12.13219738,7.1099997
45.0,1.741666675,3.78
11.0,1.191877246,2.83
8.0,1.437874556,4.38
36.0,4.313007832,1.56
23.0,43.39625931,2.3700001
1.0,4.652664662,1.39
44.8,2.123159409,7.83
2.0,1.013100386,4.0
4.0,3.444443941,5.9900002
3.0,1.029917956,1.6299999
3.0,64.20615385,1.46
14.0,2.552409411,1.75
15.0,1.070342303,22.1
20.0,19.51611519,14.44
34.0,11.814847,9.370001
20.0,3.378883601,31.640001
17.0,4.592389584,4.62
2.0,5.184751985,4.04
12.0,1.251710058,8.940001
31.0,1.20076859,20.54
3.0,2.656115532,16.52
12.0,3.296927929,22.5
11.0,6.101575375,8.24
37.0,1.621261239,16.720001
47.0,3.146972418,25.58
8.0,23.63509369,3.3600001
5.0,4.09155941,3.7999997
13.0,4.080201626,1.9300001
25.0,13.95099068,13.62
28.5,9.444846155,7.8
4.0,27.48032952,1.25
4.0,38.84066773,42.8
6.0,1.212481141,19.18
5.0,1.080495119,4.09
20.0,40.26078034,2.59
7.0,2.846819878,9.09
4.0,54.2149887,3.52
8.0,13.21701241,18.25
4.0,3.699003458,1.59
5.0,4.130330563,13.790001
5.0,20.58119202,6.25
5.0,6.42111683,4.21
44.0,4.309965134,1.56
10.0,4.79896164,2.24
3.0,7.026090145,1.5999999
3.0,2.08438778,6.4399996
3.0,25.36527062,3.0900002
22.0,24.76248741,23.900002
2.0,26.50693512,56.08
1.0,32.33215714,18.52
4.0,28.11775589,1.8699999
17.0,1.378523588,7.12
11.0,3.523523569,22.32
30.0,5.69707489,16.54
29.0,27.38665581,9.93
1.0,38.52075959,46.18
3.0,1.750359059,4.9399996
1.0,14.85701275,1.88
2.6,41.54547501,37.92
28.0,1.331750036,5.47
16.0,14.75776387,54.08
6.0,3.94290042,13.11
34.0,21.99007416,5.43
12.0,21.82343102,9.42
8.0,2.251169443,18.39
5.0,3.715127945,10.76
24.0,11.68067074,14.76
1.0,8.149575235,10.639999
9.0,3.602071047,11.530001
35.0,35.90866089,27.52
2.0,1.736975193,23.21
8.0,5.936116695,14.05
1.0,2.024060011,24.670002
3.0,7.263765335,21.99
14.0,1.832577467,12.419999
12.0,4.149312973,14.73
15.0,1.578367353,9.52
1.0,1.461082697,3.79
3.0,1.300221563,2.6699998
4.0,3.882947684,6.3
23.0,1.156816244,1.5699999
31.0,1.774330497,3.9099998
1.0,1.081079126,9.86
21.0,63.7815933,1.1
7.0,5.40561533,3.1
38.9,1.916676522,1.3499999
1.0,1.694874764,1.21
2.0,1.020053149,2.6799998
1.0,3.230535031,72.840004
18.0,2.468552113,3.8899999
47.0,2.557238341,1.9
3.0,2.99013114,3.03
1.0,1.321612239,1.81
15.0,11.32548142,1.2
5.0,1.680747986,1.92
2.0,4.724195004,3.9
1.0,3.12424779,2.5700002
1.0,19.96909905,11.53
2.4,38.93196869,1.54
3.0,6.141599655,1.24
2.0,10.2309351,2.06
2.2,1.496399522,1.99
3.0,14.13191891,4.8900003
3.0,6.556683065,2.02
1.0,2.044409514,1.8
7.0,13.88462162,6.32
2.0,2.669220686,2.21
4.0,9.125458715,1.6700001
1.0,5.971014975,4.23
2.0,24.87825394,8.09
40.0,4.818218708,4.5899997
1.0,1.526267767,22.439999
42.0,12.33635044,1.3199999
14.0,6.067589285,7.02
5.0,4.542275429,9.35
14.0,15.26683712,1.36
3.0,2.287184716,1.6099999
27.0,13.89541149,6.42
9.0,2.849863529,5.52
16.0,4.114969254,6.3199997
5.0,2.60952878,2.6299999
7.0,25.81751633,1.12
22.0,7.642860415,54.38
61.0,14.60452652,2.99
3.0,2.860728264,1.4300001
38.0,13.65011311,2.05
24.0,4.403223992,4.0699997
8.0,16.61255455,5.7299995
15.0,1.931255818,1.6700001
4.0,12.71534157,4.97
2.0,13.96313668,1.74
2.0,4.058600903,4.7799997
4.0,4.762280464,2.69
12.0,9.048459055,2.84
7.0,2.783326626,2.87
24.0,2.251889944,5.2999997
17.0,12.83441448,4.16
29.0,11.20629025,1.34
37.0,28.90879059,1.22
4.0,1.714102268,23.29
2.0,1.729247093,1.87
7.0,11.54702091,102.02
14.0,1.603832722,1.26
4.0,48.88271332,3.4900002
5.0,3.357400656,1.33
9.0,26.58070755,1.3499999
7.0,1.279444337,11.709999
52.0,7.07122135,6.92
21.0,4.065811158,1.55
8.0,1.305071712,3.3400002
2.0,33.32134629,34.989998
1.0,25.21928978,5.46
1.0,7.68272543,3.69
8.0,4.058069229,12.27
14.0,1.392273307,31.66
27.0,1.614271045,2.51
1.0,1.43,24.939999
5.0,1.564941883,2.76
1.0,5.490926745,19.510002
2.0,2.741349459,8.51
4.0,1.820300937,7.93
7.0,1.200169325,9.7
27.0,1.227725864,3.3899999
53.0,1.409593702,1.26
13.0,1.020598889,11.91
2.0,1.532613397,10.45
6.0,2.150630713,51.839996
2.0,9.32765293,1.0999999
3.0,3.207234144,27.57
32.0,1.102299214,36.58
4.0,11.28597355,9.549999
1.0,32.67594147,2.3
5.0,17.2740078,15.48
42.0,3.444516182,6.82
10.0,2.200684548,7.47
2.0,42.5202179,30.38
6.0,1.706894517,21.759998
2.0,14.07932759,18.81
11.0,4.025928021,9.6
25.0,16.11277199,2.11
17.0,6.93875265,1.03
9.0,4.846222401,10.18
31.3,1.64617455,6.8199997
18.0,1.422170997,14.7
3.0,2.14275384,16.5
42.0,20.2088604,4.75
28.0,16.17591286,24.34
113.5,1.768956781,5.91
27.0,7.651679995,13.950001
16.0,62.23706435,1.66
40.0,3.120871783,9.92
5.0,1.11462462,1.3499999
25.0,17.13158798,31.470001
1.0,35.90638352,3.34
1.0,11.49289704,30.420002
2.0,1.723016501,3.0900002
7.0,1.727642894,1.8199999
62.5,15.11504936,3.46
15.0,12.78649616,15.449999
6.0,1.142826557,2.9
2.0,4.31261921,1.44
2.0,19.54297829,1.8
5.0,21.42444229,1.17
9.0,1.985171438,5.24
4.0,38.83046723,1.53
3.0,24.3289547,2.63
28.0,21.55071259,8.26
3.0,20.35590744,5.58
18.5,17.1479969,20.69
1.0,2.328164578,6.65
77.0,6.90966034,1.5999999
3.0,8.87460327,4.1
6.0,19.85622978,5.88
18.0,11.50050545,8.62
1.0,2.399034024,4.4399996
13.0,17.8201561,7.3199997
1.0,63.47223665,5.11
5.0,10.70358849,1.9399999
4.0,1.303659797,1.14
5.0,7.051344395,1.2
2.0,1.317322851,2.83
2.0,1.153054357,20.35
1.7,1.288836599,1.0
3.5,6.40096426,2.18
4.7,10.61519242,3.25
2.0,38.07836151,1.4499999
1.0,1.00505364,1.06
5.0,4.601175309,14.140001
13.0,1.059544564,1.11
50.0,1.025045872,1.08
2.4,2.140906573,24.24
5.0,11.28417015,6.04
2.8,1.706882835,1.6299999
15.5,63.1325226,5.55
17.0,5.925836565,2.69
3.0,1.94356668,5.5099998
4.5,9.45316124,12.35
36.0,2.504364014,1.73
1.0,1.470301152,2.85
1.0,1.242533088,3.36
12.1,1.670167685,3.0299997
5.0,57.5176239,1.06
15.0,1.107224822,3.8200002
5.0,3.0542202,6.79
3.0,2.898064137,4.32
7.0,30.05044174,1.75
3.0,3.427459955,1.1800001
4.0,4.624752045,1.04
2.0,11.62128449,1.5600001
8.0,6.490193845,3.7
2.0,1.937290669,2.6000001
7.0,8.65875244,1.4100001
52.0,1.299692512,1.69
5.0,1.855275035,48.739998
1.0,3.769208908,7.24
7.6,4.55385828,6.29
3.5,6.51372051,1.71
1.3,8.0854969,1.21
22.2,1.507522464,109.61
22.5,9.14739609,8.1
7.5,27.17226029,6.6499996
2.0,32.79916382,6.2500005
1.0,1.280574084,1.05
3.5,26.51655007,1.08
2.5,2.701778889,3.1100001
1.0,5.269325735,2.01
8.0,30.63650131,1.4300001
20.0,71.78442385,27.51
32.0,1.373457909,28.66
3.0,12.21031952,21.530003
61.0,35.02967835,2.9800003
7.0,14.67937184,4.88
3.5,4.434751988,2.06
2.0,11.85890293,6.34
35.0,25.02809716,3.44
11.0,3.947379113,21.65
4.5,3.420857191,6.13
31.0,2.146751881,47.380005
2.0,21.10358238,82.47
15.0,2.37749362,6.38
38.0,11.68755818,2.21
6.0,2.17284298,63.430004
21.0,7.695138455,60.98
3.0,11.97859764,30.349998
2.0,14.64129257,1.68
6.0,5.88892269,4.81
13.0,1.734639526,24.029999
5.0,4.035034657,23.36
1.7,1.285043836,1.87
1.5,1.238770008,24.31
5.5,19.83879471,2.27
9.2,4.221150399,10.42
8.0,23.05646897,13.280001
2.0,1.394252658,17.740002
16.0,7.788359165,2.06
4.0,6.4100194,21.4
16.0,67.55716705,11.23
21.0,1.351992965,61.07
5.0,5.084335325,45.9
12.0,12.95212364,4.08
23.0,25.68342018,4.21
6.0,1.988664508,9.7
3.0,3.016326189,10.969999
8.0,2.866974354,25.95
1.0,2.696616888,2.54
1.0,1.581075788,2.5
41.0,2.780577898,4.09
8.0,1.417200446,26.240002
4.0,1.385309816,7.45
10.5,7.5372777,12.17
16.0,7.932168005,69.51
18.0,1.451128483,10.07
11.0,1.840451598,3.63
17.0,1.065397263,2.3
26.0,4.893643856,2.9599998
1.0,1.452208638,2.23
19.0,37.93759156,3.18
4.0,12.90710354,5.83
37.0,6.14060068,1.39
16.4,8.097572325,3.87
5.4,23.57411003,1.8499999
1.0,6.214107035,11.530001
2.9,6.978374005,1.76
5.5,43.03276825,2.3600001
1.0,1.466169358,6.4300003
1.0,1.140809417,3.33
21.5,1.293450475,10.71
7.6,3.49955225,2.32
2.0,14.28147984,1.7900001
1.0,3.699310303,34.33
12.0,4.277731419,2.23
45.0,5.301327705,1.99
69.2,7.982951165,10.09
8.6,3.936149597,10.08
1.0,1.424581647,2.6699998
9.2,4.491571904,46.879997
17.5,2.771928311,1.0
20.0,9.17326832,4.99
3.1,2.072081805,1.12
7.0,5.55553627,1.27
32.7,14.50772381,2.66
26.0,2.33113885,2.65
2.5,5.78749275,48.21
27.3,11.18823529,5.89
49.5,5.236501695,17.56
2.5,7.31285858,8.110001
24.0,1.520752192,1.1600001
9.7,9.462599755,1.9200001
1.6,20.17654228,1.72
3.0,10.11459542,4.84
8.6,27.88303757,4.3
21.0,12.77318192,1.93
1.5,1.652448058,2.9099998
2.3,2.164780855,5.19
1.2,5.11115074,2.9099998
3.1,4.954486847,6.77
4.9,1.114153981,2.13
1.0,2.178640366,2.49
3.8,3.012405396,33.73
22.8,51.1032982,36.66
65.5,11.25961972,72.69
3.0,6.713029385,5.6
14.2,2.496469736,3.9700003
4.0,12.115098,37.68
9.3,2.551826239,2.21
18.2,14.48979855,3.06
24.0,7.24518347,1.51
1.3,21.97145844,79.93
11.3,5.81929302,29.16
2.0,35.17000199,1.5
2.0,21.69516754,10.809999
17.9,8.1897316,2.9
15.0,15.60975838,28.07
33.0,3.585752011,17.85
18.1,7.03698683,26.3
1.0,15.85383892,1.96
5.0,8.665060045,20.49
10.0,5.237526895,7.74
3.0,9.37757492,8.4
11.0,2.525708676,1.0699999
33.0,1.059689999,6.8199997
5.0,1.390462637,62.05
9.0,2.451468468,2.9699998
42.0,1.846368075,12.53
3.0,1.235369563,23.34
3.0,1.421132684,1.9200001
5.0,1.591700435,18.150002
21.0,1.431996823,8.74
3.0,3.120195151,3.44
4.0,10.05565357,9.03
3.0,1.057814002,14.1
22.0,8.63092613,5.19
45.0,11.97591305,42.8
24.0,1.507814646,13.559999
34.0,6.692327025,48.93
8.0,2.86255455,3.81
24.0,3.043321371,25.85
8.5,3.122832775,23.18
1.5,11.90790558,13.000001
9.3,8.271219255,110.89
3.0,14.87175751,5.38
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44.0,2.367835284,45.96
6.0,9.290693285,8.3
13.0,6.92009163,1.75
10.0,12.8741827,5.77
5.0,7.534250735,2.24
21.0,14.394454,1.32
20.0,6.47271347,1.48
12.0,1.485815168,1.3
29.0,1.470686913,2.6499999
46.0,2.631582499,13.61
22.0,3.658107281,1.3499999
8.0,3.507339239,2.4399998
11.0,9.38621521,1.08
21.0,1.363355041,5.6800003
2.0,2.211771965,2.9899998

Newly Data Points Hidden Behind the Previously Plotted data in Plotly in the same graph -- plotly.express as px, and plotly.graph_objects as go

I am plotting some data points on a graph.
Output:
Now, I plotted some ambulance locations (Dataset is in two excel files) Does not work
As we can see above, the new plotted data as ambulance location is hidden due to previous plotted data. How do we solve this?
Here is my code (Dataset is in two excel files):
import plotly.express as px
import plotly.graph_objects as go
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
from openpyxl import load_workbook
import numpy as np
import pandas as pd
from mplcursors import cursor
import numpy as np;
np.random.seed(0)
import seaborn as sns;
from matplotlib.pyplot import figure
import pandas as pd
import numpy as np
import io
file_loc_1 = "AgeGroupData_time_to_treatment.xlsx"
df_centroid_Coord = pd.read_excel(file_loc_1, index_col=None, na_values=['NA'])
df_centroid_Coord
file_loc2Amb = "Ambulance IDs.xlsx"
df_station = pd.read_excel(file_loc2Amb, index_col=None, na_values=['NA'], usecols="A:D")
df_station.rename(columns={'Longtitude':'x', 'Latitude':'y'}, inplace=True)
df_centroid_Coord['Ambulance_Treatment_Time'] = df_centroid_Coord ['Base_TT']
fig = px.scatter(df_centroid_Coord, x="x", y="y",
title="Southern Region Centroids",
color='Ambulance_Treatment_Time',
log_x=True,
size_max=60,
color_continuous_scale='Reds',
range_color=(0.5,2),
)
fig.update_traces(marker={'size': 4, 'symbol': 1})
fig.add_trace(go.Scatter(x=df_station['x'],
y=df_station['y'],
mode='markers+text',
text=df_station['Ambulance station name'],
textposition='top center',
showlegend=True,
marker=dict(
size=12,
symbol=2,
color='black'
)
)
)
fig.update_layout(width=1000, height=800, paper_bgcolor="LightSteelBlue")
fig.show()
Working Code: (Dataset is inside code)
import plotly.express as px
import plotly.graph_objects as go
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import geopandas as gpd
from openpyxl import load_workbook
import numpy as np
import pandas as pd
from io import StringIO
import numpy as np;
np.random.seed(0)
import seaborn as sns;
data = '''
x y Base_TT
14.1315559 55.75811117 1.871884861
14.66228957 57.02751992 1.599971112
14.49407157 56.06563489 1.307165278
13.21996788 55.4786748 1.411554445
14.00501286 55.72120854 1.968138334
12.73736102 56.71097302 1.309849028
14.56668525 56.74872925 1.719116945
13.24138764 56.41359089 2.000620417
14.94308088 56.54283706 1.668724723
14.5744739 56.05695327 1.266861528
13.06800876 56.6356658 1.58923875
14.69964193 56.47959746 1.960050139
13.02976922 55.48474858 1.2549575
13.16901029 56.23281882 1.429789167
13.05906805 55.85369617 1.553721944
13.7382052 55.60193648 1.326429166
13.72987233 56.34767237 1.709020555
14.58803736 56.29060354 1.444833472
13.1687123 55.90031115 1.527546805
15.02422205 56.27331725 1.692005
12.91010076 56.24668905 1.090544167
13.2053785 55.45149986 1.438993611
13.37256031 55.61560233 1.632310694
13.26737288 56.19829081 1.869085
13.23818589 55.42501479 1.646565973
14.50187617 56.70356708 1.880334584
14.70189174 56.19209891 1.31886875
13.34482545 56.27124013 1.81529125
12.8785792 55.94059005 1.418415555
12.93136079 55.85161507 1.40189875
12.89164916 55.99473803 1.45182125
13.10496461 56.22286779 1.456101528
15.38666411 56.9824605 1.922860277
13.32430726 56.00137807 1.941762638
13.51541698 56.45311516 1.768057778
15.0413443 57.17170192 2.024309722
12.8606001 55.97622841 1.401550417
13.46749777 56.03019927 1.883145
14.72922746 56.88394501 1.273883612
14.55821135 56.07486606 1.331545972
12.93914018 56.60663923 1.351015694
13.9743559 55.67602686 1.874753334
14.64564524 57.04544092 1.668993889
12.66824186 56.24236656 1.333240139
14.0105656 56.75454085 1.378468056
13.60274138 55.39377504 1.582678472
15.75049672 56.25307533 1.564625833
13.60115485 55.43869065 1.577717361
15.7653804 56.19008971 1.372651944
13.1832264 56.95189695 1.748949583
15.60524952 56.25387079 1.3378925
14.23746798 56.04634612 1.200910417
14.35058672 56.00206764 1.385281667
13.60986899 56.97533616 2.631095833
13.45346163 56.38047383 1.811332917
15.3486521 56.43454959 2.049025556
12.83048942 56.22734439 1.118363333
13.84310479 56.34878109 1.626188472
13.03675856 55.97916057 1.681367638
14.42929903 56.1012924 1.947557639
13.2062902 56.12552876 1.535386944
13.34357303 57.04397378 1.86576
14.03682213 57.00630325 1.720512917
15.18750805 56.64153377 1.769290139
14.47260163 56.46087204 2.272563194
15.44375218 56.24557042 1.541445833
14.46883407 56.73038276 1.778631806
13.10870739 55.81852133 1.51138625
12.86285426 56.80305259 1.40997375
13.49439127 56.98298455 2.250301528
13.70360937 56.16769145 1.196677639
14.08919483 55.52417158 1.591170417
13.55740496 55.77068518 1.535861945
14.18123581 55.66859517 1.996627778
15.23403607 56.25512928 1.548016667
15.70489096 56.40608375 2.047458055
14.37376278 56.55025699 2.067875139
14.502587 56.64966538 2.082391666
13.02508849 56.832684 1.706181111
13.14274787 55.77410524 1.375705139
14.84565179 56.5068175 1.885311528
14.20034005 56.28870701 1.691717361
13.8812731 55.59433892 1.5328325
14.14035572 56.80042421 1.561514584
13.76343201 55.80887467 1.749841528
13.89449225 56.25041347 1.46041875
15.17289149 57.00988892 1.521688473
14.77824552 56.9199763 1.376412361
13.27891612 55.57837586 1.622874028
14.24058672 56.70224862 1.666531805
14.37017794 56.76585965 2.005441666
13.52451801 55.79725389 1.521532778
13.71088665 55.46685563 1.222417361
14.9588848 56.96508403 1.38658
14.82826594 56.75834768 1.485242778
13.1940657 56.38595264 1.792493889
14.84402516 56.90210837 1.235783333
14.46031787 56.18229581 1.681354444
14.63590428 56.35364532 1.650511111
13.34460314 55.52535888 1.473964166
14.62952158 57.00946017 1.6048425
13.71970327 55.68262124 1.564198333
13.13107617 56.34907413 1.663111806
13.48159711 56.89298141 2.3218075
13.81128734 55.80934654 1.869087639
14.15612237 56.82748426 1.526720834
13.25994595 57.06979504 1.953888333
13.36341923 55.45371566 1.683035417
13.53607897 55.91421314 1.662510139
14.84502823 56.65954652 1.808187361
13.5819665 56.40896643 1.723420972
14.10802101 56.09040464 1.322613333
12.59101931 56.18696069 1.285278334
15.22304196 57.13557739 1.861801667
13.93212119 56.63709467 1.879722361
14.04083953 56.20670871 1.426582917
13.20977325 55.70320438 1.036230556
14.50691645 56.31726101 1.512323056
14.04385954 55.43396451 1.518698611
14.62176554 56.14695594 1.436045973
12.94595993 56.48094227 1.284676667
13.43761668 56.76664816 2.052952223
13.4881 55.91363347 1.748883611
13.30499035 56.44143388 1.901664722
14.59756066 56.91952527 1.202981944
13.2574875 55.70389081 1.129174861
13.47396568 56.67724669 2.238806528
12.73718764 56.44136418 1.683367917
13.83580567 55.5220256 1.431533473
14.59492984 57.16207179 1.922699306
14.64495938 57.11730653 1.864279583
14.4312567 57.05363464 1.896975417
13.41144319 55.83179083 1.403585
14.22184539 56.81892717 1.746305417
13.50202498 56.38107009 1.659256389
12.68761018 56.18880561 1.355646945
14.94282338 56.71353347 1.63897125
15.02454518 56.77643118 1.68773
15.57297141 56.25402442 1.414539028
12.964589 56.43631667 1.382988472
13.45825395 55.45494721 1.820561111
14.63191383 56.76690666 1.694100278
13.14103724 55.81002734 1.48003625
15.21769188 56.21922061 1.580773194
14.10257692 55.63210186 1.773493889
13.17668301 56.07117614 1.724194167
15.84692703 56.23448315 1.807886528
13.44350876 55.42779971 1.808543611
15.1857457 56.28216487 1.575294861
13.31802754 56.51349734 1.770477639
12.8935671 56.83951966 1.609666389
15.86386582 56.27030588 2.006792778
13.34532169 55.88483834 1.414042917
14.02227635 55.66743196 1.754815834
15.54234554 56.37096273 1.768464167
14.17727266 55.85726695 1.794488889
13.02668442 55.86216815 1.630864584
14.02130349 56.97024678 1.49533125
14.70175234 56.21006795 1.380317916
13.4056505 56.3618967 1.847926389
14.52330627 56.29935591 1.458468611
15.45924302 56.19160552 1.353913194
13.64578835 55.52007228 1.636670139
15.54272866 56.39791466 1.825142222
13.77315439 57.01298115 2.218526667
15.81485761 56.2436834 1.651075833
13.50857285 55.7970615 1.55940875
13.2405817 55.37112529 1.525214028
13.77433597 56.9770554 1.827475
13.02797412 55.52067001 1.181577917
14.94264555 56.83032305 1.482392778
14.79657364 56.5696361 1.996696389
14.42283094 56.53252583 2.141961944
13.30174382 55.41692656 1.592043888
12.91793298 56.39959593 1.43073125
12.90894568 56.85775049 1.762173056
13.75637136 56.52764363 1.831847639
14.47776811 56.08353697 1.370715
13.22852136 55.64056743 1.272487639
13.58430026 56.34609492 1.82778375
13.49063969 56.66846469 2.12446875
13.10834154 56.8070164 1.948958889
14.04485542 56.0450056 1.215075973
14.13354678 55.66826782 1.915392223
15.50829522 56.24532218 1.578224028
15.13989252 56.99196316 1.574268333
14.38668231 56.75695784 1.967270138
14.37062878 56.73890974 2.157887638
14.17479551 55.97406075 1.204406945
12.94883204 55.51937 1.279889722
13.58529757 56.31914974 1.783001805
13.27981504 55.91988672 1.720325555
13.59881528 56.83145749 2.139697778
13.59915061 56.82247642 2.074659723
13.40296577 56.03836683 1.7947475
14.29429163 55.57045392 1.731105417
12.80578871 56.65827783 1.060809167
14.53129548 56.96421286 1.476721806
13.26306176 55.57815222 1.600958055
14.49804298 56.99103484 1.81823625
13.4719759 55.50904044 1.673925972
13.91167961 56.78067047 1.325035834
14.12918279 56.57573794 1.790625556
14.04255435 55.48787141 1.424878194
14.04189635 56.80869138 1.407714861
14.79691051 56.50674739 1.909035139
13.45649939 55.4998595 1.623631389
14.03861222 55.6495892 1.614189445
14.04342496 55.45193356 1.590481667
13.71133755 56.89556193 2.093918334
14.17229995 56.09085224 1.279623194
13.6748116 56.06854362 1.526770972
12.70417007 56.71935248 1.316963472
13.30434416 56.0909687 1.68737375
14.56792192 56.64092518 2.210113889
13.34417417 55.91178236 1.465459028
14.08469442 56.39572484 1.527290833
13.5484731 55.58186417 1.765038889
14.94294571 56.63267787 1.661855694
14.08079768 55.8835409 1.572365695
13.07503267 55.85394699 1.526148195
13.87707642 56.84325494 1.201778611
14.74834811 56.4797057 2.201030833
14.71780758 56.2190903 1.312630416
14.84688074 56.20134805 1.064184305
13.9756613 56.83511828 1.106081945
13.37482084 56.32555295 1.955326527
13.28496775 55.80313232 1.281214445
13.48814841 55.50025435 1.612392361
13.33917893 56.02853829 1.843289861
13.78947363 55.99783619 1.712659583
13.94116263 56.3137174 1.441294722
12.94495248 55.89677924 1.427171389
13.93984079 56.9426305 1.49043875
15.12394257 57.14469774 1.834394167
13.43537424 56.82053079 1.990671805
14.3348616 55.98401349 1.42602875
15.07312973 56.51587606 1.730443055
13.06228068 55.46729248 1.334419722
15.09067168 57.05489271 1.720642222
15.3000038 56.43467672 2.145260556
14.42783245 56.2001194 1.928752916
12.62154803 56.21451481 1.284935278
14.5647095 56.91941521 1.203295972
12.82352863 56.06545274 1.310144583
15.12128092 56.32716729 1.653416527
15.41977285 57.00032105 1.830578333
13.0108648 56.48199917 1.334979166
13.79627271 56.29442363 1.608296806
13.69390585 55.9788893 1.636546111
13.85415898 56.51959889 1.940849583
13.42994326 56.16451808 1.556413611
13.94165947 56.29575144 1.459196945
13.63833274 55.73566343 1.742808889
13.78097707 56.26731808 1.453552362
'''
df_centroid_Coord = pd.read_csv(StringIO(data), delim_whitespace=True)
data = '''
ID "Ambulance station name" Longtitude Latitude
0 1 AImhult 14.128734 56.547992
1 2 Angelhdm 12.870739 56.242114
2 3 Alvesta 14.549503 56.920740
3 4 "Ostra Ljungby" 13.057450 56.188099
4 5 Broby 14.080958 56.254481
5 6 Bromölla 14.466869 56.072272
6 7 Försláv 12.814913 56.350098
7 9 Hasslehdm 13.778234 56.161536
8 10 Haganas 12.556995 56.206016
9 11 Hörby 13.643265 55.849811
10 12 "Halmstad.Vaster" 12.819960 56.674306
'''
df_station = pd.read_csv(StringIO(data), delim_whitespace=True)
df_station.rename(columns={'Longtitude':'x', 'Latitude':'y'}, inplace=True)
df_centroid_Coord['Ambulance_Treatment_Time'] = df_centroid_Coord ['Base_TT']
fig = px.scatter(df_centroid_Coord, x="x", y="y",
title="Southern Region Centroids",
color='Ambulance_Treatment_Time',
log_x=True,
size_max=60,
color_continuous_scale='Reds',
range_color=(0.5,2),
)
fig.update_traces(marker={'size': 4, 'symbol': 1})
fig.add_trace(go.Scatter(x=df_station['x'],
y=df_station['y'],
mode='markers+text',
text=df_station['Ambulance station name'],
textposition='top center',
showlegend=True,
marker=dict(
size=8,
symbol=2,
color='black'
)
)
)
fig.show()
If I read the same data from externals files, it does not work. Like
if I plot the data of map coordniates (from Excel file) and then
plot ambulance data (from Excel file), ambulance symbol in the
diamond shape appears behind the map. It is not showing on the front.
When dataset inside the code, the ambulance data is shown and not hidden
Increased ambulance size - When dataset inside the code, the ambulance data is shown and not hidden

I think that there is no problem, I tried the same code with 2 Excel files with your data and I got the plot below:
import plotly.express as px
import plotly.graph_objects as go
from openpyxl import load_workbook
import pandas as pd
import numpy as np
import io
file_loc_1 = "AgeGroupData_time_to_treatment.xlsx"
df_centroid_Coord = pd.read_excel(file_loc_1, index_col=None, na_values=['NA'])
df_centroid_Coord
file_loc2Amb = "Ambulance IDs.xlsx"
df_station = pd.read_excel(file_loc2Amb, index_col=None, na_values=['NA'], usecols="A:D")
df_station.rename(columns={'Longtitude':'x', 'Latitude':'y'}, inplace=True)
df_centroid_Coord['Ambulance_Treatment_Time'] = df_centroid_Coord ['Base_TT']
fig = px.scatter(df_centroid_Coord, x="x", y="y",
title="Southern Region Centroids",
color='Ambulance_Treatment_Time',
log_x=True,
size_max=60,
color_continuous_scale='Reds',
range_color=(0.5,2),
)
fig.update_traces(marker={'size': 4, 'symbol': 1})
fig.add_trace(go.Scatter(x=df_station['x'],
y=df_station['y'],
mode='markers+text',
text=df_station['Ambulance station name'],
textposition='top center',
showlegend=True,
marker=dict(
size=12,
symbol=2,
color='black'
)
)
)
fig.update_layout(width=1000, height=800, paper_bgcolor="LightSteelBlue")
fig.show()

Scatter plot looks good but line plot looks weird on non-monotonically increasing data set

I have a monotonically increasing data set as shown below.
R,M
7.0868,1.8102943986273166
7.087,1.810312919954896
7.0872,1.8102755711577103
7.0875,1.8102573284176724
7.0876,1.810237664390435
7.0887,1.810218897273047
7.0891,1.8102001649403308
7.0893,1.810178092508343
7.0894,1.8101553469354064
7.0902,1.8101359159985828
7.0907,1.810114549302785
7.0913,1.81009305646246
7.0916,1.8100731291478405
7.0919,1.8100505894431602
7.0923,1.8100245482326576
7.0933,1.810004843857531
7.0941,1.809981589365771
7.0948,1.8099565489521152
7.0949,1.8099337348073137
7.0957,1.8099052572558645
7.096,1.8098801233168886
7.0963,1.8098547472451978
7.0968,1.8098277762241366
7.0976,1.8098018568760064
7.0988,1.8097719856008248
7.099,1.8097424026714641
7.0994,1.8097145467653863
7.1002,1.8096846260698558
7.1005,1.8096553317621344
7.1016,1.8096207945226712
7.1023000000000005,1.8095909693292185
7.1031,1.8095572406207299
7.1038,1.8095238119406782
7.1043,1.8094894673202357
7.1053,1.8094538233723965
7.1064,1.8094182142472666
7.1067,1.8093818127358254
7.1079,1.8093437811755255
7.1088000000000005,1.8093041362243816
7.1098,1.809264041823682
7.1107000000000005,1.8092243783159143
7.1112,1.8091814057573945
7.1126000000000005,1.8091391534332957
7.1136,1.809094359509292
7.1148,1.809048533354942
7.1158,1.8090036536620597
7.1169,1.8089557015211883
7.1181,1.8089046493876206
7.1193,1.8088552101687183
7.1204,1.8088020551150032
7.1219,1.808747338797958
7.1225000000000005,1.8086919257417675
7.1247,1.8086350956553856
7.1258,1.8085748348942912
7.1275,1.808514553392964
7.1291,1.8084534203833889
7.1306,1.8083867543300092
7.1325,1.8083196215972281
7.1338,1.80825088250088
7.136,1.808178483206244
7.1374,1.8081044657602499
7.1392,1.8080287144430973
7.1412,1.8079480881686774
7.1431000000000004,1.8078648303094877
7.1456,1.8077807725153732
7.1476,1.8076892427316402
7.1503000000000005,1.807599230113512
7.1525,1.8075034794947091
7.1552,1.8074049113668145
7.1578,1.8073014641377354
7.1612,1.8071891350684717
7.1637,1.8070785128831843
7.1675,1.80696372890561
7.1701,1.8068419878230761
7.1737,1.8067122501065405
7.1779,1.8065802344915192
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9.8802,2.0321140981371753
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10.3324,2.053499145209982
10.3495,2.0533064233065197
10.3668,2.0530215567767636
10.3844,2.0526369031524108
10.402000000000001,2.0521636845730233
10.42,2.0515392027607855
10.4381,2.0508116828399494
10.4563,2.0499535127064785
10.4748,2.0489576534963168
10.4937,2.0478138062588847
10.5128,2.046511793430433
10.532,2.0450378909729627
10.5515,2.043383966625784
10.5714,2.0415367556333575
10.591000000000001,2.039475707572078
10.6113,2.0371915999694594
10.6318,2.034681992486926
10.6523,2.031875344830552
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10.7156,2.021729410221098
10.737,2.0176679331240632
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11.1148,0.6620484741207461
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11.1163,0.643905710436412
11.1173,0.7471776126414391
11.1188,0.6253614804987849
11.119,0.7631699685933497
11.1209,1.8187639653487393
11.121,0.7788332436619917
11.1226,0.6064184938116979
11.1233,0.7941958537153351
11.1252,0.8092591741702967
11.1277,0.5870678163436467
11.1302,0.8385380585848173
11.1326,0.8527702605010601
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11.1377,0.8804746147417499
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11.1443,0.5471696019139972
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11.1506,0.9456674682901905
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11.1539,1.7659104790175046
11.1555,0.9702722709283456
11.1563,0.526626294457235
11.1578,0.982282581805736
11.16,0.994125843269314
11.1625,1.005758474384689
11.1651,1.0172259729870816
11.1667,1.0285290632922044
11.1681,1.7351407435690962
11.1694,1.0396641713997514
11.1708,1.0506369116951209
11.1715,0.5057129912122545
11.1728,1.06145775881892
11.1752,1.0721969265059947
11.177100000000001,1.0826438965199432
11.178700000000001,1.0930140692041797
11.1804,1.7010859834099117
11.180900000000001,1.1032438870571097
11.1824,1.113341990904366
11.184000000000001,1.123299806468843
11.1857,1.1331296145407677
11.1873,1.1428308979922694
11.1889,1.152401698215316
11.1903,1.161856869794937
11.1904,0.48445364974714356
11.1919,1.663399436215842
11.192,1.1711910394172824
11.1934,1.1804087961074725
11.1942,1.1895091584397912
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11.1974,1.2073817550934374
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11.199300000000001,1.2248195347259854
11.2006,1.233382363999008
11.2012,1.6217221083677575
11.201600000000001,1.2418573170671454
11.2026,1.2502123181321048
11.2036,1.2584707455312942
11.2044,1.2666389953934527
11.205300000000001,1.2747462832375793
11.2059,1.282698337211604
11.2071,1.2905889671447655
11.2075,1.2983932387539374
11.208400000000001,1.5756312874271927
11.2089,1.3061289118283488
11.2093,1.3137413392392563
11.209900000000001,1.3212844363707013
11.2105,1.3287440211738633
11.2111,1.3361240786859585
11.2118,1.3434226680704695
11.2121,1.3506419841724973
11.2127,1.35780576619357
11.2131,1.3718370881026203
11.2135,1.5246558611202008
11.213700000000001,1.364846987030096
11.214,1.5138285906034126
11.2142,1.385586125420797
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11.2146,1.3990472473875148
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11.215,1.392352319217516
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11.215300000000001,1.4056725030722297
11.2154,1.4562337169860362
11.2155,1.4251295454595927
11.2156,1.4314822279446662
R,M
7.0868,1.8102943986273166
7.087,1.810312919954896
7.0872,1.8102755711577103
7.0875,1.8102573284176724
7.0876,1.810237664390435
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7.0893,1.810178092508343
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7.0916,1.8100731291478405
7.0919,1.8100505894431602
7.0923,1.8100245482326576
7.0933,1.810004843857531
7.0941,1.809981589365771
7.0948,1.8099565489521152
7.0949,1.8099337348073137
7.0957,1.8099052572558645
7.096,1.8098801233168886
7.0963,1.8098547472451978
7.0968,1.8098277762241366
7.0976,1.8098018568760064
7.0988,1.8097719856008248
7.099,1.8097424026714641
7.0994,1.8097145467653863
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7.1016,1.8096207945226712
7.1023000000000005,1.8095909693292185
7.1031,1.8095572406207299
7.1038,1.8095238119406782
7.1043,1.8094894673202357
7.1053,1.8094538233723965
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7.1067,1.8093818127358254
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7.1219,1.808747338797958
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7.3192,1.8026419692687126
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7.4039,1.8013374225262055
7.434,1.8010610354808818
7.469,1.8008525385497174
7.4728,1.8008375055547212
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7.5623000000000005,1.8009237058574081
7.5682,1.800958157045992
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7.58,1.8010363488385235
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7.5923,1.8011350711403118
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7.619,1.801396979723438
7.6262,1.801481231944994
7.6332,1.8015680286606623
7.6412,1.8016656871159082
7.6485,1.8017686065785499
7.6562,1.8018805264670845
7.664,1.8020043241836483
7.6723,1.8021374134198185
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7.8660000000000005,1.807171119554773
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7.91,1.808815338202358
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8.5218,1.8504805320192779
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8.8087,1.881922204419208
8.8655,1.8890064461692662
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9.7469,2.0181299858892676
9.7574,2.019339103824116
9.768,2.0205367574186544
9.7784,2.0217329512312534
9.789200000000001,2.022924537891196
9.8004,2.024103911848606
9.8115,2.0252755733660237
9.822700000000001,2.026442290408354
9.8339,2.027595562850575
9.8451,2.0287425260513627
9.8566,2.02987789695615
9.8683,2.031004255291417
9.8802,2.0321140981371753
9.8917,2.033211262029186
9.9039,2.03429873554374
9.9159,2.035370601551793
9.9284,2.036428080910105
9.9404,2.03746680777716
9.953100000000001,2.038486511766415
9.9657,2.039488307130752
9.9784,2.040471399292025
9.9911,2.0414350165353037
10.0042,2.0423773356028083
10.0178,2.043291283530465
10.030800000000001,2.0441838862321724
10.044500000000001,2.0450464474687147
10.0585,2.0458827447198584
10.0716,2.0466886270813385
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10.0998,2.0482043647433352
10.113900000000001,2.0489096859138938
10.1285,2.0495761215721746
10.1431,2.0501996224170225
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10.2344,2.0529609887501064
10.2505,2.053231208698309
10.266300000000001,2.053430956645957
10.2827,2.0535609020452807
10.2992,2.053618768726303
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10.3495,2.0533064233065197
10.3668,2.0530215567767636
10.3844,2.0526369031524108
10.402000000000001,2.0521636845730233
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10.4563,2.0499535127064785
10.4748,2.0489576534963168
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10.5714,2.0415367556333575
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10.6318,2.034681992486926
10.6523,2.031875344830552
10.6732,2.028806016957831
10.6943,2.0254302835993974
10.7156,2.021729410221098
10.737,2.0176679331240632
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10.7804,2.0083569404787207
10.8025,2.003033377167351
10.8245,1.9972070467548615
10.8468,1.99084498211035
10.8693,1.9838848370723352
10.8916,1.976265341594595
10.913400000000001,1.9679245337116287
10.9359,1.9587753705415543
10.958,1.9487087547075432
10.9801,1.937613087921291
11.0016,1.925359737260557
11.0228,1.911811244861433
11.043700000000001,1.896814109152176
11.0641,1.880201767746232
11.0839,1.861791757645824
11.103,1.8413880752742964
11.1143,0.6798071334659402
11.1148,0.6620484741207461
11.115400000000001,0.7308628786759206
11.1163,0.643905710436412
11.1173,0.7471776126414391
11.1188,0.6253614804987849
11.119,0.7631699685933497
11.1209,1.8187639653487393
11.121,0.7788332436619917
11.1226,0.6064184938116979
11.1233,0.7941958537153351
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11.1539,1.7659104790175046
11.1555,0.9702722709283456
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11.1694,1.0396641713997514
11.1708,1.0506369116951209
11.1715,0.5057129912122545
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11.178700000000001,1.0930140692041797
11.1804,1.7010859834099117
11.180900000000001,1.1032438870571097
11.1824,1.113341990904366
11.184000000000001,1.123299806468843
11.1857,1.1331296145407677
11.1873,1.1428308979922694
11.1889,1.152401698215316
11.1903,1.161856869794937
11.1904,0.48445364974714356
11.1919,1.663399436215842
11.192,1.1711910394172824
11.1934,1.1804087961074725
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11.195500000000001,1.1985002253694752
11.1974,1.2073817550934374
11.1981,1.2161518746407876
11.199300000000001,1.2248195347259854
11.2006,1.233382363999008
11.2012,1.6217221083677575
11.201600000000001,1.2418573170671454
11.2026,1.2502123181321048
11.2036,1.2584707455312942
11.2044,1.2666389953934527
11.205300000000001,1.2747462832375793
11.2059,1.282698337211604
11.2071,1.2905889671447655
11.2075,1.2983932387539374
11.208400000000001,1.5756312874271927
11.2089,1.3061289118283488
11.2093,1.3137413392392563
11.209900000000001,1.3212844363707013
11.2105,1.3287440211738633
11.2111,1.3361240786859585
11.2118,1.3434226680704695
11.2121,1.3506419841724973
11.2127,1.35780576619357
11.2131,1.3718370881026203
11.2135,1.5246558611202008
11.213700000000001,1.364846987030096
11.214,1.5138285906034126
11.2142,1.385586125420797
11.2144,1.5083287553910607
11.214500000000001,1.5027740924000998
11.2146,1.3990472473875148
11.2147,1.491494982843448
11.2148,1.4122266556473997
11.2149,1.4187122779735373
11.215,1.392352319217516
11.2151,1.4799814651383028
11.215200000000001,1.4682238422417109
11.215300000000001,1.4056725030722297
11.2154,1.4562337169860362
11.2155,1.4251295454595927
11.2156,1.4314822279446662
The data is imported using Pandas with the code below.
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('data.txt')
df = df.sort_values(by=['R'])
plt.plot(df['R'], df['M'])
plt.gcf().set_size_inches(2.55*8,1*8)
plt.xlabel(r'$r$ $(km)$')
plt.ylabel(r'$M/M_\odot$')
plt.show()
exit()
I've shorted data short by X ('R') and it look weird where Y is not monotonically increasing as picture below
Also, if I short the data by Y ('M'), the plot doesn't look so well where X is not monotonically increasing.
Scatter plot looks as shown below.
I have no idea for an equation to fit this plot. Is there any method or package for connecting the point properly?
Edit1:
I've tried doing spline fit. The result is as below.
import numpy as np
tck = interpolate.splrep(df['R'], df['M'],)
xnew= np.linspace(min(df['R'].to_numpy()),max(df['R'].to_numpy()),1000)
ynew = interpolate.splev(xnew, tck)
plt.plot(xnew, ynew)

I guess in this particular case a solution to get the data in the desired order is to sort them by the polar angle of the points in a cartesian plane.
X = <your data>
order = np.argsort(np.arctan2(X[:,1], X[:,0]))
plt.plot(X[order,0], X[order,1])
plt.show()

How to set seaborn jointplot axis to log scale

How to set axis to logarithmic scale in a seaborn jointplot? I can't find any log arguments in seaborn.jointplot
Notebook
import seaborn as sns
import pandas as pd
df = pd.read_csv("https://storage.googleapis.com/mledu-datasets/california_housing_train.csv", sep=",")
g = sns.jointplot(x="total_bedrooms",
y="median_house_value",
data = df,
kind="reg",
logx=True
)
300 rows of sample data of the relevant columns, incase the data link dies
median_house_value,total_bedrooms
66900.0,1283.0
80100.0,1901.0
85700.0,174.0
73400.0,337.0
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74000.0,236.0
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58400.0,1175.0
48100.0,309.0
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62000.0,483.0
48600.0,248.0
70400.0,464.0
45000.0,378.0
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25000.0,33.0
44000.0,386.0
27500.0,24.0
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50000.0,95.0
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53500.0,397.0
100000.0,139.0
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42500.0,136.0
53400.0,283.0
60800.0,262.0
58600.0,382.0
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67500.0,387.0
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40000.0,199.0
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66900.0,290.0
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65600.0,1997.0
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100000.0,820.0
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112500.0,54.0
65400.0,499.0
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60600.0,625.0
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68900.0,714.0
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100000.0,176.0
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67500.0,187.0
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100000.0,367.0
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500001.0,438.0
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66000.0,780.0
61000.0,306.0
89600.0,754.0
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130400.0,859.0
145200.0,2315.0
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110900.0,2186.0
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500001.0,562.0
69600.0,1529.0
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22500.0,1743.0
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345500.0,82.0
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169100.0,847.0
240600.0,157.0
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66300.0,109.0
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214300.0,661.0
158200.0,946.0
143400.0,1070.0
217400.0,845.0
308600.0,481.0
111400.0,849.0
42500.0,10.0
173400.0,268.0
187200.0,702.0
214500.0,751.0
63000.0,525.0
221000.0,1946.0
90000.0,68.0
231800.0,786.0
206100.0,520.0
100000.0,63.0
274600.0,565.0
84700.0,1527.0

After you create the plot, you can set the axes to be log scale, using matplotlib's ax.set_xscale('log') and ax.set_yscale('log').
In this case, we need to get the axis from the JointGrid created by jointplot. If you catch the JointGrid returned as g, then the joint axis is g.ax_joint.
For example:
g = sns.jointplot(x="predictions",
y="targets",
data = calibration_data,
kind="reg",
logx=True,
)
g.ax_joint.set_xscale('log')
g.ax_joint.set_yscale('log')