How to set seaborn jointplot axis to log scale - python
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
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80100.0,1901.0
85700.0,174.0
73400.0,337.0
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74000.0,236.0
82400.0,680.0
48500.0,168.0
58400.0,1175.0
48100.0,309.0
86500.0,801.0
62000.0,483.0
48600.0,248.0
70400.0,464.0
45000.0,378.0
69100.0,587.0
94900.0,322.0
25000.0,33.0
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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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88500.0,246.0
91200.0,666.0
102800.0,104.0
64000.0,389.0
84700.0,440.0
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58600.0,382.0
66400.0,366.0
67500.0,387.0
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63100.0,275.0
67700.0,581.0
40000.0,199.0
62200.0,634.0
70700.0,340.0
60300.0,545.0
61200.0,325.0
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96000.0,268.0
60600.0,395.0
70800.0,454.0
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80800.0,530.0
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53600.0,162.0
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63500.0,256.0
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47500.0,18.0
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32500.0,49.0
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67500.0,95.0
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53800.0,25.0
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51300.0,57.0
43900.0,280.0
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81300.0,94.0
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65600.0,1997.0
84700.0,354.0
100000.0,820.0
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82600.0,705.0
112500.0,54.0
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61400.0,277.0
65900.0,800.0
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100000.0,176.0
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72100.0,221.0
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104100.0,566.0
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500001.0,438.0
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142500.0,1512.0
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81000.0,913.0
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65900.0,636.0
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87500.0,85.0
94800.0,1229.0
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51300.0,2634.0
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61000.0,306.0
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220500.0,463.0
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500001.0,562.0
69600.0,1529.0
321900.0,399.0
148200.0,361.0
22500.0,1743.0
76600.0,67.0
50000.0,166.0
230200.0,1652.0
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116500.0,876.0
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193800.0,74.0
161100.0,711.0
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66300.0,109.0
81700.0,875.0
122900.0,682.0
214300.0,661.0
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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')
Related
show cluster of point with folium
I have a dataframe with a list of coordinates. I want to group on the map near points and show a dot on the map with the number of points on it like this. I'm using geopandas and folium. Following this example this is my code from folium.plugins import MarkerCluster import geopandas as gdp from shapely.geometry import Point, LineString, Polygon, MultiPoint building_list = [(-1,-1),(10.6,-1),(10.6,4.4),(-1,4.4)] building = Polygon(building_list) gdf_building = gpd.GeoDataFrame({'building':['Building'], 'geometry':[building]}) locations = df[['x', 'y']] locationlist = locations.values.tolist() print(locationlist) map_building = gdf_building.explore(style_kwds={'color':'black','weight':3,'fillColor':'gray','fillOpacity':0.2}) marker_cluster = MarkerCluster().add_to(map_building) #add a marker to the cluster, not the map for location in locationlist: marker = folium.Marker( location=location, ) marker_cluster.add_child(marker) folium.LayerControl().add_to(map_building); map_building this is the result of the print of the location list, as you can see there are actual points. [[-10.975653734125247, 14.223493380357969], [-3.0008170610241343, 4.952113879109663], [1.2889747510247407, 2.204612243849062], [1.8083739029012205, 1.7796964504195971], [3.420669729369738, 0.7404826646612623], [4.754666507090696, -0.5524039121778554], [5.53171781359429, 1.310518688820813], [4.318223656736064, -0.8753972819321212], [1.389494271129264, 16.407899298913147], [2.82329764017852, 0.9072912833977023], [-4.497070654458373, 11.375240775117195], [4.388070857027747, 2.026226808689793], [5.020759388491676, 2.756120554023195], [6.001047947560095, 3.640750189810609], [4.748181698919979, 0.6264535181440452], [2.6528944739060116, 3.4802260546999184], [0.6549886054089478, 2.3877498852531], [0.9196638849958464, 2.150417536934342], [5.108903278970173, 1.7149961891938221], [4.023317826279737, -0.7706685254504961], [0.903562864987411, 2.0796324380334292], [1.9513434378658632, 1.3693238231850704], [5.644520571004791, 0.9779979070472828], [4.885749810717222, 1.8987377699798547], [0.8962029416395766, 2.1062557612048933], [4.8, 3.1279064752127708], [-0.04508236022751255, 3.0568571249844774], [4.800000000000001, 1.9140378892025933], [-1.989743839191096, 3.371668995303246], [1.1656083298774806, 1.807252953229595], [1.8713291261582097, 1.143179456971009], [4.874256276815991, 1.442130685409675], [1.4180968883141027, 1.6105182232356952], [3.190937527417693, 0.7681078921163724], [0.6152561767926022, 2.4352310754461803], [4.943357091884491, 1.3116890391870286], [4.669880127812657, -0.9679152111322677], [3.614139957564433, 4.902972429576663], [5.669098065784623, 3.913649249046372], [3.813973864130181, -0.7323384613836201], [-0.5199470910058632, 4.940176537119433], [0.6045522916902855, 2.4454362761206676], [3.997321854441272, -0.40367758748908944], [-0.7749524129532896, 3.7357518435721646], [4.398949303156029, 1.5109906728678215], [0.3021200538833131, 2.729947426419629], [5.644520571004791, 1.1699695341101362], [0.3021200538833131, 2.729947426419629], [4.038514768738497, -0.2998527712422736], [5.463665551081531, 4.137680366555347], [0.8962029416395773, 2.157030296469887], [3.4127967638900434, -0.15739477788275646], [5.3627337631140914, 0.47173132205910173], [-17.039850506402114, 17.631059209559115], [4.693589821903688, 2.645652867319201], [1.652915853616594, 1.4812029360906247], [0.7676072335427175, 2.7523632254899515], [5.108903278970173, 1.6583572860511704], [0.4132781963362031, 2.904134388098682], [3.259934813790278, -0.18044453177741993], [0.8945938791669944, 2.1032269377270914], [2.57881016023833, 3.112861191160806], [3.592593833132784, 1.4836895239891579], [3.317682346796072, -0.406574363983617], [5.23674698889199, -0.4896527267915487], [1.652915853616594, 1.3490348417130233], [4.085742861634555, 0.6349594490258132], [4.0072168062713, -1.0764854252471776], [3.9664315265442545, -0.39306568870652514], [0.6031981581824843, 2.377481733947807], [3.6039665183706195, -0.2228663982705159], [1.8714318174071345, 1.1838500546775474], [1.8733352672556218, 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[4.388524835038002, -0.22441713017397236], [5.9749455766628685, -0.5105871937116166], [0.2900620352731954, 2.6317207883891633], [0.8945938791669943, 2.1382788324299487], [-16.642526220238658, 19.623895964392474], [-0.046436493735313444, 2.9484252862795244], [0.6031981581824842, 2.4125336286506642], [3.9771730838489914, -0.7757922401415804], [4.536770649420184, -0.20454374589753987], [0.2900620352731951, 2.707249979624113], [-0.4080404472264636, 3.4209265133546793], [4.719384382974241, -0.48143166216084854], [4.544248150302466, -0.5884133974848607], [2.768366006109903, 0.9183434148624996]] The problem is that when i display map_building I see only the map of the first polygon without any point. How to solve it?
I think its perhaps a problem that you dont have a folium map. In the docs of GeoDataFrame.explore() it says the parameter m should be linked to a map. Perhaps first you have to create a map and then use it in the explore command. Similar to this (have not tried this example in lack of geopandas installed) map_building = gdf_building.explore(style_kwds={'color':'black','weight':3,'fillColor':'gray','fillOpacity':0.2}, m=folium.Map(location=[44, -73], zoom_start=5)) I am not aware of the advantages of GeoDataFrame.explore() vs. pure folium. Perhaps, if you do not need it, you can just create a folium map and add the markercluster to it, just like it is done in your linked example. This should work: # create map m = folium.Map(location=[44, -73], zoom_start=5) marker_cluster = MarkerCluster().add_to(m) # add a marker to the cluster, not the map for location in locationlist: marker = folium.Marker(location=location) marker_cluster.add_child(marker) # add layercontrol folium.LayerControl().add_to(m); # show map m
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 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Plotting a bar plot with seaborn
The data frame I am using: https://www.kaggle.com/mustiztemiz/diabetes I have the following column: Outcome - which has values 0 or 1. I want to plot a barplot which has Outcome on the x-axis and the it's count on y-axis. My code is as follows: sns.barplot(x='Outcome', y=diabetes['Outcome'].value_counts(), data=diabetes) It is returning the following plot The output I got is wrong as 1 should be 268 in count and 0 should be 500 in count. I don't know where I did the mistake. diabetes.csv Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome 6,148,72,35,0,33.6,0.627,50,1 1,85,66,29,0,26.6,0.351,31,0 8,183,64,0,0,23.3,0.672,32,1 1,89,66,23,94,28.1,0.167,21,0 0,137,40,35,168,43.1,2.288,33,1 5,116,74,0,0,25.6,0.201,30,0 3,78,50,32,88,31.0,0.248,26,1 10,115,0,0,0,35.3,0.134,29,0 2,197,70,45,543,30.5,0.158,53,1 8,125,96,0,0,0.0,0.232,54,1 4,110,92,0,0,37.6,0.191,30,0 10,168,74,0,0,38.0,0.537,34,1 10,139,80,0,0,27.1,1.441,57,0 1,189,60,23,846,30.1,0.398,59,1 5,166,72,19,175,25.8,0.587,51,1 7,100,0,0,0,30.0,0.484,32,1 0,118,84,47,230,45.8,0.551,31,1 7,107,74,0,0,29.6,0.254,31,1 1,103,30,38,83,43.3,0.183,33,0 1,115,70,30,96,34.6,0.529,32,1 3,126,88,41,235,39.3,0.704,27,0 8,99,84,0,0,35.4,0.388,50,0 7,196,90,0,0,39.8,0.451,41,1 9,119,80,35,0,29.0,0.263,29,1 11,143,94,33,146,36.6,0.254,51,1 10,125,70,26,115,31.1,0.205,41,1 7,147,76,0,0,39.4,0.257,43,1 1,97,66,15,140,23.2,0.487,22,0 13,145,82,19,110,22.2,0.245,57,0 5,117,92,0,0,34.1,0.337,38,0 5,109,75,26,0,36.0,0.546,60,0 3,158,76,36,245,31.6,0.851,28,1 3,88,58,11,54,24.8,0.267,22,0 6,92,92,0,0,19.9,0.188,28,0 10,122,78,31,0,27.6,0.512,45,0 4,103,60,33,192,24.0,0.966,33,0 11,138,76,0,0,33.2,0.42,35,0 9,102,76,37,0,32.9,0.665,46,1 2,90,68,42,0,38.2,0.503,27,1 4,111,72,47,207,37.1,1.39,56,1 3,180,64,25,70,34.0,0.271,26,0 7,133,84,0,0,40.2,0.696,37,0 7,106,92,18,0,22.7,0.235,48,0 9,171,110,24,240,45.4,0.721,54,1 7,159,64,0,0,27.4,0.294,40,0 0,180,66,39,0,42.0,1.893,25,1 1,146,56,0,0,29.7,0.564,29,0 2,71,70,27,0,28.0,0.586,22,0 7,103,66,32,0,39.1,0.344,31,1 7,105,0,0,0,0.0,0.305,24,0 1,103,80,11,82,19.4,0.491,22,0 1,101,50,15,36,24.2,0.526,26,0 5,88,66,21,23,24.4,0.342,30,0 8,176,90,34,300,33.7,0.467,58,1 7,150,66,42,342,34.7,0.718,42,0 1,73,50,10,0,23.0,0.248,21,0 7,187,68,39,304,37.7,0.254,41,1 0,100,88,60,110,46.8,0.962,31,0 0,146,82,0,0,40.5,1.781,44,0 0,105,64,41,142,41.5,0.173,22,0 2,84,0,0,0,0.0,0.304,21,0 8,133,72,0,0,32.9,0.27,39,1 5,44,62,0,0,25.0,0.587,36,0 2,141,58,34,128,25.4,0.699,24,0 7,114,66,0,0,32.8,0.258,42,1 5,99,74,27,0,29.0,0.203,32,0 0,109,88,30,0,32.5,0.855,38,1 2,109,92,0,0,42.7,0.845,54,0 1,95,66,13,38,19.6,0.334,25,0 4,146,85,27,100,28.9,0.189,27,0 2,100,66,20,90,32.9,0.867,28,1 5,139,64,35,140,28.6,0.411,26,0 13,126,90,0,0,43.4,0.583,42,1 4,129,86,20,270,35.1,0.231,23,0 1,79,75,30,0,32.0,0.396,22,0 1,0,48,20,0,24.7,0.14,22,0 7,62,78,0,0,32.6,0.391,41,0 5,95,72,33,0,37.7,0.37,27,0 0,131,0,0,0,43.2,0.27,26,1 2,112,66,22,0,25.0,0.307,24,0 3,113,44,13,0,22.4,0.14,22,0 2,74,0,0,0,0.0,0.102,22,0 7,83,78,26,71,29.3,0.767,36,0 0,101,65,28,0,24.6,0.237,22,0 5,137,108,0,0,48.8,0.227,37,1 2,110,74,29,125,32.4,0.698,27,0 13,106,72,54,0,36.6,0.178,45,0 2,100,68,25,71,38.5,0.324,26,0 15,136,70,32,110,37.1,0.153,43,1 1,107,68,19,0,26.5,0.165,24,0 1,80,55,0,0,19.1,0.258,21,0 4,123,80,15,176,32.0,0.443,34,0 7,81,78,40,48,46.7,0.261,42,0 4,134,72,0,0,23.8,0.277,60,1 2,142,82,18,64,24.7,0.761,21,0 6,144,72,27,228,33.9,0.255,40,0 2,92,62,28,0,31.6,0.13,24,0 1,71,48,18,76,20.4,0.323,22,0 6,93,50,30,64,28.7,0.356,23,0 1,122,90,51,220,49.7,0.325,31,1 1,163,72,0,0,39.0,1.222,33,1 1,151,60,0,0,26.1,0.179,22,0 0,125,96,0,0,22.5,0.262,21,0 1,81,72,18,40,26.6,0.283,24,0 2,85,65,0,0,39.6,0.93,27,0 1,126,56,29,152,28.7,0.801,21,0 1,96,122,0,0,22.4,0.207,27,0 4,144,58,28,140,29.5,0.287,37,0 3,83,58,31,18,34.3,0.336,25,0 0,95,85,25,36,37.4,0.247,24,1 3,171,72,33,135,33.3,0.199,24,1 8,155,62,26,495,34.0,0.543,46,1 1,89,76,34,37,31.2,0.192,23,0 4,76,62,0,0,34.0,0.391,25,0 7,160,54,32,175,30.5,0.588,39,1 4,146,92,0,0,31.2,0.539,61,1 5,124,74,0,0,34.0,0.22,38,1 5,78,48,0,0,33.7,0.654,25,0 4,97,60,23,0,28.2,0.443,22,0 4,99,76,15,51,23.2,0.223,21,0 0,162,76,56,100,53.2,0.759,25,1 6,111,64,39,0,34.2,0.26,24,0 2,107,74,30,100,33.6,0.404,23,0 5,132,80,0,0,26.8,0.186,69,0 0,113,76,0,0,33.3,0.278,23,1 1,88,30,42,99,55.0,0.496,26,1 3,120,70,30,135,42.9,0.452,30,0 1,118,58,36,94,33.3,0.261,23,0 1,117,88,24,145,34.5,0.403,40,1 0,105,84,0,0,27.9,0.741,62,1 4,173,70,14,168,29.7,0.361,33,1 9,122,56,0,0,33.3,1.114,33,1 3,170,64,37,225,34.5,0.356,30,1 8,84,74,31,0,38.3,0.457,39,0 2,96,68,13,49,21.1,0.647,26,0 2,125,60,20,140,33.8,0.088,31,0 0,100,70,26,50,30.8,0.597,21,0 0,93,60,25,92,28.7,0.532,22,0 0,129,80,0,0,31.2,0.703,29,0 5,105,72,29,325,36.9,0.159,28,0 3,128,78,0,0,21.1,0.268,55,0 5,106,82,30,0,39.5,0.286,38,0 2,108,52,26,63,32.5,0.318,22,0 10,108,66,0,0,32.4,0.272,42,1 4,154,62,31,284,32.8,0.237,23,0 0,102,75,23,0,0.0,0.572,21,0 9,57,80,37,0,32.8,0.096,41,0 2,106,64,35,119,30.5,1.4,34,0 5,147,78,0,0,33.7,0.218,65,0 2,90,70,17,0,27.3,0.085,22,0 1,136,74,50,204,37.4,0.399,24,0 4,114,65,0,0,21.9,0.432,37,0 9,156,86,28,155,34.3,1.189,42,1 1,153,82,42,485,40.6,0.687,23,0 8,188,78,0,0,47.9,0.137,43,1 7,152,88,44,0,50.0,0.337,36,1 2,99,52,15,94,24.6,0.637,21,0 1,109,56,21,135,25.2,0.833,23,0 2,88,74,19,53,29.0,0.229,22,0 17,163,72,41,114,40.9,0.817,47,1 4,151,90,38,0,29.7,0.294,36,0 7,102,74,40,105,37.2,0.204,45,0 0,114,80,34,285,44.2,0.167,27,0 2,100,64,23,0,29.7,0.368,21,0 0,131,88,0,0,31.6,0.743,32,1 6,104,74,18,156,29.9,0.722,41,1 3,148,66,25,0,32.5,0.256,22,0 4,120,68,0,0,29.6,0.709,34,0 4,110,66,0,0,31.9,0.471,29,0 3,111,90,12,78,28.4,0.495,29,0 6,102,82,0,0,30.8,0.18,36,1 6,134,70,23,130,35.4,0.542,29,1 2,87,0,23,0,28.9,0.773,25,0 1,79,60,42,48,43.5,0.678,23,0 2,75,64,24,55,29.7,0.37,33,0 8,179,72,42,130,32.7,0.719,36,1 6,85,78,0,0,31.2,0.382,42,0 0,129,110,46,130,67.1,0.319,26,1 5,143,78,0,0,45.0,0.19,47,0 5,130,82,0,0,39.1,0.956,37,1 6,87,80,0,0,23.2,0.084,32,0 0,119,64,18,92,34.9,0.725,23,0 1,0,74,20,23,27.7,0.299,21,0 5,73,60,0,0,26.8,0.268,27,0 4,141,74,0,0,27.6,0.244,40,0 7,194,68,28,0,35.9,0.745,41,1 8,181,68,36,495,30.1,0.615,60,1 1,128,98,41,58,32.0,1.321,33,1 8,109,76,39,114,27.9,0.64,31,1 5,139,80,35,160,31.6,0.361,25,1 3,111,62,0,0,22.6,0.142,21,0 9,123,70,44,94,33.1,0.374,40,0 7,159,66,0,0,30.4,0.383,36,1 11,135,0,0,0,52.3,0.578,40,1 8,85,55,20,0,24.4,0.136,42,0 5,158,84,41,210,39.4,0.395,29,1 1,105,58,0,0,24.3,0.187,21,0 3,107,62,13,48,22.9,0.678,23,1 4,109,64,44,99,34.8,0.905,26,1 4,148,60,27,318,30.9,0.15,29,1 0,113,80,16,0,31.0,0.874,21,0 1,138,82,0,0,40.1,0.236,28,0 0,108,68,20,0,27.3,0.787,32,0 2,99,70,16,44,20.4,0.235,27,0 6,103,72,32,190,37.7,0.324,55,0 5,111,72,28,0,23.9,0.407,27,0 8,196,76,29,280,37.5,0.605,57,1 5,162,104,0,0,37.7,0.151,52,1 1,96,64,27,87,33.2,0.289,21,0 7,184,84,33,0,35.5,0.355,41,1 2,81,60,22,0,27.7,0.29,25,0 0,147,85,54,0,42.8,0.375,24,0 7,179,95,31,0,34.2,0.164,60,0 0,140,65,26,130,42.6,0.431,24,1 9,112,82,32,175,34.2,0.26,36,1 12,151,70,40,271,41.8,0.742,38,1 5,109,62,41,129,35.8,0.514,25,1 6,125,68,30,120,30.0,0.464,32,0 5,85,74,22,0,29.0,1.224,32,1 5,112,66,0,0,37.8,0.261,41,1 0,177,60,29,478,34.6,1.072,21,1 2,158,90,0,0,31.6,0.805,66,1 7,119,0,0,0,25.2,0.209,37,0 7,142,60,33,190,28.8,0.687,61,0 1,100,66,15,56,23.6,0.666,26,0 1,87,78,27,32,34.6,0.101,22,0 0,101,76,0,0,35.7,0.198,26,0 3,162,52,38,0,37.2,0.652,24,1 4,197,70,39,744,36.7,2.329,31,0 0,117,80,31,53,45.2,0.089,24,0 4,142,86,0,0,44.0,0.645,22,1 6,134,80,37,370,46.2,0.238,46,1 1,79,80,25,37,25.4,0.583,22,0 4,122,68,0,0,35.0,0.394,29,0 3,74,68,28,45,29.7,0.293,23,0 4,171,72,0,0,43.6,0.479,26,1 7,181,84,21,192,35.9,0.586,51,1 0,179,90,27,0,44.1,0.686,23,1 9,164,84,21,0,30.8,0.831,32,1 0,104,76,0,0,18.4,0.582,27,0 1,91,64,24,0,29.2,0.192,21,0 4,91,70,32,88,33.1,0.446,22,0 3,139,54,0,0,25.6,0.402,22,1 6,119,50,22,176,27.1,1.318,33,1 2,146,76,35,194,38.2,0.329,29,0 9,184,85,15,0,30.0,1.213,49,1 10,122,68,0,0,31.2,0.258,41,0 0,165,90,33,680,52.3,0.427,23,0 9,124,70,33,402,35.4,0.282,34,0 1,111,86,19,0,30.1,0.143,23,0 9,106,52,0,0,31.2,0.38,42,0 2,129,84,0,0,28.0,0.284,27,0 2,90,80,14,55,24.4,0.249,24,0 0,86,68,32,0,35.8,0.238,25,0 12,92,62,7,258,27.6,0.926,44,1 1,113,64,35,0,33.6,0.543,21,1 3,111,56,39,0,30.1,0.557,30,0 2,114,68,22,0,28.7,0.092,25,0 1,193,50,16,375,25.9,0.655,24,0 11,155,76,28,150,33.3,1.353,51,1 3,191,68,15,130,30.9,0.299,34,0 3,141,0,0,0,30.0,0.761,27,1 4,95,70,32,0,32.1,0.612,24,0 3,142,80,15,0,32.4,0.2,63,0 4,123,62,0,0,32.0,0.226,35,1 5,96,74,18,67,33.6,0.997,43,0 0,138,0,0,0,36.3,0.933,25,1 2,128,64,42,0,40.0,1.101,24,0 0,102,52,0,0,25.1,0.078,21,0 2,146,0,0,0,27.5,0.24,28,1 10,101,86,37,0,45.6,1.136,38,1 2,108,62,32,56,25.2,0.128,21,0 3,122,78,0,0,23.0,0.254,40,0 1,71,78,50,45,33.2,0.422,21,0 13,106,70,0,0,34.2,0.251,52,0 2,100,70,52,57,40.5,0.677,25,0 7,106,60,24,0,26.5,0.296,29,1 0,104,64,23,116,27.8,0.454,23,0 5,114,74,0,0,24.9,0.744,57,0 2,108,62,10,278,25.3,0.881,22,0 0,146,70,0,0,37.9,0.334,28,1 10,129,76,28,122,35.9,0.28,39,0 7,133,88,15,155,32.4,0.262,37,0 7,161,86,0,0,30.4,0.165,47,1 2,108,80,0,0,27.0,0.259,52,1 7,136,74,26,135,26.0,0.647,51,0 5,155,84,44,545,38.7,0.619,34,0 1,119,86,39,220,45.6,0.808,29,1 4,96,56,17,49,20.8,0.34,26,0 5,108,72,43,75,36.1,0.263,33,0 0,78,88,29,40,36.9,0.434,21,0 0,107,62,30,74,36.6,0.757,25,1 2,128,78,37,182,43.3,1.224,31,1 1,128,48,45,194,40.5,0.613,24,1 0,161,50,0,0,21.9,0.254,65,0 6,151,62,31,120,35.5,0.692,28,0 2,146,70,38,360,28.0,0.337,29,1 0,126,84,29,215,30.7,0.52,24,0 14,100,78,25,184,36.6,0.412,46,1 8,112,72,0,0,23.6,0.84,58,0 0,167,0,0,0,32.3,0.839,30,1 2,144,58,33,135,31.6,0.422,25,1 5,77,82,41,42,35.8,0.156,35,0 5,115,98,0,0,52.9,0.209,28,1 3,150,76,0,0,21.0,0.207,37,0 2,120,76,37,105,39.7,0.215,29,0 10,161,68,23,132,25.5,0.326,47,1 0,137,68,14,148,24.8,0.143,21,0 0,128,68,19,180,30.5,1.391,25,1 2,124,68,28,205,32.9,0.875,30,1 6,80,66,30,0,26.2,0.313,41,0 0,106,70,37,148,39.4,0.605,22,0 2,155,74,17,96,26.6,0.433,27,1 3,113,50,10,85,29.5,0.626,25,0 7,109,80,31,0,35.9,1.127,43,1 2,112,68,22,94,34.1,0.315,26,0 3,99,80,11,64,19.3,0.284,30,0 3,182,74,0,0,30.5,0.345,29,1 3,115,66,39,140,38.1,0.15,28,0 6,194,78,0,0,23.5,0.129,59,1 4,129,60,12,231,27.5,0.527,31,0 3,112,74,30,0,31.6,0.197,25,1 0,124,70,20,0,27.4,0.254,36,1 13,152,90,33,29,26.8,0.731,43,1 2,112,75,32,0,35.7,0.148,21,0 1,157,72,21,168,25.6,0.123,24,0 1,122,64,32,156,35.1,0.692,30,1 10,179,70,0,0,35.1,0.2,37,0 2,102,86,36,120,45.5,0.127,23,1 6,105,70,32,68,30.8,0.122,37,0 8,118,72,19,0,23.1,1.476,46,0 2,87,58,16,52,32.7,0.166,25,0 1,180,0,0,0,43.3,0.282,41,1 12,106,80,0,0,23.6,0.137,44,0 1,95,60,18,58,23.9,0.26,22,0 0,165,76,43,255,47.9,0.259,26,0 0,117,0,0,0,33.8,0.932,44,0 5,115,76,0,0,31.2,0.343,44,1 9,152,78,34,171,34.2,0.893,33,1 7,178,84,0,0,39.9,0.331,41,1 1,130,70,13,105,25.9,0.472,22,0 1,95,74,21,73,25.9,0.673,36,0 1,0,68,35,0,32.0,0.389,22,0 5,122,86,0,0,34.7,0.29,33,0 8,95,72,0,0,36.8,0.485,57,0 8,126,88,36,108,38.5,0.349,49,0 1,139,46,19,83,28.7,0.654,22,0 3,116,0,0,0,23.5,0.187,23,0 3,99,62,19,74,21.8,0.279,26,0 5,0,80,32,0,41.0,0.346,37,1 4,92,80,0,0,42.2,0.237,29,0 4,137,84,0,0,31.2,0.252,30,0 3,61,82,28,0,34.4,0.243,46,0 1,90,62,12,43,27.2,0.58,24,0 3,90,78,0,0,42.7,0.559,21,0 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1,119,88,41,170,45.3,0.507,26,0 2,94,68,18,76,26.0,0.561,21,0 0,102,64,46,78,40.6,0.496,21,0 2,115,64,22,0,30.8,0.421,21,0 8,151,78,32,210,42.9,0.516,36,1 4,184,78,39,277,37.0,0.264,31,1 0,94,0,0,0,0.0,0.256,25,0 1,181,64,30,180,34.1,0.328,38,1 0,135,94,46,145,40.6,0.284,26,0 1,95,82,25,180,35.0,0.233,43,1 2,99,0,0,0,22.2,0.108,23,0 3,89,74,16,85,30.4,0.551,38,0 1,80,74,11,60,30.0,0.527,22,0 2,139,75,0,0,25.6,0.167,29,0 1,90,68,8,0,24.5,1.138,36,0 0,141,0,0,0,42.4,0.205,29,1 12,140,85,33,0,37.4,0.244,41,0 5,147,75,0,0,29.9,0.434,28,0 1,97,70,15,0,18.2,0.147,21,0 6,107,88,0,0,36.8,0.727,31,0 0,189,104,25,0,34.3,0.435,41,1 2,83,66,23,50,32.2,0.497,22,0 4,117,64,27,120,33.2,0.23,24,0 8,108,70,0,0,30.5,0.955,33,1 4,117,62,12,0,29.7,0.38,30,1 0,180,78,63,14,59.4,2.42,25,1 1,100,72,12,70,25.3,0.658,28,0 0,95,80,45,92,36.5,0.33,26,0 0,104,64,37,64,33.6,0.51,22,1 0,120,74,18,63,30.5,0.285,26,0 1,82,64,13,95,21.2,0.415,23,0 2,134,70,0,0,28.9,0.542,23,1 0,91,68,32,210,39.9,0.381,25,0 2,119,0,0,0,19.6,0.832,72,0 2,100,54,28,105,37.8,0.498,24,0 14,175,62,30,0,33.6,0.212,38,1 1,135,54,0,0,26.7,0.687,62,0 5,86,68,28,71,30.2,0.364,24,0 10,148,84,48,237,37.6,1.001,51,1 9,134,74,33,60,25.9,0.46,81,0 9,120,72,22,56,20.8,0.733,48,0 1,71,62,0,0,21.8,0.416,26,0 8,74,70,40,49,35.3,0.705,39,0 5,88,78,30,0,27.6,0.258,37,0 10,115,98,0,0,24.0,1.022,34,0 0,124,56,13,105,21.8,0.452,21,0 0,74,52,10,36,27.8,0.269,22,0 0,97,64,36,100,36.8,0.6,25,0 8,120,0,0,0,30.0,0.183,38,1 6,154,78,41,140,46.1,0.571,27,0 1,144,82,40,0,41.3,0.607,28,0 0,137,70,38,0,33.2,0.17,22,0 0,119,66,27,0,38.8,0.259,22,0 7,136,90,0,0,29.9,0.21,50,0 4,114,64,0,0,28.9,0.126,24,0 0,137,84,27,0,27.3,0.231,59,0 2,105,80,45,191,33.7,0.711,29,1 7,114,76,17,110,23.8,0.466,31,0 8,126,74,38,75,25.9,0.162,39,0 4,132,86,31,0,28.0,0.419,63,0 3,158,70,30,328,35.5,0.344,35,1 0,123,88,37,0,35.2,0.197,29,0 4,85,58,22,49,27.8,0.306,28,0 0,84,82,31,125,38.2,0.233,23,0 0,145,0,0,0,44.2,0.63,31,1 0,135,68,42,250,42.3,0.365,24,1 1,139,62,41,480,40.7,0.536,21,0 0,173,78,32,265,46.5,1.159,58,0 4,99,72,17,0,25.6,0.294,28,0 8,194,80,0,0,26.1,0.551,67,0 2,83,65,28,66,36.8,0.629,24,0 2,89,90,30,0,33.5,0.292,42,0 4,99,68,38,0,32.8,0.145,33,0 4,125,70,18,122,28.9,1.144,45,1 3,80,0,0,0,0.0,0.174,22,0 6,166,74,0,0,26.6,0.304,66,0 5,110,68,0,0,26.0,0.292,30,0 2,81,72,15,76,30.1,0.547,25,0 7,195,70,33,145,25.1,0.163,55,1 6,154,74,32,193,29.3,0.839,39,0 2,117,90,19,71,25.2,0.313,21,0 3,84,72,32,0,37.2,0.267,28,0 6,0,68,41,0,39.0,0.727,41,1 7,94,64,25,79,33.3,0.738,41,0 3,96,78,39,0,37.3,0.238,40,0 10,75,82,0,0,33.3,0.263,38,0 0,180,90,26,90,36.5,0.314,35,1 1,130,60,23,170,28.6,0.692,21,0 2,84,50,23,76,30.4,0.968,21,0 8,120,78,0,0,25.0,0.409,64,0 12,84,72,31,0,29.7,0.297,46,1 0,139,62,17,210,22.1,0.207,21,0 9,91,68,0,0,24.2,0.2,58,0 2,91,62,0,0,27.3,0.525,22,0 3,99,54,19,86,25.6,0.154,24,0 3,163,70,18,105,31.6,0.268,28,1 9,145,88,34,165,30.3,0.771,53,1 7,125,86,0,0,37.6,0.304,51,0 13,76,60,0,0,32.8,0.18,41,0 6,129,90,7,326,19.6,0.582,60,0 2,68,70,32,66,25.0,0.187,25,0 3,124,80,33,130,33.2,0.305,26,0 6,114,0,0,0,0.0,0.189,26,0 9,130,70,0,0,34.2,0.652,45,1 3,125,58,0,0,31.6,0.151,24,0 3,87,60,18,0,21.8,0.444,21,0 1,97,64,19,82,18.2,0.299,21,0 3,116,74,15,105,26.3,0.107,24,0 0,117,66,31,188,30.8,0.493,22,0 0,111,65,0,0,24.6,0.66,31,0 2,122,60,18,106,29.8,0.717,22,0 0,107,76,0,0,45.3,0.686,24,0 1,86,66,52,65,41.3,0.917,29,0 6,91,0,0,0,29.8,0.501,31,0 1,77,56,30,56,33.3,1.251,24,0 4,132,0,0,0,32.9,0.302,23,1 0,105,90,0,0,29.6,0.197,46,0 0,57,60,0,0,21.7,0.735,67,0 0,127,80,37,210,36.3,0.804,23,0 3,129,92,49,155,36.4,0.968,32,1 8,100,74,40,215,39.4,0.661,43,1 3,128,72,25,190,32.4,0.549,27,1 10,90,85,32,0,34.9,0.825,56,1 4,84,90,23,56,39.5,0.159,25,0 1,88,78,29,76,32.0,0.365,29,0 8,186,90,35,225,34.5,0.423,37,1 5,187,76,27,207,43.6,1.034,53,1 4,131,68,21,166,33.1,0.16,28,0 1,164,82,43,67,32.8,0.341,50,0 4,189,110,31,0,28.5,0.68,37,0 1,116,70,28,0,27.4,0.204,21,0 3,84,68,30,106,31.9,0.591,25,0 6,114,88,0,0,27.8,0.247,66,0 1,88,62,24,44,29.9,0.422,23,0 1,84,64,23,115,36.9,0.471,28,0 7,124,70,33,215,25.5,0.161,37,0 1,97,70,40,0,38.1,0.218,30,0 8,110,76,0,0,27.8,0.237,58,0 11,103,68,40,0,46.2,0.126,42,0 11,85,74,0,0,30.1,0.3,35,0 6,125,76,0,0,33.8,0.121,54,1 0,198,66,32,274,41.3,0.502,28,1 1,87,68,34,77,37.6,0.401,24,0 6,99,60,19,54,26.9,0.497,32,0 0,91,80,0,0,32.4,0.601,27,0 2,95,54,14,88,26.1,0.748,22,0 1,99,72,30,18,38.6,0.412,21,0 6,92,62,32,126,32.0,0.085,46,0 4,154,72,29,126,31.3,0.338,37,0 0,121,66,30,165,34.3,0.203,33,1 3,78,70,0,0,32.5,0.27,39,0 2,130,96,0,0,22.6,0.268,21,0 3,111,58,31,44,29.5,0.43,22,0 2,98,60,17,120,34.7,0.198,22,0 1,143,86,30,330,30.1,0.892,23,0 1,119,44,47,63,35.5,0.28,25,0 6,108,44,20,130,24.0,0.813,35,0 2,118,80,0,0,42.9,0.693,21,1 10,133,68,0,0,27.0,0.245,36,0 2,197,70,99,0,34.7,0.575,62,1 0,151,90,46,0,42.1,0.371,21,1 6,109,60,27,0,25.0,0.206,27,0 12,121,78,17,0,26.5,0.259,62,0 8,100,76,0,0,38.7,0.19,42,0 8,124,76,24,600,28.7,0.687,52,1 1,93,56,11,0,22.5,0.417,22,0 8,143,66,0,0,34.9,0.129,41,1 6,103,66,0,0,24.3,0.249,29,0 3,176,86,27,156,33.3,1.154,52,1 0,73,0,0,0,21.1,0.342,25,0 11,111,84,40,0,46.8,0.925,45,1 2,112,78,50,140,39.4,0.175,24,0 3,132,80,0,0,34.4,0.402,44,1 2,82,52,22,115,28.5,1.699,25,0 6,123,72,45,230,33.6,0.733,34,0 0,188,82,14,185,32.0,0.682,22,1 0,67,76,0,0,45.3,0.194,46,0 1,89,24,19,25,27.8,0.559,21,0 1,173,74,0,0,36.8,0.088,38,1 1,109,38,18,120,23.1,0.407,26,0 1,108,88,19,0,27.1,0.4,24,0 6,96,0,0,0,23.7,0.19,28,0 1,124,74,36,0,27.8,0.1,30,0 7,150,78,29,126,35.2,0.692,54,1 4,183,0,0,0,28.4,0.212,36,1 1,124,60,32,0,35.8,0.514,21,0 1,181,78,42,293,40.0,1.258,22,1 1,92,62,25,41,19.5,0.482,25,0 0,152,82,39,272,41.5,0.27,27,0 1,111,62,13,182,24.0,0.138,23,0 3,106,54,21,158,30.9,0.292,24,0 3,174,58,22,194,32.9,0.593,36,1 7,168,88,42,321,38.2,0.787,40,1 6,105,80,28,0,32.5,0.878,26,0 11,138,74,26,144,36.1,0.557,50,1 3,106,72,0,0,25.8,0.207,27,0 6,117,96,0,0,28.7,0.157,30,0 2,68,62,13,15,20.1,0.257,23,0 9,112,82,24,0,28.2,1.282,50,1 0,119,0,0,0,32.4,0.141,24,1 2,112,86,42,160,38.4,0.246,28,0 2,92,76,20,0,24.2,1.698,28,0 6,183,94,0,0,40.8,1.461,45,0 0,94,70,27,115,43.5,0.347,21,0 2,108,64,0,0,30.8,0.158,21,0 4,90,88,47,54,37.7,0.362,29,0 0,125,68,0,0,24.7,0.206,21,0 0,132,78,0,0,32.4,0.393,21,0 5,128,80,0,0,34.6,0.144,45,0 4,94,65,22,0,24.7,0.148,21,0 7,114,64,0,0,27.4,0.732,34,1 0,102,78,40,90,34.5,0.238,24,0 2,111,60,0,0,26.2,0.343,23,0 1,128,82,17,183,27.5,0.115,22,0 10,92,62,0,0,25.9,0.167,31,0 13,104,72,0,0,31.2,0.465,38,1 5,104,74,0,0,28.8,0.153,48,0 2,94,76,18,66,31.6,0.649,23,0 7,97,76,32,91,40.9,0.871,32,1 1,100,74,12,46,19.5,0.149,28,0 0,102,86,17,105,29.3,0.695,27,0 4,128,70,0,0,34.3,0.303,24,0 6,147,80,0,0,29.5,0.178,50,1 4,90,0,0,0,28.0,0.61,31,0 3,103,72,30,152,27.6,0.73,27,0 2,157,74,35,440,39.4,0.134,30,0 1,167,74,17,144,23.4,0.447,33,1 0,179,50,36,159,37.8,0.455,22,1 11,136,84,35,130,28.3,0.26,42,1 0,107,60,25,0,26.4,0.133,23,0 1,91,54,25,100,25.2,0.234,23,0 1,117,60,23,106,33.8,0.466,27,0 5,123,74,40,77,34.1,0.269,28,0 2,120,54,0,0,26.8,0.455,27,0 1,106,70,28,135,34.2,0.142,22,0 2,155,52,27,540,38.7,0.24,25,1 2,101,58,35,90,21.8,0.155,22,0 1,120,80,48,200,38.9,1.162,41,0 11,127,106,0,0,39.0,0.19,51,0 3,80,82,31,70,34.2,1.292,27,1 10,162,84,0,0,27.7,0.182,54,0 1,199,76,43,0,42.9,1.394,22,1 8,167,106,46,231,37.6,0.165,43,1 9,145,80,46,130,37.9,0.637,40,1 6,115,60,39,0,33.7,0.245,40,1 1,112,80,45,132,34.8,0.217,24,0 4,145,82,18,0,32.5,0.235,70,1 10,111,70,27,0,27.5,0.141,40,1 6,98,58,33,190,34.0,0.43,43,0 9,154,78,30,100,30.9,0.164,45,0 6,165,68,26,168,33.6,0.631,49,0 1,99,58,10,0,25.4,0.551,21,0 10,68,106,23,49,35.5,0.285,47,0 3,123,100,35,240,57.3,0.88,22,0 8,91,82,0,0,35.6,0.587,68,0 6,195,70,0,0,30.9,0.328,31,1 9,156,86,0,0,24.8,0.23,53,1 0,93,60,0,0,35.3,0.263,25,0 3,121,52,0,0,36.0,0.127,25,1 2,101,58,17,265,24.2,0.614,23,0 2,56,56,28,45,24.2,0.332,22,0 0,162,76,36,0,49.6,0.364,26,1 0,95,64,39,105,44.6,0.366,22,0 4,125,80,0,0,32.3,0.536,27,1 5,136,82,0,0,0.0,0.64,69,0 2,129,74,26,205,33.2,0.591,25,0 3,130,64,0,0,23.1,0.314,22,0 1,107,50,19,0,28.3,0.181,29,0 1,140,74,26,180,24.1,0.828,23,0 1,144,82,46,180,46.1,0.335,46,1 8,107,80,0,0,24.6,0.856,34,0 13,158,114,0,0,42.3,0.257,44,1 2,121,70,32,95,39.1,0.886,23,0 7,129,68,49,125,38.5,0.439,43,1 2,90,60,0,0,23.5,0.191,25,0 7,142,90,24,480,30.4,0.128,43,1 3,169,74,19,125,29.9,0.268,31,1 0,99,0,0,0,25.0,0.253,22,0 4,127,88,11,155,34.5,0.598,28,0 4,118,70,0,0,44.5,0.904,26,0 2,122,76,27,200,35.9,0.483,26,0 6,125,78,31,0,27.6,0.565,49,1 1,168,88,29,0,35.0,0.905,52,1 2,129,0,0,0,38.5,0.304,41,0 4,110,76,20,100,28.4,0.118,27,0 6,80,80,36,0,39.8,0.177,28,0 10,115,0,0,0,0.0,0.261,30,1 2,127,46,21,335,34.4,0.176,22,0 9,164,78,0,0,32.8,0.148,45,1 2,93,64,32,160,38.0,0.674,23,1 3,158,64,13,387,31.2,0.295,24,0 5,126,78,27,22,29.6,0.439,40,0 10,129,62,36,0,41.2,0.441,38,1 0,134,58,20,291,26.4,0.352,21,0 3,102,74,0,0,29.5,0.121,32,0 7,187,50,33,392,33.9,0.826,34,1 3,173,78,39,185,33.8,0.97,31,1 10,94,72,18,0,23.1,0.595,56,0 1,108,60,46,178,35.5,0.415,24,0 5,97,76,27,0,35.6,0.378,52,1 4,83,86,19,0,29.3,0.317,34,0 1,114,66,36,200,38.1,0.289,21,0 1,149,68,29,127,29.3,0.349,42,1 5,117,86,30,105,39.1,0.251,42,0 1,111,94,0,0,32.8,0.265,45,0 4,112,78,40,0,39.4,0.236,38,0 1,116,78,29,180,36.1,0.496,25,0 0,141,84,26,0,32.4,0.433,22,0 2,175,88,0,0,22.9,0.326,22,0 2,92,52,0,0,30.1,0.141,22,0 3,130,78,23,79,28.4,0.323,34,1 8,120,86,0,0,28.4,0.259,22,1 2,174,88,37,120,44.5,0.646,24,1 2,106,56,27,165,29.0,0.426,22,0 2,105,75,0,0,23.3,0.56,53,0 4,95,60,32,0,35.4,0.284,28,0 0,126,86,27,120,27.4,0.515,21,0 8,65,72,23,0,32.0,0.6,42,0 2,99,60,17,160,36.6,0.453,21,0 1,102,74,0,0,39.5,0.293,42,1 11,120,80,37,150,42.3,0.785,48,1 3,102,44,20,94,30.8,0.4,26,0 1,109,58,18,116,28.5,0.219,22,0 9,140,94,0,0,32.7,0.734,45,1 13,153,88,37,140,40.6,1.174,39,0 12,100,84,33,105,30.0,0.488,46,0 1,147,94,41,0,49.3,0.358,27,1 1,81,74,41,57,46.3,1.096,32,0 3,187,70,22,200,36.4,0.408,36,1 6,162,62,0,0,24.3,0.178,50,1 4,136,70,0,0,31.2,1.182,22,1 1,121,78,39,74,39.0,0.261,28,0 3,108,62,24,0,26.0,0.223,25,0 0,181,88,44,510,43.3,0.222,26,1 8,154,78,32,0,32.4,0.443,45,1 1,128,88,39,110,36.5,1.057,37,1 7,137,90,41,0,32.0,0.391,39,0 0,123,72,0,0,36.3,0.258,52,1 1,106,76,0,0,37.5,0.197,26,0 6,190,92,0,0,35.5,0.278,66,1 2,88,58,26,16,28.4,0.766,22,0 9,170,74,31,0,44.0,0.403,43,1 9,89,62,0,0,22.5,0.142,33,0 10,101,76,48,180,32.9,0.171,63,0 2,122,70,27,0,36.8,0.34,27,0 5,121,72,23,112,26.2,0.245,30,0 1,126,60,0,0,30.1,0.349,47,1 1,93,70,31,0,30.4,0.315,23,0
# encoding: utf-8 import pandas import matplotlib.pyplot as plt import seaborn as sns diabetes = pandas.read_csv('diabetes.csv') # solution one: data = diabetes['Outcome'].value_counts() sns.barplot(x=data.index, y=data.values) # solution two: sns.countplot(x='Outcome', data=diabetes)
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 7.1814,1.8064383616085946 7.1856,1.806293933093177 7.1896,1.806138911817485 7.1947,1.8059806108944794 7.1993,1.80580908721362 7.2053,1.8056328160830435 7.2107,1.8054479682161961 7.2173,1.8052525284387695 7.2237,1.8050465193576883 7.2309,1.8048296381956124 7.2392,1.8045988960975694 7.2474,1.8043595071215677 7.2564,1.8041082364687364 7.2666,1.8038382553758734 7.2781,1.8035594899544896 7.2901,1.8032661790541915 7.3036,1.8029587616930072 7.3192,1.8026419692687126 7.3365,1.802313214895432 7.3559,1.8019832998820766 7.3782000000000005,1.8016547858228427 7.4039,1.8013374225262055 7.434,1.8010610354808818 7.469,1.8008525385497174 7.4728,1.8008375055547212 7.4766,1.8008241798024116 7.4805,1.8008134283846535 7.485,1.8008014895962607 7.4889,1.8007906472467445 7.4931,1.8007836940234814 7.4974,1.8007772872833665 7.502,1.8007773636348627 7.5066,1.800777214492662 7.5111,1.800772599047411 7.5161,1.8007769259270974 7.5204,1.8007812794388944 7.5251,1.8007885071607819 7.5309,1.8007961396426069 7.5354,1.800809332665 7.5404,1.800828880377808 7.5456,1.800846702941447 7.5511,1.8008692674381197 7.5565,1.8008933437321841 7.5623000000000005,1.8009237058574081 7.5682,1.800958157045992 7.5744,1.8009948449267943 7.58,1.8010363488385235 7.5863000000000005,1.8010832972433193 7.5923,1.8011350711403118 7.5989,1.8011921478121384 7.6052,1.801254330745158 7.6122000000000005,1.8013200393645774 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 7.6806,1.802276358227313 7.689,1.802430876035706 7.698,1.8025962404854161 7.7073,1.802773424707928 7.7165,1.8029615206656595 7.7261,1.8031700727690376 7.7358,1.8033901306600841 7.746,1.8036245439350242 7.7569,1.8038801831608258 7.7674,1.8041511397011663 7.7785,1.8044409511998807 7.7895,1.804751063391503 7.8016000000000005,1.8050889378483397 7.8137,1.8054500843221957 7.8260000000000005,1.8058344798814248 7.839,1.8062497893213036 7.8523000000000005,1.8066916535420319 7.8660000000000005,1.807171119554773 7.8801000000000005,1.8076802794778468 7.8950000000000005,1.8082299359839067 7.91,1.808815338202358 7.9256,1.8094485033102967 7.9419,1.8101332152002367 7.9588,1.8108583555324504 7.9762,1.8116377906606793 7.9942,1.812480154325305 8.013300000000001,1.8133886252401064 8.0323,1.8143645477074526 8.0526,1.8154146330511043 8.073500000000001,1.8165442307174358 8.0957,1.8177698166402039 8.1182,1.819085725400004 8.1417,1.8205097465554974 8.1664,1.8220480025653125 8.192,1.8237072998986206 8.2188,1.8255111058560254 8.2468,1.827455350126501 8.2759,1.8295663029422389 8.3064,1.831861429607547 8.3383,1.8343455967834263 8.3716,1.837047237198313 8.4065,1.8399908757968044 8.4431,1.8431886841980547 8.4816,1.846678932529894 8.5218,1.8504805320192779 8.5642,1.8546328838729316 8.6085,1.859161369210759 8.655,1.8641143534208833 8.7039,1.8695256342139759 8.754900000000001,1.875446560741857 8.8087,1.881922204419208 8.8655,1.8890064461692662 8.9244,1.8967439742289458 8.9863,1.905188505881128 9.0511,1.9143851329920027 9.1186,1.924363320273434 9.188600000000001,1.9351143409915226 9.2613,1.9466051165466298 9.3348,1.9586922001685116 9.4098,1.970987602510523 9.4882,1.9833342806468837 9.5729,1.9958205973858019 9.6658,2.008306966070422 9.6757,2.0095470086686014 9.6853,2.0107829319774146 9.6956,2.0120161755240176 9.706,2.013246638357084 9.7155,2.0144724860531107 9.726,2.01569779646471 9.7361,2.01691593463459 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 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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()
pyplot: how to explicitly number an axis in a human-readable way
Pyplot has a strange feature where, for large numbers, the axis are scaled by non-base ten numbers, making it nearly impossible to read numeric values: x = [49856280.352, 49860580.25, 49861011.77, 49861103.034, 49861191.295, 49862295.297, 49862311.928, 49862755.161, 49863005.142, 49863331.328, 49863795.672, 49863892.911, 49864078.203, 49864455.172, 49864628.486, 49865539.345, 49865562.414, 49865652.025, 49865860.79, 49866049.199, 49866559.841, 49866709.259, 49866976.163, 49867118.158, 49867184.515, 49867228.03, 49867703.98, 49868191.475, 49868264.993, 49868402.682, 49868547.472, 49868849.941, 49869167.486, 49869233.011, 49869388.16, 49869462.118, 49869947.616, 49869976.177, 49870146.971, 49870441.068, 49870858.267, 49870898.339, 49870966.598, 49871065.408, 49871113.361, 49871268.792, 49871332.292, 49872008.637, 49872014.321, 49872128.757, 49872276.278, 49872296.18, 49872322.098, 49872366.707, 49872370.336, 49872537.099, 49872909.555, 49872917.363, 49873131.438, 49873230.402, 49873252.129, 49873289.302, 49873382.584, 49873429.968, 49873440.124, 49873444.505, 49873507.617, 49873835.836, 49873905.902, 49873965.72, 49874080.127, 49874101.966, 49874166.944, 49874359.819, 49874388.385, 49874412.152, 49874421.629, 49874584.264, 49874755.328, 49874798.936, 49874833.007, 49875145.279, 49875310.799, 49875391.973, 49875484.389, 49875615.09, 49875616.889, 49875773.568, 49875776.696, 49875892.137, 49875953.749, 49875954.395, 49876161.776, 49876220.899, 49876321.362, 49876380.343, 49876496.107, 49876595.953, 49876644.428, 49876655.041, 49876714.369, 49876770.925, 49876788.46, 49876932.063, 49876952.641, 49877075.874, 49877105.142, 49877220.934, 49877288.062, 49877294.256, 49877308.79, 49877551.764, 49877586.774, 49877620.658, 49877666.194, 49877842.635, 49878091.505, 49878171.278, 49878181.791, 49878229.777, 49878244.476, 49878483.22, 49878541.483, 49878602.181, 49878612.309, 49878615.488, 49878677.558, 49878683.807, 49878703.616, 49878785.269, 49878793.774, 49878922.532, 49878933.228, 49878981.748, 49879041.296, 49879060.17, 49879263.424, 49879355.213, 49879449.193, 49879455.009, 49879471.561, 49879508.752, 49879538.815, 49879597.852, 49879683.744, 49879727.257, 49879751.962, 49879895.858, 49879960.524, 49880178.136, 49880196.753, 49880217.788, 49880336.479, 49880370.356, 49880396.479, 49880422.808, 49880539.652, 49880559.579, 49880624.786, 49880704.456, 49880739.891, 49880836.13, 49880886.385, 49880938.998, 49881169.02, 49881288.366, 49881305.161, 49881426.038, 49881427.956, 49881463.834, 49881617.346, 49881730.679, 49881881.14, 49881894.675, 49881979.949, 49882096.47, 49882112.031, 49882131.716, 49882159.639, 49882190.252, 49882483.949, 49882538.07, 49882583.816, 49882597.938, 49882602.861, 49882611.359, 49882648.24, 49882684.267, 49882706.434, 49882835.084, 49883043.946, 49883084.906, 49883109.752, 49883228.104, 49883288.07, 49883382.503, 49883401.475, 49883491.927, 49883574.922, 49883654.813, 49883702.522, 49883801.5, 49883814.683, 49883826.244, 49883846.713, 49883855.077, 49883978.159, 49884046.234, 49884064.489, 49884112.738, 49884138.818, 49884220.732, 49884251.195, 49884255.97, 49884361.999, 49884397.955, 49884416.274, 49884498.095, 49884516.764, 49884548.794, 49884580.933, 49884597.752, 49884624.897, 49884634.323, 49884670.05, 49884676.813, 49884733.419, 49884751.203, 49884834.288, 49884888.879, 49884902.225, 49885004.171, 49885153.972, 49885157.866, 49885173.615, 49885174.386, 49885219.196, 49885273.781, 49885347.517, 49885364.666, 49885380.826, 49885427.356, 49885509.155, 49885541.137, 49885578.287, 49885595.473, 49885612.014, 49885710.601, 49885740.394, 49885741.348, 49885841.454, 49885952.568, 49885988.633, 49886053.94, 49886058.886, 49886076.628, 49886095.714, 49886147.686, 49886164.4, 49886179.103, 49886201.971, 49886279.139, 49886312.282, 49886312.8, 49886324.598, 49886408.542, 49886481.161, 49886548.747, 49886641.616, 49886642.093, 49886668.221, 49886675.982, 49886725.046, 49886741.706, 49886821.745, 49886833.46, 49886840.54, 49886850.264, 49886873.972, 49887005.587, 49887042.942, 49887073.685, 49887076.296, 49887091.248, 49887105.67, 49887148.602, 49887159.267, 49887271.173, 49887285.866, 49887303.534, 49887369.19, 49887382.49, 49887454.943, 49887479.589, 49887498.203, 49887616.744, 49887721.935, 49887747.864, 49887786.036, 49887803.228, 49887858.683, 49887943.768, 49888058.328, 49888058.963, 49888148.314, 49888200.419, 49888346.257, 49888362.013, 49888366.362, 49888406.004, 49888450.807, 49888497.044, 49888614.062, 49888622.199, 49888628.315, 49888717.575, 49888731.575, 49888736.902, 49888765.916, 49888803.98, 49888875.799, 49888892.348, 49888934.101, 49888967.728, 49888974.393, 49888981.908, 49889075.143, 49889221.983, 49889255.367, 49889277.661, 49889298.851, 49889319.147, 49889320.207, 49889407.915, 49889413.481, 49889429.941, 49889469.243, 49889487.959, 49889532.295, 49889539.477, 49889552.03, 49889572.229, 49889585.375, 49889602.118, 49889668.329, 49889683.014, 49889697.206, 49889772.868, 49889806.44, 49889881.148, 49889916.157, 49889961.726, 49889989.911, 49889997.299, 49890021.069, 49890092.985, 49890123.557, 49890137.558, 49890249.033, 49890337.341, 49890363.69, 49890412.835, 49890438.527, 49890455.844, 49890457.272, 49890540.923, 49890552.792, 49890571.653, 49890656.799, 49890657.446, 49890771.402, 49890893.014, 49890940.859, 49891117.607, 49891188.608, 49891197.473, 49891204.319, 49891260.746, 49891286.509, 49891329.619, 49891369.244, 49891373.448, 49891400.609, 49891594.308, 49891664.373, 49891769.784, 49891812.789, 49891878.95, 49891889.874, 49891924.49, 49891962.647, 49891972.677, 49892086.585, 49892096.15, 49892131.411, 49892145.241, 49892147.07, 49892186.586, 49892221.814, 49892257.429, 49892295.906, 49892366.592, 49892414.483, 49892486.488, 49892505.361, 49892571.789, 49892614.344, 49892775.098, 49892807.812, 49892832.242, 49892837.597, 49892858.141, 49892894.853, 49892896.347, 49893017.939, 49893029.177, 49893118.449, 49893168.059, 49893232.781, 49893235.78, 49893287.331, 49893326.778, 49893406.653, 49893471.93, 49893495.472, 49893510.31, 49893593.136, 49893668.36, 49893690.138, 49893717.478, 49893841.341, 49893849.407, 49893918.451, 49893929.304, 49893965.073, 49893995.907, 49894105.083, 49894113.528, 49894207.766, 49894220.283, 49894229.309, 49894287.256, 49894296.334, 49894321.153, 49894394.072, 49894410.075, 49894426.245, 49894429.549, 49894486.508, 49894569.593, 49894596.175, 49894646.893, 49894684.32, 49894706.127, 49894730.259, 49894814.007, 49894846.182, 49894921.957, 49895075.836, 49895134.196, 49895411.552, 49895467.919, 49895581.219, 49895643.126, 49895681.785, 49895715.833, 49895717.103, 49895732.304, 49895747.613, 49895775.574, 49895797.26, 49895801.342, 49895905.897, 49895914.703, 49895933.794, 49895949.521, 49895999.669, 49896017.596, 49896189.338, 49896213.562, 49896231.62, 49896310.695, 49896462.188, 49896483.7, 49896508.985, 49896602.682, 49896631.951, 49896719.268, 49896806.399, 49896806.913, 49896872.332, 49897018.727, 49897124.196, 49897134.793, 49897213.913, 49897270.838, 49897284.028, 49897315.792, 49897330.168] plt.hist(x) plt.show() In my example, the axis should be scaled by 1e7 or 1e8, or displayed as rgular numbers--not 4.986e7. Why would anyone want this setting? How can I make the x axis numbers human-readable?
How to prevent numbers being changed to exponential form in Python matplotlib figure ax = plt.gca() ax.get_xaxis().get_major_formatter().set_useOffset(False) ax.get_xaxis().get_major_formatter().set_scientific(False) plt.draw()