matplotlib 如何在子图中绘制成对的列散点图并按列对标记着色

2cmtqfgy  于 2023-10-24  发布在  其他
关注(0)|答案(1)|浏览(121)

我想生成简单的散点图点与两种颜色的为一组因素在约100个样本从两个不同的平台生成，看看每个因素的相关性。
使用pandas查看python3，matplotlib具有x，y轴比例范围，每个平台数据点的不同颜色，大小1.所有九个数据图在单个图像文件中（多窗口）使用循环和2.每个图在一个单独的文件中。提前感谢任何帮助。我尝试了以下基本步骤来生成每个数据的图。

import pandas as pd
#import csv
import numpy as np
import matplotlib.pyplot as plt
df=pd.read_csv("test_data_np_arg.csv")
df.head()
    Samples      yield_NP   mean_NP    pct_NP  mad_NP  pct-act_NP  pct-pp_NP  mean_NP.1  insert_NP       rate_NP     yield_Arg  mean_Arg  pct_Arg  mad_Arg  pct-act_Arg  pct-pp_Arg  mean_Arg.1  insert_Arg  rate_Arg
0  sample_1  1.020000e+11  30.85476  95.83108    2.40    99.72261   97.95775      440.6       99.7  5.970000e-09  1.019400e+11  30.30078  95.7618        4     99.72261    97.95775       440.6        99.7         0
1  sample_2  1.350000e+11  39.54372  96.13075    2.69    99.73480   97.92363      446.7      103.7  1.120000e-06  1.348150e+11  38.89375  96.2904        5     99.73480    97.92363       446.7       103.7         0
2  sample_3  1.040000e+11  31.50230  95.87788    2.53    99.79733   98.28176      450.1      101.8  3.980000e-06  1.043480e+11  30.95051  95.8694        5     99.79733    98.28176       450.1       101.8         0
3  sample_4  1.080000e+11  32.88599  95.94028    2.58    99.80831   98.33480      452.7      102.2  1.351000e-06  1.080160e+11  32.25406  95.9667        5     99.80831    98.33480       452.7       102.2         0
4  sample_5  1.070000e+11  30.95200  95.87643    2.85    99.73196   97.89158      436.1       99.2  1.280000e-06  1.069150e+11  30.45470  95.8471        5     99.73196    97.89158       436.1        99.2         0

###x = range(100)
###y = range(100)
### X-axis limits
###xmin = 1e-4;
###xmax = 3e-3;
###plt.scatter(df.iloc[:,2])
###df.plot(kind = 'scatter', x = 'yield_NP', y = 'yeild_Arg')
###plt.scatter(yield_NP,yield_Arg) #line graph

x = df['yield_NP']
y = df['yield_Arg']
plt.scatter(x, y)
plt.xlabel()
plt.ylabel()
plt.title()
plt.scatter(x,y,color='blue')
plt.rcParams.update({'figure.figsize':(10,8), 'figure.dpi':100})
plt.show()

范例：
我的数据在**.csv格式的文件附件。100个样本是在一行和每个因素（值）从两个不同的平台命名为NP和Arg**在列。

**表头：**column 1样本
**平台NP：**第2至10列yield_NP mean_NP、pct_NP、mad_NP、pct-act_NP、pct-pp_NP、mean_NP、insert_NP、rate_NP
**平台Arg：**第11至19列yield_Arg、mean_Arg、pct_Arg、mad_Arg、pct-act_Arg、pct-pp_Arg、mean_Arg、insert_Arg、rate_Arg

1.散点图“产量”应为列产量_NP vs列产量_Arg，OR（列2 vs 11）
1.散点图“平均值”应为列mean_NP vs列mean_Arg，（第3列vs第12列）
1.... ... ....
1.……

输入数据test_data_np_arg.csv

Samples yield_NP    mean_NP pct_NP  mad_NP  pct-act_NP  pct-pp_NP   mean_NP insert_NP   rate_NP yield_Arg   mean_Arg    pct_Arg mad_Arg pct-act_Arg pct-pp_Arg  mean_Arg    insert_Arg  rate_Arg
sample_1    1.02E+11    30.85476    95.83108    2.4 99.72261    97.95775    440.6   99.7    5.97E-09    1.0194E+11  30.30078    95.7618 4   99.72261    97.95775    440.6   99.7    0
sample_2    1.35E+11    39.54372    96.13075    2.69    99.7348 97.92363    446.7   103.7   1.12E-06    1.34815E+11 38.89375    96.2904 5   99.7348 97.92363    446.7   103.7   0
sample_3    1.04E+11    31.5023 95.87788    2.53    99.79733    98.28176    450.1   101.8   3.98E-06    1.04348E+11 30.95051    95.8694 5   99.79733    98.28176    450.1   101.8   0
sample_4    1.08E+11    32.88599    95.94028    2.58    99.80831    98.3348 452.7   102.2   0.000001351 1.08016E+11 32.25406    95.9667 5   99.80831    98.3348 452.7   102.2   0
sample_5    1.07E+11    30.952  95.87643    2.85    99.73196    97.89158    436.1   99.2    1.28E-06    1.06915E+11 30.4547 95.8471 5   99.73196    97.89158    436.1   99.2    0
sample_6    1.02E+11    30.32588    95.82636    2.32    99.74506    98.09959    443.9   99.4    3.82E-09    1.01891E+11 29.77961    95.7173 4   99.74506    98.09959    443.9   99.4    0
sample_7    1.03E+11    30.8906 95.87171    2.41    99.72588    97.97383    442.1   99.5    7.48E-07    1.02859E+11 30.33201    95.8008 4   99.72588    97.97383    442.1   99.5    0
sample_8    1.05E+11    32.29805    95.91017    2.54    99.78956    98.12378    447.3   100.8   1.63E-06    1.04712E+11 31.69718    95.9272 5   99.78956    98.12378    447.3   100.8   0
sample_9    98732711940 29.64056    95.79516    2.26    99.73939    97.91273    437.9   97.7    2.95E-09    98732711940 29.11294    95.6311 4   99.73939    97.91273    437.9   97.7    0
sample_10   1.02E+11    30.22417    95.78791    2.41    99.69657    97.78881    437.3   97.8    1.92E-06    1.01992E+11 29.69243    95.6681 4   99.69657    97.78881    437.3   97.8    0
sample_11   1.77E+11    52.53136    96.31651    3.73    99.74559    98.03557    440.9   100.9   7.83E-07    1.7654E+11  51.61069    96.5147 6   99.74559    98.03557    440.9   100.9   0
sample_12   1.20E+11    35.07535    95.99761    2.87    99.76128    98.045  432.6   100.8   1.31E-06    1.19786E+11 34.61427    96.1337 5   99.76128    98.045  432.6   100.8   0
sample_13   98874217002 29.67494    95.79408    2.28    99.74523    97.95379    445.2   99.5    6.72E-09    98874217002 29.14371    95.6212 4   99.74523    97.95379    445.2   99.5    0
sample_14   1.09E+11    32.06474    95.89239    2.47    99.71998    97.89142    443.9   99.9    1.05E-06    1.0895E+11  31.49802    95.8792 5   99.71998    97.89142    443.9   99.9    0
sample_15   1.00E+11    29.80763    95.78573    2.42    99.75593    97.87908    435.1   99.1    2.39E-09    1.00279E+11 29.29912    95.6557 4   99.75593    97.87908    435.1   99.1    0
sample_16   1.09E+11    32.91384    95.90146    2.51    99.70689    97.85116    445.6   100.7   1.88E-06    1.09404E+11 32.29913    95.9107 5   99.70689    97.85116    445.6   100.7   0
sample_17   97816752073 28.99877    95.80532    2.52    99.77984    98.11997    426.5   99.9    3.42E-09    97816752073 28.57639    95.6557 4   99.77984    98.11997    426.5   99.9    0
sample_18   99875608784 29.8512 95.84741    2.3 99.77063    98.227  443.8   99  3.90E-10    99875608784 29.30749    95.7026 4   99.77063    98.227  443.8   99  0
sample_19   1.05E+11    31.18936    95.8826 2.51    99.71659    97.8446 443.5   101 4.05E-09    1.05462E+11 30.71611    95.8931 4   99.71659    97.8446 443.5   101 0
sample_20   1.14E+11    34.54803    95.99543    2.5 99.73037    97.95208    446.9   100.4   3.05E-09    1.14368E+11 33.94012    96.0641 5   99.73037    97.95208    446.9   100.4   0
sample_21   91070755092 28.08189    95.69757    2.39    99.49053    97.29799    424.5   97.7    7.58E-10    91070755092 27.16736    95.1967 4   99.49053    97.29799    424.5   97.7    0
sample_22   1.03E+11    30.18906    95.80496    2.32    99.7367 98.0612 444 99.9    1.45E-06    1.02681E+11 29.65553    95.6822 4   99.7367 98.0612 444 99.9    0
sample_23   94779004959 28.32233    95.74401    2.26    99.75454    98.06741    440.1   101.9   4.55E-09    94779004959 27.79295    95.4909 4   99.75454    98.06741    440.1   101.9   0
sample_24   1.10E+11    32.6702 95.93303    2.45    99.79297    98.24325    458 102.7   2.48E-09    1.09683E+11 32.10852    95.947  5   99.79297    98.24325    458 102.7   0
sample_25   1.10E+11    32.51392    95.91561    2.58    99.69296    97.88712    438.9   100.3   8.80E-07    1.09664E+11 31.98217    95.9614 5   99.69296    97.88712    438.9   100.3   0
sample_26   1.11E+11    33.18012    95.9421 2.51    99.68512    97.88962    449.1   103.5   3.25E-09    1.10539E+11 32.59671    96.0175 5   99.68512    97.88962    449.1   103.5   0
sample_27   1.09E+11    31.75891    95.89965    2.54    99.74522    97.79328    439.7   99.9    2.67E-09    1.0945E+11  31.30704    95.9617 5   99.74522    97.79328    439.7   99.9    0
sample_28   1.07E+11    32.02777    95.92396    2.57    99.72103    97.78891    454 104.8   9.02E-09    1.07001E+11 31.52126    95.959  5   99.72103    97.78891    454 104.8   0
sample_29   1.12E+11    33.64239    95.93847    2.7 99.74315    97.92744    447.5   103.1   4.11E-09    1.11869E+11 33.10581    96.0227 5   99.74315    97.92744    447.5   103.1   0
sample_30   1.12E+11    32.99544    95.93774    2.53    99.73674    97.98786    442.8   100.9   1.20E-06    1.12014E+11 32.51   96.0109 5   99.73674    97.98786    442.8   100.9   0
sample_31   1.05E+11    32.27342    95.90836    2.44    99.72944    98.04976    441.8   100.3   1.47E-06    1.04834E+11 31.37597    95.8615 4   99.72944    98.04976    441.8   100.3   0
sample_32   1.08E+11    31.67663    95.87933    2.51    99.74561    97.88388    435.8   99.6    8.24E-07    1.08499E+11 31.18913    95.9151 4   99.74561    97.88388    435.8   99.6    0
sample_33   1.12E+11    32.50864    95.92867    2.61    99.68827    97.78834    449.6   102.8   9.57E-09    1.11538E+11 31.97458    95.9779 5   99.68827    97.78834    449.6   102.8   0
sample_34   1.13E+11    34.29474    96.00631    2.78    99.83226    98.27716    446.4   101.6   2.75E-09    1.12849E+11 33.82919    96.1052 5   99.83226    98.27716    446.4   101.6   0
sample_35   99400525623 30.81942    95.8387 2.48    99.78345    98.20671    446 101.2   1.08E-06    99400525623 29.98714    95.7308 4   99.78345    98.20671    446 101.2   0
sample_36   1.15E+11    34.29637    95.97148    2.82    99.77035    98.02257    441.1   101.1   1.04E-06    1.14885E+11 33.74547    96.0615 5   99.77035    98.02257    441.1   101.1   0
sample_37   1.07E+11    32.37888    95.9508 2.61    99.7133 97.98365    444.5   100.5   1.34E-09    1.07473E+11 31.76157    95.9864 5   99.7133 97.98365    444.5   100.5   0
sample_38   1.03E+11    31.53656    95.93194    2.64    99.77947    98.19387    439.4   101 1.50E-09    1.02917E+11 31.02146    95.951  5   99.77947    98.19387    439.4   101 0
sample_39   1.06E+11    30.28286    95.87534    2.45    99.75018    98.05562    439.9   100.9   1.37E-06    1.05871E+11 29.79179    95.8127 4   99.75018    98.05562    439.9   100.9   0
sample_40   1.05E+11    31.52802    95.91706    2.47    99.79558    98.06771    427.3   98.4    1.18E-08    1.05336E+11 30.91472    95.9214 4   99.79558    98.06771    427.3   98.4    0
sample_40   1.05E+11    31.52802    95.91706    2.47    99.79558    98.06771    427.3   98.4    1.18E-08    1.05336E+11 30.91472    95.9214 4   99.79558    98.06771    427.3   98.4    0
sample_41   1.09E+11    31.29889    95.91489    2.35    99.73194    97.99713    427.2   97.5    9.77E-09    1.0949E+11  30.81643    95.9266 4   99.73194    97.99713    427.2   97.5    0
sample_42   1.03E+11    30.35667    95.89239    2.41    99.73659    98.03721    438.7   101.2   8.28E-07    1.03284E+11 29.88501    95.8842 4   99.73659    98.03721    438.7   101.2   0
sample_43   1.10E+11    32.5761 95.94972    2.74    99.71097    97.89525    454.2   103.2   2.21E-09    1.10059E+11 32.00364    95.9879 5   99.71097    97.89525    454.2   103.2   0
sample_44   1.13E+11    33.22374    96.00668    2.89    99.77643    98.12726    437.6   100 1.59E-09    1.132E+11   32.77427    96.1141 5   99.77643    98.12726    437.6   100 0
sample_45   1.20E+11    34.19891    96.00994    3.05    99.76761    98.08657    444.4   101.3   1.33E-09    1.19803E+11 33.77175    96.1266 5   99.76761    98.08657    444.4   101.3   0
sample_46   1.19E+11    35.68893    96.07089    2.56    99.7517 98.01643    439.8   100.2   2.04E-09    1.19052E+11 35.17978    96.227  5   99.7517 98.01643    439.8   100.2   0
sample_47   1.56E+11    47.59501    96.27841    3.11    99.77304    98.13004    441.7   100.9   5.48E-07    1.55554E+11 46.80516    96.4836 6   99.77304    98.13004    441.7   100.9   0
sample_48   1.28E+11    37.59036    96.09955    2.62    99.7544 98.08099    436.8   100.2   1.94E-09    1.28106E+11 37.08409    96.2848 5   99.7544 98.08099    436.8   100.2   0
sample_49   99660967688 29.62502    95.82274    2.36    99.76129    98.06697    424.5   99.1    1.82E-09    99660967688 29.11601    95.6862 4   99.76129    98.06697    424.5   99.1    0
sample_50   1.10E+11    32.38506    95.96205    2.4 99.73501    98.05385    442.5   101.1   3.24E-09    1.10417E+11 31.8706 96.0418 4   99.73501    98.05385    442.5   101.1   0
sample_51   1.01E+11    30.60525    95.90146    2.37    99.71151    97.85232    440.7   101 4.59E-06    1.00502E+11 30.02358    95.8872 4   99.71151    97.85232    440.7   101 0
sample_52   1.21E+11    36.92763    96.11334    2.85    99.693  97.84396    428.1   99.3    8.65E-07    1.20533E+11 35.97378    96.2246 5   99.693  97.84396    428.1   99.3    0
sample_53   1.12E+11    31.55724    95.92106    2.5 99.71733    97.94133    427.6   96.6    7.64E-10    1.11613E+11 31.0624 95.9483 4   99.71733    97.94133    427.6   96.6    0
sample_54   1.10E+11    34.16102    96.00414    2.69    99.72714    98.20101    434 99.7    5.51E-06    1.10452E+11 33.24734    96.0308 5   99.72714    98.20101    434 99.7    0
sample_55   1.08E+11    30.76239    95.87498    2.76    99.80626    98.26043    434.2   99.4    1.75E-09    1.07891E+11 30.31996    95.8528 5   99.80626    98.26043    434.2   99.4    0
sample_56   1.02E+11    31.01334    95.8884 2.44    99.71137    98.02674    438.1   100.1   4.23E-10    1.01508E+11 30.26782    95.8255 4   99.71137    98.02674    438.1   100.1   0
sample_57   1.10E+11    32.27204    95.93738    2.77    99.74261    98.04745    425.5   96.9    1.02E-06    1.09992E+11 31.78231    95.998  5   99.74261    98.04745    425.5   96.9    0
sample_58   1.01E+11    29.63566    95.83362    2.49    99.73006    98.03498    429.5   98.5    1.75E-09    1.01204E+11 29.13839    95.7239 4   99.73006    98.03498    429.5   98.5    0
sample_59   1.39E+11    41.53544    96.18445    2.95    99.8092 98.30391    436.1   101 7.78E-07    1.38829E+11 40.87246    96.3519 5   99.8092 98.30391    436.1   101 0
sample_60   1.49E+11    43.58239    96.22036    3.1 99.7331 98.18926    441.1   101.4   1.45E-06    1.48519E+11 42.89485    96.407  5   99.7331 98.18926    441.1   101.4   0
sample_61   94358139331 29.02033    95.80859    2.23    99.72827    98.04585    442.6   102 3.49E-09    94358139331 28.40806    95.5737 4   99.72827    98.04585    442.6   102 0
sample_62   1.02E+11    30.73124    95.88949    2.55    99.75763    98.1358 440.8   101 1.91E-08    1.01922E+11 30.14433    95.8251 4   99.75763    98.1358 440.8   101 0
sample_63   1.01E+11    30.26892    95.83543    2.38    99.7678 98.10769    440.7   101.3   1.58E-06    1.01125E+11 29.77872    95.7721 4   99.7678 98.10769    440.7   101.3   0
sample_64   1.08E+11    31.847  95.94573    2.5 99.72622    97.99772    433.5   97.6    1.79E-06    1.0784E+11  31.35237    95.991  4   99.72622    97.99772    433.5   97.6    0
sample_65   96776159556 30.06908    95.84777    2.45    99.52316    97.58877    422.8   98  1.25E-09    96776159556 29.2151 95.6652 4   99.52316    97.58877    422.8   98  0
sample_66   1.26E+11    37.48715    96.11951    2.79    99.74966    98.10085    430.1   99.5    3.11E-09    1.25599E+11 36.84727    96.2503 5   99.74966    98.10085    430.1   99.5    0
sample_67   1.31E+11    39.26691    96.14926    3.08    99.74451    97.95533    432 100.3   8.28E-07    1.31475E+11 38.58257    96.2912 5   99.74451    97.95533    432 100.3   0
sample_68   99308534044 29.89997    95.8387 2.41    99.79687    98.15405    434.1   100.5   4.05E-09    99308534044 29.4153 95.755  4   99.79687    98.15405    434.1   100.5   0
sample_69   1.04E+11    31.11425    95.90001    2.53    99.75409    98.01168    430.9   99  6.05E-07    1.04154E+11 30.60253    95.8799 4   99.75409    98.01168    430.9   99  0
sample_70   1.20E+11    36.34303    96.09302    2.69    99.75035    97.97297    437.9   101.2   4.16E-09    1.19703E+11 35.73808    96.2123 5   99.75035    97.97297    437.9   101.2   0
sample_71   1.72E+11    51.99843    96.35387    3.62    99.745  98.13036    435.3   100.3   4.90E-07    1.72425E+11 51.06496    96.5405 6   99.745  98.13036    435.3   100.3   0
sample_72   1.00E+11    30.48683    95.86119    2.45    99.70088    97.77195    433.9   98.2    2.17E-09    1.0005E+11  29.95496    95.795  4   99.70088    97.77195    433.9   98.2    0
sample_73   1.02E+11    30.7607 95.9011 2.51    99.70653    97.88851    424.1   98.2    4.36E-09    1.02104E+11 30.15563    95.8511 4   99.70653    97.88851    424.1   98.2    0
sample_74   1.12E+11    32.35504    95.93339    2.43    99.72186    97.94937    431.1   97.9    6.83E-10    1.12015E+11 31.8753 96.0092 4   99.72186    97.94937    431.1   97.9    0
sample_75   1.55E+11    46.32393    96.26027    3.47    99.75905    98.11215    422 98  1.10E-06    1.55011E+11 45.5614 96.4486 6   99.75905    98.11215    422 98  0
sample_76   97089503358 29.48785    95.82201    2.86    99.74503    97.916  434.2   99.3    8.99E-09    97089503358 28.95893    95.6086 5   99.74503    97.916  434.2   99.3    0
sample_77   1.09E+11    32.97904    95.97475    2.63    99.71645    97.81641    437.2   100.9   1.95E-06    1.09002E+11 32.38335    96.0309 5   99.71645    97.81641    437.2   100.9   0
sample_78   1.03E+11    30.14343    95.87208    2.51    99.72748    97.94631    431.7   98.7    1.47E-09    1.02866E+11 29.69167    95.8327 4   99.72748    97.94631    431.7   98.7    0
sample_79   1.02E+11    30.40873    95.86446    2.43    99.74403    98.04649    437.3   99.9    1.50E-06    1.02082E+11 29.92895    95.8244 4   99.74403    98.04649    437.3   99.9    0
sample_80   1.25E+11    37.91671    96.09157    2.74    99.74695    98.07409    440.2   101 1.56E-06    1.25097E+11 37.29407    96.2274 5   99.74695    98.07409    440.2   101 0
sample_81   1.01E+11    31.53161    95.90328    3.29    99.71049    97.88596    433.1   100.6   9.41E-07    1.01411E+11 30.83368    95.8225 5   99.71049    97.88596    433.1   100.6   0
sample_82   99173169979 29.89573    95.85684    2.47    99.74151    98.05292    437.1   101 1.35E-09    99173169979 29.40804    95.7447 4   99.74151    98.05292    437.1   101 0
sample_83   1.04E+11    30.49102    95.88768    2.7 99.73246    97.92362    425.7   96.3    8.54E-09    1.04393E+11 30.01485    95.8424 5   99.73246    97.92362    425.7   96.3    0
sample_84   97704382884 29.88968    95.84995    2.4 99.78704    98.23961    441.2   102 4.37E-09    97704382884 29.30928    95.7342 4   99.78704    98.23961    441.2   102 0
sample_85   1.15E+11    33.95192    95.99579    2.86    99.7469 98.20643    435.9   99.7    2.10E-06    1.14548E+11 33.36085    96.0645 5   99.7469 98.20643    435.9   99.7    0
sample_86   1.06E+11    30.3987 95.86954    2.49    99.73811    98.12377    437.2   99.8    3.98E-09    1.06286E+11 29.92345    95.8407 4   99.73811    98.12377    437.2   99.8    0
sample_87   1.11E+11    33.99411    95.99688    2.66    99.76458    98.1613 445 101.6   1.01E-08    1.10903E+11 33.40791    96.0659 5   99.76458    98.1613 445 101.6   0
sample_88   1.07E+11    32.28092    95.95407    2.5 99.74595    98.01535    433.9   100.6   5.82E-09    1.0669E+11  31.62522    95.9834 5   99.74595    98.01535    433.9   100.6   0
sample_89   1.12E+11    33.36572    95.98309    2.61    99.76743    98.11891    437.2   101.1   1.30E-09    1.12314E+11 32.8189 96.0525 5   99.76743    98.11891    437.2   101.1   0
sample_90   1.03E+11    30.42415    95.88804    2.43    99.52558    97.59924    418.2   95  8.03E-10    1.02866E+11 29.75168    95.8063 4   99.52558    97.59924    418.2   95  0
sample_91   1.26E+11    38.03029    96.1166 3.75    99.76455    98.16839    432.9   99.6    1.07E-06    1.25675E+11 37.41665    96.2519 5   99.76455    98.16839    432.9   99.6    0
sample_92   1.32E+11    39.33902    96.14345    2.77    99.78796    98.27147    425.6   98.8    3.83E-09    1.31768E+11 38.7008 96.3025 5   99.78796    98.27147    425.6   98.8    0
sample_93   99266572540 28.77619    95.8075 2.43    99.7336 98.01659    433.9   99.1    4.99E-09    99266572540 28.32761    95.6002 4   99.7336 98.01659    433.9   99.1    0
sample_94   1.06E+11    31.0585 95.89747    2.57    99.77842    98.20429    439.6   100 2.41E-09    1.06262E+11 30.5643 95.898  4   99.77842    98.20429    439.6   100 0
sample_95   1.39E+11    41.76222    96.20041    3.13    99.70977    98.06093    451.8   104 3.40E-09    1.38828E+11 41.01108    96.3587 5   99.70977    98.06093    451.8   104 0
sample_96   1.13E+11    34.08653    95.99108    2.54    99.79491    98.17081    435.9   99  2.31E-09    1.13469E+11 33.5711 96.0856 5   99.79491    98.17081    435.9   99  0
sample_97   1.00E+11    29.98517    95.86627    2.38    99.73274    98.04362    439.6   101.7   6.33E-09    1.00316E+11 29.38303    95.7557 4   99.73274    98.04362    439.6   101.7   0
sample_98   1.14E+11    34.66174    96.01937    2.62    99.73272    98.02972    439.7   101.8   9.26E-09    1.13837E+11 33.81398    96.0889 5   99.73272    98.02972    439.7   101.8   0
sample_99   1.22E+11    36.80748    96.08722    2.73    99.70917    98.05788    441.4   101.9   1.41E-09    1.21809E+11 36.10285    96.1936 5   99.70917    98.05788    441.4   101.9   0
sample_100  1.26E+11    37.63943    96.09338    2.8 99.77074    98.15844    451.1   104.1   1.54E-06    1.25873E+11 36.97827    96.2291 5   99.77074    98.15844    451.1   104.1   0
matplotlib

1条答案

按热度按时间
1aaf6o9v

1aaf6o9v1#

  • 根据x和y将列分成组，这样每个列表的顺序对应于xy对，然后zip将组放在一起。
  • 为了通过度量分隔点，最简单的方法是使用sns.scatterplot和hue参数。
  • 用'Samples'给点着色是没有用的，因为有太多的唯一类别。
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# create the xy groups
colx_pairs = df.columns[1:10]
coly_pairs = df.columns[10:20]

col_pairs = list(zip(colx_pairs, coly_pairs))

# create a figure with 9 subplots
fig, axes = plt.subplots(3, 3, figsize=(16, 10), tight_layout=True)

# flatten the 3 x 3 axes array for easy iteration
axes = axes.flatten()

# iterate through each ax in axes, and the corresponding xy pair
for ax, (x, y) in zip(axes, col_pairs):

    # plot the data
    sns.scatterplot(data=df, x=x, y=y, hue='Samples', ax=ax)
    
    # remove all except the last legend
    if ax != axes[8]:
        ax.get_legend().remove()

# extract the legend handles and labels
handles, labels = axes[8].get_legend_handles_labels()
# create a single figure plot
fig.legend(handles, labels, bbox_to_anchor=(1, 0.5), loc='center left', frameon=False, ncols=5)
# remove the legend from axes[8]
axes[8].get_legend().remove()

fig.savefig('figure.png')

  • 不考虑Samples着色的绘图。
# create the xy groups
colx_pairs = df.columns[1:10]
coly_pairs = df.columns[10:20]

col_pairs = list(zip(colx_pairs, coly_pairs))

# create a figure with 9 subplots
fig, axes = plt.subplots(3, 3, figsize=(16, 10), tight_layout=True)

# flatten the 3 x 3 axes array for easy iteration
axes = axes.flatten()

# iterate through each ax in axes, and the corresponding xy pair
for ax, (x, y) in zip(axes, col_pairs):

    df.plot(kind='scatter', x=x, y=y, ax=ax, marker='.')

fig.savefig('figure.png')

df

data =\
{'Samples': ['sample_1', 'sample_2', 'sample_3', 'sample_4', 'sample_5', 'sample_6', 'sample_7', 'sample_8', 'sample_9', 'sample_10', 'sample_11', 'sample_12', 'sample_13', 'sample_14', 'sample_15', 'sample_16', 'sample_17', 'sample_18', 'sample_19', 'sample_20', 'sample_21', 'sample_22', 'sample_23', 'sample_24', 'sample_25', 'sample_26', 'sample_27', 'sample_28', 'sample_29', 'sample_30', 'sample_31', 'sample_32', 'sample_33', 'sample_34', 'sample_35', 'sample_36', 'sample_37', 'sample_38', 'sample_39', 'sample_40', 'sample_40', 'sample_41', 'sample_42', 'sample_43', 'sample_44', 'sample_45', 'sample_46', 'sample_47', 'sample_48', 'sample_49', 'sample_50', 'sample_51', 'sample_52', 'sample_53', 'sample_54', 'sample_55', 'sample_56', 'sample_57', 'sample_58', 'sample_59', 'sample_60', 'sample_61', 'sample_62', 'sample_63', 'sample_64', 'sample_65', 'sample_66', 'sample_67', 'sample_68', 'sample_69', 'sample_70', 'sample_71', 'sample_72', 'sample_73', 'sample_74', 'sample_75', 'sample_76', 'sample_77', 'sample_78', 'sample_79', 'sample_80', 'sample_81', 'sample_82', 'sample_83', 'sample_84', 'sample_85', 'sample_86', 'sample_87', 'sample_88', 'sample_89', 'sample_90', 'sample_91', 'sample_92', 'sample_93', 'sample_94', 'sample_95', 'sample_96', 'sample_97', 'sample_98', 'sample_99', 'sample_100'],
 'yield_NP': [102000000000.0, 135000000000.0, 104000000000.0, 108000000000.0, 107000000000.0, 102000000000.0, 103000000000.0, 105000000000.0, 98732711940.0, 102000000000.0, 177000000000.0, 120000000000.0, 98874217002.0, 109000000000.0, 100000000000.0, 109000000000.0, 97816752073.0, 99875608784.0, 105000000000.0, 114000000000.0, 91070755092.0, 103000000000.0, 94779004959.0, 110000000000.0, 110000000000.0, 111000000000.0, 109000000000.0, 107000000000.0, 112000000000.0, 112000000000.0, 105000000000.0, 108000000000.0, 112000000000.0, 113000000000.0, 99400525623.0, 115000000000.0, 107000000000.0, 103000000000.0, 106000000000.0, 105000000000.0, 105000000000.0, 109000000000.0, 103000000000.0, 110000000000.0, 113000000000.0, 120000000000.0, 119000000000.0, 156000000000.0, 128000000000.0, 99660967688.0, 110000000000.0, 101000000000.0, 121000000000.0, 112000000000.0, 110000000000.0, 108000000000.0, 102000000000.0, 110000000000.0, 101000000000.0, 139000000000.0, 149000000000.0, 94358139331.0, 102000000000.0, 101000000000.0, 108000000000.0, 96776159556.0, 126000000000.0, 131000000000.0, 99308534044.0, 104000000000.0, 120000000000.0, 172000000000.0, 100000000000.0, 102000000000.0, 112000000000.0, 155000000000.0, 97089503358.0, 109000000000.0, 103000000000.0, 102000000000.0, 125000000000.0, 101000000000.0, 99173169979.0, 104000000000.0, 97704382884.0, 115000000000.0, 106000000000.0, 111000000000.0, 107000000000.0, 112000000000.0, 103000000000.0, 126000000000.0, 132000000000.0, 99266572540.0, 106000000000.0, 139000000000.0, 113000000000.0, 100000000000.0, 114000000000.0, 122000000000.0, 126000000000.0],
 'mean_NP': [30.85476, 39.54372, 31.5023, 32.88599, 30.952, 30.32588, 30.8906, 32.29805, 29.64056, 30.22417, 52.53136, 35.07535, 29.67494, 32.06474, 29.80763, 32.91384, 28.99877, 29.8512, 31.18936, 34.54803, 28.08189, 30.18906, 28.32233, 32.6702, 32.51392, 33.18012, 31.75891, 32.02777, 33.64239, 32.99544, 32.27342, 31.67663, 32.50864, 34.29474, 30.81942, 34.29637, 32.37888, 31.53656, 30.28286, 31.52802, 31.52802, 31.29889, 30.35667, 32.5761, 33.22374, 34.19891, 35.68893, 47.59501, 37.59036, 29.62502, 32.38506, 30.60525, 36.92763, 31.55724, 34.16102, 30.76239, 31.01334, 32.27204, 29.63566, 41.53544, 43.58239, 29.02033, 30.73124, 30.26892, 31.847, 30.06908, 37.48715, 39.26691, 29.89997, 31.11425, 36.34303, 51.99843, 30.48683, 30.7607, 32.35504, 46.32393, 29.48785, 32.97904, 30.14343, 30.40873, 37.91671, 31.53161, 29.89573, 30.49102, 29.88968, 33.95192, 30.3987, 33.99411, 32.28092, 33.36572, 30.42415, 38.03029, 39.33902, 28.77619, 31.0585, 41.76222, 34.08653, 29.98517, 34.66174, 36.80748, 37.63943],
 'pct_NP': [95.83108, 96.13075, 95.87788, 95.94028, 95.87643, 95.82636, 95.87171, 95.91017, 95.79516, 95.78791, 96.31651, 95.99761, 95.79408, 95.89239, 95.78573, 95.90146, 95.80532, 95.84741, 95.8826, 95.99543, 95.69757, 95.80496, 95.74401, 95.93303, 95.91561, 95.9421, 95.89965, 95.92396, 95.93847, 95.93774, 95.90836, 95.87933, 95.92867, 96.00631, 95.8387, 95.97148, 95.9508, 95.93194, 95.87534, 95.91706, 95.91706, 95.91489, 95.89239, 95.94972, 96.00668, 96.00994, 96.07089, 96.27841, 96.09955, 95.82274, 95.96205, 95.90146, 96.11334, 95.92106, 96.00414, 95.87498, 95.8884, 95.93738, 95.83362, 96.18445, 96.22036, 95.80859, 95.88949, 95.83543, 95.94573, 95.84777, 96.11951, 96.14926, 95.8387, 95.90001, 96.09302, 96.35387, 95.86119, 95.9011, 95.93339, 96.26027, 95.82201, 95.97475, 95.87208, 95.86446, 96.09157, 95.90328, 95.85684, 95.88768, 95.84995, 95.99579, 95.86954, 95.99688, 95.95407, 95.98309, 95.88804, 96.1166, 96.14345, 95.8075, 95.89747, 96.20041, 95.99108, 95.86627, 96.01937, 96.08722, 96.09338],
 'mad_NP': [2.4, 2.69, 2.53, 2.58, 2.85, 2.32, 2.41, 2.54, 2.26, 2.41, 3.73, 2.87, 2.28, 2.47, 2.42, 2.51, 2.52, 2.3, 2.51, 2.5, 2.39, 2.32, 2.26, 2.45, 2.58, 2.51, 2.54, 2.57, 2.7, 2.53, 2.44, 2.51, 2.61, 2.78, 2.48, 2.82, 2.61, 2.64, 2.45, 2.47, 2.47, 2.35, 2.41, 2.74, 2.89, 3.05, 2.56, 3.11, 2.62, 2.36, 2.4, 2.37, 2.85, 2.5, 2.69, 2.76, 2.44, 2.77, 2.49, 2.95, 3.1, 2.23, 2.55, 2.38, 2.5, 2.45, 2.79, 3.08, 2.41, 2.53, 2.69, 3.62, 2.45, 2.51, 2.43, 3.47, 2.86, 2.63, 2.51, 2.43, 2.74, 3.29, 2.47, 2.7, 2.4, 2.86, 2.49, 2.66, 2.5, 2.61, 2.43, 3.75, 2.77, 2.43, 2.57, 3.13, 2.54, 2.38, 2.62, 2.73, 2.8],
 'pct-act_NP': [99.72261, 99.7348, 99.79733, 99.80831, 99.73196, 99.74506, 99.72588, 99.78956, 99.73939, 99.69657, 99.74559, 99.76128, 99.74523, 99.71998, 99.75593, 99.70689, 99.77984, 99.77063, 99.71659, 99.73037, 99.49053, 99.7367, 99.75454, 99.79297, 99.69296, 99.68512, 99.74522, 99.72103, 99.74315, 99.73674, 99.72944, 99.74561, 99.68827, 99.83226, 99.78345, 99.77035, 99.7133, 99.77947, 99.75018, 99.79558, 99.79558, 99.73194, 99.73659, 99.71097, 99.77643, 99.76761, 99.7517, 99.77304, 99.7544, 99.76129, 99.73501, 99.71151, 99.693, 99.71733, 99.72714, 99.80626, 99.71137, 99.74261, 99.73006, 99.8092, 99.7331, 99.72827, 99.75763, 99.7678, 99.72622, 99.52316, 99.74966, 99.74451, 99.79687, 99.75409, 99.75035, 99.745, 99.70088, 99.70653, 99.72186, 99.75905, 99.74503, 99.71645, 99.72748, 99.74403, 99.74695, 99.71049, 99.74151, 99.73246, 99.78704, 99.7469, 99.73811, 99.76458, 99.74595, 99.76743, 99.52558, 99.76455, 99.78796, 99.7336, 99.77842, 99.70977, 99.79491, 99.73274, 99.73272, 99.70917, 99.77074],
 'pct-pp_NP': [97.95775, 97.92363, 98.28176, 98.3348, 97.89158, 98.09959, 97.97383, 98.12378, 97.91273, 97.78881, 98.03557, 98.045, 97.95379, 97.89142, 97.87908, 97.85116, 98.11997, 98.227, 97.8446, 97.95208, 97.29799, 98.0612, 98.06741, 98.24325, 97.88712, 97.88962, 97.79328, 97.78891, 97.92744, 97.98786, 98.04976, 97.88388, 97.78834, 98.27716, 98.20671, 98.02257, 97.98365, 98.19387, 98.05562, 98.06771, 98.06771, 97.99713, 98.03721, 97.89525, 98.12726, 98.08657, 98.01643, 98.13004, 98.08099, 98.06697, 98.05385, 97.85232, 97.84396, 97.94133, 98.20101, 98.26043, 98.02674, 98.04745, 98.03498, 98.30391, 98.18926, 98.04585, 98.1358, 98.10769, 97.99772, 97.58877, 98.10085, 97.95533, 98.15405, 98.01168, 97.97297, 98.13036, 97.77195, 97.88851, 97.94937, 98.11215, 97.916, 97.81641, 97.94631, 98.04649, 98.07409, 97.88596, 98.05292, 97.92362, 98.23961, 98.20643, 98.12377, 98.1613, 98.01535, 98.11891, 97.59924, 98.16839, 98.27147, 98.01659, 98.20429, 98.06093, 98.17081, 98.04362, 98.02972, 98.05788, 98.15844],
 'mean_NP.1': [440.6, 446.7, 450.1, 452.7, 436.1, 443.9, 442.1, 447.3, 437.9, 437.3, 440.9, 432.6, 445.2, 443.9, 435.1, 445.6, 426.5, 443.8, 443.5, 446.9, 424.5, 444.0, 440.1, 458.0, 438.9, 449.1, 439.7, 454.0, 447.5, 442.8, 441.8, 435.8, 449.6, 446.4, 446.0, 441.1, 444.5, 439.4, 439.9, 427.3, 427.3, 427.2, 438.7, 454.2, 437.6, 444.4, 439.8, 441.7, 436.8, 424.5, 442.5, 440.7, 428.1, 427.6, 434.0, 434.2, 438.1, 425.5, 429.5, 436.1, 441.1, 442.6, 440.8, 440.7, 433.5, 422.8, 430.1, 432.0, 434.1, 430.9, 437.9, 435.3, 433.9, 424.1, 431.1, 422.0, 434.2, 437.2, 431.7, 437.3, 440.2, 433.1, 437.1, 425.7, 441.2, 435.9, 437.2, 445.0, 433.9, 437.2, 418.2, 432.9, 425.6, 433.9, 439.6, 451.8, 435.9, 439.6, 439.7, 441.4, 451.1],
 'insert_NP': [99.7, 103.7, 101.8, 102.2, 99.2, 99.4, 99.5, 100.8, 97.7, 97.8, 100.9, 100.8, 99.5, 99.9, 99.1, 100.7, 99.9, 99.0, 101.0, 100.4, 97.7, 99.9, 101.9, 102.7, 100.3, 103.5, 99.9, 104.8, 103.1, 100.9, 100.3, 99.6, 102.8, 101.6, 101.2, 101.1, 100.5, 101.0, 100.9, 98.4, 98.4, 97.5, 101.2, 103.2, 100.0, 101.3, 100.2, 100.9, 100.2, 99.1, 101.1, 101.0, 99.3, 96.6, 99.7, 99.4, 100.1, 96.9, 98.5, 101.0, 101.4, 102.0, 101.0, 101.3, 97.6, 98.0, 99.5, 100.3, 100.5, 99.0, 101.2, 100.3, 98.2, 98.2, 97.9, 98.0, 99.3, 100.9, 98.7, 99.9, 101.0, 100.6, 101.0, 96.3, 102.0, 99.7, 99.8, 101.6, 100.6, 101.1, 95.0, 99.6, 98.8, 99.1, 100.0, 104.0, 99.0, 101.7, 101.8, 101.9, 104.1],
 'rate_NP': [5.97e-09, 1.12e-06, 3.98e-06, 1.351e-06, 1.28e-06, 3.82e-09, 7.48e-07, 1.63e-06, 2.95e-09, 1.92e-06, 7.83e-07, 1.31e-06, 6.72e-09, 1.05e-06, 2.39e-09, 1.88e-06, 3.42e-09, 3.9e-10, 4.05e-09, 3.05e-09, 7.58e-10, 1.45e-06, 4.55e-09, 2.48e-09, 8.8e-07, 3.25e-09, 2.67e-09, 9.02e-09, 4.11e-09, 1.2e-06, 1.47e-06, 8.24e-07, 9.57e-09, 2.75e-09, 1.08e-06, 1.04e-06, 1.34e-09, 1.5e-09, 1.37e-06, 1.18e-08, 1.18e-08, 9.77e-09, 8.28e-07, 2.21e-09, 1.59e-09, 1.33e-09, 2.04e-09, 5.48e-07, 1.94e-09, 1.82e-09, 3.24e-09, 4.59e-06, 8.65e-07, 7.64e-10, 5.51e-06, 1.75e-09, 4.23e-10, 1.02e-06, 1.75e-09, 7.78e-07, 1.45e-06, 3.49e-09, 1.91e-08, 1.58e-06, 1.79e-06, 1.25e-09, 3.11e-09, 8.28e-07, 4.05e-09, 6.05e-07, 4.16e-09, 4.9e-07, 2.17e-09, 4.36e-09, 6.83e-10, 1.1e-06, 8.99e-09, 1.95e-06, 1.47e-09, 1.5e-06, 1.56e-06, 9.41e-07, 1.35e-09, 8.54e-09, 4.37e-09, 2.1e-06, 3.98e-09, 1.01e-08, 5.82e-09, 1.3e-09, 8.03e-10, 1.07e-06, 3.83e-09, 4.99e-09, 2.41e-09, 3.4e-09, 2.31e-09, 6.33e-09, 9.26e-09, 1.41e-09, 1.54e-06],
 'yield_Arg': [101940000000.0, 134815000000.0, 104348000000.0, 108016000000.0, 106915000000.0, 101891000000.0, 102859000000.0, 104712000000.0, 98732711940.0, 101992000000.0, 176540000000.0, 119786000000.0, 98874217002.0, 108950000000.0, 100279000000.0, 109404000000.0, 97816752073.0, 99875608784.0, 105462000000.0, 114368000000.0, 91070755092.0, 102681000000.0, 94779004959.0, 109683000000.0, 109664000000.0, 110539000000.0, 109450000000.0, 107001000000.0, 111869000000.0, 112014000000.0, 104834000000.0, 108499000000.0, 111538000000.0, 112849000000.0, 99400525623.0, 114885000000.0, 107473000000.0, 102917000000.0, 105871000000.0, 105336000000.0, 105336000000.0, 109490000000.0, 103284000000.0, 110059000000.0, 113200000000.0, 119803000000.0, 119052000000.0, 155554000000.0, 128106000000.0, 99660967688.0, 110417000000.0, 100502000000.0, 120533000000.0, 111613000000.0, 110452000000.0, 107891000000.0, 101508000000.0, 109992000000.0, 101204000000.0, 138829000000.0, 148519000000.0, 94358139331.0, 101922000000.0, 101125000000.0, 107840000000.0, 96776159556.0, 125599000000.0, 131475000000.0, 99308534044.0, 104154000000.0, 119703000000.0, 172425000000.0, 100050000000.0, 102104000000.0, 112015000000.0, 155011000000.0, 97089503358.0, 109002000000.0, 102866000000.0, 102082000000.0, 125097000000.0, 101411000000.0, 99173169979.0, 104393000000.0, 97704382884.0, 114548000000.0, 106286000000.0, 110903000000.0, 106690000000.0, 112314000000.0, 102866000000.0, 125675000000.0, 131768000000.0, 99266572540.0, 106262000000.0, 138828000000.0, 113469000000.0, 100316000000.0, 113837000000.0, 121809000000.0, 125873000000.0],
 'mean_Arg': [30.30078, 38.89375, 30.95051, 32.25406, 30.4547, 29.77961, 30.33201, 31.69718, 29.11294, 29.69243, 51.61069, 34.61427, 29.14371, 31.49802, 29.29912, 32.29913, 28.57639, 29.30749, 30.71611, 33.94012, 27.16736, 29.65553, 27.79295, 32.10852, 31.98217, 32.59671, 31.30704, 31.52126, 33.10581, 32.51, 31.37597, 31.18913, 31.97458, 33.82919, 29.98714, 33.74547, 31.76157, 31.02146, 29.79179, 30.91472, 30.91472, 30.81643, 29.88501, 32.00364, 32.77427, 33.77175, 35.17978, 46.80516, 37.08409, 29.11601, 31.8706, 30.02358, 35.97378, 31.0624, 33.24734, 30.31996, 30.26782, 31.78231, 29.13839, 40.87246, 42.89485, 28.40806, 30.14433, 29.77872, 31.35237, 29.2151, 36.84727, 38.58257, 29.4153, 30.60253, 35.73808, 51.06496, 29.95496, 30.15563, 31.8753, 45.5614, 28.95893, 32.38335, 29.69167, 29.92895, 37.29407, 30.83368, 29.40804, 30.01485, 29.30928, 33.36085, 29.92345, 33.40791, 31.62522, 32.8189, 29.75168, 37.41665, 38.7008, 28.32761, 30.5643, 41.01108, 33.5711, 29.38303, 33.81398, 36.10285, 36.97827],
 'pct_Arg': [95.7618, 96.2904, 95.8694, 95.9667, 95.8471, 95.7173, 95.8008, 95.9272, 95.6311, 95.6681, 96.5147, 96.1337, 95.6212, 95.8792, 95.6557, 95.9107, 95.6557, 95.7026, 95.8931, 96.0641, 95.1967, 95.6822, 95.4909, 95.947, 95.9614, 96.0175, 95.9617, 95.959, 96.0227, 96.0109, 95.8615, 95.9151, 95.9779, 96.1052, 95.7308, 96.0615, 95.9864, 95.951, 95.8127, 95.9214, 95.9214, 95.9266, 95.8842, 95.9879, 96.1141, 96.1266, 96.227, 96.4836, 96.2848, 95.6862, 96.0418, 95.8872, 96.2246, 95.9483, 96.0308, 95.8528, 95.8255, 95.998, 95.7239, 96.3519, 96.407, 95.5737, 95.8251, 95.7721, 95.991, 95.6652, 96.2503, 96.2912, 95.755, 95.8799, 96.2123, 96.5405, 95.795, 95.8511, 96.0092, 96.4486, 95.6086, 96.0309, 95.8327, 95.8244, 96.2274, 95.8225, 95.7447, 95.8424, 95.7342, 96.0645, 95.8407, 96.0659, 95.9834, 96.0525, 95.8063, 96.2519, 96.3025, 95.6002, 95.898, 96.3587, 96.0856, 95.7557, 96.0889, 96.1936, 96.2291],
 'mad_Arg': [4, 5, 5, 5, 5, 4, 4, 5, 4, 4, 6, 5, 4, 5, 4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 4, 4, 5, 5, 4, 5, 5, 5, 4, 4, 4, 4, 4, 5, 5, 5, 5, 6, 5, 4, 4, 4, 5, 4, 5, 5, 4, 5, 4, 5, 5, 4, 4, 4, 4, 4, 5, 5, 4, 4, 5, 6, 4, 4, 4, 6, 5, 5, 4, 4, 5, 5, 4, 5, 4, 5, 4, 5, 5, 5, 4, 5, 5, 4, 4, 5, 5, 4, 5, 5, 5],
 'pct-act_Arg': [99.72261, 99.7348, 99.79733, 99.80831, 99.73196, 99.74506, 99.72588, 99.78956, 99.73939, 99.69657, 99.74559, 99.76128, 99.74523, 99.71998, 99.75593, 99.70689, 99.77984, 99.77063, 99.71659, 99.73037, 99.49053, 99.7367, 99.75454, 99.79297, 99.69296, 99.68512, 99.74522, 99.72103, 99.74315, 99.73674, 99.72944, 99.74561, 99.68827, 99.83226, 99.78345, 99.77035, 99.7133, 99.77947, 99.75018, 99.79558, 99.79558, 99.73194, 99.73659, 99.71097, 99.77643, 99.76761, 99.7517, 99.77304, 99.7544, 99.76129, 99.73501, 99.71151, 99.693, 99.71733, 99.72714, 99.80626, 99.71137, 99.74261, 99.73006, 99.8092, 99.7331, 99.72827, 99.75763, 99.7678, 99.72622, 99.52316, 99.74966, 99.74451, 99.79687, 99.75409, 99.75035, 99.745, 99.70088, 99.70653, 99.72186, 99.75905, 99.74503, 99.71645, 99.72748, 99.74403, 99.74695, 99.71049, 99.74151, 99.73246, 99.78704, 99.7469, 99.73811, 99.76458, 99.74595, 99.76743, 99.52558, 99.76455, 99.78796, 99.7336, 99.77842, 99.70977, 99.79491, 99.73274, 99.73272, 99.70917, 99.77074],
 'pct-pp_Arg': [97.95775, 97.92363, 98.28176, 98.3348, 97.89158, 98.09959, 97.97383, 98.12378, 97.91273, 97.78881, 98.03557, 98.045, 97.95379, 97.89142, 97.87908, 97.85116, 98.11997, 98.227, 97.8446, 97.95208, 97.29799, 98.0612, 98.06741, 98.24325, 97.88712, 97.88962, 97.79328, 97.78891, 97.92744, 97.98786, 98.04976, 97.88388, 97.78834, 98.27716, 98.20671, 98.02257, 97.98365, 98.19387, 98.05562, 98.06771, 98.06771, 97.99713, 98.03721, 97.89525, 98.12726, 98.08657, 98.01643, 98.13004, 98.08099, 98.06697, 98.05385, 97.85232, 97.84396, 97.94133, 98.20101, 98.26043, 98.02674, 98.04745, 98.03498, 98.30391, 98.18926, 98.04585, 98.1358, 98.10769, 97.99772, 97.58877, 98.10085, 97.95533, 98.15405, 98.01168, 97.97297, 98.13036, 97.77195, 97.88851, 97.94937, 98.11215, 97.916, 97.81641, 97.94631, 98.04649, 98.07409, 97.88596, 98.05292, 97.92362, 98.23961, 98.20643, 98.12377, 98.1613, 98.01535, 98.11891, 97.59924, 98.16839, 98.27147, 98.01659, 98.20429, 98.06093, 98.17081, 98.04362, 98.02972, 98.05788, 98.15844],
 'mean_Arg.1': [440.6, 446.7, 450.1, 452.7, 436.1, 443.9, 442.1, 447.3, 437.9, 437.3, 440.9, 432.6, 445.2, 443.9, 435.1, 445.6, 426.5, 443.8, 443.5, 446.9, 424.5, 444.0, 440.1, 458.0, 438.9, 449.1, 439.7, 454.0, 447.5, 442.8, 441.8, 435.8, 449.6, 446.4, 446.0, 441.1, 444.5, 439.4, 439.9, 427.3, 427.3, 427.2, 438.7, 454.2, 437.6, 444.4, 439.8, 441.7, 436.8, 424.5, 442.5, 440.7, 428.1, 427.6, 434.0, 434.2, 438.1, 425.5, 429.5, 436.1, 441.1, 442.6, 440.8, 440.7, 433.5, 422.8, 430.1, 432.0, 434.1, 430.9, 437.9, 435.3, 433.9, 424.1, 431.1, 422.0, 434.2, 437.2, 431.7, 437.3, 440.2, 433.1, 437.1, 425.7, 441.2, 435.9, 437.2, 445.0, 433.9, 437.2, 418.2, 432.9, 425.6, 433.9, 439.6, 451.8, 435.9, 439.6, 439.7, 441.4, 451.1],
 'insert_Arg': [99.7, 103.7, 101.8, 102.2, 99.2, 99.4, 99.5, 100.8, 97.7, 97.8, 100.9, 100.8, 99.5, 99.9, 99.1, 100.7, 99.9, 99.0, 101.0, 100.4, 97.7, 99.9, 101.9, 102.7, 100.3, 103.5, 99.9, 104.8, 103.1, 100.9, 100.3, 99.6, 102.8, 101.6, 101.2, 101.1, 100.5, 101.0, 100.9, 98.4, 98.4, 97.5, 101.2, 103.2, 100.0, 101.3, 100.2, 100.9, 100.2, 99.1, 101.1, 101.0, 99.3, 96.6, 99.7, 99.4, 100.1, 96.9, 98.5, 101.0, 101.4, 102.0, 101.0, 101.3, 97.6, 98.0, 99.5, 100.3, 100.5, 99.0, 101.2, 100.3, 98.2, 98.2, 97.9, 98.0, 99.3, 100.9, 98.7, 99.9, 101.0, 100.6, 101.0, 96.3, 102.0, 99.7, 99.8, 101.6, 100.6, 101.1, 95.0, 99.6, 98.8, 99.1, 100.0, 104.0, 99.0, 101.7, 101.8, 101.9, 104.1],
 'rate_Arg': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}

df = pd.DataFrame(data)
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