Información de Fisher nivel nodo: Tlacotepec
- Dra. Melanie Kolb
- M. en F. C. Gustavo Magallanes-Guijón
- Dr. Oliver López-Corona
In [2]:
= pd.read_csv("Arsénico Total_TLACOTEPEC.csv", names=header_names)
#Arsenico_Total_TLACOTEPEC_dfimport numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys, os
import datetime
from matplotlib.pylab import rcParams
%matplotlib inline
import warnings
import seaborn as sns
import functools
In [8]:
!ls *.csv > lista.txt
In [34]:
lista = pd.read_csv('lista.txt', header = None)
list_of_rows = [j for j in lista.values]
# Print list of lists i.e. rows
len(list_of_rows)
Out[34]:
19
In [35]:
filesnames = os.listdir()
filesnames = [f for f in filesnames if (f.endswith(".csv"))]
In [36]:
dfs = list()
string = "_df"
for filename in filesnames:
#print(filename)
#nueva = string.join(filename)
df = pd.read_csv(filename)
dfs.append(df)
In [37]:
merged = pd.merge(dfs[0], dfs[1])
#for i in range(len(dfs)):
for i in range(len(dfs)):
merged = pd.merge(merged, dfs[i])
In [ ]:
In [38]:
merged
Out[38]:
| 2012-12-05 | 20.3333 | 0.1646 | 301.0 | 7.85 | 0.0179 | 10462.0 | 0.0132 | 6.9 | 242.364 | 0.00048000002 | 0.0075805085 | 5.5 | 6.9633 | 12.5 | 0.17657143 | 0.4332 | 0.016157143 | 0.7268 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2014-02-26 | 21.2 | 0.279 | 24196.0 | 8.2 | 0.016 | 3777.25 | 0.0181 | 2.0 | 324.9 | 0.00018 | 0.006967 | 9.3 | 11.2 | 7.5 | 0.063 | 0.118 | 0.008933 | 0.699 |
In [39]:
merged = pd.merge(dfs[0], dfs[1])
In [55]:
merged = pd.merge(merged, dfs[18]) #rev number 8!
merged
Out[55]:
| 2012-12-05 | 20.3333 | 0.1646 | 301.0 | 7.85 | 0.0179 | 10462.0 | 0.0132 | 6.9 | 242.364 | 0.00048000002 | 0.0075805085 | 5.5 | 6.9633 | 12.5 | 0.17657143 | 0.4332 | 0.016157143 | 0.7268 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2013-01-08 | 20.5 | 0.248600 | 473.0 | 8.04 | 0.020300 | 6131.000 | 0.026300 | 3.5 | 235.5240 | 0.000600 | 0.007620 | 5.769200 | 7.12 | 15.0 | 0.294143 | 0.354500 | 0.012971 | 0.663700 |
| 1 | 2013-03-15 | 19.5 | 0.512600 | 30.0 | 8.04 | 0.034600 | 3448.000 | 0.039400 | 5.5 | 320.8248 | 0.000100 | 0.007660 | 18.634600 | 8.12 | 10.0 | 0.411714 | 0.131700 | 0.009786 | 0.831200 |
| 2 | 2013-06-28 | 25.3 | 0.129000 | 201.0 | 8.02 | 0.032000 | 5794.000 | 0.029400 | 4.5 | 180.3060 | 0.000100 | 0.007700 | 31.500000 | 5.00 | 20.0 | 0.529286 | 0.634200 | 0.006600 | 0.862100 |
| 3 | 2013-08-23 | 24.5 | 0.070200 | 20.0 | 8.29 | 0.024300 | 7270.000 | 0.019400 | 4.0 | 297.4220 | 0.000100 | 0.008250 | 7.330000 | 11.22 | 15.0 | 0.646857 | 0.150300 | 0.008900 | 0.744500 |
| 4 | 2013-09-17 | 20.2 | 0.084400 | 12997.0 | 7.99 | 0.037300 | 6105.750 | 0.018967 | 112.5 | 175.8290 | 0.001800 | 0.008800 | 460.000000 | 7.42 | 312.5 | 0.764429 | 0.708500 | 0.011200 | 2.016900 |
| 5 | 2013-10-26 | 18.1 | 0.008000 | 9804.0 | 7.94 | 0.037000 | 4941.500 | 0.018533 | 39.0 | 163.9560 | 0.000990 | 0.007883 | 67.500000 | 7.63 | 40.0 | 0.882000 | 0.881000 | 0.010067 | 1.616000 |
| 6 | 2014-02-26 | 21.2 | 0.279000 | 24196.0 | 8.20 | 0.016000 | 3777.250 | 0.018100 | 2.0 | 324.9000 | 0.000180 | 0.006967 | 9.300000 | 11.20 | 7.5 | 0.063000 | 0.118000 | 0.008933 | 0.699000 |
| 7 | 2014-03-25 | 23.5 | 0.098000 | 171.0 | 8.00 | 0.004000 | 2613.000 | 0.019200 | 2.8 | 198.0000 | 0.001320 | 0.006050 | 9.800000 | 10.00 | 10.0 | 0.028000 | 0.080000 | 0.007800 | 0.521000 |
| 8 | 2014-05-13 | 26.0 | 0.111000 | 1039.0 | 8.20 | 0.005000 | 11238.000 | 0.028900 | 6.5 | 243.6000 | 0.000440 | 0.005133 | 12.700000 | 8.00 | 10.0 | 0.011000 | 0.188000 | 0.006667 | 0.800000 |
| 9 | 2014-06-25 | 24.0 | 0.242000 | 489.0 | 7.80 | 0.029000 | 19863.000 | 0.016300 | 2.5 | 236.5000 | 0.000310 | 0.004217 | 23.100000 | 4.50 | 7.5 | 0.020500 | 0.383000 | 0.005533 | 0.913000 |
| 10 | 2014-08-12 | 23.8 | 0.097000 | 1723.0 | 8.00 | 0.039000 | 17329.000 | 0.015820 | 92.5 | 164.0000 | 0.000140 | 0.003300 | 98.000000 | 5.70 | 75.0 | 0.030000 | 1.256000 | 0.004400 | 1.943000 |
| 11 | 2014-09-28 | 23.1 | 0.178000 | 4611.0 | 8.30 | 0.030000 | 10491.500 | 0.015340 | 36.0 | 128.3000 | 0.000310 | 0.005750 | 64.000000 | 6.50 | 40.0 | 0.051000 | 0.758000 | 0.004500 | 1.258000 |
| 12 | 2015-03-21 | 23.0 | 0.208313 | 459.0 | 8.30 | 0.041200 | 3654.000 | 0.014860 | 10.6 | 175.5700 | 0.001485 | 0.008200 | 31.000000 | 6.04 | 13.0 | 0.019000 | 0.701100 | 0.004600 | 0.950613 |
| 13 | 2015-05-15 | 25.3 | 0.319010 | 20.0 | 8.10 | 0.023611 | 2909.000 | 0.014380 | 8.2 | 232.1800 | 0.000101 | 0.008000 | 15.380000 | 6.57 | 8.0 | 0.017000 | 0.070415 | 0.004750 | 1.139561 |
| 14 | 2015-06-16 | 25.8 | 0.097999 | 10.0 | 7.80 | 0.002875 | 2613.000 | 0.013900 | 5.1 | 244.1900 | 0.000096 | 0.007800 | 15.000000 | 6.42 | 10.0 | 0.018667 | 0.037583 | 0.004900 | 0.618837 |
| 15 | 2015-07-27 | 25.9 | 0.139792 | 496.0 | 8.10 | 0.037480 | 19863.000 | 0.015350 | 52.0 | 163.9700 | 0.000163 | 0.007600 | 44.000000 | 5.40 | 75.0 | 0.020333 | 0.588304 | 0.014400 | 1.042271 |
| 16 | 2015-09-13 | 22.1 | 0.096771 | 5475.0 | 7.70 | 0.030934 | 13580.000 | 0.016800 | 90.0 | 154.7100 | 0.000230 | 0.019700 | 183.330000 | 6.86 | 100.0 | 0.022000 | 1.166088 | 0.034700 | 1.853759 |
| 17 | 2015-10-29 | 22.9 | 0.158423 | 6488.0 | 8.10 | 0.027965 | 7297.000 | 0.018250 | 48.0 | 149.7600 | 0.000267 | 0.030040 | 103.640000 | 6.19 | 50.0 | 0.127000 | 1.408629 | 0.012600 | 1.983690 |
| 18 | 2016-03-05 | 22.3 | 0.032482 | 31.0 | 8.40 | 0.016863 | 1014.000 | 0.019700 | 7.3 | 276.5800 | 0.000102 | 0.040380 | 9.000000 | 14.84 | 10.0 | 0.024000 | 0.049949 | 0.005800 | 0.922659 |
| 19 | 2016-04-28 | 25.3 | 0.422454 | 187.0 | 7.90 | 0.058535 | 12033.000 | 0.021317 | 21.0 | 208.6400 | 0.000089 | 0.050720 | 20.000000 | 4.24 | 18.0 | 0.031000 | 0.426900 | 0.037367 | 1.301812 |
| 20 | 2016-06-04 | 26.5 | 0.165468 | 41.0 | 8.30 | 0.001835 | 12033.000 | 0.022933 | 5.4 | 280.2400 | 0.000075 | 0.061060 | 9.500000 | 6.94 | 15.0 | 0.063000 | 0.013219 | 0.068933 | 0.180522 |
| 21 | 2016-07-24 | 23.3 | 0.205571 | 36540.0 | 8.10 | 0.020928 | 9867.000 | 0.024550 | 10.7 | 225.2900 | 0.002547 | 0.071400 | 13.750000 | 6.55 | 325.0 | 0.026000 | 0.817734 | 0.100500 | 0.774430 |
| 22 | 2016-09-18 | 23.6 | 0.112780 | 52.0 | 7.90 | 0.045480 | 7701.000 | 0.026167 | 16.0 | 197.4100 | 0.000101 | 0.061462 | 18.000000 | 5.21 | 13.0 | 0.063000 | 0.824911 | 0.086092 | 1.368339 |
| 23 | 2016-11-07 | 21.6 | 0.060794 | 11199.0 | 7.60 | 0.015819 | 5030.000 | 0.027783 | 18.0 | 130.3700 | 0.000135 | 0.051523 | 23.000000 | 7.54 | 30.0 | 0.065500 | 0.487273 | 0.071683 | 0.959882 |
| 24 | 2017-03-14 | 19.9 | 0.318180 | 5609.5 | 7.90 | 0.025330 | 2359.000 | 0.029400 | 5.7 | 282.9100 | 0.000101 | 0.041585 | 10.000000 | 7.53 | 18.0 | 0.068000 | 0.300980 | 0.057275 | 0.950412 |
| 25 | 2017-04-23 | 30.1 | 0.134560 | 20.0 | 7.70 | 0.002950 | 53342.668 | 0.047200 | 7.0 | 325.7100 | 0.000068 | 0.031647 | 18.000000 | 7.62 | 25.0 | 0.010000 | 0.024020 | 0.042867 | 0.753732 |
| 26 | 2017-05-26 | 25.7 | 0.212530 | 15531.0 | 8.20 | 0.008660 | 104326.336 | 0.053300 | 5.5 | 326.9800 | 0.000178 | 0.021708 | 10.000000 | 6.32 | 25.0 | 0.015000 | 0.055980 | 0.028458 | 0.815892 |
| 27 | 2017-07-03 | 23.2 | 0.931213 | 11300.0 | 8.20 | 0.188686 | 155310.000 | 0.043350 | 150.0 | 132.4700 | 0.000256 | 0.011770 | 198.000000 | 6.59 | 150.0 | 0.058000 | 1.251739 | 0.014050 | 4.525243 |
| 28 | 2017-08-15 | 25.9 | 0.151516 | 4884.0 | 7.90 | 0.055305 | 155310.000 | 0.033400 | 34.0 | 175.7500 | 0.000368 | 0.009335 | 70.000000 | 6.62 | 50.0 | 0.057000 | 0.946546 | 0.003400 | 2.152425 |
| 29 | 2017-10-09 | 21.7 | 0.373770 | 21420.0 | 8.00 | 0.017500 | 155310.000 | 0.023450 | 90.0 | 122.9400 | 0.000745 | 0.006900 | 155.000000 | 7.35 | 100.0 | 0.023000 | 0.713810 | 0.009900 | 1.782493 |
| 30 | 2018-02-11 | 20.7 | 0.151770 | 74.0 | 8.10 | 0.026406 | 1036.000 | 0.013500 | 4.1 | 220.6550 | 0.000391 | 0.006533 | 120.250000 | 7.69 | 10.0 | 0.110000 | 0.487336 | 0.008300 | 0.863120 |
| 31 | 2018-03-15 | 21.6 | 0.194069 | 85.0 | 8.20 | 0.037418 | 12616.000 | 0.016500 | 2.3 | 253.6040 | 0.000036 | 0.006167 | 85.500000 | 4.50 | 25.0 | 0.010000 | 0.827015 | 0.006700 | 1.554220 |
| 32 | 2018-04-24 | 25.8 | 0.040874 | 96.0 | 8.20 | 0.004143 | 24196.000 | 0.043500 | 5.9 | 283.2720 | 0.000054 | 0.005800 | 50.750000 | 4.60 | 13.0 | 0.029000 | 0.008323 | 0.005533 | 0.722890 |
| 33 | 2018-06-07 | 27.3 | 0.101231 | 10.0 | 8.30 | 0.002905 | 22029.500 | 0.048500 | 7.3 | 319.7600 | 0.000071 | 0.005433 | 16.000000 | 4.36 | 30.0 | 0.048000 | 0.032173 | 0.004367 | 0.890980 |
| 34 | 2018-10-25 | 22.0 | 0.161588 | 2382.0 | 8.00 | 0.036460 | 19863.000 | 0.034650 | 20.0 | 125.8790 | 0.000089 | 0.005067 | 44.000000 | 7.08 | 30.0 | 0.120000 | 0.975699 | 0.003200 | 1.439380 |
| 35 | 2018-12-19 | 18.6 | 0.121305 | 41.0 | 8.00 | 0.021230 | 1956.000 | 0.020800 | 4.8 | 231.8830 | 0.000092 | 0.004700 | 44.357143 | 7.54 | 8.0 | 0.054000 | 0.415529 | 0.003400 | 0.934260 |
| 36 | 2019-02-15 | 23.5 | 0.026774 | 20.0 | 8.50 | 0.002813 | 14136.000 | 0.033500 | 9.5 | 294.3140 | 0.000737 | 0.032170 | 24.000000 | 8.98 | 13.0 | 0.053000 | 0.008934 | 0.007337 | 0.930861 |
In [59]:
from sklearn import preprocessing
df_num = merged.select_dtypes(include=[np.number])
x = df_num.values #returns a numpy array
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
df_num = pd.DataFrame(x_scaled)
df_num
Out[59]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.200000 | 0.260612 | 0.012675 | 0.488889 | 0.098822 | 0.033164 | 0.321608 | 0.010135 | 0.551774 | 0.224612 | 0.063441 | 0.000000 | 0.271698 | 0.023622 | 0.325852 | 0.247215 | 0.100426 | 0.111210 |
| 1 | 0.116667 | 0.546569 | 0.000547 | 0.488889 | 0.175354 | 0.015775 | 0.650754 | 0.023649 | 0.969833 | 0.025488 | 0.064026 | 0.028323 | 0.366038 | 0.007874 | 0.460682 | 0.088107 | 0.067685 | 0.149763 |
| 2 | 0.600000 | 0.131064 | 0.005229 | 0.466667 | 0.161439 | 0.030979 | 0.399497 | 0.016892 | 0.281151 | 0.025488 | 0.064611 | 0.056647 | 0.071698 | 0.039370 | 0.595511 | 0.446957 | 0.034943 | 0.156875 |
| 3 | 0.533333 | 0.067373 | 0.000274 | 0.766667 | 0.120229 | 0.040545 | 0.148241 | 0.013514 | 0.855136 | 0.025488 | 0.072687 | 0.003436 | 0.658491 | 0.023622 | 0.730341 | 0.101390 | 0.058582 | 0.129808 |
| 4 | 0.175000 | 0.082754 | 0.355516 | 0.433333 | 0.189804 | 0.033000 | 0.137353 | 0.746622 | 0.259209 | 0.702509 | 0.080764 | 1.000000 | 0.300000 | 0.960630 | 0.865170 | 0.500017 | 0.082220 | 0.422669 |
| 5 | 0.000000 | 0.000000 | 0.268108 | 0.377778 | 0.188198 | 0.025454 | 0.126466 | 0.250000 | 0.201019 | 0.379928 | 0.067303 | 0.135902 | 0.319811 | 0.102362 | 1.000000 | 0.623204 | 0.070572 | 0.330396 |
| 6 | 0.258333 | 0.293540 | 0.662086 | 0.666667 | 0.075809 | 0.017909 | 0.115578 | 0.000000 | 0.989806 | 0.057348 | 0.053842 | 0.007773 | 0.656604 | 0.000000 | 0.060780 | 0.078324 | 0.058924 | 0.119335 |
| 7 | 0.450000 | 0.097486 | 0.004407 | 0.444444 | 0.011587 | 0.010363 | 0.143216 | 0.005405 | 0.367869 | 0.511350 | 0.040382 | 0.008874 | 0.543396 | 0.007874 | 0.020642 | 0.051187 | 0.047276 | 0.078366 |
| 8 | 0.658333 | 0.111567 | 0.028169 | 0.666667 | 0.016939 | 0.066262 | 0.386935 | 0.030405 | 0.591355 | 0.160892 | 0.026921 | 0.015258 | 0.354717 | 0.007874 | 0.001147 | 0.128313 | 0.035629 | 0.142582 |
| 9 | 0.491667 | 0.253463 | 0.013113 | 0.222222 | 0.145383 | 0.122161 | 0.070352 | 0.003378 | 0.556558 | 0.109120 | 0.013461 | 0.038154 | 0.024528 | 0.000000 | 0.012041 | 0.267568 | 0.023981 | 0.168590 |
| 10 | 0.475000 | 0.096402 | 0.046893 | 0.444444 | 0.198902 | 0.105738 | 0.058291 | 0.611486 | 0.201235 | 0.041418 | 0.000000 | 0.203048 | 0.137736 | 0.212598 | 0.022936 | 0.891003 | 0.012333 | 0.405660 |
| 11 | 0.416667 | 0.184140 | 0.125951 | 0.777778 | 0.150735 | 0.061424 | 0.046231 | 0.229730 | 0.026269 | 0.109120 | 0.035977 | 0.128197 | 0.213208 | 0.102362 | 0.047018 | 0.535367 | 0.013361 | 0.247997 |
| 12 | 0.408333 | 0.216974 | 0.012291 | 0.777778 | 0.210676 | 0.017110 | 0.034171 | 0.058108 | 0.257940 | 0.577061 | 0.071953 | 0.055546 | 0.169811 | 0.017323 | 0.010321 | 0.494733 | 0.014388 | 0.177248 |
| 13 | 0.600000 | 0.336878 | 0.000274 | 0.555556 | 0.116542 | 0.012282 | 0.022111 | 0.041892 | 0.535385 | 0.025886 | 0.069016 | 0.021158 | 0.219811 | 0.001575 | 0.008028 | 0.044342 | 0.015930 | 0.220737 |
| 14 | 0.641667 | 0.097485 | 0.000000 | 0.222222 | 0.005566 | 0.010363 | 0.010050 | 0.020946 | 0.594246 | 0.023895 | 0.066079 | 0.020322 | 0.205660 | 0.007874 | 0.009939 | 0.020895 | 0.017472 | 0.100884 |
| 15 | 0.650000 | 0.142754 | 0.013304 | 0.555556 | 0.190767 | 0.122161 | 0.046482 | 0.337838 | 0.201088 | 0.050577 | 0.063142 | 0.084166 | 0.109434 | 0.212598 | 0.011850 | 0.414182 | 0.115108 | 0.198344 |
| 16 | 0.333333 | 0.096154 | 0.149603 | 0.111111 | 0.155734 | 0.081441 | 0.082915 | 0.594595 | 0.155705 | 0.077260 | 0.240822 | 0.390904 | 0.247170 | 0.291339 | 0.013761 | 0.826794 | 0.323741 | 0.385120 |
| 17 | 0.400000 | 0.162934 | 0.177334 | 0.555556 | 0.139844 | 0.040720 | 0.119347 | 0.310811 | 0.131445 | 0.091995 | 0.392658 | 0.215465 | 0.183962 | 0.133858 | 0.134174 | 1.000000 | 0.096608 | 0.415025 |
| 18 | 0.350000 | 0.026518 | 0.000575 | 0.888889 | 0.080428 | 0.000000 | 0.155779 | 0.035811 | 0.752990 | 0.026284 | 0.544493 | 0.007113 | 1.000000 | 0.007874 | 0.016055 | 0.029726 | 0.026721 | 0.170813 |
| 19 | 0.600000 | 0.448926 | 0.004845 | 0.333333 | 0.303450 | 0.071415 | 0.196399 | 0.128378 | 0.420016 | 0.020908 | 0.696329 | 0.031329 | 0.000000 | 0.033071 | 0.024083 | 0.298918 | 0.351148 | 0.258081 |
| 20 | 0.700000 | 0.170565 | 0.000849 | 0.777778 | 0.000000 | 0.071415 | 0.237018 | 0.022973 | 0.770927 | 0.015532 | 0.848164 | 0.008213 | 0.254717 | 0.023622 | 0.060780 | 0.003496 | 0.675574 | 0.000000 |
| 21 | 0.433333 | 0.214004 | 1.000000 | 0.555556 | 0.102183 | 0.057377 | 0.277638 | 0.058784 | 0.501617 | 1.000000 | 1.000000 | 0.017570 | 0.217925 | 1.000000 | 0.018349 | 0.578024 | 1.000000 | 0.136697 |
| 22 | 0.458333 | 0.113495 | 0.001150 | 0.333333 | 0.233582 | 0.043339 | 0.318258 | 0.094595 | 0.364977 | 0.025886 | 0.854063 | 0.026926 | 0.091509 | 0.017323 | 0.060780 | 0.583150 | 0.851918 | 0.273393 |
| 23 | 0.291667 | 0.057185 | 0.306296 | 0.000000 | 0.074840 | 0.026028 | 0.358878 | 0.108108 | 0.036414 | 0.039427 | 0.708125 | 0.037934 | 0.311321 | 0.070866 | 0.063647 | 0.342032 | 0.703837 | 0.179381 |
| 24 | 0.150000 | 0.335979 | 0.153285 | 0.333333 | 0.125742 | 0.008717 | 0.399497 | 0.025000 | 0.784013 | 0.026085 | 0.562188 | 0.009314 | 0.310377 | 0.033071 | 0.066514 | 0.208995 | 0.555755 | 0.177201 |
| 25 | 1.000000 | 0.137086 | 0.000274 | 0.111111 | 0.005967 | 0.339145 | 0.846734 | 0.033784 | 0.993776 | 0.012744 | 0.416251 | 0.026926 | 0.318868 | 0.055118 | 0.000000 | 0.011210 | 0.407674 | 0.131933 |
| 26 | 0.633333 | 0.221542 | 0.424884 | 0.666667 | 0.036526 | 0.669572 | 1.000000 | 0.023649 | 1.000000 | 0.056551 | 0.270313 | 0.009314 | 0.196226 | 0.055118 | 0.005734 | 0.034033 | 0.259592 | 0.146240 |
| 27 | 0.425000 | 1.000000 | 0.309061 | 0.666667 | 1.000000 | 1.000000 | 0.750000 | 1.000000 | 0.046707 | 0.087614 | 0.124376 | 0.423201 | 0.221698 | 0.448819 | 0.055046 | 0.887960 | 0.111511 | 1.000000 |
| 28 | 0.650000 | 0.155453 | 0.133425 | 0.333333 | 0.286164 | 1.000000 | 0.500000 | 0.216216 | 0.258822 | 0.132218 | 0.088620 | 0.141406 | 0.224528 | 0.133858 | 0.053899 | 0.670013 | 0.002055 | 0.453862 |
| 29 | 0.300000 | 0.396192 | 0.586094 | 0.444444 | 0.083837 | 1.000000 | 0.250000 | 0.594595 | 0.000000 | 0.282358 | 0.052863 | 0.328535 | 0.293396 | 0.291339 | 0.014908 | 0.503809 | 0.068859 | 0.368717 |
| 30 | 0.216667 | 0.155728 | 0.001752 | 0.555556 | 0.131501 | 0.000143 | 0.000000 | 0.014189 | 0.478901 | 0.141179 | 0.047479 | 0.252032 | 0.325472 | 0.007874 | 0.114679 | 0.342077 | 0.052415 | 0.157110 |
| 31 | 0.291667 | 0.201545 | 0.002053 | 0.666667 | 0.190435 | 0.075193 | 0.075377 | 0.002027 | 0.640384 | 0.000000 | 0.042095 | 0.175529 | 0.024528 | 0.055118 | 0.000000 | 0.584652 | 0.035971 | 0.316176 |
| 32 | 0.641667 | 0.035608 | 0.002354 | 0.666667 | 0.012352 | 0.150244 | 0.753769 | 0.026351 | 0.785787 | 0.007036 | 0.036711 | 0.099026 | 0.033962 | 0.017323 | 0.021789 | 0.000000 | 0.023981 | 0.124834 |
| 33 | 0.766667 | 0.100985 | 0.000000 | 0.777778 | 0.005726 | 0.136202 | 0.879397 | 0.035811 | 0.964615 | 0.014071 | 0.031326 | 0.022523 | 0.011321 | 0.070866 | 0.043578 | 0.017032 | 0.011990 | 0.163522 |
| 34 | 0.325000 | 0.166362 | 0.064933 | 0.444444 | 0.185308 | 0.122161 | 0.531407 | 0.121622 | 0.014404 | 0.021107 | 0.025942 | 0.084166 | 0.267925 | 0.070866 | 0.126147 | 0.690832 | 0.000000 | 0.289744 |
| 35 | 0.041667 | 0.122729 | 0.000849 | 0.444444 | 0.103799 | 0.006105 | 0.183417 | 0.018919 | 0.533930 | 0.022202 | 0.020558 | 0.084952 | 0.311321 | 0.001575 | 0.050459 | 0.290798 | 0.002055 | 0.173484 |
| 36 | 0.450000 | 0.020336 | 0.000274 | 1.000000 | 0.005234 | 0.085044 | 0.502513 | 0.050676 | 0.839904 | 0.279241 | 0.423935 | 0.040136 | 0.447170 | 0.017323 | 0.049312 | 0.000436 | 0.042519 | 0.172701 |
In [79]:
result = pd.concat([merged.select_dtypes(np.object), df_num], axis=1)
result = pd.read_csv("Arsénico Total_TLACOTEPEC.csv", names=header_names)
#Arsenico_Total_TLACOTEPEC_df
Out[79]:
| 2012-12-05 | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2013-01-08 | 0.200000 | 0.260612 | 0.012675 | 0.488889 | 0.098822 | 0.033164 | 0.321608 | 0.010135 | 0.551774 | 0.224612 | 0.063441 | 0.000000 | 0.271698 | 0.023622 | 0.325852 | 0.247215 | 0.100426 | 0.111210 |
| 1 | 2013-03-15 | 0.116667 | 0.546569 | 0.000547 | 0.488889 | 0.175354 | 0.015775 | 0.650754 | 0.023649 | 0.969833 | 0.025488 | 0.064026 | 0.028323 | 0.366038 | 0.007874 | 0.460682 | 0.088107 | 0.067685 | 0.149763 |
| 2 | 2013-06-28 | 0.600000 | 0.131064 | 0.005229 | 0.466667 | 0.161439 | 0.030979 | 0.399497 | 0.016892 | 0.281151 | 0.025488 | 0.064611 | 0.056647 | 0.071698 | 0.039370 | 0.595511 | 0.446957 | 0.034943 | 0.156875 |
| 3 | 2013-08-23 | 0.533333 | 0.067373 | 0.000274 | 0.766667 | 0.120229 | 0.040545 | 0.148241 | 0.013514 | 0.855136 | 0.025488 | 0.072687 | 0.003436 | 0.658491 | 0.023622 | 0.730341 | 0.101390 | 0.058582 | 0.129808 |
| 4 | 2013-09-17 | 0.175000 | 0.082754 | 0.355516 | 0.433333 | 0.189804 | 0.033000 | 0.137353 | 0.746622 | 0.259209 | 0.702509 | 0.080764 | 1.000000 | 0.300000 | 0.960630 | 0.865170 | 0.500017 | 0.082220 | 0.422669 |
| 5 | 2013-10-26 | 0.000000 | 0.000000 | 0.268108 | 0.377778 | 0.188198 | 0.025454 | 0.126466 | 0.250000 | 0.201019 | 0.379928 | 0.067303 | 0.135902 | 0.319811 | 0.102362 | 1.000000 | 0.623204 | 0.070572 | 0.330396 |
| 6 | 2014-02-26 | 0.258333 | 0.293540 | 0.662086 | 0.666667 | 0.075809 | 0.017909 | 0.115578 | 0.000000 | 0.989806 | 0.057348 | 0.053842 | 0.007773 | 0.656604 | 0.000000 | 0.060780 | 0.078324 | 0.058924 | 0.119335 |
| 7 | 2014-03-25 | 0.450000 | 0.097486 | 0.004407 | 0.444444 | 0.011587 | 0.010363 | 0.143216 | 0.005405 | 0.367869 | 0.511350 | 0.040382 | 0.008874 | 0.543396 | 0.007874 | 0.020642 | 0.051187 | 0.047276 | 0.078366 |
| 8 | 2014-05-13 | 0.658333 | 0.111567 | 0.028169 | 0.666667 | 0.016939 | 0.066262 | 0.386935 | 0.030405 | 0.591355 | 0.160892 | 0.026921 | 0.015258 | 0.354717 | 0.007874 | 0.001147 | 0.128313 | 0.035629 | 0.142582 |
| 9 | 2014-06-25 | 0.491667 | 0.253463 | 0.013113 | 0.222222 | 0.145383 | 0.122161 | 0.070352 | 0.003378 | 0.556558 | 0.109120 | 0.013461 | 0.038154 | 0.024528 | 0.000000 | 0.012041 | 0.267568 | 0.023981 | 0.168590 |
| 10 | 2014-08-12 | 0.475000 | 0.096402 | 0.046893 | 0.444444 | 0.198902 | 0.105738 | 0.058291 | 0.611486 | 0.201235 | 0.041418 | 0.000000 | 0.203048 | 0.137736 | 0.212598 | 0.022936 | 0.891003 | 0.012333 | 0.405660 |
| 11 | 2014-09-28 | 0.416667 | 0.184140 | 0.125951 | 0.777778 | 0.150735 | 0.061424 | 0.046231 | 0.229730 | 0.026269 | 0.109120 | 0.035977 | 0.128197 | 0.213208 | 0.102362 | 0.047018 | 0.535367 | 0.013361 | 0.247997 |
| 12 | 2015-03-21 | 0.408333 | 0.216974 | 0.012291 | 0.777778 | 0.210676 | 0.017110 | 0.034171 | 0.058108 | 0.257940 | 0.577061 | 0.071953 | 0.055546 | 0.169811 | 0.017323 | 0.010321 | 0.494733 | 0.014388 | 0.177248 |
| 13 | 2015-05-15 | 0.600000 | 0.336878 | 0.000274 | 0.555556 | 0.116542 | 0.012282 | 0.022111 | 0.041892 | 0.535385 | 0.025886 | 0.069016 | 0.021158 | 0.219811 | 0.001575 | 0.008028 | 0.044342 | 0.015930 | 0.220737 |
| 14 | 2015-06-16 | 0.641667 | 0.097485 | 0.000000 | 0.222222 | 0.005566 | 0.010363 | 0.010050 | 0.020946 | 0.594246 | 0.023895 | 0.066079 | 0.020322 | 0.205660 | 0.007874 | 0.009939 | 0.020895 | 0.017472 | 0.100884 |
| 15 | 2015-07-27 | 0.650000 | 0.142754 | 0.013304 | 0.555556 | 0.190767 | 0.122161 | 0.046482 | 0.337838 | 0.201088 | 0.050577 | 0.063142 | 0.084166 | 0.109434 | 0.212598 | 0.011850 | 0.414182 | 0.115108 | 0.198344 |
| 16 | 2015-09-13 | 0.333333 | 0.096154 | 0.149603 | 0.111111 | 0.155734 | 0.081441 | 0.082915 | 0.594595 | 0.155705 | 0.077260 | 0.240822 | 0.390904 | 0.247170 | 0.291339 | 0.013761 | 0.826794 | 0.323741 | 0.385120 |
| 17 | 2015-10-29 | 0.400000 | 0.162934 | 0.177334 | 0.555556 | 0.139844 | 0.040720 | 0.119347 | 0.310811 | 0.131445 | 0.091995 | 0.392658 | 0.215465 | 0.183962 | 0.133858 | 0.134174 | 1.000000 | 0.096608 | 0.415025 |
| 18 | 2016-03-05 | 0.350000 | 0.026518 | 0.000575 | 0.888889 | 0.080428 | 0.000000 | 0.155779 | 0.035811 | 0.752990 | 0.026284 | 0.544493 | 0.007113 | 1.000000 | 0.007874 | 0.016055 | 0.029726 | 0.026721 | 0.170813 |
| 19 | 2016-04-28 | 0.600000 | 0.448926 | 0.004845 | 0.333333 | 0.303450 | 0.071415 | 0.196399 | 0.128378 | 0.420016 | 0.020908 | 0.696329 | 0.031329 | 0.000000 | 0.033071 | 0.024083 | 0.298918 | 0.351148 | 0.258081 |
| 20 | 2016-06-04 | 0.700000 | 0.170565 | 0.000849 | 0.777778 | 0.000000 | 0.071415 | 0.237018 | 0.022973 | 0.770927 | 0.015532 | 0.848164 | 0.008213 | 0.254717 | 0.023622 | 0.060780 | 0.003496 | 0.675574 | 0.000000 |
| 21 | 2016-07-24 | 0.433333 | 0.214004 | 1.000000 | 0.555556 | 0.102183 | 0.057377 | 0.277638 | 0.058784 | 0.501617 | 1.000000 | 1.000000 | 0.017570 | 0.217925 | 1.000000 | 0.018349 | 0.578024 | 1.000000 | 0.136697 |
| 22 | 2016-09-18 | 0.458333 | 0.113495 | 0.001150 | 0.333333 | 0.233582 | 0.043339 | 0.318258 | 0.094595 | 0.364977 | 0.025886 | 0.854063 | 0.026926 | 0.091509 | 0.017323 | 0.060780 | 0.583150 | 0.851918 | 0.273393 |
| 23 | 2016-11-07 | 0.291667 | 0.057185 | 0.306296 | 0.000000 | 0.074840 | 0.026028 | 0.358878 | 0.108108 | 0.036414 | 0.039427 | 0.708125 | 0.037934 | 0.311321 | 0.070866 | 0.063647 | 0.342032 | 0.703837 | 0.179381 |
| 24 | 2017-03-14 | 0.150000 | 0.335979 | 0.153285 | 0.333333 | 0.125742 | 0.008717 | 0.399497 | 0.025000 | 0.784013 | 0.026085 | 0.562188 | 0.009314 | 0.310377 | 0.033071 | 0.066514 | 0.208995 | 0.555755 | 0.177201 |
| 25 | 2017-04-23 | 1.000000 | 0.137086 | 0.000274 | 0.111111 | 0.005967 | 0.339145 | 0.846734 | 0.033784 | 0.993776 | 0.012744 | 0.416251 | 0.026926 | 0.318868 | 0.055118 | 0.000000 | 0.011210 | 0.407674 | 0.131933 |
| 26 | 2017-05-26 | 0.633333 | 0.221542 | 0.424884 | 0.666667 | 0.036526 | 0.669572 | 1.000000 | 0.023649 | 1.000000 | 0.056551 | 0.270313 | 0.009314 | 0.196226 | 0.055118 | 0.005734 | 0.034033 | 0.259592 | 0.146240 |
| 27 | 2017-07-03 | 0.425000 | 1.000000 | 0.309061 | 0.666667 | 1.000000 | 1.000000 | 0.750000 | 1.000000 | 0.046707 | 0.087614 | 0.124376 | 0.423201 | 0.221698 | 0.448819 | 0.055046 | 0.887960 | 0.111511 | 1.000000 |
| 28 | 2017-08-15 | 0.650000 | 0.155453 | 0.133425 | 0.333333 | 0.286164 | 1.000000 | 0.500000 | 0.216216 | 0.258822 | 0.132218 | 0.088620 | 0.141406 | 0.224528 | 0.133858 | 0.053899 | 0.670013 | 0.002055 | 0.453862 |
| 29 | 2017-10-09 | 0.300000 | 0.396192 | 0.586094 | 0.444444 | 0.083837 | 1.000000 | 0.250000 | 0.594595 | 0.000000 | 0.282358 | 0.052863 | 0.328535 | 0.293396 | 0.291339 | 0.014908 | 0.503809 | 0.068859 | 0.368717 |
| 30 | 2018-02-11 | 0.216667 | 0.155728 | 0.001752 | 0.555556 | 0.131501 | 0.000143 | 0.000000 | 0.014189 | 0.478901 | 0.141179 | 0.047479 | 0.252032 | 0.325472 | 0.007874 | 0.114679 | 0.342077 | 0.052415 | 0.157110 |
| 31 | 2018-03-15 | 0.291667 | 0.201545 | 0.002053 | 0.666667 | 0.190435 | 0.075193 | 0.075377 | 0.002027 | 0.640384 | 0.000000 | 0.042095 | 0.175529 | 0.024528 | 0.055118 | 0.000000 | 0.584652 | 0.035971 | 0.316176 |
| 32 | 2018-04-24 | 0.641667 | 0.035608 | 0.002354 | 0.666667 | 0.012352 | 0.150244 | 0.753769 | 0.026351 | 0.785787 | 0.007036 | 0.036711 | 0.099026 | 0.033962 | 0.017323 | 0.021789 | 0.000000 | 0.023981 | 0.124834 |
| 33 | 2018-06-07 | 0.766667 | 0.100985 | 0.000000 | 0.777778 | 0.005726 | 0.136202 | 0.879397 | 0.035811 | 0.964615 | 0.014071 | 0.031326 | 0.022523 | 0.011321 | 0.070866 | 0.043578 | 0.017032 | 0.011990 | 0.163522 |
| 34 | 2018-10-25 | 0.325000 | 0.166362 | 0.064933 | 0.444444 | 0.185308 | 0.122161 | 0.531407 | 0.121622 | 0.014404 | 0.021107 | 0.025942 | 0.084166 | 0.267925 | 0.070866 | 0.126147 | 0.690832 | 0.000000 | 0.289744 |
| 35 | 2018-12-19 | 0.041667 | 0.122729 | 0.000849 | 0.444444 | 0.103799 | 0.006105 | 0.183417 | 0.018919 | 0.533930 | 0.022202 | 0.020558 | 0.084952 | 0.311321 | 0.001575 | 0.050459 | 0.290798 | 0.002055 | 0.173484 |
| 36 | 2019-02-15 | 0.450000 | 0.020336 | 0.000274 | 1.000000 | 0.005234 | 0.085044 | 0.502513 | 0.050676 | 0.839904 | 0.279241 | 0.423935 | 0.040136 | 0.447170 | 0.017323 | 0.049312 | 0.000436 | 0.042519 | 0.172701 |
In [80]:
result.to_csv("tlalcotepec_merged.csv",index=False, header=False)
In [83]:
os.chdir("fisher-nodo/")
In [85]:
fs = !ls *.png
import IPython.display as dp
# create list of image objects
images = []
for ea in fs:
images.append(dp.Image(filename=ea, format='png'))
# display all images
for ea in images:
dp.display_png(ea)
