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bsmith89/2017-01-22_hplc_method_comparison.ipynb

Last active January 22, 2017 20:00
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HPLC Calibration
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2017-01-22_hplc_method_comparison.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import io\n",
"import pandas as pd\n",
"import statsmodels.formula.api as sm\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"input_str = \"\"\"\n",
"Data Filename\tSample Name\tSample ID\tSample Type\tLevel\tRet. Time\tArea\tHeight\tISTD Area\tISTD Height\tArea Ratio\tHeight Ratio\tConc.\tStd. Conc.\tDeviation\t%Dev\tAccuracy\tQC Check Results\tMark\tPeak Start\tPeak End\tT.Plate#\tHETP\tmeter\tTailing F.\tTailing F(10%)\tResolution\tk'\tSeparation F.\tArea%\tHeight%\tUSP Width\tWidth(5%)\tWidth(10%)\tWidth(50%)\tRelative Retention Time\tStatistic\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_000.lcd\tNo Inject\tBLANK\tUnknown\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_001.lcd\t20 mM Mix\tSTD-0102\tUnknown\t-----\t22.457\t852768\t28727\t-----\t-----\t-----\t-----\t25.88916\t-----\t-----\t-----\t-----\tQuant.Range(High)\t V \t21.4\t23.792\t14031.02\t10.691\t93540.132\t0.987\t1.018\t2.682\t2.69\t1.136\t9.8709\t7.4039\t0.758\t1.058\t0.874\t0.441\t1.465\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_002.lcd\t10 mM Mix\tSTD-0103\tUnknown\t-----\t22.469\t409700\t13911\t-----\t-----\t-----\t-----\t12.43808\t-----\t-----\t-----\t-----\t\t\t21.5\t23.6\t14098.435\t10.639\t93989.57\t0.983\t1.009\t2.691\t2.698\t1.136\t9.809\t7.3752\t0.757\t1.046\t0.869\t0.44\t1.465\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_003.lcd\t5 mM Mix\tSTD-0104\tUnknown\t-----\t22.477\t207605\t7064\t-----\t-----\t-----\t-----\t6.30267\t-----\t-----\t-----\t-----\t\tS \t21.475\t23.408\t14118.088\t10.625\t94120.585\t0.977\t1.001\t2.696\t2.71\t1.136\t9.8237\t7.3978\t0.757\t1.045\t0.868\t0.44\t1.466\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_004.lcd\t2.5 mM Mix\tSTD-0105\tUnknown\t-----\t22.486\t107255\t3656\t-----\t-----\t-----\t-----\t3.25616\t-----\t-----\t-----\t-----\t\t\t21.617\t23.458\t14207.482\t10.558\t94716.544\t0.981\t1.001\t2.702\t2.717\t1.136\t9.8031\t7.3197\t0.755\t1.037\t0.867\t0.44\t1.466\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_005.lcd\t1 mM Mix\tSTD-0106\tUnknown\t-----\t22.49\t40177\t1391\t-----\t-----\t-----\t-----\t1.21973\t-----\t-----\t-----\t-----\t\t\t21.758\t23.258\t14253.039\t10.524\t95020.26\t0.988\t1.005\t2.717\t2.72\t1.136\t9.7247\t7.233\t0.754\t1.007\t0.854\t0.439\t1.467\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_006.lcd\t0.5 mM Mix\tSTD-0107\tUnknown\t-----\t22.486\t20335\t723\t-----\t-----\t-----\t-----\t0.61735\t-----\t-----\t-----\t-----\t\t\t21.892\t23.15\t14581.869\t10.287\t97212.459\t1.023\t1.032\t2.757\t2.728\t1.136\t9.5108\t7.068\t0.745\t0.954\t0.822\t0.434\t1.466\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_007.lcd\t0.25 mM Mix\tSTD-0108\tUnknown\t-----\t22.5\t10213\t375\t-----\t-----\t-----\t-----\t0.31005\t-----\t-----\t-----\t-----\tQuant.Range(Low)\t\t21.992\t23.117\t15203.407\t9.866\t101356.046\t1.056\t1.025\t2.79\t2.729\t1.137\t9.067\t6.6925\t0.73\t0.905\t0.787\t0.427\t1.467\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_008.lcd\t0.1 mM Mix\tSTD-0109\tUnknown\t-----\t22.536\t3205\t127\t-----\t-----\t-----\t-----\t0.0973\t-----\t-----\t-----\t-----\tQuant.Range(Low)\t\t22.117\t22.967\t17590.383\t8.527\t117269.217\t1.029\t0.973\t2.92\t2.72\t1.137\t6.9455\t4.5736\t0.68\t0.77\t0.694\t0.405\t1.47\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_009.lcd\tWater Blank\tBLANK\tUnknown\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_010.lcd\t\tP1-A01\tUnknown\t-----\t22.444\t399159\t9196\t-----\t-----\t-----\t-----\t12.11807\t-----\t-----\t-----\t-----\t\t\t21.383\t23.425\t7747.452\t19.361\t51649.677\t--\t--\t2.019\t3.257\t1.115\t0.7709\t0.2575\t1.02\t0\t0\t0.59\t1.462\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_011.lcd\t\tP1-A02\tUnknown\t-----\t22.452\t136942\t3320\t-----\t-----\t-----\t-----\t4.15743\t-----\t-----\t-----\t-----\t\tTV \t21.55\t23.242\t7567.472\t19.822\t50449.813\t--\t--\t1.097\t3.249\t1.074\t0.3944\t0.201\t1.032\t0\t0\t0.601\t1.464\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_012.lcd\t\tP1-A03\tUnknown\t-----\t22.429\t280515\t8443\t-----\t-----\t-----\t-----\t8.51615\t-----\t-----\t-----\t-----\t\t\t21.492\t23.158\t10605.709\t14.143\t70704.729\t0.913\t0.943\t2.337\t2.655\t1.133\t1.3305\t0.6336\t0.871\t1.141\t0.977\t0.509\t1.462\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_013.lcd\t\tP1-A04\tUnknown\t-----\t22.524\t2802\t3880\t-----\t-----\t-----\t-----\t0.08506\t-----\t-----\t-----\t-----\tQuant.Range(Low)\tT \t21.133\t23.233\t3325.906\t45.1\t22172.706\t0.827\t0.802\t1.655\t3.737\t1.124\t0.007\t0.2139\t1.562\t1.485\t1.343\t0.725\t1.468\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_014.lcd\t\tP1-A05\tUnknown\t-----\t22.42\t124420\t3036\t-----\t-----\t-----\t-----\t3.77725\t-----\t-----\t-----\t-----\t\t\t21.492\t23.317\t6688.794\t22.426\t44591.959\t0.902\t0.893\t1.876\t7.136\t1.1\t0.7459\t0.2736\t1.097\t1.382\t1.229\t0.629\t1.462\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_015.lcd\t\tP1-A06\tUnknown\t-----\t22.415\t121810\t3035\t-----\t-----\t-----\t-----\t3.69802\t-----\t-----\t-----\t-----\t\t\t21.508\t23.225\t7003.742\t21.417\t46691.614\t0.913\t0.904\t1.869\t6.822\t1.1\t0.6841\t0.2624\t1.071\t1.341\t1.196\t0.619\t1.462\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_016.lcd\t\tP1-A07\tUnknown\t-----\t22.439\t378203\t11270\t-----\t-----\t-----\t-----\t11.48187\t-----\t-----\t-----\t-----\t\t\t21.55\t23.267\t10462.838\t14.336\t69752.254\t0.959\t0.971\t2.03\t6.968\t1.102\t1.6615\t0.7794\t0.878\t1.162\t0.995\t0.51\t1.463\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_017.lcd\t\tP1-A08\tUnknown\t-----\t22.448\t57960\t1612\t-----\t-----\t-----\t-----\t1.75961\t-----\t-----\t-----\t-----\t\t\t21.7\t23.25\t9047.398\t16.579\t60315.988\t1.062\t1.04\t2.025\t3.211\t1.116\t0.269\t0.1285\t0.944\t1.217\t1.064\t0.551\t1.463\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_018.lcd\t\tP1-A09\tUnknown\t-----\t22.45\t187934\t5425\t-----\t-----\t-----\t-----\t5.70547\t-----\t-----\t-----\t-----\t\t\t21.633\t23.342\t9943.624\t15.085\t66290.828\t1.003\t0.996\t1.796\t3.261\t1.077\t0.8484\t0.4288\t0.901\t1.198\t1.024\t0.524\t1.464\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_019.lcd\t\tP1-A10\tUnknown\t-----\t22.437\t133258\t3734\t-----\t-----\t-----\t-----\t4.04559\t-----\t-----\t-----\t-----\t\t\t21.6\t23.092\t9008.38\t16.651\t60055.869\t0.947\t0.939\t1.905\t3.23\t1.125\t0.6401\t0.291\t0.946\t1.211\t1.055\t0.549\t1.464\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_020.lcd\t\tP1-A11\tUnknown\t-----\t22.396\t107347\t2630\t-----\t-----\t-----\t-----\t3.25896\t-----\t-----\t-----\t-----\t\t\t21.617\t23.35\t6440.648\t23.29\t42937.652\t0.982\t0.96\t1.385\t3.217\t1.077\t0.4823\t0.1887\t1.116\t1.324\t1.177\t0.647\t1.461\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_021.lcd\t\tP1-A12\tUnknown\t-----\t22.373\t75492\t1780\t-----\t-----\t-----\t-----\t2.29186\t-----\t-----\t-----\t-----\t\t\t21.533\t23.15\t5920.61\t25.335\t39470.736\t0.945\t0.938\t1.629\t2.654\t1.119\t0.278\t0.1197\t1.163\t1.314\t1.191\t0.685\t1.46\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_022.lcd\t\tP1-B01\tUnknown\t-----\t22.479\t169213\t4565\t-----\t-----\t-----\t-----\t5.13713\t-----\t-----\t-----\t-----\t\t\t21.675\t23.392\t8345.975\t17.973\t55639.832\t1.008\t1.009\t1.77\t3.212\t1.079\t0.9239\t0.4309\t0.984\t1.247\t1.093\t0.572\t1.469\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_023.lcd\t\tP1-B02\tUnknown\t-----\t22.444\t180236\t4570\t-----\t-----\t-----\t-----\t5.47178\t-----\t-----\t-----\t-----\t\t\t21.567\t23.058\t7850.693\t19.107\t52337.956\t--\t--\t1.895\t3.184\t1.126\t0.5508\t0.2563\t1.013\t0\t0\t0.586\t1.463\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_024.lcd\t\tP1-B03\tUnknown\t-----\t22.417\t81042\t1624\t-----\t-----\t-----\t-----\t2.46036\t-----\t-----\t-----\t-----\t\t\t21.667\t23.15\t4068.578\t36.868\t27123.853\t--\t--\t1.527\t3.243\t1.109\t0.3615\t0.1149\t1.406\t0\t0\t0.806\t1.461\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_025.lcd\t\tP1-B04\tUnknown\t-----\t22.454\t152047\t3752\t-----\t-----\t-----\t-----\t4.61598\t-----\t-----\t-----\t-----\t\t\t21.642\t23.283\t6865.622\t21.848\t45770.814\t1.044\t1.019\t1.741\t3.238\t1.111\t0.6656\t0.2808\t1.084\t1.366\t1.182\t0.628\t1.465\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_026.lcd\t\tP1-B05\tUnknown\t-----\t22.423\t175563\t4801\t-----\t-----\t-----\t-----\t5.32992\t-----\t-----\t-----\t-----\t\t\t21.608\t23.158\t8752.498\t17.138\t58349.988\t1.041\t0.995\t1.834\t3.237\t1.109\t0.8052\t0.3699\t0.959\t1.289\t1.089\t0.558\t1.463\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_027.lcd\t\tP1-B06\tUnknown\t-----\t22.401\t96645\t2572\t-----\t-----\t-----\t-----\t2.93403\t-----\t-----\t-----\t-----\t\t\t21.583\t23.2\t7899.697\t18.988\t52664.648\t0.918\t0.916\t1.64\t52.35\t1.094\t0.4703\t0.1955\t1.008\t1.243\t1.109\t0.584\t1.461\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_028.lcd\t\tP1-B07\tUnknown\t-----\t22.427\t125766\t3461\t-----\t-----\t-----\t-----\t3.81813\t-----\t-----\t-----\t-----\t\t\t21.608\t23.2\t8695.04\t17.251\t57966.933\t0.986\t0.985\t1.743\t3.263\t1.118\t0.5176\t0.2377\t0.962\t1.233\t1.079\t0.559\t1.463\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_029.lcd\t\tP1-B08\tUnknown\t-----\t22.44\t83561\t2089\t-----\t-----\t-----\t-----\t2.53684\t-----\t-----\t-----\t-----\t\t\t21.683\t23.325\t6692.237\t22.414\t44614.91\t1.059\t1.066\t1.605\t3.254\t1.082\t0.4029\t0.1732\t1.097\t1.315\t1.174\t0.625\t1.465\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_030.lcd\t\tP1-B09\tUnknown\t-----\t22.411\t324192\t9083\t-----\t-----\t-----\t-----\t9.84215\t-----\t-----\t-----\t-----\t\t\t21.583\t23.75\t9566.123\t15.68\t63774.151\t1.015\t0.993\t1.729\t3.209\t1.116\t0.9779\t0.5025\t0.917\t1.251\t1.055\t0.532\t1.463\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_031.lcd\t\tP1-B10\tUnknown\t-----\t22.372\t246614\t6380\t-----\t-----\t-----\t-----\t7.48695\t-----\t-----\t-----\t-----\t\t\t21.458\t23.1\t7826.482\t19.166\t52176.55\t0.853\t0.847\t1.959\t67.437\t1.085\t0.8645\t0.3686\t1.012\t1.293\t1.16\t0.59\t1.46\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_032.lcd\t\tP1-B11\tUnknown\t-----\t22.355\t134931\t4232\t-----\t-----\t-----\t-----\t4.09637\t-----\t-----\t-----\t-----\t\t\t21.533\t22.858\t11128.721\t13.479\t74191.475\t0.845\t0.866\t2.123\t3.192\t1.126\t0.4838\t0.2746\t0.848\t1.048\t0.931\t0.496\t1.459\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_033.lcd\t\tP1-B12\tUnknown\t-----\t22.359\t163141\t4909\t-----\t-----\t-----\t-----\t4.95281\t-----\t-----\t-----\t-----\t\t\t21.433\t22.925\t10761.545\t13.939\t71743.63\t0.837\t0.86\t2.155\t3.221\t1.118\t0.7451\t0.3747\t0.862\t1.165\t1.013\t0.503\t1.46\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_034.lcd\t\tP1-C01\tUnknown\t-----\t22.369\t53816\t1624\t-----\t-----\t-----\t-----\t1.6338\t-----\t-----\t-----\t-----\t\tTV \t21.608\t23.067\t10616.418\t14.129\t70776.122\t0.895\t0.911\t1.804\t4.431\t1.069\t0.2237\t0.1247\t0.868\t1.129\t0.984\t0.507\t1.461\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_035.lcd\t\tP1-C02\tUnknown\t-----\t22.355\t143438\t3891\t-----\t-----\t-----\t-----\t4.35465\t-----\t-----\t-----\t-----\t\t\t21.5\t23.517\t9384.398\t15.984\t62562.655\t1.173\t0.953\t1.791\t3.205\t1.073\t0.7089\t0.3211\t0.923\t1.537\t1.072\t0.536\t1.46\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_036.lcd\t\tP1-C03\tUnknown\t-----\t22.367\t180422\t5252\t-----\t-----\t-----\t-----\t5.47743\t-----\t-----\t-----\t-----\t\tT \t21.45\t22.942\t10037.002\t14.945\t66913.345\t0.837\t0.853\t2.069\t3.217\t1.117\t0.7966\t0.3909\t0.893\t1.18\t1.04\t0.522\t1.458\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_037.lcd\t\tP1-C04\tUnknown\t-----\t22.361\t145467\t3889\t-----\t-----\t-----\t-----\t4.41623\t-----\t-----\t-----\t-----\t\t\t21.433\t22.892\t8478.199\t17.692\t56521.329\t0.806\t0.809\t1.803\t4.07\t1.11\t0.4805\t0.2381\t0.971\t1.219\t1.114\t0.571\t1.461\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_038.lcd\t\tP1-C05\tUnknown\t-----\t22.341\t50167\t2053\t-----\t-----\t-----\t-----\t1.52301\t-----\t-----\t-----\t-----\t\tT \t21.358\t23.008\t5122.573\t29.282\t34150.484\t0.879\t0.871\t1.661\t3.238\t1.12\t0.172\t0.1416\t1.249\t1.317\t1.209\t0.677\t1.459\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_039.lcd\t\tP1-C06\tUnknown\t-----\t22.347\t472351\t13034\t-----\t-----\t-----\t-----\t14.34009\t-----\t-----\t-----\t-----\t\t\t21.35\t23.042\t9260.356\t16.198\t61735.706\t0.857\t0.871\t1.976\t9.872\t1.095\t1.647\t0.7815\t0.929\t1.283\t1.119\t0.541\t1.459\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_040.lcd\t\tP1-C07\tUnknown\t-----\t22.368\t269045\t5495\t-----\t-----\t-----\t-----\t8.16795\t-----\t-----\t-----\t-----\t\t\t21.308\t22.675\t855.412\t175.354\t5702.746\t--\t--\t0.941\t3.256\t1.131\t0.6487\t0.2791\t3.059\t0\t0\t0\t1.456\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_041.lcd\t\tP1-C08\tUnknown\t-----\t22.408\t94767\t2300\t-----\t-----\t-----\t-----\t2.87704\t-----\t-----\t-----\t-----\t\tT \t21.533\t22.808\t3345.018\t44.843\t22300.118\t--\t--\t1.273\t4.195\t1.074\t0.4212\t0.1872\t1.55\t0\t0\t0\t1.464\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_042.lcd\t\tP1-C09\tUnknown\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_043.lcd\t\tP1-C10\tUnknown\t-----\t22.353\t170922\t4505\t-----\t-----\t-----\t-----\t5.18901\t-----\t-----\t-----\t-----\t\t\t21.5\t23.058\t7923.322\t18.931\t52822.143\t0.874\t0.873\t1.808\t3.217\t1.114\t0.6934\t0.3447\t1.004\t1.257\t1.129\t0.585\t1.463\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_044.lcd\t\tP1-C11\tUnknown\t-----\t22.337\t153365\t4752\t-----\t-----\t-----\t-----\t4.656\t-----\t-----\t-----\t-----\t\t\t21.608\t22.867\t10452.157\t14.351\t69681.048\t0.914\t0.932\t1.806\t3.224\t1.119\t0.4767\t0.3337\t0.874\t1.013\t0.911\t0.514\t1.46\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_045.lcd\t\tP1-C12\tUnknown\t-----\t22.338\t100395\t2842\t-----\t-----\t-----\t-----\t3.04788\t-----\t-----\t-----\t-----\t\t\t21.592\t22.967\t8913.741\t16.828\t59424.938\t0.901\t0.91\t1.782\t3.218\t1.077\t0.3112\t0.1756\t0.946\t1.147\t1.032\t0.555\t1.459\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_046.lcd\t\tP1-D01\tUnknown\t-----\t22.363\t159153\t3894\t-----\t-----\t-----\t-----\t4.83171\t-----\t-----\t-----\t-----\t\t\t21.508\t23.092\t6474.984\t23.166\t43166.561\t0.901\t0.896\t1.676\t30.947\t1.089\t0.6012\t0.2647\t1.112\t1.299\t1.182\t0.646\t1.462\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_047.lcd\t\tP1-D02\tUnknown\t-----\t22.36\t113923\t3172\t-----\t-----\t-----\t-----\t3.45858\t-----\t-----\t-----\t-----\t\t\t21.675\t23.092\t8490.61\t17.667\t56604.066\t1.012\t1.014\t1.744\t3.256\t1.077\t0.4783\t0.2411\t0.971\t1.152\t1.036\t0.568\t1.46\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_048.lcd\t\tP1-D03\tUnknown\t-----\t22.351\t184315\t5176\t-----\t-----\t-----\t-----\t5.59563\t-----\t-----\t-----\t-----\t\t\t21.608\t23.108\t8729.171\t17.184\t58194.476\t0.999\t1.01\t1.808\t42.815\t1.085\t0.8802\t0.4728\t0.957\t1.169\t1.036\t0.557\t1.461\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_049.lcd\t\tP1-D04\tUnknown\t-----\t22.433\t150317\t3677\t-----\t-----\t-----\t-----\t4.56347\t-----\t-----\t-----\t-----\t\t\t21.533\t23.15\t7029.288\t21.339\t46861.92\t0.855\t0.848\t1.895\t3.248\t1.115\t0.5204\t0.2547\t1.07\t1.345\t1.225\t0.625\t1.467\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_050.lcd\t\tP1-D05\tUnknown\t-----\t22.381\t107950\t2938\t-----\t-----\t-----\t-----\t3.27724\t-----\t-----\t-----\t-----\t\t\t21.708\t23.242\t8277.384\t18.122\t55182.563\t1.066\t1.06\t1.632\t3.238\t1.079\t0.5908\t0.2962\t0.984\t1.201\t1.064\t0.577\t1.463\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_051.lcd\t\tP1-D06\tUnknown\t-----\t22.348\t480174\t13781\t-----\t-----\t-----\t-----\t14.5776\t-----\t-----\t-----\t-----\t\t\t21.517\t23.167\t9446.227\t15.879\t62974.844\t0.992\t1.01\t1.778\t3.226\t1.109\t1.9798\t1.2013\t0.92\t1.189\t1.033\t0.534\t1.46\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_052.lcd\t\tP1-D07\tUnknown\t-----\t22.252\t35556\t825\t-----\t-----\t-----\t-----\t1.07946\t-----\t-----\t-----\t-----\t\t\t21.567\t22.975\t6251.578\t23.994\t41677.185\t0.972\t0.96\t1.539\t3.22\t1.107\t0.1885\t0.0967\t1.126\t1.192\t1.11\t0.727\t1.455\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_053.lcd\t\tP1-D08\tUnknown\t-----\t22.326\t126683\t3375\t-----\t-----\t-----\t-----\t3.84596\t-----\t-----\t-----\t-----\t\t\t21.6\t23.075\t7747.169\t19.362\t51647.793\t0.992\t0.993\t1.752\t3.221\t1.108\t0.5028\t0.2467\t1.015\t1.217\t1.099\t0.589\t1.461\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_054.lcd\t\tP1-D09\tUnknown\t-----\t22.346\t51407\t1578\t-----\t-----\t-----\t-----\t1.56067\t-----\t-----\t-----\t-----\t\t\t21.783\t23.05\t10053.106\t14.921\t67020.703\t1.137\t1.132\t1.965\t3.244\t1.107\t0.2923\t0.1567\t0.891\t1.022\t0.922\t0.522\t1.461\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_055.lcd\t\tP1-D10\tUnknown\t-----\t22.311\t207070\t5580\t-----\t-----\t-----\t-----\t6.28642\t-----\t-----\t-----\t-----\t\t\t21.533\t23.017\t7942.565\t18.886\t52950.432\t0.966\t0.964\t1.768\t3.197\t1.105\t0.6741\t0.3336\t1.001\t1.213\t1.09\t0.578\t1.459\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_056.lcd\t\tP1-D11\tUnknown\t-----\t22.321\t40732\t1188\t-----\t-----\t-----\t-----\t1.23659\t-----\t-----\t-----\t-----\t\t\t21.683\t23.15\t9208.6\t16.289\t61390.668\t1.084\t1.07\t1.943\t3.241\t1.081\t0.201\t0.1078\t0.93\t1.11\t0.993\t0.542\t1.461\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_057.lcd\t\tP1-D12\tUnknown\t-----\t22.336\t49846\t1605\t-----\t-----\t-----\t-----\t1.51329\t-----\t-----\t-----\t-----\t\t\t21.792\t22.942\t11016.314\t13.616\t73442.09\t1.11\t1.103\t1.677\t3.202\t1.082\t0.401\t0.1916\t0.851\t0.976\t0.881\t0.494\t1.461\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_058.lcd\t20 mM Mix\tSTD-0102\tUnknown\t-----\t22.362\t791874\t24367\t-----\t-----\t-----\t-----\t24.04048\t-----\t-----\t-----\t-----\tQuant.Range(High)\t\t21.5\t23.258\t11351.98\t13.214\t75679.867\t1.144\t1.158\t2.429\t2.674\t1.138\t9.3172\t7.3494\t0.84\t1.152\t0.98\t0.486\t1.463\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_059.lcd\t10 mM Mix\tSTD-0103\tUnknown\t-----\t22.378\t380793\t11880\t-----\t-----\t-----\t-----\t11.56048\t-----\t-----\t-----\t-----\t\t\t21.533\t23.142\t11483.214\t13.063\t76554.761\t1.111\t1.129\t2.446\t2.682\t1.138\t9.3376\t7.365\t0.835\t1.116\t0.958\t0.484\t1.463\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_060.lcd\t5 mM Mix\tSTD-0104\tUnknown\t-----\t22.387\t182495\t5859\t-----\t-----\t-----\t-----\t5.54037\t-----\t-----\t-----\t-----\t\t\t21.642\t23.075\t11833.542\t12.676\t78890.281\t1.095\t1.112\t2.469\t2.689\t1.138\t8.9674\t7.2903\t0.823\t1.055\t0.919\t0.477\t1.464\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_061.lcd\t2.5 mM Mix\tSTD-0105\tUnknown\t-----\t22.387\t92178\t2994\t-----\t-----\t-----\t-----\t2.79844\t-----\t-----\t-----\t-----\t\t\t21.658\t23.075\t11992.995\t12.507\t79953.302\t1.108\t1.118\t2.474\t2.693\t1.137\t8.9899\t7.1545\t0.818\t1.031\t0.902\t0.474\t1.464\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_062.lcd\t1 mM Mix\tSTD-0106\tUnknown\t-----\t22.4\t32787\t1113\t-----\t-----\t-----\t-----\t0.99537\t-----\t-----\t-----\t-----\t\t\t21.883\t22.983\t12800.367\t11.718\t85335.782\t1.088\t1.098\t2.58\t2.696\t1.138\t9.6801\t7.288\t0.792\t0.95\t0.852\t0.456\t1.464\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_063.lcd\t0.5 mM Mix\tSTD-0107\tUnknown\t-----\t22.398\t18743\t605\t-----\t-----\t-----\t-----\t0.56901\t-----\t-----\t-----\t-----\t\t\t22\t23.142\t11556.108\t12.98\t77040.721\t--\t--\t2.548\t2.698\t1.138\t11.7505\t7.7461\t0.833\t0\t0\t0.477\t1.464\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_064.lcd\t0.25 mM Mix\tSTD-0108\tUnknown\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"2016-11-14_065.lcd\t0.1 mM Mix\tSTD-0109\tUnknown\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t-----\t\t*\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"input_handle = io.StringIO('\\n'.join(line for line in input_str.split('\\n') if line.strip()))\n",
"all_data = (pd.read_table(input_handle,\n",
" usecols=[0, 2, 6], names=['injection_id', 'sample_id', 'area'], skiprows=1,\n",
" index_col='injection_id',\n",
" na_values=['-----']))\n",
"all_data.area.fillna(0, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sample_id</th>\n",
" <th>area</th>\n",
" <th>concentration</th>\n",
" </tr>\n",
" <tr>\n",
" <th>injection_id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-11-14_001.lcd</th>\n",
" <td>STD-0102</td>\n",
" <td>852768.0</td>\n",
" <td>20.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_002.lcd</th>\n",
" <td>STD-0103</td>\n",
" <td>409700.0</td>\n",
" <td>10.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_003.lcd</th>\n",
" <td>STD-0104</td>\n",
" <td>207605.0</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_004.lcd</th>\n",
" <td>STD-0105</td>\n",
" <td>107255.0</td>\n",
" <td>2.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_005.lcd</th>\n",
" <td>STD-0106</td>\n",
" <td>40177.0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_006.lcd</th>\n",
" <td>STD-0107</td>\n",
" <td>20335.0</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_007.lcd</th>\n",
" <td>STD-0108</td>\n",
" <td>10213.0</td>\n",
" <td>0.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_008.lcd</th>\n",
" <td>STD-0109</td>\n",
" <td>3205.0</td>\n",
" <td>0.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_058.lcd</th>\n",
" <td>STD-0102</td>\n",
" <td>791874.0</td>\n",
" <td>20.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_059.lcd</th>\n",
" <td>STD-0103</td>\n",
" <td>380793.0</td>\n",
" <td>10.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_060.lcd</th>\n",
" <td>STD-0104</td>\n",
" <td>182495.0</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_061.lcd</th>\n",
" <td>STD-0105</td>\n",
" <td>92178.0</td>\n",
" <td>2.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_062.lcd</th>\n",
" <td>STD-0106</td>\n",
" <td>32787.0</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_063.lcd</th>\n",
" <td>STD-0107</td>\n",
" <td>18743.0</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_064.lcd</th>\n",
" <td>STD-0108</td>\n",
" <td>0.0</td>\n",
" <td>0.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_065.lcd</th>\n",
" <td>STD-0109</td>\n",
" <td>0.0</td>\n",
" <td>0.10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sample_id area concentration\n",
"injection_id \n",
"2016-11-14_001.lcd STD-0102 852768.0 20.00\n",
"2016-11-14_002.lcd STD-0103 409700.0 10.00\n",
"2016-11-14_003.lcd STD-0104 207605.0 5.00\n",
"2016-11-14_004.lcd STD-0105 107255.0 2.50\n",
"2016-11-14_005.lcd STD-0106 40177.0 1.00\n",
"2016-11-14_006.lcd STD-0107 20335.0 0.50\n",
"2016-11-14_007.lcd STD-0108 10213.0 0.25\n",
"2016-11-14_008.lcd STD-0109 3205.0 0.10\n",
"2016-11-14_058.lcd STD-0102 791874.0 20.00\n",
"2016-11-14_059.lcd STD-0103 380793.0 10.00\n",
"2016-11-14_060.lcd STD-0104 182495.0 5.00\n",
"2016-11-14_061.lcd STD-0105 92178.0 2.50\n",
"2016-11-14_062.lcd STD-0106 32787.0 1.00\n",
"2016-11-14_063.lcd STD-0107 18743.0 0.50\n",
"2016-11-14_064.lcd STD-0108 0.0 0.25\n",
"2016-11-14_065.lcd STD-0109 0.0 0.10"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"known = pd.Series([20, 10, 5, 2.5, 1, 0.5, 0.25, 0.1],\n",
" index=['STD-0102', 'STD-0103',\n",
" 'STD-0104', 'STD-0105',\n",
" 'STD-0106', 'STD-0107',\n",
" 'STD-0108', 'STD-0109'],\n",
" name='concentration')\n",
"\n",
"standards = all_data[lambda x: x.sample_id.str.contains('STD-0')].join(known, on='sample_id')\n",
"standards"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-100, 1000000.0)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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MEckSkd9EJFtErgi9vogMF5G1IrJDROaLyCkh9fVE5FkR2Swiv4jIuyJyaHQ/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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11395a5c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"standards = standards[lambda x: x.area > 0]\n",
"\n",
"fit0 = sm.wls('area ~ concentration - 1', data=standards,\n",
" ).fit()\n",
"fit1 = sm.wls('area ~ concentration - 1', data=standards,\n",
" weights=standards.concentration ** (-2)\n",
" ).fit()\n",
"\n",
"\n",
"fig, axs = plt.subplots(2, 2)\n",
"\n",
"axs[0, 0].scatter('concentration', 'area', data=standards)\n",
"axs[0, 1].scatter('concentration', 'area', data=standards)\n",
"axs[1, 0].scatter('concentration', 'area', data=standards)\n",
"axs[1, 1].scatter('concentration', 'area', data=standards)\n",
"\n",
"xx = np.linspace(standards.concentration.min(), standards.concentration.max())\n",
"axs[0, 0].plot(xx, fit0.params[0] * xx)\n",
"axs[0, 1].plot(xx, fit0.params[0] * xx)\n",
"axs[1, 0].plot(xx, fit1.params[0] * xx)\n",
"axs[1, 1].plot(xx, fit1.params[0] * xx)\n",
"\n",
"axs[0, 0].set_xscale('log')\n",
"axs[0, 0].set_yscale('symlog', linthreshy=1000, linscaley=0.1)\n",
"axs[0, 0].set_ylim(-100)\n",
"axs[1, 0].set_xscale('log')\n",
"axs[1, 0].set_yscale('symlog', linthreshy=1000, linscaley=0.1)\n",
"axs[1, 0].set_ylim(-100)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/bjsmith/Projects/longev/.venv/lib/python3.5/site-packages/scipy/stats/stats.py:1327: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=14\n",
" \"anyway, n=%i\" % int(n))\n"
]
},
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>WLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>area</td> <th> R-squared: </th> <td> 0.993</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>WLS</td> <th> Adj. R-squared: </th> <td> 0.993</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 1865.</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sun, 22 Jan 2017</td> <th> Prob (F-statistic):</th> <td>2.00e-15</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>15:00:06</td> <th> Log-Likelihood: </th> <td> -143.60</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 14</td> <th> AIC: </th> <td> 289.2</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 13</td> <th> BIC: </th> <td> 289.8</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>concentration</th> <td> 3.879e+04</td> <td> 898.365</td> <td> 43.181</td> <td> 0.000</td> <td> 3.69e+04</td> <td> 4.07e+04</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td> 2.261</td> <th> Durbin-Watson: </th> <td> 1.299</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.323</td> <th> Jarque-Bera (JB): </th> <td> 1.494</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td>-0.777</td> <th> Prob(JB): </th> <td> 0.474</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 2.614</td> <th> Cond. No. </th> <td> 1.00</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" WLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: area R-squared: 0.993\n",
"Model: WLS Adj. R-squared: 0.993\n",
"Method: Least Squares F-statistic: 1865.\n",
"Date: Sun, 22 Jan 2017 Prob (F-statistic): 2.00e-15\n",
"Time: 15:00:06 Log-Likelihood: -143.60\n",
"No. Observations: 14 AIC: 289.2\n",
"Df Residuals: 13 BIC: 289.8\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"=================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"---------------------------------------------------------------------------------\n",
"concentration 3.879e+04 898.365 43.181 0.000 3.69e+04 4.07e+04\n",
"==============================================================================\n",
"Omnibus: 2.261 Durbin-Watson: 1.299\n",
"Prob(Omnibus): 0.323 Jarque-Bera (JB): 1.494\n",
"Skew: -0.777 Prob(JB): 0.474\n",
"Kurtosis: 2.614 Cond. No. 1.00\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fit1.summary()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = all_data[lambda x: ~x.sample_id.str.contains('BLANK')].copy()\n",
"data['concentration_est0'] = data.area / fit0.params[0]\n",
"data['concentration_est1'] = data.area / fit1.params[0]\n",
"data = data.join(known, on='sample_id')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sample_id</th>\n",
" <th>area</th>\n",
" <th>concentration_est0</th>\n",
" <th>concentration_est1</th>\n",
" <th>concentration</th>\n",
" </tr>\n",
" <tr>\n",
" <th>injection_id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-11-14_001.lcd</th>\n",
" <td>STD-0102</td>\n",
" <td>852768.0</td>\n",
" <td>20.955538</td>\n",
" <td>21.982741</td>\n",
" <td>20.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_002.lcd</th>\n",
" <td>STD-0103</td>\n",
" <td>409700.0</td>\n",
" <td>10.067784</td>\n",
" <td>10.561289</td>\n",
" <td>10.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_003.lcd</th>\n",
" <td>STD-0104</td>\n",
" <td>207605.0</td>\n",
" <td>5.101592</td>\n",
" <td>5.351663</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_004.lcd</th>\n",
" <td>STD-0105</td>\n",
" <td>107255.0</td>\n",
" <td>2.635636</td>\n",
" <td>2.764830</td>\n",
" <td>2.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_005.lcd</th>\n",
" <td>STD-0106</td>\n",
" <td>40177.0</td>\n",
" <td>0.987292</td>\n",
" <td>1.035687</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_006.lcd</th>\n",
" <td>STD-0107</td>\n",
" <td>20335.0</td>\n",
" <td>0.499703</td>\n",
" <td>0.524198</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_007.lcd</th>\n",
" <td>STD-0108</td>\n",
" <td>10213.0</td>\n",
" <td>0.250970</td>\n",
" <td>0.263272</td>\n",
" <td>0.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_008.lcd</th>\n",
" <td>STD-0109</td>\n",
" <td>3205.0</td>\n",
" <td>0.078758</td>\n",
" <td>0.082619</td>\n",
" <td>0.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_010.lcd</th>\n",
" <td>P1-A01</td>\n",
" <td>399159.0</td>\n",
" <td>9.808754</td>\n",
" <td>10.289562</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_011.lcd</th>\n",
" <td>P1-A02</td>\n",
" <td>136942.0</td>\n",
" <td>3.365151</td>\n",
" <td>3.530105</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_012.lcd</th>\n",
" <td>P1-A03</td>\n",
" <td>280515.0</td>\n",
" <td>6.893250</td>\n",
" <td>7.231145</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_013.lcd</th>\n",
" <td>P1-A04</td>\n",
" <td>2802.0</td>\n",
" <td>0.068855</td>\n",
" <td>0.072230</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_014.lcd</th>\n",
" <td>P1-A05</td>\n",
" <td>124420.0</td>\n",
" <td>3.057441</td>\n",
" <td>3.207312</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_015.lcd</th>\n",
" <td>P1-A06</td>\n",
" <td>121810.0</td>\n",
" <td>2.993304</td>\n",
" <td>3.140031</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_016.lcd</th>\n",
" <td>P1-A07</td>\n",
" <td>378203.0</td>\n",
" <td>9.293791</td>\n",
" <td>9.749356</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_017.lcd</th>\n",
" <td>P1-A08</td>\n",
" <td>57960.0</td>\n",
" <td>1.424283</td>\n",
" <td>1.494099</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_018.lcd</th>\n",
" <td>P1-A09</td>\n",
" <td>187934.0</td>\n",
" <td>4.618206</td>\n",
" <td>4.844582</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_019.lcd</th>\n",
" <td>P1-A10</td>\n",
" <td>133258.0</td>\n",
" <td>3.274622</td>\n",
" <td>3.435138</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_020.lcd</th>\n",
" <td>P1-A11</td>\n",
" <td>107347.0</td>\n",
" <td>2.637897</td>\n",
" <td>2.767202</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_021.lcd</th>\n",
" <td>P1-A12</td>\n",
" <td>75492.0</td>\n",
" <td>1.855107</td>\n",
" <td>1.946041</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_022.lcd</th>\n",
" <td>P1-B01</td>\n",
" <td>169213.0</td>\n",
" <td>4.158164</td>\n",
" <td>4.361990</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_023.lcd</th>\n",
" <td>P1-B02</td>\n",
" <td>180236.0</td>\n",
" <td>4.429039</td>\n",
" <td>4.646142</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_024.lcd</th>\n",
" <td>P1-B03</td>\n",
" <td>81042.0</td>\n",
" <td>1.991490</td>\n",
" <td>2.089109</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_025.lcd</th>\n",
" <td>P1-B04</td>\n",
" <td>152047.0</td>\n",
" <td>3.736335</td>\n",
" <td>3.919483</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_026.lcd</th>\n",
" <td>P1-B05</td>\n",
" <td>175563.0</td>\n",
" <td>4.314206</td>\n",
" <td>4.525681</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_027.lcd</th>\n",
" <td>P1-B06</td>\n",
" <td>96645.0</td>\n",
" <td>2.374911</td>\n",
" <td>2.491325</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_028.lcd</th>\n",
" <td>P1-B07</td>\n",
" <td>125766.0</td>\n",
" <td>3.090517</td>\n",
" <td>3.242009</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_029.lcd</th>\n",
" <td>P1-B08</td>\n",
" <td>83561.0</td>\n",
" <td>2.053391</td>\n",
" <td>2.154044</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_030.lcd</th>\n",
" <td>P1-B09</td>\n",
" <td>324192.0</td>\n",
" <td>7.966549</td>\n",
" <td>8.357055</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_031.lcd</th>\n",
" <td>P1-B10</td>\n",
" <td>246614.0</td>\n",
" <td>6.060182</td>\n",
" <td>6.357241</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_036.lcd</th>\n",
" <td>P1-C03</td>\n",
" <td>180422.0</td>\n",
" <td>4.433609</td>\n",
" <td>4.650937</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_037.lcd</th>\n",
" <td>P1-C04</td>\n",
" <td>145467.0</td>\n",
" <td>3.574641</td>\n",
" <td>3.749863</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_038.lcd</th>\n",
" <td>P1-C05</td>\n",
" <td>50167.0</td>\n",
" <td>1.232781</td>\n",
" <td>1.293210</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_039.lcd</th>\n",
" <td>P1-C06</td>\n",
" <td>472351.0</td>\n",
" <td>11.607342</td>\n",
" <td>12.176313</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_040.lcd</th>\n",
" <td>P1-C07</td>\n",
" <td>269045.0</td>\n",
" <td>6.611391</td>\n",
" <td>6.935470</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_041.lcd</th>\n",
" <td>P1-C08</td>\n",
" <td>94767.0</td>\n",
" <td>2.328762</td>\n",
" <td>2.442913</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_042.lcd</th>\n",
" <td>P1-C09</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_043.lcd</th>\n",
" <td>P1-C10</td>\n",
" <td>170922.0</td>\n",
" <td>4.200161</td>\n",
" <td>4.406045</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_044.lcd</th>\n",
" <td>P1-C11</td>\n",
" <td>153365.0</td>\n",
" <td>3.768723</td>\n",
" <td>3.953459</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_045.lcd</th>\n",
" <td>P1-C12</td>\n",
" <td>100395.0</td>\n",
" <td>2.467062</td>\n",
" <td>2.587993</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_046.lcd</th>\n",
" <td>P1-D01</td>\n",
" <td>159153.0</td>\n",
" <td>3.910954</td>\n",
" <td>4.102662</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_047.lcd</th>\n",
" <td>P1-D02</td>\n",
" <td>113923.0</td>\n",
" <td>2.799493</td>\n",
" <td>2.936719</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_048.lcd</th>\n",
" <td>P1-D03</td>\n",
" <td>184315.0</td>\n",
" <td>4.529274</td>\n",
" <td>4.751291</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_049.lcd</th>\n",
" <td>P1-D04</td>\n",
" <td>150317.0</td>\n",
" <td>3.693823</td>\n",
" <td>3.874887</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_050.lcd</th>\n",
" <td>P1-D05</td>\n",
" <td>107950.0</td>\n",
" <td>2.652715</td>\n",
" <td>2.782746</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_051.lcd</th>\n",
" <td>P1-D06</td>\n",
" <td>480174.0</td>\n",
" <td>11.799580</td>\n",
" <td>12.377975</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_052.lcd</th>\n",
" <td>P1-D07</td>\n",
" <td>35556.0</td>\n",
" <td>0.873737</td>\n",
" <td>0.916566</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_053.lcd</th>\n",
" <td>P1-D08</td>\n",
" <td>126683.0</td>\n",
" <td>3.113051</td>\n",
" <td>3.265647</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_054.lcd</th>\n",
" <td>P1-D09</td>\n",
" <td>51407.0</td>\n",
" <td>1.263253</td>\n",
" <td>1.325175</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_055.lcd</th>\n",
" <td>P1-D10</td>\n",
" <td>207070.0</td>\n",
" <td>5.088445</td>\n",
" <td>5.337872</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_056.lcd</th>\n",
" <td>P1-D11</td>\n",
" <td>40732.0</td>\n",
" <td>1.000930</td>\n",
" <td>1.049994</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_057.lcd</th>\n",
" <td>P1-D12</td>\n",
" <td>49846.0</td>\n",
" <td>1.224893</td>\n",
" <td>1.284935</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_058.lcd</th>\n",
" <td>STD-0102</td>\n",
" <td>791874.0</td>\n",
" <td>19.459156</td>\n",
" <td>20.413009</td>\n",
" <td>20.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_059.lcd</th>\n",
" <td>STD-0103</td>\n",
" <td>380793.0</td>\n",
" <td>9.357436</td>\n",
" <td>9.816121</td>\n",
" <td>10.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_060.lcd</th>\n",
" <td>STD-0104</td>\n",
" <td>182495.0</td>\n",
" <td>4.484550</td>\n",
" <td>4.704375</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_061.lcd</th>\n",
" <td>STD-0105</td>\n",
" <td>92178.0</td>\n",
" <td>2.265141</td>\n",
" <td>2.376174</td>\n",
" <td>2.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_062.lcd</th>\n",
" <td>STD-0106</td>\n",
" <td>32787.0</td>\n",
" <td>0.805693</td>\n",
" <td>0.845187</td>\n",
" <td>1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_063.lcd</th>\n",
" <td>STD-0107</td>\n",
" <td>18743.0</td>\n",
" <td>0.460582</td>\n",
" <td>0.483159</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_064.lcd</th>\n",
" <td>STD-0108</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-11-14_065.lcd</th>\n",
" <td>STD-0109</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>64 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" sample_id area concentration_est0 \\\n",
"injection_id \n",
"2016-11-14_001.lcd STD-0102 852768.0 20.955538 \n",
"2016-11-14_002.lcd STD-0103 409700.0 10.067784 \n",
"2016-11-14_003.lcd STD-0104 207605.0 5.101592 \n",
"2016-11-14_004.lcd STD-0105 107255.0 2.635636 \n",
"2016-11-14_005.lcd STD-0106 40177.0 0.987292 \n",
"2016-11-14_006.lcd STD-0107 20335.0 0.499703 \n",
"2016-11-14_007.lcd STD-0108 10213.0 0.250970 \n",
"2016-11-14_008.lcd STD-0109 3205.0 0.078758 \n",
"2016-11-14_010.lcd P1-A01 399159.0 9.808754 \n",
"2016-11-14_011.lcd P1-A02 136942.0 3.365151 \n",
"2016-11-14_012.lcd P1-A03 280515.0 6.893250 \n",
"2016-11-14_013.lcd P1-A04 2802.0 0.068855 \n",
"2016-11-14_014.lcd P1-A05 124420.0 3.057441 \n",
"2016-11-14_015.lcd P1-A06 121810.0 2.993304 \n",
"2016-11-14_016.lcd P1-A07 378203.0 9.293791 \n",
"2016-11-14_017.lcd P1-A08 57960.0 1.424283 \n",
"2016-11-14_018.lcd P1-A09 187934.0 4.618206 \n",
"2016-11-14_019.lcd P1-A10 133258.0 3.274622 \n",
"2016-11-14_020.lcd P1-A11 107347.0 2.637897 \n",
"2016-11-14_021.lcd P1-A12 75492.0 1.855107 \n",
"2016-11-14_022.lcd P1-B01 169213.0 4.158164 \n",
"2016-11-14_023.lcd P1-B02 180236.0 4.429039 \n",
"2016-11-14_024.lcd P1-B03 81042.0 1.991490 \n",
"2016-11-14_025.lcd P1-B04 152047.0 3.736335 \n",
"2016-11-14_026.lcd P1-B05 175563.0 4.314206 \n",
"2016-11-14_027.lcd P1-B06 96645.0 2.374911 \n",
"2016-11-14_028.lcd P1-B07 125766.0 3.090517 \n",
"2016-11-14_029.lcd P1-B08 83561.0 2.053391 \n",
"2016-11-14_030.lcd P1-B09 324192.0 7.966549 \n",
"2016-11-14_031.lcd P1-B10 246614.0 6.060182 \n",
"... ... ... ... \n",
"2016-11-14_036.lcd P1-C03 180422.0 4.433609 \n",
"2016-11-14_037.lcd P1-C04 145467.0 3.574641 \n",
"2016-11-14_038.lcd P1-C05 50167.0 1.232781 \n",
"2016-11-14_039.lcd P1-C06 472351.0 11.607342 \n",
"2016-11-14_040.lcd P1-C07 269045.0 6.611391 \n",
"2016-11-14_041.lcd P1-C08 94767.0 2.328762 \n",
"2016-11-14_042.lcd P1-C09 0.0 0.000000 \n",
"2016-11-14_043.lcd P1-C10 170922.0 4.200161 \n",
"2016-11-14_044.lcd P1-C11 153365.0 3.768723 \n",
"2016-11-14_045.lcd P1-C12 100395.0 2.467062 \n",
"2016-11-14_046.lcd P1-D01 159153.0 3.910954 \n",
"2016-11-14_047.lcd P1-D02 113923.0 2.799493 \n",
"2016-11-14_048.lcd P1-D03 184315.0 4.529274 \n",
"2016-11-14_049.lcd P1-D04 150317.0 3.693823 \n",
"2016-11-14_050.lcd P1-D05 107950.0 2.652715 \n",
"2016-11-14_051.lcd P1-D06 480174.0 11.799580 \n",
"2016-11-14_052.lcd P1-D07 35556.0 0.873737 \n",
"2016-11-14_053.lcd P1-D08 126683.0 3.113051 \n",
"2016-11-14_054.lcd P1-D09 51407.0 1.263253 \n",
"2016-11-14_055.lcd P1-D10 207070.0 5.088445 \n",
"2016-11-14_056.lcd P1-D11 40732.0 1.000930 \n",
"2016-11-14_057.lcd P1-D12 49846.0 1.224893 \n",
"2016-11-14_058.lcd STD-0102 791874.0 19.459156 \n",
"2016-11-14_059.lcd STD-0103 380793.0 9.357436 \n",
"2016-11-14_060.lcd STD-0104 182495.0 4.484550 \n",
"2016-11-14_061.lcd STD-0105 92178.0 2.265141 \n",
"2016-11-14_062.lcd STD-0106 32787.0 0.805693 \n",
"2016-11-14_063.lcd STD-0107 18743.0 0.460582 \n",
"2016-11-14_064.lcd STD-0108 0.0 0.000000 \n",
"2016-11-14_065.lcd STD-0109 0.0 0.000000 \n",
"\n",
" concentration_est1 concentration \n",
"injection_id \n",
"2016-11-14_001.lcd 21.982741 20.00 \n",
"2016-11-14_002.lcd 10.561289 10.00 \n",
"2016-11-14_003.lcd 5.351663 5.00 \n",
"2016-11-14_004.lcd 2.764830 2.50 \n",
"2016-11-14_005.lcd 1.035687 1.00 \n",
"2016-11-14_006.lcd 0.524198 0.50 \n",
"2016-11-14_007.lcd 0.263272 0.25 \n",
"2016-11-14_008.lcd 0.082619 0.10 \n",
"2016-11-14_010.lcd 10.289562 NaN \n",
"2016-11-14_011.lcd 3.530105 NaN \n",
"2016-11-14_012.lcd 7.231145 NaN \n",
"2016-11-14_013.lcd 0.072230 NaN \n",
"2016-11-14_014.lcd 3.207312 NaN \n",
"2016-11-14_015.lcd 3.140031 NaN \n",
"2016-11-14_016.lcd 9.749356 NaN \n",
"2016-11-14_017.lcd 1.494099 NaN \n",
"2016-11-14_018.lcd 4.844582 NaN \n",
"2016-11-14_019.lcd 3.435138 NaN \n",
"2016-11-14_020.lcd 2.767202 NaN \n",
"2016-11-14_021.lcd 1.946041 NaN \n",
"2016-11-14_022.lcd 4.361990 NaN \n",
"2016-11-14_023.lcd 4.646142 NaN \n",
"2016-11-14_024.lcd 2.089109 NaN \n",
"2016-11-14_025.lcd 3.919483 NaN \n",
"2016-11-14_026.lcd 4.525681 NaN \n",
"2016-11-14_027.lcd 2.491325 NaN \n",
"2016-11-14_028.lcd 3.242009 NaN \n",
"2016-11-14_029.lcd 2.154044 NaN \n",
"2016-11-14_030.lcd 8.357055 NaN \n",
"2016-11-14_031.lcd 6.357241 NaN \n",
"... ... ... \n",
"2016-11-14_036.lcd 4.650937 NaN \n",
"2016-11-14_037.lcd 3.749863 NaN \n",
"2016-11-14_038.lcd 1.293210 NaN \n",
"2016-11-14_039.lcd 12.176313 NaN \n",
"2016-11-14_040.lcd 6.935470 NaN \n",
"2016-11-14_041.lcd 2.442913 NaN \n",
"2016-11-14_042.lcd 0.000000 NaN \n",
"2016-11-14_043.lcd 4.406045 NaN \n",
"2016-11-14_044.lcd 3.953459 NaN \n",
"2016-11-14_045.lcd 2.587993 NaN \n",
"2016-11-14_046.lcd 4.102662 NaN \n",
"2016-11-14_047.lcd 2.936719 NaN \n",
"2016-11-14_048.lcd 4.751291 NaN \n",
"2016-11-14_049.lcd 3.874887 NaN \n",
"2016-11-14_050.lcd 2.782746 NaN \n",
"2016-11-14_051.lcd 12.377975 NaN \n",
"2016-11-14_052.lcd 0.916566 NaN \n",
"2016-11-14_053.lcd 3.265647 NaN \n",
"2016-11-14_054.lcd 1.325175 NaN \n",
"2016-11-14_055.lcd 5.337872 NaN \n",
"2016-11-14_056.lcd 1.049994 NaN \n",
"2016-11-14_057.lcd 1.284935 NaN \n",
"2016-11-14_058.lcd 20.413009 20.00 \n",
"2016-11-14_059.lcd 9.816121 10.00 \n",
"2016-11-14_060.lcd 4.704375 5.00 \n",
"2016-11-14_061.lcd 2.376174 2.50 \n",
"2016-11-14_062.lcd 0.845187 1.00 \n",
"2016-11-14_063.lcd 0.483159 0.50 \n",
"2016-11-14_064.lcd 0.000000 0.25 \n",
"2016-11-14_065.lcd 0.000000 0.10 \n",
"\n",
"[64 rows x 5 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.to_excel('/Users/bjsmith/Desktop/hplc_method_comparison.xlsx')\n",
"data"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
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