190416_arrayOfDataFitting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import *\n",
    "import scipy as scipy\n",
    "from scipy import optimize\n",
    "from matplotlib import gridspec\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a reference (stand alone) notebook that goes along with <a href='http://emilygraceripka.com/blog/20'><b>this</b></a> blog post on how to work with experimental data which has many scans. <br>\n",
    "This is helpful for things like fluorescence, XRD, or NMR spectroscopy data for a system monitored over time. <br>\n",
    "Further, you may find this helpful for a general understanding of how to work with ndarrays and dataframes, and how to go between the two data types. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<a id='TOC'></a>\n",
    "## Table of Contents (TOC)\n",
    "1. <a href='#1'><b>Data Cleaning</b></a><br>\n",
    "  1.1 <a href='#1.1'>Reading in data</a><br>\n",
    "  1.2 <a href='#1.2'>Selecting range of rows</a><br>\n",
    "  1.3 <a href='#1.3'>Changing index</a><br>\n",
    "  1.4 <a href='#1.4'>Deleting columns</a><br>\n",
    "  1.5 <a href='#1.5'>Renaming columns</a><br>\n",
    "  1.6 <a href='#1.6'>Plotting data</a><br>\n",
    "2. <a href='#2'><b>Fitting Data to Gaussian Peaks</b></a><br>\n",
    "  2.1 <a href='#2.1'>Iterative fitting over dataframe</a><br>\n",
    "  2.2 <a href='#2.2'>Converting nddata Fitting parameters to a dataframe</a><br>\n",
    "3. <a href='#3'><b>Extracting Fitting Information</b></a><br>\n",
    "  3.1 <a href='#3.1'>Normalized Peak Intensity Shift</a><br>\n",
    "  3.2 <a href='#3.2'>Wavelength Shift</a><br>\n",
    "  3.3 <a href='#3.3'>FWHM Shift</a><br>  \n",
    "4. <a href='#4'><b>Saving Dataframes</b></a><br>\n",
    "  4.1 <a href='#4.1'>Pickle format</a><br>\n",
    "  4.2 <a href='#4.2'>CSV format</a><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1'></a>\n",
    "# 1. Data Cleaning \n",
    "<a id='1.1'></a>\n",
    "## 1.1 Reading in data <a href='#TOC'><b>(back to TOC)</b></a><br>\n",
    "We can turn a $.csv$ file into a dataframe (df) by using Panda's $read\\_csv$ function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16987, 606)\n"
     ]
    }
   ],
   "source": [
    "# *** RUN THIS CELL TWICE ***\n",
    "# Example path:\n",
    "path = r'SampleFluorescenceData_190402_EGR.csv'\n",
    "\n",
    "df = pd.read_csv(path)\n",
    "print np.shape(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize the original raw data, we can show $df$ or use Panda's $head()$ function to just see the first five rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Wavelength (nm)</td>\n",
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       "      <td>Wavelength (nm)</td>\n",
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       "      <td>Wavelength (nm)</td>\n",
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       "      <td>Wavelength (nm)</td>\n",
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       "      <td>Wavelength (nm)</td>\n",
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       "      <th>1</th>\n",
       "      <td>430</td>\n",
       "      <td>0.058042798</td>\n",
       "      <td>430</td>\n",
       "      <td>-0.01974665</td>\n",
       "      <td>430</td>\n",
       "      <td>0.089482792</td>\n",
       "      <td>430</td>\n",
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       "      <td>0.048927508</td>\n",
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       "      <td>-0.050527506</td>\n",
       "      <td>430</td>\n",
       "      <td>0.04026616</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>431.0599976</td>\n",
       "      <td>-0.00970205</td>\n",
       "      <td>431.0599976</td>\n",
       "      <td>0.029207313</td>\n",
       "      <td>431.0599976</td>\n",
       "      <td>0.059682488</td>\n",
       "      <td>431.0599976</td>\n",
       "      <td>0.06060851</td>\n",
       "      <td>431.0599976</td>\n",
       "      <td>0.078460604</td>\n",
       "      <td>...</td>\n",
       "      <td>431.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>431.0599976</td>\n",
       "      <td>0.019929253</td>\n",
       "      <td>431.0599976</td>\n",
       "      <td>0.061345302</td>\n",
       "      <td>431.0599976</td>\n",
       "      <td>0.081356205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>431.9599915</td>\n",
       "      <td>0.009579737</td>\n",
       "      <td>431.9599915</td>\n",
       "      <td>0.009718739</td>\n",
       "      <td>431.9599915</td>\n",
       "      <td>0.019442203</td>\n",
       "      <td>431.9599915</td>\n",
       "      <td>0.067501113</td>\n",
       "      <td>431.9599915</td>\n",
       "      <td>0.030053195</td>\n",
       "      <td>...</td>\n",
       "      <td>431.9599915</td>\n",
       "      <td>0.020747319</td>\n",
       "      <td>431.9599915</td>\n",
       "      <td>0.010255569</td>\n",
       "      <td>431.9599915</td>\n",
       "      <td>0.010158989</td>\n",
       "      <td>431.9599915</td>\n",
       "      <td>0.041944109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>433.0299988</td>\n",
       "      <td>0.029176353</td>\n",
       "      <td>433.0299988</td>\n",
       "      <td>0.048106991</td>\n",
       "      <td>433.0299988</td>\n",
       "      <td>0.048642393</td>\n",
       "      <td>433.0299988</td>\n",
       "      <td>-0.010162911</td>\n",
       "      <td>433.0299988</td>\n",
       "      <td>0.039304312</td>\n",
       "      <td>...</td>\n",
       "      <td>433.0299988</td>\n",
       "      <td>0.030374518</td>\n",
       "      <td>433.0299988</td>\n",
       "      <td>0.02032066</td>\n",
       "      <td>433.0299988</td>\n",
       "      <td>-0.030248033</td>\n",
       "      <td>433.0299988</td>\n",
       "      <td>-0.009978446</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>433.9299927</td>\n",
       "      <td>0.019258175</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>0.029845698</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>-0.068960764</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>-0.029745085</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>0.019614382</td>\n",
       "      <td>...</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>0</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>-0.081288427</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>-0.010362587</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>0.112422712</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>435</td>\n",
       "      <td>0.029816924</td>\n",
       "      <td>435</td>\n",
       "      <td>0.00974412</td>\n",
       "      <td>435</td>\n",
       "      <td>0.029070893</td>\n",
       "      <td>435</td>\n",
       "      <td>-0.019309679</td>\n",
       "      <td>435</td>\n",
       "      <td>0.02913272</td>\n",
       "      <td>...</td>\n",
       "      <td>435</td>\n",
       "      <td>0.020968316</td>\n",
       "      <td>435</td>\n",
       "      <td>0.010163222</td>\n",
       "      <td>435</td>\n",
       "      <td>0</td>\n",
       "      <td>435</td>\n",
       "      <td>0.050819721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>436.0599976</td>\n",
       "      <td>-0.009748205</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>-0.04886869</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>0.009688607</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>-0.009797773</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>0.039117508</td>\n",
       "      <td>...</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>0.030630687</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>0.050461724</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>-0.010179464</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>436.9599915</td>\n",
       "      <td>0</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>-0.009863586</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>0.048343278</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>-0.040247522</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>0.059689019</td>\n",
       "      <td>...</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>0.101620853</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>0.05064676</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>-0.010054597</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>0.03065322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>438.0299988</td>\n",
       "      <td>0.049147293</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>0.029712679</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>-0.009679324</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>0</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>-0.030050183</td>\n",
       "      <td>...</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>-0.0102416</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>-0.010024761</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>-0.009985422</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>0.040140893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0.019724838</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>-0.029381232</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0.019620346</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0.077636741</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0.009827141</td>\n",
       "      <td>...</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0.040346988</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0.029959256</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0.070338324</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>440</td>\n",
       "      <td>0.038385138</td>\n",
       "      <td>440</td>\n",
       "      <td>0.029257732</td>\n",
       "      <td>440</td>\n",
       "      <td>0.01921488</td>\n",
       "      <td>440</td>\n",
       "      <td>-0.050328646</td>\n",
       "      <td>440</td>\n",
       "      <td>0.020295709</td>\n",
       "      <td>...</td>\n",
       "      <td>440</td>\n",
       "      <td>0.030318955</td>\n",
       "      <td>440</td>\n",
       "      <td>0.02973064</td>\n",
       "      <td>440</td>\n",
       "      <td>0.040341694</td>\n",
       "      <td>440</td>\n",
       "      <td>0.010304922</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>441.0599976</td>\n",
       "      <td>0.038362313</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>0.009880447</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>0.038805943</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>-0.009912473</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>-0.010376673</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>0.052354377</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>0.040707085</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>0.091667436</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>441.9599915</td>\n",
       "      <td>-0.048075076</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>0.028893938</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>-0.01947799</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>-0.119486213</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>0.039522175</td>\n",
       "      <td>...</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>0.030505165</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>0</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>0.030446962</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>0.020385489</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>443.0299988</td>\n",
       "      <td>-0.009615107</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>0</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>0.067498505</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>-0.029801818</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>-0.049323283</td>\n",
       "      <td>...</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>0.040189289</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>0.02037905</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>-0.060119037</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>0.020148089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>443.9299927</td>\n",
       "      <td>0.019217649</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>-0.0196454</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>0.019588254</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>-0.059078962</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>0.009745734</td>\n",
       "      <td>...</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>0.010003401</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>0.039931718</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>0.041184466</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>-0.030719137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>445</td>\n",
       "      <td>0.06854815</td>\n",
       "      <td>445</td>\n",
       "      <td>-0.05869291</td>\n",
       "      <td>445</td>\n",
       "      <td>-0.019449577</td>\n",
       "      <td>445</td>\n",
       "      <td>0.009975063</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>445</td>\n",
       "      <td>0.061513856</td>\n",
       "      <td>445</td>\n",
       "      <td>-0.010287431</td>\n",
       "      <td>445</td>\n",
       "      <td>0.040981926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>446.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>0.039113685</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>0.009918273</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>-0.019814536</td>\n",
       "      <td>...</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>-0.020323345</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>0.030507337</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>-0.010131507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>446.9599915</td>\n",
       "      <td>0.019136557</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>0.009885428</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>0.038827788</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>0.099999994</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>-0.010100601</td>\n",
       "      <td>...</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>0.060862422</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>0</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>-0.009960555</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>0.010321302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>448.0299988</td>\n",
       "      <td>0.048575759</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>-0.059414174</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>0</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>0</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>-0.038583968</td>\n",
       "      <td>...</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>-0.009847365</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>-0.04975174</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>0.020094039</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>0.031662267</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>448.9299927</td>\n",
       "      <td>0.009576159</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>-0.009899716</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>0</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>-0.098154694</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>0.02023677</td>\n",
       "      <td>...</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>0</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>0.050648812</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>0.062532566</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>0.062263273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>450</td>\n",
       "      <td>0</td>\n",
       "      <td>450</td>\n",
       "      <td>-0.009917191</td>\n",
       "      <td>450</td>\n",
       "      <td>0.019483684</td>\n",
       "      <td>450</td>\n",
       "      <td>-0.030045068</td>\n",
       "      <td>450</td>\n",
       "      <td>-0.019980419</td>\n",
       "      <td>...</td>\n",
       "      <td>450</td>\n",
       "      <td>-0.041053854</td>\n",
       "      <td>450</td>\n",
       "      <td>-0.030399757</td>\n",
       "      <td>450</td>\n",
       "      <td>-0.030295685</td>\n",
       "      <td>450</td>\n",
       "      <td>0.030969979</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0.029142341</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0.05895647</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0.048794769</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0.070239522</td>\n",
       "      <td>...</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>-0.020151744</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0.009994104</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0.069865167</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0.010252309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0.077900581</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0.009864657</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0.030193539</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0.101774953</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0.071114361</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>-0.020314882</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0.059989803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>453.0299988</td>\n",
       "      <td>0.057893261</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>0.019438611</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>0.059153512</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>-0.009876348</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>0.009747064</td>\n",
       "      <td>...</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>-0.020612607</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>0.061185159</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>0</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>0.040225666</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>453.9299927</td>\n",
       "      <td>-0.009666319</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>0.019313782</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>-0.029357366</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>0.010008607</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>-0.02892793</td>\n",
       "      <td>...</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>0.051856462</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>0.041105323</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>0.050291188</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>0.030289873</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>455</td>\n",
       "      <td>0.087446563</td>\n",
       "      <td>455</td>\n",
       "      <td>-0.070473582</td>\n",
       "      <td>455</td>\n",
       "      <td>-0.029303458</td>\n",
       "      <td>455</td>\n",
       "      <td>-0.028950822</td>\n",
       "      <td>455</td>\n",
       "      <td>-0.019725811</td>\n",
       "      <td>...</td>\n",
       "      <td>455</td>\n",
       "      <td>0.080286622</td>\n",
       "      <td>455</td>\n",
       "      <td>0</td>\n",
       "      <td>455</td>\n",
       "      <td>0.020089397</td>\n",
       "      <td>455</td>\n",
       "      <td>0.060551625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>456.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>-0.039386749</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>0.059674177</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>-0.071171485</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>0.009976555</td>\n",
       "      <td>...</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>0.040707495</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>-0.009733117</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>-0.029928472</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>0.07045798</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0.01934685</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0.070386417</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0.029720921</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>-0.029944602</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0.010093975</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0.010278971</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0.050750602</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>458.0299988</td>\n",
       "      <td>0.058948938</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>0.030063735</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>0.058647014</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>-0.010041673</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>0.010067452</td>\n",
       "      <td>...</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>0.009980339</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>0.010098358</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>-0.010003101</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>0.0303395</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16957</th>\n",
       "      <td>No. of scans                     1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16958</th>\n",
       "      <td>Time (min)                       0.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16959</th>\n",
       "      <td>Multicell holder                  Multicell</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16960</th>\n",
       "      <td>Multi zero                       OFF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16961</th>\n",
       "      <td>Device</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16962</th>\n",
       "      <td>Set temperature (�C)             25.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16963</th>\n",
       "      <td>Monitor                          Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16964</th>\n",
       "      <td>Method Log     :</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16965</th>\n",
       "      <td>Method Name    : Default</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16966</th>\n",
       "      <td>Date/Time stamp: 2/22/2018 9:30:33 AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16967</th>\n",
       "      <td>Method Modifications:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16968</th>\n",
       "      <td>Overlayed Graphs Changed: 2/22/2018 9:44:41 AM</td>\n",
       "      <td>Old:OFF</td>\n",
       "      <td>New:ON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16969</th>\n",
       "      <td>Cell Changer Changed: 2/22/2018 9:44:41 AM</td>\n",
       "      <td>Old:OFF</td>\n",
       "      <td>New:ON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16970</th>\n",
       "      <td>Cell2 Changed: 2/22/2018 9:44:41 AM</td>\n",
       "      <td>Old:OFF</td>\n",
       "      <td>New:ON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16971</th>\n",
       "      <td>Auto Temp Setting Changed: 2/22/2018 9:44:41 AM</td>\n",
       "      <td>Old:OFF</td>\n",
       "      <td>New:ON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16972</th>\n",
       "      <td>Monitoring device: Changed: 2/22/2018 9:44:41 AM</td>\n",
       "      <td>Old:PCB 150</td>\n",
       "      <td>New:Block</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16973</th>\n",
       "      <td>Block Temp Changed: 2/22/2018 9:44:41 AM</td>\n",
       "      <td>Old:  20.00</td>\n",
       "      <td>New:  25.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16974</th>\n",
       "      <td>Block (�C): Changed: 2/22/2018 9:44:41 AM</td>\n",
       "      <td>Old:OFF</td>\n",
       "      <td>New:ON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>Ex. Slit Width (nm): Changed: 2/22/2018 9:44:4...</td>\n",
       "      <td>Old:5</td>\n",
       "      <td>New:2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16976</th>\n",
       "      <td>Em. Slit Width (nm): Changed: 2/22/2018 9:44:4...</td>\n",
       "      <td>Old:5</td>\n",
       "      <td>New:2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16977</th>\n",
       "      <td>Cycle Mode Changed: 2/22/2018 9:44:41 AM</td>\n",
       "      <td>Old:OFF</td>\n",
       "      <td>New:ON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16978</th>\n",
       "      <td>Cycle number: Changed: 2/22/2018 9:44:41 AM</td>\n",
       "      <td>Old:15</td>\n",
       "      <td>New:1000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16979</th>\n",
       "      <td>Cycle time (s): Changed: 2/22/2018 9:44:41 AM</td>\n",
       "      <td>Old:     1.0000</td>\n",
       "      <td>New:     0.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16980</th>\n",
       "      <td>Excitation Wavelength Changed: 2/22/2018 9:44:...</td>\n",
       "      <td>Old: 350.00</td>\n",
       "      <td>New: 400.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16981</th>\n",
       "      <td>Emission Wavelength Changed: 2/22/2018 9:44:41 AM</td>\n",
       "      <td>Old: 400.00</td>\n",
       "      <td>New: 390.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16982</th>\n",
       "      <td>Stop Wavelength Changed: 2/22/2018 9:44:41 AM</td>\n",
       "      <td>Old: 600.00</td>\n",
       "      <td>New: 750.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16983</th>\n",
       "      <td>Cycle time (s): Changed: 2/22/2018 11:06:47 AM</td>\n",
       "      <td>Old:     0.5000</td>\n",
       "      <td>New:    20.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16984</th>\n",
       "      <td>Cycle time (s): Changed: 2/27/2018 9:38:31 AM</td>\n",
       "      <td>Old:    20.0000</td>\n",
       "      <td>New:     0.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16985</th>\n",
       "      <td>End Method Modifications</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16986</th>\n",
       "      <td>[Time]</td>\n",
       "      <td>189.0413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16987 rows × 606 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Sample2_1        Unnamed: 1  \\\n",
       "0                                        Wavelength (nm)  Intensity (a.u.)   \n",
       "1                                                    430       0.058042798   \n",
       "2                                            431.0599976       -0.00970205   \n",
       "3                                            431.9599915       0.009579737   \n",
       "4                                            433.0299988       0.029176353   \n",
       "5                                            433.9299927       0.019258175   \n",
       "6                                                    435       0.029816924   \n",
       "7                                            436.0599976      -0.009748205   \n",
       "8                                            436.9599915                 0   \n",
       "9                                            438.0299988       0.049147293   \n",
       "10                                           438.9299927       0.019724838   \n",
       "11                                                   440       0.038385138   \n",
       "12                                           441.0599976       0.038362313   \n",
       "13                                           441.9599915      -0.048075076   \n",
       "14                                           443.0299988      -0.009615107   \n",
       "15                                           443.9299927       0.019217649   \n",
       "16                                                   445        0.06854815   \n",
       "17                                           446.0599976                 0   \n",
       "18                                           446.9599915       0.019136557   \n",
       "19                                           448.0299988       0.048575759   \n",
       "20                                           448.9299927       0.009576159   \n",
       "21                                                   450                 0   \n",
       "22                                           451.0599976       0.029142341   \n",
       "23                                           451.9599915                 0   \n",
       "24                                           453.0299988       0.057893261   \n",
       "25                                           453.9299927      -0.009666319   \n",
       "26                                                   455       0.087446563   \n",
       "27                                           456.0599976                 0   \n",
       "28                                           456.9599915        0.01934685   \n",
       "29                                           458.0299988       0.058948938   \n",
       "...                                                  ...               ...   \n",
       "16957              No. of scans                     1000               NaN   \n",
       "16958            Time (min)                       0.5000               NaN   \n",
       "16959        Multicell holder                  Multicell               NaN   \n",
       "16960               Multi zero                       OFF               NaN   \n",
       "16961                                             Device               NaN   \n",
       "16962             Set temperature (�C)             25.00               NaN   \n",
       "16963             Monitor                          Block               NaN   \n",
       "16964                                   Method Log     :               NaN   \n",
       "16965                           Method Name    : Default               NaN   \n",
       "16966              Date/Time stamp: 2/22/2018 9:30:33 AM               NaN   \n",
       "16967                              Method Modifications:               NaN   \n",
       "16968     Overlayed Graphs Changed: 2/22/2018 9:44:41 AM           Old:OFF   \n",
       "16969         Cell Changer Changed: 2/22/2018 9:44:41 AM           Old:OFF   \n",
       "16970                Cell2 Changed: 2/22/2018 9:44:41 AM           Old:OFF   \n",
       "16971    Auto Temp Setting Changed: 2/22/2018 9:44:41 AM           Old:OFF   \n",
       "16972   Monitoring device: Changed: 2/22/2018 9:44:41 AM       Old:PCB 150   \n",
       "16973           Block Temp Changed: 2/22/2018 9:44:41 AM       Old:  20.00   \n",
       "16974          Block (�C): Changed: 2/22/2018 9:44:41 AM           Old:OFF   \n",
       "16975  Ex. Slit Width (nm): Changed: 2/22/2018 9:44:4...             Old:5   \n",
       "16976  Em. Slit Width (nm): Changed: 2/22/2018 9:44:4...             Old:5   \n",
       "16977           Cycle Mode Changed: 2/22/2018 9:44:41 AM           Old:OFF   \n",
       "16978        Cycle number: Changed: 2/22/2018 9:44:41 AM            Old:15   \n",
       "16979      Cycle time (s): Changed: 2/22/2018 9:44:41 AM   Old:     1.0000   \n",
       "16980  Excitation Wavelength Changed: 2/22/2018 9:44:...       Old: 350.00   \n",
       "16981  Emission Wavelength Changed: 2/22/2018 9:44:41 AM       Old: 400.00   \n",
       "16982      Stop Wavelength Changed: 2/22/2018 9:44:41 AM       Old: 600.00   \n",
       "16983     Cycle time (s): Changed: 2/22/2018 11:06:47 AM   Old:     0.5000   \n",
       "16984      Cycle time (s): Changed: 2/27/2018 9:38:31 AM   Old:    20.0000   \n",
       "16985                           End Method Modifications               NaN   \n",
       "16986                                            [Time]           189.0413   \n",
       "\n",
       "              Sample2_2        Unnamed: 3        Sample2_3        Unnamed: 5  \\\n",
       "0       Wavelength (nm)  Intensity (a.u.)  Wavelength (nm)  Intensity (a.u.)   \n",
       "1                   430       -0.01974665              430       0.089482792   \n",
       "2           431.0599976       0.029207313      431.0599976       0.059682488   \n",
       "3           431.9599915       0.009718739      431.9599915       0.019442203   \n",
       "4           433.0299988       0.048106991      433.0299988       0.048642393   \n",
       "5           433.9299927       0.029845698      433.9299927      -0.068960764   \n",
       "6                   435        0.00974412              435       0.029070893   \n",
       "7           436.0599976       -0.04886869      436.0599976       0.009688607   \n",
       "8           436.9599915      -0.009863586      436.9599915       0.048343278   \n",
       "9           438.0299988       0.029712679      438.0299988      -0.009679324   \n",
       "10          438.9299927      -0.029381232      438.9299927       0.019620346   \n",
       "11                  440       0.029257732              440        0.01921488   \n",
       "12          441.0599976       0.009880447      441.0599976       0.038805943   \n",
       "13          441.9599915       0.028893938      441.9599915       -0.01947799   \n",
       "14          443.0299988                 0      443.0299988       0.067498505   \n",
       "15          443.9299927        -0.0196454      443.9299927       0.019588254   \n",
       "16                  445       -0.05869291              445      -0.019449577   \n",
       "17          446.0599976                 0      446.0599976       0.039113685   \n",
       "18          446.9599915       0.009885428      446.9599915       0.038827788   \n",
       "19          448.0299988      -0.059414174      448.0299988                 0   \n",
       "20          448.9299927      -0.009899716      448.9299927                 0   \n",
       "21                  450      -0.009917191              450       0.019483684   \n",
       "22          451.0599976        0.05895647      451.0599976       0.048794769   \n",
       "23          451.9599915       0.077900581      451.9599915       0.009864657   \n",
       "24          453.0299988       0.019438611      453.0299988       0.059153512   \n",
       "25          453.9299927       0.019313782      453.9299927      -0.029357366   \n",
       "26                  455      -0.070473582              455      -0.029303458   \n",
       "27          456.0599976      -0.039386749      456.0599976       0.059674177   \n",
       "28          456.9599915       0.070386417      456.9599915       0.029720921   \n",
       "29          458.0299988       0.030063735      458.0299988       0.058647014   \n",
       "...                 ...               ...              ...               ...   \n",
       "16957               NaN               NaN              NaN               NaN   \n",
       "16958               NaN               NaN              NaN               NaN   \n",
       "16959               NaN               NaN              NaN               NaN   \n",
       "16960               NaN               NaN              NaN               NaN   \n",
       "16961               NaN               NaN              NaN               NaN   \n",
       "16962               NaN               NaN              NaN               NaN   \n",
       "16963               NaN               NaN              NaN               NaN   \n",
       "16964               NaN               NaN              NaN               NaN   \n",
       "16965               NaN               NaN              NaN               NaN   \n",
       "16966               NaN               NaN              NaN               NaN   \n",
       "16967               NaN               NaN              NaN               NaN   \n",
       "16968            New:ON               NaN              NaN               NaN   \n",
       "16969            New:ON               NaN              NaN               NaN   \n",
       "16970            New:ON               NaN              NaN               NaN   \n",
       "16971            New:ON               NaN              NaN               NaN   \n",
       "16972         New:Block               NaN              NaN               NaN   \n",
       "16973       New:  25.00               NaN              NaN               NaN   \n",
       "16974            New:ON               NaN              NaN               NaN   \n",
       "16975           New:2.5               NaN              NaN               NaN   \n",
       "16976           New:2.5               NaN              NaN               NaN   \n",
       "16977            New:ON               NaN              NaN               NaN   \n",
       "16978          New:1000               NaN              NaN               NaN   \n",
       "16979   New:     0.5000               NaN              NaN               NaN   \n",
       "16980       New: 400.00               NaN              NaN               NaN   \n",
       "16981       New: 390.00               NaN              NaN               NaN   \n",
       "16982       New: 750.00               NaN              NaN               NaN   \n",
       "16983   New:    20.0000               NaN              NaN               NaN   \n",
       "16984   New:     0.5000               NaN              NaN               NaN   \n",
       "16985               NaN               NaN              NaN               NaN   \n",
       "16986               NaN               NaN              NaN               NaN   \n",
       "\n",
       "             Sample2_4        Unnamed: 7        Sample2_5        Unnamed: 9  \\\n",
       "0      Wavelength (nm)  Intensity (a.u.)  Wavelength (nm)  Intensity (a.u.)   \n",
       "1                  430       0.048062135              430       0.048927508   \n",
       "2          431.0599976        0.06060851      431.0599976       0.078460604   \n",
       "3          431.9599915       0.067501113      431.9599915       0.030053195   \n",
       "4          433.0299988      -0.010162911      433.0299988       0.039304312   \n",
       "5          433.9299927      -0.029745085      433.9299927       0.019614382   \n",
       "6                  435      -0.019309679              435        0.02913272   \n",
       "7          436.0599976      -0.009797773      436.0599976       0.039117508   \n",
       "8          436.9599915      -0.040247522      436.9599915       0.059689019   \n",
       "9          438.0299988                 0      438.0299988      -0.030050183   \n",
       "10         438.9299927       0.077636741      438.9299927       0.009827141   \n",
       "11                 440      -0.050328646              440       0.020295709   \n",
       "12         441.0599976      -0.009912473      441.0599976                 0   \n",
       "13         441.9599915      -0.119486213      441.9599915       0.039522175   \n",
       "14         443.0299988      -0.029801818      443.0299988      -0.049323283   \n",
       "15         443.9299927      -0.059078962      443.9299927       0.009745734   \n",
       "16                 445       0.009975063              445                 0   \n",
       "17         446.0599976       0.009918273      446.0599976      -0.019814536   \n",
       "18         446.9599915       0.099999994      446.9599915      -0.010100601   \n",
       "19         448.0299988                 0      448.0299988      -0.038583968   \n",
       "20         448.9299927      -0.098154694      448.9299927        0.02023677   \n",
       "21                 450      -0.030045068              450      -0.019980419   \n",
       "22         451.0599976                 0      451.0599976       0.070239522   \n",
       "23         451.9599915       0.030193539      451.9599915                 0   \n",
       "24         453.0299988      -0.009876348      453.0299988       0.009747064   \n",
       "25         453.9299927       0.010008607      453.9299927       -0.02892793   \n",
       "26                 455      -0.028950822              455      -0.019725811   \n",
       "27         456.0599976      -0.071171485      456.0599976       0.009976555   \n",
       "28         456.9599915      -0.029944602      456.9599915                 0   \n",
       "29         458.0299988      -0.010041673      458.0299988       0.010067452   \n",
       "...                ...               ...              ...               ...   \n",
       "16957              NaN               NaN              NaN               NaN   \n",
       "16958              NaN               NaN              NaN               NaN   \n",
       "16959              NaN               NaN              NaN               NaN   \n",
       "16960              NaN               NaN              NaN               NaN   \n",
       "16961              NaN               NaN              NaN               NaN   \n",
       "16962              NaN               NaN              NaN               NaN   \n",
       "16963              NaN               NaN              NaN               NaN   \n",
       "16964              NaN               NaN              NaN               NaN   \n",
       "16965              NaN               NaN              NaN               NaN   \n",
       "16966              NaN               NaN              NaN               NaN   \n",
       "16967              NaN               NaN              NaN               NaN   \n",
       "16968              NaN               NaN              NaN               NaN   \n",
       "16969              NaN               NaN              NaN               NaN   \n",
       "16970              NaN               NaN              NaN               NaN   \n",
       "16971              NaN               NaN              NaN               NaN   \n",
       "16972              NaN               NaN              NaN               NaN   \n",
       "16973              NaN               NaN              NaN               NaN   \n",
       "16974              NaN               NaN              NaN               NaN   \n",
       "16975              NaN               NaN              NaN               NaN   \n",
       "16976              NaN               NaN              NaN               NaN   \n",
       "16977              NaN               NaN              NaN               NaN   \n",
       "16978              NaN               NaN              NaN               NaN   \n",
       "16979              NaN               NaN              NaN               NaN   \n",
       "16980              NaN               NaN              NaN               NaN   \n",
       "16981              NaN               NaN              NaN               NaN   \n",
       "16982              NaN               NaN              NaN               NaN   \n",
       "16983              NaN               NaN              NaN               NaN   \n",
       "16984              NaN               NaN              NaN               NaN   \n",
       "16985              NaN               NaN              NaN               NaN   \n",
       "16986              NaN               NaN              NaN               NaN   \n",
       "\n",
       "             ...             Sample2_299      Unnamed: 597      Sample2_300  \\\n",
       "0            ...         Wavelength (nm)  Intensity (a.u.)  Wavelength (nm)   \n",
       "1            ...                     430      -0.019919526              430   \n",
       "2            ...             431.0599976                 0      431.0599976   \n",
       "3            ...             431.9599915       0.020747319      431.9599915   \n",
       "4            ...             433.0299988       0.030374518      433.0299988   \n",
       "5            ...             433.9299927                 0      433.9299927   \n",
       "6            ...                     435       0.020968316              435   \n",
       "7            ...             436.0599976       0.030630687      436.0599976   \n",
       "8            ...             436.9599915       0.101620853      436.9599915   \n",
       "9            ...             438.0299988        -0.0102416      438.0299988   \n",
       "10           ...             438.9299927                 0      438.9299927   \n",
       "11           ...                     440       0.030318955              440   \n",
       "12           ...             441.0599976      -0.010376673      441.0599976   \n",
       "13           ...             441.9599915       0.030505165      441.9599915   \n",
       "14           ...             443.0299988       0.040189289      443.0299988   \n",
       "15           ...             443.9299927       0.010003401      443.9299927   \n",
       "16           ...                     445                 0              445   \n",
       "17           ...             446.0599976      -0.020323345      446.0599976   \n",
       "18           ...             446.9599915       0.060862422      446.9599915   \n",
       "19           ...             448.0299988      -0.009847365      448.0299988   \n",
       "20           ...             448.9299927                 0      448.9299927   \n",
       "21           ...                     450      -0.041053854              450   \n",
       "22           ...             451.0599976      -0.020151744      451.0599976   \n",
       "23           ...             451.9599915       0.101774953      451.9599915   \n",
       "24           ...             453.0299988      -0.020612607      453.0299988   \n",
       "25           ...             453.9299927       0.051856462      453.9299927   \n",
       "26           ...                     455       0.080286622              455   \n",
       "27           ...             456.0599976       0.040707495      456.0599976   \n",
       "28           ...             456.9599915       0.010093975      456.9599915   \n",
       "29           ...             458.0299988       0.009980339      458.0299988   \n",
       "...          ...                     ...               ...              ...   \n",
       "16957        ...                     NaN               NaN              NaN   \n",
       "16958        ...                     NaN               NaN              NaN   \n",
       "16959        ...                     NaN               NaN              NaN   \n",
       "16960        ...                     NaN               NaN              NaN   \n",
       "16961        ...                     NaN               NaN              NaN   \n",
       "16962        ...                     NaN               NaN              NaN   \n",
       "16963        ...                     NaN               NaN              NaN   \n",
       "16964        ...                     NaN               NaN              NaN   \n",
       "16965        ...                     NaN               NaN              NaN   \n",
       "16966        ...                     NaN               NaN              NaN   \n",
       "16967        ...                     NaN               NaN              NaN   \n",
       "16968        ...                     NaN               NaN              NaN   \n",
       "16969        ...                     NaN               NaN              NaN   \n",
       "16970        ...                     NaN               NaN              NaN   \n",
       "16971        ...                     NaN               NaN              NaN   \n",
       "16972        ...                     NaN               NaN              NaN   \n",
       "16973        ...                     NaN               NaN              NaN   \n",
       "16974        ...                     NaN               NaN              NaN   \n",
       "16975        ...                     NaN               NaN              NaN   \n",
       "16976        ...                     NaN               NaN              NaN   \n",
       "16977        ...                     NaN               NaN              NaN   \n",
       "16978        ...                     NaN               NaN              NaN   \n",
       "16979        ...                     NaN               NaN              NaN   \n",
       "16980        ...                     NaN               NaN              NaN   \n",
       "16981        ...                     NaN               NaN              NaN   \n",
       "16982        ...                     NaN               NaN              NaN   \n",
       "16983        ...                     NaN               NaN              NaN   \n",
       "16984        ...                     NaN               NaN              NaN   \n",
       "16985        ...                     NaN               NaN              NaN   \n",
       "16986        ...                     NaN               NaN              NaN   \n",
       "\n",
       "           Unnamed: 599      Sample2_301      Unnamed: 601      Sample2_302  \\\n",
       "0      Intensity (a.u.)  Wavelength (nm)  Intensity (a.u.)  Wavelength (nm)   \n",
       "1          -0.030978296              430      -0.050527506              430   \n",
       "2           0.019929253      431.0599976       0.061345302      431.0599976   \n",
       "3           0.010255569      431.9599915       0.010158989      431.9599915   \n",
       "4            0.02032066      433.0299988      -0.030248033      433.0299988   \n",
       "5          -0.081288427      433.9299927      -0.010362587      433.9299927   \n",
       "6           0.010163222              435                 0              435   \n",
       "7           0.050461724      436.0599976      -0.010179464      436.0599976   \n",
       "8            0.05064676      436.9599915      -0.010054597      436.9599915   \n",
       "9          -0.010024761      438.0299988      -0.009985422      438.0299988   \n",
       "10          0.040346988      438.9299927       0.029959256      438.9299927   \n",
       "11           0.02973064              440       0.040341694              440   \n",
       "12          0.052354377      441.0599976       0.040707085      441.0599976   \n",
       "13                    0      441.9599915       0.030446962      441.9599915   \n",
       "14           0.02037905      443.0299988      -0.060119037      443.0299988   \n",
       "15          0.039931718      443.9299927       0.041184466      443.9299927   \n",
       "16          0.061513856              445      -0.010287431              445   \n",
       "17          0.030507337      446.0599976                 0      446.0599976   \n",
       "18                    0      446.9599915      -0.009960555      446.9599915   \n",
       "19          -0.04975174      448.0299988       0.020094039      448.0299988   \n",
       "20          0.050648812      448.9299927       0.062532566      448.9299927   \n",
       "21         -0.030399757              450      -0.030295685              450   \n",
       "22          0.009994104      451.0599976       0.069865167      451.0599976   \n",
       "23          0.071114361      451.9599915      -0.020314882      451.9599915   \n",
       "24          0.061185159      453.0299988                 0      453.0299988   \n",
       "25          0.041105323      453.9299927       0.050291188      453.9299927   \n",
       "26                    0              455       0.020089397              455   \n",
       "27         -0.009733117      456.0599976      -0.029928472      456.0599976   \n",
       "28          0.010278971      456.9599915                 0      456.9599915   \n",
       "29          0.010098358      458.0299988      -0.010003101      458.0299988   \n",
       "...                 ...              ...               ...              ...   \n",
       "16957               NaN              NaN               NaN              NaN   \n",
       "16958               NaN              NaN               NaN              NaN   \n",
       "16959               NaN              NaN               NaN              NaN   \n",
       "16960               NaN              NaN               NaN              NaN   \n",
       "16961               NaN              NaN               NaN              NaN   \n",
       "16962               NaN              NaN               NaN              NaN   \n",
       "16963               NaN              NaN               NaN              NaN   \n",
       "16964               NaN              NaN               NaN              NaN   \n",
       "16965               NaN              NaN               NaN              NaN   \n",
       "16966               NaN              NaN               NaN              NaN   \n",
       "16967               NaN              NaN               NaN              NaN   \n",
       "16968               NaN              NaN               NaN              NaN   \n",
       "16969               NaN              NaN               NaN              NaN   \n",
       "16970               NaN              NaN               NaN              NaN   \n",
       "16971               NaN              NaN               NaN              NaN   \n",
       "16972               NaN              NaN               NaN              NaN   \n",
       "16973               NaN              NaN               NaN              NaN   \n",
       "16974               NaN              NaN               NaN              NaN   \n",
       "16975               NaN              NaN               NaN              NaN   \n",
       "16976               NaN              NaN               NaN              NaN   \n",
       "16977               NaN              NaN               NaN              NaN   \n",
       "16978               NaN              NaN               NaN              NaN   \n",
       "16979               NaN              NaN               NaN              NaN   \n",
       "16980               NaN              NaN               NaN              NaN   \n",
       "16981               NaN              NaN               NaN              NaN   \n",
       "16982               NaN              NaN               NaN              NaN   \n",
       "16983               NaN              NaN               NaN              NaN   \n",
       "16984               NaN              NaN               NaN              NaN   \n",
       "16985               NaN              NaN               NaN              NaN   \n",
       "16986               NaN              NaN               NaN              NaN   \n",
       "\n",
       "           Unnamed: 603      Sample2_303      Unnamed: 605  \n",
       "0      Intensity (a.u.)  Wavelength (nm)  Intensity (a.u.)  \n",
       "1            0.04026616              NaN               NaN  \n",
       "2           0.081356205              NaN               NaN  \n",
       "3           0.041944109              NaN               NaN  \n",
       "4          -0.009978446              NaN               NaN  \n",
       "5           0.112422712              NaN               NaN  \n",
       "6           0.050819721              NaN               NaN  \n",
       "7                     0              NaN               NaN  \n",
       "8            0.03065322              NaN               NaN  \n",
       "9           0.040140893              NaN               NaN  \n",
       "10          0.070338324              NaN               NaN  \n",
       "11          0.010304922              NaN               NaN  \n",
       "12          0.091667436              NaN               NaN  \n",
       "13          0.020385489              NaN               NaN  \n",
       "14          0.020148089              NaN               NaN  \n",
       "15         -0.030719137              NaN               NaN  \n",
       "16          0.040981926              NaN               NaN  \n",
       "17         -0.010131507              NaN               NaN  \n",
       "18          0.010321302              NaN               NaN  \n",
       "19          0.031662267              NaN               NaN  \n",
       "20          0.062263273              NaN               NaN  \n",
       "21          0.030969979              NaN               NaN  \n",
       "22          0.010252309              NaN               NaN  \n",
       "23          0.059989803              NaN               NaN  \n",
       "24          0.040225666              NaN               NaN  \n",
       "25          0.030289873              NaN               NaN  \n",
       "26          0.060551625              NaN               NaN  \n",
       "27           0.07045798              NaN               NaN  \n",
       "28          0.050750602              NaN               NaN  \n",
       "29            0.0303395              NaN               NaN  \n",
       "...                 ...              ...               ...  \n",
       "16957               NaN              NaN               NaN  \n",
       "16958               NaN              NaN               NaN  \n",
       "16959               NaN              NaN               NaN  \n",
       "16960               NaN              NaN               NaN  \n",
       "16961               NaN              NaN               NaN  \n",
       "16962               NaN              NaN               NaN  \n",
       "16963               NaN              NaN               NaN  \n",
       "16964               NaN              NaN               NaN  \n",
       "16965               NaN              NaN               NaN  \n",
       "16966               NaN              NaN               NaN  \n",
       "16967               NaN              NaN               NaN  \n",
       "16968               NaN              NaN               NaN  \n",
       "16969               NaN              NaN               NaN  \n",
       "16970               NaN              NaN               NaN  \n",
       "16971               NaN              NaN               NaN  \n",
       "16972               NaN              NaN               NaN  \n",
       "16973               NaN              NaN               NaN  \n",
       "16974               NaN              NaN               NaN  \n",
       "16975               NaN              NaN               NaN  \n",
       "16976               NaN              NaN               NaN  \n",
       "16977               NaN              NaN               NaN  \n",
       "16978               NaN              NaN               NaN  \n",
       "16979               NaN              NaN               NaN  \n",
       "16980               NaN              NaN               NaN  \n",
       "16981               NaN              NaN               NaN  \n",
       "16982               NaN              NaN               NaN  \n",
       "16983               NaN              NaN               NaN  \n",
       "16984               NaN              NaN               NaN  \n",
       "16985               NaN              NaN               NaN  \n",
       "16986               NaN              NaN               NaN  \n",
       "\n",
       "[16987 rows x 606 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df\n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1.2'></a>\n",
    "## 1.2 Selecting range of rows <a href='#TOC'><b>(back to TOC)</b></a><br>\n",
    "We can select a range of rows of $df$ by selecting a slice using integer indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Sample2_1</th>\n",
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       "      <th>Sample2_5</th>\n",
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       "      <th>...</th>\n",
       "      <th>Sample2_299</th>\n",
       "      <th>Unnamed: 597</th>\n",
       "      <th>Sample2_300</th>\n",
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       "      <th>Sample2_301</th>\n",
       "      <th>Unnamed: 601</th>\n",
       "      <th>Sample2_302</th>\n",
       "      <th>Unnamed: 603</th>\n",
       "      <th>Sample2_303</th>\n",
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       "      <td>749.0200195</td>\n",
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       "      <td>0.009653814</td>\n",
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       "      <td>0.039798222</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>750</td>\n",
       "      <td>0.049861383</td>\n",
       "      <td>750</td>\n",
       "      <td>-0.038783364</td>\n",
       "      <td>750</td>\n",
       "      <td>0.009997201</td>\n",
       "      <td>750</td>\n",
       "      <td>0.010114804</td>\n",
       "      <td>750</td>\n",
       "      <td>-0.050363116</td>\n",
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       "      <td>750</td>\n",
       "      <td>-0.020354992</td>\n",
       "      <td>750</td>\n",
       "      <td>0</td>\n",
       "      <td>750</td>\n",
       "      <td>-0.010105195</td>\n",
       "      <td>750</td>\n",
       "      <td>-0.009824052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Sample2_1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>Sample2_1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>Collection Time: 2/27/2018 9:47:46 AM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 606 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Sample2_1    Unnamed: 1    Sample2_2  \\\n",
       "318                            746.9400024  -0.009663143  746.9400024   \n",
       "319                            748.0499878  -0.019659111  748.0499878   \n",
       "320                            749.0200195  -0.009596192  749.0200195   \n",
       "321                                    750   0.049861383          750   \n",
       "322                                    NaN           NaN          NaN   \n",
       "323                              Sample2_1           NaN          NaN   \n",
       "324                              Sample2_1           NaN          NaN   \n",
       "325  Collection Time: 2/27/2018 9:47:46 AM           NaN          NaN   \n",
       "\n",
       "       Unnamed: 3    Sample2_3    Unnamed: 5    Sample2_4    Unnamed: 7  \\\n",
       "318  -0.019745091  746.9400024  -0.029714443  746.9400024   0.040749796   \n",
       "319   0.028764287  748.0499878   0.020575708  748.0499878             0   \n",
       "320   0.009653814  749.0200195   0.039798222  749.0200195  -0.039246853   \n",
       "321  -0.038783364          750   0.009997201          750   0.010114804   \n",
       "322           NaN          NaN           NaN          NaN           NaN   \n",
       "323           NaN          NaN           NaN          NaN           NaN   \n",
       "324           NaN          NaN           NaN          NaN           NaN   \n",
       "325           NaN          NaN           NaN          NaN           NaN   \n",
       "\n",
       "       Sample2_5    Unnamed: 9     ...       Sample2_299  Unnamed: 597  \\\n",
       "318  746.9400024    0.04909084     ...       746.9400024  -0.050462741   \n",
       "319  748.0499878   0.009988414     ...       748.0499878   0.030135309   \n",
       "320  749.0200195  -0.029143473     ...       749.0200195             0   \n",
       "321          750  -0.050363116     ...               750  -0.020354992   \n",
       "322          NaN           NaN     ...               NaN           NaN   \n",
       "323          NaN           NaN     ...               NaN           NaN   \n",
       "324          NaN           NaN     ...               NaN           NaN   \n",
       "325          NaN           NaN     ...               NaN           NaN   \n",
       "\n",
       "     Sample2_300 Unnamed: 599  Sample2_301  Unnamed: 601  Sample2_302  \\\n",
       "318  746.9400024   0.05077998  746.9400024   0.010015725  746.9400024   \n",
       "319  748.0499878  0.020186728  748.0499878   0.121364132  748.0499878   \n",
       "320  749.0200195   0.01045686  749.0200195             0  749.0200195   \n",
       "321          750            0          750  -0.010105195          750   \n",
       "322          NaN          NaN          NaN           NaN          NaN   \n",
       "323          NaN          NaN          NaN           NaN          NaN   \n",
       "324          NaN          NaN          NaN           NaN          NaN   \n",
       "325          NaN          NaN          NaN           NaN          NaN   \n",
       "\n",
       "     Unnamed: 603 Sample2_303 Unnamed: 605  \n",
       "318   -0.04050592         NaN          NaN  \n",
       "319   0.092554502         NaN          NaN  \n",
       "320   0.020063199         NaN          NaN  \n",
       "321  -0.009824052         NaN          NaN  \n",
       "322           NaN         NaN          NaN  \n",
       "323           NaN         NaN          NaN  \n",
       "324           NaN         NaN          NaN  \n",
       "325           NaN         NaN          NaN  \n",
       "\n",
       "[8 rows x 606 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[318:326]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clean up the data by re-assigning $df$ to only the rows with the data of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df[1:322]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing this to the original $df$, we can see that the old first and the last 16 thousand or so rows were removed from $df$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <td>431.9599915</td>\n",
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       "      <td>433.0299988</td>\n",
       "      <td>0.030374518</td>\n",
       "      <td>433.0299988</td>\n",
       "      <td>0.02032066</td>\n",
       "      <td>433.0299988</td>\n",
       "      <td>-0.030248033</td>\n",
       "      <td>433.0299988</td>\n",
       "      <td>-0.009978446</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>433.9299927</td>\n",
       "      <td>0.019258175</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>0.029845698</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>-0.068960764</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>-0.029745085</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>0.019614382</td>\n",
       "      <td>...</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>0</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>-0.081288427</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>-0.010362587</td>\n",
       "      <td>433.9299927</td>\n",
       "      <td>0.112422712</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>435</td>\n",
       "      <td>0.029816924</td>\n",
       "      <td>435</td>\n",
       "      <td>0.00974412</td>\n",
       "      <td>435</td>\n",
       "      <td>0.029070893</td>\n",
       "      <td>435</td>\n",
       "      <td>-0.019309679</td>\n",
       "      <td>435</td>\n",
       "      <td>0.02913272</td>\n",
       "      <td>...</td>\n",
       "      <td>435</td>\n",
       "      <td>0.020968316</td>\n",
       "      <td>435</td>\n",
       "      <td>0.010163222</td>\n",
       "      <td>435</td>\n",
       "      <td>0</td>\n",
       "      <td>435</td>\n",
       "      <td>0.050819721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>436.0599976</td>\n",
       "      <td>-0.009748205</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>-0.04886869</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>0.009688607</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>-0.009797773</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>0.039117508</td>\n",
       "      <td>...</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>0.030630687</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>0.050461724</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>-0.010179464</td>\n",
       "      <td>436.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>436.9599915</td>\n",
       "      <td>0</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>-0.009863586</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>0.048343278</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>-0.040247522</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>0.059689019</td>\n",
       "      <td>...</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>0.101620853</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>0.05064676</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>-0.010054597</td>\n",
       "      <td>436.9599915</td>\n",
       "      <td>0.03065322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>438.0299988</td>\n",
       "      <td>0.049147293</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>0.029712679</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>-0.009679324</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>0</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>-0.030050183</td>\n",
       "      <td>...</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>-0.0102416</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>-0.010024761</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>-0.009985422</td>\n",
       "      <td>438.0299988</td>\n",
       "      <td>0.040140893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0.019724838</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>-0.029381232</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0.019620346</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0.077636741</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0.009827141</td>\n",
       "      <td>...</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0.040346988</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0.029959256</td>\n",
       "      <td>438.9299927</td>\n",
       "      <td>0.070338324</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>440</td>\n",
       "      <td>0.038385138</td>\n",
       "      <td>440</td>\n",
       "      <td>0.029257732</td>\n",
       "      <td>440</td>\n",
       "      <td>0.01921488</td>\n",
       "      <td>440</td>\n",
       "      <td>-0.050328646</td>\n",
       "      <td>440</td>\n",
       "      <td>0.020295709</td>\n",
       "      <td>...</td>\n",
       "      <td>440</td>\n",
       "      <td>0.030318955</td>\n",
       "      <td>440</td>\n",
       "      <td>0.02973064</td>\n",
       "      <td>440</td>\n",
       "      <td>0.040341694</td>\n",
       "      <td>440</td>\n",
       "      <td>0.010304922</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>441.0599976</td>\n",
       "      <td>0.038362313</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>0.009880447</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>0.038805943</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>-0.009912473</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>-0.010376673</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>0.052354377</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>0.040707085</td>\n",
       "      <td>441.0599976</td>\n",
       "      <td>0.091667436</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>441.9599915</td>\n",
       "      <td>-0.048075076</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>0.028893938</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>-0.01947799</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>-0.119486213</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>0.039522175</td>\n",
       "      <td>...</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>0.030505165</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>0</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>0.030446962</td>\n",
       "      <td>441.9599915</td>\n",
       "      <td>0.020385489</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>443.0299988</td>\n",
       "      <td>-0.009615107</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>0</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>0.067498505</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>-0.029801818</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>-0.049323283</td>\n",
       "      <td>...</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>0.040189289</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>0.02037905</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>-0.060119037</td>\n",
       "      <td>443.0299988</td>\n",
       "      <td>0.020148089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>443.9299927</td>\n",
       "      <td>0.019217649</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>-0.0196454</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>0.019588254</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>-0.059078962</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>0.009745734</td>\n",
       "      <td>...</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>0.010003401</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>0.039931718</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>0.041184466</td>\n",
       "      <td>443.9299927</td>\n",
       "      <td>-0.030719137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>445</td>\n",
       "      <td>0.06854815</td>\n",
       "      <td>445</td>\n",
       "      <td>-0.05869291</td>\n",
       "      <td>445</td>\n",
       "      <td>-0.019449577</td>\n",
       "      <td>445</td>\n",
       "      <td>0.009975063</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>445</td>\n",
       "      <td>0.061513856</td>\n",
       "      <td>445</td>\n",
       "      <td>-0.010287431</td>\n",
       "      <td>445</td>\n",
       "      <td>0.040981926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>446.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>0.039113685</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>0.009918273</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>-0.019814536</td>\n",
       "      <td>...</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>-0.020323345</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>0.030507337</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>446.0599976</td>\n",
       "      <td>-0.010131507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>446.9599915</td>\n",
       "      <td>0.019136557</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>0.009885428</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>0.038827788</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>0.099999994</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>-0.010100601</td>\n",
       "      <td>...</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>0.060862422</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>0</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>-0.009960555</td>\n",
       "      <td>446.9599915</td>\n",
       "      <td>0.010321302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>448.0299988</td>\n",
       "      <td>0.048575759</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>-0.059414174</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>0</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>0</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>-0.038583968</td>\n",
       "      <td>...</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>-0.009847365</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>-0.04975174</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>0.020094039</td>\n",
       "      <td>448.0299988</td>\n",
       "      <td>0.031662267</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>448.9299927</td>\n",
       "      <td>0.009576159</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>-0.009899716</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>0</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>-0.098154694</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>0.02023677</td>\n",
       "      <td>...</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>0</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>0.050648812</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>0.062532566</td>\n",
       "      <td>448.9299927</td>\n",
       "      <td>0.062263273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>450</td>\n",
       "      <td>0</td>\n",
       "      <td>450</td>\n",
       "      <td>-0.009917191</td>\n",
       "      <td>450</td>\n",
       "      <td>0.019483684</td>\n",
       "      <td>450</td>\n",
       "      <td>-0.030045068</td>\n",
       "      <td>450</td>\n",
       "      <td>-0.019980419</td>\n",
       "      <td>...</td>\n",
       "      <td>450</td>\n",
       "      <td>-0.041053854</td>\n",
       "      <td>450</td>\n",
       "      <td>-0.030399757</td>\n",
       "      <td>450</td>\n",
       "      <td>-0.030295685</td>\n",
       "      <td>450</td>\n",
       "      <td>0.030969979</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0.029142341</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0.05895647</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0.048794769</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0.070239522</td>\n",
       "      <td>...</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>-0.020151744</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0.009994104</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0.069865167</td>\n",
       "      <td>451.0599976</td>\n",
       "      <td>0.010252309</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0.077900581</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0.009864657</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0.030193539</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0.101774953</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0.071114361</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>-0.020314882</td>\n",
       "      <td>451.9599915</td>\n",
       "      <td>0.059989803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>453.0299988</td>\n",
       "      <td>0.057893261</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>0.019438611</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>0.059153512</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>-0.009876348</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>0.009747064</td>\n",
       "      <td>...</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>-0.020612607</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>0.061185159</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>0</td>\n",
       "      <td>453.0299988</td>\n",
       "      <td>0.040225666</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>453.9299927</td>\n",
       "      <td>-0.009666319</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>0.019313782</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>-0.029357366</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>0.010008607</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>-0.02892793</td>\n",
       "      <td>...</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>0.051856462</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>0.041105323</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>0.050291188</td>\n",
       "      <td>453.9299927</td>\n",
       "      <td>0.030289873</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>455</td>\n",
       "      <td>0.087446563</td>\n",
       "      <td>455</td>\n",
       "      <td>-0.070473582</td>\n",
       "      <td>455</td>\n",
       "      <td>-0.029303458</td>\n",
       "      <td>455</td>\n",
       "      <td>-0.028950822</td>\n",
       "      <td>455</td>\n",
       "      <td>-0.019725811</td>\n",
       "      <td>...</td>\n",
       "      <td>455</td>\n",
       "      <td>0.080286622</td>\n",
       "      <td>455</td>\n",
       "      <td>0</td>\n",
       "      <td>455</td>\n",
       "      <td>0.020089397</td>\n",
       "      <td>455</td>\n",
       "      <td>0.060551625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>456.0599976</td>\n",
       "      <td>0</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>-0.039386749</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>0.059674177</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>-0.071171485</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>0.009976555</td>\n",
       "      <td>...</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>0.040707495</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>-0.009733117</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>-0.029928472</td>\n",
       "      <td>456.0599976</td>\n",
       "      <td>0.07045798</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0.01934685</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0.070386417</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0.029720921</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>-0.029944602</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0.010093975</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0.010278971</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0</td>\n",
       "      <td>456.9599915</td>\n",
       "      <td>0.050750602</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>458.0299988</td>\n",
       "      <td>0.058948938</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>0.030063735</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>0.058647014</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>-0.010041673</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>0.010067452</td>\n",
       "      <td>...</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>0.009980339</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>0.010098358</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>-0.010003101</td>\n",
       "      <td>458.0299988</td>\n",
       "      <td>0.0303395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>458.9299927</td>\n",
       "      <td>0.009714491</td>\n",
       "      <td>458.9299927</td>\n",
       "      <td>-0.068917312</td>\n",
       "      <td>458.9299927</td>\n",
       "      <td>-0.029025048</td>\n",
       "      <td>458.9299927</td>\n",
       "      <td>0.041109547</td>\n",
       "      <td>458.9299927</td>\n",
       "      <td>0.060088933</td>\n",
       "      <td>...</td>\n",
       "      <td>458.9299927</td>\n",
       "      <td>0.081090674</td>\n",
       "      <td>458.9299927</td>\n",
       "      <td>0.02086659</td>\n",
       "      <td>458.9299927</td>\n",
       "      <td>-0.040108696</td>\n",
       "      <td>458.9299927</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>720.9799805</td>\n",
       "      <td>-0.01960131</td>\n",
       "      <td>720.9799805</td>\n",
       "      <td>0</td>\n",
       "      <td>720.9799805</td>\n",
       "      <td>0.049992502</td>\n",
       "      <td>720.9799805</td>\n",
       "      <td>0.038642492</td>\n",
       "      <td>720.9799805</td>\n",
       "      <td>0.009880349</td>\n",
       "      <td>...</td>\n",
       "      <td>720.9799805</td>\n",
       "      <td>0.101469278</td>\n",
       "      <td>720.9799805</td>\n",
       "      <td>-0.009725261</td>\n",
       "      <td>720.9799805</td>\n",
       "      <td>0.030019211</td>\n",
       "      <td>720.9799805</td>\n",
       "      <td>0.040095028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>721.9699707</td>\n",
       "      <td>-0.030403763</td>\n",
       "      <td>721.9699707</td>\n",
       "      <td>0.029168408</td>\n",
       "      <td>721.9699707</td>\n",
       "      <td>-0.040619444</td>\n",
       "      <td>721.9699707</td>\n",
       "      <td>0.009790772</td>\n",
       "      <td>721.9699707</td>\n",
       "      <td>-0.019780243</td>\n",
       "      <td>...</td>\n",
       "      <td>721.9699707</td>\n",
       "      <td>-0.010031097</td>\n",
       "      <td>721.9699707</td>\n",
       "      <td>0.020477327</td>\n",
       "      <td>721.9699707</td>\n",
       "      <td>0</td>\n",
       "      <td>721.9699707</td>\n",
       "      <td>0.010422312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>722.9500122</td>\n",
       "      <td>0.009974963</td>\n",
       "      <td>722.9500122</td>\n",
       "      <td>0.029331248</td>\n",
       "      <td>722.9500122</td>\n",
       "      <td>-0.059704464</td>\n",
       "      <td>722.9500122</td>\n",
       "      <td>-0.029100785</td>\n",
       "      <td>722.9500122</td>\n",
       "      <td>-0.039747208</td>\n",
       "      <td>...</td>\n",
       "      <td>722.9500122</td>\n",
       "      <td>0</td>\n",
       "      <td>722.9500122</td>\n",
       "      <td>0.020124977</td>\n",
       "      <td>722.9500122</td>\n",
       "      <td>0</td>\n",
       "      <td>722.9500122</td>\n",
       "      <td>0.069646895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>723.9400024</td>\n",
       "      <td>-0.009988214</td>\n",
       "      <td>723.9400024</td>\n",
       "      <td>0.049539775</td>\n",
       "      <td>723.9400024</td>\n",
       "      <td>0.048324119</td>\n",
       "      <td>723.9400024</td>\n",
       "      <td>0.059947845</td>\n",
       "      <td>723.9400024</td>\n",
       "      <td>0.049787406</td>\n",
       "      <td>...</td>\n",
       "      <td>723.9400024</td>\n",
       "      <td>-0.091998197</td>\n",
       "      <td>723.9400024</td>\n",
       "      <td>0.010125762</td>\n",
       "      <td>723.9400024</td>\n",
       "      <td>-0.020136526</td>\n",
       "      <td>723.9400024</td>\n",
       "      <td>-0.060152184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>725.0700073</td>\n",
       "      <td>0</td>\n",
       "      <td>725.0700073</td>\n",
       "      <td>-0.068465687</td>\n",
       "      <td>725.0700073</td>\n",
       "      <td>0.029325513</td>\n",
       "      <td>725.0700073</td>\n",
       "      <td>0.010250312</td>\n",
       "      <td>725.0700073</td>\n",
       "      <td>0.020286445</td>\n",
       "      <td>...</td>\n",
       "      <td>725.0700073</td>\n",
       "      <td>-0.010171595</td>\n",
       "      <td>725.0700073</td>\n",
       "      <td>0.060730588</td>\n",
       "      <td>725.0700073</td>\n",
       "      <td>-0.081549443</td>\n",
       "      <td>725.0700073</td>\n",
       "      <td>-0.040242665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>726.0499878</td>\n",
       "      <td>0.019699387</td>\n",
       "      <td>726.0499878</td>\n",
       "      <td>0.050172597</td>\n",
       "      <td>726.0499878</td>\n",
       "      <td>-0.020331196</td>\n",
       "      <td>726.0499878</td>\n",
       "      <td>0.039575357</td>\n",
       "      <td>726.0499878</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>726.0499878</td>\n",
       "      <td>0</td>\n",
       "      <td>726.0499878</td>\n",
       "      <td>0</td>\n",
       "      <td>726.0499878</td>\n",
       "      <td>0.010124224</td>\n",
       "      <td>726.0499878</td>\n",
       "      <td>0.029865606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>727.039978</td>\n",
       "      <td>0.019562773</td>\n",
       "      <td>727.039978</td>\n",
       "      <td>0.009793457</td>\n",
       "      <td>727.039978</td>\n",
       "      <td>-0.030029429</td>\n",
       "      <td>727.039978</td>\n",
       "      <td>0</td>\n",
       "      <td>727.039978</td>\n",
       "      <td>0.089313179</td>\n",
       "      <td>...</td>\n",
       "      <td>727.039978</td>\n",
       "      <td>-0.030049883</td>\n",
       "      <td>727.039978</td>\n",
       "      <td>0.010150429</td>\n",
       "      <td>727.039978</td>\n",
       "      <td>0</td>\n",
       "      <td>727.039978</td>\n",
       "      <td>-0.040288061</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>728.0200195</td>\n",
       "      <td>-0.029214425</td>\n",
       "      <td>728.0200195</td>\n",
       "      <td>0.009989811</td>\n",
       "      <td>728.0200195</td>\n",
       "      <td>-0.030139243</td>\n",
       "      <td>728.0200195</td>\n",
       "      <td>0.039690413</td>\n",
       "      <td>728.0200195</td>\n",
       "      <td>0.050188709</td>\n",
       "      <td>...</td>\n",
       "      <td>728.0200195</td>\n",
       "      <td>0.092161179</td>\n",
       "      <td>728.0200195</td>\n",
       "      <td>0.010136642</td>\n",
       "      <td>728.0200195</td>\n",
       "      <td>0.03004236</td>\n",
       "      <td>728.0200195</td>\n",
       "      <td>-0.03010265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>729.0100098</td>\n",
       "      <td>0.029423593</td>\n",
       "      <td>729.0100098</td>\n",
       "      <td>-0.010160537</td>\n",
       "      <td>729.0100098</td>\n",
       "      <td>0.020323757</td>\n",
       "      <td>729.0100098</td>\n",
       "      <td>0.029779335</td>\n",
       "      <td>729.0100098</td>\n",
       "      <td>-0.029732114</td>\n",
       "      <td>...</td>\n",
       "      <td>729.0100098</td>\n",
       "      <td>-0.020259524</td>\n",
       "      <td>729.0100098</td>\n",
       "      <td>-0.029865306</td>\n",
       "      <td>729.0100098</td>\n",
       "      <td>0.10317149</td>\n",
       "      <td>729.0100098</td>\n",
       "      <td>0.060030017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>730</td>\n",
       "      <td>0.019390931</td>\n",
       "      <td>730</td>\n",
       "      <td>0.020698149</td>\n",
       "      <td>730</td>\n",
       "      <td>0.060053449</td>\n",
       "      <td>730</td>\n",
       "      <td>-0.040436715</td>\n",
       "      <td>730</td>\n",
       "      <td>0.06859383</td>\n",
       "      <td>...</td>\n",
       "      <td>730</td>\n",
       "      <td>-0.042262301</td>\n",
       "      <td>730</td>\n",
       "      <td>0.061686508</td>\n",
       "      <td>730</td>\n",
       "      <td>-0.040138073</td>\n",
       "      <td>730</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>730.9799805</td>\n",
       "      <td>-0.02935392</td>\n",
       "      <td>730.9799805</td>\n",
       "      <td>0.058735427</td>\n",
       "      <td>730.9799805</td>\n",
       "      <td>-0.029988306</td>\n",
       "      <td>730.9799805</td>\n",
       "      <td>-0.009725261</td>\n",
       "      <td>730.9799805</td>\n",
       "      <td>0.009961647</td>\n",
       "      <td>...</td>\n",
       "      <td>730.9799805</td>\n",
       "      <td>0.019655246</td>\n",
       "      <td>730.9799805</td>\n",
       "      <td>0.051384293</td>\n",
       "      <td>730.9799805</td>\n",
       "      <td>0.051307838</td>\n",
       "      <td>730.9799805</td>\n",
       "      <td>0.010035325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>731.9699707</td>\n",
       "      <td>0.038828921</td>\n",
       "      <td>731.9699707</td>\n",
       "      <td>-0.009737667</td>\n",
       "      <td>731.9699707</td>\n",
       "      <td>0.088459916</td>\n",
       "      <td>731.9699707</td>\n",
       "      <td>-0.019510292</td>\n",
       "      <td>731.9699707</td>\n",
       "      <td>0.019777503</td>\n",
       "      <td>...</td>\n",
       "      <td>731.9699707</td>\n",
       "      <td>0.050386969</td>\n",
       "      <td>731.9699707</td>\n",
       "      <td>-0.049242649</td>\n",
       "      <td>731.9699707</td>\n",
       "      <td>0.019992404</td>\n",
       "      <td>731.9699707</td>\n",
       "      <td>0.070286065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>732.9500122</td>\n",
       "      <td>0.059944849</td>\n",
       "      <td>732.9500122</td>\n",
       "      <td>-0.019701134</td>\n",
       "      <td>732.9500122</td>\n",
       "      <td>0.091590002</td>\n",
       "      <td>732.9500122</td>\n",
       "      <td>-0.059161678</td>\n",
       "      <td>732.9500122</td>\n",
       "      <td>0.019539647</td>\n",
       "      <td>...</td>\n",
       "      <td>732.9500122</td>\n",
       "      <td>0.050482105</td>\n",
       "      <td>732.9500122</td>\n",
       "      <td>-0.070905469</td>\n",
       "      <td>732.9500122</td>\n",
       "      <td>0.049843494</td>\n",
       "      <td>732.9500122</td>\n",
       "      <td>-0.061491162</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>733.9400024</td>\n",
       "      <td>-0.04997851</td>\n",
       "      <td>733.9400024</td>\n",
       "      <td>-0.010042581</td>\n",
       "      <td>733.9400024</td>\n",
       "      <td>0.009803633</td>\n",
       "      <td>733.9400024</td>\n",
       "      <td>0.010058743</td>\n",
       "      <td>733.9400024</td>\n",
       "      <td>0.009731508</td>\n",
       "      <td>...</td>\n",
       "      <td>733.9400024</td>\n",
       "      <td>0.030123508</td>\n",
       "      <td>733.9400024</td>\n",
       "      <td>-0.065186217</td>\n",
       "      <td>733.9400024</td>\n",
       "      <td>0.071067438</td>\n",
       "      <td>733.9400024</td>\n",
       "      <td>0.020062394</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>735.0700073</td>\n",
       "      <td>0.020344226</td>\n",
       "      <td>735.0700073</td>\n",
       "      <td>-0.029536862</td>\n",
       "      <td>735.0700073</td>\n",
       "      <td>0.038783364</td>\n",
       "      <td>735.0700073</td>\n",
       "      <td>0.038467087</td>\n",
       "      <td>735.0700073</td>\n",
       "      <td>0.019514669</td>\n",
       "      <td>...</td>\n",
       "      <td>735.0700073</td>\n",
       "      <td>0.020178581</td>\n",
       "      <td>735.0700073</td>\n",
       "      <td>-0.050160512</td>\n",
       "      <td>735.0700073</td>\n",
       "      <td>0.063070923</td>\n",
       "      <td>735.0700073</td>\n",
       "      <td>-0.010136539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>736.0499878</td>\n",
       "      <td>-0.01974548</td>\n",
       "      <td>736.0499878</td>\n",
       "      <td>-0.02958259</td>\n",
       "      <td>736.0499878</td>\n",
       "      <td>-0.019719005</td>\n",
       "      <td>736.0499878</td>\n",
       "      <td>0.080227047</td>\n",
       "      <td>736.0499878</td>\n",
       "      <td>-0.029359091</td>\n",
       "      <td>...</td>\n",
       "      <td>736.0499878</td>\n",
       "      <td>-0.060844522</td>\n",
       "      <td>736.0499878</td>\n",
       "      <td>-0.0200445</td>\n",
       "      <td>736.0499878</td>\n",
       "      <td>0.010203041</td>\n",
       "      <td>736.0499878</td>\n",
       "      <td>-0.030404996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>737.039978</td>\n",
       "      <td>0.019765189</td>\n",
       "      <td>737.039978</td>\n",
       "      <td>-0.009977153</td>\n",
       "      <td>737.039978</td>\n",
       "      <td>0.030119574</td>\n",
       "      <td>737.039978</td>\n",
       "      <td>0</td>\n",
       "      <td>737.039978</td>\n",
       "      <td>0.009668094</td>\n",
       "      <td>...</td>\n",
       "      <td>737.039978</td>\n",
       "      <td>-0.010331966</td>\n",
       "      <td>737.039978</td>\n",
       "      <td>0.010147855</td>\n",
       "      <td>737.039978</td>\n",
       "      <td>-0.019990204</td>\n",
       "      <td>737.039978</td>\n",
       "      <td>0.040010404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>738.0200195</td>\n",
       "      <td>0.089331798</td>\n",
       "      <td>738.0200195</td>\n",
       "      <td>0.020149305</td>\n",
       "      <td>738.0200195</td>\n",
       "      <td>0.049740851</td>\n",
       "      <td>738.0200195</td>\n",
       "      <td>0.020401293</td>\n",
       "      <td>738.0200195</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>738.0200195</td>\n",
       "      <td>-0.059977807</td>\n",
       "      <td>738.0200195</td>\n",
       "      <td>0.020286649</td>\n",
       "      <td>738.0200195</td>\n",
       "      <td>0.050432716</td>\n",
       "      <td>738.0200195</td>\n",
       "      <td>-0.020295709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>739.0100098</td>\n",
       "      <td>-0.068090729</td>\n",
       "      <td>739.0100098</td>\n",
       "      <td>0.030309154</td>\n",
       "      <td>739.0100098</td>\n",
       "      <td>-0.0098173</td>\n",
       "      <td>739.0100098</td>\n",
       "      <td>-0.009982131</td>\n",
       "      <td>739.0100098</td>\n",
       "      <td>0.019613419</td>\n",
       "      <td>...</td>\n",
       "      <td>739.0100098</td>\n",
       "      <td>0.030593202</td>\n",
       "      <td>739.0100098</td>\n",
       "      <td>-0.010032203</td>\n",
       "      <td>739.0100098</td>\n",
       "      <td>-0.039917771</td>\n",
       "      <td>739.0100098</td>\n",
       "      <td>-0.019917937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>740</td>\n",
       "      <td>0.019321058</td>\n",
       "      <td>740</td>\n",
       "      <td>-0.020103129</td>\n",
       "      <td>740</td>\n",
       "      <td>0.029960454</td>\n",
       "      <td>740</td>\n",
       "      <td>0.078507565</td>\n",
       "      <td>740</td>\n",
       "      <td>-0.06922058</td>\n",
       "      <td>...</td>\n",
       "      <td>740</td>\n",
       "      <td>0.01032045</td>\n",
       "      <td>740</td>\n",
       "      <td>-0.010153727</td>\n",
       "      <td>740</td>\n",
       "      <td>0.030720709</td>\n",
       "      <td>740</td>\n",
       "      <td>0.0103714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>740.9699707</td>\n",
       "      <td>-0.029506069</td>\n",
       "      <td>740.9699707</td>\n",
       "      <td>-0.039340261</td>\n",
       "      <td>740.9699707</td>\n",
       "      <td>0.019856241</td>\n",
       "      <td>740.9699707</td>\n",
       "      <td>0.019869654</td>\n",
       "      <td>740.9699707</td>\n",
       "      <td>-0.01022725</td>\n",
       "      <td>...</td>\n",
       "      <td>740.9699707</td>\n",
       "      <td>0</td>\n",
       "      <td>740.9699707</td>\n",
       "      <td>0.061307065</td>\n",
       "      <td>740.9699707</td>\n",
       "      <td>-0.050199796</td>\n",
       "      <td>740.9699707</td>\n",
       "      <td>-0.019710647</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>741.9400024</td>\n",
       "      <td>0.009647295</td>\n",
       "      <td>741.9400024</td>\n",
       "      <td>0.050401703</td>\n",
       "      <td>741.9400024</td>\n",
       "      <td>0.009681105</td>\n",
       "      <td>741.9400024</td>\n",
       "      <td>0.098170109</td>\n",
       "      <td>741.9400024</td>\n",
       "      <td>0.019248351</td>\n",
       "      <td>...</td>\n",
       "      <td>741.9400024</td>\n",
       "      <td>-0.050551515</td>\n",
       "      <td>741.9400024</td>\n",
       "      <td>0.03094315</td>\n",
       "      <td>741.9400024</td>\n",
       "      <td>0.010108055</td>\n",
       "      <td>741.9400024</td>\n",
       "      <td>-0.009933643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>743.0499878</td>\n",
       "      <td>0.01946529</td>\n",
       "      <td>743.0499878</td>\n",
       "      <td>-0.029370876</td>\n",
       "      <td>743.0499878</td>\n",
       "      <td>-0.029882263</td>\n",
       "      <td>743.0499878</td>\n",
       "      <td>-0.068393439</td>\n",
       "      <td>743.0499878</td>\n",
       "      <td>0.040081766</td>\n",
       "      <td>...</td>\n",
       "      <td>743.0499878</td>\n",
       "      <td>-0.041175988</td>\n",
       "      <td>743.0499878</td>\n",
       "      <td>-0.090466812</td>\n",
       "      <td>743.0499878</td>\n",
       "      <td>-0.051022489</td>\n",
       "      <td>743.0499878</td>\n",
       "      <td>-0.020974914</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>744.0200195</td>\n",
       "      <td>-0.019522481</td>\n",
       "      <td>744.0200195</td>\n",
       "      <td>0</td>\n",
       "      <td>744.0200195</td>\n",
       "      <td>0.03913473</td>\n",
       "      <td>744.0200195</td>\n",
       "      <td>-0.049763128</td>\n",
       "      <td>744.0200195</td>\n",
       "      <td>0.00980421</td>\n",
       "      <td>...</td>\n",
       "      <td>744.0200195</td>\n",
       "      <td>0.019946544</td>\n",
       "      <td>744.0200195</td>\n",
       "      <td>0.030432753</td>\n",
       "      <td>744.0200195</td>\n",
       "      <td>0</td>\n",
       "      <td>744.0200195</td>\n",
       "      <td>0.04034739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>745</td>\n",
       "      <td>0.010068871</td>\n",
       "      <td>745</td>\n",
       "      <td>0.009819131</td>\n",
       "      <td>745</td>\n",
       "      <td>0</td>\n",
       "      <td>745</td>\n",
       "      <td>0.059964024</td>\n",
       "      <td>745</td>\n",
       "      <td>0.028821489</td>\n",
       "      <td>...</td>\n",
       "      <td>745</td>\n",
       "      <td>-0.010163427</td>\n",
       "      <td>745</td>\n",
       "      <td>0</td>\n",
       "      <td>745</td>\n",
       "      <td>0</td>\n",
       "      <td>745</td>\n",
       "      <td>0.02040733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>745.9699707</td>\n",
       "      <td>-0.009758192</td>\n",
       "      <td>745.9699707</td>\n",
       "      <td>-0.048799053</td>\n",
       "      <td>745.9699707</td>\n",
       "      <td>0.02933641</td>\n",
       "      <td>745.9699707</td>\n",
       "      <td>-0.019521909</td>\n",
       "      <td>745.9699707</td>\n",
       "      <td>0.059904754</td>\n",
       "      <td>...</td>\n",
       "      <td>745.9699707</td>\n",
       "      <td>0.029886134</td>\n",
       "      <td>745.9699707</td>\n",
       "      <td>-0.029707087</td>\n",
       "      <td>745.9699707</td>\n",
       "      <td>-0.030369598</td>\n",
       "      <td>745.9699707</td>\n",
       "      <td>-0.060375538</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>746.9400024</td>\n",
       "      <td>-0.009663143</td>\n",
       "      <td>746.9400024</td>\n",
       "      <td>-0.019745091</td>\n",
       "      <td>746.9400024</td>\n",
       "      <td>-0.029714443</td>\n",
       "      <td>746.9400024</td>\n",
       "      <td>0.040749796</td>\n",
       "      <td>746.9400024</td>\n",
       "      <td>0.04909084</td>\n",
       "      <td>...</td>\n",
       "      <td>746.9400024</td>\n",
       "      <td>-0.050462741</td>\n",
       "      <td>746.9400024</td>\n",
       "      <td>0.05077998</td>\n",
       "      <td>746.9400024</td>\n",
       "      <td>0.010015725</td>\n",
       "      <td>746.9400024</td>\n",
       "      <td>-0.04050592</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>748.0499878</td>\n",
       "      <td>-0.019659111</td>\n",
       "      <td>748.0499878</td>\n",
       "      <td>0.028764287</td>\n",
       "      <td>748.0499878</td>\n",
       "      <td>0.020575708</td>\n",
       "      <td>748.0499878</td>\n",
       "      <td>0</td>\n",
       "      <td>748.0499878</td>\n",
       "      <td>0.009988414</td>\n",
       "      <td>...</td>\n",
       "      <td>748.0499878</td>\n",
       "      <td>0.030135309</td>\n",
       "      <td>748.0499878</td>\n",
       "      <td>0.020186728</td>\n",
       "      <td>748.0499878</td>\n",
       "      <td>0.121364132</td>\n",
       "      <td>748.0499878</td>\n",
       "      <td>0.092554502</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>749.0200195</td>\n",
       "      <td>-0.009596192</td>\n",
       "      <td>749.0200195</td>\n",
       "      <td>0.009653814</td>\n",
       "      <td>749.0200195</td>\n",
       "      <td>0.039798222</td>\n",
       "      <td>749.0200195</td>\n",
       "      <td>-0.039246853</td>\n",
       "      <td>749.0200195</td>\n",
       "      <td>-0.029143473</td>\n",
       "      <td>...</td>\n",
       "      <td>749.0200195</td>\n",
       "      <td>0</td>\n",
       "      <td>749.0200195</td>\n",
       "      <td>0.01045686</td>\n",
       "      <td>749.0200195</td>\n",
       "      <td>0</td>\n",
       "      <td>749.0200195</td>\n",
       "      <td>0.020063199</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>750</td>\n",
       "      <td>0.049861383</td>\n",
       "      <td>750</td>\n",
       "      <td>-0.038783364</td>\n",
       "      <td>750</td>\n",
       "      <td>0.009997201</td>\n",
       "      <td>750</td>\n",
       "      <td>0.010114804</td>\n",
       "      <td>750</td>\n",
       "      <td>-0.050363116</td>\n",
       "      <td>...</td>\n",
       "      <td>750</td>\n",
       "      <td>-0.020354992</td>\n",
       "      <td>750</td>\n",
       "      <td>0</td>\n",
       "      <td>750</td>\n",
       "      <td>-0.010105195</td>\n",
       "      <td>750</td>\n",
       "      <td>-0.009824052</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>321 rows × 606 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sample2_1    Unnamed: 1    Sample2_2    Unnamed: 3    Sample2_3  \\\n",
       "1            430   0.058042798          430   -0.01974665          430   \n",
       "2    431.0599976   -0.00970205  431.0599976   0.029207313  431.0599976   \n",
       "3    431.9599915   0.009579737  431.9599915   0.009718739  431.9599915   \n",
       "4    433.0299988   0.029176353  433.0299988   0.048106991  433.0299988   \n",
       "5    433.9299927   0.019258175  433.9299927   0.029845698  433.9299927   \n",
       "6            435   0.029816924          435    0.00974412          435   \n",
       "7    436.0599976  -0.009748205  436.0599976   -0.04886869  436.0599976   \n",
       "8    436.9599915             0  436.9599915  -0.009863586  436.9599915   \n",
       "9    438.0299988   0.049147293  438.0299988   0.029712679  438.0299988   \n",
       "10   438.9299927   0.019724838  438.9299927  -0.029381232  438.9299927   \n",
       "11           440   0.038385138          440   0.029257732          440   \n",
       "12   441.0599976   0.038362313  441.0599976   0.009880447  441.0599976   \n",
       "13   441.9599915  -0.048075076  441.9599915   0.028893938  441.9599915   \n",
       "14   443.0299988  -0.009615107  443.0299988             0  443.0299988   \n",
       "15   443.9299927   0.019217649  443.9299927    -0.0196454  443.9299927   \n",
       "16           445    0.06854815          445   -0.05869291          445   \n",
       "17   446.0599976             0  446.0599976             0  446.0599976   \n",
       "18   446.9599915   0.019136557  446.9599915   0.009885428  446.9599915   \n",
       "19   448.0299988   0.048575759  448.0299988  -0.059414174  448.0299988   \n",
       "20   448.9299927   0.009576159  448.9299927  -0.009899716  448.9299927   \n",
       "21           450             0          450  -0.009917191          450   \n",
       "22   451.0599976   0.029142341  451.0599976    0.05895647  451.0599976   \n",
       "23   451.9599915             0  451.9599915   0.077900581  451.9599915   \n",
       "24   453.0299988   0.057893261  453.0299988   0.019438611  453.0299988   \n",
       "25   453.9299927  -0.009666319  453.9299927   0.019313782  453.9299927   \n",
       "26           455   0.087446563          455  -0.070473582          455   \n",
       "27   456.0599976             0  456.0599976  -0.039386749  456.0599976   \n",
       "28   456.9599915    0.01934685  456.9599915   0.070386417  456.9599915   \n",
       "29   458.0299988   0.058948938  458.0299988   0.030063735  458.0299988   \n",
       "30   458.9299927   0.009714491  458.9299927  -0.068917312  458.9299927   \n",
       "..           ...           ...          ...           ...          ...   \n",
       "292  720.9799805   -0.01960131  720.9799805             0  720.9799805   \n",
       "293  721.9699707  -0.030403763  721.9699707   0.029168408  721.9699707   \n",
       "294  722.9500122   0.009974963  722.9500122   0.029331248  722.9500122   \n",
       "295  723.9400024  -0.009988214  723.9400024   0.049539775  723.9400024   \n",
       "296  725.0700073             0  725.0700073  -0.068465687  725.0700073   \n",
       "297  726.0499878   0.019699387  726.0499878   0.050172597  726.0499878   \n",
       "298   727.039978   0.019562773   727.039978   0.009793457   727.039978   \n",
       "299  728.0200195  -0.029214425  728.0200195   0.009989811  728.0200195   \n",
       "300  729.0100098   0.029423593  729.0100098  -0.010160537  729.0100098   \n",
       "301          730   0.019390931          730   0.020698149          730   \n",
       "302  730.9799805   -0.02935392  730.9799805   0.058735427  730.9799805   \n",
       "303  731.9699707   0.038828921  731.9699707  -0.009737667  731.9699707   \n",
       "304  732.9500122   0.059944849  732.9500122  -0.019701134  732.9500122   \n",
       "305  733.9400024   -0.04997851  733.9400024  -0.010042581  733.9400024   \n",
       "306  735.0700073   0.020344226  735.0700073  -0.029536862  735.0700073   \n",
       "307  736.0499878   -0.01974548  736.0499878   -0.02958259  736.0499878   \n",
       "308   737.039978   0.019765189   737.039978  -0.009977153   737.039978   \n",
       "309  738.0200195   0.089331798  738.0200195   0.020149305  738.0200195   \n",
       "310  739.0100098  -0.068090729  739.0100098   0.030309154  739.0100098   \n",
       "311          740   0.019321058          740  -0.020103129          740   \n",
       "312  740.9699707  -0.029506069  740.9699707  -0.039340261  740.9699707   \n",
       "313  741.9400024   0.009647295  741.9400024   0.050401703  741.9400024   \n",
       "314  743.0499878    0.01946529  743.0499878  -0.029370876  743.0499878   \n",
       "315  744.0200195  -0.019522481  744.0200195             0  744.0200195   \n",
       "316          745   0.010068871          745   0.009819131          745   \n",
       "317  745.9699707  -0.009758192  745.9699707  -0.048799053  745.9699707   \n",
       "318  746.9400024  -0.009663143  746.9400024  -0.019745091  746.9400024   \n",
       "319  748.0499878  -0.019659111  748.0499878   0.028764287  748.0499878   \n",
       "320  749.0200195  -0.009596192  749.0200195   0.009653814  749.0200195   \n",
       "321          750   0.049861383          750  -0.038783364          750   \n",
       "\n",
       "       Unnamed: 5    Sample2_4    Unnamed: 7    Sample2_5    Unnamed: 9  \\\n",
       "1     0.089482792          430   0.048062135          430   0.048927508   \n",
       "2     0.059682488  431.0599976    0.06060851  431.0599976   0.078460604   \n",
       "3     0.019442203  431.9599915   0.067501113  431.9599915   0.030053195   \n",
       "4     0.048642393  433.0299988  -0.010162911  433.0299988   0.039304312   \n",
       "5    -0.068960764  433.9299927  -0.029745085  433.9299927   0.019614382   \n",
       "6     0.029070893          435  -0.019309679          435    0.02913272   \n",
       "7     0.009688607  436.0599976  -0.009797773  436.0599976   0.039117508   \n",
       "8     0.048343278  436.9599915  -0.040247522  436.9599915   0.059689019   \n",
       "9    -0.009679324  438.0299988             0  438.0299988  -0.030050183   \n",
       "10    0.019620346  438.9299927   0.077636741  438.9299927   0.009827141   \n",
       "11     0.01921488          440  -0.050328646          440   0.020295709   \n",
       "12    0.038805943  441.0599976  -0.009912473  441.0599976             0   \n",
       "13    -0.01947799  441.9599915  -0.119486213  441.9599915   0.039522175   \n",
       "14    0.067498505  443.0299988  -0.029801818  443.0299988  -0.049323283   \n",
       "15    0.019588254  443.9299927  -0.059078962  443.9299927   0.009745734   \n",
       "16   -0.019449577          445   0.009975063          445             0   \n",
       "17    0.039113685  446.0599976   0.009918273  446.0599976  -0.019814536   \n",
       "18    0.038827788  446.9599915   0.099999994  446.9599915  -0.010100601   \n",
       "19              0  448.0299988             0  448.0299988  -0.038583968   \n",
       "20              0  448.9299927  -0.098154694  448.9299927    0.02023677   \n",
       "21    0.019483684          450  -0.030045068          450  -0.019980419   \n",
       "22    0.048794769  451.0599976             0  451.0599976   0.070239522   \n",
       "23    0.009864657  451.9599915   0.030193539  451.9599915             0   \n",
       "24    0.059153512  453.0299988  -0.009876348  453.0299988   0.009747064   \n",
       "25   -0.029357366  453.9299927   0.010008607  453.9299927   -0.02892793   \n",
       "26   -0.029303458          455  -0.028950822          455  -0.019725811   \n",
       "27    0.059674177  456.0599976  -0.071171485  456.0599976   0.009976555   \n",
       "28    0.029720921  456.9599915  -0.029944602  456.9599915             0   \n",
       "29    0.058647014  458.0299988  -0.010041673  458.0299988   0.010067452   \n",
       "30   -0.029025048  458.9299927   0.041109547  458.9299927   0.060088933   \n",
       "..            ...          ...           ...          ...           ...   \n",
       "292   0.049992502  720.9799805   0.038642492  720.9799805   0.009880349   \n",
       "293  -0.040619444  721.9699707   0.009790772  721.9699707  -0.019780243   \n",
       "294  -0.059704464  722.9500122  -0.029100785  722.9500122  -0.039747208   \n",
       "295   0.048324119  723.9400024   0.059947845  723.9400024   0.049787406   \n",
       "296   0.029325513  725.0700073   0.010250312  725.0700073   0.020286445   \n",
       "297  -0.020331196  726.0499878   0.039575357  726.0499878             0   \n",
       "298  -0.030029429   727.039978             0   727.039978   0.089313179   \n",
       "299  -0.030139243  728.0200195   0.039690413  728.0200195   0.050188709   \n",
       "300   0.020323757  729.0100098   0.029779335  729.0100098  -0.029732114   \n",
       "301   0.060053449          730  -0.040436715          730    0.06859383   \n",
       "302  -0.029988306  730.9799805  -0.009725261  730.9799805   0.009961647   \n",
       "303   0.088459916  731.9699707  -0.019510292  731.9699707   0.019777503   \n",
       "304   0.091590002  732.9500122  -0.059161678  732.9500122   0.019539647   \n",
       "305   0.009803633  733.9400024   0.010058743  733.9400024   0.009731508   \n",
       "306   0.038783364  735.0700073   0.038467087  735.0700073   0.019514669   \n",
       "307  -0.019719005  736.0499878   0.080227047  736.0499878  -0.029359091   \n",
       "308   0.030119574   737.039978             0   737.039978   0.009668094   \n",
       "309   0.049740851  738.0200195   0.020401293  738.0200195             0   \n",
       "310    -0.0098173  739.0100098  -0.009982131  739.0100098   0.019613419   \n",
       "311   0.029960454          740   0.078507565          740   -0.06922058   \n",
       "312   0.019856241  740.9699707   0.019869654  740.9699707   -0.01022725   \n",
       "313   0.009681105  741.9400024   0.098170109  741.9400024   0.019248351   \n",
       "314  -0.029882263  743.0499878  -0.068393439  743.0499878   0.040081766   \n",
       "315    0.03913473  744.0200195  -0.049763128  744.0200195    0.00980421   \n",
       "316             0          745   0.059964024          745   0.028821489   \n",
       "317    0.02933641  745.9699707  -0.019521909  745.9699707   0.059904754   \n",
       "318  -0.029714443  746.9400024   0.040749796  746.9400024    0.04909084   \n",
       "319   0.020575708  748.0499878             0  748.0499878   0.009988414   \n",
       "320   0.039798222  749.0200195  -0.039246853  749.0200195  -0.029143473   \n",
       "321   0.009997201          750   0.010114804          750  -0.050363116   \n",
       "\n",
       "        ...       Sample2_299  Unnamed: 597  Sample2_300  Unnamed: 599  \\\n",
       "1       ...               430  -0.019919526          430  -0.030978296   \n",
       "2       ...       431.0599976             0  431.0599976   0.019929253   \n",
       "3       ...       431.9599915   0.020747319  431.9599915   0.010255569   \n",
       "4       ...       433.0299988   0.030374518  433.0299988    0.02032066   \n",
       "5       ...       433.9299927             0  433.9299927  -0.081288427   \n",
       "6       ...               435   0.020968316          435   0.010163222   \n",
       "7       ...       436.0599976   0.030630687  436.0599976   0.050461724   \n",
       "8       ...       436.9599915   0.101620853  436.9599915    0.05064676   \n",
       "9       ...       438.0299988    -0.0102416  438.0299988  -0.010024761   \n",
       "10      ...       438.9299927             0  438.9299927   0.040346988   \n",
       "11      ...               440   0.030318955          440    0.02973064   \n",
       "12      ...       441.0599976  -0.010376673  441.0599976   0.052354377   \n",
       "13      ...       441.9599915   0.030505165  441.9599915             0   \n",
       "14      ...       443.0299988   0.040189289  443.0299988    0.02037905   \n",
       "15      ...       443.9299927   0.010003401  443.9299927   0.039931718   \n",
       "16      ...               445             0          445   0.061513856   \n",
       "17      ...       446.0599976  -0.020323345  446.0599976   0.030507337   \n",
       "18      ...       446.9599915   0.060862422  446.9599915             0   \n",
       "19      ...       448.0299988  -0.009847365  448.0299988   -0.04975174   \n",
       "20      ...       448.9299927             0  448.9299927   0.050648812   \n",
       "21      ...               450  -0.041053854          450  -0.030399757   \n",
       "22      ...       451.0599976  -0.020151744  451.0599976   0.009994104   \n",
       "23      ...       451.9599915   0.101774953  451.9599915   0.071114361   \n",
       "24      ...       453.0299988  -0.020612607  453.0299988   0.061185159   \n",
       "25      ...       453.9299927   0.051856462  453.9299927   0.041105323   \n",
       "26      ...               455   0.080286622          455             0   \n",
       "27      ...       456.0599976   0.040707495  456.0599976  -0.009733117   \n",
       "28      ...       456.9599915   0.010093975  456.9599915   0.010278971   \n",
       "29      ...       458.0299988   0.009980339  458.0299988   0.010098358   \n",
       "30      ...       458.9299927   0.081090674  458.9299927    0.02086659   \n",
       "..      ...               ...           ...          ...           ...   \n",
       "292     ...       720.9799805   0.101469278  720.9799805  -0.009725261   \n",
       "293     ...       721.9699707  -0.010031097  721.9699707   0.020477327   \n",
       "294     ...       722.9500122             0  722.9500122   0.020124977   \n",
       "295     ...       723.9400024  -0.091998197  723.9400024   0.010125762   \n",
       "296     ...       725.0700073  -0.010171595  725.0700073   0.060730588   \n",
       "297     ...       726.0499878             0  726.0499878             0   \n",
       "298     ...        727.039978  -0.030049883   727.039978   0.010150429   \n",
       "299     ...       728.0200195   0.092161179  728.0200195   0.010136642   \n",
       "300     ...       729.0100098  -0.020259524  729.0100098  -0.029865306   \n",
       "301     ...               730  -0.042262301          730   0.061686508   \n",
       "302     ...       730.9799805   0.019655246  730.9799805   0.051384293   \n",
       "303     ...       731.9699707   0.050386969  731.9699707  -0.049242649   \n",
       "304     ...       732.9500122   0.050482105  732.9500122  -0.070905469   \n",
       "305     ...       733.9400024   0.030123508  733.9400024  -0.065186217   \n",
       "306     ...       735.0700073   0.020178581  735.0700073  -0.050160512   \n",
       "307     ...       736.0499878  -0.060844522  736.0499878    -0.0200445   \n",
       "308     ...        737.039978  -0.010331966   737.039978   0.010147855   \n",
       "309     ...       738.0200195  -0.059977807  738.0200195   0.020286649   \n",
       "310     ...       739.0100098   0.030593202  739.0100098  -0.010032203   \n",
       "311     ...               740    0.01032045          740  -0.010153727   \n",
       "312     ...       740.9699707             0  740.9699707   0.061307065   \n",
       "313     ...       741.9400024  -0.050551515  741.9400024    0.03094315   \n",
       "314     ...       743.0499878  -0.041175988  743.0499878  -0.090466812   \n",
       "315     ...       744.0200195   0.019946544  744.0200195   0.030432753   \n",
       "316     ...               745  -0.010163427          745             0   \n",
       "317     ...       745.9699707   0.029886134  745.9699707  -0.029707087   \n",
       "318     ...       746.9400024  -0.050462741  746.9400024    0.05077998   \n",
       "319     ...       748.0499878   0.030135309  748.0499878   0.020186728   \n",
       "320     ...       749.0200195             0  749.0200195    0.01045686   \n",
       "321     ...               750  -0.020354992          750             0   \n",
       "\n",
       "     Sample2_301  Unnamed: 601  Sample2_302  Unnamed: 603 Sample2_303  \\\n",
       "1            430  -0.050527506          430    0.04026616         NaN   \n",
       "2    431.0599976   0.061345302  431.0599976   0.081356205         NaN   \n",
       "3    431.9599915   0.010158989  431.9599915   0.041944109         NaN   \n",
       "4    433.0299988  -0.030248033  433.0299988  -0.009978446         NaN   \n",
       "5    433.9299927  -0.010362587  433.9299927   0.112422712         NaN   \n",
       "6            435             0          435   0.050819721         NaN   \n",
       "7    436.0599976  -0.010179464  436.0599976             0         NaN   \n",
       "8    436.9599915  -0.010054597  436.9599915    0.03065322         NaN   \n",
       "9    438.0299988  -0.009985422  438.0299988   0.040140893         NaN   \n",
       "10   438.9299927   0.029959256  438.9299927   0.070338324         NaN   \n",
       "11           440   0.040341694          440   0.010304922         NaN   \n",
       "12   441.0599976   0.040707085  441.0599976   0.091667436         NaN   \n",
       "13   441.9599915   0.030446962  441.9599915   0.020385489         NaN   \n",
       "14   443.0299988  -0.060119037  443.0299988   0.020148089         NaN   \n",
       "15   443.9299927   0.041184466  443.9299927  -0.030719137         NaN   \n",
       "16           445  -0.010287431          445   0.040981926         NaN   \n",
       "17   446.0599976             0  446.0599976  -0.010131507         NaN   \n",
       "18   446.9599915  -0.009960555  446.9599915   0.010321302         NaN   \n",
       "19   448.0299988   0.020094039  448.0299988   0.031662267         NaN   \n",
       "20   448.9299927   0.062532566  448.9299927   0.062263273         NaN   \n",
       "21           450  -0.030295685          450   0.030969979         NaN   \n",
       "22   451.0599976   0.069865167  451.0599976   0.010252309         NaN   \n",
       "23   451.9599915  -0.020314882  451.9599915   0.059989803         NaN   \n",
       "24   453.0299988             0  453.0299988   0.040225666         NaN   \n",
       "25   453.9299927   0.050291188  453.9299927   0.030289873         NaN   \n",
       "26           455   0.020089397          455   0.060551625         NaN   \n",
       "27   456.0599976  -0.029928472  456.0599976    0.07045798         NaN   \n",
       "28   456.9599915             0  456.9599915   0.050750602         NaN   \n",
       "29   458.0299988  -0.010003101  458.0299988     0.0303395         NaN   \n",
       "30   458.9299927  -0.040108696  458.9299927             0         NaN   \n",
       "..           ...           ...          ...           ...         ...   \n",
       "292  720.9799805   0.030019211  720.9799805   0.040095028         NaN   \n",
       "293  721.9699707             0  721.9699707   0.010422312         NaN   \n",
       "294  722.9500122             0  722.9500122   0.069646895         NaN   \n",
       "295  723.9400024  -0.020136526  723.9400024  -0.060152184         NaN   \n",
       "296  725.0700073  -0.081549443  725.0700073  -0.040242665         NaN   \n",
       "297  726.0499878   0.010124224  726.0499878   0.029865606         NaN   \n",
       "298   727.039978             0   727.039978  -0.040288061         NaN   \n",
       "299  728.0200195    0.03004236  728.0200195   -0.03010265         NaN   \n",
       "300  729.0100098    0.10317149  729.0100098   0.060030017         NaN   \n",
       "301          730  -0.040138073          730             0         NaN   \n",
       "302  730.9799805   0.051307838  730.9799805   0.010035325         NaN   \n",
       "303  731.9699707   0.019992404  731.9699707   0.070286065         NaN   \n",
       "304  732.9500122   0.049843494  732.9500122  -0.061491162         NaN   \n",
       "305  733.9400024   0.071067438  733.9400024   0.020062394         NaN   \n",
       "306  735.0700073   0.063070923  735.0700073  -0.010136539         NaN   \n",
       "307  736.0499878   0.010203041  736.0499878  -0.030404996         NaN   \n",
       "308   737.039978  -0.019990204   737.039978   0.040010404         NaN   \n",
       "309  738.0200195   0.050432716  738.0200195  -0.020295709         NaN   \n",
       "310  739.0100098  -0.039917771  739.0100098  -0.019917937         NaN   \n",
       "311          740   0.030720709          740     0.0103714         NaN   \n",
       "312  740.9699707  -0.050199796  740.9699707  -0.019710647         NaN   \n",
       "313  741.9400024   0.010108055  741.9400024  -0.009933643         NaN   \n",
       "314  743.0499878  -0.051022489  743.0499878  -0.020974914         NaN   \n",
       "315  744.0200195             0  744.0200195    0.04034739         NaN   \n",
       "316          745             0          745    0.02040733         NaN   \n",
       "317  745.9699707  -0.030369598  745.9699707  -0.060375538         NaN   \n",
       "318  746.9400024   0.010015725  746.9400024   -0.04050592         NaN   \n",
       "319  748.0499878   0.121364132  748.0499878   0.092554502         NaN   \n",
       "320  749.0200195             0  749.0200195   0.020063199         NaN   \n",
       "321          750  -0.010105195          750  -0.009824052         NaN   \n",
       "\n",
       "    Unnamed: 605  \n",
       "1            NaN  \n",
       "2            NaN  \n",
       "3            NaN  \n",
       "4            NaN  \n",
       "5            NaN  \n",
       "6            NaN  \n",
       "7            NaN  \n",
       "8            NaN  \n",
       "9            NaN  \n",
       "10           NaN  \n",
       "11           NaN  \n",
       "12           NaN  \n",
       "13           NaN  \n",
       "14           NaN  \n",
       "15           NaN  \n",
       "16           NaN  \n",
       "17           NaN  \n",
       "18           NaN  \n",
       "19           NaN  \n",
       "20           NaN  \n",
       "21           NaN  \n",
       "22           NaN  \n",
       "23           NaN  \n",
       "24           NaN  \n",
       "25           NaN  \n",
       "26           NaN  \n",
       "27           NaN  \n",
       "28           NaN  \n",
       "29           NaN  \n",
       "30           NaN  \n",
       "..           ...  \n",
       "292          NaN  \n",
       "293          NaN  \n",
       "294          NaN  \n",
       "295          NaN  \n",
       "296          NaN  \n",
       "297          NaN  \n",
       "298          NaN  \n",
       "299          NaN  \n",
       "300          NaN  \n",
       "301          NaN  \n",
       "302          NaN  \n",
       "303          NaN  \n",
       "304          NaN  \n",
       "305          NaN  \n",
       "306          NaN  \n",
       "307          NaN  \n",
       "308          NaN  \n",
       "309          NaN  \n",
       "310          NaN  \n",
       "311          NaN  \n",
       "312          NaN  \n",
       "313          NaN  \n",
       "314          NaN  \n",
       "315          NaN  \n",
       "316          NaN  \n",
       "317          NaN  \n",
       "318          NaN  \n",
       "319          NaN  \n",
       "320          NaN  \n",
       "321          NaN  \n",
       "\n",
       "[321 rows x 606 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1.3'></a>\n",
    "## 1.3 Changing index <a href='#TOC'><b>(back to TOC)</b></a><br>\n",
    "It is very useful for your index column (i.e. the first column in bold of the table) to be something other than just the index number. In this data set, we have alternating columns of intensity and wavelength values, where the wavelength values make the most sense as the row index because it is repeated identically throughout the data.<br><br>\n",
    "To assign one of the wavelength columns as the index, we first want to relabel the $'Sample2\\_1'$ column as $'Wavelength\\ (nm)'$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Emily\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.py:2746: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  **kwargs)\n"
     ]
    },
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       "  Wavelength (nm)   Unnamed: 1    Sample2_2   Unnamed: 3    Sample2_3  \\\n",
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       "\n",
       "   Sample2_301  Unnamed: 601  Sample2_302  Unnamed: 603 Sample2_303  \\\n",
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     },
     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "df.rename(columns={'Sample2_1': 'Wavelength (nm)'}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And reformat the entire dataframe to be of the type $float$ instead of the $string$ type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "And finally assign our relabeled $'Wavelength\\ (nm)'$ column as our $df$ index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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      ],
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       "                 Unnamed: 1   Sample2_2  Unnamed: 3   Sample2_3  Unnamed: 5  \\\n",
       "Wavelength (nm)                                                               \n",
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       "\n",
       "                  Sample2_4  Unnamed: 7   Sample2_5  Unnamed: 9   Sample2_6  \\\n",
       "Wavelength (nm)                                                               \n",
       "430.000000       430.000000    0.048062  430.000000    0.048928  430.000000   \n",
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       "433.929993       433.929993   -0.029745  433.929993    0.019614  433.929993   \n",
       "\n",
       "                     ...       Sample2_299  Unnamed: 597  Sample2_300  \\\n",
       "Wavelength (nm)      ...                                                \n",
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       "431.059998           ...        431.059998      0.000000   431.059998   \n",
       "431.959992           ...        431.959992      0.020747   431.959992   \n",
       "433.029999           ...        433.029999      0.030375   433.029999   \n",
       "433.929993           ...        433.929993      0.000000   433.929993   \n",
       "\n",
       "                 Unnamed: 599  Sample2_301  Unnamed: 601  Sample2_302  \\\n",
       "Wavelength (nm)                                                         \n",
       "430.000000          -0.030978   430.000000     -0.050528   430.000000   \n",
       "431.059998           0.019929   431.059998      0.061345   431.059998   \n",
       "431.959992           0.010256   431.959992      0.010159   431.959992   \n",
       "433.029999           0.020321   433.029999     -0.030248   433.029999   \n",
       "433.929993          -0.081288   433.929993     -0.010363   433.929993   \n",
       "\n",
       "                 Unnamed: 603  Sample2_303  Unnamed: 605  \n",
       "Wavelength (nm)                                           \n",
       "430.000000           0.040266          NaN           NaN  \n",
       "431.059998           0.081356          NaN           NaN  \n",
       "431.959992           0.041944          NaN           NaN  \n",
       "433.029999          -0.009978          NaN           NaN  \n",
       "433.929993           0.112423          NaN           NaN  \n",
       "\n",
       "[5 rows x 605 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('Wavelength (nm)')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1.4'></a>\n",
    "## 1.4 Deleting columns <a href='#TOC'><b>(back to TOC)</b></a><br>\n",
    "Another helpful aspect of dataframes is that we can easily drop columns based on their label. In this dataset, we see that the wavelength column is repeated over and over, so we want to delete all of these. Conveniently, the labels of these columns are of the form $'Sample2\\_\\#'$, so to drop them, all we need to do is create alist of strings of this form, increasing the $'\\#'$ from 2 to the number of rows divided by two (because this column occurs in every other position)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Sample2_2', 'Sample2_3', 'Sample2_4', 'Sample2_5', 'Sample2_6', 'Sample2_7', 'Sample2_8', 'Sample2_9', 'Sample2_10', 'Sample2_11']\n"
     ]
    }
   ],
   "source": [
    "stringBase = 'Sample2'\n",
    "stringArray = []\n",
    "for i in range(1,(np.shape(df)[1]/2)):\n",
    "    temp_string = stringBase+'_'+str(i+1)\n",
    "    stringArray.append(temp_string)\n",
    "print stringArray[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we just feed the list into the Panda's $drop$ function, and see that $df$ is much neater now. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Sample2_303</th>\n",
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       "      <th>Wavelength (nm)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>0.010135</td>\n",
       "      <td>0.030968</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.061345</td>\n",
       "      <td>0.081356</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>431.959992</th>\n",
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       "      <td>-0.029152</td>\n",
       "      <td>-0.039360</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>433.029999</th>\n",
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       "      <td>-0.010570</td>\n",
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       "      <td>0.020321</td>\n",
       "      <td>-0.030248</td>\n",
       "      <td>-0.009978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-0.049085</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "<p>5 rows × 304 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Unnamed: 1  Unnamed: 3  Unnamed: 5  Unnamed: 7  Unnamed: 9  \\\n",
       "Wavelength (nm)                                                               \n",
       "430.000000         0.058043   -0.019747    0.089483    0.048062    0.048928   \n",
       "431.059998        -0.009702    0.029207    0.059682    0.060609    0.078461   \n",
       "431.959992         0.009580    0.009719    0.019442    0.067501    0.030053   \n",
       "433.029999         0.029176    0.048107    0.048642   -0.010163    0.039304   \n",
       "433.929993         0.019258    0.029846   -0.068961   -0.029745    0.019614   \n",
       "\n",
       "                 Unnamed: 11  Unnamed: 13  Unnamed: 15  Unnamed: 17  \\\n",
       "Wavelength (nm)                                                       \n",
       "430.000000         -0.039297     0.000000     0.049792     0.069252   \n",
       "431.059998         -0.029884     0.009626    -0.049555     0.039333   \n",
       "431.959992         -0.069307     0.048137    -0.029152    -0.039360   \n",
       "433.029999          0.059814     0.019370    -0.039072     0.029213   \n",
       "433.929993         -0.049085     0.009721     0.038927     0.000000   \n",
       "\n",
       "                 Unnamed: 19      ...       Unnamed: 589  Unnamed: 591  \\\n",
       "Wavelength (nm)                   ...                                    \n",
       "430.000000          0.038574      ...          -0.010229      0.000000   \n",
       "431.059998          0.029028      ...           0.020483      0.051043   \n",
       "431.959992          0.019405      ...           0.050032      0.049353   \n",
       "433.029999          0.000000      ...           0.000000     -0.031027   \n",
       "433.929993          0.039839      ...           0.010084      0.010202   \n",
       "\n",
       "                 Unnamed: 593  Unnamed: 595  Unnamed: 597  Unnamed: 599  \\\n",
       "Wavelength (nm)                                                           \n",
       "430.000000           0.050478      0.049519     -0.019920     -0.030978   \n",
       "431.059998           0.010135      0.030968      0.000000      0.019929   \n",
       "431.959992           0.020095     -0.061382      0.020747      0.010256   \n",
       "433.029999          -0.010570     -0.010119      0.030375      0.020321   \n",
       "433.929993           0.000000      0.020233      0.000000     -0.081288   \n",
       "\n",
       "                 Unnamed: 601  Unnamed: 603  Sample2_303  Unnamed: 605  \n",
       "Wavelength (nm)                                                         \n",
       "430.000000          -0.050528      0.040266          NaN           NaN  \n",
       "431.059998           0.061345      0.081356          NaN           NaN  \n",
       "431.959992           0.010159      0.041944          NaN           NaN  \n",
       "433.029999          -0.030248     -0.009978          NaN           NaN  \n",
       "433.929993          -0.010363      0.112423          NaN           NaN  \n",
       "\n",
       "[5 rows x 304 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(stringArray, inplace=True, axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1.5'></a>\n",
    "## 1.5 Renaming columns <a href='#TOC'><b>(back to TOC)</b></a><br>\n",
    "Renaming column labels is easy. We just need to create two things:<br><br>\n",
    "(1) a list of the current labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Unnamed: 1',\n",
       " 'Unnamed: 3',\n",
       " 'Unnamed: 5',\n",
       " 'Unnamed: 7',\n",
       " 'Unnamed: 9',\n",
       " 'Unnamed: 11',\n",
       " 'Unnamed: 13',\n",
       " 'Unnamed: 15',\n",
       " 'Unnamed: 17',\n",
       " 'Unnamed: 19']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_columnNames = df.columns.get_values().tolist()\n",
    "df_columnNames[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and (2) a list of the new labels we want to use. I'm going to simplify the labels to simply be $'Scan\\ \\#'$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_columnNames_new = []\n",
    "stringBase = 'Scan '\n",
    "for i in range(1,(np.shape(df)[1])):\n",
    "    temp_string = stringBase+str(i)\n",
    "    df_columnNames_new.append(temp_string)\n",
    "df_columnNames_new = np.array(df_columnNames_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then simply feed in our old and new labels to the $rename$ function. <br>\n",
    "This function takes in list of tuples for the $columns$ arguement.<br>\n",
    "To create a tuple from our two lists, we can use the function $zip$ from the standard Python library which creates a list of tuples using the $i^{th}$ elements from each list fed into it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Wavelength (nm)</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>431.959992</th>\n",
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       "    <tr>\n",
       "      <th>433.029999</th>\n",
       "      <td>0.029176</td>\n",
       "      <td>0.048107</td>\n",
       "      <td>0.048642</td>\n",
       "      <td>-0.010163</td>\n",
       "      <td>0.039304</td>\n",
       "      <td>0.059814</td>\n",
       "      <td>0.019370</td>\n",
       "      <td>-0.039072</td>\n",
       "      <td>0.029213</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.031027</td>\n",
       "      <td>-0.010570</td>\n",
       "      <td>-0.010119</td>\n",
       "      <td>0.030375</td>\n",
       "      <td>0.020321</td>\n",
       "      <td>-0.030248</td>\n",
       "      <td>-0.009978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433.929993</th>\n",
       "      <td>0.019258</td>\n",
       "      <td>0.029846</td>\n",
       "      <td>-0.068961</td>\n",
       "      <td>-0.029745</td>\n",
       "      <td>0.019614</td>\n",
       "      <td>-0.049085</td>\n",
       "      <td>0.009721</td>\n",
       "      <td>0.038927</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.039839</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010084</td>\n",
       "      <td>0.010202</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020233</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.081288</td>\n",
       "      <td>-0.010363</td>\n",
       "      <td>0.112423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 304 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Scan 1    Scan 2    Scan 3    Scan 4    Scan 5    Scan 6  \\\n",
       "Wavelength (nm)                                                               \n",
       "430.000000       0.058043 -0.019747  0.089483  0.048062  0.048928 -0.039297   \n",
       "431.059998      -0.009702  0.029207  0.059682  0.060609  0.078461 -0.029884   \n",
       "431.959992       0.009580  0.009719  0.019442  0.067501  0.030053 -0.069307   \n",
       "433.029999       0.029176  0.048107  0.048642 -0.010163  0.039304  0.059814   \n",
       "433.929993       0.019258  0.029846 -0.068961 -0.029745  0.019614 -0.049085   \n",
       "\n",
       "                   Scan 7    Scan 8    Scan 9   Scan 10      ...       \\\n",
       "Wavelength (nm)                                              ...        \n",
       "430.000000       0.000000  0.049792  0.069252  0.038574      ...        \n",
       "431.059998       0.009626 -0.049555  0.039333  0.029028      ...        \n",
       "431.959992       0.048137 -0.029152 -0.039360  0.019405      ...        \n",
       "433.029999       0.019370 -0.039072  0.029213  0.000000      ...        \n",
       "433.929993       0.009721  0.038927  0.000000  0.039839      ...        \n",
       "\n",
       "                 Scan 295  Scan 296  Scan 297  Scan 298  Scan 299  Scan 300  \\\n",
       "Wavelength (nm)                                                               \n",
       "430.000000      -0.010229  0.000000  0.050478  0.049519 -0.019920 -0.030978   \n",
       "431.059998       0.020483  0.051043  0.010135  0.030968  0.000000  0.019929   \n",
       "431.959992       0.050032  0.049353  0.020095 -0.061382  0.020747  0.010256   \n",
       "433.029999       0.000000 -0.031027 -0.010570 -0.010119  0.030375  0.020321   \n",
       "433.929993       0.010084  0.010202  0.000000  0.020233  0.000000 -0.081288   \n",
       "\n",
       "                 Scan 301  Scan 302  Scan 303  Unnamed: 605  \n",
       "Wavelength (nm)                                              \n",
       "430.000000      -0.050528  0.040266       NaN           NaN  \n",
       "431.059998       0.061345  0.081356       NaN           NaN  \n",
       "431.959992       0.010159  0.041944       NaN           NaN  \n",
       "433.029999      -0.030248 -0.009978       NaN           NaN  \n",
       "433.929993      -0.010363  0.112423       NaN           NaN  \n",
       "\n",
       "[5 rows x 304 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={i:j for i,j in zip(df_columnNames,df_columnNames_new)}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last two columns have NaN values, so going to drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scan 1</th>\n",
       "      <th>Scan 2</th>\n",
       "      <th>Scan 3</th>\n",
       "      <th>Scan 4</th>\n",
       "      <th>Scan 5</th>\n",
       "      <th>Scan 6</th>\n",
       "      <th>Scan 7</th>\n",
       "      <th>Scan 8</th>\n",
       "      <th>Scan 9</th>\n",
       "      <th>Scan 10</th>\n",
       "      <th>...</th>\n",
       "      <th>Scan 293</th>\n",
       "      <th>Scan 294</th>\n",
       "      <th>Scan 295</th>\n",
       "      <th>Scan 296</th>\n",
       "      <th>Scan 297</th>\n",
       "      <th>Scan 298</th>\n",
       "      <th>Scan 299</th>\n",
       "      <th>Scan 300</th>\n",
       "      <th>Scan 301</th>\n",
       "      <th>Scan 302</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wavelength (nm)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>430.000000</th>\n",
       "      <td>0.058043</td>\n",
       "      <td>-0.019747</td>\n",
       "      <td>0.089483</td>\n",
       "      <td>0.048062</td>\n",
       "      <td>0.048928</td>\n",
       "      <td>-0.039297</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.049792</td>\n",
       "      <td>0.069252</td>\n",
       "      <td>0.038574</td>\n",
       "      <td>...</td>\n",
       "      <td>0.040734</td>\n",
       "      <td>0.040934</td>\n",
       "      <td>-0.010229</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050478</td>\n",
       "      <td>0.049519</td>\n",
       "      <td>-0.019920</td>\n",
       "      <td>-0.030978</td>\n",
       "      <td>-0.050528</td>\n",
       "      <td>0.040266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431.059998</th>\n",
       "      <td>-0.009702</td>\n",
       "      <td>0.029207</td>\n",
       "      <td>0.059682</td>\n",
       "      <td>0.060609</td>\n",
       "      <td>0.078461</td>\n",
       "      <td>-0.029884</td>\n",
       "      <td>0.009626</td>\n",
       "      <td>-0.049555</td>\n",
       "      <td>0.039333</td>\n",
       "      <td>0.029028</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052168</td>\n",
       "      <td>0.010187</td>\n",
       "      <td>0.020483</td>\n",
       "      <td>0.051043</td>\n",
       "      <td>0.010135</td>\n",
       "      <td>0.030968</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.019929</td>\n",
       "      <td>0.061345</td>\n",
       "      <td>0.081356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431.959992</th>\n",
       "      <td>0.009580</td>\n",
       "      <td>0.009719</td>\n",
       "      <td>0.019442</td>\n",
       "      <td>0.067501</td>\n",
       "      <td>0.030053</td>\n",
       "      <td>-0.069307</td>\n",
       "      <td>0.048137</td>\n",
       "      <td>-0.029152</td>\n",
       "      <td>-0.039360</td>\n",
       "      <td>0.019405</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010191</td>\n",
       "      <td>0.010512</td>\n",
       "      <td>0.050032</td>\n",
       "      <td>0.049353</td>\n",
       "      <td>0.020095</td>\n",
       "      <td>-0.061382</td>\n",
       "      <td>0.020747</td>\n",
       "      <td>0.010256</td>\n",
       "      <td>0.010159</td>\n",
       "      <td>0.041944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433.029999</th>\n",
       "      <td>0.029176</td>\n",
       "      <td>0.048107</td>\n",
       "      <td>0.048642</td>\n",
       "      <td>-0.010163</td>\n",
       "      <td>0.039304</td>\n",
       "      <td>0.059814</td>\n",
       "      <td>0.019370</td>\n",
       "      <td>-0.039072</td>\n",
       "      <td>0.029213</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029989</td>\n",
       "      <td>-0.038470</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.031027</td>\n",
       "      <td>-0.010570</td>\n",
       "      <td>-0.010119</td>\n",
       "      <td>0.030375</td>\n",
       "      <td>0.020321</td>\n",
       "      <td>-0.030248</td>\n",
       "      <td>-0.009978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433.929993</th>\n",
       "      <td>0.019258</td>\n",
       "      <td>0.029846</td>\n",
       "      <td>-0.068961</td>\n",
       "      <td>-0.029745</td>\n",
       "      <td>0.019614</td>\n",
       "      <td>-0.049085</td>\n",
       "      <td>0.009721</td>\n",
       "      <td>0.038927</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.039839</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019645</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010084</td>\n",
       "      <td>0.010202</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020233</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.081288</td>\n",
       "      <td>-0.010363</td>\n",
       "      <td>0.112423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 302 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Scan 1    Scan 2    Scan 3    Scan 4    Scan 5    Scan 6  \\\n",
       "Wavelength (nm)                                                               \n",
       "430.000000       0.058043 -0.019747  0.089483  0.048062  0.048928 -0.039297   \n",
       "431.059998      -0.009702  0.029207  0.059682  0.060609  0.078461 -0.029884   \n",
       "431.959992       0.009580  0.009719  0.019442  0.067501  0.030053 -0.069307   \n",
       "433.029999       0.029176  0.048107  0.048642 -0.010163  0.039304  0.059814   \n",
       "433.929993       0.019258  0.029846 -0.068961 -0.029745  0.019614 -0.049085   \n",
       "\n",
       "                   Scan 7    Scan 8    Scan 9   Scan 10    ...     Scan 293  \\\n",
       "Wavelength (nm)                                            ...                \n",
       "430.000000       0.000000  0.049792  0.069252  0.038574    ...     0.040734   \n",
       "431.059998       0.009626 -0.049555  0.039333  0.029028    ...    -0.052168   \n",
       "431.959992       0.048137 -0.029152 -0.039360  0.019405    ...     0.010191   \n",
       "433.029999       0.019370 -0.039072  0.029213  0.000000    ...     0.029989   \n",
       "433.929993       0.009721  0.038927  0.000000  0.039839    ...     0.019645   \n",
       "\n",
       "                 Scan 294  Scan 295  Scan 296  Scan 297  Scan 298  Scan 299  \\\n",
       "Wavelength (nm)                                                               \n",
       "430.000000       0.040934 -0.010229  0.000000  0.050478  0.049519 -0.019920   \n",
       "431.059998       0.010187  0.020483  0.051043  0.010135  0.030968  0.000000   \n",
       "431.959992       0.010512  0.050032  0.049353  0.020095 -0.061382  0.020747   \n",
       "433.029999      -0.038470  0.000000 -0.031027 -0.010570 -0.010119  0.030375   \n",
       "433.929993       0.000000  0.010084  0.010202  0.000000  0.020233  0.000000   \n",
       "\n",
       "                 Scan 300  Scan 301  Scan 302  \n",
       "Wavelength (nm)                                \n",
       "430.000000      -0.030978 -0.050528  0.040266  \n",
       "431.059998       0.019929  0.061345  0.081356  \n",
       "431.959992       0.010256  0.010159  0.041944  \n",
       "433.029999       0.020321 -0.030248 -0.009978  \n",
       "433.929993      -0.081288 -0.010363  0.112423  \n",
       "\n",
       "[5 rows x 302 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Scan 303','Unnamed: 605'], inplace=True, axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='1.6'></a>\n",
    "## 1.6 Plotting data  <a href='#TOC'><b>(back to TOC)</b></a><br>\n",
    "We can then nicely plot $df$ to see what the data looks like for each scan across the range of wavelengths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#fig = figure(figsize=(4,3))\n",
    "#gs = gridspec.GridSpec(1,1)\n",
    "#ax1 = fig.add_subplot(gs[0])\n",
    "#df.plot(y=df_columnNames_new[10:-1],legend=False,colormap='jet',xlim=(500,700),title='Full Spectra',ax=ax1)\n",
    "#df.plot(y=df_columnNames_new[10:-1],legend=False,colormap='jet',xlim=(565,610),ylim=(40,65),title='Zoom-in on top of peak',ax=ax1)\n",
    "#fig.tight_layout()\n",
    "#fig.savefig(\"dataframe_plot.png\", format=\"png\",dpi=1000)\n",
    "#fig.savefig(\"dataframe_plot_zoom.png\", format=\"png\",dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d3a1c88>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GOhwrhAv0jmNmcac2/7KN2Ckrq2xgHX+Za3ZJtMCRAiEhoiyFrBdVwdnW3UcA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20607b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WwQj7FEaciwoLWZB9mCeCJovun6Vi9hnO3tcwvvup7JhlWYy3J2s7D3smobcf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nK0ielVK8gzu+zBFzBnsI7lgYgUg5nPJ/DBT8Cgp5BQbtgW/N/vgbe+1S6GJWWhYxEEumXokCuv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\nOU5S9IlEIjHHSYo+kUgk5jj/DwiR1qdtCftJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d3bc358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y=df_columnNames_new[10:-1],legend=False,colormap='jet',xlim=(500,700),title='Full Spectra')\n",
    "df.plot(y=df_columnNames_new[10:-1],legend=False,colormap='jet',xlim=(565,610),ylim=(40,65),title='Zoom-in on top of peak')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='2'></a>\n",
    "# 2. Fitting data to Gaussian peaks \n",
    "When working with spectral data, you often want to extract out information about the change in features over time. In this example, we have a single gaussian-shape peak for each scan. So naturally, we should fit each scan to a single gaussian function and then extract out the amplitude, center, and standard deviation of the peak. <br><br>\n",
    "I wrote a [blog post](http://emilygraceripka.com/blog/16), and have a [reference notebook](https://github.com/emilyripka/BlogRepo/blob/master/181119_PeakFitting.ipynb) for fitting gaussian peaks, so will not go into too much detail here.<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='2.1'></a>\n",
    "## 2.1 Iterative fitting over dataframe <a href='#TOC'><b>(back to TOC)</b></a><br>\n",
    "First, you need to define a function $\\_1G$ of the form of a gaussian distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def _1G(wavelength_labels, amp1,cen1,sigma1):\n",
    "    return amp1*(1./(sigma1*(np.sqrt(2.*np.pi))))*(np.exp(-((wavelength_labels-cen1)**2.)/((2.*sigma1)**2.)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then create ndarrays of zeros to store the gaussian fitting parameters in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "popt = np.zeros((np.shape(df)[1],3))\n",
    "pcov = np.zeros((np.shape(df)[1],3,3))\n",
    "perr = np.zeros((np.shape(df)[1],3))\n",
    "pars = np.zeros((popt.shape[0],3))\n",
    "peak = np.zeros((popt.shape[0],np.shape(df)[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Next, we fit the first Scan to the gaussian function, and assign the fitting parameters to the ndarrys we created above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xAxis = np.nan_to_num(np.array(df.index))\n",
    "guess = [55,590,20]\n",
    "i = 10\n",
    "scanString = 'Scan '+str(i)\n",
    "popt[i,:], pcov[i,:] = scipy.optimize.curve_fit(_1G, xAxis, df[scanString], p0=guess)\n",
    "peak[i,:] = _1G(xAxis, *popt[i,:])\n",
    "perr[i,:] = np.sqrt(np.diag(pcov[i,:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we iterate over the entire number of scans, feeding in the guess p0 parameters as the parameters from the previous scan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(11,np.shape(df)[1]-1):\n",
    "    j = i-1\n",
    "    scanString = 'Scan '+str(i)\n",
    "    popt[i,:], pcov[i,:] = scipy.optimize.curve_fit(_1G, xAxis, df[scanString], p0=popt[j,:])\n",
    "    peak[i,:] = _1G(xAxis, *popt[i,:])\n",
    "    perr[i,:] = np.sqrt(np.diag(pcov[i,:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='2.2'></a>\n",
    "## 2.2 Converting nddata Fitting parameters to a dataframe <a href='#TOC'><b>(back to TOC)</b></a><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then want to take the fitting parameters on peak center, peak amplitude, and peak standard deviation, and visualize the data.<br> We want to be able to watch a shift in these three parameters over the course of the experimental time. <br>\n",
    "To do this, we need to create an x-axis ndarray, time_array, which contains the time increments for the experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scan_length = 0.5          #minutes\n",
    "time_vector_length = np.shape(df)[1]\n",
    "time_vector = np.arange(0,time_vector_length*scan_length,scan_length)[:]\n",
    "\n",
    "time_array = np.zeros((time_vector.shape[0]))\n",
    "for i in range(time_vector.shape[0]):\n",
    "    time_array[i] = time_vector[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we add this time_array to the current fitting parameters array by stacking the two together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_array = np.reshape(time_array, (np.shape(time_array)[0],1))\n",
    "fitData_ndarray = ((np.hstack((time_array,popt))).T)[:,10:-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can convert this ndarry to a dataframe for easier data visualization and manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1334.559471</td>\n",
       "      <td>590.029123</td>\n",
       "      <td>11.524225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.5</td>\n",
       "      <td>1425.311939</td>\n",
       "      <td>590.066343</td>\n",
       "      <td>10.875346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1428.683301</td>\n",
       "      <td>591.119620</td>\n",
       "      <td>10.300490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.5</td>\n",
       "      <td>1428.463385</td>\n",
       "      <td>591.989966</td>\n",
       "      <td>10.290524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1431.618267</td>\n",
       "      <td>591.906336</td>\n",
       "      <td>10.151259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0            1           2          3\n",
       "0  5.0  1334.559471  590.029123  11.524225\n",
       "1  5.5  1425.311939  590.066343  10.875346\n",
       "2  6.0  1428.683301  591.119620  10.300490\n",
       "3  6.5  1428.463385  591.989966  10.290524\n",
       "4  7.0  1431.618267  591.906336  10.151259"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitData_df = pd.DataFrame(fitData_ndarray.T)\n",
    "fitData_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly we will want to rename the columns to their respective fitting parameters, as well as re-assign the index to the time_array we made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Norm. Peak Amplitude</th>\n",
       "      <th>Wavelength (nm)</th>\n",
       "      <th>FWHM (nm)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time (min)</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>1334.559471</td>\n",
       "      <td>590.029123</td>\n",
       "      <td>23.048450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.5</th>\n",
       "      <td>1425.311939</td>\n",
       "      <td>590.066343</td>\n",
       "      <td>21.750691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>1428.683301</td>\n",
       "      <td>591.119620</td>\n",
       "      <td>20.600979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.5</th>\n",
       "      <td>1428.463385</td>\n",
       "      <td>591.989966</td>\n",
       "      <td>20.581048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>1431.618267</td>\n",
       "      <td>591.906336</td>\n",
       "      <td>20.302518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Norm. Peak Amplitude  Wavelength (nm)  FWHM (nm)\n",
       "Time (min)                                                  \n",
       "5.0                  1334.559471       590.029123  23.048450\n",
       "5.5                  1425.311939       590.066343  21.750691\n",
       "6.0                  1428.683301       591.119620  20.600979\n",
       "6.5                  1428.463385       591.989966  20.581048\n",
       "7.0                  1431.618267       591.906336  20.302518"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fitData_df.rename(columns={0:'Time (min)', 1:'Norm. Peak Amplitude',2:'Wavelength (nm)',3:'FWHM (nm)'}, inplace=True)\n",
    "fitData_df['FWHM (nm)'] = fitData_df['FWHM (nm)']*2\n",
    "fitData_df = fitData_df.set_index('Time (min)')\n",
    "fitData_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='3'></a>\n",
    "# 3. Extracting Fitting Information\n",
    "To visualize the fitting parameters, we can easily plot a shift in them over time. <br>This data can then be fit to linear, mono- or bi-exponential curves depending on the trend.\n",
    "<a id='3.1'></a>\n",
    "## 3.1 Normalized Peak Intensity Shift <a href='#TOC'><b>(back to TOC)</b></a><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#fig = figure(figsize=(4,3))\n",
    "#gs = gridspec.GridSpec(1,1)\n",
    "#ax1 = fig.add_subplot(gs[0])\n",
    "#fitData_df['Norm. Peak Amplitude'].plot(legend=False,style=[\"ro\"],title=\"Norm. Peak Amplitude Shift\",xlim=(-1,160),ax=ax1)\n",
    "#fitData_df['Wavelength (nm)'].plot(legend=False,style=[\"ro\"],title=\"Wavelength Shift\",xlim=(-1,160),ax=ax1)\n",
    "#fitData_df['FWHM (nm)'].plot(legend=False,style=[\"ro\"],title=\"Peak Width Shift\",xlim=(-1,160),ax=ax1)\n",
    "#fig.tight_layout()\n",
    "#fig.savefig(\"dataframe_fitting_amplitude.png\", format=\"png\",dpi=1000)\n",
    "#fig.savefig(\"dataframe_fitting_wavelength.png\", format=\"png\",dpi=1000)\n",
    "#fig.savefig(\"dataframe_fitting_FWHM.png\", format=\"png\",dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x31246e10>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ichIuuaT1vPYwBwcz6035NarXrm3smA0bUpCorE6ayfj4vCw91A0Okq6Q9LCk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\nxsHBzMymcXAwM7NpHBzMzGya/w8Pt+QpJLGd7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x32da7a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitData_df['Norm. Peak Amplitude'].plot(legend=False,style=[\"ro\"],title=\"Norm. Peak Amplitude Shift\",xlim=(-1,160))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='3.2'></a>\n",
    "## 3.2 Wavelength Shift <a href='#TOC'><b>(back to TOC)</b></a><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f726a90>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NHATMupoDQLfL9xYos91kkZeQMGt7DgC2Z7vJq66CuXPLnePVRM3angOA7VGrwebNe5aP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\nB0074brU7yaPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x34973780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitData_df['Wavelength (nm)'].plot(legend=False,style=[\"ro\"],title=\"Wavelength Shift\",xlim=(-1,160))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='3.3'></a>\n",
    "## 3.3 FWHM Shift <a href='#TOC'><b>(back to TOC)</b></a><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x321ac748>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x333cf208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitData_df['FWHM (nm)'].plot(legend=False,style=[\"ro\"],title=\"Peak Width Shift\",xlim=(-1,160))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<a id='4'></a>\n",
    "# 4. Saving dataframes\n",
    "Perhaps you want to save your dataframe to use in another notebook, or to send it to another user to work with your data. <br><br>\n",
    "For the former, it makes sense to save in the pickle format, which saves the dataframe as an as-is object, and can be read in and looks identical to what you were working with previously.\n",
    "<a id='4.1'></a>\n",
    "## 4.1 Pickle format <a href='#TOC'><b>(back to TOC)</b></a><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_pickle('CleanData.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_pickle = pd.read_pickle('CleanData.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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       "                   Scan 1    Scan 2    Scan 3    Scan 4    Scan 5    Scan 6  \\\n",
       "Wavelength (nm)                                                               \n",
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       "\n",
       "                   Scan 7    Scan 8    Scan 9   Scan 10    ...     Scan 293  \\\n",
       "Wavelength (nm)                                            ...                \n",
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       "\n",
       "                 Scan 294  Scan 295  Scan 296  Scan 297  Scan 298  Scan 299  \\\n",
       "Wavelength (nm)                                                               \n",
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       "\n",
       "                 Scan 300  Scan 301  Scan 302  \n",
       "Wavelength (nm)                                \n",
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       "\n",
       "[5 rows x 302 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pickle.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='4.2'></a>\n",
    "## 4.2 CSV format <a href='#TOC'><b>(back to TOC)</b></a><br>\n",
    "If instead you want to send your clean dataframe to another user who isn't keen on python (how sad :[), then you can save your data in the CSV format, however be warned if you try to read this back in, you index will be screwed up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('CleanData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_csv = pd.read_csv('CleanData.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       "   Wavelength (nm)    Scan 1    Scan 2    Scan 3    Scan 4    Scan 5  \\\n",
       "0       430.000000  0.058043 -0.019747  0.089483  0.048062  0.048928   \n",
       "1       431.059998 -0.009702  0.029207  0.059682  0.060609  0.078461   \n",
       "2       431.959992  0.009580  0.009719  0.019442  0.067501  0.030053   \n",
       "3       433.029999  0.029176  0.048107  0.048642 -0.010163  0.039304   \n",
       "4       433.929993  0.019258  0.029846 -0.068961 -0.029745  0.019614   \n",
       "\n",
       "     Scan 6    Scan 7    Scan 8    Scan 9    ...     Scan 293  Scan 294  \\\n",
       "0 -0.039297  0.000000  0.049792  0.069252    ...     0.040734  0.040934   \n",
       "1 -0.029884  0.009626 -0.049555  0.039333    ...    -0.052168  0.010187   \n",
       "2 -0.069307  0.048137 -0.029152 -0.039360    ...     0.010191  0.010512   \n",
       "3  0.059814  0.019370 -0.039072  0.029213    ...     0.029989 -0.038470   \n",
       "4 -0.049085  0.009721  0.038927  0.000000    ...     0.019645  0.000000   \n",
       "\n",
       "   Scan 295  Scan 296  Scan 297  Scan 298  Scan 299  Scan 300  Scan 301  \\\n",
       "0 -0.010229  0.000000  0.050478  0.049519 -0.019920 -0.030978 -0.050528   \n",
       "1  0.020483  0.051043  0.010135  0.030968  0.000000  0.019929  0.061345   \n",
       "2  0.050032  0.049353  0.020095 -0.061382  0.020747  0.010256  0.010159   \n",
       "3  0.000000 -0.031027 -0.010570 -0.010119  0.030375  0.020321 -0.030248   \n",
       "4  0.010084  0.010202  0.000000  0.020233  0.000000 -0.081288 -0.010363   \n",
       "\n",
       "   Scan 302  \n",
       "0  0.040266  \n",
       "1  0.081356  \n",
       "2  0.041944  \n",
       "3 -0.009978  \n",
       "4  0.112423  \n",
       "\n",
       "[5 rows x 303 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_csv.head()"
   ]
  }
 ],
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