This notebook shows how to use pandas to analyze some generated measurement data.

example notebook: pandas for measurement data analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas for measurement data analysis\n",
    "\n",
    "This is a short demonstration of the usage of pandas, numpy and scipy for a fairly standard data analysis task. This notebook will only run in python3, because I use some greek characters as variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np  # numerical stuff\n",
    "import scipy as cp  # we need this for curve-fitting\n",
    "import pandas as pd # this is what it's all about\n",
    "import os # interact with the filesystem\n",
    "\n",
    "import matplotlib.pyplot as plt # plotting\n",
    "plt.style.use('ggplot') # choose the plotting style, run plt.style.available to list all available styles\n",
    "\n",
    "# actually show the plots in the notebook (commands starting with % are ipython magic commands, run %magic to learn more)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate fake measurement data\n",
    "\n",
    "We'll pretend that we have measured the resistance of some piece of material under different values of relative humidities. For every value of the relative humidity, we will create a comma separated value file (csv-file) that contains measurements of electrical current as a function of a voltage. We will use a more-or-less random relationship between the resistance and the relative humidity (actually, I'm just showing jupyter notebook's LaTeX parsing capabilities):\n",
    "\n",
    "$R = -900 \\cdot \\frac{\\xi}{100} + 1000$,\n",
    "\n",
    "where $\\xi$ is the relative humidity.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def resistance(ξ):\n",
    "    return -9*ξ + 1000\n",
    "\n",
    "def createFakeMeasurements(number_of_measurements):\n",
    "    \"\"\"\n",
    "    Create a number of fake measurement files with the relative humidity value in the filename.\n",
    "    \"\"\"\n",
    "    \n",
    "    try:\n",
    "        os.mkdir('data') #create a folder for our csv files\n",
    "    except:\n",
    "        pass #folder already exists, do nothing\n",
    "    \n",
    "    \n",
    "    for ξ in np.linspace(0, 100, number_of_measurements):\n",
    "        df = pd.DataFrame() \n",
    "        df['voltage'] = np.linspace(1,10,100)                                       \n",
    "        df['current'] = df.voltage / resistance(ξ) + 0.001*np.random.randn(len(df)) # add a random number to simulate noise\n",
    "        df.to_csv(\"data/RH=%.1f.csv\" % ξ,index=False) # go here: https://pyformat.info/ if you don't understand the %-sign\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "createFakeMeasurements(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data\n",
    "\n",
    "We are now going to load the data from the files that we have just generated, and combine everything into one dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# create an empty list that will hold the dataframes for the different files\n",
    "dataframes = [] \n",
    "\n",
    "for filename in os.listdir('data/'):\n",
    "    # read the csv-file into a dataframe\n",
    "    df = pd.read_csv('data/' + filename)\n",
    "    \n",
    "    # get the relative humidity from the filename\n",
    "    ξ = float(filename[3:-4]) \n",
    "    \n",
    "    # create a new dataframe column and assign the relative humidity to every row\n",
    "    df['relative_humidity'] = ξ\n",
    "    \n",
    "    # add the dataframe to the list\n",
    "    dataframes.append(df)\n",
    "\n",
    "#combine all dataframes into one\n",
    "df = pd.concat(dataframes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>voltage</th>\n",
       "      <th>current</th>\n",
       "      <th>relative_humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.090909</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.181818</td>\n",
       "      <td>-0.000126</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.272727</td>\n",
       "      <td>0.001368</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.363636</td>\n",
       "      <td>0.002396</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.454545</td>\n",
       "      <td>0.001334</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.545455</td>\n",
       "      <td>0.000734</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.636364</td>\n",
       "      <td>0.002404</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.727273</td>\n",
       "      <td>0.001938</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.818182</td>\n",
       "      <td>0.004092</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.909091</td>\n",
       "      <td>0.002155</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>-0.000368</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.090909</td>\n",
       "      <td>0.002665</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.181818</td>\n",
       "      <td>0.002737</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.272727</td>\n",
       "      <td>0.002639</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.363636</td>\n",
       "      <td>0.002366</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.454545</td>\n",
       "      <td>0.002564</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.545455</td>\n",
       "      <td>0.001701</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.636364</td>\n",
       "      <td>0.002942</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.727273</td>\n",
       "      <td>0.002347</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.818182</td>\n",
       "      <td>0.003005</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.909091</td>\n",
       "      <td>0.002652</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.001892</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.090909</td>\n",
       "      <td>0.002557</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.181818</td>\n",
       "      <td>0.001152</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3.272727</td>\n",
       "      <td>0.002420</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3.363636</td>\n",
       "      <td>0.002703</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.454545</td>\n",
       "      <td>0.002041</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3.545455</td>\n",
       "      <td>0.001484</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3.636364</td>\n",
       "      <td>0.003528</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>7.363636</td>\n",
       "      <td>0.067884</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>7.454545</td>\n",
       "      <td>0.069241</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>7.545455</td>\n",
       "      <td>0.070618</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>7.636364</td>\n",
       "      <td>0.070714</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>7.727273</td>\n",
       "      <td>0.070744</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>7.818182</td>\n",
       "      <td>0.071397</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>7.909091</td>\n",
       "      <td>0.071475</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.072904</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>8.090909</td>\n",
       "      <td>0.074791</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>8.181818</td>\n",
       "      <td>0.077563</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>8.272727</td>\n",
       "      <td>0.074442</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>8.363636</td>\n",
       "      <td>0.075756</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>8.454545</td>\n",
       "      <td>0.077809</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>8.545455</td>\n",
       "      <td>0.078018</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>8.636364</td>\n",
       "      <td>0.078022</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>8.727273</td>\n",
       "      <td>0.078838</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>8.818182</td>\n",
       "      <td>0.078928</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>8.909091</td>\n",
       "      <td>0.082497</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.083628</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>9.090909</td>\n",
       "      <td>0.082730</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>9.181818</td>\n",
       "      <td>0.084226</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>9.272727</td>\n",
       "      <td>0.084920</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>9.363636</td>\n",
       "      <td>0.084625</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>9.454545</td>\n",
       "      <td>0.087501</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>9.545455</td>\n",
       "      <td>0.088127</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>9.636364</td>\n",
       "      <td>0.089183</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>9.727273</td>\n",
       "      <td>0.088605</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>9.818182</td>\n",
       "      <td>0.089878</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>9.909091</td>\n",
       "      <td>0.090924</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.089207</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      voltage   current  relative_humidity\n",
       "0    1.000000  0.000334                0.0\n",
       "1    1.090909  0.000104                0.0\n",
       "2    1.181818 -0.000126                0.0\n",
       "3    1.272727  0.001368                0.0\n",
       "4    1.363636  0.002396                0.0\n",
       "5    1.454545  0.001334                0.0\n",
       "6    1.545455  0.000734                0.0\n",
       "7    1.636364  0.002404                0.0\n",
       "8    1.727273  0.001938                0.0\n",
       "9    1.818182  0.004092                0.0\n",
       "10   1.909091  0.002155                0.0\n",
       "11   2.000000 -0.000368                0.0\n",
       "12   2.090909  0.002665                0.0\n",
       "13   2.181818  0.002737                0.0\n",
       "14   2.272727  0.002639                0.0\n",
       "15   2.363636  0.002366                0.0\n",
       "16   2.454545  0.002564                0.0\n",
       "17   2.545455  0.001701                0.0\n",
       "18   2.636364  0.002942                0.0\n",
       "19   2.727273  0.002347                0.0\n",
       "20   2.818182  0.003005                0.0\n",
       "21   2.909091  0.002652                0.0\n",
       "22   3.000000  0.001892                0.0\n",
       "23   3.090909  0.002557                0.0\n",
       "24   3.181818  0.001152                0.0\n",
       "25   3.272727  0.002420                0.0\n",
       "26   3.363636  0.002703                0.0\n",
       "27   3.454545  0.002041                0.0\n",
       "28   3.545455  0.001484                0.0\n",
       "29   3.636364  0.003528                0.0\n",
       "..        ...       ...                ...\n",
       "70   7.363636  0.067884               99.0\n",
       "71   7.454545  0.069241               99.0\n",
       "72   7.545455  0.070618               99.0\n",
       "73   7.636364  0.070714               99.0\n",
       "74   7.727273  0.070744               99.0\n",
       "75   7.818182  0.071397               99.0\n",
       "76   7.909091  0.071475               99.0\n",
       "77   8.000000  0.072904               99.0\n",
       "78   8.090909  0.074791               99.0\n",
       "79   8.181818  0.077563               99.0\n",
       "80   8.272727  0.074442               99.0\n",
       "81   8.363636  0.075756               99.0\n",
       "82   8.454545  0.077809               99.0\n",
       "83   8.545455  0.078018               99.0\n",
       "84   8.636364  0.078022               99.0\n",
       "85   8.727273  0.078838               99.0\n",
       "86   8.818182  0.078928               99.0\n",
       "87   8.909091  0.082497               99.0\n",
       "88   9.000000  0.083628               99.0\n",
       "89   9.090909  0.082730               99.0\n",
       "90   9.181818  0.084226               99.0\n",
       "91   9.272727  0.084920               99.0\n",
       "92   9.363636  0.084625               99.0\n",
       "93   9.454545  0.087501               99.0\n",
       "94   9.545455  0.088127               99.0\n",
       "95   9.636364  0.089183               99.0\n",
       "96   9.727273  0.088605               99.0\n",
       "97   9.818182  0.089878               99.0\n",
       "98   9.909091  0.090924               99.0\n",
       "99  10.000000  0.089207               99.0\n",
       "\n",
       "[10000 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case you worry about storing the same value of the relative humidity in multiple cells, don't! We now have our data in the sort of format that is common in traditional databases, and it gives us great flexibility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# calculate the power dissipation and resistance for all measurements at once\n",
    "df['power'] = df.voltage * df.current\n",
    "df['resistance'] = df.voltage / df.current\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>voltage</th>\n",
       "      <th>current</th>\n",
       "      <th>relative_humidity</th>\n",
       "      <th>power</th>\n",
       "      <th>resistance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000721</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000721</td>\n",
       "      <td>1387.389092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.090909</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000097</td>\n",
       "      <td>12315.187148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.181818</td>\n",
       "      <td>0.001378</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001628</td>\n",
       "      <td>857.863671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.272727</td>\n",
       "      <td>0.002443</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003109</td>\n",
       "      <td>521.036781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.363636</td>\n",
       "      <td>0.002018</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002752</td>\n",
       "      <td>675.789009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.454545</td>\n",
       "      <td>-0.000541</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.000786</td>\n",
       "      <td>-2690.634604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.545455</td>\n",
       "      <td>0.001817</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002809</td>\n",
       "      <td>850.382184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.636364</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001044</td>\n",
       "      <td>2563.971379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.727273</td>\n",
       "      <td>0.002908</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005022</td>\n",
       "      <td>594.025614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.818182</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>8223.012144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.909091</td>\n",
       "      <td>0.001787</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003411</td>\n",
       "      <td>1068.427527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.002070</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004139</td>\n",
       "      <td>966.410255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.090909</td>\n",
       "      <td>0.001738</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003634</td>\n",
       "      <td>1202.972176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.181818</td>\n",
       "      <td>0.002429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005300</td>\n",
       "      <td>898.151619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.272727</td>\n",
       "      <td>0.001644</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003737</td>\n",
       "      <td>1382.227846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.363636</td>\n",
       "      <td>0.000605</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001431</td>\n",
       "      <td>3903.894172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.454545</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005411</td>\n",
       "      <td>1113.496916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.545455</td>\n",
       "      <td>0.003327</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008468</td>\n",
       "      <td>765.188417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.636364</td>\n",
       "      <td>0.003308</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008721</td>\n",
       "      <td>796.948144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.727273</td>\n",
       "      <td>0.003251</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008867</td>\n",
       "      <td>838.882036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.818182</td>\n",
       "      <td>0.002658</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007492</td>\n",
       "      <td>1060.092887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.909091</td>\n",
       "      <td>0.002750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>1057.785214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.003413</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.010239</td>\n",
       "      <td>879.011636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.090909</td>\n",
       "      <td>0.002375</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.007340</td>\n",
       "      <td>1301.633303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.181818</td>\n",
       "      <td>0.002861</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009103</td>\n",
       "      <td>1112.165023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3.272727</td>\n",
       "      <td>0.004191</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.013717</td>\n",
       "      <td>780.831072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3.363636</td>\n",
       "      <td>0.002576</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.008666</td>\n",
       "      <td>1305.532571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.454545</td>\n",
       "      <td>0.003495</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012074</td>\n",
       "      <td>988.420458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3.545455</td>\n",
       "      <td>0.002758</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.009779</td>\n",
       "      <td>1285.408700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3.636364</td>\n",
       "      <td>0.003919</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.014250</td>\n",
       "      <td>927.969109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>7.363636</td>\n",
       "      <td>0.008049</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.059267</td>\n",
       "      <td>914.895619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>7.454545</td>\n",
       "      <td>0.007788</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.058054</td>\n",
       "      <td>957.215741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>7.545455</td>\n",
       "      <td>0.006834</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.051567</td>\n",
       "      <td>1104.082657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>7.636364</td>\n",
       "      <td>0.008055</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.061514</td>\n",
       "      <td>947.984072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>7.727273</td>\n",
       "      <td>0.008223</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.063544</td>\n",
       "      <td>939.671763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>7.818182</td>\n",
       "      <td>0.007629</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.059643</td>\n",
       "      <td>1024.829750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>7.909091</td>\n",
       "      <td>0.009143</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.072309</td>\n",
       "      <td>865.089872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.007598</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.060785</td>\n",
       "      <td>1052.885785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>8.090909</td>\n",
       "      <td>0.008240</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.066671</td>\n",
       "      <td>981.884446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>8.181818</td>\n",
       "      <td>0.008887</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.072711</td>\n",
       "      <td>920.663823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>8.272727</td>\n",
       "      <td>0.007354</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.060837</td>\n",
       "      <td>1124.944914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>8.363636</td>\n",
       "      <td>0.007057</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.059022</td>\n",
       "      <td>1185.157650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>8.454545</td>\n",
       "      <td>0.009176</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.077583</td>\n",
       "      <td>921.327488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>8.545455</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.061917</td>\n",
       "      <td>1179.399319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>8.636364</td>\n",
       "      <td>0.010873</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.093905</td>\n",
       "      <td>794.275089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>8.727273</td>\n",
       "      <td>0.008652</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.075511</td>\n",
       "      <td>1008.670551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>8.818182</td>\n",
       "      <td>0.008440</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.074425</td>\n",
       "      <td>1044.821141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>8.909091</td>\n",
       "      <td>0.008481</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.075556</td>\n",
       "      <td>1050.502310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.008393</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.075534</td>\n",
       "      <td>1072.365836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>9.090909</td>\n",
       "      <td>0.010996</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.099964</td>\n",
       "      <td>826.747673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>9.181818</td>\n",
       "      <td>0.009546</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.087646</td>\n",
       "      <td>961.888473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>9.272727</td>\n",
       "      <td>0.009733</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.090255</td>\n",
       "      <td>952.672398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>9.363636</td>\n",
       "      <td>0.012485</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116907</td>\n",
       "      <td>749.977927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>9.454545</td>\n",
       "      <td>0.009247</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.087422</td>\n",
       "      <td>1022.493049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>9.545455</td>\n",
       "      <td>0.009033</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.086224</td>\n",
       "      <td>1056.726597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>9.636364</td>\n",
       "      <td>0.009165</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.088314</td>\n",
       "      <td>1051.465995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>9.727273</td>\n",
       "      <td>0.009548</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.092874</td>\n",
       "      <td>1018.802186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>9.818182</td>\n",
       "      <td>0.007750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.076090</td>\n",
       "      <td>1266.876496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>9.909091</td>\n",
       "      <td>0.011167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.110651</td>\n",
       "      <td>887.386810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.011507</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.115069</td>\n",
       "      <td>869.042216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      voltage   current  relative_humidity     power    resistance\n",
       "0    1.000000  0.000721                1.0  0.000721   1387.389092\n",
       "1    1.090909  0.000089                1.0  0.000097  12315.187148\n",
       "2    1.181818  0.001378                1.0  0.001628    857.863671\n",
       "3    1.272727  0.002443                1.0  0.003109    521.036781\n",
       "4    1.363636  0.002018                1.0  0.002752    675.789009\n",
       "5    1.454545 -0.000541                1.0 -0.000786  -2690.634604\n",
       "6    1.545455  0.001817                1.0  0.002809    850.382184\n",
       "7    1.636364  0.000638                1.0  0.001044   2563.971379\n",
       "8    1.727273  0.002908                1.0  0.005022    594.025614\n",
       "9    1.818182  0.000221                1.0  0.000402   8223.012144\n",
       "10   1.909091  0.001787                1.0  0.003411   1068.427527\n",
       "11   2.000000  0.002070                1.0  0.004139    966.410255\n",
       "12   2.090909  0.001738                1.0  0.003634   1202.972176\n",
       "13   2.181818  0.002429                1.0  0.005300    898.151619\n",
       "14   2.272727  0.001644                1.0  0.003737   1382.227846\n",
       "15   2.363636  0.000605                1.0  0.001431   3903.894172\n",
       "16   2.454545  0.002204                1.0  0.005411   1113.496916\n",
       "17   2.545455  0.003327                1.0  0.008468    765.188417\n",
       "18   2.636364  0.003308                1.0  0.008721    796.948144\n",
       "19   2.727273  0.003251                1.0  0.008867    838.882036\n",
       "20   2.818182  0.002658                1.0  0.007492   1060.092887\n",
       "21   2.909091  0.002750                1.0  0.008000   1057.785214\n",
       "22   3.000000  0.003413                1.0  0.010239    879.011636\n",
       "23   3.090909  0.002375                1.0  0.007340   1301.633303\n",
       "24   3.181818  0.002861                1.0  0.009103   1112.165023\n",
       "25   3.272727  0.004191                1.0  0.013717    780.831072\n",
       "26   3.363636  0.002576                1.0  0.008666   1305.532571\n",
       "27   3.454545  0.003495                1.0  0.012074    988.420458\n",
       "28   3.545455  0.002758                1.0  0.009779   1285.408700\n",
       "29   3.636364  0.003919                1.0  0.014250    927.969109\n",
       "..        ...       ...                ...       ...           ...\n",
       "70   7.363636  0.008049                1.0  0.059267    914.895619\n",
       "71   7.454545  0.007788                1.0  0.058054    957.215741\n",
       "72   7.545455  0.006834                1.0  0.051567   1104.082657\n",
       "73   7.636364  0.008055                1.0  0.061514    947.984072\n",
       "74   7.727273  0.008223                1.0  0.063544    939.671763\n",
       "75   7.818182  0.007629                1.0  0.059643   1024.829750\n",
       "76   7.909091  0.009143                1.0  0.072309    865.089872\n",
       "77   8.000000  0.007598                1.0  0.060785   1052.885785\n",
       "78   8.090909  0.008240                1.0  0.066671    981.884446\n",
       "79   8.181818  0.008887                1.0  0.072711    920.663823\n",
       "80   8.272727  0.007354                1.0  0.060837   1124.944914\n",
       "81   8.363636  0.007057                1.0  0.059022   1185.157650\n",
       "82   8.454545  0.009176                1.0  0.077583    921.327488\n",
       "83   8.545455  0.007246                1.0  0.061917   1179.399319\n",
       "84   8.636364  0.010873                1.0  0.093905    794.275089\n",
       "85   8.727273  0.008652                1.0  0.075511   1008.670551\n",
       "86   8.818182  0.008440                1.0  0.074425   1044.821141\n",
       "87   8.909091  0.008481                1.0  0.075556   1050.502310\n",
       "88   9.000000  0.008393                1.0  0.075534   1072.365836\n",
       "89   9.090909  0.010996                1.0  0.099964    826.747673\n",
       "90   9.181818  0.009546                1.0  0.087646    961.888473\n",
       "91   9.272727  0.009733                1.0  0.090255    952.672398\n",
       "92   9.363636  0.012485                1.0  0.116907    749.977927\n",
       "93   9.454545  0.009247                1.0  0.087422   1022.493049\n",
       "94   9.545455  0.009033                1.0  0.086224   1056.726597\n",
       "95   9.636364  0.009165                1.0  0.088314   1051.465995\n",
       "96   9.727273  0.009548                1.0  0.092874   1018.802186\n",
       "97   9.818182  0.007750                1.0  0.076090   1266.876496\n",
       "98   9.909091  0.011167                1.0  0.110651    887.386810\n",
       "99  10.000000  0.011507                1.0  0.115069    869.042216\n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a variable that refers to the measurements at a single relative humidity value only\n",
    "dfRH = df[df.relative_humidity == 1.0]\n",
    "dfRH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x8a6c7f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2m6Pa/c///A+///3vKS8v58EHH2T16tXYbDays7P51a9+RW1tLVdffTUXXHBBuM+5556L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eu9fN49VUViSBAPTEolJSqYH79/nMKTzRw90szfykr5wYwkvjUxgVd2ldHaZUgMYN+pBob1CyUi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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8a43c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot several measurements\n",
    "for ξ in df.relative_humidity.unique()[::20]:\n",
    "    dfRH = df[df.relative_humidity == ξ]\n",
    "    plt.plot(dfRH.voltage, dfRH.current, label=\"%.1f %%RH\" % ξ)\n",
    "    \n",
    "\n",
    "plt.xlabel('Voltage (V)')\n",
    "plt.ylabel('Current (A)')\n",
    "plt.legend(loc='upper left')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregating the data\n",
    "\n",
    "Eventually we would like to be able to plot a graph that shows the electrical resistance as a function of the relative humidity. This means that we have to calculate the resistance for every measurement. We will do that by calculating the following things for each subset of data in which the relative humidity was the same\n",
    "\n",
    "* the mean resistance\n",
    "* the standard deviation of the resistance\n",
    "* the standard error of the resistance\n",
    "\n",
    "The mean and standard deviation can be found by using the `mean` and `std` function of numpy. For the standard error we have to create a function ourselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def stderr(values):\n",
    "    \"\"\"\n",
    "    Calculates the standard error of `values`. Values should be numpy array-like\n",
    "    \"\"\"\n",
    "    return values.std()/np.sqrt(len(values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grouped = df.groupby('relative_humidity')\n",
    "dfAgg = grouped.aggregate({'resistance': [np.std, np.mean, stderr]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">resistance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>stderr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relative_humidity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>1602.034439</td>\n",
       "      <td>1029.146587</td>\n",
       "      <td>160.203444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>1436.261380</td>\n",
       "      <td>1216.021430</td>\n",
       "      <td>143.626138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>368.717822</td>\n",
       "      <td>1042.360498</td>\n",
       "      <td>36.871782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>2017.702400</td>\n",
       "      <td>802.755751</td>\n",
       "      <td>201.770240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>993.818495</td>\n",
       "      <td>1106.223647</td>\n",
       "      <td>99.381850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.1</th>\n",
       "      <td>209.776023</td>\n",
       "      <td>987.755804</td>\n",
       "      <td>20.977602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.1</th>\n",
       "      <td>436.593000</td>\n",
       "      <td>1043.044177</td>\n",
       "      <td>43.659300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.1</th>\n",
       "      <td>1527.744211</td>\n",
       "      <td>1208.757380</td>\n",
       "      <td>152.774421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.1</th>\n",
       "      <td>538.758520</td>\n",
       "      <td>1017.264707</td>\n",
       "      <td>53.875852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.1</th>\n",
       "      <td>2824.877508</td>\n",
       "      <td>677.883175</td>\n",
       "      <td>282.487751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.1</th>\n",
       "      <td>922.109888</td>\n",
       "      <td>995.075462</td>\n",
       "      <td>92.210989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.1</th>\n",
       "      <td>392.133495</td>\n",
       "      <td>944.395010</td>\n",
       "      <td>39.213349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.1</th>\n",
       "      <td>2076.823539</td>\n",
       "      <td>735.255651</td>\n",
       "      <td>207.682354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.1</th>\n",
       "      <td>653.756495</td>\n",
       "      <td>973.145931</td>\n",
       "      <td>65.375649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.1</th>\n",
       "      <td>1680.095437</td>\n",
       "      <td>865.085523</td>\n",
       "      <td>168.009544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.2</th>\n",
       "      <td>1431.357449</td>\n",
       "      <td>740.525665</td>\n",
       "      <td>143.135745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.2</th>\n",
       "      <td>394.523252</td>\n",
       "      <td>945.233792</td>\n",
       "      <td>39.452325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.2</th>\n",
       "      <td>321.011968</td>\n",
       "      <td>866.741786</td>\n",
       "      <td>32.101197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.2</th>\n",
       "      <td>195.199054</td>\n",
       "      <td>880.065927</td>\n",
       "      <td>19.519905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.2</th>\n",
       "      <td>4756.238319</td>\n",
       "      <td>1333.002915</td>\n",
       "      <td>475.623832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.2</th>\n",
       "      <td>908.585909</td>\n",
       "      <td>713.539067</td>\n",
       "      <td>90.858591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.2</th>\n",
       "      <td>7257.168341</td>\n",
       "      <td>1315.736175</td>\n",
       "      <td>725.716834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.2</th>\n",
       "      <td>991.306960</td>\n",
       "      <td>727.021350</td>\n",
       "      <td>99.130696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.2</th>\n",
       "      <td>167.311149</td>\n",
       "      <td>798.629528</td>\n",
       "      <td>16.731115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.2</th>\n",
       "      <td>2381.352883</td>\n",
       "      <td>1048.339144</td>\n",
       "      <td>238.135288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.3</th>\n",
       "      <td>648.577825</td>\n",
       "      <td>849.810473</td>\n",
       "      <td>64.857782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.3</th>\n",
       "      <td>380.398716</td>\n",
       "      <td>835.899702</td>\n",
       "      <td>38.039872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.3</th>\n",
       "      <td>1851.991837</td>\n",
       "      <td>871.953695</td>\n",
       "      <td>185.199184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.3</th>\n",
       "      <td>671.592548</td>\n",
       "      <td>866.534276</td>\n",
       "      <td>67.159255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.3</th>\n",
       "      <td>727.120464</td>\n",
       "      <td>848.830855</td>\n",
       "      <td>72.712046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70.7</th>\n",
       "      <td>70.134252</td>\n",
       "      <td>367.516875</td>\n",
       "      <td>7.013425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71.7</th>\n",
       "      <td>42.886895</td>\n",
       "      <td>359.488219</td>\n",
       "      <td>4.288689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72.7</th>\n",
       "      <td>75.129365</td>\n",
       "      <td>357.553669</td>\n",
       "      <td>7.512936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73.7</th>\n",
       "      <td>32.477496</td>\n",
       "      <td>333.325235</td>\n",
       "      <td>3.247750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74.7</th>\n",
       "      <td>35.828998</td>\n",
       "      <td>332.487983</td>\n",
       "      <td>3.582900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.8</th>\n",
       "      <td>42.066098</td>\n",
       "      <td>322.853816</td>\n",
       "      <td>4.206610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76.8</th>\n",
       "      <td>27.384450</td>\n",
       "      <td>313.569672</td>\n",
       "      <td>2.738445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77.8</th>\n",
       "      <td>23.417233</td>\n",
       "      <td>303.028915</td>\n",
       "      <td>2.341723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78.8</th>\n",
       "      <td>31.884285</td>\n",
       "      <td>294.131373</td>\n",
       "      <td>3.188428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79.8</th>\n",
       "      <td>64.015510</td>\n",
       "      <td>292.207797</td>\n",
       "      <td>6.401551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80.8</th>\n",
       "      <td>24.485104</td>\n",
       "      <td>272.599436</td>\n",
       "      <td>2.448510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81.8</th>\n",
       "      <td>68.672830</td>\n",
       "      <td>272.114966</td>\n",
       "      <td>6.867283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82.8</th>\n",
       "      <td>24.822744</td>\n",
       "      <td>255.376871</td>\n",
       "      <td>2.482274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83.8</th>\n",
       "      <td>25.177548</td>\n",
       "      <td>249.892141</td>\n",
       "      <td>2.517755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84.8</th>\n",
       "      <td>16.635265</td>\n",
       "      <td>235.590117</td>\n",
       "      <td>1.663527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85.9</th>\n",
       "      <td>19.014167</td>\n",
       "      <td>232.710781</td>\n",
       "      <td>1.901417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86.9</th>\n",
       "      <td>12.470321</td>\n",
       "      <td>217.152942</td>\n",
       "      <td>1.247032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87.9</th>\n",
       "      <td>13.672296</td>\n",
       "      <td>208.948988</td>\n",
       "      <td>1.367230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88.9</th>\n",
       "      <td>16.567045</td>\n",
       "      <td>203.325018</td>\n",
       "      <td>1.656705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89.9</th>\n",
       "      <td>10.980351</td>\n",
       "      <td>192.736415</td>\n",
       "      <td>1.098035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90.9</th>\n",
       "      <td>13.438713</td>\n",
       "      <td>183.521386</td>\n",
       "      <td>1.343871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91.9</th>\n",
       "      <td>9.240913</td>\n",
       "      <td>173.747430</td>\n",
       "      <td>0.924091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92.9</th>\n",
       "      <td>10.535475</td>\n",
       "      <td>164.650922</td>\n",
       "      <td>1.053548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93.9</th>\n",
       "      <td>6.673396</td>\n",
       "      <td>153.935492</td>\n",
       "      <td>0.667340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94.9</th>\n",
       "      <td>7.356380</td>\n",
       "      <td>146.058795</td>\n",
       "      <td>0.735638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96.0</th>\n",
       "      <td>5.769546</td>\n",
       "      <td>136.984251</td>\n",
       "      <td>0.576955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97.0</th>\n",
       "      <td>6.120658</td>\n",
       "      <td>127.680270</td>\n",
       "      <td>0.612066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98.0</th>\n",
       "      <td>4.365614</td>\n",
       "      <td>118.159108</td>\n",
       "      <td>0.436561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99.0</th>\n",
       "      <td>4.058754</td>\n",
       "      <td>108.720007</td>\n",
       "      <td>0.405875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.0</th>\n",
       "      <td>4.426185</td>\n",
       "      <td>99.868837</td>\n",
       "      <td>0.442618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    resistance                         \n",
       "                           std         mean      stderr\n",
       "relative_humidity                                      \n",
       "0.0                1602.034439  1029.146587  160.203444\n",
       "1.0                1436.261380  1216.021430  143.626138\n",
       "2.0                 368.717822  1042.360498   36.871782\n",
       "3.0                2017.702400   802.755751  201.770240\n",
       "4.0                 993.818495  1106.223647   99.381850\n",
       "5.1                 209.776023   987.755804   20.977602\n",
       "6.1                 436.593000  1043.044177   43.659300\n",
       "7.1                1527.744211  1208.757380  152.774421\n",
       "8.1                 538.758520  1017.264707   53.875852\n",
       "9.1                2824.877508   677.883175  282.487751\n",
       "10.1                922.109888   995.075462   92.210989\n",
       "11.1                392.133495   944.395010   39.213349\n",
       "12.1               2076.823539   735.255651  207.682354\n",
       "13.1                653.756495   973.145931   65.375649\n",
       "14.1               1680.095437   865.085523  168.009544\n",
       "15.2               1431.357449   740.525665  143.135745\n",
       "16.2                394.523252   945.233792   39.452325\n",
       "17.2                321.011968   866.741786   32.101197\n",
       "18.2                195.199054   880.065927   19.519905\n",
       "19.2               4756.238319  1333.002915  475.623832\n",
       "20.2                908.585909   713.539067   90.858591\n",
       "21.2               7257.168341  1315.736175  725.716834\n",
       "22.2                991.306960   727.021350   99.130696\n",
       "23.2                167.311149   798.629528   16.731115\n",
       "24.2               2381.352883  1048.339144  238.135288\n",
       "25.3                648.577825   849.810473   64.857782\n",
       "26.3                380.398716   835.899702   38.039872\n",
       "27.3               1851.991837   871.953695  185.199184\n",
       "28.3                671.592548   866.534276   67.159255\n",
       "29.3                727.120464   848.830855   72.712046\n",
       "...                        ...          ...         ...\n",
       "70.7                 70.134252   367.516875    7.013425\n",
       "71.7                 42.886895   359.488219    4.288689\n",
       "72.7                 75.129365   357.553669    7.512936\n",
       "73.7                 32.477496   333.325235    3.247750\n",
       "74.7                 35.828998   332.487983    3.582900\n",
       "75.8                 42.066098   322.853816    4.206610\n",
       "76.8                 27.384450   313.569672    2.738445\n",
       "77.8                 23.417233   303.028915    2.341723\n",
       "78.8                 31.884285   294.131373    3.188428\n",
       "79.8                 64.015510   292.207797    6.401551\n",
       "80.8                 24.485104   272.599436    2.448510\n",
       "81.8                 68.672830   272.114966    6.867283\n",
       "82.8                 24.822744   255.376871    2.482274\n",
       "83.8                 25.177548   249.892141    2.517755\n",
       "84.8                 16.635265   235.590117    1.663527\n",
       "85.9                 19.014167   232.710781    1.901417\n",
       "86.9                 12.470321   217.152942    1.247032\n",
       "87.9                 13.672296   208.948988    1.367230\n",
       "88.9                 16.567045   203.325018    1.656705\n",
       "89.9                 10.980351   192.736415    1.098035\n",
       "90.9                 13.438713   183.521386    1.343871\n",
       "91.9                  9.240913   173.747430    0.924091\n",
       "92.9                 10.535475   164.650922    1.053548\n",
       "93.9                  6.673396   153.935492    0.667340\n",
       "94.9                  7.356380   146.058795    0.735638\n",
       "96.0                  5.769546   136.984251    0.576955\n",
       "97.0                  6.120658   127.680270    0.612066\n",
       "98.0                  4.365614   118.159108    0.436561\n",
       "99.0                  4.058754   108.720007    0.405875\n",
       "100.0                 4.426185    99.868837    0.442618\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfAgg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x8e971d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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avg08Ymb/5Zz7FnAl8G3n3CGAAyYBo4FHnHMHmVnY7VEXmspqePft1m11W6F6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\np6OoRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8c38c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.errorbar(dfAgg.index,dfAgg.resistance['mean'],yerr=dfAgg.resistance['stderr'])\n",
    "ax.set_ylim(0,2000)\n",
    "ax.set_xlim(20,100)\n",
    "ax.set_xlabel('Relative humidity')\n",
    "ax.set_ylabel('Resistance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregating the data again: finding the original relationship\n",
    "\n",
    "Now we are going to find the mathematical relationship between the resistance and the relative humidity. The graph shown above displays a linear trend with lots of noise at the lower values of the relative humidity.\n",
    "\n",
    "If all goes well, we should be able to find the same mathematical relation between resistance and relative humidity that we used to generate the data at the start of this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We'll use a first order polynomial fit the find the relationship between resistance and relative humidity, and use the standard error as as weights, such that values with a higher standard error have a lower weight in the fit algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   -9.19314468,  1018.83322483])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#p is an array of polynomial coefficients, highest order first\n",
    "p = cp.polyfit(dfAgg.index, dfAgg.resistance['mean'], w=1/dfAgg.resistance['stderr'],deg=1)\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the graph again, but now with a nice dashed fit-line through it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x9f1d2b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8888LJxIrT6tWrQqnFhg1ahRvvvkmDz30EP7+/gwcOBB/f3/27NlDTEwMn376KT/88ENh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\nCFGTVbSG8xbQE/gWGAHEAZ2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CJTR6/V/Ed2zLlUOHMnj8TRhNmrk9PuEZknBqstC6mLu2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\nvmFY2a7Prnwp8Ei5UWP7CcIATG7R3+22u0qixIe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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8ea0ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.errorbar(dfAgg.index,dfAgg.resistance['mean'],yerr=dfAgg.resistance['stderr'],label='mean of measured values')\n",
    "formula = r\"$R={:.2f} \\cdot \\xi + {:.2f}$\".format(*p)\n",
    "ax.plot(np.linspace(20,100),cp.polyval(p, np.linspace(20,100)), color='k', linestyle='--', label=formula)\n",
    "ax.set_ylim(0,2000)\n",
    "ax.set_xlim(20,100)\n",
    "ax.set_xlabel('Relative humidity')\n",
    "ax.set_ylabel(r'Resistance ($\\mathrm{\\Omega}$)')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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