Pandas_Well_Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Today we are going to really start using pandas.  Lets review the libraries or packages and what they have done so far.\n",
    "1.  We always start byt typing %matploltlib inline.  This is a built in magic command that enables us to plot the data right into our ipython notebook https://ipython.org/ipython-doc/3/interactive/magics.html \n",
    "2.  we import matplotlib.pyplot as plt.  This turns on all the graphing capabilities and then uses the shorthand plt. for when we call functions from matplotlib.  This used to be called pylab but was updated to pyplot.  Then we say fig,ax=plt.subplots() and all the plot functions go into fig and ax\n",
    "3.  we import numpy as np.  This turns on math functions and we use the shorthand np.\n",
    "4.  from scipy we import stats.  scipy gives us a lot of analysis functions and we use linear regression from stats.\n",
    "5.  Now we are also going to use pandas.  Pandas is database management.  It lets us take complicated datasets and anlyze them.  You can think of it like a supercharged excel where you combine the organization of excel with the power of a programming language.  It can do amazing things and I am still learning every day.  So lets get started!\n",
    "1.  What is pandas? http://pandas.pydata.org/index.html  and here is the documentation http://pandas.pydata.org/pandas-docs/stable/\n",
    "1.  import pandas as pd!!!!!!\n",
    "1.  On a final note you can see I made a numbered list in markdown. To do that you type a number a period and then two spaces.\n",
    "1.  Also in terms of line numnbers.  I turn my line numbers on so it is easier to debug.  Do this under view\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing files\n",
    "We are going to start by finding our csv file and reading it in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I only want to list the csv files so I can see what I can read in.  so I will do ls \\*.csv   the star is a wildcard that means everything and then .csv is only ones that end in .csv.  Today we are going to look at data from well water chemistry in Bangladesh. Specifically arsenic concentrations and if people drink the water.  We will also look at the rest of the chemistry.  We are looking at well water arsenic because drinking water with arsenic has negative long term health impacts.  The US standard for arsenic is 10 ppb or 10 ug/L.  The bangladesh standard is 50 ppb.  Lets see what we can learn!  We are going to try and learn about how many people drink water with 10 or 50 ppb arsenic.  (to show the star I had to type \\\\*)\n",
    "\n",
    "The data is on the edblogs siteh ttps://edblogs.columbia.edu/eescx3050-001-2015-3/category/classes/class-10-start-pandas/ or on the github site https://github.com/bmaillou/BigDataPython/blob/master/well_data.csv.  "
   ]
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   "execution_count": 4,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/bmaillou/Documents/work-teaching/python/fall21/BigDataPython'"
      ]
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     "execution_count": 4,
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     "output_type": "execute_result"
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   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brian.csv                    gdp2 - Copy.csv\r\n",
      "CoreEM09GC01-extra-line.csv  gdp2.csv\r\n",
      "CoreEM09GC01.csv             gdp2015.csv\r\n",
      "GDP-Lifespan - Copy.csv      gdp_and_lifespan.csv\r\n",
      "GDP-Lifespan.csv             gdp_only.csv\r\n",
      "GDP_Lifespan_all_data.csv    gdp_only_download.csv\r\n",
      "Libby_Thesis_Data.csv        mystery.csv\r\n",
      "Well-As.csv                  twoD1.csv\r\n",
      "central_park.csv             weekly_mlo.csv\r\n",
      "fldav_ljo.csv                well_data.csv\r\n",
      "fldav_ljo_Yasna.csv          well_sites.csv\r\n",
      "gdp.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls *.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we read in a well_data.csv.  But I want to use pandas and not numpy.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we are going to read in some data and try to analyze it.  open the well_data.csv.  It is for wells from Bangladesh. every well has an id#, a latitude and longitude, Depth, if people drink it and then some concentration data.  lets use readcsv to get read in.  In Pandas you are trying to get your data into a dataframe which is like an excel sheet.  It will have column titles and an index for rows.  It is all about the dataframes.  When using pandas people name things 'df' a lot.  That is shorthand for dataframe.  I am not a good namer.   \n",
    "\n",
    "I am going to just name it df today.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('well_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is now magically in the computers memory even if we can't see it we can access it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This is important.  Your output may not look like my output.  It changes between computers depending on default settings when you installed.  Don't worry.  If you see data of descriptions you are fine.\n",
    "\n",
    "just typing well_data will give us some descriptions of what we got!  It used the first row for column names!  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Well_ID</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Drink</th>\n",
       "      <th>Si</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Fe</th>\n",
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       "      <th>Cl</th>\n",
       "      <th>SO4</th>\n",
       "      <th>Br</th>\n",
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       "  </thead>\n",
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       "      <td>90.31</td>\n",
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       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83</td>\n",
       "      <td>23.80</td>\n",
       "      <td>91.33</td>\n",
       "      <td>50</td>\n",
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       "      <td>48084.33842</td>\n",
       "      <td>0.936358</td>\n",
       "      <td>2085.570979</td>\n",
       "      <td>54666.48199</td>\n",
       "      <td>1.260031</td>\n",
       "      <td>...</td>\n",
       "      <td>76.207449</td>\n",
       "      <td>26.104825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.019154</td>\n",
       "      <td>78.97747</td>\n",
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       "      <td>83.0137</td>\n",
       "      <td>5.6188</td>\n",
       "      <td>0.1512</td>\n",
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       "      <th>4</th>\n",
       "      <td>84</td>\n",
       "      <td>23.98</td>\n",
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       "      <td>39790.24000</td>\n",
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       "      <td>91.27</td>\n",
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       "      <td>Y</td>\n",
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       "      <td>13771.370000</td>\n",
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       "      <td>1.498350</td>\n",
       "      <td>...</td>\n",
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       "      <td>3958.51</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>60</td>\n",
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       "      <td>12.793100</td>\n",
       "      <td>...</td>\n",
       "      <td>33.180360</td>\n",
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       "      <td>0.0413</td>\n",
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       "</table>\n",
       "<p>759 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Well_ID    Lat    Lon  Depth Drink           Si         P             S  \\\n",
       "0          2  23.74  90.31     45     Y          NaN       NaN           NaN   \n",
       "1         14  23.62  90.60     60     Y          NaN       NaN           NaN   \n",
       "2         23  23.94  91.46     60     Y          NaN       NaN           NaN   \n",
       "3         83  23.80  91.33     50     Y  48084.33842  0.936358   2085.570979   \n",
       "4         84  23.98  90.81    150     Y          NaN       NaN           NaN   \n",
       "..       ...    ...    ...    ...   ...          ...       ...           ...   \n",
       "754    12516  24.71  90.41    160     Y  32379.64000  0.197380   3669.430000   \n",
       "755    12654  24.36  91.27     60     Y  25561.12000  0.090570  13771.370000   \n",
       "756    72641  24.38  90.90     45     N  31319.48000  1.162550     38.300000   \n",
       "757    76175  23.90  90.65     60     N  30605.53000  1.556120   4168.520000   \n",
       "758   141499  23.60  91.34     50     N          NaN       NaN           NaN   \n",
       "\n",
       "              Ca         Fe  ...         Na         Mg        K        Mn  \\\n",
       "0            NaN        NaN  ...        NaN        NaN      NaN       NaN   \n",
       "1            NaN        NaN  ...        NaN        NaN      NaN       NaN   \n",
       "2            NaN        NaN  ...        NaN        NaN      NaN       NaN   \n",
       "3    54666.48199   1.260031  ...  76.207449  26.104825      NaN  1.019154   \n",
       "4            NaN        NaN  ...        NaN        NaN      NaN       NaN   \n",
       "..           ...        ...  ...        ...        ...      ...       ...   \n",
       "754  39790.24000   0.341200  ...  26.299280  13.330000  1850.53  0.340000   \n",
       "755  57630.63000   1.498350  ...  19.723360  33.740000  3958.51  1.200000   \n",
       "756  60905.16000  22.417560  ...  14.114910  21.700000  3372.45  1.560000   \n",
       "757  66756.16000  12.793100  ...  33.180360  25.100000  4102.56  1.640000   \n",
       "758          NaN        NaN  ...        NaN        NaN      NaN       NaN   \n",
       "\n",
       "            As          Sr       F       Cl      SO4      Br  \n",
       "0          NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "1          NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "2          NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "3     78.97747  253.528674  0.1669  83.0137   5.6188  0.1512  \n",
       "4          NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "..         ...         ...     ...      ...      ...     ...  \n",
       "754    0.13000  331.900000  0.3976  57.0935  10.4236  0.0391  \n",
       "755   17.39000  215.750000     NaN      NaN      NaN     NaN  \n",
       "756  112.37000  251.660000  0.2567  35.4774   0.1471  0.0363  \n",
       "757  248.93000  298.240000  0.2361  64.4598  11.8111  0.0413  \n",
       "758        NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "\n",
       "[759 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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    "df"
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  {
   "cell_type": "code",
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   "metadata": {
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we didn't set an index it just numbers each row and calls that the index.  But that doesn't help us.  I think we could set the well_id to the index.  When you look at your data above. see how the numbers on the left have no title but are a little offset.  That is the index.  But what is an index.  I am not sure.  It is sort of like a master column that helps us organize the data.  It will make more sense when we get to timeseries analysis.  That is where pandas shines even more.  But lets set and index and use well_id as that is the most important factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.set_index('Well_ID')"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
    {
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       "      <td>83.0137</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>13.330000</td>\n",
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       "      <td>0.3976</td>\n",
       "      <td>57.0935</td>\n",
       "      <td>10.4236</td>\n",
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       "      <td>24.36</td>\n",
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       "      <td>57630.63000</td>\n",
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       "      <td>73.230000</td>\n",
       "      <td>19.723360</td>\n",
       "      <td>33.740000</td>\n",
       "      <td>3958.51</td>\n",
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       "      <td>215.750000</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>35.4774</td>\n",
       "      <td>0.1471</td>\n",
       "      <td>0.0363</td>\n",
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       "      <th>76175</th>\n",
       "      <td>23.90</td>\n",
       "      <td>90.65</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>30605.53000</td>\n",
       "      <td>1.556120</td>\n",
       "      <td>4168.520000</td>\n",
       "      <td>66756.16000</td>\n",
       "      <td>12.793100</td>\n",
       "      <td>178.870000</td>\n",
       "      <td>33.180360</td>\n",
       "      <td>25.100000</td>\n",
       "      <td>4102.56</td>\n",
       "      <td>1.640000</td>\n",
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       "      <td>298.240000</td>\n",
       "      <td>0.2361</td>\n",
       "      <td>64.4598</td>\n",
       "      <td>11.8111</td>\n",
       "      <td>0.0413</td>\n",
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       "      <td>23.60</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>759 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Lat    Lon  Depth Drink           Si         P             S  \\\n",
       "Well_ID                                                                   \n",
       "2        23.74  90.31     45     Y          NaN       NaN           NaN   \n",
       "14       23.62  90.60     60     Y          NaN       NaN           NaN   \n",
       "23       23.94  91.46     60     Y          NaN       NaN           NaN   \n",
       "83       23.80  91.33     50     Y  48084.33842  0.936358   2085.570979   \n",
       "84       23.98  90.81    150     Y          NaN       NaN           NaN   \n",
       "...        ...    ...    ...   ...          ...       ...           ...   \n",
       "12516    24.71  90.41    160     Y  32379.64000  0.197380   3669.430000   \n",
       "12654    24.36  91.27     60     Y  25561.12000  0.090570  13771.370000   \n",
       "72641    24.38  90.90     45     N  31319.48000  1.162550     38.300000   \n",
       "76175    23.90  90.65     60     N  30605.53000  1.556120   4168.520000   \n",
       "141499   23.60  91.34     50     N          NaN       NaN           NaN   \n",
       "\n",
       "                  Ca         Fe          Ba         Na         Mg        K  \\\n",
       "Well_ID                                                                      \n",
       "2                NaN        NaN         NaN        NaN        NaN      NaN   \n",
       "14               NaN        NaN         NaN        NaN        NaN      NaN   \n",
       "23               NaN        NaN         NaN        NaN        NaN      NaN   \n",
       "83       54666.48199   1.260031   96.159587  76.207449  26.104825      NaN   \n",
       "84               NaN        NaN         NaN        NaN        NaN      NaN   \n",
       "...              ...        ...         ...        ...        ...      ...   \n",
       "12516    39790.24000   0.341200   77.770000  26.299280  13.330000  1850.53   \n",
       "12654    57630.63000   1.498350   73.230000  19.723360  33.740000  3958.51   \n",
       "72641    60905.16000  22.417560  176.020000  14.114910  21.700000  3372.45   \n",
       "76175    66756.16000  12.793100  178.870000  33.180360  25.100000  4102.56   \n",
       "141499           NaN        NaN         NaN        NaN        NaN      NaN   \n",
       "\n",
       "               Mn         As          Sr       F       Cl      SO4      Br  \n",
       "Well_ID                                                                     \n",
       "2             NaN        NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "14            NaN        NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "23            NaN        NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "83       1.019154   78.97747  253.528674  0.1669  83.0137   5.6188  0.1512  \n",
       "84            NaN        NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "...           ...        ...         ...     ...      ...      ...     ...  \n",
       "12516    0.340000    0.13000  331.900000  0.3976  57.0935  10.4236  0.0391  \n",
       "12654    1.200000   17.39000  215.750000     NaN      NaN      NaN     NaN  \n",
       "72641    1.560000  112.37000  251.660000  0.2567  35.4774   0.1471  0.0363  \n",
       "76175    1.640000  248.93000  298.240000  0.2361  64.4598  11.8111  0.0413  \n",
       "141499        NaN        NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "\n",
       "[759 rows x 20 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can undue the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <td>90.81</td>\n",
       "      <td>150</td>\n",
       "      <td>Y</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>...</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>754</th>\n",
       "      <td>12516</td>\n",
       "      <td>24.71</td>\n",
       "      <td>90.41</td>\n",
       "      <td>160</td>\n",
       "      <td>Y</td>\n",
       "      <td>32379.64000</td>\n",
       "      <td>0.197380</td>\n",
       "      <td>3669.430000</td>\n",
       "      <td>39790.24000</td>\n",
       "      <td>0.341200</td>\n",
       "      <td>...</td>\n",
       "      <td>26.299280</td>\n",
       "      <td>13.330000</td>\n",
       "      <td>1850.53</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.13000</td>\n",
       "      <td>331.900000</td>\n",
       "      <td>0.3976</td>\n",
       "      <td>57.0935</td>\n",
       "      <td>10.4236</td>\n",
       "      <td>0.0391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>12654</td>\n",
       "      <td>24.36</td>\n",
       "      <td>91.27</td>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <td>25561.12000</td>\n",
       "      <td>0.090570</td>\n",
       "      <td>13771.370000</td>\n",
       "      <td>57630.63000</td>\n",
       "      <td>1.498350</td>\n",
       "      <td>...</td>\n",
       "      <td>19.723360</td>\n",
       "      <td>33.740000</td>\n",
       "      <td>3958.51</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>17.39000</td>\n",
       "      <td>215.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>756</th>\n",
       "      <td>72641</td>\n",
       "      <td>24.38</td>\n",
       "      <td>90.90</td>\n",
       "      <td>45</td>\n",
       "      <td>N</td>\n",
       "      <td>31319.48000</td>\n",
       "      <td>1.162550</td>\n",
       "      <td>38.300000</td>\n",
       "      <td>60905.16000</td>\n",
       "      <td>22.417560</td>\n",
       "      <td>...</td>\n",
       "      <td>14.114910</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>3372.45</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>112.37000</td>\n",
       "      <td>251.660000</td>\n",
       "      <td>0.2567</td>\n",
       "      <td>35.4774</td>\n",
       "      <td>0.1471</td>\n",
       "      <td>0.0363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>76175</td>\n",
       "      <td>23.90</td>\n",
       "      <td>90.65</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>30605.53000</td>\n",
       "      <td>1.556120</td>\n",
       "      <td>4168.520000</td>\n",
       "      <td>66756.16000</td>\n",
       "      <td>12.793100</td>\n",
       "      <td>...</td>\n",
       "      <td>33.180360</td>\n",
       "      <td>25.100000</td>\n",
       "      <td>4102.56</td>\n",
       "      <td>1.640000</td>\n",
       "      <td>248.93000</td>\n",
       "      <td>298.240000</td>\n",
       "      <td>0.2361</td>\n",
       "      <td>64.4598</td>\n",
       "      <td>11.8111</td>\n",
       "      <td>0.0413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>758</th>\n",
       "      <td>141499</td>\n",
       "      <td>23.60</td>\n",
       "      <td>91.34</td>\n",
       "      <td>50</td>\n",
       "      <td>N</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>759 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Well_ID    Lat    Lon  Depth Drink           Si         P             S  \\\n",
       "0          2  23.74  90.31     45     Y          NaN       NaN           NaN   \n",
       "1         14  23.62  90.60     60     Y          NaN       NaN           NaN   \n",
       "2         23  23.94  91.46     60     Y          NaN       NaN           NaN   \n",
       "3         83  23.80  91.33     50     Y  48084.33842  0.936358   2085.570979   \n",
       "4         84  23.98  90.81    150     Y          NaN       NaN           NaN   \n",
       "..       ...    ...    ...    ...   ...          ...       ...           ...   \n",
       "754    12516  24.71  90.41    160     Y  32379.64000  0.197380   3669.430000   \n",
       "755    12654  24.36  91.27     60     Y  25561.12000  0.090570  13771.370000   \n",
       "756    72641  24.38  90.90     45     N  31319.48000  1.162550     38.300000   \n",
       "757    76175  23.90  90.65     60     N  30605.53000  1.556120   4168.520000   \n",
       "758   141499  23.60  91.34     50     N          NaN       NaN           NaN   \n",
       "\n",
       "              Ca         Fe  ...         Na         Mg        K        Mn  \\\n",
       "0            NaN        NaN  ...        NaN        NaN      NaN       NaN   \n",
       "1            NaN        NaN  ...        NaN        NaN      NaN       NaN   \n",
       "2            NaN        NaN  ...        NaN        NaN      NaN       NaN   \n",
       "3    54666.48199   1.260031  ...  76.207449  26.104825      NaN  1.019154   \n",
       "4            NaN        NaN  ...        NaN        NaN      NaN       NaN   \n",
       "..           ...        ...  ...        ...        ...      ...       ...   \n",
       "754  39790.24000   0.341200  ...  26.299280  13.330000  1850.53  0.340000   \n",
       "755  57630.63000   1.498350  ...  19.723360  33.740000  3958.51  1.200000   \n",
       "756  60905.16000  22.417560  ...  14.114910  21.700000  3372.45  1.560000   \n",
       "757  66756.16000  12.793100  ...  33.180360  25.100000  4102.56  1.640000   \n",
       "758          NaN        NaN  ...        NaN        NaN      NaN       NaN   \n",
       "\n",
       "            As          Sr       F       Cl      SO4      Br  \n",
       "0          NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "1          NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "2          NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "3     78.97747  253.528674  0.1669  83.0137   5.6188  0.1512  \n",
       "4          NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "..         ...         ...     ...      ...      ...     ...  \n",
       "754    0.13000  331.900000  0.3976  57.0935  10.4236  0.0391  \n",
       "755   17.39000  215.750000     NaN      NaN      NaN     NaN  \n",
       "756  112.37000  251.660000  0.2567  35.4774   0.1471  0.0363  \n",
       "757  248.93000  298.240000  0.2361  64.4598  11.8111  0.0413  \n",
       "758        NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "\n",
       "[759 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df #since we have an index it prints the index name on its own row."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we could just read in the data with the index set.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('well_data.csv',index_col='Well_ID')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you don't know the column name you can use the column number!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('well_data.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The first great trick of pandas!\n",
    "The describe function.  It gives you amazing summary statistics lickety-split!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Si</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>K</th>\n",
       "      <th>Mn</th>\n",
       "      <th>As</th>\n",
       "      <th>Sr</th>\n",
       "      <th>F</th>\n",
       "      <th>Cl</th>\n",
       "      <th>SO4</th>\n",
       "      <th>Br</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>759.000000</td>\n",
       "      <td>759.000000</td>\n",
       "      <td>759.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>343.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>407.000000</td>\n",
       "      <td>413.000000</td>\n",
       "      <td>411.000000</td>\n",
       "      <td>397.000000</td>\n",
       "      <td>405.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.789249</td>\n",
       "      <td>90.641199</td>\n",
       "      <td>65.554677</td>\n",
       "      <td>40101.151444</td>\n",
       "      <td>0.809323</td>\n",
       "      <td>3407.292389</td>\n",
       "      <td>41129.291921</td>\n",
       "      <td>5.556200</td>\n",
       "      <td>89.078507</td>\n",
       "      <td>29.441511</td>\n",
       "      <td>20.487685</td>\n",
       "      <td>5068.337278</td>\n",
       "      <td>1.309343</td>\n",
       "      <td>89.688641</td>\n",
       "      <td>186.770328</td>\n",
       "      <td>0.216924</td>\n",
       "      <td>42.371061</td>\n",
       "      <td>9.300930</td>\n",
       "      <td>0.056895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.578493</td>\n",
       "      <td>0.578800</td>\n",
       "      <td>42.186161</td>\n",
       "      <td>10117.680290</td>\n",
       "      <td>0.902860</td>\n",
       "      <td>5364.247733</td>\n",
       "      <td>20161.130827</td>\n",
       "      <td>5.153779</td>\n",
       "      <td>54.172650</td>\n",
       "      <td>17.888604</td>\n",
       "      <td>11.359487</td>\n",
       "      <td>5566.741424</td>\n",
       "      <td>0.978969</td>\n",
       "      <td>101.530582</td>\n",
       "      <td>90.501136</td>\n",
       "      <td>0.194702</td>\n",
       "      <td>34.464444</td>\n",
       "      <td>14.287154</td>\n",
       "      <td>0.087262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>22.780000</td>\n",
       "      <td>89.610000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12605.576700</td>\n",
       "      <td>0.008210</td>\n",
       "      <td>-41.390000</td>\n",
       "      <td>3577.160000</td>\n",
       "      <td>-0.003680</td>\n",
       "      <td>5.630000</td>\n",
       "      <td>4.428421</td>\n",
       "      <td>2.490000</td>\n",
       "      <td>18.855854</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.470000</td>\n",
       "      <td>-0.010200</td>\n",
       "      <td>0.226100</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.005800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.285000</td>\n",
       "      <td>90.155000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>33200.310900</td>\n",
       "      <td>0.151957</td>\n",
       "      <td>149.635000</td>\n",
       "      <td>26996.273955</td>\n",
       "      <td>1.706806</td>\n",
       "      <td>53.423976</td>\n",
       "      <td>15.171753</td>\n",
       "      <td>14.020000</td>\n",
       "      <td>2804.793027</td>\n",
       "      <td>0.545000</td>\n",
       "      <td>14.026849</td>\n",
       "      <td>119.685000</td>\n",
       "      <td>0.113000</td>\n",
       "      <td>18.931200</td>\n",
       "      <td>0.407100</td>\n",
       "      <td>0.019300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.790000</td>\n",
       "      <td>90.650000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>40021.490000</td>\n",
       "      <td>0.507850</td>\n",
       "      <td>1220.877945</td>\n",
       "      <td>40166.830000</td>\n",
       "      <td>3.931310</td>\n",
       "      <td>79.674488</td>\n",
       "      <td>23.775190</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>3563.100000</td>\n",
       "      <td>1.183136</td>\n",
       "      <td>54.400000</td>\n",
       "      <td>174.617542</td>\n",
       "      <td>0.166700</td>\n",
       "      <td>31.298100</td>\n",
       "      <td>3.344400</td>\n",
       "      <td>0.032700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.300000</td>\n",
       "      <td>91.130000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>45369.825000</td>\n",
       "      <td>1.189271</td>\n",
       "      <td>4341.695000</td>\n",
       "      <td>52976.458285</td>\n",
       "      <td>8.531585</td>\n",
       "      <td>113.711543</td>\n",
       "      <td>41.495813</td>\n",
       "      <td>24.780000</td>\n",
       "      <td>4979.045000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>129.433314</td>\n",
       "      <td>233.535000</td>\n",
       "      <td>0.239700</td>\n",
       "      <td>52.962050</td>\n",
       "      <td>11.973300</td>\n",
       "      <td>0.053800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>24.770000</td>\n",
       "      <td>91.650000</td>\n",
       "      <td>523.000000</td>\n",
       "      <td>70304.057950</td>\n",
       "      <td>5.477616</td>\n",
       "      <td>45035.460000</td>\n",
       "      <td>116040.620000</td>\n",
       "      <td>30.192230</td>\n",
       "      <td>293.440000</td>\n",
       "      <td>105.318060</td>\n",
       "      <td>104.545670</td>\n",
       "      <td>44273.150000</td>\n",
       "      <td>6.271782</td>\n",
       "      <td>700.890000</td>\n",
       "      <td>681.287906</td>\n",
       "      <td>1.595800</td>\n",
       "      <td>217.525000</td>\n",
       "      <td>125.317000</td>\n",
       "      <td>0.994800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Lat         Lon       Depth            Si           P  \\\n",
       "count  759.000000  759.000000  759.000000    407.000000  407.000000   \n",
       "mean    23.789249   90.641199   65.554677  40101.151444    0.809323   \n",
       "std      0.578493    0.578800   42.186161  10117.680290    0.902860   \n",
       "min     22.780000   89.610000    0.000000  12605.576700    0.008210   \n",
       "25%     23.285000   90.155000   45.000000  33200.310900    0.151957   \n",
       "50%     23.790000   90.650000   50.000000  40021.490000    0.507850   \n",
       "75%     24.300000   91.130000   70.000000  45369.825000    1.189271   \n",
       "max     24.770000   91.650000  523.000000  70304.057950    5.477616   \n",
       "\n",
       "                  S             Ca          Fe          Ba          Na  \\\n",
       "count    407.000000     407.000000  407.000000  407.000000  407.000000   \n",
       "mean    3407.292389   41129.291921    5.556200   89.078507   29.441511   \n",
       "std     5364.247733   20161.130827    5.153779   54.172650   17.888604   \n",
       "min      -41.390000    3577.160000   -0.003680    5.630000    4.428421   \n",
       "25%      149.635000   26996.273955    1.706806   53.423976   15.171753   \n",
       "50%     1220.877945   40166.830000    3.931310   79.674488   23.775190   \n",
       "75%     4341.695000   52976.458285    8.531585  113.711543   41.495813   \n",
       "max    45035.460000  116040.620000   30.192230  293.440000  105.318060   \n",
       "\n",
       "               Mg             K          Mn          As          Sr  \\\n",
       "count  407.000000    343.000000  407.000000  407.000000  407.000000   \n",
       "mean    20.487685   5068.337278    1.309343   89.688641  186.770328   \n",
       "std     11.359487   5566.741424    0.978969  101.530582   90.501136   \n",
       "min      2.490000     18.855854    0.000000    0.000000   34.470000   \n",
       "25%     14.020000   2804.793027    0.545000   14.026849  119.685000   \n",
       "50%     18.250000   3563.100000    1.183136   54.400000  174.617542   \n",
       "75%     24.780000   4979.045000    1.850000  129.433314  233.535000   \n",
       "max    104.545670  44273.150000    6.271782  700.890000  681.287906   \n",
       "\n",
       "                F          Cl         SO4          Br  \n",
       "count  413.000000  411.000000  397.000000  405.000000  \n",
       "mean     0.216924   42.371061    9.300930    0.056895  \n",
       "std      0.194702   34.464444   14.287154    0.087262  \n",
       "min     -0.010200    0.226100    0.120000    0.005800  \n",
       "25%      0.113000   18.931200    0.407100    0.019300  \n",
       "50%      0.166700   31.298100    3.344400    0.032700  \n",
       "75%      0.239700   52.962050   11.973300    0.053800  \n",
       "max      1.595800  217.525000  125.317000    0.994800  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A hint of what is to come!  But we just got all of our summary statistics.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That boxplot was hard to see.  What if we just look at As and Fe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "df.boxplot(column=['As','Fe'],ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this plotting is a little diffrent then how we have been plotting.  Pandas has some built in plotting so you can make some really nice and quick plots. But these plots are a little harder to customize. So we will be doing both types of plotting depending on the goal.  The goal could be a quick view versus a profesional looking plot.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also just get a list of our columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Lat', 'Lon', 'Depth', 'Drink', 'Si', 'P', 'S', 'Ca', 'Fe', 'Ba', 'Na',\n",
       "       'Mg', 'K', 'Mn', 'As', 'Sr', 'F', 'Cl', 'SO4', 'Br'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why did the columns not have parantheses?   I am learning this.  But each dataframe has attributes and methods.  Methods uses paranthesis.  Think of it as having to do something.  An attribute just tells you about the dataframe and doesn't need parantheses.  Methods can take extra arguments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember NaN is not a number.  We are going to use this to our advantage! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "shape still gives us the shape.  We can call it two different ways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(759, 20)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(759, 20)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stop and think for a second.  What does this shape mean?  \n",
    "It means we are starting to analyze a lot of data.  It is a dataset with 759 rows or wells and 20 columns or different parameters.  This will already get hard to deal with in excel!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We have to slow down and learn some Pandas basics.  this is a critical section.   Take your time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now how do we get at our data.  How do we slice it.  There are many ways. lets go through them all.\n",
    "\n",
    ".ix\n",
    "\n",
    ".loc\n",
    "\n",
    ".iloc\n",
    "\n",
    "[]\n",
    "\n",
    "We are going to do a lot of practice and then I tried to make a cheat sheet/table.  Take lots of notes.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[] works like normal except you can only use integers on rows and names on columns.  you can't use integers on both rows and columns.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am putting .head() on the print statements to save paper. You don't need them.  It just shows the first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Drink</th>\n",
       "      <th>Si</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>K</th>\n",
       "      <th>Mn</th>\n",
       "      <th>As</th>\n",
       "      <th>Sr</th>\n",
       "      <th>F</th>\n",
       "      <th>Cl</th>\n",
       "      <th>SO4</th>\n",
       "      <th>Br</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Well_ID</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>2</th>\n",
       "      <td>23.74</td>\n",
       "      <td>90.31</td>\n",
       "      <td>45</td>\n",
       "      <td>Y</td>\n",
       "      <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>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>14</th>\n",
       "      <td>23.62</td>\n",
       "      <td>90.60</td>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <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>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>23</th>\n",
       "      <td>23.94</td>\n",
       "      <td>91.46</td>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <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>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>83</th>\n",
       "      <td>23.80</td>\n",
       "      <td>91.33</td>\n",
       "      <td>50</td>\n",
       "      <td>Y</td>\n",
       "      <td>48084.33842</td>\n",
       "      <td>0.936358</td>\n",
       "      <td>2085.570979</td>\n",
       "      <td>54666.48199</td>\n",
       "      <td>1.260031</td>\n",
       "      <td>96.159587</td>\n",
       "      <td>76.207449</td>\n",
       "      <td>26.104825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.019154</td>\n",
       "      <td>78.97747</td>\n",
       "      <td>253.528674</td>\n",
       "      <td>0.1669</td>\n",
       "      <td>83.0137</td>\n",
       "      <td>5.6188</td>\n",
       "      <td>0.1512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>23.98</td>\n",
       "      <td>90.81</td>\n",
       "      <td>150</td>\n",
       "      <td>Y</td>\n",
       "      <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>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",
       "</div>"
      ],
      "text/plain": [
       "           Lat    Lon  Depth Drink           Si         P            S  \\\n",
       "Well_ID                                                                  \n",
       "2        23.74  90.31     45     Y          NaN       NaN          NaN   \n",
       "14       23.62  90.60     60     Y          NaN       NaN          NaN   \n",
       "23       23.94  91.46     60     Y          NaN       NaN          NaN   \n",
       "83       23.80  91.33     50     Y  48084.33842  0.936358  2085.570979   \n",
       "84       23.98  90.81    150     Y          NaN       NaN          NaN   \n",
       "\n",
       "                  Ca        Fe         Ba         Na         Mg   K        Mn  \\\n",
       "Well_ID                                                                         \n",
       "2                NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "14               NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "23               NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "83       54666.48199  1.260031  96.159587  76.207449  26.104825 NaN  1.019154   \n",
       "84               NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "\n",
       "               As          Sr       F       Cl     SO4      Br  \n",
       "Well_ID                                                         \n",
       "2             NaN         NaN     NaN      NaN     NaN     NaN  \n",
       "14            NaN         NaN     NaN      NaN     NaN     NaN  \n",
       "23            NaN         NaN     NaN      NaN     NaN     NaN  \n",
       "83       78.97747  253.528674  0.1669  83.0137  5.6188  0.1512  \n",
       "84            NaN         NaN     NaN      NaN     NaN     NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:].head()  #I am including head to shorten my printouts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Well_ID\n",
      "2          NaN\n",
      "14         NaN\n",
      "23         NaN\n",
      "83    78.97747\n",
      "84         NaN\n",
      "Name: As, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df['As'].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Well_ID\n",
      "2          NaN\n",
      "14         NaN\n",
      "23         NaN\n",
      "83    78.97747\n",
      "84         NaN\n",
      "Name: As, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print (df[:]['As'].head()) #This is the same as the one above showing the rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Well_ID\n",
      "330     10.233204\n",
      "333           NaN\n",
      "342           NaN\n",
      "356           NaN\n",
      "374     18.365596\n",
      "389     59.285003\n",
      "397    115.834040\n",
      "398           NaN\n",
      "402     17.755544\n",
      "403     81.859568\n",
      "410           NaN\n",
      "414           NaN\n",
      "415     87.102492\n",
      "417           NaN\n",
      "418    386.827954\n",
      "420     79.798479\n",
      "421    142.409968\n",
      "434           NaN\n",
      "475    270.785974\n",
      "478     56.883257\n",
      "Name: As, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print (df[30:50]['As'])  #This prints rows 30-50.  \n",
    "\n",
    "#Don't get confused as well_ID is our index and the name of the row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Well_ID\n",
      "330     10.233204\n",
      "342           NaN\n",
      "374     18.365596\n",
      "397    115.834040\n",
      "402     17.755544\n",
      "410           NaN\n",
      "415     87.102492\n",
      "418    386.827954\n",
      "421    142.409968\n",
      "475    270.785974\n",
      "Name: As, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print (df[30:50:2]['As']) #we skipped by twos!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But you can pass a list to the columns you want! SEE the double brackets???  It is a list in the brackets!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>As</th>\n",
       "      <th>Depth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Well_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>10.233204</td>\n",
       "      <td>45</td>\n",
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       "      <th>342</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>18.365596</td>\n",
       "      <td>45</td>\n",
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       "      <th>397</th>\n",
       "      <td>115.834040</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>17.755544</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
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       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>87.102492</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>386.827954</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>142.409968</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>270.785974</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 As  Depth\n",
       "Well_ID                   \n",
       "330       10.233204     45\n",
       "342             NaN     30\n",
       "374       18.365596     45\n",
       "397      115.834040     45\n",
       "402       17.755544     30\n",
       "410             NaN     60\n",
       "415       87.102492     60\n",
       "418      386.827954     65\n",
       "421      142.409968    150\n",
       "475      270.785974     55"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[30:50:2][['As','Depth']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the order doesn't matter.  So somehow it is smart about rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>As</th>\n",
       "      <th>Depth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Well_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 As  Depth\n",
       "Well_ID                   \n",
       "330       10.233204     45\n",
       "342             NaN     30\n",
       "374       18.365596     45\n",
       "397      115.834040     45\n",
       "402       17.755544     30\n",
       "410             NaN     60\n",
       "415       87.102492     60\n",
       "418      386.827954     65\n",
       "421      142.409968    150\n",
       "475      270.785974     55"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['As','Depth']][30:50:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>Depth</th>\n",
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       "      <th>Well_ID</th>\n",
       "      <th></th>\n",
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       "      <td>65</td>\n",
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       "      <th>421</th>\n",
       "      <td>150</td>\n",
       "      <td>142.409968</td>\n",
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       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>55</td>\n",
       "      <td>270.785974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Depth          As\n",
       "Well_ID                   \n",
       "330         45   10.233204\n",
       "342         30         NaN\n",
       "374         45   18.365596\n",
       "397         45  115.834040\n",
       "402         30   17.755544\n",
       "410         60         NaN\n",
       "415         60   87.102492\n",
       "418         65  386.827954\n",
       "421        150  142.409968\n",
       "475         55  270.785974"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Depth','As']][30:50:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What I am teaching you is easy and hard at the same time.  Take your time.  It is a lot.  I am showing you how to get at data.  I just showed you brackets and now I am going to show you .loc.  Also remember I just add .head to shorten the printouts.  you can remove it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ".loc only uses names of the index and the columns. \n",
    "\n",
    "THIS IS DIFFERENT.  It is saying if my index matches this name then print it. This is a little confusing our the wells have numbers for names\n",
    "\n",
    "Sometimes I put print sometimes not. It doesn't always matter and sometimes one looks nicer than the other.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Drink</th>\n",
       "      <th>Si</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>K</th>\n",
       "      <th>Mn</th>\n",
       "      <th>As</th>\n",
       "      <th>Sr</th>\n",
       "      <th>F</th>\n",
       "      <th>Cl</th>\n",
       "      <th>SO4</th>\n",
       "      <th>Br</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Well_ID</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>2</th>\n",
       "      <td>23.74</td>\n",
       "      <td>90.31</td>\n",
       "      <td>45</td>\n",
       "      <td>Y</td>\n",
       "      <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>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>14</th>\n",
       "      <td>23.62</td>\n",
       "      <td>90.60</td>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <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>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>23</th>\n",
       "      <td>23.94</td>\n",
       "      <td>91.46</td>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <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>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>83</th>\n",
       "      <td>23.80</td>\n",
       "      <td>91.33</td>\n",
       "      <td>50</td>\n",
       "      <td>Y</td>\n",
       "      <td>48084.33842</td>\n",
       "      <td>0.936358</td>\n",
       "      <td>2085.570979</td>\n",
       "      <td>54666.48199</td>\n",
       "      <td>1.260031</td>\n",
       "      <td>96.159587</td>\n",
       "      <td>76.207449</td>\n",
       "      <td>26.104825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.019154</td>\n",
       "      <td>78.97747</td>\n",
       "      <td>253.528674</td>\n",
       "      <td>0.1669</td>\n",
       "      <td>83.0137</td>\n",
       "      <td>5.6188</td>\n",
       "      <td>0.1512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>23.98</td>\n",
       "      <td>90.81</td>\n",
       "      <td>150</td>\n",
       "      <td>Y</td>\n",
       "      <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>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",
       "</div>"
      ],
      "text/plain": [
       "           Lat    Lon  Depth Drink           Si         P            S  \\\n",
       "Well_ID                                                                  \n",
       "2        23.74  90.31     45     Y          NaN       NaN          NaN   \n",
       "14       23.62  90.60     60     Y          NaN       NaN          NaN   \n",
       "23       23.94  91.46     60     Y          NaN       NaN          NaN   \n",
       "83       23.80  91.33     50     Y  48084.33842  0.936358  2085.570979   \n",
       "84       23.98  90.81    150     Y          NaN       NaN          NaN   \n",
       "\n",
       "                  Ca        Fe         Ba         Na         Mg   K        Mn  \\\n",
       "Well_ID                                                                         \n",
       "2                NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "14               NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "23               NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "83       54666.48199  1.260031  96.159587  76.207449  26.104825 NaN  1.019154   \n",
       "84               NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "\n",
       "               As          Sr       F       Cl     SO4      Br  \n",
       "Well_ID                                                         \n",
       "2             NaN         NaN     NaN      NaN     NaN     NaN  \n",
       "14            NaN         NaN     NaN      NaN     NaN     NaN  \n",
       "23            NaN         NaN     NaN      NaN     NaN     NaN  \n",
       "83       78.97747  253.528674  0.1669  83.0137  5.6188  0.1512  \n",
       "84            NaN         NaN     NaN      NaN     NaN     NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:].head()  #gives us all rows with all indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Drink</th>\n",
       "      <th>Si</th>\n",
       "      <th>P</th>\n",
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       "      <th>Fe</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>K</th>\n",
       "      <th>Mn</th>\n",
       "      <th>As</th>\n",
       "      <th>Sr</th>\n",
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       "      <th>SO4</th>\n",
       "      <th>Br</th>\n",
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       "    <tr>\n",
       "      <th>Well_ID</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>101</th>\n",
       "      <td>24.40</td>\n",
       "      <td>90.26</td>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <td>34311.71514</td>\n",
       "      <td>0.117534</td>\n",
       "      <td>2618.717799</td>\n",
       "      <td>42646.99574</td>\n",
       "      <td>1.843156</td>\n",
       "      <td>58.666191</td>\n",
       "      <td>28.281030</td>\n",
       "      <td>22.578377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.192692</td>\n",
       "      <td>28.070949</td>\n",
       "      <td>123.043018</td>\n",
       "      <td>0.1994</td>\n",
       "      <td>38.1123</td>\n",
       "      <td>7.5180</td>\n",
       "      <td>0.0552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>24.02</td>\n",
       "      <td>89.67</td>\n",
       "      <td>45</td>\n",
       "      <td>N</td>\n",
       "      <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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0739</td>\n",
       "      <td>48.8797</td>\n",
       "      <td>27.1446</td>\n",
       "      <td>0.0304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>23.39</td>\n",
       "      <td>91.35</td>\n",
       "      <td>45</td>\n",
       "      <td>Y</td>\n",
       "      <td>47417.95635</td>\n",
       "      <td>1.095644</td>\n",
       "      <td>113.180915</td>\n",
       "      <td>46848.09017</td>\n",
       "      <td>11.740445</td>\n",
       "      <td>131.582974</td>\n",
       "      <td>23.585986</td>\n",
       "      <td>20.501162</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.091605</td>\n",
       "      <td>96.885674</td>\n",
       "      <td>161.537333</td>\n",
       "      <td>0.1106</td>\n",
       "      <td>38.2706</td>\n",
       "      <td>0.3645</td>\n",
       "      <td>0.0403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>24.61</td>\n",
       "      <td>91.18</td>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <td>37289.99489</td>\n",
       "      <td>2.448648</td>\n",
       "      <td>13.335397</td>\n",
       "      <td>65129.07627</td>\n",
       "      <td>8.923465</td>\n",
       "      <td>134.435231</td>\n",
       "      <td>15.872622</td>\n",
       "      <td>24.259731</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.528849</td>\n",
       "      <td>80.627214</td>\n",
       "      <td>271.210408</td>\n",
       "      <td>0.2701</td>\n",
       "      <td>29.8010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>22.96</td>\n",
       "      <td>89.77</td>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <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>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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       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>22.94</td>\n",
       "      <td>89.97</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>44023.88418</td>\n",
       "      <td>1.172086</td>\n",
       "      <td>1023.167741</td>\n",
       "      <td>80183.25742</td>\n",
       "      <td>6.349396</td>\n",
       "      <td>159.171636</td>\n",
       "      <td>32.822761</td>\n",
       "      <td>27.926934</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.671612</td>\n",
       "      <td>77.006865</td>\n",
       "      <td>359.682075</td>\n",
       "      <td>0.1746</td>\n",
       "      <td>78.9229</td>\n",
       "      <td>2.6963</td>\n",
       "      <td>0.0487</td>\n",
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       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>24.17</td>\n",
       "      <td>90.81</td>\n",
       "      <td>45</td>\n",
       "      <td>Y</td>\n",
       "      <td>40523.43773</td>\n",
       "      <td>0.091676</td>\n",
       "      <td>2848.048146</td>\n",
       "      <td>40703.88184</td>\n",
       "      <td>1.869486</td>\n",
       "      <td>69.657716</td>\n",
       "      <td>29.684875</td>\n",
       "      <td>21.333230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.291619</td>\n",
       "      <td>39.249817</td>\n",
       "      <td>173.403095</td>\n",
       "      <td>0.0716</td>\n",
       "      <td>35.8091</td>\n",
       "      <td>8.4836</td>\n",
       "      <td>0.0207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>22.84</td>\n",
       "      <td>91.56</td>\n",
       "      <td>60</td>\n",
       "      <td>N</td>\n",
       "      <td>48375.82211</td>\n",
       "      <td>0.979053</td>\n",
       "      <td>1420.255478</td>\n",
       "      <td>52694.25919</td>\n",
       "      <td>13.020352</td>\n",
       "      <td>129.253387</td>\n",
       "      <td>23.804323</td>\n",
       "      <td>20.638614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.111451</td>\n",
       "      <td>131.248945</td>\n",
       "      <td>232.340847</td>\n",
       "      <td>0.1152</td>\n",
       "      <td>40.7122</td>\n",
       "      <td>3.8367</td>\n",
       "      <td>0.0397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Lat    Lon  Depth Drink           Si         P            S  \\\n",
       "Well_ID                                                                  \n",
       "101      24.40  90.26     60     Y  34311.71514  0.117534  2618.717799   \n",
       "107      24.02  89.67     45     N          NaN       NaN          NaN   \n",
       "110      23.39  91.35     45     Y  47417.95635  1.095644   113.180915   \n",
       "112      24.61  91.18     60     Y  37289.99489  2.448648    13.335397   \n",
       "116      22.96  89.77     60     Y          NaN       NaN          NaN   \n",
       "130      22.94  89.97     60     N  44023.88418  1.172086  1023.167741   \n",
       "153      24.17  90.81     45     Y  40523.43773  0.091676  2848.048146   \n",
       "156      22.84  91.56     60     N  48375.82211  0.979053  1420.255478   \n",
       "\n",
       "                  Ca         Fe          Ba         Na         Mg   K  \\\n",
       "Well_ID                                                                 \n",
       "101      42646.99574   1.843156   58.666191  28.281030  22.578377 NaN   \n",
       "107              NaN        NaN         NaN        NaN        NaN NaN   \n",
       "110      46848.09017  11.740445  131.582974  23.585986  20.501162 NaN   \n",
       "112      65129.07627   8.923465  134.435231  15.872622  24.259731 NaN   \n",
       "116              NaN        NaN         NaN        NaN        NaN NaN   \n",
       "130      80183.25742   6.349396  159.171636  32.822761  27.926934 NaN   \n",
       "153      40703.88184   1.869486   69.657716  29.684875  21.333230 NaN   \n",
       "156      52694.25919  13.020352  129.253387  23.804323  20.638614 NaN   \n",
       "\n",
       "               Mn          As          Sr       F       Cl      SO4      Br  \n",
       "Well_ID                                                                      \n",
       "101      1.192692   28.070949  123.043018  0.1994  38.1123   7.5180  0.0552  \n",
       "107           NaN         NaN         NaN  0.0739  48.8797  27.1446  0.0304  \n",
       "110      2.091605   96.885674  161.537333  0.1106  38.2706   0.3645  0.0403  \n",
       "112      0.528849   80.627214  271.210408  0.2701  29.8010      NaN     NaN  \n",
       "116           NaN         NaN         NaN     NaN      NaN      NaN     NaN  \n",
       "130      1.671612   77.006865  359.682075  0.1746  78.9229   2.6963  0.0487  \n",
       "153      2.291619   39.249817  173.403095  0.0716  35.8091   8.4836  0.0207  \n",
       "156      3.111451  131.248945  232.340847  0.1152  40.7122   3.8367  0.0397  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[101:156]  \n",
    "#gives us all rows with all indexes but the numbers have to match an index. The numbers had to match Well_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lat          24.4\n",
       "Lon         90.26\n",
       "Depth          60\n",
       "Drink           Y\n",
       "Si        34311.7\n",
       "P        0.117534\n",
       "S         2618.72\n",
       "Ca          42647\n",
       "Fe        1.84316\n",
       "Ba        58.6662\n",
       "Na         28.281\n",
       "Mg        22.5784\n",
       "K             NaN\n",
       "Mn        1.19269\n",
       "As        28.0709\n",
       "Sr        123.043\n",
       "F          0.1994\n",
       "Cl        38.1123\n",
       "SO4         7.518\n",
       "Br         0.0552\n",
       "Name: 101, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[101]   # just call one index.  This is well 101"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[102]   # if the index doesn't exist you get an error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we can use column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Well_ID\n",
       "2          NaN\n",
       "14         NaN\n",
       "23         NaN\n",
       "83    78.97747\n",
       "84         NaN\n",
       "Name: As, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:]['As'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plus it can seperata with commas as well as multiple brackets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Well_ID\n",
       "2          NaN\n",
       "14         NaN\n",
       "23         NaN\n",
       "83    78.97747\n",
       "84         NaN\n",
       "Name: As, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,'As'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can look at the type. It shows if you just get one column then it turns from a dataframe to a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print (type(df.loc[:]['As']))\n",
    "print (type(df.loc[:,'As']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This suble difference can sometimes be important.  A series is more like a set of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Well_ID\n",
      "330     10.233204\n",
      "342           NaN\n",
      "374     18.365596\n",
      "397    115.834040\n",
      "402     17.755544\n",
      "410           NaN\n",
      "415     87.102492\n",
      "418    386.827954\n",
      "421    142.409968\n",
      "475    270.785974\n",
      "481           NaN\n",
      "488           NaN\n",
      "500           NaN\n",
      "Name: As, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print (df.loc[330:500:2]['As']  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also add a list of names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>As</th>\n",
       "      <th>Depth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Well_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>10.233204</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>18.365596</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>115.834040</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>17.755544</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>87.102492</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>386.827954</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>142.409968</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>270.785974</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>NaN</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 As  Depth\n",
       "Well_ID                   \n",
       "330       10.233204     45\n",
       "342             NaN     30\n",
       "374       18.365596     45\n",
       "397      115.834040     45\n",
       "402       17.755544     30\n",
       "410             NaN     60\n",
       "415       87.102492     60\n",
       "418      386.827954     65\n",
       "421      142.409968    150\n",
       "475      270.785974     55\n",
       "481             NaN     60\n",
       "488             NaN     45\n",
       "500             NaN     60"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[330:500:2][['As','Depth']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## iloc\n",
    "iloc only uses integers.  So now this is row numbers. NOT the index.  look the Well_ID compared to the iloc numberss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Drink</th>\n",
       "      <th>Si</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>K</th>\n",
       "      <th>Mn</th>\n",
       "      <th>As</th>\n",
       "      <th>Sr</th>\n",
       "      <th>F</th>\n",
       "      <th>Cl</th>\n",
       "      <th>SO4</th>\n",
       "      <th>Br</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Well_ID</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>2</th>\n",
       "      <td>23.74</td>\n",
       "      <td>90.31</td>\n",
       "      <td>45</td>\n",
       "      <td>Y</td>\n",
       "      <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>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>14</th>\n",
       "      <td>23.62</td>\n",
       "      <td>90.60</td>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <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>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>23</th>\n",
       "      <td>23.94</td>\n",
       "      <td>91.46</td>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <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>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>83</th>\n",
       "      <td>23.80</td>\n",
       "      <td>91.33</td>\n",
       "      <td>50</td>\n",
       "      <td>Y</td>\n",
       "      <td>48084.33842</td>\n",
       "      <td>0.936358</td>\n",
       "      <td>2085.570979</td>\n",
       "      <td>54666.48199</td>\n",
       "      <td>1.260031</td>\n",
       "      <td>96.159587</td>\n",
       "      <td>76.207449</td>\n",
       "      <td>26.104825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.019154</td>\n",
       "      <td>78.97747</td>\n",
       "      <td>253.528674</td>\n",
       "      <td>0.1669</td>\n",
       "      <td>83.0137</td>\n",
       "      <td>5.6188</td>\n",
       "      <td>0.1512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>23.98</td>\n",
       "      <td>90.81</td>\n",
       "      <td>150</td>\n",
       "      <td>Y</td>\n",
       "      <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>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",
       "</div>"
      ],
      "text/plain": [
       "           Lat    Lon  Depth Drink           Si         P            S  \\\n",
       "Well_ID                                                                  \n",
       "2        23.74  90.31     45     Y          NaN       NaN          NaN   \n",
       "14       23.62  90.60     60     Y          NaN       NaN          NaN   \n",
       "23       23.94  91.46     60     Y          NaN       NaN          NaN   \n",
       "83       23.80  91.33     50     Y  48084.33842  0.936358  2085.570979   \n",
       "84       23.98  90.81    150     Y          NaN       NaN          NaN   \n",
       "\n",
       "                  Ca        Fe         Ba         Na         Mg   K        Mn  \\\n",
       "Well_ID                                                                         \n",
       "2                NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "14               NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "23               NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "83       54666.48199  1.260031  96.159587  76.207449  26.104825 NaN  1.019154   \n",
       "84               NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "\n",
       "               As          Sr       F       Cl     SO4      Br  \n",
       "Well_ID                                                         \n",
       "2             NaN         NaN     NaN      NaN     NaN     NaN  \n",
       "14            NaN         NaN     NaN      NaN     NaN     NaN  \n",
       "23            NaN         NaN     NaN      NaN     NaN     NaN  \n",
       "83       78.97747  253.528674  0.1669  83.0137  5.6188  0.1512  \n",
       "84            NaN         NaN     NaN      NaN     NaN     NaN  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>Lon</th>\n",
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       "      <th>Mn</th>\n",
       "      <th>As</th>\n",
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       "      <th>SO4</th>\n",
       "      <th>Br</th>\n",
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       "    <tr>\n",
       "      <th>Well_ID</th>\n",
       "      <th></th>\n",
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3058</th>\n",
       "      <td>23.92</td>\n",
       "      <td>90.47</td>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1166</td>\n",
       "      <td>110.2480</td>\n",
       "      <td>34.2441</td>\n",
       "      <td>0.0313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3060</th>\n",
       "      <td>23.62</td>\n",
       "      <td>91.56</td>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <td>37199.96208</td>\n",
       "      <td>0.949837</td>\n",
       "      <td>13.694978</td>\n",
       "      <td>51862.73844</td>\n",
       "      <td>10.061950</td>\n",
       "      <td>119.569875</td>\n",
       "      <td>11.641918</td>\n",
       "      <td>19.904506</td>\n",
       "      <td>7806.94907</td>\n",
       "      <td>1.376403</td>\n",
       "      <td>154.676413</td>\n",
       "      <td>230.823815</td>\n",
       "      <td>0.2422</td>\n",
       "      <td>12.7629</td>\n",
       "      <td>0.1512</td>\n",
       "      <td>0.0201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3103</th>\n",
       "      <td>23.33</td>\n",
       "      <td>90.12</td>\n",
       "      <td>130</td>\n",
       "      <td>Y</td>\n",
       "      <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>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>3112</th>\n",
       "      <td>24.43</td>\n",
       "      <td>91.05</td>\n",
       "      <td>45</td>\n",
       "      <td>Y</td>\n",
       "      <td>41513.82677</td>\n",
       "      <td>1.697027</td>\n",
       "      <td>14.933618</td>\n",
       "      <td>47308.53575</td>\n",
       "      <td>14.961449</td>\n",
       "      <td>133.853170</td>\n",
       "      <td>22.606914</td>\n",
       "      <td>17.730658</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.913210</td>\n",
       "      <td>130.108906</td>\n",
       "      <td>182.378072</td>\n",
       "      <td>0.1951</td>\n",
       "      <td>22.9864</td>\n",
       "      <td>0.2077</td>\n",
       "      <td>0.0278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3179</th>\n",
       "      <td>23.18</td>\n",
       "      <td>90.78</td>\n",
       "      <td>50</td>\n",
       "      <td>Y</td>\n",
       "      <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>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",
       "</div>"
      ],
      "text/plain": [
       "           Lat    Lon  Depth Drink           Si         P          S  \\\n",
       "Well_ID                                                                \n",
       "3058     23.92  90.47     60     Y          NaN       NaN        NaN   \n",
       "3060     23.62  91.56     60     Y  37199.96208  0.949837  13.694978   \n",
       "3103     23.33  90.12    130     Y          NaN       NaN        NaN   \n",
       "3112     24.43  91.05     45     Y  41513.82677  1.697027  14.933618   \n",
       "3179     23.18  90.78     50     Y          NaN       NaN        NaN   \n",
       "\n",
       "                  Ca         Fe          Ba         Na         Mg           K  \\\n",
       "Well_ID                                                                         \n",
       "3058             NaN        NaN         NaN        NaN        NaN         NaN   \n",
       "3060     51862.73844  10.061950  119.569875  11.641918  19.904506  7806.94907   \n",
       "3103             NaN        NaN         NaN        NaN        NaN         NaN   \n",
       "3112     47308.53575  14.961449  133.853170  22.606914  17.730658         NaN   \n",
       "3179             NaN        NaN         NaN        NaN        NaN         NaN   \n",
       "\n",
       "               Mn          As          Sr       F        Cl      SO4      Br  \n",
       "Well_ID                                                                       \n",
       "3058          NaN         NaN         NaN  0.1166  110.2480  34.2441  0.0313  \n",
       "3060     1.376403  154.676413  230.823815  0.2422   12.7629   0.1512  0.0201  \n",
       "3103          NaN         NaN         NaN     NaN       NaN      NaN     NaN  \n",
       "3112     0.913210  130.108906  182.378072  0.1951   22.9864   0.2077  0.0278  \n",
       "3179          NaN         NaN         NaN     NaN       NaN      NaN     NaN  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[101:110:2] #this is row numbers now. so the index is not matching."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and column number.  But we use a column seperator......."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Well_ID\n",
       "3058         NaN\n",
       "3060    0.949837\n",
       "3103         NaN\n",
       "3112    1.697027\n",
       "3179         NaN\n",
       "Name: P, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[101:110:2,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Depth</th>\n",
       "      <th>Drink</th>\n",
       "      <th>Si</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Well_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3058</th>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3060</th>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <td>37199.96208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3103</th>\n",
       "      <td>130</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3112</th>\n",
       "      <td>45</td>\n",
       "      <td>Y</td>\n",
       "      <td>41513.82677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3179</th>\n",
       "      <td>50</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Depth Drink           Si\n",
       "Well_ID                          \n",
       "3058        60     Y          NaN\n",
       "3060        60     Y  37199.96208\n",
       "3103       130     Y          NaN\n",
       "3112        45     Y  41513.82677\n",
       "3179        50     Y          NaN"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[101:110:2,2:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just to boggles your bind a little....."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Depth</th>\n",
       "      <th>P</th>\n",
       "      <th>Fe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Well_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3058</th>\n",
       "      <td>60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3060</th>\n",
       "      <td>60</td>\n",
       "      <td>0.949837</td>\n",
       "      <td>10.061950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3103</th>\n",
       "      <td>130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3112</th>\n",
       "      <td>45</td>\n",
       "      <td>1.697027</td>\n",
       "      <td>14.961449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3179</th>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Depth         P         Fe\n",
       "Well_ID                            \n",
       "3058        60       NaN        NaN\n",
       "3060        60  0.949837  10.061950\n",
       "3103       130       NaN        NaN\n",
       "3112        45  1.697027  14.961449\n",
       "3179        50       NaN        NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[101:110:2,[2,5,8]]  #I just had it show columns 2,5,8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ix was phased out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'ix'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-43-6c367ed50da5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m101\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m110\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5137\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5138\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5139\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5141\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'ix'"
     ]
    }
   ],
   "source": [
    "print (df.ix[101:110:2,[2,5,8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dot notation\n",
    "I am not sure if that is the official name but here is how it works"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What I didn't show you is a dot notation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Well_ID\n",
       "2          NaN\n",
       "14         NaN\n",
       "23         NaN\n",
       "83    78.97747\n",
       "84         NaN\n",
       "Name: As, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.As.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dot notation only works if you name your columns well.  No minus signs or spaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Well_ID\n",
       "233          NaN\n",
       "237          NaN\n",
       "275          NaN\n",
       "279          NaN\n",
       "280     5.364619\n",
       "283          NaN\n",
       "287          NaN\n",
       "290          NaN\n",
       "292    53.097829\n",
       "295          NaN\n",
       "Name: As, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.As[20:30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All examples in one place so maybe we can make sense of them?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "|Name                                     |Description|\n",
    "|-----------------------------------------|-----------------------|\n",
    "|[]||\n",
    "|well_data[:]          |             all data |\n",
    "|well_data[:]['As']    |            all arsenic data |\n",
    "|well_data.loc[:,'As']                    |basically the same as above|\n",
    "|well_data['As']                    |all arsenic data.|\n",
    "|well_data[1:10]['As']              |arsenic data from rows 1-10 excluding 10|\n",
    "|well_data[1:10:2]['As']            |same but skipping by two|\n",
    "|well_data[1:10:2][['As','Depth']]  |for As and depth.  note the double brackets.|\n",
    "|well_data[['As','Depth']][1:10:2]  |order doesn't matter|\n",
    "|well_data[['Depth','As']][1:10:2]  |order doesn't matter.|  \n",
    "|you can't use column numbers...................................  | |\n",
    "\n",
    "<br/>\n",
    "<br/>\n",
    "\n",
    "|loc|Description |\n",
    "|---------------------------------:|--------------------------------:|\n",
    "|well_data.loc[:]  |gives us all rows with all columns|\n",
    "|well_data.loc[101:156]  |needs to be an index  Gives us by index number not row number.|\n",
    "|well_data.loc[101:156:2] | and we can skip|\n",
    "|well_data.loc[101:156:2]['As'] | and we can do column names|\n",
    "|well_data.loc[:]['As']|  |\n",
    "|well_data.loc[:,'As'] | is the same as above. I have bugs where one works but other doesn't|\n",
    "|well_data.loc[101:156:2][['As','Depth']]|  we can do multiple columns|\n",
    "|................................................................................| .|\n",
    "\n",
    "<br/>\n",
    "<br/>\n",
    "\n",
    "|iloc| |\n",
    "|-----------------------------------------|-----------------------|\n",
    "|well_data.iloc[:]                   |gives it all.|\n",
    "|well_data.iloc[101:110:2]           |does row numbers.|\n",
    "|well_data.iloc[101:110:2,5]         |row number by column number|\n",
    "|well_data.iloc[101:110:2,2:5]        |mulitple row multiple number|\n",
    "|well_data.iloc[101:110:2,[2,5,8]]    |select columns|\n",
    "|...........................................................................|.|\n",
    "\n",
    "<br/>\n",
    "<br/>\n",
    "\n",
    "|ix |phased out|\n",
    "|-----------------------------------------|-----------------------|\n",
    "\n",
    "\n",
    "<br/>\n",
    "<br/>\n",
    "\n",
    "|Dot notation. |This can be very nice.|\n",
    "|-----------------------------------------|-----------------------|\n",
    "|well_data.As       |gives all arsenic data|\n",
    "|well_data.As[1:5]  |gives rows 1-5|\n",
    "|...............................................................||\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can use boolean choices to get the data you want.  For example I gave the description if people drink or don't drink from their well.  Lets count that.  \n",
    "\n",
    "value_counts is a great first function.  It just counts for you.  Simple but very helpful.\n",
    "\n",
    "I am going to do the same thing many different ways!  value_counts is a function that counts each "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    614\n",
       "N    144\n",
       "Name: Drink, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Drink'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "is the same as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    614\n",
       "N    144\n",
       "Name: Drink, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Drink.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is the same as (I am trying to teach you pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    614\n",
       "N    144\n",
       "Name: Drink, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,3].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is the same as (I am trying to teach you pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    614\n",
       "N    144\n",
       "Name: Drink, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,'Drink'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you should be able to access your data.  I always forget the semantics.  Look online or back at your cheat sheets.  That is why I made the cheat sheet above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can sub-select data very easily.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can return a boolean based on results.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Well_ID\n",
       "2          True\n",
       "14         True\n",
       "23         True\n",
       "83         True\n",
       "84         True\n",
       "          ...  \n",
       "12516      True\n",
       "12654      True\n",
       "72641     False\n",
       "76175     False\n",
       "141499    False\n",
       "Name: Drink, Length: 759, dtype: bool"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Drink']=='Y'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also do it with the dot notation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Well_ID\n",
       "2          True\n",
       "14         True\n",
       "23         True\n",
       "83         True\n",
       "84         True\n",
       "          ...  \n",
       "12516      True\n",
       "12654      True\n",
       "72641     False\n",
       "76175     False\n",
       "141499    False\n",
       "Name: Drink, Length: 759, dtype: bool"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Drink=='Y'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we only want data from wells people drink from?  we can ask for that.  Remember I just added the .head() to save paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Depth</th>\n",
       "      <th>Drink</th>\n",
       "      <th>Si</th>\n",
       "      <th>P</th>\n",
       "      <th>S</th>\n",
       "      <th>Ca</th>\n",
       "      <th>Fe</th>\n",
       "      <th>Ba</th>\n",
       "      <th>Na</th>\n",
       "      <th>Mg</th>\n",
       "      <th>K</th>\n",
       "      <th>Mn</th>\n",
       "      <th>As</th>\n",
       "      <th>Sr</th>\n",
       "      <th>F</th>\n",
       "      <th>Cl</th>\n",
       "      <th>SO4</th>\n",
       "      <th>Br</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Well_ID</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",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.74</td>\n",
       "      <td>90.31</td>\n",
       "      <td>45</td>\n",
       "      <td>Y</td>\n",
       "      <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>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>14</th>\n",
       "      <td>23.62</td>\n",
       "      <td>90.60</td>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <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>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.94</td>\n",
       "      <td>91.46</td>\n",
       "      <td>60</td>\n",
       "      <td>Y</td>\n",
       "      <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>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>83</th>\n",
       "      <td>23.80</td>\n",
       "      <td>91.33</td>\n",
       "      <td>50</td>\n",
       "      <td>Y</td>\n",
       "      <td>48084.33842</td>\n",
       "      <td>0.936358</td>\n",
       "      <td>2085.570979</td>\n",
       "      <td>54666.48199</td>\n",
       "      <td>1.260031</td>\n",
       "      <td>96.159587</td>\n",
       "      <td>76.207449</td>\n",
       "      <td>26.104825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.019154</td>\n",
       "      <td>78.97747</td>\n",
       "      <td>253.528674</td>\n",
       "      <td>0.1669</td>\n",
       "      <td>83.0137</td>\n",
       "      <td>5.6188</td>\n",
       "      <td>0.1512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>23.98</td>\n",
       "      <td>90.81</td>\n",
       "      <td>150</td>\n",
       "      <td>Y</td>\n",
       "      <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>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",
       "</div>"
      ],
      "text/plain": [
       "           Lat    Lon  Depth Drink           Si         P            S  \\\n",
       "Well_ID                                                                  \n",
       "2        23.74  90.31     45     Y          NaN       NaN          NaN   \n",
       "14       23.62  90.60     60     Y          NaN       NaN          NaN   \n",
       "23       23.94  91.46     60     Y          NaN       NaN          NaN   \n",
       "83       23.80  91.33     50     Y  48084.33842  0.936358  2085.570979   \n",
       "84       23.98  90.81    150     Y          NaN       NaN          NaN   \n",
       "\n",
       "                  Ca        Fe         Ba         Na         Mg   K        Mn  \\\n",
       "Well_ID                                                                         \n",
       "2                NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "14               NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "23               NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "83       54666.48199  1.260031  96.159587  76.207449  26.104825 NaN  1.019154   \n",
       "84               NaN       NaN        NaN        NaN        NaN NaN       NaN   \n",
       "\n",
       "               As          Sr       F       Cl     SO4      Br  \n",
       "Well_ID                                                         \n",
       "2             NaN         NaN     NaN      NaN     NaN     NaN  \n",
       "14            NaN         NaN     NaN      NaN     NaN     NaN  \n",
       "23            NaN         NaN     NaN      NaN     NaN     NaN  \n",
       "83       78.97747  253.528674  0.1669  83.0137  5.6188  0.1512  \n",
       "84            NaN         NaN     NaN      NaN     NaN     NaN  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Drink=='Y'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we only wanted arsenic concentrations where people drink the water?\n",
    "\n",
    "This is weird again.  \n",
    "\n",
    "You are saying only give me As."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Well_ID\n",
       "2          NaN\n",
       "14         NaN\n",
       "23         NaN\n",
       "83    78.97747\n",
       "84         NaN\n",
       "Name: As, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Drink=='Y']['As'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the crazy world of pandas where you put the Arsenic doesn't matter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Well_ID\n",
       "2          NaN\n",
       "14         NaN\n",
       "23         NaN\n",
       "83    78.97747\n",
       "84         NaN\n",
       "Name: As, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['As'][df.Drink=='Y'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Say you wanted to do an intervention.  You would want to go to the houses with the highest arsenic first.  So we could ask what are the well id's for people who drink water and their arsenic is greater than 250 ppb.  This would be poeple with high exposure!  We would need to use an and statement.  \n",
    "\n",
    "In pandas you do this two ways.  \n",
    "* np.logical_and().  \n",
    "* Else you can use the & but YOU NEED PARANTHESE.  ** REMEMBER THIS!!!!!** It will come back and help you. \n",
    "\n",
    "We would try to convince these households to switch.  The drinking water standard is 10 ppb.  This is really crazy high exposure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Well_ID\n",
       "475     270.785974\n",
       "2821    285.971884\n",
       "2841    506.750799\n",
       "2977    282.519542\n",
       "4545    439.690000\n",
       "4689    267.553524\n",
       "4793    271.752307\n",
       "4987    255.620635\n",
       "5060    368.900000\n",
       "5557    351.206317\n",
       "5717    700.890000\n",
       "5788    422.070000\n",
       "6137    309.920000\n",
       "6583    339.300000\n",
       "7007    304.690000\n",
       "8051    308.880000\n",
       "8522    256.610000\n",
       "9362    299.530000\n",
       "Name: As, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['As'][(df.Drink=='Y') & (df.As>250)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A second way to do boolean and in pandas.  Remember.  When you split a line at a comma in a fucntion you don't need to use the \\.  I do this to make the packets print better. You don't need to do it.  But also line breaks can just make things cleaner and easier to see"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Well_ID\n",
       "475     270.785974\n",
       "2821    285.971884\n",
       "2841    506.750799\n",
       "2977    282.519542\n",
       "4545    439.690000\n",
       "4689    267.553524\n",
       "4793    271.752307\n",
       "4987    255.620635\n",
       "5060    368.900000\n",
       "5557    351.206317\n",
       "5717    700.890000\n",
       "5788    422.070000\n",
       "6137    309.920000\n",
       "6583    339.300000\n",
       "7007    304.690000\n",
       "8051    308.880000\n",
       "8522    256.610000\n",
       "9362    299.530000\n",
       "Name: As, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['As'][np.logical_and(df.Drink=='Y'\n",
    "                        ,df.As>250)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can we look at who drinks from their wells and if they don't drink is it beacuse it has more arsenic?\n",
    "\n",
    "Another way to word this.\n",
    "\n",
    "What is the average arsenic in wells people drink from?\n",
    "\n",
    "What is the averarge arsenic in wells people don't drink from.\n",
    "\n",
    "Use decribe...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arsenic of wells where people drink\n",
      "count    336.000000\n",
      "mean      72.484421\n",
      "std       91.571489\n",
      "min        0.000000\n",
      "25%        9.962403\n",
      "50%       39.975000\n",
      "75%       99.294435\n",
      "max      700.890000\n",
      "Name: As, dtype: float64\n",
      "\n",
      "Arsenic of wells where people don't drink\n",
      "count     71.000000\n",
      "mean     171.105792\n",
      "std      107.308224\n",
      "min        1.368709\n",
      "25%       81.614239\n",
      "50%      150.250000\n",
      "75%      250.245000\n",
      "max      473.340000\n",
      "Name: As, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#wells people drink from\n",
    "print('Arsenic of wells where people drink')\n",
    "print (df['As'][df.Drink=='Y'].describe() )\n",
    "\n",
    "# wells people don't drinkfrom \n",
    "print ('\\nArsenic of wells where people don\\'t drink') #to put the ' in the line I added a \\ before it\n",
    "print (df['As'][df.Drink=='N'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do the results above show?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am going to come back to groupby here and there and we will do a whole packet on it.  But when your brain can think through groupby it makes things simpler.  So here we are going to groupby drink and then describe As.  It should do what we just did in one line and make a nicer output.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drink</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>N</th>\n",
       "      <td>71.0</td>\n",
       "      <td>171.105792</td>\n",
       "      <td>107.308224</td>\n",
       "      <td>1.368709</td>\n",
       "      <td>81.614239</td>\n",
       "      <td>150.250</td>\n",
       "      <td>250.245000</td>\n",
       "      <td>473.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>336.0</td>\n",
       "      <td>72.484421</td>\n",
       "      <td>91.571489</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.962403</td>\n",
       "      <td>39.975</td>\n",
       "      <td>99.294435</td>\n",
       "      <td>700.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count        mean         std       min        25%      50%  \\\n",
       "Drink                                                                \n",
       "N       71.0  171.105792  107.308224  1.368709  81.614239  150.250   \n",
       "Y      336.0   72.484421   91.571489  0.000000   9.962403   39.975   \n",
       "\n",
       "              75%     max  \n",
       "Drink                      \n",
       "N      250.245000  473.34  \n",
       "Y       99.294435  700.89  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Drink')['As'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Could we disply this data?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='As')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you remember what a boxplot shows? I found this next picture on stackoverflow.  No need to import. Just for your reference.  \n",
    "http://stackoverflow.com/questions/17725927/boxplots-in-matplotlib-markers-and-outliers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i7ZMd0hx3Ou1yznLZ45rtts892yPTM8Ur1NvNx2y9oa/FBp+NSZsK/S5Rm/3bAu4FXg8qDd4S4hmqEkYOmwzvirgVWRVVEp0bkxFL2+xLM4oTiVuM7024mJiV5J9snqKSKrFFaCvfNq401u2E7TPpb3e0ZVzeWZiZtGtDls1usz12e6nZO3JO77u/f+jA59zpg7N504e+508VfCqcLPp8eO4I61HdkujSM8c6jo+cmDj57tTrshfl3RUPTzecqT/bVvnpnNj5DRdKL76o4bzkcHkP3L3mryvVBt4oqeu5hb+tUb/pzq6GM431TQ13zzUfbNnWmnQv/X7ug2MPyx8dbzvwOKHd9YliB65joPNKV053eI/zU/Nn5r3Off79Cc8zX+we2PYy+JXpIP/g5NDN17uHvd4oviW9fT/SMnrs3eb3RmPksWfj5RPbP4R9DJwM/RT1OfZL7FTsV9q3xO8pP5Kmw2YsZ1lmr86Zzz2e95n/tNC1RF4eWJ1/edCK2CLP0SCMgOXi5HGd+FSCMmGCeJoUyqDMMM/YxnSCnERxYVZjYWaZYX3B1sRexZHPuY0rhNuFx4BXlo+Lb4l/XKBHsEGoWrhcpES0SKxgTa54lkSiJFXKXFpUek6mXfaEXJy8lYKYIqo4odSv/FDlluo5tWL1dA0/TV0tglandqGOly6f7gu9Y/qBa9UMCAZD62oNc41Cjc1MpExZzYDZd/Mxiz7L21ZF1kE2kjbDtsV2jvZE+xaHnY7WTrxOH5wbXPJdQ9303SnuQx4XPbd62Xhzeb/2qVwfA9//8xvubEzfZOpH8uumlvpHBKwNJAcOBJ0N3hyiE7IU2hiWHm4UASKaIndEmUbjou/F7Io1jp3bfJ7mDd/ZVfGO8T8SihPXJg4lpScLJd9J8UvlTh3Ycn7rzm1eaTJpM9tb0vN3hGSY7ZTL5N3FlAWyfuwe2fNkb032vhzqPo39xP0DBy7lZh+MyLM8xH7ofv76/MmC1ELjIpPDe46QjmaXjB7jO652Qvek7inNMuVymQqx03xn2M4yVjJUscCVZHDB7+Lu6os1Ty8tXZG56nPt0PXuG5x13jdLb/XX4+/INlg2+jdtv3u8uaHldevKfbEHpg9DHu1ru/y4r325Q7Zzfdfh7qGnas/2937ud31+c0DsZeGg8mvmN0mjOeMpnxy+zcw70+f/V+2N/k4gaAGQD/NMj/3wnAAg7wYAUrcB4GEEwIkCgJsuQHdXA9SyGiARu/6+PxCAAySYc3IDESALNGCmaQd8YOacDPbCjPICaAA9sA6yhLAjsogRzA/jkP0wH7yHjKAIKoaaoIHobpjl9aALmDhmj6VilVg/joTTw0XhynEv8Ox4W5iRtRAQghEhndBMxBNtiAeJz0lipEjSTQYigydDJcMCoz3jKcZZJgemSjKO7E9uoUhS9lI+M7sx18NMJ48VsG5mHWXzZutkt2C/w6HDUcupx9nC5cI1wp3AQ+Ap4pXireNz4Bvn3yOgKjAieELIX1heeE7kvmihWOAaDXGC+EuJq5K5UpHStjLyshTZKble+dsKxxXTlHyVdVU4VaZUn6idU9+rEappo6WkzaW9ovNJd0ivR79t7T2D1nUPDDuMBozHTWbMgDkB7nMkK5I1gw3ZltNOzF7DwcEx2qnAud7lnRvFXcPD23Ob10nvVp8xX6YNKhs9Nm31q6B2+M8FSgS5Bu8KqQ9dCDeNOBI5Hx0Q07PZglYfr5FQk6SYfDl17ZaubTHbBdP7Mgoy7XbN7C7Yq5B9b1/QAa7c13lP8gcLV4pFj+qW2h3fdDKl7GTFwFnFqpMXVKqHL5+6tvEG083q+g2N8s0i9ywelrWTO2W6Z57l9cu86H51/PXhtz3v/SbmP7F/ufAN/FCZ0Z1dmc9eqFt8tnR7ufxn7Ir26v6BrNYc2IEAkIK1BkNgD3xhbSEN5IEKcBN0wrrBMsKNKCM2SDCsCJTBKsBbFIdKo3YoDT2KtqBfMEHMFtuK1WCjsFLmgsvB3cMjeAP8Fvxt/BLBkLCT8IjISvQmniZ+IxmT8knvGfQZ8hkmGS3gnC8xeTFdg5kwjfyMoks5yczEnMw8xuLN0sFqwdrEto6tkd2UvY3DlWMQZqYLXLncctyPeTbzcvPW8jnzvedPEaAIVAgaCY4K5QnbiDCLDIpeFTuwJkzcRIJX4qPkHamD0sEyJrKScuzyJAW8IkmJWZldhU2VpDqvNq7er9GmeVfrrnabzkvdb/rMa1UMnNeFGcYb0YxDTbxMLc10zTUsdCwtrTZZp9mcsn1gN+Ug4GjuFAHfaQWuh90K3Qs8Tno2en310Vyf7vtko/CmeL9Of/GA4MDCoOvBHSGjoYvh3BHqkW5RidFHY5piP9B44iziExPOJg4ks6bYp+Zueb5NKm3b9pEdITtZM9uy4vcQ9u7Owe3bc0AgtyUvI9+r0PSw/hH9Ev1juidkT+HK7lcknhE4e6fK/zzXhcHqe5c6r0xfV72x9ebjepYGsyZa85nWiQcmj660q3aUdg32/Hj2tW/s+cjA+Ksfr5E3jCOc7yTGrCaKJrW/ZH8/Mxs537GYudSy/OPn/Or8o/DpZ4PVJkWwDjjD+uc2UAQugXbwAWGAtSF7hIYUI03IB5QbNUPj0TPoAMaGWWOZWBO2DCszqbhbuCW8MT4b30+QJewgDBLXEctIJFIM6RmDLsMxRhTWQnqZzJhuk3XJdylOlPfMGSxiLE2svqwzbAfZFdmfcERzUjgruUy4XnGn8AjzdPAe4PPnNxGQE+QUXBQaFK4TOSQaJWa3RkWcV4IgMS/5VeqL9HeZZTlmeQkFI0U/pXTlYyp1qk/Vvmvwa1prZWi36JL1fPUvGRDht2qD8RqTfDNu82pLH2s2m267ow4xTu4uaq4D7j4e7V5W3k/XB/vObdzph1Bj/XsDtYNKQxhCd4QzRpRH2ceA2Bu0mHjhhJakhJTALZ/TKtJTdvRlLGWiu0hZrLvV98TtfZbjvm/iwJ6DSnkv8vcU6hd9La46urGU8djZE9on75QZljedNjvTVulU9ey824Wuaouam5dlrhy+Rrq+rXapbu8tqdtddzIatZommktbHe/jHtx+FPdYvn2043iXVw/n057e3H6b5ysDF145Do6/Thhefpsxir3LGEPHd37Afdw++fmzxZeUqWNf939L+G72ffbHuWmH6ZczoTMzs4mzE3O+c53zpvPnFygLsQs9i5qLxYtfl6yXypYWl92WL/7Efnr9vLCCrLivnKPPf1ywOqxXwgMhm8Dy49DKyncpAIiHAFjOW1lZLFtZWS6HycYrAJoif/2fQ1em/09U+pqO2uR60+nXfx//B0KhywfCps/NAAAACXBIWXMAAAsTAAALEwEAmpwYAAABnWlUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iWE1QIENvcmUgNS40LjAiPgogICA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPgogICAgICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgICAgICAgICB4bWxuczpleGlmPSJodHRwOi8vbnMuYWRvYmUuY29tL2V4aWYvMS4wLyI+CiAgICAgICAgIDxleGlmOlBpeGVsWERpbWVuc2lvbj41OTE8L2V4aWY6UGl4ZWxYRGltZW5zaW9uPgogICAgICAgICA8ZXhpZjpQaXhlbFlEaW1lbnNpb24+NjQzPC9leGlmOlBpeGVsWURpbWVuc2lvbj4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+CvgflrcAAEAASURBVHgB7J0LfBNV2v8nQEsqICk3i/iX4q5uEXdb3FfFa4srWlxd0V1oK7gtLmAV1pZ9ZVu8LLiCLerasqLtitryKqaI2nqjVdQUL7ReIOkKpuIlQcHWckkQStIW6P/MTDKTNDNtkpkkM8lvPv00Z86c6/eZnGfOb85MNBaLJS4uLj4+foh7Gzx48CBm0zAbhQ0EQAAEQAAEVE5gkMrbj+aDAAiAAAiAwMAE4O0GZoQUIAACIAACaicAb6d2C6L9IAACIAACAxOAtxuYEVKAAAiAAAioncAQtXcA7QcBtRDodtrtTtJYrU6njVdLo9FOEIgWApjbRYsl0Q9FEHA2lmfP0WhcfxnlVtq9Ud3tpvJszbyExCUZGUsSE+Zp0p5saO12Nbh9fXYan4XJuzCvuK7Z6jqODxAAATkIwNvJQRFlgICLgDajsOZpYwXZG1nW9FJjYbKWspuq542fur29bJWj9yWT6aVex4r67G0zJ8/Lq7HTuZIW1pjurUgnoRlGGzm6zmKcYl3z4qWTltVZ6ePYQAAE5CAAbycHRZQBAh4E4rQ6sjcsif5POU0rp86nqNwVDYXnadlE2imZxSuqsqgNOatrWtmo03TJJBCnJSm045LT7qrWk9295c2d7GH8BwEQkEwA3k4yQhQAAoIEGA3TWlf+Iz3PK5zicnWupFNmFY4k/iynrkMob4+dmfUJHUIcCIBAcASwSiU4bsgFAn4RYG/ODUvy9nUkK1mqQlFHKNMBJzXOfbDTbu92Ul83N5ZPv5MkmVGeOcyvSpAIBEBgYALwdgMzQgoQkEggzr/82y4dv41NmVW1qjL7PJ3bDfqXHalAAAT6IQAlsx84OBRtBN5///233nornL3qoWhBs8e/Km8y21Ywy1UorXZCeF3d+vXrW1tdNxH9ayxSgYDKCMDbqcxgaK4UAp9++ukHH3wgpYRA845NSiZZDpja+2Zst+4lUakZEzzmbz2Udkp+3YxUiixgWVTa2DdLKPdfeeUV8oL4UNaAskEgwgTg7SJsAFQftQQYNzYuI/98Mrdbs/JT73UnH9cUk46nls9iFm56MtAtbGwiC1h6lk9f3WD1PIAwCICAFALwdlLoIS8ICBDocdKerbOd9W/Jy8zkcYJtjyUW72bepEIeSthdU7h2eUtcUX1hRhKb/7jdSmdx0rInpZv2KPPEXsvMSXWtTAwdiw0EQEASAXg7SfiQGQS8CdDvUlk0lV5ReWTppXOYd6kMS8l+oc14WfqaBxMTFublLdQkPJjTmF5rrCpll1zS71J5+E56ecq2qYmF5Y1kGacuLf/eiiwS8+LkBMzwvAljDwSCJKDBr7kGSQ7ZVEigtLTUZrOtWbMmIm3fXZP3YM4GUvXIMuP6wrSItEGs0szMzIKCgpkzZ4olQDwIqJ0A5nZqtyDarxoCU7KrV9SWkOYeWTp1Tl6lyWp3vypTNV1AQ0FAvQTg7dRrO7RcfQSmzCp+ydF2r0GfntT65KyM+Wl5jex7o9XXFbQYBFRGAE+Xq8xgaK7qCWiT0jKyyR9VqvquoAMgoCICmNupyFhoKgiAAAiAQJAEMLcLEhyyqZSA0+k8dOiQShsfumafPHkydIWjZBBQAgF4OyVYAW0IH4GXX35548aN4atPJTUlJiaqpKVoJggESQDeLkhwyKZSAvPmzYvUEwhKJkaeQFBy89A2EJBOAPftpDNECSAAAiAAAkonAG+ndAuhfSAAAiAAAtIJwNtJZ4gSQAAEQAAElE4A3k7pFkL7QAAEQAAEpBOAt5POECWAAAiAAAgonQC8ndIthPaBAAiAAAhIJwBvJ50hSgABEAABEFA6AXg7pVsI7QMBEAABEJBOAE+XS2eIElRDgPx+W1dXl2qaG8aGFhYWTpkyJYwVoioQCDcB/JpruImjPhAAARAAgfATgJIZfuaoEQRAAARAINwE4O3CTRz1gQAIgAAIhJ8AvF34maNGEAABEACBcBOAtws3cdQHAiAAAiAQfgLwduFnjhpBAARAAATCTQDeLtzEUR8IgAAIgED4CcDbhZ85agQBEAABEAg3AXi7cBNHfSAAAiAAAuEnAG8XfuaoEQRAAARAINwE4O3CTRz1gQAIgAAIhJ8AvF34maNGEAABEACBcBOAtws3cdQHAiAAAiAQfgLwduFnjhpBAARAAATCTQDeLtzEUR8IgAAIgED4CeD37cLPHDWGiUCn3d5JUfFarU6rFauy20kSiB2M1nin3e7spqhhWi358+1kN32cHNUNEzjomxwxIKAOApjbqcNOaGVABDpMNYUazfzExKWzMhYlJMzR5L1jsvcpodNqWp+nWVxt6hMf1btOU03pHE3CIkImI3F+QsLcvPLdxLPxm/Od4ox5CYlLEslRzfrGdv4IQiCgcgLwdio3IJrvQ8BUnbdkak5nmeHp3t6NjaaXem0LSqzPTE1cVtPKpe1orFy5snTrBnp+w0VGe8BenZ3wcM7ydIP5JULG1PuCzXyRaemDiQkbW10Ob3fltGec+WU2W5mxfiJFbZ1e3RHtUNC/2CEAbxc7to6Jnna21jw8fwOVpS8vzNC5eqy7trjhpixqb87kOvewPi4j/9HqUjKgE6kzRrbd1flbNlET9ebFGSlsl+N1KYXNxjMp6rXJ+Xtof+c8oCuvKs+eoNNNSMtcqE+nqPaeGKGDbsYAAXi7GDByDHXR2ViaQ7qbXpw5zKvX2t8XV5CIzXXNfLTTGUNDubO1ev4mAmZ2psvVuTho0/LKiFfbsKWZKL3ajOwMNzfr5pxtqYbCCTwvhEBA3QTg7dRtP7S+DwH2JpSvPhmnpWd6PQ3W2JnM9SHDuPYk34UnpyUlk5S7re3u9M7djdULNZNayDIWdxQ+QSAKCMDbRYER0QWeADtAuzVMPt4VStL6RMVKRBzd0STmv0CXOcfWbW+1tusuL8oiibZPn8Td0hPIgygQUBUBeDtVmQuNHYgAO3XzncC5Rvl2z/WHA5UVXceZuZ3ofTiOWLwu7ffZs/JKa8oMZQRAY7M1ujCgN7FLAN4udm0fjT3XTkhOJf2ytpO7UF7bAauJ7I/MTuMmMV6Ho35Hqx1L97F1f18wTmszTebitKQ+DCZkZJJVPNhAIGoIwNtFjSnREZpARv5K8r+lsM57VG/fXLyGxM+dlUb+e26+97E8j0ZRODmvvoCA2VzjsU6HdM/evGEtuUNXclOazrezhKGvF/RNhhgQUAUBeDtVmAmN9JtA8qwV+gKq5c7FxXWuZ8Wc9rrC8dtbqNR6S4bnBIa5hfcjvzrD7yrUmXBCZumtBdSRpZeurjGxPei2m1YmTu+hUhdYCsfRUfSj5YWlNVbmaYTG0uwjWfpbhbygOgGg1bFOQGOxWOLi4uLj44e4t8GDBw9iNg2zxToh9F+FBMi7VO6bmnOESp2Ym7Z3wwYqtSC3euXvPQbujuaah4tLf9xGpjXUmQVVy0rzJsTE+hXyLpVy8oA5lZo+MY3au2FbXG7ZveX5U3Rs553vFCY8s9Zl7/OrmpblTYtR4VeF5zyaPCABeLsBESGBSgnYN+YlvraBbnx6k23xNAGlTqUdk9psu2lZ4tS9DJh7bI0Xe4PpZh5D9H2EQ2qlyA8CkSYAJTPSFkD9oSKgm1tN3hlGr6TfdmnissoGq9cLIUNVqwrK1aU96jBfRp4pp7Y9lqhZ32DqcPJLVclLtOHqVGBENDFwApjbBc4MOVRFoNPe+llD88cNDVZTK5WSd191YXJMiJYDG6mj1bS9udHUUPd1q31sXvkjhRnxA2dCChBQKwF4O7VaDu0GARAAARDwnwCUTP9ZISUIgAAIgIBaCcDbqdVyaDcIgAAIgID/BPDb5f6zQkrVE9j/2WcbMzNPnTwZ8Z70erRBM2gQpdFEtkkjJ07Mee01XXJyZJuB2kEgdATg7ULHFiUrjoDlvfd+PW9exsqVEW/Zizfc8MP27Wwzcl5//ewrrohskzbPmXPAbIa3i6wVUHtICcDbhRQvClccgSFabUJiYsSbNTgujpvNDR0xIuJNGjR4cMSZoAEgEFICuG8XUrwoHARAAARAQBEE4O0UYQY0AgRAAARAIKQE4O1CiheFgwAIgAAIKIIAvJ0izIBGgAAIgAAIhJQAvF1I8aJwEAABEAABRRCAt1OEGdAIEAABEACBkBJQtrdzOu12e4fd3sm/ot2XRn/HfFP3G+N3Uc72DtlfqO9XZ/ttPg6CAAgETIAbZIS//t3OAYegwKrs9je5c3+7cJP8LUAg3QCdFcgRRVFK9XbO9rdKs+ckJCxKzFiamDg/QbOsvMH1U9Q8fefuhso5mszdMpwSAxfV2VozR6Nx/SWM327n2+F/aH9jOV+IJvsd5leiqf466/y0Ms8jC9OAjLz1Nc2d/teKlCAAAgIEyG/bls7RkEEmcWkGGWQS5uaV7/a6iqV/zH1eQuISZgha39guUEYgUftNDcs0mg0m8QHLaSIJ3N/3hEeDqrHb2ljIF6Ipb7Aybeyvsx3NlXM9sjANyFhdzv6KfSA9VHha8tvl+/bt6+joOHz48M8//3z8+PGurq6enp6TJ0+eOnWqNyKbzVhAUbOpdIPZxtZvM9cvoGMKdjm4BlmeLsgtSCWRWR6R3NGAAv4U5ahKp+4pqaiqqHi6rGydvulYQDV4JrY10X1JrfiJjfSjszZjxWyKWlBh7OrtPWaz1Jakk93ZubVkF1tABD4sKXnn738PKEuIElelp6+kKPbPYjCEqBb/i33+uuv2bNnif3r1p7RVZZHRg1pnMLN96bKZy+jxhHrB7BpldlWkzi7Q77PZ9hnr76HHnxLXdzaYzjs+qigqyEol5Ve5y/ctZldV1uzckqqqiqcrytaV6ffxw51v2v5jbE+nkwanf+IeQQfsbC87LqVXkT52OWxGfQk9yFAFluDb0H8LI3CUUp63c7zAnIXcOcdSOWauounn6j3djEWfK4e3o2vovyi29rfNbZ619zHXT2a3c3YfOGYxC5yvDjP9zcliO+JXZ4+Z9aTvBVWur2VvL1OCDG7e3dCY+YS3EzN1rHk72q9Q1D167jvFgHGw19m5X9FDvMOgN3Df96/05BKzYJ8Yvt7en5r0T9caxY8zRyz0F1nU2znM5Dp4Vb35mLiDOdZmtrgcmLsqh2WXgEdy1NKjqGsm4EdnSXc9xyVSOFuCeGvd9avoU3FKZre17rVNFJVa8fsUr9/cHJYyawb5teUNpSYPCbGbEtcEApxT91uUc2v+fFLeM5PHz9doCksbPJrgrqa9YcnkyYsKazrdEZ2tdfMnTV6aX9ePTB9QZ90FUxS5w8fvIAQCIBAgAWdr9XwyyqTPzkzxyqlNyyujR5ktzeQbps3IzhjmOmzdnLMt1VA4wSu1186B1tKtN5u4r7/XMfdOv+sPqD11K49QVMvMyeTGzZyMUqEbNNZHx0/+e2Ihf8jZujph0oOT8q3uKgQ+/eqsbz7nAanCrW+ZEY5RnLfrYf1Xii6uLxnt2CQS1fK1/Hdu+9bks6+dUWdZZTRcX5JLDv24fObi0sa+aZIyywxl1Nqc+Xm0wyM3+eZPvplKLXmkclY/vwcdUGc76dU6Tmtr4+ppU8m34syK4ile1wN9W4R9EAABMQI99IGkYT7foNOSksmB3VZupHfubqxeqJnUQlFuz0fn9N3itDoq1ac433TiMcmzKh8xGxdUldBD37blDyaU+6xUSL7XYoij1j6YwDg8Z+vKhMktVOoCS2WyeLHkiN+dpahWepDpsJo2Fs/auo1cD1TN8p519FuP0g8OUWgDJZ02bJ+cYldSw7QBlz5Ml3xeGvnLmJ2dd9+k6T8ur9xdmNHH2UzIKHyk3vn3mTnzTZVUyzYqtWhVc3GyP1X5k4aijiy9dP5StmtZBebyy1No548NBEAgCALMxXSSzyW1qyTOsXXbW63tusuLsras2bR9+qSxZsdc79Gf+AY2z3EyG2xpt7t3gxhk4rW65JQ08ndtdvb6zElbty3d0pqf511dfHJGlaV+/qSZDyY0xlEtPVRqrqX52oFGGT87S3ek5c4lCXeyPbreYJ6dkcKhYCNV/V9xczsXTdcpFDzbTqJVJCQI/VX3rzb0X+Ww5IzVxgqK2vQZu5zSO3VyZvE9ZIENcXUUdVdj6Xn+uTE/5diJevM6umqyJemS4eoYEvgHAkERYKY77cx/gfzcEBGvS/t99qy80hpauaGoxmarV2qniRthHr6TfOuXLnWPOf2su/QqQXBHm7ywwTiSoj7uUx2TOD4582lDARllSOPPNzT+fiBXRzL52Vm6+Cz90za6arLF6ZKjydWRHilubjdMp6NJb7Laa/pIB+27idJOpacl+eVDhiVn3ttkpHzTanVSTchMDcf6lkwETGvD+rV088m2vrTuotJZ/dcVUGeJ7DkuLf+esprHlq59cFryOlPhOLYm/AcBEAiIgFY7ltySoFr326kpXteNTmuziZR0cZpXLImZkJE5kVpK5m9emzZlhdFIfEmclvq6Ou/FNWl3GQtJyT2U9lyh8cErrx87E3RCpThb1xe6RpkvC0v3NA90VR1QZ9uJk0t7tKls0aVLX5uamWxrvJwZj/1orAqSKG9ul5SxgFy4UMvr6BvF/NbRWEOkc6poZZqf9LXJadPSBLaUZL7QgUNCc0xarMid4nNJ1W1tWDRp5hGqYIXDsUJf0LPm5kXFDdxFonBVgXf24sI6erVOy9KlxY39rH8Rrg6xIAACNIHkvHp6erS5ptmLh715w1oyzJTcJDTKkPHIxwtqpzBDzJQU8pFCpWdelpbGxPQnAPrKp4Jf5OMUdZGP06Wc1vJpk7e3UDeZbVVmPdWy5v6E4j1Co5RHvwLpLOPlddMK76VX62xbm1i636MgtQeV5+0o7bWl5vMJ6Usz6lpdDq/DVLNk+nJ60cfKDB/irQcGMLZPDtEI76Kcrcs0CXPyauzWOvLo5Wr2gW67aeXUO8+vX5ncpxB7IxHTe6jce23lU7TaKdnlK/S5PWtmzi9u7JOQIm8zIFHkbjB9wK/O9jBrMDtdz73qFtY1EamhZ830h2ta+xaOfRAAAT8ITMgsvbWAvhe+usbEJu8mX+3E6eRO2AILq5rQj5YXlrJPWDsbS7OPZOlvFfKCruxUO2V3ikmjbBrWzx3wWmfnfCtPM0+Tt8feupqs9y6u2U+PZvaN+VN7Cgwz+l5Stz85bRJxddcbbXNTdMNSsp826imKOLxSn/Us7IrKdnbtgh+dJZUyo0y7ne1CGntVTS1f6rHOnO2Fiv8r73k75vkNR1ttCf1AzOz03HvoxySpFWW13o92tr1QlHsr/fwjeVI7q8pgkfDYh0hRDjO5/Judrj/WZqBrcf2lv9DUJlSX7e2yEveznK7jxqqSWqPX0zH7DGXucuinYd5mH5Tpr7OOTyrIM4Xu2rMq2Af4bMYqNvLWono8Yy5kDuE4PG8nzKW3N9aet2M4OFzPUKem35NLHqejbs0t22XjHnZzvF3g/t6R8adqgBdKfKXPcj9EK8iY1FW0gHl6fTaVuqLC4P7acg/GWcq4rzl56k5Pv0rCd9tVW1blPf78ZNSXeT812GWhb+txg0ZZvYUpp7/O/tREv7/C/ZdlYMcl14svyABbIvBEn2/jFB+jId4uLi4uPj5+iHsbPHjwIGbTMFsEPbndVLNoag7dgNSKp035urA3pdtJLta0zDJl1wrPIJZa+dnqiHfWz3aqOtlHpaUOm23GmjUR70V1RsbebfRqJrLlGgzJGRlMMGL/XsjMvKSg4NyZMyPWgkhVbDctS5y6l649/R5b48XeowwzAlCh+9bT1TqdneTxBmY1ALvCM06r7eexJTpL0Fu/nQ26VLVkVKCSyaPTpWVXmWuJakfWxS7S5L1jsoo9VMDnkTUUr2VdHSmUBOg/WYv3KizinfVqDXZAIEYIkEUZDvNl5C4Vte2xRM36BlOH+ykCEsWMACH81tOMPQYWdpAJlasjdfXbWboxUb0p2tsR8uQVKut7HfST3WVJWwrzFk3LeLLRGq0WianORqsR0S/1EdCmFDb2rjMbb60q219duHTatMLy6F0CFlOd9T4XFfcEgnfz2D0teayb/FGFQgejLS6mOhttxkN/1EtgXEraLPKXFxOjTEx1ljsnlT634xqKAAiAAAiAAAgETQDeLmh0yAgCIAACIKAaAvB2qjEVGgoCIAACIBA0AXi7oNEhIwiAAAiAgGoIwNupxlRoKAiAAAiAQNAE4O2CRoeMIAACIAACqiEAbyePqU6dPNlznLzHFRsIgAAIhIpA97FjoSo6BsqFt5PHyD9+9ln19OnylIVSQAAEQECIwCNjxvQ4HEJHEDcwAXi7gRkhBQiAAAiAgNoJwNup3YJoPwiAAAiAwMAE4O0GZoQUIAACIAACaicAb6d2C6L9IAACIAACAxOAtxuYEVKAAAiAAAionQC8ndotiPaDAAiAAAgMTADebmBGSAECIAACIKB2AvB2arcg2g8CIAACIDAwAVX8muvA3VBCit6TJ48fPKiElqANYgR6OjvFDsV4fG9vb9eRIziBY/w0iO7uw9vJZl+bxfLEr34lW3EoKAQETjgc/3PnnSEoWPVFdh89+vrChYPj41Xfk6juALmkjur+hbZz8Hay8R31y18u/OQT2YpDQSEg8FFpqcNmC0HBqi9y6Omnz37ppXNnzlR9T6K6A6u02qjuX2g7h/t2oeWL0kEABEAABJRAAN5OCVZAG0AABEAABEJLAN4utHxROgiAAAiAgBIIwNspwQpoAwiAAAiAQGgJwNuFli9KBwEQAAEQUAIBeDslWAFtAAEQAAEQCC0BeLvQ8kXpIAACIAACSiAAb6cEK6ANIAACIAACoSUAbycP38FDhw4bN06eslAKCIAACAgROP2sszQajdARxA1MAO9SGZiRPynGT5166xtv+JMSaUAABEAgOAJ3f/NNcBmRixDA3A6nAQiAAAiAQPQTwNwu+m2MHnIEvvvuu56jR7ldBEAABGKHAOZ28tj666+/Xr58uTxloZSQETjnnHPOPvvskBWv4oLJdcC3336r4g7ERtNvvfXW7u7u2Oir/L3E3E4epqNGjZoxY4Y8ZaGUkBGw2WzkV9xCVryKC05MTCTnsIo7EBtN/9Of/jR48ODY6Kv8vYS3k4fp6NGjr776annKQikhI0C8HZRMQbrE25FN8BAilUPglltuUU5jVNcSKJnymAxKpjwcQ1wKlEwxwFAyxcgoKh5KphRzYG4nhR6fF0omz0LBoR07dpzs7ITi7GsiMrGDkumLRWkxUDKlWATeTgo9Pi+UTJ6FgkNkbof7doL2YYRMKJmCbBQUCSVTijGgZEqhx+eFksmzUHCIjOkjR45UcAMj1jQomRFDH0jFUDIDodU3LeZ2fYkEtw8lMzhuYc4FJVMMOJRMMTKKioeSKcUc8HZS6PF5oWTyLBQc+u1vf+uw2RTcwIg1DUpmxNAHUjGUzEBo9U0LJbMvkeD2oWQGxy3MuYhe9/3334e5UlVUByVTFWaCkinFTJjbSaHH54WSybNQcIjMYLoG4QpPwEJQMgWgKC8KSqYUm8DbSaHH54WSybNQcIiM6Q4FNy+CTYOSGUH4/lcNJdN/Vr4pcZ3ryySYGCiZwVALe5533333448/Dnu1KqgQSqYKjERRUDKlmAlzOyn0+LxQMnkWCg5dc801WKUiaB8omYJYlBYJJVOKReDtpNDj80LJ5FkoOIS3QosZB0qmGBlFxUPJlGIOKJlS6PF5oWTyLBQcIt7uCH4DQchAUDKFqCguDkqmFJNgbieFHp8XSibPQsEh8uYwKJmC9oGSKYhFaZFQMqVYBN5OCj0+L5RMnoWCQ3iXiphxoGSKkVFUPJRMKeaAkimFHp8XSibPQsEh/OKPmHGgZIqRUVQ8lEwp5oC3k0KPzwslk2eBkAoJQMlUhdGgZEoxE5RMKfT4vFAyeRYKDpEZDPl9OwU3MGJNg5IZMfSBVAwlMxBafdNibteXSHD7UDKD4xbmXOSt0L/+9a/DXKkqqoOSqQozQcmUYibM7aTQ4/NCyeRZKDhExvSeo0cV3MCINQ1KZsTQB1IxlMxAaPVNC2/Xl0hw+1Ayg+MW5lxkTMdboQWZQ8kUxKK0SCiZUiwCJVMKPT4vlEyehYJDZEzHb5cL2gdKpiAWpUVCyZRiEcztpNDj80LJ5FkoOETeCk05nTMU3MJINQ1KZqTIB1QvlMyAcPVJDG/XB0iQu1AygwQX3mx4K7QYbyiZYmQUFQ8lU4o54O2k0OPzEiXzueeeKykp4aMQUh4B8p5M+zff/Pj55xFvWpfHYpmDX30VP3x4ZJv0bWvrmG+/PTeyjUDtAxEgSmZ1dXV8fPxACXFcgAC8nQCUIKKgZAYBLfxZhvzmNz+9+uobd9wR/qr71HjC4Rh13nls5OcVFZrBg/skCPPu6DPP/FVGRpgrRXWBEoCSGSgxz/Qai8USFxdHLhaGuLfBgwcPYjYNs3mmRhgEQAAEQAAE1EgAazLlsRrWZMrDMcSlEBWIbCGuRJXFr1692mg0qrLpsdRorMmUYm0omVLo8XmhZPIsFBzKy8tTcOsi2bSrrrpq/PjxkWwB6vaDAJRMPyCJJoGSKYoGB6KPgNVqJZ1KTk6Ovq6hRyAAAv0TgJLZPx9/j0LJ9JdURNM1MltEm6DQyqFkKtQw3s2CkunNI7A9KJmB8RJLDSVTjIyi4qFkipkDSqYYGUXFQ8mUYg4omVLoIa/KCJCpHWlxBpbaq8xuaC4IyEAASqYMEEkRUDLl4RjiUsgdO9y0E2QMJVMQi9IioWRKsQiUTCn0+LxQMnkWCg7B1YkZB0qmGBlFxUPJlGIOKJlS6CGvygisXLmStJj9r7Kmo7kgAALSCEDJlMbPnRtKppuEoj+Jn4OrE7QQlExBLEqLhJIpxSLwdlLo8XmhZPIsFByK+BMInXZ7J8vH6dxvbe9WDCsomYoxRX8NgZLZH52BjsHbDUTIv+P4xR//OKks1f7G8jkaDfNXbPVpe0djqetoWt7H7U6f414RHc3VCzWa+YkZVibh7prMpZPGbzANkMuriFDuXHnllUlJSaGsAWXLQID84g95j7EMBcVkEfB28pgdSqY8HENcCnn2IKDHDyZkFL5g0TONWrOxwerdOvvm6cvpmPSqKlP15Ula76N998ZNy1tckUVRKeyBCdOKUwsqLkseIFffUkK2DyUzZGjlLBhKphSaWJMphR6fF0omz0LBIfaV0AE9Yx6fnBZHUT0U1TKzpqO3eJy7d52mmm1seCA/585B6eiUrsmcLiXzvnLuSOQDUDIjbwM/WgAl0w9IokkwtxNFE9ABKJkB4YpUYuLnAnJ1dDudzjPTC1LJrIxavrnZ7m65sy7vzlR97fnufeazva44g9E208prTO57cs7G8mwSOTdN8/ebN1AUM5lztpsaa1ZmpFW30s6vs7VhWZpmbjadd3VdKx1jNW2sLJ6ryatrqClkpFQ23qs2WXegZMqKM1SFQcmUQhbeTgo9Pi+UTJ6FgkPkrdDsi6EDamNP0rS5xVUky7b8GtbddVvrXmspypvlkiWZ0uzr08Y3ppW/1Nt7b1Xa9pypD9dZiatsLE54amnaI47ejaa2GbTLpN2b3dr6cV3ll9tamIzW8skz92Y3baxpfNpY0XLz5E/pOuxfV67poTa8OLN5ttFwWTrVcvPK3a5pIZNJ7n9QMuUmGpLyoGRKwQpvJ4UenxdKJs9CwaEg12S2Os9Mm3UZ6VfLnXXMupLt5Tkj9fkTtBQ31yPC5tYWKplqb2xo3M8Q+PLmhs72xqfWUDOMhcztuaQZs3JZNrqUjMWF+W5OunHp1MSUJLLbzfizL9udw5Iz8opJ4qwVjvLL0zJmFxa4E4fqE0pmqMjKWi6UTCk4cd9OCj0+L5RMnoWCQwHLmK6+OHso3a31RdtnrtlS3jC7MvmptakrSpMpZyu5pcduVlMjReUmJ2m7nc645LwCQx6lS+lsrSZHz9SxSahu9007st/JT9R0Cxt7O1obV6ZN+pKd7DHJPROPTZlGUZWuUkLzQZTM0BSMUuUkQJRMOYuLsbIwt5PH4FAy5eEY4lKCnNsxrRqXkUffpdtw8/yEqXFllVOYG3Bce+PoXef5GRnXZmZem5FxOflLSzpgNZFYO+/YuOSeAWdjadqSyYUZDY6XXOs/PY/SYbJGJtQblMxQE5alfCiZUjDC20mhx+eFksmzUHCIeSl0cmANdJLnwRnPpk2ZrXdJkQvzyGTLa4vXJlHUphdrWt2x9ifTsm2T6GQmk5WNjGfKYfyiOxVRL611Ty1vSTU0ZtDzQjqemy/yiegQKT+EG5TMEMKVr2gomVJYwttJocfnhZLJs1BwKAhvZ7eajrQ07mf80JRZhSNJ7wrqL2PFSXv7j2SXea48OTOPHPoyZ/KyyjqTqXF1WuK2vOIrLsk8k6L25kxe32jtdFrfKiVrMjdtrmnscPIurYcp+cvqut2tpvX5OaQ8IoruttrZeJblAVMDybi/nd0LyX+syQwJVrkLxZpMKUTh7aTQ4/NCyeRZKDhEnrdjH7nzr43OTyvzFk1dSuTL+xMy3iEvQdGmLSxKvz4/I56i6NesjJ9Oa4zb5s/X5H1sT3nUqCfTsr133vzw1OkH8gxVhWmUNmW1uZ44vK3TJ81PmESvwUzN0jmdP31SvXgy7di2TM7cbD9/RjrVs2H+g5NLJxTrSeKWnGLjB+sfnL+JeLiHi6s/baxZnUPcJPVMXjnrdP1rfGCpoGQGxitCqaFkSgGP30CQQo/Pe+jQoZaWlquvvpqPQigGCTiddrJKRasb5nVXz0nWpMRptcRHim2dTucwLZvH2U31l1KsBInxH3744bnnnouXh0nEGOrsr7766k033YSXhwXHGd4uOG7IpUoC+O1yVZoNjQYBOQhAyZSDIn67XB6KKCViBKBkRgx9IBVDyQyEVt+0eN6uL5Hg9rEmMzhuYc4V0Cuhw9y2yFaHNZmR5e9n7ViT6ScowWRQMgWxIDI6CbBLVIJ9xjw6maBXIBAjBKBkymNorMmUh2OIS6HfCZ2XF+JKVFk8lExVmA1KphQzwdtJocfnhZLJs1BwKLi3Qiu4Q7I1DUqmbChDWRCUTCl0oWRKoYe8KiMAJVNlBkNzQUA+ApjbycMSSqY8HENcCpRMMcBQMsXIKCoeSqYUc8DbSaHH54WSybNQcIh+k0p1tYIbGLGmQcmMGPpAKoaSGQitvmmhZPYlgv0oJsD+lCt5W2YU9xFdAwEQECSAuZ0gloAjoWQGjCwSGYJ4K3QkmhmBOqFkRgB64FVCyQycGZ8D3o5nISUEJVMKvbDlhZIphhpKphgZRcVDyZRijuCVzGPHjn377bdS6kZeEIhlAhqNpre3N5YJoO8gIJHABRdc4P87soN/c9jixYs/+eQTrevF7RLbHPnsXV1d5HcMzjyT/OJKMBvJfvjw4fHjxweTOTR5Ojo6hjFbaIoPd6k9PT0//fTTWWedJaVicolGsg8fPlxKIXLlPX78+IkTJ04//XS5CpRSDmFLWpKQkBB0IadOnfrhhx8mTpwYdAlKy2iz2UiTEhMTldOwvXv3nn322eQ6KbgmkfvWJPugQVEi6f3888933313YWGhnzSC93ZkYrd+/XryI5B+1qTwZM3NzQUFBcR/B9dOBf7iDxE9srKyZs+eHVyPlJbrm2++ue6660wmk5SGKeo3EPLz81NTU++8804pPZIrr/Rf/CFXEmeccYZEA8nVHVnKWblyJZl8P/jgg7KUJkshEn/xh0xOyBAn5ZpGll7IVci6devMZrP/pQXv7fyvIxZS4rfLVWFlvBVazExRc9kq1sHoiCe/XR4dHYlIL6JkShsRdp6VYk2mJw3FhrFKRcw0WJMpRkZR8ViTKcUcmNtJocfnxZpMnoWCQ3gltJhxsCZTjIyi4rEmU4o54O2k0OPzQsnkWSg4hKfLxYwDJVOMjKLioWRKMcdgsqCFrOAkG1mow21kzQ+3iZU+derUX/3qV1GzJpMsSLv00kuDXpNJlMzHHnvsmmuuEcMV/vgpU6acf/75ZFVm+KsORY3k1vrll18ucU0mWUNht9sV8i6V884779e//rVC1mQSJXPEiBFSFhWTMYRMEKNpTSY5T9LS0hS1JpMomTfeeCNBHdxXjBho0qRJZJwPLrvSck2YMOHCCy8kMw0/Gxb883Z+VhAjyRS4JjNGyKObshCQviZTlmagkP4JSFyT2X/hUX80UG/ndDqjZjoX9caNhg46KUorXz/YV0Lj7p0cROW1jBwtQhlRT0Ca/wlsSttcPi1hWjU5zaNgcxJJy26Xqy9KWJNJ98gpV4cib2GrqSFPo6k2ydkj8gQC2cLeNycxDfmehr3eACoMZE2m09RQqdFkymqZAJoaiqRq+e74uSaTPuGiajBgbG5vTktIqG4N/nsUgLezN5deurSFSpHxUjsU560fZTpbS9M0CUSPT0xM0OQ1tgePj6sssmsy7a11GRqmRwkJaXnVcnSI61lEAs7GyuLi0uINZGIn6+kW/rdCO62NGZqEhFmzyH3HtNIGGU610BjE7zWZ1srC/NJi8kR8kqyWCU2v/ChVXd8dP9ZkOhuKMxKY0S0hQVPZ2O4HA1UksZcmXtpCUTopjbVYLPv27SNvmSIvviIvYiFvMyIvwSJvaTp58iR5FRB5lYBrsxnT2Wqy9A53nDo/bRUUVaA3ktcCGevL6D4VGdTZEXerHU10J/QGo9FQwhpJ7T1ie2bRk35VmeU83aqYzQ0u9J8O+luTXmGka3KYs7hw6GsOaQ0WfS5FZclqmZC2V7zwqPvuGCtSyehmsdksxnpiJIoqaRPvvYqOGCvYoY3SSzjtKP+8He0hKoyW+gLyffXxdg4bKaStjbgPB9nMxqY2OQcouS3iMOvrze5CHXrCsKDevRv85549e4qLi4PPLyGnuSK9wGBzF2ApImd4epWSLeBu6gCfxFBi3s5ha6NPOdcZ12ZskvANGKAVkg4zX9HUJrdxjFXkbEs3unclFS135lWrVu3cudPPUs164rgFvZ23ZWzmJmX21t1P1X13cnJyyGzE3XzfT4dBb+C++2ZmdLP4plJbjMNI/E+VxVwrNCAEcMr5pWS2Vs+6s8iQn5Zk7zMtdrbXFBOdZlZDc3N5Nq0Lkm3y1BrFyjUEFqVNyc5MoQMU5bQ252xLNRRmMHvcP6e9nTyX1c4I30QAb2022bljYoEIKpnJ2XWlGdz8PmkaGYj6LuxwMh1y9cjeampVrdbptJuKiQo9q7y5ucF9xo2fWtkqZpc+8eQ9meyrMvvEh2bX2VqzjUrNS3EbJ2VaPkVta7Z6nk5KMY3fSqYoqnZTDT0WlDc0N5a7xoLEyZVenRXNG6kDqvvuDKRkajOyM7QumtbqnG1FhsJkb7jeo1u7qVnCfTDvkkO15zRlTr3TYMtL1vZ1LAGfcuzc7vvvv29raztw4ADRM48cOXL06NHOzk6iapLLBOYSu4iZDjvoSzp+bmcpoftX4rp2cJjJhWuRQSVXEo42QxWZqJIt3WDhLoZ624z05VB6UYW+qog5Sv/LrVVJp+hLNnpul15lpoP05jDq6Y4UVZEOkZ65tlqPLrPpwv+f1gGENq4lAnO7NkMq3RkDm8ZhriJ79YH0xcBsXBWhDTjMuaR9Hso/26Msl3WUa5oBsfjO7doM9GDAff3N9Cy2KBDLDFhnGBKo5rszEAuH0UAbgGwlHgOyw2YsIt+f9CK93i0LkhS5tfzwN1C5kThOy28FBtr/sF8fTskM4pRjlcxvH2HQ+P4razYRB1dgMBPRyGYzlxBYqWXmNiJZ9ppp7Z7Sc2c0c1sitcQQCSK+dQqNo0wcm9RmNOhr9SWkb/SWywphQeDjKg65kjlAh1wNYW6oFLh1PYeBNhhlcCnLjiqyV2RQxMntMDLkBf4Z3e3z8XbMxRbF9Y6c/VUkfwnzTeAMoaAA843w9XbMtYjiTCNJyWT9use4aa4iX61UXl9XkFVEm6KK785ASibdO+LVavW1VUWu0c3lHiRfKYqCC+UBRowtIrchafdTT19RldQTZ+QgX37a/QR4yrnu233+8fsffPDBxx9/3NTU9Omnn37++ec7duwgOn6rqUJgQKKoMqONngS5/QTdXxt93a0Qb+cwCzeb3Hx0j6WsiRyGMvqcoJdCBIWPM/TBgwffe+89blf2gJncfBbcypgVEEx9DnpZBz/EsN4iV29xN4a5SamUu9bk/q7g5vbU7ks5fpWKj/Ngr05KAhlTVzCbG0ioP9vKiNF85nakwQo0Dfn6E3XHTyJ95nbsZUeu3sxlZy6z+FORi1dsQC3fnVdeeYX8JqKfGC2GMjJmsFdXtCynoitFVw+ZC3SBca/MxlzpBnrKud6Tefa5Fwxxb9xbxJiXh01us2TSD5Q7yVpwe+X4yctTS8wN+Uk6bQNpRFZmslskbm2oJstDyzLSBNoW9ihtcqaxydj37hVphlbnbi/bJm3GrFnU0k1kh9zDI+vdczP5Byza2/29G0Syh/o9mcmZNUZjX9ma6VAK2xOKqNuTcsh1HHcLz9pcRw5lpiW5ElB26zY6hzcB98Fwf2pT0vw6Vfq0NmtWmjuGPKWwnHyXM9J0/red/GKZ/4klp9QlEeNsstprKNYGzvZ2UmZSklaBppH2nkzaJplpyS5iztbq5S1UakUglnFljcyHer47Ab0nMzkjM5daaiVMndY6MshlTeOGaztzKvrc4I8MfvFatdmNbRl24n/ooZs8Ajd55hoyt8sn/WhnB7dkV14/Tzn/1mSyrtbrvl0TPa0sYIVMm5HMKohKVu89c/LzEiSSyZir7FSinrGX25wo7JrqpVb4uYAu5ErmAJDoe6gVnAjYa6mqMJi8F86xHUz1mAsOUGSkD7MN9pi62egrVbd2YWSE9KJ6S0DNJGc72QLKIiUxq/Zzk092SkSs5DM3or8+kTVN4Eqmx9TNZiDtL3Dd3rYx04jA7qdKgSw5r5q+O/4omR5A6LUUFWRZLKOLZPGTb2Ykp9K51cIeWZQb9Bqigzrl/HwCgUXgqCVaKa/M2PQFRKlJzcoi/6kSPT/WKhcYaZnDUpSeXqJvoh2zw0IGUNdJEBQ+rqehVjK5ioQCrvGlpKKshNmIaFFQb7Ex1yOuBSk2IzGdehYO0Cs40unTimypBRXue402o8cZl6U3+qu8cdDCukqF1OowElu45H1GKk+vopVnBZrGbyWzTV+U67ZMVpV7EQS53iWRrrEgq8So6IeQuNOBNgXrm9Xy3RlIyXTUF6VnlbCrKZh7w67hWoYrRU9qEQk7LLVkOOAmJEGccgF5O4E+sosnBA4oNop4O2YQZf6lc19X0t4g8Cmhl+wEItU1ArF9c627MeoLyH5WFq3Zk++AaoagfrGq65RzWAw0/fQscpWdXsKLH9FoGoeDfuK2X+Mp7GDUfXcc9CPR7q2girmmZ5lLvlJUmOnY5gR2ygX6Vmg3SJV/si8tFHq/NXnAjr63FejdLfKezOeee47MrBQHJsgOKa4fsjQoUm+FZs43n5NKSaYh78m8/vrryc94ycI5SgpRkoFYpOQ9meQcjo+P74ew+OBG9XOonwKj5lDwv2+nagTsihyhLgwZoiUHhY4MFEd+SY78dtRAqcJ+PPgOhb2poa9Qp9ORV2WS/6GvyqsG5nzziqF3lGQasiSN/DTd8OHDfVoZwxFKMhBrBmKmyZMn9/8DdeKDGznj6C1mLRqjc7uYtTc6DgIgAAKxScCvN4fFJpqAeq2EX/wJqMGxmZioQGSLzb733+tAfvGn/5JwNIQE/PzFnxC2QM1Fx+6sVl6rRfA9mfJ2JLpLw++4itlX+nsyxUpGvIwEBnpPpoxVRWFRUDKj0KjokhgB9pXQkfhBV7EWIR4EQCBMBKBkygMaSqY8HENcSvh/zTXEHZKteCiZsqEMZUFQMqXQhZIphR6fF0omz0LBIeLtFNy6SDYNSmYk6ftdN5RMv1EJJISSKQAFUdFKgH1PZnjflhmtLNEvEFAZASiZ8hgMSqY8HENcCvFzcHWCjKFkCmJRWiSUTCkWgbeTQo/PCyWTZ6HgEPlNerIpuIERaxqUzIihD6RiKJmB0OqbFkpmXyLYj2ICWJMZxcZF10CgfwKY2/XPx9+jUDL9JRXRdOTZAzx+IGgBKJmCWJQWCSVTikXg7aTQ4/NCyeRZKDiEd6mIGQdKphgZRcVDyZRiDiiZUughr8oIsDft8ByCysyG5oKAHAQwt5ODIkVByZSHY4hLwdPlYoChZIqRUVQ8lEwp5oC3k0KPzwslk2eh4BCUTDHjQMkUI6OoeCiZUswBJVMKPeQFARAAARBQBwHM7eSxE5RMeTiGuBTyBAL7EEKI61Ff8VAyVWEzKJlSzIT3ZEqhx+eFksmzUHAI61PEjAMlU4yMouKhZEoxB5RMKfSQFwRAAARAQB0EoGSqw05oJQiAAAiAgBQC8HZS6CEvCIAACICAOgjA26nDTmglCIAACICAFALwdlLoIS8IgAAIgIA6CMDbqcNOaCUIgAAIgIAUAvB2FOV02u12p18Une3WVlNrO5fWKZaXjfevUK40BGgCgaCzt1tNpla/MRNziTK2W9vFD4rmCvMB5nzrpxOezel7rnoeEwr3U6xTDWyE+qSMONZqfrXFaacHGCt/JtJjk9DgFMi3xK+aYyFRjHs7e11ptiYhITExMUGTVt3MuzEf29sbqws1moTM4urWdjtz1NlQmpGQMC0/n+TVVJu4vGyZ0woLZyUkaPKqm/kz16dQRPQhYG0sJ+bIyKfRlTZY+xz13G1trE7TaBIzi5tbaS/lbK3R9N3y+rjB5vJpCdOqPczhrMnm8yQWN3qWr7yws7GcnG8Js2g2GQ0eA6JPU/ueq601eXw/2VB2jQcHUoCdcJ9W2coX5TR55EkobuRObz4JQgMTsLeWZpAzmowwZIApbO7ngsreWl2YpklILCbDEHvh4WwtJREZ7ABT2Mz5PLuJLnNaPjPAkFHLOnAzkIIlYLFY9u3b19HRcfjw4Z9//vn48eNdXV09PT0nT548depUb1RvxjKKSi0yGI0GfQlLw9Am0GFHW1MufTjX2ObgDjeVpVJUkYWJMOsLyGG9md6x1JK0RRYmncNSy8VzGREQI+AwVhFcFUYbSeAw67mwT/q2KsYeeiNnLUdtFrFPmZ7davX08XQ9b63eXlsTY+IsPtJhJtXlVlRVVVRUlJWUGVhb+lSmkAhjVTrpEsOm16wnvXWF+zRP4Fx1mAmN3LIqFxw9OemprCqzZ0Y3Gz7SXJVF5ZawbMrK9Mpm49kVRYVthHVqkd5oNOpLiMnIVsKdsp4NbWsipyIxkt5jgKHzUgX1zDnsYE7oXGaAYU51V3yvubaAGJOJ9ywPYWECVOx6O4cx3ePks9QWkbOryvfEcZjJCUXORKPn2Okwk5M3tazJDdVCp8mtJUkMdDEub0fGbDJEVXjldOfAZ18CtqpUgq6M9nX05qCH99QK9y4bScfTlxN9LOUwV1QYPOzjIEXl1lq4PL02Ymtm472dQ0+qK6pvc3jk4zMoLESfq1Rqift8Y3fLjH1bKXSuOiz1VQYLn5LJW+vhvmzGCjcbsysZc94W1ZtVwYbvmsJC5KykCgzuRjnq6TEiy3cwcFjoCzuKPzPpHOzVXlmT+/RnrpuZU9rCDDCuYpkrtnTfMt2V4tOLQAx7u16HzcaPdOw55+vtDCX0GJxeVmtsMtTXG8xsFsbbpVdww42DvtqmCsgYYqGvtuiLZ4vDxuRlr8h46A5bG7nCaLORysnWZmzydbB84hgK9b2AcM9geBPRMAhTGm96WZOxqb6+3mh2DweepNrqSRIDf8RGhvMKo4UebtwTPnY0oYuit6xa33IcNtpMbjuZjU0e192elYUj3Hfs62XON59LAeFz1buBDIUSno3DmEqlN1noS7p094SPmTuyZEhsie8JinPYG6rIntcA02uhJ2i+87A2WiQik776JmaAMboHGG9tg5mgM9fTjnrmai+rosnhsNBfBm83SRyl1wBjMzexgoBIG2MqOpa9nZeh2bHQ54ttY89F+sK6hHFjrNTGDM1keHAPxay3Yy/cbPoC5vylz2Ha/3Gbw2YsIkfSi/T6CtdUgyRhZoRcmpgNsAN6lt7METAz8k2f64+mMhe5gpISNpTFX3O4spr16VQqZ5peM5kkFhnIKMDof24lkwwVFrOhvirXZatU3js62vRFpOz0Cj3zSduRbF6m5BoZngCLgpXKmRo9zzeuCSLnKnecDjhoNjwxehJcRPec1io4b+cgnt5srK9iLvTovpdx+hvOYS+cAewwc7tU9+nHZbQZXCd0akFJgeuMJjM6ZtJGTkGjOyEtR7sGHJuRH2C8R482IzEvGWAq9FVFtN2YzUvkcBcXm5/R7+3oGZTQ5mVv5tKpoJYfal1HWa+W6vq2uycERKhkhxuqiMliYWV3t/PjpCFyspHbQa6i2gz0uEoPu/TGns31ns6QPRDt/4VMQcfZmBt1nveThLwdi93tmVzCHeWNkb7hkaW3sCAZkxUxg7W3t+M5OwxlZKjnxnrmepkq4fKT4aOIMyKfK6wh+i6al3gr5O1Ez1WPptqaSDncXTgz8f+s1Mbk9bzUcOVxWNjrMtc1B85hD5YBBVnpqNbnapodB1JLDGxp7AoA2ijsZI5KZVQHRxNzp5pzfkb+ajmLu9/cxsge3LlKX+S5FxYE1NQoThzt3s5hJF9vwc1D7GbGDu6miKe12RHEfU+YOCnmphEzh3OYXZMLrvRc+sKNXWpRZWyzGCrYI1W0ksAOTwXc2c6e5SWCq2I8GxBtYYcLCgfNHSj74G1yNeA54DLezu3YXBxYjO7bomRNkO/8j5Ux2ckIY74CIj/Tm5meqqSWmduIhNwHq40ePRiR0zWL4q5CmBK4wahPtrDt2gzEg7uWQTGVMhzcF2GuZvRzrrobyggYrO933Rmqt9Bo2sz1NBtyOnqK+2wu5j4fM+3DOezmGOgn47pKuJtwHtnZC+iCeos7jp3DMSOJuZaclZ5bLqN8GOlLnyyjzWaooK+B6DA97jAZPaZ6zBVSn6+Pu5JY/Yz2X/zRppiNRu/F1uz5o03RsgHKVJmZ0653FE9z7Xt/0KlcjxyQkDYtM5fawJSnTSlu7C0ka4W1Wmtd8eSb11QUziKJGxvmExktOy1JS+U7zLqEyTnVzda8FG3dJnJaTkt2V2pvb2fqce97Vxq9e9pMEXvokrXNFLXJ1F6TncJ2nzxKR1EpOgFCVnqFNhOfNC2TojZ44iJPJhCxOS2JjnNaGwl1avrktfQeuy2dPH5pmdFRmOZZrlZLHirJ0JEcpkpSWi5tPXZztre6QpH80CbR/bGS8zCF6Rhlp5ulSyIt9tzoRgueq65EzubStVSZkS3C2lxKomdOSuRLWD59/HKKDJ1ebJjDacQMTivOYZ5VACF79bTJVr25elofc/FFtHNPF1DJmVnUhnb6kDZlVmOvgxlgqLrCaTevTSmclUIM3DB/E7n4SNPpqPwasy5pcs7a5tbKFG0zfeJmcqMa1a6IM5fvoyJCsbsmk7nAoaf/6VXcTfs2QxUnizETAHYlFT+ZEFh4wso7rnvFzEpC/r4xfZuE6BIO5rrbY+LCXianC13txep1l2v6yy2gYNC5F5UQKOyEjJmdEOnSRYldtMJJc2QVC5kEeXJmVgS1tZFVQW1m+pZ+agmZ27ELATxA0zPOLHoNJ1Mpbz6yUiaXZOKXxnnkCWuQvXJ3613kQp6+vK/ibur0+nWusjImLy+wy3DayEqcNnMtM7ejZ3p95730MhaKXsOJczgok5MRg1MgSQGGCu5ONEuaWc5dUO8uu40+Sz2maCSelSj/h8FPAABAAElEQVRdZzUz1ebPcMYoZGrIzhH5O7vsCeOzjsldS4x+RruS2a9ZbUY9fcWRVUKet6K3IjKGsI6NVSzp5ZRkATedhB1Z+n7hyVHmJnNqCSd9sc9FGdrY8cdISsylx2bm6Rn3SWxkxtAibszut5Gxc5D9xrLqLhuuojUaemMVS+bLzKzATtczFyjMRYOHc+plZMx6bk0Fm9n1n0nscp+OWnKjP72oiTEbYw7W7r3Mk2euBSmu06Oo3tUIr9LCvWOm19i4hCnGB3Przv09V1kZ0yLYcM8T22EuIid8EXsJYaNPVXK5RufCOSzIrr9II7NWO6vMY4BhHZtLeKQVS0s94U0x9zvIFQU9IvFOixxlROxU/iRkn8xxryRgHlGlL9BtBpKxwPXUjY2pts/97P7aGSPHYtjbsSccRa5c+Y1Vxt232VzLhR2sgJ5Ohhsqq4z95veaDXp2CVVRFb+ajzlpbLUlTMpc5j/3HBi7kio1K4uuMMvjyegYOdP86iZ7szM9i0ZXUm/m8jBjvftpSNcd03QybSar1hiH5UpopqfS3BN7XG42wHgF9xTcQK+OdW3pBR6Tw152Sa3LTiV6t7/tU1gEdh3sfZqsXNLHdPIsnLsJjBd3L20XPFeZlMzzhYI3p8lhZr2Pe8bArtNxwSHPRnM1kccW6dWAOIfd6Pv/ZF2X9wDj9mTM5QX3PIy5lp7RpWfRZ7R7XZvNoGfXH6dXGfgvAl0juQNNJ0zNpc8EqsK9hIpcnHkYp8TzVRj9tzN2juK3y8kJI7Qxr1TUaulbIcxG3ktH36LjYtpbW+26pOQkoftKJAOd3KnV6vgCXKU4ySdXCBOHf30I9EXPHKYjPbmx9hDi65mqT8leu0w1JEYgPXtI4IBXAZHYEWXj2Y2+5yrb0L4E+22+C44QAuXC6bdHSjvY15J9Rwxnq8mqS+5ngGHs6fsFIHegidW4cUtp3Y5oe+DtIooflYMACIAACISFQIy/FTosjFEJCIAACIBApAnA20XaAqgfBEAABEAg9ATg7ULPGDWAAAiAAAhEmgC8XaQtgPpBAARAAARCTwDeLvSMUQMIgAAIgECkCcDbyWOB9vb2zZs3y1MWSgkZgR07dmzfvj1kxau44Ndff33v3r0q7kBsNJ388vCJEydio6/y9xLeTh6m5OEXDBbyoAxlKYcPHz5w4EAoa1Br2fv27evs7FRr62Om3Xv27CEPg8dMd2XuKJ63kxkoigMBEAABEFAgAczt5DEKlEx5OIa4FCiZYoChZIqRUVQ8lEwp5oC3k0KPzwslk2eh4BCUTDHjQMkUI6OoeCiZUswBJVMKPeQFARAAARBQBwHM7eSxE5RMeTiGuBQomWKAoWSKkVFUPJRMKeaAt5NCj88LJZNnoeAQlEwx40DJFCOjqHgomVLMASVTCj3kBQF/CfxgNB6yWDp/+qnr0KGuzs6TDodGozlFUYM1msHDhg0dMSJh1Khh48dPuOCCxIkT/S0U6UAABPwmMMTvlEjYHwGiZH744YezZ8/uLxGORZoAUTK7urouu+yy8DSEeLj9TU0/tX7TvretO37Y8fgxJ04fdTxuPKUdcWLECEpDDaZ6NadOnuo8RnXYE787oLHvjne+NCL+ZNIv/t/YyZPPu+aakWeeGZ6mEiUzNTV1IhxteHAHWwtRMhcuXDhkCMbtYAiCWjDUfPNAyfRlosAYomQeP3481A1rM5u/rq//fud/DzoGHx9z3pEzfnfaZenU8OFsvfHMB/vfsyUOZoc07uCP33/3zSfDGnd9+srb4yaMOvuiC/9n3rzBcXGeiWUPEyXzl7/8pezFokB5CUDJlMITSqYUesgLAl4E/vvaa19tbWzfbz+UNLVz8tXDz7nA63DgOw7j+yMtHyUeME+44Jdpf7zp//3P/wReBnKAAAjQBODt5DkPoGTKwzHEpYROyTTW1Ox6a+vB3sSffnFNwkWZsvejt/HFYd8aJk0YnvrHP5w7fbrs5UPJlB1pKAqEkimFKpRMKfT4vFAyeRYKDoVCyfyirs646Y39Q8Yfvqhg+Dm/SQhN9zUZtx7PuPXzTxt+qHjl7FfevOT2uWddeKGMVUHJlBFm6IqCkimFLeZ2Uughb0wT2L9z58fPPLf/2GntF/35tElSRUv/UTo+2ZK0+9Vzf3P2zPvvHYQFC/6DQ8rYJoDn7eSxP54ul4djiEuR8enyt1c//Grpky1nzPp5ziPhdHWEUMIl1x+5/ZnmIxP/M2vu588/LwszPF0uC8ZQF4Kny6UQhreTQo/PCyWTZ6HgkCxPl5vfeeeZW+Y2/zT68J+fjf/tNZHq7pDf5Xbkb3r/vW825t99rKNDYjPwdLlEgOHJDiVTCmcomVLoIW/MEXj3kX/tNn53eMaKQePGKaTzPZ+9O+bzqqvm3XBhTo5CmoRmgIACCWBuJ49RoGTKwzHEpUhRMo/s27ch57aPf9LZ5z6pHFdHgMVddM2ROzdurf/i1b8VBc0PSmbQ6MKZEUqmFNrwdlLo8XmhZPIsFBwKWsn8YvPm/7tr2ZeXL6em/0WZ/XPOedgYd2HlrJwfzeYgWgglMwho4c8CJVMKcyiZUughb0wQ2PLQw6Yvfur581rl97b3wIGRLxXMuGPOBbNmKb+1aCEIhJMA5nby0IaSKQ/HEJcShJJZc8fd/3WerQpXR+Bpxo79efGLr738Sf3DawJiCSUzIFyRSgwlUwp5eDsp9Pi8UDJ5FgoOBaRkHj90qOIPc3afe2vXpfMU3CeBpp3ILvnUlvTiwiUCx0SioGSKgFFWNJRMKfaAkimFHvJGLYH23bs3L/vH4bznqdNOU2knT5i2nvPF83n6as0gXNSq1IZotpwE8DWQhyaUTHk4hrgUP5XMna+8on+w8vBdr6jX1RGQQ9JmfJ25pmLmH4/bbANyhZI5ICIlJICSKcUK8HZS6PF5oWTyLBQc8kfJ/GzDhm2vfvTzn59QcD/8bdrQseMP/LX26bn57V9+2X8eKJn981HIUSiZUgwBJVMKPeSNNgJbSx/bael23nhvlHXstOfuuGHRzZMz5f9xhigDhe5EMQHM7eQxLpRMeTiGuJT+lcy3H17z6Z6u6HN1BOrx2/9T/6SevPNMDDCUTDEyioqHkinFHPB2UujxeaFk8iwUHOpHyXz7wYc+P5h44pb7FNx8SU07eseGho3vtWzaJFgKlExBLEqLhJIpxSJQMqXQQ94oIfBa4dIvE1K7L8+Lkv6Id2P4y8VXX5c6FW/UFEeEI9FKAHM7eSwLJVMejiEuRVDJfGv5fV8OnhwLro7QPfan0jff2vXhf57tQxpKZh8gytyFkinFLvB2UujxeaFk8iwUHPJVMt8t/VdLZ1L39EUKbrXMTTuVvfrD+s++eP11z3KhZHrSUGwYSqYU00DJlEIPedVNoP7+lS2alK5LstXdjaBar3v13ow/XJyK12kGRQ+Z1EgAczt5rAYlUx6OIS7FU8nccv8/TMeTYtPVEcz2Wx42bHyndetWFjmUzBCfevIUDyVTCkd4Oyn0+LxQMnkWCg5xSuYH6578fH9899X5Cm5syJt2JPephmfe+Ip5LAFKZshxy1EBlEwpFKFkSqGHvKok0Lx+veGzg91/WK7K1svd6OHPLsh64K6zLrxQ7oJRHggoiwDmdvLYA0qmPBxDXApRMp+7//7mT76Hq+NIH/vLM6/8s/yF9ev37t3LRSKgTAJQMqXYBd5OCj0+L5RMnoWCQy1bt27/2Hxk1kMKbmMEmmZf8H9vra08dvRoBOpGlYEQgJIZCK2+aaFk9iWC/WglsG/HjvqSx3/M2xitHZTSrzOc1qE1985/+UUphSAvCCiZAOZ28lgHSqY8HENWyrGOjndKHvv593k/mLeHrBIVF9z67Y5DF8+suX2hivsQA02HkinFyPB2UujxeaFk8iwUGdqcv+T0xWuO/mzvPHJAkQ2McKNsHfuHnpV8POWq2oJlEW4KqhcnACVTnM3AR6BkDswIKdROQJ+bd+raPw875wLSkZZD49TenVC0f1Ji1+mDjpCS29/STxr683UPPBCKWlAmCESQAOZ28sCHkikPxxCUUlf4N+dvrmNd3betLVAyBRnv/OjNjvYfyKGk3+fsP3jygycrBJMhMrIEoGRK4Q9vJ4UenxdKJs9CSaF3HnzQPnJS4mW/YxsFJVPMOETJ7HIcZ4+Oyr7rq51f7X7jDbHEiI8UASiZUshDyZRCD3kVTaD52Wd3mfaOuXWJZyuhZHrS4MKcksnFtK8tumHZkrN++1suBgEQUDUBzO3kMR+UTHk4ylfK7rfe2r3d1MfVQckUA8wpmVyCpII1Df8sOXXiBBeDQMQJQMmUYgJ4Oyn0+LxQMnkWCgi1ffnlp89vGr2g71ILKJlixvFUMrk08XOWvnDbfG4XgYgTgJIpxQRQMqXQQ14lEug9deq5P85O/NsTg4YM8W0flExfJiTGV8lkk9ma3jvd+ukf/10mmAuRIKAiApjbyWMsKJnycJSjlE1/Wai9+Q5BVwclUwywr5LJpky89HcHh5/17urVYhkRH04CUDKl0Ia3k0KPzwslk2cR0dCW4uJjv7xs2Llpgq2AkimIhUQKKpls4jNuvO2bH458tuF5sbyIDxsBKJlSUEPJlEIPeZVF4MN//7vV+vPY2Yv6aRaUTEE4Ykoml/jgk/dPv+O2X1x5JReDAAioiwDmdvLYC0qmPBwllPLF669/88W3/bs6KJligMWUTC79mMWr3nt8ncNm42IQCD8BKJlSmMPbSaHH54WSybOIRKhjz57PN76cePt9/VcOJVOMTz9KJpdl6C1LXrqrgNtFIPwEoGRKYQ4lUwo95FUKgapbshMW/lOr0w3YICiZgogGVDLZXIc+ahi9v2VW+b8EC0EkCCiZAOZ28lgHSqY8HIMq5ZX8JZprsv1xdVAyxQAPqGSyGUdfkXkgbpzhcTyQIAYytPFQMqXwhbeTQo/PCyWTZxHe0NZ/PnQ06Ve6qZf5Uy2UTDFK/iiZbN6xt8z/+su9X7z2mlhRiA8dASiZUthCyZRCD3kjTGDHhg3Gz1rHzF3qfzugZAqy8lPJ5PJ2/OtvN626/4yUFC4GARBQOAHM7eQxEJRMeTgGUopl+/Yv3vs4IFcHJVMMsJ9KJpf99AUr6u9bwe0iEB4CUDKlcIa3k0KPzwslk2cRlhB5W3HjmrKRd/wzoNqgZIrh8l/JZEvQjhzZk571cv5isQIRHwoCUDKlUIWSKYUe8kaMwEu3/6U7I3v4uamBtgBKpiCxQJVMtpCf3nrxnATHjPuWC5aJSBBQFAHM7eQxB5RMeTj6V0rDP1YcP+eiIFwdlEwxwIEqmWw5Z/z+1u++P2zS68WKRby8BKBkSuEJbyeFHp8XSibPIsSh5meeabOd1P3uliDqgZIpBi1QJZMr54y8Zc21DW0mExeDQOgIQMmUwhZKphR6yBtuAt998MH7Tz8/ZnHwr+SHkilos+CUTLYo8hNLtkcX3173imDJiAQBhRDA3E4eQ0DJlIdjv6X0OBzbytZJcXVQMsUAB6dksqVpBg06dd38zXfkixWOeLkIQMmUQhLeTgo9Pi+UTJ5FyEKv3nGX5ua7pBQPJVOMXtBKJlvgmLSLD489/52HVomVj3hZCEDJlIIRSqYUesgbPgJblt/bNvyc0Vf/QWKVUDIFAUpRMrkCOzY89j9Xpl44dy4XgwAIKIcA5nby2AJKpjwcRUppqqxscwyV7uqgZIoApqQomVyZ43Lv+ezNd3/49FMuBgF5CUDJlMIT3k4KPT4vlEyehdyhr997b3dTy+isO6UXDCVTjKFEJZMrdtySkq2r13C7CMhLAEqmFJ5QMqXQQ96QEzh+6FDNosWj7vm3XDVByRQkKYuSyZZs273jtKbXsp6tFKwIkSAQKQKY28lDHkqmPBx9SnmtcNngWyStTPEsEkqmJw3PsCxKJltg4pTfHptwwZsPrPQsH2FZCEDJlIIR3k4KPT4vlEyehXyht4qKO6dcMfIc2V60DyVTzDhyKZls+aNnztl/sKvp2efEqkN8cASgZAbHjc0FJVMKPeQNIYHmior/7jkwbo7MT3FByRS0mYxKJlf+oXX3Z+Tf9osrr+RiEACBCBLA3E4e+FAy5eHoLqW1vn7X51/K7uqgZLoB9/2UUcnkih69ZFXj42vJOwG4GAQkEoCSKQUgvJ0UenxeKJk8C8mhw99/3/TcC2MWPCC5pL4FQMnsS8S9L6+S6S6Vip+Vvwm/CsThkByAkikFIZRMKfSQNyQEns+Zq5m9bPj4M0NROpRMQaqhUDLZig4Z3hhr//YPjzwsWC8iQSBsBDC3kwc1lEx5OFJU7d0FPRffGCJXByVTzEyhUDLZukZPv/HAiYQPn3pKrGrE+08ASqb/rHxTwtv5MgkmBkpmMNR88rz7r8ePDJswatrVPkfkiYCSKcYxREomW93o2Xd8+an5y/p6sdoR7ycBKJl+ghJMBiVTEAsiI0Dgv5s2fW74fFTuspDWDSVTEG/olEyuugOPFMxZ99jICRO4GARAIJwEMLeThzaUTIkc9+/c+fnrb4fa1UHJFDNT6JRMrsaE24pfLbyH20UgCAJQMoOAxmWBt+NQSApAyZSCj/wcaMNDDyfeFfKFDFAyxcwUUiWTrXT4+PGDrrzl5cV3i7UB8QMSgJI5IKJ+EkDJ7AcODoWJQM38BT0Zfxpx3oVhqA9KpiDkMCiZbL1tb75wXoLjd/ffJ9gMRIJA6AhgbicPWyiZQXPcUrz86C+mhcfVQckUM1MYlEy26vE3zPtm/9FPqjeItQTx/RCAktkPnAEPwdsNiMivBFAy/cLkk+iDJ9btPzF87O+k/karT8HCEVAyhblQVBiUTK7qM/78t13vbv/mgw+4GAT8JAAl009QgsmgZApiQWQ4CPy3rm5nw4e63KJwVOauA0qmm4TXZ9iUTK7Wnx5ZcmvlEyPOOIOLQQAEQkoAczt58ELJDJQjvQhz82thdnVQMsXMFDYlk2vAsNvufXnJ/3K7CPhDAEqmP5TE0sDbiZEJLB5KZkC8Tp048faqksS7SgLKJT0xlEwxhuFUMtk2kNfl9F4166X8JWJNQrwvASiZvkz8j4GS6T8rpJSNwMa5uSdmLhj5i1/JVqLfBUHJFEQVfiWTbcaBhs3jTx244aGVgq1CJAjISABzO3lgQsn0n+PmJQUnLro+Iq4OSqaYmcKvZLItGZs5u/3YoA+feFKsYYj3JAAl05NGoGF4u0CJCaeHkinMxSe24Z//7Bzzi5GXTPc5Eo4IKJlilMOvZHItGTMn/8ude/776qtcDAJiBKBkipHxJx5Kpj+UkEYeAtv/858vzT+OyVosT3FBlQIlUxBbpJRMrjE//bt4ZuEdZ19yCReDAAjISwBzO3l4QskckGPLK698vaM1sq4OSqaYmSKlZHLtOePu0oY1/+o8cICLQcCXAJRMXyb+x8Db+c+qv5RQMvujQ1Hfbd++o7Z+5Pzl/ScL9VEomWKEI6hkck0aecfDm+6I5Lyfa4liA1AypZgGSqYUesjrF4Gf29tfXrx01D1r/Uod4kRQMgUBR1zJZFtl+/Iz7cd1Oc+tF2wkIkFACgHM7aTQ4/NCyeRZ+IRevvOvI+9+1Cc6AhFQMsWgR1zJZBuWeP5FXb++5uUleAhP2FBQMoW5+BcLb+cfp4FSQckUI/TivNz4PxUOjo8XSxDOeCiZYrSVoGSybdNNm35Qd+5b9z0g1tRYjoeSKcX6UDKl0EPeAQjo5y90/Pb6Ub+9fIB0YTwMJVMQtkKUTK5th2uf+cVZw9Pvxo/hcUgQkEoAczupBNn8UDJ9Ob75v8s6z09XlKuDkulrJjZGIUom17xRNy/4as+BD9bhqXMOCR2AkumFI8AdeLsAgYkkh5LZB8zbD62yJf5izBXX9omP7C6UTDH+ylEyuRaOm/vXPbut5MEVLgYBKJlSzgEomVLoIa8wgbdXPbzvcO/o2QuFD0c0FkqmIH6lKZlcIw+se+CiP16XdsstXAwCIBAcAcztguPWNxeUTI7Ie/8qs/54TJmuDkomZ6Y+AaUpmVzzxi556JOX3vimsZGLieUAlEwp1oe3k0KPzwslk2Xx+YYNlm/az7itkEejpBCUTDFrKFDJ5Jp6RsGarWuf/vGLL7iYmA1AyZRieiiZUughrxcB48aNnxs+G/uXe71iFbYDJVPQIIpVMrnWtj269E+PPDj2l7/kYhAAgYAIYG4XEC7RxFAyTS+/vMPwicJdHZRMsTNYsUom1+Dxy8rqlvzv8cOHuZgYDEDJlGJ0eDsp9Pi8Ma5k7qip2fn+J2P+cj9PRJEhKJliZlGyksm1eeyK9fqFdzli2OFByeROhiACUDKDgIYsXgS+ev99Q+WGpKWKeDeYV8uEdqBkClGhlK9kcs0+8PAd816oThg5kotBAAT8IYC5nT+UBk4Ts0rm5y++uL3mdbW4OiiZYqey8pVMruVj7/3P83Pmnezp4WJiJwAlU4qt4e2k0OPzxqaSuWPzZuOWbYnzFb0shTcSRUHJ9KThGVaFksk1eNzKZ6tuyXb+/DMXEyMBKJlSDA0lUwq9mM67Q6831n805s4V6qIAJVPQXipSMrn2Hyxd/Kcn/qU7+2wuBgEQ6IcA5nb9wAngUKwpmcYXXzS++7HqXB2UTLFzWkVKJteFMcVP1tz1vzH1HB6UTM76QQTg7YKAJpAlppTMxscf/9L09ZgF/xAAoewoKJli9lGXksn1Ium+J2uL/mHZvp2Lie4AlEwp9oWSKYVeLOZ9t3TNtz8cPWOeWn+KBUqm4FmrRiWT60jHU/dfMe+P5193HReDAAj4EsDczpdJMDExomS+8fdiq+2Uel0dlEyxk1uNSibXl3F3rfroOf1/X32Vi4nWAJRMKZaFt5NCj88bC0rmK3cvPTTs/42Z9Re+22oLQckUs5hKlUyuO+MKH/mk0WR49F9cTFQGoGRKMSuUTCn0Yijvi7fldk69duylM9TeZyiZghZUtZLJ9ahd/9Sk0ZrrVqhsnTDXfgRCSgBzO3nwRrGS2XP8+HO3zD6VOT8KXB2UTLHTXdVKJteppJy7LMeHv3xXARcTZQEomVIMCm8nhR6fN1qVzLYvvvi/2+aPWPzo8HPO53ur2hCUTDHTqV3J5PqVNOu2oxekb5x7GxcTTQEomVKsCSVTCr0oz/sZeaju1fpx/xtV90KgZAqetdGhZHJdO/a9tbP6wTn/qUjEs+cclJgPYG4nzykQfUrm1kcf+2/j51Hm6qBkip3u0aFkcr0bfnbymOVP6wuLTa+/zkVGQQBKphQjwttJocfnjTIl89W7C74/Mmj0/GK+h1ERgpIpZsaoUTK5Dg6OiztzWfknLze8U1LKRao9ACVTigWhZEqhF4V5D+7d+9rigt6rs8Zc+rso7B5FQckUNGuUKZmeffz5nc3DDnz9x3XlnpEIxyABzO3kMXp0KJlGvf7N4n+MWFoera4OSqbY6R5lSqZnN0+/dvaRyVc+84ebD+3d6xmvxjCUTClWGyIlM/JyBKJAyXxnxcrvfjiYdLc6fpSVIx9QgFEyjweUJUYS00rm/xsXrZ0ddeGVjonnv/y34qk3XHvx/Pnq7SaUTCm2g5IphV6U5D2yf/+bfy/u/NWl42b8KUq6JN4NKJmCbKJYyfTs74EXykcPctzyRJlnJMIxQgBKpjyGVq+S+dHTT29avPTEzEWx4OqgZIqd7lGsZHp2eey8QvtZadWzc3747DPPeLWEoWRKsRSUTCn0+LwqVTLJlK7juGZc0Tq+J1EdgpIpZt7oVjI9ez3qqpnHzk2r/1f5OWm/uqZYZauOoWR6mjLQMJTMQIlFSfqW2lrT/208edlNo66Ird9JgZIpeAbHiJLp2fcDdc+NOGhJv6dw/OTJnvEIRysBKJnyWFZdSuaW5cs/ff39kX9/KtZcHZRMsdM9RpRMz+6PnXW74+Ib6leubqqs9IxXchhKphTrwNtJocfnVYuSSX4DrPqWOT8kTBy76AG+9TETwtPlYqaOvqfLxXrqGZ/4m0tGFT7eYm6v+cui/Tt2eB5SZhhKphS7QMmUQk9NeTs7OhrXPPLj0ZOj5t1DXjOhpqbL2lYomYI4Y1DJ9ORweNcnPe++8ovfTrnm78s84xGOJgKY28ljTYUrmU1PPvlC/pIfky4YO395LLs6KJlip3sMKpmeKEZdcMkZhY98e/hU9Z9ubVHwb6BDyfS0WqBhrMkMlJhwesUqmbveeKOlZnPnmF+csSxWFl4KW4iJxZpMMTixsyZTjACJP+Om+cfa2ppef+Zbw7ZLFi2Y8Otf95M4IoegZErBDiVTCj1F5/1+584d1RsOHjsZNz1b98vzpLa1S9vZrYkb4YgXLii+m+oWOcRliLe1DbV3n5o0sZOLcge0HYeocaOd7l3fz/jOLmrY0G7fA4HGhE3JPGGjTgyjtG4oJ45RVDw1xL3bT7PplMOpMF+H+qFkDuk+GtcTf1LMCt1d8fEDGaj76LCOtsHxE4+PG3qiD4HuQ8N7hh8bNrRPNL/bfTSBEj39+GSyhDo+em/Q5/Vn//o88ojCoCFhNoUsPUAhAgSgZApACSJKUUrmkX37Gh74x7tr/m1LvmTswn9IdnUJnzw/9ubpp8+7bkTWZePe3zXMh89pb1ymK6o93Seei0jY9daYmy/T/bNi6L5D7lOu6/R/XzbuZtff6c/v5G4lJuzcNJaN/3DXiO4urW2v7vnLdPOePY0rLuhAqJXMg+9SK290/a36M0XcFtlO/ERV30itWk2t+iNV+TLlOcyf2EfV3U2nr1xP2Y9RxM9ZP6BW5VD72JxB9zPwjP0rmZ17E++/bFTWdSPmTdctXTXK1tW3gs5do7Om6yw+8Vy6zr26Z3PHZV03zLB7cLf7omXfWy5DE3Nn3Zhgc6fu3pv471z63FhaPqrjqJb4yF1bx5Da9xwNk+MZd8XvxhQ+9t3xYdV/ytm+bp27XZH/hJIpxQZhOnukNFEVeRWiZJ5wOreVlVl2fHHi/MuTCpfIgs6yaUTpu6cequ6i9sY9sGLwE4uGjXuj64LR/KD91fPDn6OoK4YL19Z9aGTljUMNVO/jb9gnjXaPcxS1792hhutP/nUqRca+nu7eS646yuTXfviPEY+/Sz30hv287tOy/phwRUFc8pYhr1LUE3+xC1cQSGxolcxuqnEtdc1dlJZ4uG5q+DnUGDKT66ZeWEBRd1ErZ9Lhl/9IvTCMyiNhkmYftepOKnkBdf9N1L5qqjyHmlVA1a2lqAIqWQRmIH0NLG1/SmbXyHk5cbc82P2XidTOtfEvbBly+yhd7V0e5jg6ct6iwRTVy12weNcd/9VbI4tXa6Y/2LVpxhF+ctt1+qbVmnnLyARY09NN6c7tOouZ2HXvHZWVM2RKQdemLMeep3R3XDfir/f1PrF6EHWf84IR/FnnXUVI9pJunEvdONe46anWm+dM+f21lywghozwBiVTigHg7aTQ4/MmJyffc889/H7YQ92dnR+tW2dp+vzUBReP/V/5ftyk6/Rn1558bvuhRNKj86j/DB1zR/GgjmNDKLe369wzurhCvLddI55nXZ3hwCRPkarr9MrVmlse77li6vF4D1HL1jycuLo7qo9eQPxil/aG63v+kmXb+fW46fMc7FAoXpNfR9IuTtccCtW7jw9+QO2iqIxp1Bgalmtrf4+yUtSCK5ndeOqKAqpyLdV+JZU0nGq8k6IuoLJvokXL4WdR2f9HpcTR3m7JVe7MYfz83S13nD7oiGCF+7YMvWHdsdsupN+mPenJEccvS3h19yBy2eL2WwkNxZ6m7VtGx9bTiau74j7n3TN+9jy2713tR9TJrKscZ412eMQP35QzhJp6YnkW7Rd1E08Vv3HwkvjEJ1ZrnrjGK7tHltAGJ2Td1X3s2H/rnm2dk5OSOeOS228PbX39ll5Whjd89guo34NuWanfRDg4IIEIKpkHvv32vYceev6227+1a0YXPTn297kDtjaABEOdy98+yo3eunNPeuXtGnl/3uDSV5w3UJSNn7ZxSYbseinhTYqaUnCC+lq3s1m376hrhNz3wdDdFPXq37RZ00fdvHj0vi7XVVfnUQ2ZIlwwkR7+uvfGdV7SQ670H9pyKnsGO/PjSg4yEEIlk5nYkWat+zOtTH7U7GohcYHUJGqMe642JoWO39dBz/PI5ChtJj0RJJv1PSopkdp1L5W2kpkRMpHh/NePkjnuGperY9rTdd41Xu2y1I54+5quJ0pPecVyO0d1JSvoQeaKczW7mhN37RnhOk26RmxaTaIH//XGEUSxfOUDtwzeNYiwmT6rh5HLtbu2UJNGx3+4JG7648dludzh2hVQIH748LHzCgZnF+/e+fVzt2R9/OSTAWWXMTGUTCkw4e2k0OPzRkTJ/O6DD94sXl779we+7T599LInxv4hj2+QbKHuYe4BihRp30sEK+q8JHY5ifb9RUMvXHf0V+N7PVQtz4rjLe/Su7vXxu00Dnnob/F/vU7XsIe+/Tbuqs7/6J0P3HcymewYB/91+kjXPZtuDXXuyXFkqnB05D/zBl83rff9nCHzqo+No077qjnxw62JHW6/SPIFsYXw6fJ46oZnqPyHqWuYH8F9dzX10Re0S/uGTPfO6rvkxPod3fZj5MhZdKC9nnpzGqX9jnrZQt3wW8q5j9r1AfUNk4Y+HJatn6fL40ccd0/jSFPi97xLTbn+BBvTvXf03x49uermI/Fd5DJFYOvcO9jKRNdsGfLJ83EP5CVk/WMUvUhpaFf+K47H1/XMu54+/EKx9pWdrvvB5Ew4b2IPibTUnv6fq3qG7Rnx+Ncn86d1du4d+eHWUTv3uC8c6Hxh3YaPHz9q7tKEBf9s2XPguZv+f3tnAhdVvT3w3+wzbLILrmCuSQ+w+ks+FTBLtMytQl/6MNPC0lBfBlaWS4VliZZP6WWJaaHlVq9cnsu4pbQJlgqug4qCCMzINgszzP/cOzPAsDnALPfOnKsf5t7f/S3nfM+9v3N/5/7uvU9LP/jgzuXLdpWAEIxkdgQ4eruO0Ksva+dI5pnMzO9mJR76/KtCj/sCX1sdOGZKvSi2W1N7bl3AfXKFMahYcMDz00jNtEFKQs9N8BHqG7es5l+8RMB7fXmyeNK0km2ZVBf22XQ3uHgXiqoDe5YPeqI0TVr9UiQk87KvUYOcwEE15BI/HmYojBKN2qHodknyWR9tnJ/o4yEeKZu5QsJ76cVOTSd0Nm635W2IZPaPGtfy/g7tEXcmQQ+QofPIvPepeg5+Q01Igf9NF/BzEAccOon8CIFNmKJygbp1dyKJjF9PSvaSFbOpYV/RapJlR4cHkczuof2aitoopfiAZCepTTQEFdVen07hLd9/F9xUMwN7uqT8GtXDTF1blTav9IV/ly8A33aQvzULPJbGPbgidJB80lvFn62lAgZbvqAndIpUE6fCQeI1f0jggnM12+Jrdk7nzc1U3dwVMHWKqJjoZe+5/feipJFU9tyU+Ph0mfyyz+trL8k536cs/un15Av/+5/dBIBIpsCFXw3RQc7GCFIHa8HiEMk8fvz4M888Y1MUt86ezduzJ//XP3T+PfSDngiMGGzT5swrFx5/TyKdrd5FTyeBWXNz3+Es3qGpqnDTlPELIGsRT14hcW86Rzyy1nDRLuxZlTLGe8UeDjxLQOpu9Igq41bxTsSKDp8VjuipEgbLd0klxWU8d1+YjO758STugkzN8QVwg6f2s1WlgSL+gt2+BWrSr664uYj33KIimSXu3QcMuWfOjmTwfoAkvkzS15ESGL2FUjfzGi29O1MJvaeTlHEEhsnePkRxjJx4lMypJGvXkag3ydAoQnqR9CwS1atRUVttQiTz/r4hgUHdW2lAU+jz0juc5fvldFBRSNllqiaRCKsqRIoiamxXXOTuE1Rj9ogCPQYMDaYudAhRDX5BQvYIFHXzcunUwEGlqxYGLljJu6kmoSLNoJeLt8R7VBFdoJ+y+EDAzjE1n1aI567kwGXWJDj2+vrOPyYc27fhrT66Fvv+4XC5geOmEzL9+uGfCjfvOJ35XcgjDz/43HNCd+MI1UbiQCRz1qxZfHwool180du1C1uTQjaNZMJMy5ytW6/98ptCXlEb+oDnzHfFnTo1EcG2CbJd3qtKtduWGScyFJ+l5t8tn9QgrLReOGO9cJVU2XA2CpWprC7GpQkdrCd7mspJDQpDPU1DQ5EyMJjKU7BLcuJZzVwhL/4SmbhWSYU3iUjhpxvUXlcH5W07J5OS2rjw6emJMFyF6SfkOFG9Dk/QUQs8hAeLh+lGqNiHvm+nIVtXkumZpGQDtZdydeAZ5CSsH7Vin6W1OZkGCdReyyYJFmSWGydGqsU7syEKLZy6pT7MuXyKO0lS7YpvPNKrMiUIfbWxRACD+0aLgKpDX+co3P0qqXW1VyrlXFU313gSoptIX2ZVlXKGDjT4zkZ1OGYzeMQTZMQTirwzf50+fva56V36hPYYPuyBsWNtJA1GMjsCFr1dR+jVl7VFJFNfW3vm++9vnDxZfPmaPjhEO+DRoIeH1jdpxzX5ab8FB7Vb/l1m6Njkp32Lw5Sf7dALqOilnlSKlk0RkNk1i8arvY2uiK8hWqFINfRZsfRbrmIZoedB8oupEUDTeeocCE6G9aHjoXVKVXSau5KskCqqfvGHtEh6r+aa6LCvriMdiU3nZNbJDita6JCHEQ8hCQLXdZyUVBIP2t1V0t4uyHxaaMEPpCiBeupgH8xVSTD6xcvfkG5vNqzStuutzMmkG/bYESsemlE+rCeMRWHxOLyH9/Z/q+HREYGQQ4R62Wb35VvI4szqfr4mz6YWEpGmWxiEcgUXC4XDetLpGp4UJqE0CXrDEwhkpM508NAtEHLhW3H+bHWYJ++LPSRkto6+QhCe/oLXb4WpCWNGx/949w8n8J+Qa9Ifbu3c/8emr7tHhHX/+9/7xsZaVzick9kRnnjfriP06stacU4mjOROf/vtT6+nZEyI/+OnowXe/f1eX+s/7TVHubqqi74z5vCIH+f4Nr8dm/12rAuYMYfbrWdFYHClj1+Vj1+1T5AWBjDdgrSBxukMwl/e9Y0fElCg1oTFQ6/P3fwTPeNO7bZ/PRm6VNWNeG4eEvjxOt9iysG5HV8pyn9WExncsAvjn/1CFLJQBRFL957UZD9qpETdJeJPm0bNg2/3Yrs5mYosairm2TxKNNVVKow5NYGanOIfBU8ZkH37aJE15MRKEpJEguqHQzCZlWzYRBKfojIEhZIielBYdJBs72XXp+5amZNJiOT4225bCKk6504dAJv9Ph7iJguu8fGrDAyGA6DSx7MqsA913RMYVGmY0wQxzwmx3p8c8CTBVcunkh8XSAwPnhdkgXp6GKUVH4O3DQQcP0tdAsABtmAlWTy7qiEVUuoDT7asehZiCZrQPiSfdpCyn7xX9dHY+ak72nKW/ukS+1TAi2+LX1h2ucrz6MavM579x6Fly3L377e0/L3y4ZzMexFqbT+O7VqjY/m+jkcyb/7557Wff759Pq+s4CYJ6FHTfWDn1+fB7QHLZbBJTrXnF9Ppg+Qg77ODxhZil6rMByfEG6ZrNmi+7mUZcB9uW6bPsiniCXtEJJszNEkzl3qWwMONkBNb+Ce2UNVMbPTQMXRvhZ6Lv9V/KqWerxL2VC2Y6pEyyhfWUzLLBzV4Pr1Bg5au2i6SyafDcNsXku0gSxiZ+RXpZghXCsn4DWT3TLLkDxICd/ASyNSRZtKe/ZCELDT6v/7zKF+4ZAMJmUTemmWWzdYbrUQyCw5QD0GG9CFbVlIzculF/2kkNdWmbhGKwNvVhazBhNR6MZWiCXu5YrGn54LYwKGR5ES2fvmOu3DbT065Oc6qF91WETcSWbviv4p+ZpYVHn9bMHCpISquGfxmzeHpoglrAgdOrdk2r+GBVtc+s1bE3t7ip6YQMqX85s1Lp49e3fr9yfUbAvveF/y3sD4xMZ0MM3HbJTJGMtuFzVgI35PZEXodLVuWn3/jl19K8vJu5l6s0XN1gaG6kLDgITEdrdfh5dXw2kyYeFk3XBNqKnhEqGuQwtcYniUQqcyu6A2Swzs5Sa3ZZAdqmAQxMSssNnxPJkRvaQGbfRkmvF0Flqa7HPJWzKYcLXhPZtNCraXAK99IA+PCJoQrBZ4NzE0dJHAxV9vgqKiv0J5vxaxv1WZrELApPbqPW3iRlN709HIP7N/X//77HxgzxvGXszZTmYEVo7ezjlEsn5NZ8Oeft//88+7ly8VXr1VWq4lfkDYgVDzgYe9e9pp+Zx2NWVkLRDJzbT8nk41o5Od23HNOJhv1YqDMZef+0Fw8yy+5wS255dHZz7d3r4DevXs89JBPz573lBbnZN4TUSsZMJLZCpw27Golkll08WLR2bNlMlnljYKym0VajrDWJ7DGv7v7o8P8+z7QhjYwa4cJ2C6S2WHRHFxBK5FMB0vmdM37DnyQwH96uXP6ZFn+hfxLx05t2e4h4fv36OrTo4dnaGiPQYM8OtPPqZirj5FMcx5t28KxXdt43TN3ZVHRrfPn7167dvfWrbs3iyrulFLTpTsFcmAuR2A3z74R7oGN7nnds0rMYE0CNoxkWlNMe9dl9UimvRVgf3vyy5fU13LJnRs8+R1dRYlEJPAOCvDq3t0b3uESEtLtoYd4wmai/uzX234aoLdrP+sSmazs6tWKwsLKkpKbMtlvZ89H+PurNLV8Lx+9h3eNZwA/uIdb6P3uAQHtbwNLWpUARjJbwomRzJbIOCq9PF+munG59vZ1fXkJr0rOqSwXekh+vnVj3NChvsHB4qAg8IL+993n2dwQ0FEyM7xdjGS2ZiC4t1yUm1tdWqosL1eVlSkVCjWsVFTUKBQ6lYojFIq9vETe3u5+fp369CkpquBNmd8ZD77WiDp4H0YyWzIARjJbIuOodK+QUPjfsHWF7GrJV+/79+5ddeeO4vff8+/erSkvr9XphJ6eQi8voZsbNRe0UydJp05iX1+Y+enfqxd+irYhQJcb28Ej25XFxeW3b8MnctSVlerq6ho4aNRq2NSq1bCuranRVlZCil6l0uv1fA8PvpsbvARdBP+9vOAwcvf19Q4N7dynT6P5VMfOlpVVWGfeYEML4bp1CWAks1meGMlsFgvTEvt1dRvQg3p6o26B6+/iy5fvFhYqS0rUd+/Cf1VVla6qqgb+VlfX8vkCkUggkfAkEj6siMXgF7nu1MJxd5e4uYk8PQWenp7QrQUENOrQ6ppwmhV7eDtwME05wiUJl0c9vgN7a6qrwdnodTpYV1VXc3g8nVZbo9FwayGXTq9W62pqqIdX9Xq1Ugk/8B9sSWqoBTJQO+DhnZoaFfgqlUqjVhOdjlNbq9Vq9ZAB/BbUBusgBqTr9dQRIBbzBAKuSMSHFZFIKJHAlREcBHBlBCtwceTm7+/brRs4OQstDXMyP/t6b/iQJyzMj9kcQgAjmS1hx0hmS2QYlZ59+JvFyXMsfE8m9HhKuVx+82Z1WRk4Rbi4h27W4AXVcHGvUtWq1XBxXwudpEbD0VJX6tA3Ei5XyOdD38gTi6HfhtEhdNQ8+i8MIgVCoZ7Hgy5az+XCf3iCBOJbAvrtoNAJC4VCPmTQ6WCEYBhWcsRi8KlQM5fDgf9QIXSqUBy8LNUhmz9P3DTFuvDbH8k89fXXhz75hNK8thbUBkcCmEhtLdfge0AvSAF/VltLOPRjp5RLInpQGFIgP+29YBekUG/LgIVeAaxUJVAVl6urreVxubBB7TVUTr//G4oQ2ipgBj5kgJrBQYIk4L0At0gE71TnQE7IBhc13t5EJAKs8N8jKMjA1/wtVVT1xqW6msD/khJy5Qr55RdT6r1/S0pKLp2/dn9E1L2zYg7HEVDcua4p44k0jHjYA04F+lxxHI4GLRde+bNngLDGi+qYcGEsgV+zjm/a5Gmht2tGC5jnAv99TO9pNc8B4wOYZEcNHmrBTyqJSgXDBrhlQ/XkMHiAfru6Gvwi9KvQh0Mi9KVUTo0GhhNUTZAMObVaqo+tqaF6bL2eZygIu+hRCpWL9hFQIZXN4Dv0lFswjGcoVwH9Nvyn81Odv2Ed3pQKPTwtm7Eg7Xpe2bnTp0sXqnULlvaP7UY9+qigpsbf398ojUEmQCk2fJ+yhcZBB8jJvKW0tPTMmTMjRoxgnmjtlCgrK6sbvbSzPMOKVVZWnjhxIi4ujmFytV+cv/76y83N7b777mt/FUwqCaGUH3/8cfz48UwSqkOynD9/Hsrff//9HaqFSYV37dr11FNPwVCNSUI1J0udm2i4UmH6pDPtJqHYlStX/h4Xt+KDD5qropm09nu7oUOHpqamDhs2rJlaWZgEviEpKemXtoznGK7l008/HR8fb+uPENkNwuXLl0eNGgXHt91atHVDiYmJ4eHhs2fPtnVD9qkfLkdgilYV3GJwlmXJkiUwlli6dKmzKAQjETGENiUSR34g0Iow165dm5ub+2+LPyVPBwmt2D5WhQSQABJAAkiAeQTQ2zHPJigREkACSAAJWJsAejtrE8X6kAASQAJIgHkE0NsxzyYoERJAAkgACVibQPtnqVRUVMDdzvbPhbW2Jh2sD2aUKZVKeM6yg/UwpzjMFxAI4PEYJ3m3HsyFBo28vOgPwzKHcgckqa6uhtlxIpHxc+8dqIkpRRUKhTc88OMsC7zqHWapOM2cDjCLkxlIDY9i63QwsdnCI6793s7CBjAbEkACSAAJIAGHE2hrJBMudxwus3UEUMF1jkLhLNpQTCiNnMY8JiM7hYFUYBrnOXOMpnEKy9QdZs517lAHnBN2BtDHdaiDa5u3y1odJYnKYP1hrspbEcGR+FCLhDP9SBHrFVLk7Y7h0BpJJBHTM9ivENUJ5efsm87hZOSw2zqq/CMxHIlk/HgIiEWs2MduZUx+LmdfOocTx3LLGJVxunNHtS8lRkL3bhIJJ/1IkVFPJ/hRZEVIJBl57T+H2uDtFFkrHpl/hvRv9VUpLGCqSJcMKErJhqcss/emEbIpdnUWC6RuRURVls+ACVGZ0uxsaWo0ObPp+eAlR1rJzoZdqiPpKSkrUjbBW/hYfbipcuJCY8n6bP2RI3plbv9Fo+PSc9jAvxUZ89PnJa5IgSfig1htGaOGTnfu5KRHjVYlyuRyWfbeBEJmx2YUtWJMNu1SrPB55AwhHbotLJPJCgoKiouLy8rKysvL4c453PozvG0ZXoEGN2mNizw72oAmPlNpSmPlrzI3c2+uSXJlJmiVtNe0ycrf3PXRSVK5SXRZMpgpeiO7bWRQRpYJqmzMZbEq2evh8Ao/ZTJO9kbYjM42bZpMxr5fWWYCIfFstoyRudOdO0ppprTuhMmlezcZ+46vZiSmTyXKA2V24LCzcGynSPeJnJwt25tESNNLBZUiPz+/qIgOFKtUeTlZjI6kiftPjutPYYMgcH7WlKPh0nkx9FbdH5WiiFLIqI8iLytHUbePgSshk3eviKm74gmKigcZG112q2iFjBop8nLyGG0hI+NWIvTmFirKyepAdMOGFlXlbT1Kwqf3Nxmnf1QiIUez8hseTuw0DWkpmmRuGTx3bHh0NVu1OGZyjOnkz8+YcjRZOi/EPKO5hRh77pgJrcpJj5w9XZa7C1KbHHnmCrV6yFnk7fIyxs9OliZGBCmKzIQgqqKtKXBXYvy+rKzVk6m7YLAMiNzaRCDzUkzYUhUdyZgngSiT+ci4KGcrpc/qfVlHVhv18RmQbtY9MUF6MxmoTzjWJxRlbSPR0yNMKaqcrSlwR2/1EVDIaCGfAZHMdA71SrS8plLkpMA91/Grs7L2mY644Mj0vJZLOG6PKn/fUUL6N474HckxnEXOZho8dxx3qDVqWZVzJCOGEwovSzZdaFEZ2HTuNFQIbgdEzpbKp4eIGzuWNh9yhkjm9evXCwsL79y5A/HMu3fvwrN08GwTRDVhUAyRP0KSC6nBpTITxg3RdZFMWSolU6rMMO5U5kKYJllq3DKkOe4vPDvX/GIQSZ4tzdyVmUoNg2BJMAyOC6WUQnUq5FJxp2RZXVzAccpQLTevDaSaiUWHmJJMY32lNDUcNJIWGjIpN8JWcn2gw6yk3TdaUqhOEPrAM49kFkopfZKlhjzK3I2wtZcpFqoT3LSizKWOrwaRf4NG0RtzwZxMNo1JgRZ/c6mOwCySyehzp0U9zHaw6Nwxk7vJhlKevStz18ZkY+9mDP2x69ypV4q62ZQkpfyP4fSpi2S245AjtLe78iGcls0taVk5wCxJmgtzOuTyXKrzDE/LLZRDJ5tLxe5JZl1fQ5/b4anSejkdt6bMXd+cNpC23tw7KKVp1DFB3RxS5lL6JOyqy5C7EXaF198Rc5w60HLueqqfb2ZJy66TS0nd6KoX2HBwJGTKTBnom5Qklb5wMaU56leZ3YwudFK2yQBNvB19sUXqfDlYjPJ2qfSZ4Cg9Wm23MA2M1sTbpUrljDZNqyoZdjb2dsw+dyxQSM+mc8cSfeg8MmkanCCGqyvq+oRN545RSfrWYzJMuqHcz15qNJK6F5xRO7trw9dcRSN+PnxMx4c3O8BnVA1/4fN6sLhxT82HFmIHrIG/xmX+gOD5adnyoHSYMZcQURenURUxJ5wkDonLPpXd+O4VyC9uGPSjtmPgc1zzt8Ea3MOj9Imrn3JaxCCFSEjc1uzsxgN5WqH+8JdaqOl/U+DCp+4WXn7WbkiOiwiid8MfRT4E1hgyyVHcP7dZfYi43gAmuY2/qvzdYKj4qBBTlFYBX56kFtO2MR9zfryDwDjb8hVbicEGKlrgoCAxo03Tdn4MP3furRC7zp1762PMERITl0Dm58MW+84dgwqqrBXQZx0N9YGgrHFZNHrAIpImz/VuR3dt/HZ5jz5h8A4ww2LwduD2aH83oFAWp4LPIqmgm1SkBw9YFJ6auy8xyFu8D1qPj6vrevL2ZcD00LSYCJNUDv0Vh1j4FXF6KkR4BKiRT3WacREhRrlVeRmLzpDw9RENI9+O00kc0r9VsvkrJDCNSDnZ6CvyM9LzIynJ4ynV6EWVd2QRNTKv84aOU4ZqWdw/olWFTNI1cmXx4+tvSR5JB4WiYxhiIZPADX7FEeMTyLZFeYqUIPooKirKMlokB3Ix1jQNNLB0ldHnzr2UYN25cy+FGuzPJ2Qydb1bBGmsOncMOognHymMUYD/gQ7DGx6BGzD6AxjbJcIVb9FuyNHm7trSJxCokaXZfbtT1LAyyRDIlGdDDA1uqOw1RaGM41Am/ihlydHRqZmnKFGVMhjqx2fmUnLKpZQ+u2TUul5OD/wZfE+IltL0xyht6vo0+L4uLBC0SNork9MW2mWyEBWnZc5tSJPorfwawn0Q9zPlkVNxGVOoOZsOpCfvlZn2MvJXmU1Fww3hfTrcF72Rijyz3TR0JLM+Zo7nDmMOPuXe5Oj4VMP9JfresDGQzsJzpwlTQ4dgvG/Xru7acN/OguftqLaVu6DLrL8PIc9MgvsS4fHx8JekZtbdcGkiJqMSwNtR/b5hid7YYFoN+Ox6feJTs42TOxglfTPCGG6ghlNGqFuM826yM5MgCb5gTv1NzWSJQqCjMjszOdqoUXjSeqnxKkqe3eCIi8/MZsQtyGZM0iBJKZNS9KPjo+FPav3lIGtNU5iZnGCyTHzd6YPnTgObO3BVST0kZlqSNtLX9AZxWHjuNOKolO0CzepmqbTjkOvoW6EN7y2DSKeJMDt+WxYb3jBH3dtim0ItY3dChVSgLbssRB9vTUR2NtM4mz7wVkY2dgYtd27G10w2ORBb7j2YvqdtFuqot2M6DZQPCSABJIAEkAAhFj1djqCQABJAAkgACbCaAHo7VpsPhUcCSAAJIAGLCKC3swgTZkICSAAJIAFWE0Bvx2rzofBIAAkg+oy8GAAAN+5JREFUASRgEQH0dhZhwkxIAAkgASTAagLo7VhtPhQeCSABJIAELCKA3s4iTJgJCSABJIAEWE0AvR2rzYfCIwEkgASQgEUE0NtZhAkzIQEkgASQAKsJoLdjtflQeCSABJAAErCIAHo7izBhJiSABJAAEmA1AfR2rDYfCo8EkAASQAIWEUBvZxEmzIQEkAASQAKsJoDejtXmQ+GRABJAAkjAIgLo7SzChJmQABJAAkiA1QTQ27HafCg8EkACSAAJWEQAvZ1FmDATEkACSAAJsJoAejtWmw+FRwJIAAkgAYsIoLezCBNmQgJIAAkgAVYTQG/HavOh8EgACSABJGARAfR2FmHCTEgACSABJMBqAujtWG0+FB4JIAEkgAQsIoDeziJMmAkJIAEkgARYTQC9HavNh8IjASSABJCARQTQ21mECTMhASSABJAAqwmgt2O1+VB4JIAEkAASsIgAejuLMGEmJIAEkAASYDUB9HasNh8KjwSQABJAAhYRQG9nESbMhASQABJAAqwmgN6O1eZD4ZEAEkACSMAiAujtLMKEmZAAEkACSIDVBNDbsdp8KDwSQAJIAAlYRAC9nUWYMBMSQAJIAAmwmgB6O1abD4VHAkgACSABiwigt7MIE2ZCAkgACSABVhNAb8dq86HwSAAJIAEkYBEB9HYWYcJMSAAJIAEkwGoC6O1YbT4UHgkgASSABCwigN7OIkyYCQkgASSABFhNAL0dq82HwiMBJIAEkIBFBPgW5cJMSAAJWINA0fnz5YWF1aWlNSqVVquFKvmwuLu7+/h49+gR2Lu3NRrBOpAAEmiGAHq7ZqBgEhKwFoHiy5ev/fxzQe7VQtnNqtJyNV+sE3lyhB5qnkQgEOhrazX6WrG2kmgqeaoKsU7jHeTX476ugff37TVsmFdwsLXEwHqQABLgyGQyOOuEQiF1jUkvPB6PSy8cekFGSAAJtJVAwR9/5B44fOn3c/Jyndr/Pk3gffyu/QUDHr5HPVqt6vxJTeElj+LLYsU1/0CvgUPC+40e7RcSco+CuBsJIIF7EUBvdy9CuB8JtIXAb5s3Z+//+TaEKrtFagcOdesd0ZbSZnnVOdLaS795Fv3ZradfxOiY8AkTzHbjBhJAAm0hgN6uLbQwLxJogUCtVvu/j1efPfjr3YAB2r89Lg77ewsZ25Os+W2v4NxhH83tqAmxg59/vj1VYBkk4PIE0Nu5/CGAADpMYG/qynOHfi3rOUQ0LolwbTXPWSW7KPh1m0/ZuaiJjz3ywgsdlhorQAKuRQC9nWvZG7W1LoFfNm48/s2P5b1ieGPnWrfmlmqrvV3Ekf7Ht/JyzPOT/jZuXEvZMB0JIIFGBNDbNQKCm0jAIgKyU6f2rt5QzO/JmfK2RQWsmqnm2gWB9PMefjVPL0nxxKmbVmWLlTkrAfR2zmpZ1MuGBH54+90zp68rR7wk6vugDZu5V9U1WT+5/bpl8BPDH02afa+8uB8JuDoB9HaufgSg/m0icC0ra8fytcUhQyWjE9tU0HaZa7an9iQ3JqcuxkGe7SBjzU5AAL2dExgRVbATgQMfrT555GzNmDcFPULt1KRlzSj/OOT184Yxr0yNGPeEZSUwFxJwOQLo7VzO5Khw+wis/8esAo8w4VNJ7Stuh1L6DfP7hUimrH7fDm1hE0iAdQRsNVuadSBQYCTQEoGis2dXjny68IEEJrs6EJ4zM+1PQdgn46ZW3bnTki6YjgRclgCO7VzW9Ki4RQSObth8dNdR/ewNFuVmQKaaohuSLfOmvv+vnkOGMEAcFAEJMIUAejumWALlYCCBb5IWXSrUk3+uYKBsrYukXzdr5LMxQ6c/13o23IsEXIcARjJdx9aoadsIbHo55U/9fWx0daAn5+XPDx69njnvrbbpjLmRgPMSQG/nvLZFzTpAIG3Us5e6jJE8PrMDdTi4KGfSohxV1y0v/8vBcmDzSIAZBNDbMcMOKAVjCGiqqtIenVTyj08EEcMZI1Q7BXF7cnae38hPn5nVzvJYDAk4EQH0dk5kTFSlwwTk166tiHuuPOk7oV9QhytjRAWCwaNvR81ZNSaeEdKgEEjAcQTQ2zmOPbbMMALXfvtt3aw39cm7bfcdA4doLOgXfjfhi1WxT+k0GocIgI0iASYQQG/HBCugDI4nIDtx4qvFa7WvbnG8KDaQgOvuUTF/96q4KeqqKhtUj1UiARYQQG/HAiOhiLYmcPHgwcwPv6qds8nWDTmyfi63esGOj+Keqy4tdaQY2DYScBAB9HYOAo/NMobAuYPSbz/aUvPifxgjkQ0F0SbvXvXMy3cLCmzYBlaNBBhJAL0dI82CQtmLwLkff/z+s+91czLs1aDj29Et2PbJP5Lw7WKOtwRKYF8C6O3syxtbYxKB/JMnd376XU3CaiYJZQ9Zal/f8cnkl2u1Wns0hm0gAWYQQG/HDDugFHYncOPXX7csXVc716nv1bVMVTP/u49Hxetra1vOgnuQgFMRQG/nVOZEZSwkUHT+/Ka3PtXNdc4ZmBZCqH4lc00cPodnIS3MxnoC6O1Yb0JUoK0EqkpKNsxZont1c1sLOlt+obBk7IqPx051Nr1QHyTQHAH0ds1RwTTnJQA3q9bGz9Yt+NZ5VWyDZoLQ+xSj3vhkwvQ2lMGsSICdBNDbsdNuKHV7Cax58jllQnp7SzthOX7I/cWD/pkx4xUn1A1VQgINCKC3awADV52dwJqJz5fGvcHx9XN2RdumnyByxKXA2K/nvt62YpgbCbCKAHo7VpkLhe0AgYyZ84rDpwh6h3egDqctKhz69Fltz92L33daDVExlyeA3s7lDwHXAPDN/DeveD0sfPBx11C3PVqKn3glu4B36JN17SmMZZAA4wmgt2O8iVDADhPY/2Ha+cpA/ojnOlyTk1fAnZR89Pj1377d4eR6onouSQC9nUua3ZWUPrHhq5M5CsG4JFdSuv268hJWHPjmgOzkyfZXgSWRACMJoLdjpFlQKCsRuHDw4MEffuf+Y6mV6nOJampmpme+/Sk8legS2qKSLkMAvZ3LmNr1FC2+fHn7R19xXvzE9VTvqMaahLWf/3NuR2vB8kiASQTQ2zHJGiiLVQlseXmRNvFLq1bpKpVxfPzuRL/6+XOJrqIw6ukCBNDbuYCRXVLFT5+eKR+VTPh8l9TeCkoLBz5yvUvsN/PetEJdWAUSYAAB9HYMMAKKYG0C37zyelG/0fx+D1m7Yteqjx8dn1vlf/BjDAW7lt2dVVv0ds5qWdfV6+DK1ec1XYSPTHJdBNbTnD9u/s+/3Pxr927rVYk1IQHHEEBv5xju2KqNCGRt3fnzH7cF4+bZqH5XrPafH/zw+X9v5eS4ou6osxMRQG/nRMZ0eVVu5uRIv95Dpqa6PAkrA9C+9Pm3ye9auVKsDgnYlwB6O/vyxtZsRgA+w/3Na+9rXsDvG9gAMZd757HX//OPF21QNVaJBOxEAL2dnUBjM7Ym8O/JiYrRr+MkTBtxFvb/v+vBsZnzcYqmjQBjtTYngN7O5oixATsQ2LrgjcKesUKchGlL1oLYKefKfaSr19qyEawbCdiKAHo7W5HFeu1G4NCatbkKb2H0FLu16LINCSa8duz4pdx9+1yWACrOXgLo7dhrO5ScIpB34MDJo1e4E/FLpPY6Hp5fsytts+LmTXu1h+0gAesQQG9nHY5Yi0MIlBcWbl+5qXZGmkNad9lGVU9/uCkRLy9c1v5sVRy9HVsth3IDgYzE15VPL0MUdibAC+5658F/bk7EjyjZGTw21yEC6O06hA8LO5DAllf+Vfq3ycIuvRwog8s2LXho1CV9v/2pK12WACrOOgLo7VhnMhSYIrD/g48vaEO4g59AHI4iwBv78qnssjPbtztKAGwXCbSJAHq7NuHCzIwgcPb770/+dps/Fj/A5mBzcP6Z+tPGn4ovXnSwHNg8ErCAAHo7CyBhFiYRKMrN/SF9F3f6h0wSynVlUU9J2zLvbdfVHzVnDwH0duyxFUpKE9j6r2WaZz5AGAwhwPX2Ln1k1pfPz2GIPCgGEmiJAHq7lshgOhMJfDVrfvH/TecEdmaicK4qkzDy0cuS8B+X4GjbVY8AluiN3o4lhkIxCfnp3Q8u8PqKHhqFMJhGQDRm1m8XFL9uyWSaYCgPEqgjgN6uDgWuMJrAn7t2nT5bJnxyNqOldGHhuM+9f/Tb/xX++acLM0DVGU0AvR2jzYPCGQgUX7jw85adkoTlCITJBCSJH+17Gz+Dx2QTubRs6O1c2vxsUf6HlLc7Pf8GW6R1WTl5IpF+5NTvEl92WQKoOJMJoLdjsnVQNorA1pfm8qKfdvPzQxzMJ+D7YFSpX59D777HfFFRQlcjgN7O1SzOMn3/927qXd8Qn8HRLJPbhcUNevK5KwXl2Zk4Y8WFDwJGqo7ejpFmQaFoAr9nZuZfK+381DTkwS4CgQn/+v37gzd+/51dYqO0zk0AvZ1z25fF2hWeOXN6557AGfhlGVYa0e+V5QeWf6CvrWWl9Ci0MxJAb+eMVnUKnfa8vcxvTqpTqOKKSnD5/Nonnt8+8xVXVB51ZiQB9HaMNIvLC7VtVqL+8ak8odDlSbAYgP8DD5X2CNu7eAmLdUDRnYgAejsnMqazqLLn7aV3g+/3H/R3Z1HIdfUIGvVMQZnu5OcbXBcBas4YAujtGGMKFIQmcOqLL2+WaQJHT0YezkEgYOrc88d+v3rsmHOog1qwlwB6O/bazgklv3L8+F+Hfgl4LskJdXNhlfxfXnYo7d+q8nIXZoCqO54AejvH2wAlMBDQVFUd+HB14Fx8PZgTHhHiZ+Z8Nxu/CuSElmWRSujtWGQsJxd166xXJPHznFxJV1XPM7SfJmzEzgWvuSoA1NvxBNDbOd4GKAEQ2D3/NW34o53u64c0nJWAX8yYUp6vdFWasyqIejGcAHo7hhvIJcSTfrSqiO/vO3yUS2jrwkoGTJx5JffGXzt3ujADVN1hBNDbOQw9Nmwg8Me27y7k3QyaOAOBuAIB/xkpJzN/wM/guYKtmaYjejumWcS15Ck4fRpeDxb0QrJrqe3a2gbOW7Hv7WWuzQC1dwAB9HYOgI5NGgjASxT3vvOe/6v4ejDXOiI4XC4ZPytzxkzXUhu1dTQB9HaOtoALt//VP2eInn2F6vtwcTECPv0iq/sN+37BQhfTG9V1JAHsaBxJ35Xb3r1goTZipFfvMFeG4Mq6+w0fXczzkX68ypUhoO72JIDezp60sS0jgf+9/0GRINDv748jEVcmAFM0L1wozN661ZUhoO52I4Dezm6osSEjgaxNX129IQ8an4BEkEDQ8wv/+OnQFXyLJh4KtieA3s72jLGFBgQuHDqUs/9k0D8XNEjDVZcm4P/ye4dWrau4fdulKaDytieA3s72jLEFE4HbFy8eWbcxaO4yUwL+IgGKgOesJdtexrdo4sFgWwLo7WzLF2tvSODHhYs6v4azEhoiwXWKgMTXlz965jcvvIA4kIDtCKC3sx1brNmMQMbkafxJ882ScAMJmAh4D4xU9R2+az4+k2Aigr/WJoDeztpEsb7mCHyXOKdm8NhOffo2txPTkABFwHf46DuS4APL30McSMAWBNDb2YIq1mlG4PvkN+RdBgZExZil4gYSaEIgcOzUa6Wao/9e32QPJiCBjhJAb9dRgli+dQIH3k+9Vdsp8LFJrWfDvUjAQMA//pXzpy+d+e47BIIErEsAvZ11eWJtZgROfbFRdqsyeOLzZqm4gQRaJRA8M+WP/53I3bu31Vy4Ewm0jQB6u7bxwtyWE/h1y5YzJ/8KfC7J8iKYEwkYCPjOePPwuo0FOTkIBAlYiwB6O2uRxHrMCOQdOnRm3/GgmSlmqbiBBCwm0PWNtT8tebeqtNTiEpgRCbRGAL1da3RwX/sI3Dpz5uhnWwJeWd6+4lgKCRgIdE5et/X5F+HLUAgECXScAHq7jjPEGswI3Lly5Yd3VnSe/4FZKm4ggXYRkLz03sZJz7arKBZCAmYE0NuZ4cCNDhJQyuXb5y8KSl7TwXqwOBIwEHDz9RU8u/DLp6cgECTQQQLo7ToIEIvXE4CI05Z/JAQlf1KfhGtIoMMEPENCuaOnbop/rsM1YQUuTQC9nUub37rKfzFukt8b6fgtcutSxdqAgPf9D5PYyZkJ+CJNPBzaTwC9XfvZYcmGBD6Le9LjlQ+5fH7DRFxHAtYi0Cl8sDJi9PZX5lmrQqzH1Qigt3M1i9tE3y8mPCtJeFvcqZNNasdKkQBNwDdqePWgUV9Pxe8A4wHRHgLo7dpDDcs0JPD52Ini6W97hYQ0TMR1JGALAl73P6h9ZNyWaTNsUTnW6dwE0Ns5t31trt2XE591m/6We2CgzVvCBpAATaDToKFVYbHfJr6MPJBAmwigt2sTLsxsRuDzMU+5v/S+e5duZqm4gQRsTKDz8FGqQWO+/ie+f9XGoJ2revR2zmVPe2mjVam+eGKcz+v/Fnl52atNbAcJ1BPweuD/tP/3xLYZM+uTcA0JtEoAvV2reHBncwQ0VVWfjZ3gk7KeLxI1tx/TkIA9CHR6cHj135/e+Ow/7NEYtsF+Aujt2G9D+2pQevny55Ond122CR82sC94bK0ZAj73D+JNmvd53FPN7MMkJGBOAL2dOQ/capXA1ZMnt81/q+sb/241F+5EAvYj4NUjxGtB2hdPjofouv1axZZYSAC9HQuN5iCRf9323YG1X3Z5A18M5iADYLMtEBB5evq9+Z8N8c+VXL7cQhZMRgIEvR0eBBYROLXhi9/3HO88932LcmMmJGB3AkEp679buDh3/367t4wNsoMAejt22MmxUu5Z+t7ZM/ldEt9yrBjYOhJonUDw62uObtmV9WVG69lwr2sSQG/nmnZvg9bfzpx1tZwX8I+5bSiDWZGAgwgEvbzsrz8uSz9a5aD2sVnmEkBvx1zbOFwy+ILPpmkzlOGPdXt6hsOFQQGQgIUEAqa+mldYvW3mSxbmx2wuQgC9nYsYus1qlslkn415ijf2pU4PxbS5MBZAAg4lEPz0i9WD4jLwUTyHWoFpjaO3Y5pFGCHPqa+2bEn8V+fFGzy6hzJCIBQCCbSRgE/k33mTXv1y7MTbubltLIrZnZMAejvntGtHtPrvW2+fzb7SYwl+l7UjFLGs4wl49ujl+0b6t6+9dXJjhuOlQQkcTQC9naMtwKT24Ubdd3PmX6sSBjw7m0lyoSxIoP0Eur+1/sLl0u/m4DSr9jN0jpLo7ZzDjlbQ4ubp058/OaHyb6O6PfuiFarDKpAAYwj4PDmtzL/f5qkJVcXFjBEKBbE3Ab69G8T2GEng0MdpeT//3nXx54yUDoVCAh0l0HnUs4qQsK8SX416dnzk5MkdrQ7Ls5AAju1YaDSrigzRy2+en3X5tqbrwjSrVoyVIQFmEfDud3/wwk9O7ju5OzmFWZKhNHYhgN7OLpiZ2sjZ3bs/H/eM8sExnSfghzGZaiSUy6oEur701h2PXhvjp1zLyrJqxVgZ0wlgJJPpFrKdfD+9ufj6rcrgN9bbrgmsGQkwkEDAyPGV9//fvjVreh078djrrzFQQhTJFgRwbGcLqkyvM3fv3vQnJ94QdQl+cRHTZUX5kIANCHh06dL51Q9kd9RfT59588wZG7SAVTKOAEcmkwkEAqFQyDctPB6PSy8cemGcyChQxwj89823bt4o83lugcjLq2M12bv0ZYV3lU5o71axPYsJBEoqg92qLc7OiIxl537THdjW+/8iYv+1gBECoRA2I4BjO5uhZV7FOTt2fD520i1+cNDsJaxzdczDiRI5AwHfgQ8HzPvo0q3qzVMSLh896gwqoQ4tEMCxXQtgnCv5zpUrR1Z9UqLkBL2QwuGy9RIHx3YMPyrZOLarQ1qWe15zYEuX0KCx7y5j7zlSpw6uNCWA3q4pE2dLObjyY1nWaRI1JmDoY6zWDb0dw83Ham9nYHtn3/ba7EMPPDV6yMyZDKeN4rWVAHq7thJjU/7fv/46e8f32pCI4Gec4dRFb8fwg88JvJ2BcNFXaR7VxYMmP33/6NEMZ47iWU4AvZ3lrNiU89z+/We+2a4QePlMmCXx9WWT6C3Lit6uZTaM2OM03g5oys+f1ki/D/QXPzRtavcHH2QEXxSiYwTQ23WMH/NKXz1+/PfM7WV3NcKYJ33DHm5ZQJjcqGlxr9pdrqnx8Ww5Q5OSmgq3GqIXeCrvPWlSLdYQrVCkbVLHPRIY4e00RFFMtALi39korRaU0RC+B2nl2VVDHrHHPRRsbTe0Qgj/3nBbq8PW+5r1dho1v2Vb86sKRYLgqjaopRZXabhEqHUX3fvg1FSIhZ6qjmhddPwA9/f9waHdh7z0gl+vXh2pCss6nAB6O4ebwGoCyE6c+PO7XbfvKPQPjug8pJVbdGJZlvuCBbxV0uJQUX3rmmu+8VPqe+wFOxTDght2KMLj73qv2lOfH9Zil6pefaycqL12vCjecsmwS7/8v3fD/EwF1Z77Vko+o0rVrthRHeqrL74k+fhF/qC1ldMGtXmquoO9XSXJySS7fyBhk8hDI0lIN0IqyYn3yMEqEuFOcs6S8StJRH8zPtSGIc9ZOj2UzFxKuvlQ62dXk+2H6ETTn4iFZPxwekNDfl9HfqT3ztxAgnyI4irZvpD0fp+MfMCUm5G/jbydprDT4S9EJwaq3p1Q3kBe8fG3vVYdNCWMrNm2TN7Q22mu+cRPEZh2G371n0rvdBORgmN+c1N4hqShCzVzJyhMBfkFWZ0+XsDLJ+TJperJw5WkUnR6m2TVFt2Wk6Xu5nW1Y6to/w6Sc7xXRN+HEhJ8Q/GLj+1AyIgi9b0bI8RBIdpF4OrRozm7vi8pVugiYoMnj2q1Do99qyXnsjkExmFm+cQnPuTHztaFeRCNhtT4agebuTqiKXQHVzfjTa23UE+VK+OvWsMJ6wteTbIvVqxYqt4SVVN8TgJOdPE2t10v095O7fVJrFjaR/el9G7NOe+XJnnMWKo9/A4/n2g/aLurMxPW7huVeeSjhYQ8Sl7bQTxMXWzeOnKwF3lrFjWqG5pF1i4k3XYQf9Neg4xZU8jZSSRxFik5TbZvIhv+SV77LxHfplxdXJKpKjnZvoGEGEYOGrJ7EskJpRrSXiCrZ5K4hSRnJSkiZCazXV0jm8hP+23ew5XuIUMjzfZorrmtOqh/6U0d0XBqKknoyEYDO2H2FwIyRrdgcC1VTMTJTuFLI3WBIqK5CK6Os2pHZSDhH18v/mylcNRIfpgnFSEoOOAz9x3OjP9Uje3D2RzrNnUMf25P3qdbyNzMqo67Oqg/aNQkMmrS5Z8yr/7rje79eodPeabb3/5GiYcLqwigt2OVuZoIm7Nr14U9+0sqtJxBw7vExzXZ3zShMm5e5aADASfeMdsFfdCn2WTx66qwno16H2M2xTXO8h2KMJML1FzzI2u4YT1VRO3pvkr5QlQF5AuN0i6I9F6lAVcKC//serGU6Ff9p9QHuiq/2ieXKsc+Rorf4U/cUWnuEejsDP6jvU27umHkrXlm4cqC44RMMqZ4w1CPUMHGhou2gOx7iiyZTqUF9SJiOdnyA6mEmGQBmQ7urS4WWkAIbNI15G8iOYQkfkQ5Qq0PiVpIooYTxUoydINZ0w1bYea6z6DSVwd5kj0S80/siH95m0+m1gweWeXTbChSLZJHarbVD9qENX28SXwNHDDFlbWfSuUwwoNlxAv8zw7yNZVc4klIKeXqBi5UjQ2rIkQYMkb82Vul7ln+n47UDYXj03pLlyemkCemXN+78+bSlUE9Ow946sk+MTHWqx5rsjkB9HY2R2yjBk5+/vnFw8dUQm/ew493fXBom1oxxRnrCgl/+ZA6EpZPgUth96GzaxKnyRtdFAdGlQXWZQdPtotHpmqoFFHFsCjjDk2h26pssvwt6HRg4cohtjlS25Xunm5m8foOryn4yfvHMTW7TC7TWIzxP7+/R4k48lFS9BeB7rNbPyKm3XXIo+TEDrK9Mxn/KMmCz98Oazyw4weSlIR69fz7UesA2vtB4l2fTPL3Ul7TkFJ5tb6ekj9ItyhScpBkPUqWmFxjg3KMX1VzasxlpAZ2cFRcEszYAurqUzIrB/dUmmURVcRNaJBQ6v7pJbI8ksoTOEhu2iG8+BO4TI3h2ktTSV1djRpEOza15FeNbrDaa9kC7uL/1sU5TeWs8Rs8eiIZPbFAuqfw869/35zZb2TMoClTrFEx1mFzAujtbI7Yug3kZ2Vd+GnP9exz2p79JaNnBfVpeqeoHQ1qBq+o/rSQf/EXwafrOSfWCwqId9o0RYsVqd2k35KXMhpcOKvdzx6ULH7P/Ln1ShIyknrTV9VFvwVrar8cI5nxHlkllZMKr9NZ/CpP3eCouywY5FWSPBlF4uAm6rbZwR+o9ZmZpJsH6T2TRF0lWevI2XVUkLPRyI/KJyRi6se4lFyA8S/xbqSzhuT8QJ5cY8pURYKGUx5RdZWkbyCvPUo+WkMSd1D3/y6fJip30v9Blg3yTIpRv8Kg6s92CIqvCQ6v50svcVZM8Vy+v8YQjWyYrW694LSA9NH2pcOVdCJfftFj53vCHy+RELi8aLB0C6KC6odfFIW8WXVzt/u52ep3/bgFp31khaTbIGVocINjtUGpdq92iR1DYseUnj6VdfBYzne7ewx+cOC4p4L7W+VkbLdQWPAeBMy7p3tkxt0OI6C6e/fUF198lTDzwOoNlzmBAW+uD56a5G0dV0cpJfSs7NZXMWLanW07NDBOzF8vkKlbVLbqkuAE0VNhTNNSdU0g99RNHUltL57UqYAqq+k3uzZ/pXhCQuDU6ZxPpRU3vxANXKryvuY3YZR4ZxFc4IuSt3UyVcDcX62C5EMcMoEs+YQMnUXmwN07iDtm0gJ7kIi6x7EOkYLbdGILfyAcCmHMya839lXg1WAKiyGMCUW7JZCidWTJq2RFEpmzg5RkkpCFxKOALJlCTkBMUEY2fN9CA6xIFqkCgyvCospe3aRYnkTdAN56zK1lwd2y3yFDp1JhTNMiunmN0/dZ6pZe/nrhpwcgjgketGYiIQtiAycM8Tw7VTkpWLBgjX7VeN3hV7znzhEQP84Xk7wutHwwm2puz6/foEe6zEx2m7nsyh3dD4uWb3tlbvY338AHI9tTF5axPQEc29mecQdagDPnzM6d106eKrx8jXTpI/r7hIAHHu5AffcuKgxWzM3wOzGdJysShzbwZw1K8i8cpMKYhjsohnT3vophfQkZLuw3sNPiNZyLReJuPVWBUSXb9rsrKok3TDEv9Jn7be2qHdxlk3jk2Zq3p8E0PK9fhwg08TD+YcESYpqI5z+cRKwkOVepW3QQY0xfRxIzieo4yVhHMmZSg7CgZvXRkB9nkpErSX/65lxDhQ03/+rmtkCQ861MUllFPDoT/m2y9geSuIFsmUnIU2Tq05SnzBtLtOMau8yGFbJkXRMWX/nSMc/P9vA1T7RwDJSKvoTo+qCGoc6qsMeoOPngQd6fThKe2C2Y+xgRiiqmnVQ/WSgkHhofz9pfXvGNXaHS7KHuQ6fsUMBkq9A3A365JunXt2E91oQk9vERT3oBaiw5eThL+tvprbuCH+gfOnTIAHwy3ZqYrVAXejsrQLR6FeDk/tyx49pvp4svXNL4dtP3i+j6TLLVW2mxQhF10e0ubOESlQ5jzsho9mpZEzZcS9bUT/YUelYFUtff4sMpgifXVrlfcsuH/uuFCsojVHAFs7XNugbYybQF3E/d0nsYyaFvIV1eQ0KSSBA8RTeazHEna1eSggJqNkrTJec1ooAHDJoGuugwZlxdGJMuCY/uedNP5uW8R6LeJ2IZNSFz+hTaw1USfoITuDoDIb2wkpBI6mBrdik4Db2TLtQPrisaL8Lg6qEjhSdK69I1PvTNYJg8tSK79stV+s2xBC7IDPOKFWX6fsG2cnV1EsCK/5ARZMgIOHmv/2/HjczvT33xVZeB/Xs+MnhAXFzDbLjuKALo7RxFvpl25deuXZRKb/z+p/zGtRqfrryQsIDX5nL5dreRGm7760Pr55KAS6qf1wJBSwhjxjeaXGDSRkNPyAwNqg9ywp6qszDXQLdtkPrsuzD3RdfVMGv8mMB9YJsfuTO1Y79ffjcSRUjWBfL0cGOjCpiK+SiF5DIxPUIAPV0UCYOr+7qJFA0EzN9OckaT6abiMCfF41HjfBZVARXGjGky4IPSqjyyW0beeoDkr6bq8qf9X0kWEdNTXRpUz/RVn5avaDSXyNAX6q94zJ9DhzAmZ+hSTaPZUnXaakrhrnB9WTrdbf8U3tSMcp9KiRScXZTxMZj96/UzptWVs/kKvFE6KO4ZQp6pLC6+8tvB65k/nEz/wq9/n9AHI/uNGiVm22e2bM7Ljg3gfTs7wm6uKbgSPPfTT4c//HDztOczk1JOn8wt6x7u//q/u7z0VudR423p6jgKkwvTFPrMHxK4+UAnKqHCK30678lVSuMMTLXnJ0O8J7zrY8orvLCHCkXWhzHVnptfCdxhKKv22D+FP3SpquFD6+ARtr7IeykDHmzQBkZSCKD3q7roO/c9/dhBDUZMzcFhSFrUEkJ+oKKXsMDDAwcJeRqCikISEUbObofpI8Z08Fu9DX4LnpkbS5aspqKdRcdIxiaIsZGs7eTEdnLwQ5JRUD91swAeIX+qfpOuyfjnyEJq6gpc6XiDF6VncsIdvrVrSNQDDXMxe50OEpwoqutkhL+sDpzwiv+FQjEoJDvg+RmpnTacenYFluIDAfGxvsevmXwjHcaMbRDGLD7mP/9tP7osKT7tuSpb/68xDR9aJ/LTbl8S7di+KuKhGwiHGVWT5PACsfdatQ/dhJ3/eAQGdn3iH4GJS7zmriyWhJzc9/NXU1/Yljj7+OrVV6TgjnGxNwG7jxvsrSAT29NpNHmHDxefPXv7wuW7t+4QvwB9YKgoOr7LwEG2F9f9+Dq3nVuoSduL5wTMTVKOGFQpFOrz4bb/O6Kd71A+bsF/qoZRjy4ZFnoSeSGVn1rU4p3fkqn/MQ9jZpMt2aItdNm5a6v/NcjQ/xsKQC/m9mNkzTbog2AKxnDVxG/FU4cEkj7w7FSJ0aEaMzL3B+6lzXmTrE0iOcNI/nES9z4Jo71axJuk8j3y0VgS8SjJOUSefJ/07mzUAvwcuU25xvSV1DzMsxuoMZxheXq9aU1DTvxA3cxruiiOkaww8hYdFIVR49AfyIqxVD0wb8XwoELTIkxLqTrrm76efwLEWi/8uNR32uwyeELcHTxQNjdlEvUZ4YHPaj+T1j/WYricqntigZqNSXT9zMOY+Qd5KQehrBdd9g5U2GDx/HIOd8GOcsrHiapfWCpc8KLHl4RMXKF8wfyAbFDETqsCN7fAx8cTAv/JnV9/Lr167twv30hX/du7e5du9/cNeOCB+6Kj7SSKazeDbw6zk/1v/P570fnz8itXS65er7xboesUQAK68UL6Bz481E4S3KMZoUYN1+C1wibP/GrUcCWuEhp7Fj5ka5KHSoTqm6RTTWrUEkKUpuIGIeAai3IHbV0c/OYwDVHB+zCFjV9W2ex7MiERlnu+1pJ6waZpMNOQhoXFGxZhwnqjN4c1KxJ9OMGepi9KhSOwwSGkFmqoo9H8OKESwZlpmmMmrKrgupu9FbOdh1mzYls9EYI6RScPaa9dFpTe4FeUeQT7B/YKhdeS+Q8Y0C083OrNYYVAAL2dTQ6Dytu3b/31V1l+vuLa9bLCOxDB14q99D6BxC/Yvc9A7/54NLcHu4O9XXtEdq0ylng71yJisbbFp39WXgfPd4tfXspRV3sFdA7o1dWze3ffnj27RUaKPD0trgkztkgAvV2LaCzcoamsLL54sfTGDWVRUdXt23du3a4sLq2trdVJOhEf/1r/rqLO3f0iBuPXkC3k2Uo29HatwGHCLvR2VrEC3Oko/fNX7U0Zt/Q2p1JOquUiidgzwM87KNCtc2evLl18QkK63H+/VdpyqUrQ21lq7rLr18tv3FAUF6tLS5UKRVVJSfkduaq8XK3WcITuxNOrRtKJ6xPEDwx269nPo7PpBo6l1WO+exNAb3dvRg7Ngd7ORvgVsquaW1fUd4r4lWWkXM5TlXNVSqGbROLt5Rbg6+nrC8/8Sfz8PAMCfHr06NS1q43EYHu1rj5LBaLn8JqSiuLi6tLSKoWiprJSVVGhq6rSVFdX3y2vVFTolCplVYUOXBpPrBWIeG4eWrGH1t1b6N2L2yNQ7N89qHt3th8EKD8SQAJMJuAd2ovAf/NFcfVKyZ0CoighN8r4l0trq09zVFUQBdVrNCIPMd9NInDzcPftJPFwF3t4wEwZCIcKvbzc4Vl4X19Pf3/YdLWAE8u8XU11tVIuBxelUatramp0SuqhUZgvqAEXVVurrq6uUat5arVSra6F79ao1WqtFoKKHNiE3DU11A6lWqdS1WpqNOoarVrN0ddquTyeUFTD4XPFYiIWablijtBNL3LjevQQ9fXleHYS+nT27NLF/EjDLSSABJCAIwl497oP/jeVALrHilu31PKiKrm8rEqhLa6ovV7M16m5GmWtRgkf9BLoavS6GlKjJXyeRCLi8Ph8sYgnFIvdRQIhny8ScQUCjlAoFomoxzgEAqFQCFk4YjFPIBCIRLAuEothExqCvRIPDz6PB4kCd3fD/UXGOtH2e7uNH354MC2NAwuXS3kULpfo9eB4aiFFr+fCul5fC2/ChweV6f+GOeywDumwbtgEU/H0eiioh0K1tVQhSII/enjSSM+hylM79IRjqEcHs5I5UJSqhsflagls6LU6vZ7HhTzwrxYSOVzIUcshKmgHihOOjseDqmEdHFsNj8sRCFWEC3ISNzdienZbp9OqVSo3d/oh3qZHUHMpUCWTF6WyWsCHf/VvNrG6tGA3q9fZUoVwJ1RRoRKIqcnrTFk6pj6cMnD9xReYzaNnimptlwMOBp36rk+ntsynoM5l5i4aNfXYjFAEc5JtskBPaZN6W660suKuu4dXfbs1NUSp5EEnXKvl6LQibQ0PukkddLtEqK/V1eqhT5dQvTN0xXoe9KlUBw0dai0U4VK9MnTLdO9NtwidLOUDoGumemJq0UFW6KCpJEI5Aw7hQ99MNw9b0FdTvTos4EEgk0ks8AXQEOygfqEWDlQCCZTXoKqu8yaQVFs7b/v2h4daOq29/ffthkZGPhYZGdqrFyUQCEwLRElMS8mX0JQo8WjF4SoA1rhcPhcycnRCoZYuAm5ST3fHlMqwHy4RAAHk4XLF3t58uIKggNhjOX/+/Lp169auXWt5Y0bVLC9g35xLly6NiYmJtuWjPPYkcPPmzddff/3rr7+2L8XWWuug+mlpab169Ro3blxrbbBnn1KpnDhx4t69ey0XGc56yzPbP+emTZtAwunTp9uoafurHxcX9/3334tg0Gb7hRr5waNLcHuouhq6cdiEgFwtDDx0OlinhkN0BtjFUan44NL0+hqdDjp/qiDsbTCIgnEI0ekI7QsM/gVqhkPnxKlT3C5d0j//3EJt2j+2I+7uI55/ftiwYRa2xPBs7u7uXl5ejz76KMPltFw8cN5hYWEjRoywvAiTc16+fFkikcTGxjJZyDbJtnXr1r59+8IVSZtKMTZzZWUldFVOow5wPnr0KDgkZzrkDAaC84ixR1GbBCtfuzY3N9fyInYaOVkuEOZEAkgACSABJGB1AujtrI4UK0QCSAAJIAHGEUBvxziToEBIAAkgASRgdQLtv283ePBgHx+HvFvc6hCoCr29vR955BGbVO2gSiMjI4OCghzUuPWbdXNzGz58uPXrdVyNAwcO7NGjh+Pat3LLfD7/scces3KlDq2ud+/e9p9IYlONH3/8cXhYwKZN2LPynj17tmmmWPvnZNpTK2wLCSABJIAEkEBHCGAksyP0sCwSQAJIAAmwg0BbvZ0KnnxwjgUeBIHFWbShbEJp5DTmMR1kTmEgFZjGec4co2mcwjJ1h5lznTvUAeeEnQH0cR3q4Nrm7bJWR0miMlh/mKvyVkRwJHDXEV6lypl+pIj1CinydsdwaI0kkojpGexXiOqE8nP2TedwMnLYbR1V/pEYjkQyfjw85BSxYh+7lTH5uZx96RxOHMstY1TG6c4d1b6UGAndu0kknPQjRUY9neBHkRUhkWTktf8caoO3U2SteGT+GdLfVu/RsZc5FOmSAUUp2XK5PHtvGiGbYldn2atp27SjyvIZMCEqU5qdLU2NJmc2PR+85IhtWrJbraoj6SkpK1I2EQLvLmXxosqJC40l67P1R47olbn9F42OS89hsTqU6Pnp8xJXpMwmJIjVljFawenOnZz0qNGqRJlcLsvem0DI7NiMIpYfcCbxFSt8HjkD0wlN2+35lclkBQUFxcXFZWVl5eXl8Op/eJcy9cJlHbxmGV7mYlrk2dGG6uMzlaY0Vv4qczP35pokV2aCVkl7TZus/M1dH50klZtElyWDmaI3sttGBmVkmaDKxlwWq5K9Hg6v8FMm42RvhM3obNOmyWTs+5VlJhASz2bLGJk73bmjlGZK606YXLp3k7Hv+GpGYvpUojxQZgcOOwvHdop0n8jJ2bK9SYQ0vVRQKfLz84uK6ECxSpWXk8XoSJq4/+S4/hQ2CALnZ005Gi6dF0Nv1f1RKYoohYz6KPKychR1+xi4EjJ594qYuiueoKh4kLHRZbeKVsiokSIvJ4/RFjIybiVCb26hopysDkQ3bGhRVd7WoyR8en+TcfpHJcLrqLLyGx5O7DQNaSmaZG4ZPHdseHQ1W7U4ZnKM6eTPz5hyNFk6L8Q8o7mFGHvumAmtykmPnD1dlrsLUpsceeYKtXrIWeTt8jLGz06WJkYEKYrMhCCqoq0pcFdi/L6srNWTqbtgsAyI3NpEIPNSTNhSFR3JmCeBKJP5yLgoZyulz+p9WUdWG/XxGZBu1j0xQXozGeD12abjG9KLsraR6OkRphRVztYUuKO3+ggoZLSQz4BIZjoHM61a2FApclLgnuv41VlZ+0xHXHBkel4L2R2arMrfd5SQ/o0jfkdyDGeRs5kGzx2HHm0NG1flHMmI4YR+YN67sencMdcmLnK2VD49RNzYsbT5kDNEMq9fv15YWHjnzh2IZ96Fr5tWVFRVVUFUEwbFEPkjJLmQGlwqM2HcEF0XyZSlUjKlygzjTmUuhGmSpcYtQ5rj/sIL2ZtfDCLJs6WZuzJTqWEQLAmGwXGhlFKoToVcKu6ULKuLCzhOGarl5rWBVDOx6BBTkmmsr5SmhoNG0kJDJuVG2EquD3SYlbT7RksK1QlCH3jmkcxCKaVPstSQR5m7Ebb2MsVCdYKbVpS51PHVIPJv0Ch6Yy6Yk8mmMSnQ4m8u1RGYRTIZfe60qIfZDhadO2ZyN9lQyrN3Ze7amGzs3YyhP3adO/VKUTebkqSU/zGcPnWRzHYccoT2dlc+hNOyuSUtKweYJUlzYU6HXJ5LdZ7habmFcuhkc6nYPcms62voczs8VVovp+PWlLnrm9MG0tabewelNI06JqibQ8pcSp+EXXUZcjfCrvD6O2KOUwdazl1P9fPNLGnZdXIpqRtd9QIbDo6ETJkpA32TkqTSFy6mNEf9KrOb0YVOyjYZoIm3oy+2SJ0vB4tR3i6VPhMcpUer7RamgdGaeLtUqZzRpmlVJcPOxt6O2eeOBQrp2XTuWKIPnUcmTYMTxHB1RV2fsOncMSpJ33pMhkk3lPvZS41GUveCM2pnd214c5hoxM+Hj+moL9DCJyEMf+GNLLC4cU/NhxZiB6yBv8Zl/oDg+WnZ8qB0mDGXEFEXp1EVMSecJA6Jyz6V3fjuFcgvbhj0o7Zjxo8n87fBGtzDo/SJq59yWsQghUhI3Nbs7MYDeVqh/vCXWqjpf1PgwqfuFl5+1m5IjosIonfDH0U+BNYYMslR3D+3WX2IuN4AJrmNv6r83WCo+KgQU5RWUWQICZq2G+V3/KZ3EBhnW75iKzHYQEULHBQkZrRp2s6N4efOvRVi17lzb32MOUJi4hLI/HzYYt+5Y1BBlbUC+qyjoT4QlDUui0YPWETS5Lne7eiuje/J7NEnDF5zZ1gM3g7cHu3vBhTK4lRiMYRMxWJFevCAReGpufsSg7zF+6D1+Li6ridvXwZMD02LiTBJ5dBfcUhElEUC0FMhwiNAjXyq04yLCDEWU+VlLDpDwtdHmKYYWFSdzTKJQ/q3SjZ/hQSmESknG31FfkZ6fiQleTylGr2o8o4sokbmdd7QZrJaVLG4f0SrCpkqaeTK4sfX35I8kg4KRccwxEImgRv8iiPGJ5Bti/IUKUH0UVRUlGW0SA7kYqxpGmhg6Sqjz517KcG6c+deCjXYn0/IZOp6twjSWHXuGHQQTz5SGKMA/0MNVOARuAGjP4CxXSJc8Rbthhxt7q4tfQKBGlma3bc7RQ0rkwyBTHk2xNDghspeUxTKOA5l4o9SlhwdnZp5ihJVKYOhfnxmLiWnXErps0tGrevl9MCfwfeEaClNf4zSpq5PS6UXCFok7ZXJaQvtMlmIitMy5zakSfRWfg3hPoj7mfLIqbiMKdScTQfSk/fKTHsZ+avMpqLhhvA+He6L3khFntluGjqSWR8zx3OHMQefcm9ydHyq4f4SfW/YGEhn4bnThKmhQzDet2tXd224b2fB83ZU28pd0GXW34eQZybBfYnw+Hj4S1Iz6264NBGTUQng7ah+37BEb2wwrQZ8dr0+8anZxskdjJK+GWEMN1DDKSPULcZ5N9mZSZAUH0/F7OEcYIlCoKMyOzM52qhReNJ6qfEqSp7d4IiLz8xmxC3IZkzSIEkpk1L0o+Oj4U9q/eUga01TmJmcYLJMfN3pg+dOA5s7cFVJPSRmWpI20tf0BnFYeO404qiU7QLN6maptOOQ6+g3EAzvLYNIp4kwO35bFhveMEfd22KbQi1jd0KFVKAtuyxEH29NRHY20zibPvBWRjZ2Bi13bsbXTDY5EFvuPZi+p20W6qi3YzoNlA8JIAEkgASQACEWPV2OoJAAEkACSAAJsJoAejtWmw+FRwJIAAkgAYsIoLezCBNmQgJIAAkgAVYTQG/HavOh8EgACSABJGARAfR2FmHCTEgACSABJMBqAujtWG0+FB4JIAEkgAQsIoDeziJMmAkJIAEkgARYTQC9HavNh8IjASSABJCARQT+H5gO9R8Zi2wOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 67,
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='boxplot_structure.png',width=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cool boxplots\n",
    "\n",
    "But we really want two boxplots.  One for people who drink and one for people who don't drink.  I wasn't sure how to do it?  So I googled pandas boxplot.  Here are two of the links I got.  See if you can figure it out!  If you scroll down on the first link you should find the answer....   You will want your boxplots grouped.  (only spend 2 minutes on this and I will come help you.  Don't go down a rabbit hole on this.  Answer at the ened)\n",
    "* click on the first link\n",
    "* scroll down to where it says boxplots.\n",
    "* Now scroll a little further to where you see the boxplots that say \"grouped by x\"\n",
    "* look in the code.  \n",
    "* see if you can find a keyword argument in the parantheses that could hep you and figure out what column to pass\n",
    "\n",
    "\n",
    "https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.boxplot.html\n",
    "\n",
    "http://stackoverflow.com/questions/23232989/boxplot-stratified-by-column-in-python-pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc0542e8>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2tpaNGzce0rZx40Zqa2tzqmj4KmqfAnB+ROyS9GZgg6RfFT4YESEpSllxGi4LAaqrq2lt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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc05f320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What difference do you notice about the arsenic concentrations of peeople drinking from their wells?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Whenever you are comparing two populations a t-test should pop into your head!\n",
    "\n",
    "# t-test or student t-test\n",
    "\n",
    "A t-test tells you if there is a significant difference between two means.  Actually it tells you the probability that they are the same.  Back to our friend the p-value!  The [first website seems to have a good explanation](https://www.investopedia.com/terms/t/t-test.asp).\n",
    "\n",
    "Whenever you are comparing two means you run a t-test. \n",
    "\n",
    "I am going to repeat this.  If you ever compare two populations with a mean you need to run a t-test to see if the differences are statistically signifcant.\n",
    "\n",
    "You then need to choose if it is\n",
    "1.  Paired [scipy.stats.ttest_rel](https://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.stats.ttest_rel.html)\n",
    "2.  not paired [scipy.stats.ttest_ind](https://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.stats.ttest_ind.html)\n",
    "\n",
    "By paired we mean you repeated the measure on the same thing.  Can you track the same thing across two samples.  For example The exam score of the same person before and after an intervention \n",
    "\n",
    "By not paired we mean two different populations.  Imagine we fed 100 people carrots and 100 people steak and we weighed them and wanted to know if their exam scores was differnt. That is not paired and also the worst experiment ever!\n",
    "\n",
    "In terms of our arsenic example if we measured the same wells twice it would be paired.  if we measured different sets of wells it is unpaired.  \n",
    "\n",
    "Finally, if you are doing unpaired you need to decide if the groups have the same or unequal variance.  It is statiscally safer to choose unequal variance.  But you can always look at your variance and decide.  \n",
    "\n",
    "Your results are a t statistic and a p-value.  We want our p-value less than 0.05 or 0.01 again!\n",
    "\n",
    "Back to our wells.  We will run an unparied t-test with unequal variance. \n",
    "\n",
    "so lets pass our arrays from aboce with Arsenic for Drink=Y and Drink=N\n",
    "\n",
    "We are asking if the difference we see with our eyes in the boxplot is statistically significant for arsenic.  You need the stats to verify!\n",
    "\n",
    "### THIS WILL FAIL!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=nan, pvalue=nan)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(df['As'][df.Drink=='Y']\\\n",
    "                ,df['As'][df.Drink=='N'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It failed b/c we have NaN's in our data (Not a Number).  NaN's are nice as they keep track where we don't have data.  \n",
    "\n",
    "But scipy does not handle NaN's well.  \n",
    "\n",
    "So we need to get rid of them then do the math.  \n",
    "\n",
    "In pandas terms we need to drop the NaN's using the function dropna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Well_ID\n",
       "83        78.977470\n",
       "101       28.070949\n",
       "110       96.885674\n",
       "112       80.627214\n",
       "153       39.249817\n",
       "            ...    \n",
       "12363     26.980000\n",
       "12440     21.740000\n",
       "12461    117.820000\n",
       "12516      0.130000\n",
       "12654     17.390000\n",
       "Name: As, Length: 336, dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['As'][df.Drink=='Y'].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So try again!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-7.209206229150192, pvalue=1.4829579464861492e-10)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(df['As'][df.Drink=='Y'].dropna()\n",
    "               ,df['As'][df.Drink=='N'].dropna()\n",
    "                ,equal_var=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is a small p-value!!!!!\n",
    "\n",
    "So a signficant difference!  \n",
    "\n",
    "You could say the mean arsenic concentration is lower in well where people drink then where they don't drink (p<0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What wells do people drink from?\n",
    "For our final exercise. Lets put it together and get data and then see if we can plot it.  I want to know the number of people who are drinking from there wells based on the arsenic concentrations.  Can we do the reverse.  if the arsenic is <10,10-50, and >50 what is the value counts of drinking and not drinking.  I chose these numbers because 10 ppb is the EPA and WHO drinking water limit.  50 ppb is the Bangladesh drinking water limit.  We see negative health effects at 10ppb.  Drinking water with 10 ppb arsenic is bad for you!   It increases your risk of cardiovascular disease, cancers, and death!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I would first just try and break the data into 3 groups and print out the results.  So use your way of selecting data and select data based on the levels of arsenic.  To do between 10 and 50 you will need to use an and statement and how to do those is different.  you need to use a boolean function to choose two data sets! works by taking two arguments and then returning what happens the same way as if you did an and.  but it works better.  Remember we did this above.\n",
    "\n",
    "Three Groups\n",
    "1.  <10 ppb arsenic\n",
    "1.  10-50 ppb arsenic\n",
    "1.  \\>50 ppb arsenic\n",
    "\n",
    "1.  Print out the number of people drinking from wells with arsenic less than 10.  you can use value_counts() and your selection method.\n",
    "\n",
    "This is the graph we want to make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11506128>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NmwcrKytcvHgRf/3rX9G3b19Mnz7dGLUREZGB6Q0BCwsLSJKEffv2YcGCBViwYAFKS0uN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\nIgVjCBARKRhDgIhIwRgCREQK9n+NxbLr/WaNWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b2fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First start by counting who drinks less than 10.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "people drinking with arsenic <10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Y    84\n",
       "N     1\n",
       "Name: Drink, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print ('people drinking with arsenic <10')\n",
    "\n",
    "df['Drink'][df.As<=10].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.  Next use determine the  people drinking from wells with arsenic more than 50. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "people drinking with >50 \n",
      "Y    147\n",
      "N     66\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.  Now use your logical_and() or & and parantheses to determine between 10 and 50.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "people drinking with 10-50 \n",
      "Y    105\n",
      "N      4\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the data one by one is painful.  Lets work on getting to our bar chart.  This is a bad way of looking at the data.  I would like to make bar plot.  Here is my goal.  Can we get there?    Follow the next steps after the plot and see how it goes!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "python does not make bar plots easy. But let's make one anyway\n",
    "\n",
    "First lets look up bar plot.  http://matplotlib.org/examples/api/barchart_demo.html  This is the example on all the web pages.   We can make sense of it.  Lets do one step at a time.  What plt.bar wants is (x,y,width).  lets do it for As<10 first.  Here is our data again.  The x location and width become arbitrary to make it look pretty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    84\n",
       "N     1\n",
       "Name: Drink, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Drink'][df.As<=10].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we want to plot 84 Drink, 1 Doesn't drink.  I will do it longhand first time.\n",
    "\n",
    "remember it is\n",
    "ax.bar(x,y,width)\n",
    "\n",
    "x=where to plot it and is a bit of a dumy variable\n",
    "y=the height of the bar\n",
    "width=how wide you want the bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 1 artists>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar for people who drink\n",
    "fig,ax=plt.subplots()\n",
    "ax.bar(0,84,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default width is 0.8 starting from 0.  Now we need to add the doesn't drink.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 1 artists>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar for people who drink and don't drink\n",
    "fig,ax=plt.subplots()\n",
    "ax.bar(0,84,.8)\n",
    "ax.bar(0.8,1,.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to add colors and labels for a legend.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8862c5afd0>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.bar(0,84,0.8,color='g',label='Drink')\n",
    "ax.bar(0.8,1,0.8,color='r',label=\"Doesn't Drink\") \n",
    "#I did double quotes so I could print the single quote\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this is a disaster.  We can't hard wire it all. We need to be better in our programming and be what people call pythonic. now instead of setting the x-axis to zero lets use np.arange.  Then also lets set the width. We will also make the second bar start at one width  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7fdf6d2880>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots() \n",
    "width=0.35\n",
    "xvalues=np.arange(1)\n",
    "ax.bar(xvalues,84,width,color='g',label='Drink')\n",
    "ax.bar(xvalues+width,1,width,color='r',label=\"Doesn't Drink\") \n",
    "#I did double quotes so I could print the single quote\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are starting to make progress.  But we need the other two sets of bars.  We will need a set of yes and no values.  so we need yes[0],yes[1],yes[2] representing our values.  I would make a nump array of zeros and then fill it in.  So to make a numpy array of zeros.  then fill in the array.  we know the length has to be three."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "yes=np.zeros(3)\n",
    "print (yes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now do the same for no.  then set each one equal to the correct result that you have above where you printed out the results.  don't print the results like you did above.  set them to yes,no given the correct array spot.  At the end you should now have yes and no set for the three levels.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  0.  0.]\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will show you how to add the first yes and no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes [84.  0.  0.]\n",
      "no [1. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "yes=np.zeros(3)\n",
    "no=np.zeros(3)\n",
    "yes[0],no[0]=df['Drink'][df.As<=10].value_counts() \n",
    "print ('yes',yes)\n",
    "print ('no',no)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now can you do the other two?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  84.  105.  147.] [  1.   4.  66.]\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can do a bar plot of yes and no.  Go copy and past your barplot code from above.  but now make the x-axis have an np.arange of 3 b/c we want 3 locations.  And don't use the hardwired number put in your new yes and no arrays you just made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x106ed908>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qVTBKCQpv2vfee+/x+uuvExoaSt++fSksLOzmqr2XmZnJli1bqK+vJy4ujtzc\nXJqbm4He33dw7vb15r777LPPWLNmDWPGjPGEw7Jlyzhw4ADQ+/vPm/b15v6rq6sjKyuL1tZWWltb\nmTFjBpMnT/YrO3UxlIiIiWmOVxERE1PIi4iYmEJeRMTEFPIiIiamkBcRMTGFvIiIiSnkRURMTCEv\nImJi/w/e2FQzwHwEuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106ede10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you are looking great with a wonderful graph.  lets label everything.  We just need an x-axis labeled correctly.  Also, I would put all the code in one cell so it always works smoothly.  If we go back to our webpage with the example we can use ax.set_xticks(xvalues+width/2) to get us the xticks we want.  then we can add ax.set_xticklabels(('names','names','names')).  We can also use ax.set_xlabel() and ax.set_ylabel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10f052e8>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NmwcrKytcvHgRf/3rX9G3b19Mnz7dGLUREZGB6Q0BCwsLSJKEffv2YcGCBViwYAFKS0uN\nURsRERmY3kFlOnTogPXr12Pnzp1ITk5GRUUFHj58aIzaiIjIwPQeCcTGxqJt27bYvn07bG1tkZub\ni2XLlhmjNmplJElq1g8iJeLIYmQ0HJmqZeP+a7nq++7U2RzUvn17nb+OJElCSUlJ01RHREQmo7M5\n6N69eygtLa3z0ZAAmDlzJlQqFdzc3ORphYWF0Gg0cHZ2xpgxY1BUVCTPi4iIgJOTE1xcXHDo0KGn\nfFtERNQQOkOgsLCw3oc+M2bMQEJCQo1pkZGR0Gg0SE9Ph5+fHyIjIwEAqampiI2NRWpqKhISEjB/\n/nxUVlY+5VsjIiJ9dDYHDRo0qN6TZRkZGfWu2NfXF5mZmTWmxcfHIykpCQAQEhICtVqNyMhIxMXF\nITg4GJaWlnBwcICjoyNSUlIwbNiwJ3grRET0pHSGwONf4E2hoKAAKpUKAKBSqVBQUAAAuHnzZo0v\nfHt7e+Tm5jb59omIqCa9/QQqKyvxj3/8AxkZGVi1ahVu3LiB/Px8+Pj4PNWG9V2Wp2teeHi4/Lda\nrYZarX6qOoiIWhutVgutVtugZfWGwPz582FmZoZjx45h1apVaN++PebPn4+zZ88+cWEqlQr5+fmw\ntbVFXl4ebGxsAAB2dnbIzs6Wl8vJyYGdnV2d66geAkREVNvjP5BXr16tc1m9ncVOnz6NLVu24Jln\nngEAWFtbN7rHcEBAAKKiogAAUVFRCAwMlKfHxMSgrKwMGRkZuHr16lMfaRARkX56jwTatGmDiooK\n+fmtW7dgZqb/DtTBwcFISkrC7du30aNHD6xZswYrVqxAUFAQtm3bBgcHB3z77bcAAFdXVwQFBcHV\n1RUWFhbYsmULe3ASERmB3h7DO3fuxLfffotz584hJCQEu3fvxtq1axEUFGSsGmXsMdyyscdpy8b9\n13I1qsfwI1OnTsXgwYNx9OhRAEBcXBz69+/ftBUSEZFJ6A2B999/HyNGjMCMGTNgZWVljJqIiMhI\n9Dbu9+nTB9988w28vb3h4+ODsLAw7Nu3zxi1ERGRgTX4LqL5+fmIjY3FRx99hLt378pDThoTzwm0\nbGxTbtm4/1qupzonMGvWLKSlpUGlUmH48OHYs2cPvLy8mrxIIiIyPr3NQYWFhSgvL0fnzp1hbW2N\n5557DpaWlsaojYiIDEzvkcDevXsBAGlpaUhISMDIkSNRUVGBnJwcgxdHRESGpTcE9u/fj+TkZCQn\nJ6OoqAijRo2Cr6+vMWojIiID0xsCP/74I3x9fbFkyRJ0797dGDUREZGR6A2BzZs3G6MOIiIyAf03\nASIiolaLIUBEpGA6Q8DPzw8A8Mc//tFoxRARkXHpPCeQl5eHn376CfHx8ZgyZQqEEDVu7zxo0CCj\nFEhERIajMwRWr16NNWvWIDc3F2FhYbXmJyYmGrQwIiIyPJ0hMGnSJEyaNAlr1qzBqlWrjFkTEREZ\nid5LRFetWoW4uDgcP34ckiRhxIgR8Pf3N0ZtRERkYHqvDlqxYgU2bdqEAQMGoH///ti0aRNWrlxp\njNqIiMjA9N5K2s3NDRcuXIC5uTkAoKKiAp6enrh8+bJRCqyuJYw7zNvZ6sZbEbds3H8t11PdSlqS\nJBQVFaFr164AgKKioqf+Mo6IiMDOnTthZmYGNzc37NixA/fv38fkyZORlZUlD0LfuXPn2i8Of6pN\nG1a4qQsgInoyepuDVq5ciUGDBiE0NBQhISEYPHgw3n333UZvMDMzE3//+9/x888/4/Lly6ioqEBM\nTAwiIyOh0WiQnp4OPz8/REZGNnobRETUMHpDIDg4GCdPnsSECRPw2muv4eTJk5gyZUqjN9ixY0dY\nWlriwYMHKC8vx4MHD9C9e3fEx8cjJCQEABASEsIhLImIjEBvcxAAdO/eHa+++mqTbNDa2hphYWHo\n2bMnnnnmGYwdOxYajQYFBQVQqVQAAJVKhYKCgibZHhER6dagEGhK165dw6efforMzEx06tQJkyZN\nws6dO2ssI0mS7vMO1fuoOQDobahKiYhaJq1WC61W26BljR4CZ8+exYsvviifaJ44cSJOnjwJW1tb\n5Ofnw9bWFnl5ebCxsal7BSONWCwRUQukVquhVqvl56tXr9a5bL3nBMrLy9GvX78mKwwAXFxccOrU\nKfzrX/+CEAJHjhyBq6sr/P39ERUVBQCIiopCYGBgk26XiIhqq/dIwMLCAi4uLsjKykKvXr2aZIMe\nHh6YPn06vL29YWZmhkGDBmHOnDkoLS1FUFAQtm3bJl8iSkREhqW3s5ivry/Onz8PHx8fWFlZVb1I\nkhAfH2+UAqtr1p1VAHZY0aNZ779w7jt9uP9arqfqLPanP/2pzhUSEVHLpzcE1Go1MjMz8euvv2L0\n6NHy9f1ERNTy6e0s9uWXX2LSpEmYO3cuACAnJwcTJkwweGFERGR4ekPg888/x4kTJ9CxY0cAgLOz\nM3777TeDF0ZERIanNwTatm2Ltm3bys/Ly8t5ToCIqJXQGwIjRozAunXr8ODBAxw+fBiTJk3ioDJE\nRK2E3hCIjIxEt27d4Obmhq1bt2LcuHFYu3atMWojIiID03t1kLm5OUJCQjB06FBIkgQXFxc2BxER\ntRJ6Q+D777/Hm2++iT59+gAArl+/Lh8REBFRy6Y3BJYuXYrExEQ4OjoCqLoL6Lhx4xgCREStgN5z\nAh07dpQDAAD69OkjXy5KREQtm84jgT179gAAvL29MW7cOAQFBQEAvvvuO3h7exunOiIiMiidIbB/\n/375BLCNjQ2SkpIAAN26dcPvv/9unOqIiMigdIbAV199ZcQyiIjIFPSeGL5+/To+++wzZGZmyjeO\nM9WtpImIqGnpDYHAwEDMnj0b/v7+MDOrOo/MfgJE1Nw05++l5jzWgd4QaNeuHRYtWmSMWoiIGq25\nfs0232iqojcE3nrrLYSHh2Ps2LE1biQ3aNAggxZGRESGpzcErly5gujoaCQmJsrNQQCQmJho0MKI\niMjw9IbAd999h4yMDLRp06bJNlpUVITZs2fjypUrkCQJO3bsgJOTEyZPnoysrCx5oPnOnTs32TaJ\niKg2vT2G3dzccPfu3Sbd6OLFizFu3DikpaXh0qVLcHFxQWRkJDQaDdLT0+Hn54fIyMgm3SYREdWm\n90jg7t27cHFxwZAhQ+RzAk9ziWhxcTGSk5MRFRVVVYCFBTp16oT4+Hi5Q1pISAjUajWDgIjIwPSG\nwOrVq5t0gxkZGejWrRtmzJiBixcvYvDgwfj0009RUFAAlUoFAFCpVCgoKGjS7RIRUW16Q0CtVjfp\nBsvLy/Hzzz9j8+bNGDJkCJYsWVLrF78kSbqv+a1+PtoBQO8mLY+IqMXTarXQarUNWlZvCLRv317+\nQi4rK8PDhw/Rvn17lJSUNKo4e3t72NvbY8iQIQCA//qv/0JERARsbW2Rn58PW1tb5OXlwcbGpu4V\njGzUZomIFEOtVtf4AV9fi47eE8P37t1DaWkpSktL8a9//Qv/8z//g/nz5ze6OFtbW/To0QPp6ekA\ngCNHjmDAgAHw9/eXzxNERUUhMDCw0dsgIqKGkUQj+jN7enriwoULjd7oxYsXMXv2bJSVlaFv377Y\nsWMHKioqEBQUhBs3bui8RFSSJCC80Zs1vPDm3T3c1Jr1/gvnvtOn2e8/U9eggwTT/9uSJElnDXqb\ngx6NKwAAlZWVOHfuHJ555pmnKsjDwwNnzpypNf3IkSNPtV4iInoyekOg+rgCFhYWcHBwQFxcnMEL\nIyIiw9MbAhxXgIio9dIZArrOJj86Kli1apVhKiIiIqPRGQJWVla1rtW/f/8+tm3bhtu3bzMEiIha\nAZ0hsGzZMvnvkpISbNq0CTt27MCUKVMQFhZmlOKIiMiw6j0ncOfOHXzyySf4xz/+genTp+Pnn39G\nly5djFUbEREZWL1HAnv37sWcOXNw6dIldOjQwZh1ERGREejsLGZmZoY2bdrA0tKy9oskqdG3jXga\nzbqzCsAOR3o06/0Xzn2nT7Pff6auQYcW21mssrLSYAUREVHzoPfeQURE1HoxBIiIFIwhQESkYAwB\nIiIFYwgQESkYQ4CISMEYAkRECsYQICJSMIYAEZGCMQSIiBTMZCFQUVEBLy8v+Pv7AwAKCwuh0Wjg\n7OyMMWPGoKioyFSlEREphslCYOPGjXB1dZUHromMjIRGo0F6ejr8/PwQGRlpqtKIiBTDJCGQk5OD\ngwcPYvbs2fKd7eLj4xESEgIACAkJwb59+0xRGhGRopgkBN5++21s2LABZmb/v/mCggKoVCoAgEql\nQkFBgSlKIyJSlHpHFjOEAwcOwMbGBl5eXtBqtXUuI0lSrfGNZYnV/nYA0LuJCyQiauG0Wq3O79fH\nGT0EfvrpJ8THx+PgwYP4/fffUVJSgmnTpkGlUiE/Px+2trbIy8uDjY1N3SsYadx6iYhaGrVaDbVa\nLT9fvXq1zmWN3hy0fv16ZGdnIyMjAzExMRg1ahSio6MREBCAqKgoAEBUVBQCAwONXRoRkeKYvJ/A\no2afFStW4PDhw3B2dsaxY8ewYsUKE1dGRNT6Gb05qLoRI0ZgxIgRAABra2scOXLElOUQESmOyY8E\niIjIdBgCREQKxhAgIlIwhgARkYIxBIiIFIwhQESkYAwBIiIFYwgQESkYQ4CISMEYAkRECsYQICJS\nMIYAEZGCMQSIiBSMIUBEpGAMASIiBWMIEBEpGEOAiEjBGAJERArGECAiUjCjh0B2djZGjhyJAQMG\nYODAgdi0aRMAoLCwEBqNBs7OzhgzZgyKioqMXRoRkeIYPQQsLS3xySef4MqVKzh16hQ+//xzpKWl\nITIyEhqNBunp6fDz80NkZKSxSyMiUhyjh4CtrS08PT0BAO3bt0f//v2Rm5uL+Ph4hISEAABCQkKw\nb98+Y5dGRKQ4Jj0nkJmZifPnz2Po0KEoKCiASqUCAKhUKhQUFJiyNCIiRbAw1Ybv3buH1157DRs3\nbkSHDh1qzJMkCZIk1f3CxGp/OwDobagKiYhaJq1WC61W26BlTRICDx8+xGuvvYZp06YhMDAQQNWv\n//z8fNja2iIvLw82NjZ1v3ikEQslImqB1Go11Gq1/Hz16tU6lzV6c5AQArNmzYKrqyuWLFkiTw8I\nCEBUVBQAICoqSg4HIiIyHKMfCfzzn//Ezp074e7uDi8vLwBAREQEVqxYgaCgIGzbtg0ODg749ttv\njV0aEZHiGD0Ehg8fjsrKyjrnHTlyxMjVEBEpG3sMExEpGEOAiEjBGAJERArGECAiUjCGABGRgjEE\niIgUjCFARKRgDAEiIgVjCBARKRhDgIhIwRgCREQKxhAgIlIwhgARkYIxBIiIFIwhQESkYAwBIiIF\nYwgQESmYSQaaJ2qOJEkydQk6CSFMXQK1Us3qSCAhIQEuLi5wcnLCn//8Z1OXQwojmumDyJCaTQhU\nVFRg4cKFSEhIQGpqKnbt2oW0tDRTl0VE1Ko1mxBISUmBo6MjHBwcYGlpiSlTpiAuLs7UZRERtWrN\n5pxAbm4uevToIT+3t7fH6dOnTVhR47BdmYhakmYTAs35y/NJNNev2dbx6RJRU2s2IWBnZ4fs7Gz5\neXZ2Nuzt7WsvGG68mhqjOX/ZNougDTd1Abo1g09Hp2ax7wDuv0ZqNvuvDpJoJm0E5eXl6NevH44e\nPYru3bvDx8cHu3btQv/+/U1dGhFRq9VsjgQsLCywefNmjB07FhUVFZg1axYDgIjIwJrNkQARERlf\ns7lEtCXbvHkzHB0dYWZmhsLCwhrzFi1aBCcnJ3h4eOD8+fMmqlA5Zs6cCZVKBTc3N3laYWEhNBoN\nnJ2dMWbMGBQVFdX52vDwcNjb28PLywteXl744Ycf5HkRERFwcnKCi4sLDh06ZPD3QQ0TGhqKPn36\nyPvs4sWL8jz+32sYhkAjlZWV4cGDBwCA4cOH4+jRo+jVq1eNZQ4ePIhff/0VV69exZdffol58+aZ\nolRFmTFjBhISEmpMi4yMhEajQXp6Ovz8/BAZGVnnayVJwtKlS3H+/HmcP38eL7/8MgAgNTUVsbGx\nSE1NRUJCAubPn4/KykqDvxcC7t69W+98SZLw0UcfyfvMw8MDAP/vPQmGwBNKS0tDWFgYXFxccPXq\nVQCAp6cpM9C8AAAK8ElEQVRnrQAAgPj4eISEhAAAhg4diqKiIhQUFBi1XqXx9fVFly5dakyrvh9C\nQkKwb98+na+vq3U0Li4OwcHBsLS0hIODAxwdHZGSktK0hVOdhgwZgqlTpyIxMVFnP5e6pvP/XsMx\nBBrg/v372LFjB3x9fTFnzhwMHDgQly9fln916FJXB7icnBxDl0uPKSgogEqlAgCoVKp6vww+++wz\neHh4YNasWXKz0c2bN2tcrmxvb4/c3FzDFk0AgPT0dAQHB2Pz5s0YMGAAIiIikJeXV2OZlStXwsPD\nA0uXLkVZWRkA/t97EgyBBnj++eexfft2/Pd//zeSk5MxY8YMWFlZNei1j/9Kac7XCyuBJEk698G8\nefOQkZGBCxcu4Pnnn0dYWFi96yHDMzMzwyuvvII9e/bg+PHjuHbtGnr27ImzZ88CqDpXk56ejjNn\nzqCwsLDGjSf5f69hGAINsGfPHtjZ2WHixIn405/+hBs3bjTodY93gMvJyYGdnZ2hyiQdVCoV8vPz\nAQB5eXmwsbEBUHX+wMvLC+PHjwcA2NjYyCExe/ZsucmH+9G0iouLsXXrVgQEBODatWvYsWOHfOLf\n1tYWANCmTRuEhoZynzUCQ6ABNBoNYmJikJycjE6dOuHVV1+FRqNBVlZWrWWr//oICAjA119/DQA4\ndeoUOnfuLDdLkPEEBAQgKioKABAVFYXAwEAAwI4dO3D+/HkcOHAAAGo0M+zdu1f+ogkICEBMTAzK\nysqQkZGBq1evwsfHx8jvQpmmTp2KwYMHIysrC9HR0UhMTMTUqVPRtm1bAP+/z4QQ2LdvX419xv97\nDSSoUVJSUkR2drYQQoiNGzcKe3t7YWlpKbp37y7eeOMNebkFCxaIvn37Cnd3d3Hu3DlTlasYU6ZM\nEc8//7ywtLQU9vb2Yvv27eLOnTvCz89PODk5CY1GI+7evVvna6dNmybc3NyEu7u7ePXVV0V+fr48\nb926daJv376iX79+IiEhwVhvR/Hi4+NFRUWFzvmjRo0Sbm5uYuDAgWLatGni/v378jz+32sYdhYj\nIlIwNgcRESkYQ4CISMEYAkRECsYQICJSMIYAEZGCMQSIiBSMIUBNZt++fTAzM8Mvv/xi8G2dO3cO\nixcvbvDyDx8+xIoVK+Ds7IzBgwfjxRdfrHW3UWOLi4tDWlraE78uKSkJJ0+elJ9v3boV0dHRTVla\ng6nVapw7d84k26amwRCgJrNr1y6MHz8eu3btqnN+eXl5k21r8ODB2LhxY4OX/+CDD1BQUIArV67g\n3Llz2LdvH0pLS5usnsbYu3cvUlNT65xXUVGh83WJiYn46aef5Odz587FtGnTmry+huD9eFoBU/dW\no9ahtLRU9OrVS2RlZQkXFxd5emJiohg+fLgICAgQ/fr1E/fv3xfjxo0THh4eYuDAgSI2NlYIIcTZ\ns2fFiBEjxODBg8XYsWNFXl6eEEKIESNGiOXLlwsfHx/h7OwskpOT5fWOHz9e3nZoaKjc23fPnj01\nart//77o2rWrKC0trbP2b775Ru51unz5cnm6lZWVeO+994SHh4cYNmyYKCgoEEIIkZ+fLwIDA4WH\nh4fw8PAQJ0+eFEIIER0dLXx8fISnp6eYO3eu3NO1rvX885//FNbW1qJ3797Cy8tLXLt2TYwYMUIs\nWbJEeHt7i48//ljs379fDB06VHh5eYnRo0eLgoICkZGRIWxtbYWdnZ3w9PQUycnJ4sMPPxQfffSR\nEEKI8+fPi6FDhwp3d3cxYcIEuXe0rs+xusTEROHr6yteeeUV0a9fP/Hmm2+KyspK+T28/fbbYsCA\nAcLPz0/cunVLCCGEWq0WixcvFp6enmLgwIEiJSWlYf9gqNlgCFCT2Llzp5g7d64QQghfX1+5m35i\nYqKwsrISmZmZQgghdu/eXeO2GsXFxaKsrEy88MIL4vbt20IIIWJiYsTMmTOFEFVfMsuWLRNCCHHw\n4EExevRoeb2PQuCPf/yjePvtt+V1Pn5biIsXLwovL686687NzRU9e/YUt2/fFuXl5WLUqFFi3759\nQgghJEkSBw4ckLexdu1aIYQQQUFBYuPGjUIIISorK0VxcbFITU0V/v7+ory8XAghxLx588TXX39d\n73pCQ0NrBJZarRYLFiyo8338/e9/F2FhYUIIIcLDw8XHH38sz6v+3M3NTRw/flwIIcSqVavEkiVL\n6v0cq0tMTBTt2rUTGRkZoqKiQmg0GrF79275PXzzzTdCCCHWrFkjFi5cKISoCpc5c+YIIYQ4fvy4\nGDhwYJ2fMzVfbA6iJrFr1y5MmjQJADBp0qQaTUI+Pj7yoDvu7u44fPgwVqxYgRMnTqBjx4745Zdf\ncOXKFYwePRpeXl5Yt25djfv1T5w4EQAwaNAgZGZm1tr20aNHsWDBAvl5586dG1z3mTNnMHLkSHTt\n2hXm5uZ4/fXXcfz4cQBVd6Z85ZVXAFQ1Pz3admJiojxSlSRJ6NixI44ePYpz587B29sbXl5eOHbs\nGDIyMupdD1D7dseTJ0+W/87OzsaYMWPg7u6Ojz76qEbT0eOvA4CSkhIUFxfD19cXQNUAOo/eC6D/\ncwSq9pWDgwPMzMwQHByMEydOAKi6pfOj2qZOnSpPlyQJwcHBAKoG9CkpKUFJSUmd66bmycLUBVDL\nV1hYiMTERPzv//4vJElCRUUFJEnChg0bAKDG2AtOTk44f/48vv/+e7z//vvw8/PDhAkTMGDAgBrt\n3NU9umOkubm5zvMKdX0pPuLo6IgbN26gtLQUHTp0qDFPkqQarxVCyO3clpaW8nQzM7Ma265reyEh\nIVi/fn2t6fWt5/E29eqf1VtvvYVly5Zh/PjxSEpKQnh4uM73WJfHa2zI51i9HiEEzMxq/06s/hnp\nWwc1fzwSoKe2e/duTJ8+HZmZmcjIyMCNGzfQu3dvJCcn11o2Ly8P7dq1w+uvv45ly5bh/Pnz6Nev\nH27duoVTp04BqLqSR9cJ07poNBp8/vnn8vPHB5J/9tlnMWvWLCxevBgPHz4EANy6dQu7d++Gj48P\nkpKScOfOHVRUVCAmJgYjRoyod3t+fn744osvAFSdwC0pKYGfnx92796NW7duAagKRn3jTnTo0KHW\nr+bqX9wlJSXo3r07AOCrr76q8brHT2oLIdCxY0d06dJF/pUeHR0NtVpdbw2PS0lJQWZmJiorKxEb\nG4vhw4cDACorK/Hdd98BAL755hv5aEMIgdjYWADAiRMn0Llz51pBS80bQ4CeWkxMDCZMmFBj2muv\nvYZdu3bVGsnr8uXLGDp0KLy8vLBmzRq8//77sLS0xO7du7F8+XJ4enrCy8urxiWQ1VVf16O/33//\nfdy9exdubm7w9PSEVqut9bq1a9eiW7ducHV1hZubG/z9/dGpUyfY2toiMjISI0eOhKenJ7y9veHv\n71/nth4937hxIxITE+Hu7g5vb2+kpaWhf//+WLt2LcaMGQMPDw+MGTNGHshG13qmTJmCDRs2YPDg\nwbh+/XqtZcPDwzFp0iR4e3ujW7du8jx/f3/s3bsXgwYNqtEsA1SNl/DOO+/Aw8MDly5dwqpVq/R+\njtWnDRkyBAsXLoSrqyv69u0r71crKyukpKTAzc0NWq1WXq8kSWjXrh0GDRqE+fPnY9u2bXVuj5ov\n3kqaiAAAWq0WH3/8Mfbv319rXl1HH9Q68EiAiADUP/4y2/lbLx4JEBEpGI8EiIgUjCFARKRgDAEi\nIgVjCBARKRhDgIhIwRgCREQK9n+NxbLr/WaNWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b32da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that is a great looking graph. You just need to add a figure caption.  I would write something like\n",
    "\n",
    "Number of wells categorized by if the respondents drink or don't drink from the well and stratified by arsenic concentration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a total bonus and if you have time you could change it from the number of wells to the proportion of wells in each category.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x19af3cc0>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xLAAOr/a/Rb827eU7AesqxwEPR8RS4E+SDitZdygwOSL+D/B5YHlEHBIRBwMP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\nfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19035a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I thought it might be nice to stack the bars since they add up to 100..... See http://matplotlib.org/examples/pylab_examples/bar_stacked.html  It is \"easy\"  I used the bottom keyword. Then I removed the width offset and tweaked a few other things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a6b4c18>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p4IplBwGTI+J/AZ8HlkbEgRFxAPCQpK2A64C/i4iPk5zQL0+3DaAuIv4aOAf4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1991f080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some homework hints.\n",
    "\n",
    "for the homework you will need to make scatter plots.  They are easy to make in pandas.  Here is one of Arsenic versus Iron.  You can label your axes and change the color of your symbols."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Iron mg/L')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "ax.scatter(df['As'],df['Fe'],c='xkcd:vomit')\n",
    "ax.set_xlabel('Arsenic ug/L')\n",
    "ax.set_ylabel('Iron mg/L')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Below is a simpler method\n",
    "We will learn more about this but I did advanced Python.\n",
    "1. I defined bins\n",
    "1.  I used groubpy to group the data by if people drink\n",
    "1.  I added a cut value to also group by the bins I cut and set by.\n",
    "1.  I then filled the group by the counts.\n",
    "1.  I then unstack and transpose and flip the matrix.  \n",
    "1. Then I can plot that new data.\n",
    "1.  The fun part is I can change the bins and it automtacillay updates!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percent of wells used')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins=[0,10,50,1000]\n",
    "df_No_Yes=df.groupby(['Drink',pd.cut(df['As'],bins)])\\\n",
    "                        .As.count().unstack().transpose()\n",
    "\n",
    "fig,ax=plt.subplots(1,1) \n",
    "width=0.35\n",
    "xvalues=np.arange(df_No_Yes.shape[0])\n",
    "ax.bar(xvalues,df_No_Yes.Y,width,color='g',label='Drink')\n",
    "ax.bar(xvalues+width,df_No_Yes.N,width,color='r',label=\"Doesn't Drink\") \n",
    "#I did double quotes so I could print the single quote\n",
    "\n",
    "ax.legend(loc='best')  \n",
    "#You can try numbers 1-8 for location. see http://matplotlib.org/1.3.1/users/legend_guide.html\n",
    "\n",
    "ax.set_xticks(xvalues+width/2)\n",
    "ax.set_xticklabels(df_No_Yes.index.values)#('<10','10-50','>50'))\n",
    "ax.set_xlabel('Arsenic Concentration ppb')\n",
    "ax.set_ylabel('Percent of wells used')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Answers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'As'}, xlabel='Drink'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='As',by='Drink')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "people drinking with <10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Y    84\n",
       "N     1\n",
       "Name: Drink, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print ('people drinking with <10')\n",
    "\n",
    "df['Drink'][df.As<=10].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "people drinking with >50 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Y    147\n",
       "N     66\n",
       "Name: Drink, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print ('\\npeople drinking with >50 ')\n",
    "\n",
    "df['Drink'][df.As>=50].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "people drinking with 10-50\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Y    105\n",
       "N      4\n",
       "Name: Drink, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print ('people drinking with 10-50')\n",
    "\n",
    "df['Drink'][(df.As<=50)&(df.As>=10)].value_counts() \n",
    "\n",
    "# using np.logical_and\n",
    "#df['Drink'][np.logical_and(df.As<=50,df.As>=10)].value_counts() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 84. 105. 147.] [ 1.  4. 66.]\n"
     ]
    }
   ],
   "source": [
    "yes=np.zeros(3)\n",
    "no=np.zeros(3)\n",
    "yes[0],no[0]=df['Drink'][df.As<=10].value_counts() \n",
    "yes[1],no[1]=df['Drink'][(df.As<=50)&(df.As>=10)].value_counts()\n",
    "yes[2],no[2]=df['Drink'][df.As>=50].value_counts() \n",
    "print (yes,no)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8862f94070>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,1) \n",
    "width=0.35\n",
    "xvalues=np.arange(3)\n",
    "ax.bar(xvalues,yes,width,color='g',label='Drink')\n",
    "ax.bar(xvalues+width,no,width,color='r',label=\"Doesn't Drink\") #I did double quotes so I could print the single quote\n",
    "ax.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of wells')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yes=np.zeros(3)\n",
    "no=np.zeros(3)\n",
    "yes[0],no[0]=df['Drink'][df.As<=10].value_counts() \n",
    "yes[1],no[1]=df['Drink'][(df.As<=50)&(df.As>=10)].value_counts()\n",
    "yes[2],no[2]=df['Drink'][df.As>=50].value_counts() \n",
    "\n",
    "\n",
    "fig,ax=plt.subplots() \n",
    "width=0.35\n",
    "xvalues=np.arange(3)\n",
    "ax.bar(xvalues,yes,width,color='g',label='Drink')\n",
    "ax.bar(xvalues+width,no,width,color='r',label=\"Doesn't Drink\") \n",
    "#I did double quotes so I could print the single quote\n",
    "ax.legend(loc='best')\n",
    "ax.set_xticks(xvalues+width/2)\n",
    "ax.set_xticklabels(('<10','10-50','>50'))\n",
    "ax.set_xlabel('Arsenic Concentration ppb')\n",
    "ax.set_ylabel('Number of wells')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percent of wells used')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "yes=np.zeros(3)\n",
    "no=np.zeros(3)\n",
    "yes[0],no[0]=df['Drink'][df.As<=10].value_counts()\\\n",
    "            /df['Drink'][df.As<=10].count()*100 \n",
    "    \n",
    "yes[1],no[1]=df['Drink'][(df.As<=50)&(df.As>=10)].value_counts()\\\n",
    "            /df['Drink'][(df.As<=50)&(df.As>=10)].count()*100\n",
    "\n",
    "yes[2],no[2]=df['Drink'][df.As>=50].value_counts()\\\n",
    "            /df['Drink'][df.As>=50].count()*100\n",
    "\n",
    "\n",
    "fig,ax=plt.subplots(1,1) \n",
    "width=0.35\n",
    "xvalues=np.arange(3)\n",
    "ax.bar(xvalues,yes,width,color='g',label='Drink')\n",
    "ax.bar(xvalues+width,no,width,color='r',label=\"Doesn't Drink\") #I did double quotes so I could print the single quote\n",
    "ax.legend(loc='best')\n",
    "ax.set_xticks(xvalues+width/2)\n",
    "ax.set_xticklabels(('<10','10-50','>50'))\n",
    "ax.set_xlabel('Arsenic Concentration ppb')\n",
    "ax.set_ylabel('Percent of wells used')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percent of wells used')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yes=np.zeros(3)\n",
    "no=np.zeros(3)\n",
    "yes[0],no[0]=df['Drink'][df.As<=10].value_counts()\\\n",
    "            /df['Drink'][df.As<=10].count()*100 \n",
    "    \n",
    "yes[1],no[1]=df['Drink'][(df.As<=50)&(df.As>=10)].value_counts()\\\n",
    "            /df['Drink'][(df.As<=50)&(df.As>=10)].count()*100\n",
    "\n",
    "yes[2],no[2]=df['Drink'][df.As>=50].value_counts()\\\n",
    "            /df['Drink'][df.As>=50].count()*100\n",
    "\n",
    "\n",
    "fig,ax=plt.subplots(1,1) \n",
    "width=0.35\n",
    "xvalues=np.arange(3)\n",
    "ax.bar(xvalues,yes,width,color='g',label='Drink')\n",
    "ax.bar(xvalues,no,width,color='r',bottom=yes,label=\"Doesn't Drink\") \n",
    "#I did double quotes so I could print the single quote\n",
    "ax.legend(loc=3)  #You can try numbers 1-8 for location. see http://matplotlib.org/1.3.1/users/legend_guide.html\n",
    "ax.set_xticks(xvalues+width/2)\n",
    "ax.set_xticklabels(('<10','10-50','>50'))\n",
    "ax.set_xlabel('Arsenic Concentration ppb')\n",
    "ax.set_ylabel('Percent of wells used')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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