{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Desalination Plant Numbers\n",
    "## Christina Cook\n",
    "## Dominic Suares\n",
    "## Youxi Li\n",
    "## Linzhi Xie\n",
    "## Erik Mei"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction and Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Research Question: \n",
    ">In case of the total drought in California, how many desalination plant projects we need to supply residential use water for population who live in urban areas in California? \n",
    "\n",
    "## Hypothesis: \n",
    "Suppose that a $1 billion desalination plant project can produce 50 million gallons water per day and the average residential water use  is about 90 gallons per capita per day. After researching online, we got that urban population in Califonia is [(1)](http://ruralhealth.stanford.edu/health-pros/factsheets/) 28,102,643 and the average water each person use at home per day is about [(2)](https://water.usgs.gov/edu/qa-home-percapita.html) 80-100 gallons. We estimate the total number of desalination plant projects to supply residential-use water for urban population in California by the formula:\n",
    "> **the total residential-use water** / **one desalination plant project can produce** = (28102643 * 90) / 5e7 = 51 desalination plants\n",
    "\n",
    "Our hypothesis is that we need at least 51 desalination plant projects to supply urban residential-use water in Califonia. \n",
    "\n",
    "References:\n",
    "- (1) http://ruralhealth.stanford.edu/health-pros/factsheets/ (For urban population in Califonia: 28,102,643)\n",
    "- (2) https://water.usgs.gov/edu/qa-home-percapita.html (For How much water does the average person use at home per day? )\n",
    "\n",
    "## Background:\n",
    "\n",
    "### 1. Why is this question of interest? \n",
    "California is going to suffer from a crippling megadrought in the next century. If our global temperature raises by 2 degrees celsius, the chance that this occurs is merely 30-60 percent. However, if we continue on the path that we are currently, it is almost certain that we will face a drought significantly worse than any previously recorded drought.(1) Our water infrastructure is not prepared for this, as evidenced by the most recent drought. A drought lasting many years will exhaust all of our freshwater, leaving us with two options: desalination and importing water. Importing water isn't preferable because of transportation costs and the need to rely on other states for a huge amount of our basic needs. Desalination is innefficient, requiring huge investments to obtain even normal prices for the water produced by desalinating ocean water. However, if we want to retain our independence from other states and avoid building massive amounts of infrastructure to import water, we need to build these expensive plants.  Not only is this an expensive problem, but it is a problem that requires planning and foresight: the billion dollar desalination plant in Oceanside, for example, took 14 years to build. (2) For these reasons, it's critical to start thinking of how many desalination plants we need to support our population. \n",
    "\n",
    "We are interested in this question since we noticed that California has been under the situation of drought for five years. Even though recently, the news mentioned that \"2017 could be the end of a dry spell\"(3), we still think that it is necessary to prepare for the next drought. After doing some research online, we think that building some desalination plant projects may be a good strategy. Someone may argue that it is too expensive and it is not realistic. However, based on the article,\"Israel Proves the Desalination Era Is Here\", the desalination project mentioned in the article can produce the low-cost water. \" Desalination used to be an expensive energy hog, but the kind of advanced technologies being employed at Sorek have been a game changer. Water produced by desalination costs just a third of what it did in the 1990s. Sorek can produce a thousand liters of drinking water for 58 cents. Israeli households pay about US$30 a month for their water — similar to households in most U.S. cities, and far less than Las Vegas (US\\$47) or Los Angeles (US\\$58).\" (4) In the near future, such desalination project may be a win-win method to solve water shortage problems. \n",
    "\n",
    "References: \n",
    "- (1) https://www.ecowatch.com/megadroughts-2031955357.html\n",
    "- (2) http://www.latimes.com/local/california/la-me-carlsbad-desalination-20150604-story.html#page=1\n",
    "- (3) http://www.latimes.com/local/california/la-me-storms-drought-20170111-story.html (News about 2017 could be the end of a dry spell)\n",
    "- (4) https://www.scientificamerican.com/article/israel-proves-the-desalination-era-is-here/ (The article about other countries' successful desalination projects and why the desalination project is feasible)\n",
    "\n",
    "\n",
    "### 2. What background information led you to your hypothesis, and why is this important?\n",
    "In order to solve our question, we have to figure out total urban population and total residential-use water in California and how many water a desalination plant project can  produce per day. Based on the link below, we can know these important information. We mentioned them in the hypothesis part as well. \n",
    "\n",
    "References: \n",
    "- (1) http://ruralhealth.stanford.edu/health-pros/factsheets/ (For urban population in Califonia: 28,102,643)\n",
    "- (2) https://water.usgs.gov/edu/qa-home-percapita.html (For How much water does the average person use at home per day? )\n",
    "- (3) http://www.mercurynews.com/2014/05/29/nations-largest-ocean-desalination-plant-goes-up-near-san-diego-future-of-the-california-coast/ (One $1-billion desalination plant project can produce 50 million gallons water per day.)\n",
    "\n",
    "### 3. What areas do we consider as major urban areas? \n",
    "\n",
    "- Based on this website https://en.wikipedia.org/wiki/List_of_California_urban_areas, we will set the cut-off point at the population larger than 50000. \n",
    "\n",
    "### 4. How many gallons water in these urban areas each people are using per day?\n",
    "\n",
    "- Based on the dataset, the column 'R-GPCD_Calculated' will be the number of gallons water each people use per day. \n",
    "- More details about description of our dataset used: Please read the \"Urban_Water_Supplier_Monitoring_Data_Definitions.pdf\"\n",
    "\n",
    "### 5. How many water can each desalination plant project produce per day?\n",
    "- Based on the article: One $1-billion desalination plant project can produce 50 million gallons water per day.(http://www.mercurynews.com/2014/05/29/nations-largest-ocean-desalination-plant-goes-up-near-san-diego-future-of-the-california-coast/ ) \n",
    "\n",
    "### 6. How many desalination plant projects we need for a given urban areas?\n",
    "\n",
    "- The number of desalination plant projects= the total residence-use water per day / 50 million gallons\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\n",
    "### Data Description\n",
    "\n",
    "\n",
    "| Database Name | Description | # of Observations | Brief Useage | \n",
    "|:------------ |:-----------|:------------------:|:--------------|\n",
    "| **[Urban_Water_Supplier_Monitoring_Reports.csv](https://data.ca.gov/dataset/drinking-water-%E2%80%93-urban-water-supplier-monitoring-%E2%80%93-drinc)**| Drinking Water Information Clearinghouse (DRINC) includes water activities such as the amount of water being served to different regions and counties. There are 31 columns in this dataset and for for information, please read the pdf file: 'Urban_Water_Supplier_Monitoring_Data_Definitions.pdf' | 9356  | This dataset allows us to find information we need to solve our research question. We will forcus on these columns, R-GPCD Calculated,Supplier_Name,Hydrologic_Region,County,Reporting_Month\tPopulation_Served,R-GPCD_Calculated  | \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Proposed Methods\n",
    "\n",
    "**1. How 'clean' is the data? To the extent that it requires cleaning, how will you do so?**\n",
    "\n",
    "- Our data set has more information than we need so we plan on dropping the unecessary columns first. \n",
    "\n",
    "**2. How will you analyze the data? Be as specific as you can. Briefly mention any pre-processing steps that are required for your methods (for example, checking data distributions and performing any transformations that may be required). Include a brief outline of how you will apply your chosen method(s).** \n",
    "\n",
    "- We will seperate our data set based on the year since we noticed that the raw dataset contains different years data. It is  convenient for us to work on the data set which contains the same year data and extract information we need to answer our question.  \n",
    "\n",
    "**3. What do you plan to report? Briefly mention any key visualizations you plan to create, and/or the kind of result you will be able to report that addresses your question (this could be, for example, the outcome of some statistical test(s), prediction error on a model, a model fit parameter, etc.).**\n",
    "\n",
    "- We will report our result as a list and at the same time, we will build three models to predict how many desalination plant projects we need based on the supplier name, county name or hydrologic name. For example, if we want to find how many desalination projects we need to substitude one water supplier, we will call the function  estimate_supplier to figure it out. For more details,we will show them in the result part.\n",
    "\n",
    "** 4.Include a list of packages you expect to use for you project. If you plan to use packages we have not used in class, add a very brief description about them (a few words is sufficient). **\n",
    "\n",
    "- We plan to use panda, matplotlib.pyplot, and numpy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning and Pre-processing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Packages  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9356, 31)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Urban_Water_Supplier_Monitoring_Reports.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop unnecessary columns\n",
    "df=df.drop(['PWSID'],axis=1)\n",
    "df=df.drop(['Days_Allowed/Week'],axis=1)\n",
    "df=df.drop(['Actions_Implemented'],axis=1)\n",
    "df=df.drop(['Complaints'],axis=1)\n",
    "df=df.drop(['Follow-ups'],axis=1)\n",
    "df=df.drop(['Penalties_Rate'],axis=1)\n",
    "df=df.drop(['Penalties_Other'],axis=1)\n",
    "df=df.drop(['Warnings'],axis=1)\n",
    "df=df.drop(['ID'],axis=1)\n",
    "df=df.drop(['Ag_Cert'],axis=1)\n",
    "df=df.drop(['Current_Ag'],axis=1)\n",
    "df=df.drop(['Current_Production_Calculated'],axis=1)\n",
    "df=df.drop(['Mandatory Restrictions'],axis=1)\n",
    "df=df.drop(['2013_Production'],axis=1)\n",
    "df=df.drop(['2013_Ag'],axis=1)\n",
    "df=df.drop(['Current_Recycled'],axis=1)\n",
    "df=df.drop(['Current_Non-revenue'],axis=1)\n",
    "df=df.drop(['Percent_Reduction'],axis=1)\n",
    "df=df.drop(['2013_Production_Calculated'],axis=1)\n",
    "df=df.drop(['CII'],axis=1)\n",
    "df=df.drop(['Days-in-Month'],axis=1)\n",
    "df=df.drop(['Percent_Residential_Use'],axis=1)\n",
    "df=df.drop(['Current_Production'],axis=1)\n",
    "df=df.drop(['Units'],axis=1)\n",
    "df=df.drop(['R_GPCD_Reported '],axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4066, 6)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Figuring out urban areas which we are interested in.\n",
    "df=df.drop(df[df.Population_Served<=50000].index)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Supplier_Name</th>\n",
       "      <th>Hydrologic_Region</th>\n",
       "      <th>County</th>\n",
       "      <th>Reporting_Month</th>\n",
       "      <th>Population_Served</th>\n",
       "      <th>R-GPCD_Calculated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>East Bay Municipal Utilities District</td>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>ALAMEDA</td>\n",
       "      <td>4/15/2016 0:00</td>\n",
       "      <td>1400000</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>East Bay Municipal Utilities District</td>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>ALAMEDA</td>\n",
       "      <td>3/15/2016 0:00</td>\n",
       "      <td>1400000</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>East Bay Municipal Utilities District</td>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>ALAMEDA</td>\n",
       "      <td>2/15/2016 0:00</td>\n",
       "      <td>1400000</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>East Bay Municipal Utilities District</td>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>ALAMEDA</td>\n",
       "      <td>1/15/2016 0:00</td>\n",
       "      <td>1400000</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>East Bay Municipal Utilities District</td>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>ALAMEDA</td>\n",
       "      <td>12/15/2015 0:00</td>\n",
       "      <td>1390000</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>East Bay Municipal Utilities District</td>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>ALAMEDA</td>\n",
       "      <td>11/15/2015 0:00</td>\n",
       "      <td>1390000</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>East Bay Municipal Utilities District</td>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>ALAMEDA</td>\n",
       "      <td>10/15/2015 0:00</td>\n",
       "      <td>1390000</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>East Bay Municipal Utilities District</td>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>ALAMEDA</td>\n",
       "      <td>9/15/2015 0:00</td>\n",
       "      <td>1390000</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>East Bay Municipal Utilities District</td>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>ALAMEDA</td>\n",
       "      <td>8/15/2015 0:00</td>\n",
       "      <td>1390000</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>East Bay Municipal Utilities District</td>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>ALAMEDA</td>\n",
       "      <td>7/15/2015 0:00</td>\n",
       "      <td>1390000</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Seperate to three dataframe based on reporting_year ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2016=df[df.Reporting_Month.str.contains(\"2016\") == True]\n",
    "df2015=df[df.Reporting_Month.str.contains(\"2015\") == True]\n",
    "df2014=df[df.Reporting_Month.str.contains(\"2014\") == True]\n",
    "#Remove all rows from above dataframes that have missing data (that have NaN values) \n",
    "df2016= df2016.dropna()\n",
    "df2015= df2015.dropna()\n",
    "df2014= df2014.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add one column \"Total_Res_use_perDay\" which is based on the population served \n",
    "#and average residential water use per-capita-per-day.\n",
    "df2016=df2016.assign(Total_Res_use_PerDay = df2016['R-GPCD_Calculated']*df2016['Population_Served'])\n",
    "df2015=df2015.assign(Total_Res_use_PerDay = df2015['R-GPCD_Calculated']*df2015['Population_Served'])\n",
    "df2014=df2014.assign(Total_Res_use_PerDay = df2014['R-GPCD_Calculated']*df2014['Population_Served'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Supplier_Name</th>\n",
       "      <th>Hydrologic_Region</th>\n",
       "      <th>County</th>\n",
       "      <th>Reporting_Month</th>\n",
       "      <th>Population_Served</th>\n",
       "      <th>R-GPCD_Calculated</th>\n",
       "      <th>Total_Res_use_PerDay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>East Bay Municipal Utilities District</td>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>ALAMEDA</td>\n",
       "      <td>4/15/2016 0:00</td>\n",
       "      <td>1400000</td>\n",
       "      <td>59</td>\n",
       "      <td>82600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>East Bay Municipal Utilities District</td>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>ALAMEDA</td>\n",
       "      <td>3/15/2016 0:00</td>\n",
       "      <td>1400000</td>\n",
       "      <td>53</td>\n",
       "      <td>74200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>East Bay Municipal Utilities District</td>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>ALAMEDA</td>\n",
       "      <td>2/15/2016 0:00</td>\n",
       "      <td>1400000</td>\n",
       "      <td>54</td>\n",
       "      <td>75600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>East Bay Municipal Utilities District</td>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>ALAMEDA</td>\n",
       "      <td>1/15/2016 0:00</td>\n",
       "      <td>1400000</td>\n",
       "      <td>53</td>\n",
       "      <td>74200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Yorba Linda Water District</td>\n",
       "      <td>South Coast</td>\n",
       "      <td>ORANGE</td>\n",
       "      <td>4/15/2016 0:00</td>\n",
       "      <td>78539</td>\n",
       "      <td>117</td>\n",
       "      <td>9189063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show the form of 2016 dataframe\n",
    "df2016.head(n=5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Histogram Part\n",
    "#### Plot the histogram to show the potential distribution of  residential water usage gallons-per-capita-day in different years\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. Plot the histogram of number of residential gallons-per-capita-day in 2016\n",
    "fig = plt.figure(figsize=(15, 8))\n",
    "cm=plt.cm.get_cmap('RdYlBu_r')\n",
    "data=df2016['R-GPCD_Calculated']\n",
    "n, bins, patches = plt.hist(data)\n",
    "bin_centers = 0.5 * (bins[:-1] + bins[1:])\n",
    "plt.xlabel('Gallons of Water ')\n",
    "plt.ylabel('# Counts')\n",
    "plt.title(r'$\\mathrm{Histogram\\ of\\ Residential\\ Water\\ Usage\\ in\\ 2016\\ (gallons-per-capita-day)}$')\n",
    "col = bin_centers - min(bin_centers)\n",
    "col /= max(col)\n",
    "for c, p in zip(col, patches):\n",
    "    plt.setp(p, 'facecolor', cm(c))\n",
    "plt.grid(True)\n",
    "#number of residential gallons-per-capita-day\n",
    "\n",
    "# 2. Plot the histogram of number of residential gallons-per-capita-day in 2015\n",
    "fig = plt.figure(figsize=(15, 8))\n",
    "cm=plt.cm.get_cmap('RdYlGn_r')\n",
    "data=df2015['R-GPCD_Calculated']\n",
    "n, bins, patches = plt.hist(data)\n",
    "bin_centers = 0.5 * (bins[:-1] + bins[1:])\n",
    "plt.xlabel('Gallons of Water ')\n",
    "plt.ylabel('# Counts')\n",
    "plt.title(r'$\\mathrm{Histogram\\ of\\ Residential\\ Water\\ Usage\\ in\\ 2015\\ (gallons-per-capita-day)}$')\n",
    "col = bin_centers - min(bin_centers)\n",
    "col /= max(col)\n",
    "for c, p in zip(col, patches):\n",
    "    plt.setp(p, 'facecolor', cm(c))\n",
    "plt.grid(True)\n",
    "\n",
    "\n",
    "# 3. Plot the histogram of number of residential gallons-per-capita-day in 2014\n",
    "fig = plt.figure(figsize=(15, 8))\n",
    "cm=plt.cm.get_cmap('PuOr')\n",
    "data=df2014['R-GPCD_Calculated']\n",
    "n, bins, patches = plt.hist(data)\n",
    "bin_centers = 0.5 * (bins[:-1] + bins[1:])\n",
    "plt.xlabel('Gallons of Water ')\n",
    "plt.ylabel('# Counts')\n",
    "plt.title(r'$\\mathrm{Histogram\\ of\\ Residential\\ Water\\ Usage\\ in\\ 2014\\ (gallons-per-capita-day)}$')\n",
    "col = bin_centers - min(bin_centers)\n",
    "col /= max(col)\n",
    "for c, p in zip(col, patches):\n",
    "    plt.setp(p, 'facecolor', cm(c))\n",
    "plt.grid(True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Chart Part\n",
    "###### For R-GPCD_Calculated in each County\n",
    "R-GPCD: Residential Gallons-per-capita-day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f363111de48>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#A bar chart showing R-GPCD_Calculated in each county based on df206\n",
    "countyList = df2016['County'].unique()\n",
    "countyList.sort()\n",
    "countyGPCDdict = {}\n",
    "gpcdList16 = []\n",
    "for i in range(len(countyList)):\n",
    "    currRGPCD = df2016[df2016['County'] == countyList[i]]['R-GPCD_Calculated'].mean()\n",
    "    countyGPCDdict[countyList[i]] = currRGPCD\n",
    "\n",
    "countyGPCDdict = pd.Series(countyGPCDdict)\n",
    "countyGPCDdict.sort_values(inplace = True)\n",
    "countyGPCDdict.plot(kind = 'bar', grid = True, figsize = (20,8), title = 'Average Gallons Per Capita Day for each county in 2016')\n",
    "# randList = list(range(0,len(countyList)))\n",
    "# plt.figure(figsize=(20,15))\n",
    "# plt.bar(randList, gpcdList16, align='center', alpha=0.5)\n",
    "# plt.xlabel('Counties')\n",
    "# plt.ylabel('Average R-GPCD in 2016')\n",
    "# plt.ylim(ymax = 220)\n",
    "# plt.xticks(randList, countyList, fontsize = 8, rotation = 'vertical')\n",
    "# plt.title(r'$\\mathrm{Bar\\ of\\ Residential\\ Gallons-per-capita-day(gallons-per-capita-day)\\ 2016}$')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f362af7d828>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "#A bar chart showing R-GPCD_Calculated in each county based on df2015\n",
    "countyList = df2015['County'].unique()\n",
    "countyList.sort()\n",
    "countyGPCDdict = {}\n",
    "gpcdList16 = []\n",
    "for i in range(len(countyList)):\n",
    "    currRGPCD = df2015[df2015['County'] == countyList[i]]['R-GPCD_Calculated'].mean()\n",
    "    countyGPCDdict[countyList[i]] = currRGPCD\n",
    "\n",
    "countyGPCDdict = pd.Series(countyGPCDdict)\n",
    "countyGPCDdict.sort_values(inplace = True)\n",
    "countyGPCDdict.plot(kind = 'bar', grid = True, figsize = (20,8), title = 'Average Gallons Per Capita Day for each county in 2015')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f362aff7588>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "#A bar chart showing R-GPCD_Calculated in each county based on df2014\n",
    "countyList = df2014['County'].unique()\n",
    "countyList.sort()\n",
    "countyGPCDdict = {}\n",
    "gpcdList16 = []\n",
    "for i in range(len(countyList)):\n",
    "    currRGPCD = df2014[df2014['County'] == countyList[i]]['R-GPCD_Calculated'].mean()\n",
    "    countyGPCDdict[countyList[i]] = currRGPCD\n",
    "\n",
    "countyGPCDdict = pd.Series(countyGPCDdict)\n",
    "countyGPCDdict.sort_values(inplace = True)\n",
    "countyGPCDdict.plot(kind = 'bar', grid = True, figsize = (20,8), title = 'Average Gallons Per Capita Day for each county in 2014')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Chart "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot a line chart showing the tendency of the average water use (units in gallons)\n",
    "mean_14 = df2014['R-GPCD_Calculated'].mean()\n",
    "mean_15 = df2015['R-GPCD_Calculated'].mean()\n",
    "mean_16 = df2016['R-GPCD_Calculated'].mean()\n",
    "\n",
    "years = [2014,2015,2016]\n",
    "waterUsage = [mean_14, mean_15, mean_16]\n",
    "\n",
    "f = plt.figure(figsize=(6, 8))\n",
    "ax = plt.axes()\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Average Water Use per-capita-per-day (in gallons)')\n",
    "plt.title(r'$\\mathrm{Trend\\ of\\ Average\\ Water\\ Use\\ per-capita-day\\ from\\ 2014\\ to\\ 2016 }$')\n",
    "ax.set_xticks(years)\n",
    "plt.ylim((50,150))\n",
    "\n",
    "plt.plot(years, waterUsage)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is the function which helps us find the population based on the dataframe\n",
    "def estimate_popuation(df):\n",
    "    supplier_list =list(df['Supplier_Name'].unique())\n",
    "    ttpop=0\n",
    "    for i in range(len(supplier_list)):\n",
    "        df1 = df[df['Supplier_Name'] == supplier_list[i]]\n",
    "        ttpop=ttpop+df1.groupby('Supplier_Name')['Population_Served'].mean()[0]\n",
    "    return(round(ttpop))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F4BXAPRHxpQmpTpIkHbRGDfRi+EFmbgO+Cnw1IhYAL2p3YZIkqTUahf0XR5uYmY8Al7SnHEmS1GpjnsbPzI9NZCGSJKk9xu0uNyIeMxGFSJKk9mgY9hHxTODvJqgWSZLUBmNes4+I1wB/CZw9ceVIkqRWa9RA73PAiZm5caKKkSRJrdfoNP4HgIvtREeSpMNbo9b4/x34LPCtiStHkiS1WqPT+GTm5yPivokqRpIktd64t95l5g8mohBJktQeDY/sI+JE4IXA0nLSvcBlmXljuwuTJEmtMeaRfUT8F+DLFH3k/7L8CuBfIuI9E1OeJEk6WI2O7M8DTs7MnbUTI+LjwPXAh9tZmCRJao1G1+yHgKNHmX5UOU+SJB0GGh3Zvx34QUSsA+4upy0HVgJvaXdhkiSpNcYM+8z8bkSsAs5k7wZ6v8rM3RNRnCRJOnjj3Wc/BPyifnpEdGbmQNuqkiRJLTPuffZjuKGlVUiSpLZpNOrdO8eaBXS2pxxJktRqjY7s/xpYBHTVfXWOs54kSTqENLpmfw3wrcy8un5GRPxJ+0qSJEmt1Cjs3wQ8OMa81W2oRZIktUGjIW5/B8wEiIgj6+atb3NdkiSpRca79n5hRCwBLpiIYiRJUus1GgjnDcBdFAPg3B0Rr5+wqiRJUss0OrL/EXAbxT31twE/noiCJEmqoqGhZOfuyRlaplF3uXdGxHuBFwCfyswvTVxZkiQdHjKTh7fuZH3/dtZvHmT95u1s2FzzuH+QDeX3D7zgZF73xGMnvMbxusv9s/Lhf56AWiRJOmRkJv2Du/YK7j0BXhvsg+wY5Yh94dyZ9HZ10DN/Nid0L6F3/mxOPnr+JOzJOGEvSVIVbRncNRLeRXDvHd7r+4vv23buO+5b1+wZ9MyfTe/8Dh6/4ojicVcHvfM76C2nd3fNpmPm9EnYs9EZ9pKkyti+c3ddiA+WR+ZlmJchPjC4a59158yczpELOujpms2pyxbS01UE93Cw984v5s2bffhF5+FXsSRpyhnctZuN/YN7h3d/zZF4OW3z9n1DfNaMacURd1cHjz5yPn+4avaeo/CuDnrKx52zZxARk7B37Tdu2EfER4EPAduA7wKPBd6RmV9oc22SpIrbuXuIBwYG92rYtqG//vr4IJu27Nhn3ZnTg56Ra+KdPOmExfSUR9/DR+K982ezYM7MyoZ4s5o5sn9OZr47Il4M3AG8BPgJYNhLkka1eyh5cMtgzVH3vg3b1m8e5MEtg2Tuve60gO4ysJctmsvjjl00Etw98zvK6+OzWTR3FtOmTe0Qb1YzYT+z/P484GuZ+chU/4QkSVPV0FDy0NYdNde/9w7vjWWYbxwYZPfQ3ikeAYvnzR5pxPbYZQvoqWnY1lOG+OLO2Uw3xFuqmbD/14i4ieI0/p9HRDewvb1lSZImUmayeduu8l7xUe4XLxu2bejfzs7duc/6i+bOLBuzdbCqt2vvI/Hy8ZLO2cyc7gjpk6GZsP8A8FHgkczcHRFbKTrakSQd4jKTgcFdexq21d0fvr5m2o5d+94rPr9jxsj17yccP694PNJKvQjx7q7ZzJ5x6Nxmpn01E/Y/z8wzhp9k5paI+ClwRoN1JElttnVHbYjX3WJW09Bt64597xXvHL5XvKuDM5YvGrmtrLZhW09XB3NmGeJVMGbYl8PaLgXmRMTpwPAFlPnA3PE2HBEdFA35Zpev8/XM/EDdMrOBfwYeBzwIvDIz74iIFcCNwO/KRX9R05ufJFXa9p3Dt5nVXA/v3/sWsw2bB+kf5V7xjpnTyqPvDk4+ej5nndgzco18+Jp4z/wOOg/De8V14Br9ts8G3ggsAz5eM70feF8T2x4EzsrMgYiYCVwZEd/JzF/ULHMe8FBmroyIc4GPAK8s592amac1uR+SdMjbsWuIjQN7Xwuv73Z1ff92Ht66c591Z02fNtK5y6OO7OKpfd379NzWM7+D+R3VvVdcB67RQDiXAJdExEsz8xv7u+HMTGCgfDqz/Kpv1fFC4IPl468Dnwr/SiUdZnbtHuLBLTvGbNg2fKr9wVHuFZ8+LejpKoL62MVzOfO4I/Zp2Nbb1cHCud4rrgPX6DT+a8uOc1ZExDvr52fmx0dZrX4b04GrgZXApzPzqrpFlgJ3l9vbFRGPAIvLecdFxLXAZuD9mfnTMV7jfOB8gOXLl49XkiQ1bWgoR0K8/v7wjTXPHxgYZGiUe8WXdBZH4ksXdnD68oUj94f3jNxm1sHied4rrvZrdBp/Xvm980A3npm7gdMiYiFwaUSckpm/bWLV+4HlmflgRDwO+FZEnJyZm0d5jYuAiwBWr1697/0gklRnvCFJhxu7bewfZFd9igOL580aaYl+0lHz9z0SL0N8hreZ6RDR6DT+Z8rvFxzsi2TmwxFxBXAOUBv29wLHAPdExAxgAfBgeQlgsFz36oi4FVgFrDnYWiRVV2ayeXvdkKT1Ddv6B5sakrSvZ8moDdu6O2cza4YhrsNLM33jdwN/CqyoXT4z/1MT6+0sg34O8GyKBni1LgPeAPwceBnww8zMct1N5X39xwN9wG1N75WkyhmoGVd8rCFJ12/ezvad+4b44TgkqdRKzdx78W3gp8D3gX1v1hzbURQN/KYD04CvZublEXEhsCYzLwMuBj4fEbcAm4Bzy3WfBlwYETuBIeDPMnPTfry2pMPEth27666H1w6EsueofMso94rXD0k6ciS+V8cvs5k7y9vMNLVF1o9AUL9AxNrD5Ra41atX55o1numXDgWDu3aPdK9a2/FLs0OSHlnTycue8cSnzpCkUjMi4urMXD3ecs183L08Iv4oM/+tBXVJqoiHtuxg3YYBbtkwwH0Pb9urYdv6zdt5aJR7xccakrT2dHpvVwfz5xjiUis1E/ZvA94XEYPAToqe9DIz57e1MkmHhAcHBrl5/QC3bOhn3YYB1q0fYN2Gfh4Y2HPP+PRpQXdnMZrZMUc4JKl0qBk37DOzayIKkTR5MpONA4Pcsn6AdRsGuHl9/8hR+6aajmA6Z8+gr7eTs07soa+ni5W9nfT1dHLUgjkOSSodwhp1qlM/0E0CD2Tm3e0tSVK7ZCYb+geLMC+D/ZYN/dy8foBHtu057d7VMYNVvV0856Re+nq76OvppK+3kyPnd3h6XToMNTqy/5+jTDsiImYBr8rMtW2qSdJBykzuf2R7edq9f+TU+7oNA/TXNIhbOHcmq3q6eN5jj6Kvp5NVZbB3d8021KUKadSpzjNGmx4Rq4G/pbg9TtIkGhpK7ntk254wXz/AzRsGuHXDAAM1I6ItnjeLlT2dvOi0pfT1drKyp5O+ni6WdM4y1KUpYL9vPs3MNRFxwF3oStp/Q0PJPQ9tGzk6v3l9P7eU19Rrxyrv7ppNX08nLz1jKSt7u1jVUwT74s7Zk1i9pMm232EfEb3sO3qdpBbYPZTctWlrceq9DPN1G4pgr+0Zrnf+bPp6unjl44+hr6erOFrv7mTRvFmTWL2kQ1WjBnqfZN9QPwJ4EsXteJIO0K7dQ9y5aWtx+r0M9nUbBrh14wA7du0J9aMXdLCyt4vXPGExq3o7WdnTxcqeThbMmTmJ1Us63DQ6sq/vii6BB4F3ZuaG9pUkVcfO3UPc8cCWve5PX7d+gNsf2LLXQCxLF85hVW8nT+1bUl5PL06/d3UY6pIOXqMGepdMZCHS4Wxw127ueGAr68rb2G6pCfXhIVIj4JhFc+nr6eTpJ3azqjz9fkJ3J/Nm23e7pPZpdBr/XynGif9uZu6sm3c88Ebgjsz8bFsrlA4h23fu5raNW0auoxet3/u588Gt7C5DfVrAsYvnsbKnk2ef1Etfb9Hy/YTuTubMclQ1SROv0eHEnwLvBD4REZuAjUAHxVC3twKfysxvt71CaRJs27GbWzcWDeRqe5O788EtlJnO9GnBsYuLI/XnPeaokdvZju+e51Cpkg4pjU7j/x54N/DuiFhBMWTtNuDmzNw6IdVJbbZlcBe3bhzYpze5ux/ayvCAkDOmBcctmcejj+ri+acePdL5zIolc5k9w1CXdOhr6kJhZt4B3NHWSqQ2GhjcNXKUfsuGPS3g73lo28gyM6cHxy/p5DHLFvDSM5aVp987OXbxPGbNmDaJ1UvSwbFVkCpl8/adrKtpIHfzhgFuWd/PfY9sH1lm1oxpnNDdyRnLF/HK1ccUfb/3dnLsEXOZMd1Ql1Q9hr0OS49s3TnS8r22sdzvN+8J9dkzprGyp5MnHL945Ha2vt4ulh8x1xHaJE0phr0OaZu27NirN7nhxnIb+wdHlpkzczp9vZ08aeVi+nq6WFW2fl+6yGFXJQmaCPuIeDLwQeDYcvkAMjOPb29pmioykwe37Nir05nho/UHBvYeS31lTydPX9U9cjtbX28nRy+YwzRDXZLG1MyR/cXAO4Crgd3jLCuNKTPZ2D+4Z9jVml7lHtq691jqfT2dPPPE8h71ctjVoxY4lrokHYhmwv6RzPxO2ytRZWQm6zcP7tOb3LoNAzyybU+oL5gzk1W9nZxzylHl9fTilrYex1KXpJZqJuyviIj/AXwTGLlQmpnXtK0qHRYyk/se2c669Xv3JnfL+gH6a8ZSXzR3Jn29XfzxY49iVXmUvrK3k+5OQ12SJkIzYf+E8vvqmmkJnNX6cnQoGhpK7n14W8319LIDmvX9bKkZS31J5yz6erp48RlLy4FcisZyjqUuSZNr3LDPzGdMRCGafLuHknse2ron0GtawW/buSfUe7pm09fbyctXHzPSUG5lTydHOJa6JB2SmmmNvwD4APC0ctKPgQsz85F2Fqb22T2U3LVp6z69yd26cYDtO/cMu3rUgg5W9nTyqjOXl9fTO1nZ3cWCuQ67KkmHk2ZO438W+C3wivL564DPAS9pV1FqjZ27h7jzwa179Sa3bn0/tz2whR279h5LfWVPJ086obhPfWVvMZb6fMdSl6RKaCbsT8jMl9Y8vyAi1rarIO2/HbuGuPPBLSO9yRXX0we47YEBdu7OkeWOOWIOfT1d/OGq7pHb2U7o6aTTsdQlqdKa+S+/LSKekplXwkgnO9vGWUdtMLhrN7c/sGWfa+p3PLCFXeW4qxGw/Ii59PV0cdaje4pb2nq6OKFnHnNnGeqSNBU189//z4FLymv3AWwC3tjOoqa67Tv3jKVe26vcnZu2srsM9WkBKxbPY2VPJ2ef3Muq3qKR3AndnY6lLknaSzOt8dcCp0bE/PL55rZXNUVs21GE+nCYD3dAc9emrZSZzvRpwYrFc3nUkcV96ivL0+/HLZlnqEuSmjJm2EfEazPzCxHxzrrpAGTmx9tcW2VsKcdSL+5PLzqdWbdhgLsf2kqWoT5zenDcknmcfPQCXnT60pF+31c4lrok6SA1OrKfV37vGmVejjJtyuvfvnNPqNf0/X7vw3uaOMyaPo3ju+dx6jELednjlo0Mu3rs4rnMdCx1SVIbjBn2mfmZ8uH3M/M/aueVjfSmrEe27dyrv/fhcL//kb3HUj+hu5PVKxbx6t7lI+OpLz9iLjMMdUnSBGqmgd4ngTOamFY5D2/dMXJ0PtIBzYZ+1m/eeyz1lT2d/MHxi1nZ28mq8vT7skVzHUtdknRIaHTN/g+AJwHdddft5wOVbxl21sd+xG0PbBl5PnfWdPp6OnlqX/fICG19PV0sXehY6pKkQ1ujI/tZQGe5TO11+83Ay9pZ1KHgxacvpWPm9JHx1I+a32GoS5IOS42u2f8Y+HFE/FNm3jmBNR0S3vrMvskuQZKklmjmmv3Wcjz7k4GO4YmZ6RC3kiQdBpppFv5F4CbgOOAC4A7gV22sSZIktVAzYb84My8GdmbmjzPzPwEe1UuSdJho5jT+zvL7/RHxPOA+4Ij2lSRJklqpmbD/UDkIzl9S3F8/H3hHW6uSJEkt08xAOJeXDx8BntHeciRJUqs16lTnkzToAz8z/6ItFUmSpJZqdGS/ZsKqkCRJbdOoU51LJrIQSZLUHuNes4+IKxjldL6d6kiSdHhopjX+u2oedwAvBXa1pxxJktRqzbTGv7pu0n9ExC8GchrpAAAMaklEQVTbVI8kSWqxcXvQi4gjar6WRMTZwIIm1uuIiF9GxHURcX1EXDDKMrMj4isRcUtEXBURK2rmvbec/rvyNSVJ0gFo5jT+1RTX7IPi9P3twHlNrDcInJWZAxExE7gyIr6Tmb+oWeY84KHMXBkR5wIfAV4ZEScB51IMvnM08P2IWJWZu5veM0mSBDR3Gv+4A9lwZiYwUD6dWX7VN/R7IfDB8vHXgU9FRJTTv5yZg8DtEXELcCbw8wOpRZKkqayZ0/gdEfHOiPhmRHwjIt4eER3jrVeuOz0i1gIbgO9l5lV1iywF7gbIzF0UvfQtrp1euqecNtprnB8RayJizcaNG5spS5KkKaWZUe/+meJ0+ieBT5WPP9/MxjNzd2aeBiwDzoyIUw600AavcVFmrs7M1d3d3a3evCRJh71mrtmfkpkn1Ty/IiJu2J8XycyHy/v1zwF+WzPrXuAY4J6ImEHR8O/BmunDlpXTJEnSfmrmyP6aiHji8JOIeAJNdKUbEd0RsbB8PAd4NnBT3WKXAW8oH78M+GF5rf8y4Nyytf5xQB/g7X6SJB2AZo7sHwf8LCLuKp8vB34XEb+haIf32DHWOwq4JCKmU3yo+GpmXh4RFwJrMvMy4GLg82UDvE0ULfDJzOsj4qvADRR3ALzZlviSJB2YKA6kGywQcWyj+Zl5Z0srOgirV6/ONWscv0eSNDVExNWZuXq85Zq59e7OiDgVeGo56aeZed3BFihJkiZGM7fevQ34ItBTfn0hIt7a7sIkSVJrNHPN/jzgCZm5BSAiPkLRuc0n21mYJElqjWZa4wdQ2zhudzlNkiQdBpo5sv8ccFVEXFo+fxFFK3pJknQYaKaB3scj4kfAU8pJb8rMa9talSRJapkxw77s//7PgJXAb4C/K/uvlyRJh5FG1+wvAVZTBP1zgY9NSEWSJKmlGp3GPykzHwMQERdjd7WSJB2WGh3Z7xx+4Ol7SZIOX42O7E+NiM3l4wDmlM+Dok/8+W2vTpIkHbQxwz4zp09kIZIkqT2a6VRHkiQdxgx7SZIqzrCXJKniDHtJkirOsJckqeIMe0mSKs6wlySp4gx7SZIqzrCXJKniDHtJkirOsJckqeIMe0mSKs6wlySp4gx7SZIqzrCXJKniDHtJkirOsJckqeIMe0mSKs6wlySp4gx7SZIqzrCXJKniDHtJkirOsJckqeIMe0mSKs6wlySp4gx7SZIqzrCXJKniDHtJkirOsJckqeIMe0mSKs6wlySp4gx7SZIqzrCXJKniDHtJkirOsJckqeIMe0mSKs6wlySp4toW9hFxTERcERE3RMT1EfG2UZZZFBGXRsSvI+KXEXFKzbw7IuI3EbE2Ita0q05JkqpuRhu3vQv4y8y8JiK6gKsj4nuZeUPNMu8D1mbmiyPiRODTwDNr5j8jMx9oY42SJFVe247sM/P+zLymfNwP3AgsrVvsJOCH5TI3ASsiorddNUmSNBVNyDX7iFgBnA5cVTfrOuAl5TJnAscCy8p5Cfx7RFwdEedPRJ2SJFVRO0/jAxARncA3gLdn5ua62R8G/iYi1gK/Aa4FdpfznpKZ90ZED/C9iLgpM38yyvbPB84HWL58ebt2Q5Kkw1Zbj+wjYiZF0H8xM79ZPz8zN2fmmzLzNOD1QDdwWznv3vL7BuBS4MzRXiMzL8rM1Zm5uru7u017IknS4audrfEDuBi4MTM/PsYyCyNiVvn0T4CfZObmiJhXNuojIuYBzwF+265aJUmqsnaexn8y8DrgN+Vpeiha3y8HyMx/AB4NXBIRCVwPnFcu1wtcWnxeYAbwpcz8bhtrlSSpstoW9pl5JRDjLPNzYNUo028DTm1TaZIkTSn2oCdJUsUZ9pIkVZxhL0lSxRn2kiRVnGEvSVLFGfaSJFWcYS9JUsUZ9pIkVZxhL0lSxRn2kiRVnGEvSVLFGfaSJFWcYS9JUsUZ9pIkVZxhL0lSxRn2kiRVnGEvSVLFGfaSJFWcYS9JUsUZ9pIkVZxhL0lSxRn2kiRVnGEvSVLFGfaSJFWcYS9JUsUZ9pIkVZxhL0lSxRn2kiRVnGEvSVLFGfaSJFWcYS9JUsUZ9pIkVZxhL0lSxRn2kiRVnGEvSVLFGfaSJFWcYS9JUsUZ9pIkVZxhL0lSxRn2kiRVnGEvSVLFGfaSJFWcYS9JUsUZ9pIkVZxhL0lSxRn2kiRVnGEvSVLFGfaSJFWcYS9JUsW1Lewj4piIuCIiboiI6yPibaMssygiLo2IX0fELyPilJp550TE7yLiloh4T7vqlCSp6tp5ZL8L+MvMPAl4IvDmiDipbpn3AWsz87HA64G/AYiI6cCngecCJwGvGmVdSZLUhLaFfWben5nXlI/7gRuBpXWLnQT8sFzmJmBFRPQCZwK3ZOZtmbkD+DLwwnbVKklSlU3INfuIWAGcDlxVN+s64CXlMmcCxwLLKD4U3F2z3D3s+0FBkiQ1YUa7XyAiOoFvAG/PzM11sz8M/E1ErAV+A1wL7N7P7Z8PnF8+HYiI3x1kybWWAA+0cHtSVflekZrT6vfKsc0s1Nawj4iZFEH/xcz8Zv38MvzfVC4bwO3AbcAc4JiaRZcB9472Gpl5EXBRaysvRMSazFzdjm1LVeJ7RWrOZL1X2tkaP4CLgRsz8+NjLLMwImaVT/8E+En5AeBXQF9EHFfOPxe4rF21SpJUZe08sn8y8DrgN+Vpeiha3y8HyMx/AB4NXBIRCVwPnFfO2xURbwH+NzAd+GxmXt/GWiVJqqy2hX1mXgnEOMv8HFg1xrx/A/6tDaXtj7ZcHpAqyPeK1JxJea9EZk7G60qSpAlid7mSJFXclAr7sbrwjYgjIuJ7EbGu/L6onH5iRPw8IgYj4l2jbG96RFwbEZdP9L5I7dTK90pE3BERv4mItRGxZjL2R2qXFr9XFkbE1yPipoi4MSL+oFV1TqmwZ+wufN8D/CAz+4AflM8BNgF/AXxsjO29jaJnQKlqWv1eeUZmnubteaqgVr5X/gb4bmaeCJxKC/NlSoV9gy58XwhcUi52CfCicpkNmfkrYGf9tiJiGfA84B8noHRpQrXyvSJVWaveKxGxAHgaxS3rZOaOzHy4VXVOqbCvVdeFb29m3l/O+j3Q28QmPgG8GxhqR33SoaIF75UE/j0iri57vJQq6SDfK8cBG4HPlZeH/zEi5rWqtikZ9o268M3i9oSGtyhExB8DGzLz6vZVKU2+g32vlJ6SmWdQjGL55oh4WusrlSZXC94rM4AzgL/PzNOBLew59X/QplzYj9GF7/qIOKqcfxSwYZzNPBl4QUTcQTEi31kR8YU2lSxNiha9V8jMe8vvG4BLKUa1lCqjRe+Ve4B7MnN4wLivU4R/S0ypsG/Qhe9lwBvKx28Avt1oO5n53sxclpkrKLry/WFmvrYNJUuTolXvlYiYFxFdw4+B5wC/bX3F0uRoYa78Hrg7Ih5VTnomcEPL6pxKnepExFOAn1KMsDd8rf19FNdXvkrRle+dwCsyc1NEHAmsAeaXyw8AJ9WeoomIpwPvysw/nqj9kNqtVe8VihG+Li3XnwF8KTP/20Tth9RurcyViDiNotH3LIpB4d6UmQ+1pM6pFPaSJE1FU+o0viRJU5FhL0lSxRn2kiRVnGEvSVLFGfaSJFWcYS9pRBSujIjn1kx7eUR8dzLrknRwvPVO0l4i4hTgaxR9fM8ArgXOycxbD2KbMzJzV4tKlLSfDHtJ+4iIj1L0zT0P6M/Mv4qINwBvpujw42fAWzJzKCIuoujWcw7wlcy8sNzGPcAXgLOBvwaWAX9KMSTor+11Upo4Mya7AEmHpAuAa4AdwOryaP/FwJMyc1cZ8OcCXwLeU/YMNgO4IiK+npnD3XxuKAf1ICLuB47NzB0RsXDC90iawgx7SfvIzC0R8RVgIDMHI+JZwOOBNUVX4MwB7i4Xf1VEnEfx/+Roim5yh8P+KzWbvR74QkR8G/jWBOyGpJJhL2ksQ+zp6zuAz2bmf61dICL6gLcBZ2bmw+Xojx01i2ypeXw28IfAC4D3RcRjM3N326qXNMLW+JKa8X3gFRGxBCAiFkfEcorBPPqBzeUwnmePtnJETAeWZeYPgXdTDJAzd0Iql+SRvaTxZeZvIuIC4PsRMQ3YCfwZxehdNwA3UYzs9R9jbGIG8KVyuNtpwMcys7/9lUsCW+NLklR5nsaXJKniDHtJkirOsJckqeIMe0mSKs6wlySp4gx7SZIqzrCXJKniDHtJkiru/wD2Knmac7cvWQAAAABJRU5ErkJggg=="
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2. Plot line chart to show the change of urban population in California\n",
    "#2014 urban population:\n",
    "point1=estimate_popuation(df2014)\n",
    "#2015 urban population:\n",
    "point2=estimate_popuation(df2015)\n",
    "#2016 urban population:\n",
    "point3=estimate_popuation(df2016)\n",
    "\n",
    "years = [2014,2015,2016]\n",
    "population = [point1, point2, point3]\n",
    "\n",
    "f = plt.figure(figsize=(8, 6))\n",
    "ax = plt.axes()\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel('Population (Units *10^7)')\n",
    "plt.title(r'$\\mathrm{Trend\\ of\\ Urban\\ Population\\ from\\ 2014\\ to\\ 2016 }$')\n",
    "ax.set_xticks(years)\n",
    "plt.plot(years, population)\n",
    "plt.ylim((2.9e7,3.18e7))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis and Results\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Estimating how many desalination plant projects we need in case of total drought based on a Counties' name or Water Supplier Names or Hydrologic_Region's name (From the list below)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "hydro_list =list(df['Hydrologic_Region'].unique())\n",
    "supplier_list =list(df2016['Supplier_Name'].unique())\n",
    "county_list = list(df['County'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function based on the name of County\n",
    "def estimate_county(county,df):\n",
    "        gallons_water_per_project=5e7   # Number of gallons water per desalination plants project can produced per day    \n",
    "        df1=df[df.County.str.contains(county) == True]\n",
    "        m_ppw=df1['R-GPCD_Calculated'].mean() #Mean of gallons of water each person need per day\n",
    "# Find the population based on new data frame df1. \n",
    "# We use groupby and take the mean since we noticed that even under the same supplier district, \n",
    "# the population served each month is slightly changing so we decided to take the mean of the the population served in these months in the data frame.\n",
    "        ttpop=df1.groupby('Supplier_Name')['Population_Served'].mean()\n",
    "        ttpop=round(ttpop.sum())\n",
    "        ttwater=ttpop*m_ppw \n",
    "        the_number_of_projectsneed= round(ttwater /gallons_water_per_project)\n",
    "        if the_number_of_projectsneed==0:     #In case, it is zero. We should prepare for at least one for this situation\n",
    "                 the_number_of_projectsneed=1\n",
    "        return (the_number_of_projectsneed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function based on the name of Supplier_Name\n",
    "def estimate_supplier(supplier,df):\n",
    "        gallons_water_per_project=5e7  # Number of gallons water per desalination plants project can produced per day    \n",
    "        df1=df[df.Supplier_Name.str.contains(supplier) == True] #Get on new data frame which contains the information which related to our interests.\n",
    "        m_ppw=df1['R-GPCD_Calculated'].mean() #Mean of gallsons of water each person need per day\n",
    "        # Find the population based on the new data frame\n",
    "        ttpop=df1.groupby('Supplier_Name')['Population_Served'].mean()\n",
    "        ttpop=round(ttpop.sum())\n",
    "        ttwater=ttpop*m_ppw \n",
    "        the_number_of_projectsneed= round(ttwater /gallons_water_per_project)\n",
    "        if the_number_of_projectsneed==0: #In case, it is zero. We should prepare for at least one for this situation\n",
    "                 the_number_of_projectsneed=1\n",
    "        return (the_number_of_projectsneed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function based on the name of Hydrologic_Region\n",
    "def estimate_Hydrologic_Region(Hydrologic_Region,df):\n",
    "        gallons_water_per_project=5e7   # Number of gallons water per desalination plants project can produced per day    \n",
    "        df1=df[df.Hydrologic_Region.str.contains(Hydrologic_Region) == True]\n",
    "        m_ppw=df1['R-GPCD_Calculated'].mean() #Mean of gallsons of water each person need per day\n",
    "        # Find the population based on new data frame\n",
    "        ttpop=df1.groupby('Supplier_Name')['Population_Served'].mean()\n",
    "        ttpop=round(ttpop.sum())\n",
    "        ttwater=round(ttpop*m_ppw )\n",
    "        the_number_of_projectsneed= round(ttwater /gallons_water_per_project)\n",
    "        if the_number_of_projectsneed==0: #In case, it is zero. We should prepare for at least one for this situation\n",
    "                 the_number_of_projectsneed=1\n",
    "        return (the_number_of_projectsneed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "Overview: \n",
    "1. We have built three models to predict how many desalination plant projects we need in the previous part. In the result part,we will show how to use them.\n",
    "\n",
    "2. We are going to make one dataframe to show our results based on Hydrologic_Region\n",
    "> (There are two columns in the dataframe. One is for the Hydrologic_Region name and another one is for  number of projects we need)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Predicting the number of desalination plants "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prediction based on the county's names: (we will call the function we wrote before)\n",
    "#Example: \n",
    "county_example = estimate_county('ALAMEDA',df2014)\n",
    "\n",
    "# Prediction based on the supplier names: (we will call the function we wrote before)\n",
    "#Example:\n",
    "supplier_example = estimate_supplier('Milpitas  City of',df2014)\n",
    "\n",
    "\n",
    "#  Prediction based on the Hydrologic_Region: (we will call the function we wrote before)\n",
    "# Example:\n",
    "hydrologic_example = estimate_Hydrologic_Region('San Joaquin River',df2014)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "By building three models to predict deslaination plants, we can deterimine the number of plants, supplier, and hydrologic religions affected. \n",
      "\n",
      "Based on the county's name:\n",
      "For example, in Alameda in 2014, there can be 5 desalination plant project(s)\n",
      "\n",
      "Based on the supplier name:\n",
      "For example: In Milpitas City in 2014, it needs 1 desalination plant project(s)\n",
      "\n",
      "Based on the Hydrolic Region:\n",
      "For example: In the San Joaquin River region in 2014, it needs 3 desalination plant project(s)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"By building three models to predict deslaination plants, we can deterimine the number of plants, supplier, and hydrologic religions affected. \\n\")\n",
    "print(\"Based on the county's name:\")\n",
    "print(\"For example, in Alameda in 2014, there can be\", county_example, \"desalination plant project(s)\\n\")\n",
    "\n",
    "print(\"Based on the supplier name:\")\n",
    "print(\"For example: In Milpitas City in 2014, it needs\", supplier_example, \"desalination plant project(s)\\n\")\n",
    "\n",
    "print(\"Based on the Hydrolic Region:\")\n",
    "print(\"For example: In the San Joaquin River region in 2014, it needs\", hydrologic_example, \"desalination plant project(s)\\n\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Predicting desalination plant projects based on hydrologic region"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the Hydrolic Region, we can predict a certain amount of plant projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hydrologic_Region</th>\n",
       "      <th>num_Projects_needed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Francisco Bay</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Coast</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Joaquin River</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sacramento River</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>North Coast</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tulare Lake</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Colorado River</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Central Coast</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>South Lahontan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hydro_list =list(df['Hydrologic_Region'].unique())\n",
    "h_list=[]\n",
    "for x in hydro_list:\n",
    "    h_list.append(round((estimate_Hydrologic_Region(x,df2014)+estimate_Hydrologic_Region(x,df2015)+\n",
    "                  estimate_Hydrologic_Region(x,df2016))/3))\n",
    "\n",
    "hydro_se = {'Hydrologic_Region' : hydro_list,\n",
    "                'num_Projects_needed': h_list }\n",
    "hydro_df = pd.DataFrame(hydro_se)\n",
    "hydro_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Desalination Plant Projects Need is 56 to supply the urban residential-use water in California\n"
     ]
    }
   ],
   "source": [
    "t=hydro_df['num_Projects_needed'].sum()\n",
    "print('Total Number of Desalination Plant Projects Need is',t, \"to supply the urban residential-use water in California\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion / Dicussion\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Number of Desalination Plant Projects Need is 56 to supply the urban residential-use water in California\n"
     ]
    }
   ],
   "source": [
    "t=hydro_df['num_Projects_needed'].sum()\n",
    "print('Total Number of Desalination Plant Projects Need is',t, \"to supply the urban residential-use water in California\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If each plant is a billion dollar plant supplying 50 million gallons, our hypothesis was very close to the concluded result (56 vs 51)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion:\n",
    "**1.** When we are trying to loop through the unique list of the supplier name or counties' names, we realized that dataframes of different years don't contain the exact same counties' names or supplier's names. Hence, we decided to conclude our result based on the Hydrologic_Region's name. At the same time, we build a model to predict the amount of desalination plant projects needed based on suppliers' names or the counties' names. \n",
    "\n",
    "**2. Why did we switch our project?**\n",
    "\n",
    "We switched our project idea for a few reasons. \n",
    "\n",
    "We did not have any domain knowledge for the problem that we were trying to solve. Our best guesses at water consumption were just that - guesses. We thought that it would be more practical and more useful to look at how many desalination plants would be needed, rather than where they should be.\n",
    "\n",
    "The second reason had to do with the algorithm itself. Our original plan, to constrain the centroids to be along the coast, was something that no one had attempted to do before (as far as we'd found). So we simplified our problem a bit, to \"Constrain the size of the clusters to be a specific size\" as there could only be a certain amount of people serviced by a desalination plant. We also chose to limit our observations to only be along the coast, to make it so that the desalination plant would only service coastal populations. However, this lowered the amount of observations to 743, which would have costed us points in the end. Both because of the complexity of limiting the cluster size and because of the small number of observations, we chose to go with a problem that was better in nearly every way.\n",
    "\n",
    "**3. What are the pitfalls and potential confounds of your data and/or methods?**\n",
    "\n",
    "Our project is very hypothetical since we are unsure of many variables. For example, if we are placing the desalination plant in a more population dense area, the water delivery will be more efficient, but the desalination plant itself will take up a huge space. Therefore placing the plant in the most population dense area might not be the best idea, and we need to come up with a constraint of population density for where we can place the plants.\n",
    "Furthremore, Since the implementation of desalination plants takes relatively long time, and our project is using population as a independent variable, we might also need to consider the growth and decay of population, or the population shifts in California.\n",
    "The water pipe distance and cost to maintain to transport water to plants would also cause the number of plants to change.\n",
    "\n",
    "**Confounds in our design:** \n",
    "\n",
    "The average water use and total population are two key factors to solve our research question. We noticed that the trend of average water use per day is decreasing from 2014 to 2016.  As for urban population, we know that the population in California is still growing so in that case, it will be hard to predict the exact number of desalination projects we need in case of total drought in future. Also, if we have more time, we could build another two models to predict the population and the average residential water usage per-capita-day. After that, these two models will help us get more accurate result. This is also data that is collected up to 2016. If we had current data, it would make this estimate even more accurate. \n",
    "\n"
   ]
  }
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