time-series-first-analysis_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Welcome to Big Data!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Today we are going to analyze Rainfall data from central Park to get you started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We are going to do this all in a Jupyter notebook which was formerly called iPython.  This will take some getting used to. You actually do your programming in a window in a web browser or in a program.  But you are not online.  You are running off of your own computer.  But we will teach you all you need to do.  Make sure to type everything you see correctly. All syntax matters including capital letters and punctuation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, Lets get you started.  But make sure you read the syllabus and get a binder to keep the handouts.  You need to do the code.org also!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to use [Anaconda python](http://continuum.io/).    We have used [Enthought Canopy](https://www.enthought.com/products/canopy/) but anaconda seems to work better.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look on the course site for directions to open Anaconda and then you can move around directories.  Remember nothing gets saved on the school computers. so whatever you save each day you need to email to yourself or save some home\n",
    "\n",
    "Open Jupyter notebook like in the video"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are in iPython!!!! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You see that I am using Markdown to add these notes. You make markdown by choosing Markdown above you can change from code to Markdown.  Notes are essential to understanding everything.  Please make a new cell and add some notes!  We will do more of this later.  If you download the notebook from Courseworks under syllabus you can double click on the notes and see how I added links!  I will show you how to add equations and images later!  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we wanted to analyze precipitation data from Central Park. What questions could we ask and answer?  I have downloaded the data from 1876 onward!  I want to know the precipiation in the month I was born?  I want to know the annual average precipiation in Central Park and I also want to know what monthly Precipitation looks like.   Lets see if we can figure that out today.  We are going to dive in really deep the first class and show you the power of programming.  Then over the course of the semester I will teach you how to do this on your own.  So enjoy the first analysis, see what we can do and then work on learning it yourself during the semester."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing we will do is import the python libraries that we will use.  When programming you don't use the whole Python programming language.  You have a basic part of Python that is always turned-on but also you import or turn on the parts of the language that you will use for your analysis.  You will learn about different libraries as the semester progresses.  \n",
    "\n",
    "Type in what you see below.  Then to execute the code hit shift-enter.  The little circle will fill in on the top right as the computer computes code.  It needs to be exact, uppercase, lowercase etc.   TYPE EXACTLY!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib # for plot data\n",
    "import matplotlib.pylab as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to get our data.  I will use github a lot this semester. It is a website that allows you to update and share code and to keep track of it.  It is like Dropbox on steroids.  I got the data from NOAA https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USW00094728/detail. \n",
    "\n",
    "The information for this class is at this website\n",
    "https://github.com/bmaillou/BigDataPython\n",
    "\n",
    "This is the cool part.  We don't need to get it.  We can just link it below.  But you can look at the data and download if you want.  follow the link to central park csv file. Then click on it and look at the raw data.  csv files are a great way of getting data. csv means commas seperated variables.  It is just a text file with data seperated by commas.  now copy the link so we can add it below.  The link goes to the url. Then we are using the pandas reader to get the data.  We are then indexing by Date to organize. In pandas data is usually called \"df\" for dataframes.  It is almost like a monster excel sheet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "url='https://raw.githubusercontent.com/bmaillou/BigDataPython/master/CentralPark2022.csv'\n",
    "df = pd.read_csv(url,index_col='DATE',parse_dates=['DATE']) #This is central park data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the data is in the computer as df.  You could just type df to see it. I typed df.head() to see the first 5 rows.  Type df.tail() to see the last 5. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>STATION</th>\n",
       "      <th>NAME</th>\n",
       "      <th>DASF</th>\n",
       "      <th>MDSF</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>SNOW</th>\n",
       "      <th>SNWD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</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>2022-08-24</th>\n",
       "      <td>USW00094728</td>\n",
       "      <td>NY CITY CENTRAL PARK, NY US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-25</th>\n",
       "      <td>USW00094728</td>\n",
       "      <td>NY CITY CENTRAL PARK, NY US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-26</th>\n",
       "      <td>USW00094728</td>\n",
       "      <td>NY CITY CENTRAL PARK, NY US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-27</th>\n",
       "      <td>USW00094728</td>\n",
       "      <td>NY CITY CENTRAL PARK, NY US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-28</th>\n",
       "      <td>USW00094728</td>\n",
       "      <td>NY CITY CENTRAL PARK, NY US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                STATION                         NAME  DASF  MDSF  PRCP  SNOW  \\\n",
       "DATE                                                                           \n",
       "2022-08-24  USW00094728  NY CITY CENTRAL PARK, NY US   NaN   NaN  0.00   0.0   \n",
       "2022-08-25  USW00094728  NY CITY CENTRAL PARK, NY US   NaN   NaN  0.00   0.0   \n",
       "2022-08-26  USW00094728  NY CITY CENTRAL PARK, NY US   NaN   NaN  0.24   0.0   \n",
       "2022-08-27  USW00094728  NY CITY CENTRAL PARK, NY US   NaN   NaN  0.00   0.0   \n",
       "2022-08-28  USW00094728  NY CITY CENTRAL PARK, NY US   NaN   NaN  0.00   0.0   \n",
       "\n",
       "            SNWD  \n",
       "DATE              \n",
       "2022-08-24   0.0  \n",
       "2022-08-25   0.0  \n",
       "2022-08-26   0.0  \n",
       "2022-08-27   0.0  \n",
       "2022-08-28   0.0  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can do a lot with the data.  For some quick ideas type df. with the period and hit tab. The list of functions comes up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But lets make a quick and dirty plot.  To plot all the data you type df.plot() and then to show the plot you type plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to know what type of data I have.  So i need to check the data type,  show how to type data1.  Hit tab and then see what we can do.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION     object\n",
       "NAME        object\n",
       "DASF       float64\n",
       "MDSF       float64\n",
       "PRCP       float64\n",
       "SNOW       float64\n",
       "SNWD       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But pandas has better built in functions.  try df.info() to learn more about your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 56064 entries, 1869-02-28 to 2022-08-28\n",
      "Data columns (total 7 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   STATION  56064 non-null  object \n",
      " 1   NAME     56064 non-null  object \n",
      " 2   DASF     56 non-null     float64\n",
      " 3   MDSF     56 non-null     float64\n",
      " 4   PRCP     56064 non-null  float64\n",
      " 5   SNOW     55901 non-null  float64\n",
      " 6   SNWD     39565 non-null  float64\n",
      "dtypes: float64(5), object(2)\n",
      "memory usage: 3.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are only interested in precipitation today.  So lets just plot it. We can subchoose just that column.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 30,
     "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['PRCP'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use describe to look at the description of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>DASF</th>\n",
       "      <th>MDSF</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>SNOW</th>\n",
       "      <th>SNWD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>56.0</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>56064.000000</td>\n",
       "      <td>55901.000000</td>\n",
       "      <td>39565.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.172679</td>\n",
       "      <td>0.124627</td>\n",
       "      <td>0.074042</td>\n",
       "      <td>0.279201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.786808</td>\n",
       "      <td>0.352954</td>\n",
       "      <td>0.625393</td>\n",
       "      <td>1.451665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6.627500</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>8.280000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       DASF       MDSF          PRCP          SNOW          SNWD\n",
       "count  56.0  56.000000  56064.000000  55901.000000  39565.000000\n",
       "mean    2.0   5.172679      0.124627      0.074042      0.279201\n",
       "std     0.0   5.786808      0.352954      0.625393      1.451665\n",
       "min     2.0   0.310000      0.000000      0.000000      0.000000\n",
       "25%     2.0   2.010000      0.000000      0.000000      0.000000\n",
       "50%     2.0   4.020000      0.000000      0.000000      0.000000\n",
       "75%     2.0   6.627500      0.050000      0.000000      0.000000\n",
       "max     2.0  40.000000      8.280000     27.300000     26.000000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stop and think for a second.  What do the results mean above?  They say we have over 50,000 precipitation values!  We know something about them and we have done this really quickly!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can you just show the describe for Precitation?  Use what you learned above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    56064.000000\n",
       "mean         0.124627\n",
       "std          0.352954\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.050000\n",
       "max          8.280000\n",
       "Name: PRCP, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are lucky this year.  The data is now in inches!  They used to give you the data in 10's of mm.  That is cm's but I am npt sure why they called it that.  So lets convert to cm as practice.  \n",
    "1 in = 2.54 cm.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 36,
     "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.PRCP*2.54/1).plot()  #we are doing the math and plotting at the same time!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, stop and think about the data?  What are the biggest rainstorms we have gotten?  How does this compare to some hurricanes you hear about on the news?? or some other big storm near you? What about Ida?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets convert to cm so we can save it forever!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.PRCP=df.PRCP*2.54/1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check and make sure it worked using describe. Does the data make sense? \n",
    "\n",
    "Make sure not to convert twice above.  Everytime you convert is saves it.  Your numbers will keep getting smaller and smaller.....\n",
    "\n",
    "If you convert more than once you have to go up and re-read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    56064.000000\n",
       "mean         0.316553\n",
       "std          0.896502\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.127000\n",
       "max         21.031200\n",
       "Name: PRCP, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I like inches!  So lets convert back and then describe again.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    56064.000000\n",
       "mean         0.124627\n",
       "std          0.352954\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.050000\n",
       "max          8.280000\n",
       "Name: PRCP, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.PRCP=df.PRCP/2.54\n",
    "df.PRCP.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "can we just get yearly rainfall?  We can resample the data and sum all the rainfall for each year!  Plus look and I am adding a comment to the code below!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "1869-12-31    35.85\n",
       "1870-12-31    39.25\n",
       "1871-12-31    51.38\n",
       "1872-12-31    43.99\n",
       "1873-12-31    47.99\n",
       "              ...  \n",
       "2018-12-31    65.55\n",
       "2019-12-31    53.03\n",
       "2020-12-31    45.35\n",
       "2021-12-31    59.73\n",
       "2022-12-31    28.02\n",
       "Freq: A-DEC, Name: PRCP, Length: 154, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PRCP'].resample('A').sum()  #This just gave us yearly rainfall!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we could plot the yearly data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 41,
     "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['PRCP'].resample('A').sum().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also make it a scatter plot with points and no line.  This is the power of plotting with matplotlib. You can change anything! \n",
    "\n",
    "go to http://matplotlib.org/1.3.1/examples/pylab_examples/filledmarker_demo.html\n",
    "and choose your own color and style.  if you want to turn the line off do linestyle='none'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yHwyVt1YwaNdqYug8EKfGp3oGsSIkaZPrdQl5+5W355aPEAab9ct3lgdDnH1RxTbUOLqNN5kcm+SOq+6IXabq1A++yLhQuQFUaecoTvPoNZ98koUzyp5LPMn87/3m4c4zH2XMi9+uqPnH4+yLUOfrz0OSMhXCXOpJDFrco+y4kDeqvmhI0gOxzCCWpDANOnmVHYzzUtRqV3H2Rd0P/k5xy1SVFgKJe0Kq6/HkXsFFQzpVYeGMdnHb5PJoH6/CJXaR9wXi7Is0bahV+J6HUfa9rSTqel8liWHa5IOoybvX8yycdS2yKpfYVa89F/k9x1ljNA9lNGOlzWuI6yAXjarX5OssyzvxVarRVKn7Xaciu+OV1b2z6KvnYfIaSo+xOPK6Aqx8F8p2db9MHkaVLrGrrKjvueyTdpEn4mHyWpXm3DxP2pXvQtlSVNe7JOkJ6YSTtJtYv/QXnbfQvst+iuqOV3b3ziK7Ag6T16pMT56kK3iRCl/jtZ+shuSHPO3AMJIMNe+X/qLzFuJ32U9RQ/qrOEQ+rWHzmnYd5CKntBj2pJ1XWoNqrsniMjn0aQeGFfcSu1/6oXvByytvRX6Xca4Y2p/TnpbOK524TRlpr1Kq0gyRhbh5zfKKr9eaBI5z8vTJzK8o49476LUYTr+4VZs2+SxusGQVUKre/t0v/f3kkbeivss4J/huz+n13Kw+c9Drq3qDOqn2vLa0B9qs27TjrEmQ5Y3uOCeyXnk8tXCKk6dPnvGerbhVmyCfRc0mq4Ay7Amn7DbofukHCs1bUb0jhpnLpttzs/rMOhm2XPcL5N3atCH9dxm3opPlvhp00k6yGE6LX++FLBpSiPYbLLbT2LJvS+JL16zaOYdplw2hDbpf+ovOW1Ft3MPMZRP3/2k+sy6yKNf97rv1+y7TtFN3xoJestxXg+4d9Pus3O7PpO1gn+bRbTBU1oNBkg7E6ff5aQdohTJkvF/6i85bnM8btiwkmcum/bntfycdDDRKc+NkUa77DWzq/C4PPXzIV350ZazpDLqVm85YMLlr0s+66awlry96X/UqLxM3TvSNW1R17pq8RhbGDWB5fX6dR+jllbcs9kWSuWxu/sbNvvoTq333vbuX/D3o8zoDyh9/44+DGN1bxMjZLPZ9v5Ni5/4b+y9jA08qg8pNeyyYuHHCz77p7FL3Vb8y2i9uDRPkS22TL7s9M6/Pr9IIvaTyyltW+yLpXDYtcW569mpP/uj0R7lh9obSbp4WNXI2i30/6L5bt33TTav5Jc36DmXf6E6ThsreeC27B0ten1/nrnF55a3sshBH2ZWSXqq2KtKgINfvpGVmS04qVSg3WajsiNeyFwDJ6/OrMkIvjbzyVnZZiCPUm6xFpSvtvu/WI6dfzb/XyNFNX9jE2avOXnLDvgrlpnRp23nSPLq1yZfZnln258vzqrAvQr3JGmq63NPda+nX9j9qi4W0UNUbr+7lTzFc9ufL80LfF6EGlFDT5Z6uR06VFhEqyjBBPqjBUHVQ9iAoyVcIN+6qlK40beZ1vac1TGyo7I3XOKoUNLPs5RA33yF8PyGkQcKUtkdOqCettIaNDbUN8kV1DctKlt0A4+Q7hO+n11ww48vH+do7vhbkfsqKTm6Dv4MyauUh7pdhY0Ntg3yoXdZ6iXtpOqgQxs13CN9Pv7k4Qj4hDyuEE2zWkgbHJJWRomrloe6XYbt61mbumk6hdlnrJc68OXHm/4iT78ZcI9PvJ+1c1v0+K81aAFWRdu2DIuc3TyLNvDRxv4O0c8GnkdWaFFlp7W+AsWVjHHrnocLXDgg2yLcXrlUrVi1+OSEvqhBnIq44hXDQyaJ1QLabHJvk0DsPpfp+0k48FSdAhXpC7iVuEE5zgg1h4rpe0gTHECthIaWpc3/PL8zzlj97C425Rm6T9HUTZJDvDGKtg2H3vbsL+2Liag8KM3tnuO7Xr+s7i2acQjjoZNHrgNz0hU2pvp80B3i3E83K5Su5+U03s3pydc8TTqg1WUgWhNPMdhpaLbNdmuBY1spW/cpQSKttddvfz51+jss+f1mqGXbTih3kzWy5mX3HzG6P/j7PzO42s4ein+dmlaheB8M1d13DT4//lJm9M0EEh25B4SONj3Bw+8Gel6ZxCmG3KZev+/XrmNk7g+20ngfe/MJ834LT6+BIc4D3KsDX3HUN+9+2HzM744QTck0WkgXhNNMnh1TL7JQmOBY1hXS7QWWojDT10m+/5t1s1S72jVczuwbYAJzl7pvN7BPAE+7+cTO7FjjX3T/U7z3i3niNM9l/CDdT0tz4TNPboNvNpJXLV/LVd3x18TWDuqX1uyG19datibu5DdpHSRZMCOVGetKbY71uKPa6gZnV5G559B5J2wum6K6OccpQKN0vs5zML/feNWb2UmAPsAu4JgryDwAb3f2oma0FZt394n7vEzfId345oQaHtHfMkxbCXvkfWzbGyY+cjHVA9vsO77zqzsQHeJoCHPpkUlnNstjrZAoM3Z0wz94joQTHfkIvQ+2y7D5aRJD/EnATMAV8MAryT7r7OW3P+bm7n9FkY2ZXA1cDXPCyCy596j1PxRrg0/7l5LFjk9SG8q6ZDZKm1pzkPfx6T3yApynAoU/BnMVBOahCkrT2n/T96y70MtQpqxNnrkHezDYDM+7+H8xsIwmDfLvlL1nuC1cvLP7drwbSOa/0zW+6mQ/86gcW/z97ZJbL917Ogi8kvmxNUhvKu2YWRxYFO4+DY5gTg7uz6QubmF+YZ2zZGF//7a9nXmtM06wx7EGZdhh/3PJYpZpsHuo65cEgeQf5m4DfAU4Bq4CzgC8D/4qEzTX2YnPes3RbnBpItx17xa1XcOLUCZ47/dzi8+JetiapDaWtmWUpi4IdysHRa4Rs1vdYyhoUE/dk2n4C6qVbeaxSTTavkadVaFbKWmEjXjtq8p8EftZ24/U8d//PfV/fJchDvBpI5449ceoE8wvzZzwvzkkjSW0oy5rTMIU+i4IdysGRR5ND53e74AucPH0y1mdkGYzinEx7nei66SxjoZysBwl15Gle8p5Koawg/yJgP3Ah8Ciwzd2f6Pf6i//lxf7A9x9Y/HuYGsgwwTdJbSjLHhFVKPRFzPuRdZNDkqDZ+Rl57JdBJ9M4J7l+ZSyEk3VWU3PUQRHHdmXmrhl76Zjf/Vd3Z1IDGSb4JqkNZVVzCrXQtx+sY8vGWGbLUjWBJZF1k8Og3kf9PqOM/TLoJBdq7bwlTlAbpXsHRZShyiz/d9G5F2W2bFxr0ENn8I0z6CHJMmZZLXcX4kCYbsOuO7UGA7XuP2RRyx9m33XTb3DY7JHZvp9Rxn7ptmTd7nt3c81d12A7Lfh25l7L87XKCYzWsnwhHttLpF1tJM2j28pQw6jSijBpl2g79PChxdV1Vt24KtM89lq1p9eya0mXcRuUr6z2Xa/vduLGiYGfUcbSeSGv5BRHv3LSUvU8JlFEGaLKy/+NijSFPuvA2inOwdoqrGmWcSvKMAGlrGDU6ySX50k9K3GDWpUqYcMoogwNE+SDnk++brIa6ZpVW19n23hjrsFvffG3OHn6JMc/fHxJ2/Bln7+s5/uE0MYa57vt1dw0zGuzzkNVbs5XoYdPkfK+GV6ZG695BfkiDsAy5H3zqtvBuvXWrZxaOMXx+eNLCmuV+md3M0wAzSL4ximjod6c7yaEHj6jZKSDfK/uc3VYfq6IwBr3YK167S1tAG3MNXpexcR5ba8BT91OEqPUI0WSqUyQtxebr3pvtjXtOi8/F1pgzav2Fmrf/Dj974d5bedJoupXS5KfSgV53pNt8B00edfYsjGWL1ue2WjGopuF6n5ZXFQ7dJoA2q/2/9V3fDXVVAWdOgdmhXRSr5OqN+lWLshD9jcPp/fE22Erl6/EcU6ePpnppGZVL0RlKaodOk0A7VeBWD25eqipCnqdYOp+Ui9DVW5o91PJhbyzGijQGlgzOTZJY0eDxo7G4vJzY8vGznj+c6efW5zTJMnKRP1WDeocVBTaikchK2ogSbfVtgbVkNtXSzr0zkNLVknqfG238gHPr0+8enI1uzftZmp8qu/gr6wWvW70WSJv1IS87GIRSrnxmsfNw8v3Xs4vTv2CiRUT3HHVHZhZ7Nr9sJOarVqxqjK9IkITcjt0ktr/oDb/3ffu5vfu+j0cz72GXoeaa5bqcEO7MtMaQD5rLk6vn+b4h48vXupe9vnLmBqfYnJskjuuumMxgPTa2XFqjf2GabfmvE/zvqMu6ykOspRkSos4UxXc8857CgmycaYdGCWjNMVCV2lHUaV5sLbYkW+dI9Emd036WTedteTz4w4/7jeqrYyh8XVSh5GReYx6TDv6Nc5I5lFShykW0LQGvbUHkIkbJ/zsm85OvbP7DUWveiGS4WV5shpmSgtVOs5U9YrEMEG+8oOhksqzr3fZvSLq2sOnrvnqZ5heR+qKWT+V6UIZQpCvq7rebKtrvgYZ9mZhCJUOyY6CvFRq3pMk6pqvQULudSTFq1TvGslH8AsXpFTXfA3S3uvI3dn0hU3ML8wztmyMxlxDtXKJTUG+JuraTayu+Rqk1X3z8r2XL2mqml+YZ/O+zbVvrpLslDbiVbLVqvnNHpkF8hmPUIa65iuO6fXTLPjCGdtHabSmDE81+ZrIai3a0NQ1X3GNanOVZEdBvkZa857UTV3zFceoNldJdtRcIxKwUW6ukmyoJi8SsFFvrpLhKciLBG6Um6tkeGquERGpMQV5EZEaU5AXEamxgUHezFaZ2bfM7Htm9kMz2xltP8/M7jazh6Kf5+afXBERSSJOTf454DJ3fxVwCfBmM3stcC1wj7u/HLgn+ltERAIyMMhHc9Y/G/05Fj0c2ArsibbvAa7II4EiIpJerDZ5M1tuZt8FHgfudvf7gAvc/ShA9PP8Hq+92swOm9nhY8eOZZRsERGJI1aQd/fT7n4J8FLgNWb2yrgf4O63uPsGd9+wZs2alMkUEZE0EvWucfcngVngzcBjZrYWIPr5eNaJExGR4cTpXbPGzM6Jfp8A3gD8HXAbsCN62g5Ak2mIiAQmzrQGa4E9Zrac5klhv7vfbmb3AvvN7N3Ao8C2HNMpIjJyWovYs5ZL077HwCDv7t8HfrnL9p8Br0/7wSIi0ltrEfth1w7QBGUiIgGa2TuTyeIwmtZARCRAWa3+pSAvIhKgqfEpGjsa+PUOR7k/7fsoyIuIBKhzVbC0FORFRALUvirYML1rFORFKqIx12Bi1wS205jYNUFjrlF2kiRni6uCqblGpN46u9OdOHWCzfs2K9DLQAryIhUws3eG4/PHl2w7Pn+8OVBGpA8FeZEK6NWdLqtudlJfGgwlUgFT41PcduVtbFy3cXHb7JHZ5k05kT5UkxepgM7udLNHZtl2YBsHt2teQOlPNXmRCmjvTvfMyWeYGp/i4PaDTK+fLjtpEjgFeZGKWOxOJ5KAmmtERGpMQV5EpMYU5EVEakxBXkSkxhTkRURqzNy9uA8zOwY8AqwGfhrzZWcDT1XoucpbGGnVPmtS3vJLQ5H5+ifuvibm65dy98IfwOEEz72lYs8d+bwFklbtM+Ut1zSEkK84jyo013ylYs9NIoT05pG3ENKqfZZcCOmtUt5CyNdAhTbXLH6o2WF331D4BxdAeaueuuYLlLcqyjpfZdXkbynpc4ugvFVPXfMFylsVZZqvUmryIiJSjCq0yYuISEoK8iIiNZZZkDezPzWzx83sB23bLjGzb5rZd83ssJm9Jtr+jmhb67FgZpdE/7vUzP7GzP6Pmf1XM7Os0phGwnyNm9nnovR/z8w2tr1m1sweaMvz+cXnZqkeeXuVmd0b5eErZnZWtP2NZnZ/tP1+M7us7TVB7bMoTbHz1vb/C83sWTP7YNu2oPZbwn1WmeMsSlOSvFXmWDOzl5lZw8x+ZGY/NLP3RdvPM7O7zeyh6Oe50fZsj7Ws+mICvwG8GvhB27a7gLdEv88As11e90vAw21/fwv4VcCAr7ZeX9YjSb6A9wKfi34/H7gfWBb9PQtsKDMvMfP2v4HfjH5/F/DR6PdfBl4c/f5K4B9D3WdJ89b2//8JHAA+2LYtqP2WJl/R9qCPsxTlsTLHGrAWeHX0+xTwIPAK4BPAtdH2a4E/jH7P9FjLrCbv7n8BPNG5GWjVls4GftLlpVcC+wDMbC1wlrvf680cfR64Iqs0ppEwX68A7ole9zjwJBBsF68eebsY+Ivo97uBt0bP/Y67t/L5Q2CVma0McZ9BsrwBmNkVwMM08xaspPlqE/RxBonzVpljzd2Puvu3o9+fAX4EvATYCuyJnraHaB9kfazl3Sb/fuCTZvYPwB8B13V5zr8hKnw0M/7jtv/9ONoWmvfTPV/fA7aa2QozWw9cCrys7XWfiy4fPxLC5XEPPwBaC4duY2n6W94KfMfdn6M6+wx65M3MXgB8CNjZ43Wh77c4+6yKxxn0zlsljzUzW0ezpn4fcIG7H4XmiYDmFUmnoY+1vIP8vwc+4O4vAz4A/En7P83sV4Dj7t5qg+u2M0Ls49krX39K84s/DHwK+AZwKvrfO9z9l4DXRY/fKTLBCbwLeK+Z3U/z0vJk+z/N7F8Afwi8p7Wpy3uEuM+gd952Arvd/dkur6nCfhu0z6p6nEHvvFXuWDOzF9JsEny/uw9c4iuzYy3jtqd1LG1Pe4rn++Ib8HTH83cDv9/RdvV3bX9fCXw2gDa1RPlqe943gFd02f5vgc+Una9ueev43z8HvtX290tptif+Wuj7LEnegL8EjkSPJ2k2GfxuqPstyT6LtlXiOEuTt7b/BX2sAWPA14Fr2rY9AKxt2ycPtP0vs2Mt75r8T4DfjH6/DHio9Q8zW0bz8uvW1jZvXrI8Y2avjS6x3gmEuBx913yZ2WR06Y+ZvRE45e5/G11Sro62jwGbaV6GBqfVEyHaP38A/Lfo73OAO4Dr3P2vW8+v0D7rmTd3f527r3P3dTRrhR9z989UZb/1ylfbtqoeZ/3KY2WOteg7/hPgR+5+c9u/bgN2RL/vINoHmR9rGZ6p9gFHgXmal1HvBn6d5l3v79Fsg7q07fkbgW92eZ8NNHfK3wOfIaoxl3gGjp0vmrWQB2jeWPlfNKcHBXhB9Pzv07yR8mlgeQC1i255ex/NGsSDwMd5/orlD4D/B3y37XF+iPssad46XncDUe+aEPdb0nxV5ThLUR4rc6xF8cKjNLWOnRngRTRvHj8U/Twven6mx5qmNRARqTGNeBURqTEFeRGRGlOQFxGpMQV5EZEaU5AXEakxBXmpJTM7HQ1r/2E0S+E1UV/r9uccNLN7o983tc1a+GzbLIafN7ONZvaULZ3R8Q3l5EwkGXWhlFoys2fd/YXR7+cDe4G/dvfro23nAH8DPAvMuPtc22tnafaVPxz9vTH6e3OBWRDJhGryUnvenKXwauB32yareivwFZojQbeXlTaRvCnIy0hw94dplvfWTH+tqXf3Rb8P8rqO5pp/mlNSRTK1ouwEiBTIAMzsAuCfAX/l7m5mp8zslf78LI3d/KWaa6SKVJOXkWBmFwGngcdpzq1+LjBnZkdozoOiJhupJQV5qT0zW0Nz9sLPeLOnwZXAm/35mScvRUFeakrNNVJXE2b2XZrzeJ8C/gdwc7Qyz4XAN1tPdPc5M3vazH7F3e/r8X6vi96v5UZ3/1IuKRfJkLpQiojUmJprRERqTEFeRKTGFORFRGpMQV5EpMYU5EVEakxBXkSkxhTkRURq7P8DN1MWbcnUCSMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['PRCP'].resample('A').sum().plot(marker='8',color='g',fillstyle='top',linestyle='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can you use describe to give statistics on the annual data? How much rain fell on the wettest year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    154.000000\n",
       "mean      45.370714\n",
       "std        8.485716\n",
       "min       26.090000\n",
       "25%       39.335000\n",
       "50%       44.415000\n",
       "75%       49.307500\n",
       "max       80.560000\n",
       "Name: PRCP, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if we only want to plot some of the data?  Lets plot from 1900 to 1920..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 45,
     "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.PRCP['1900':'1920'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or for just one year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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j8bQt1JM5o+RjFENmJpZH0DJYtSBsCrk71tACRlhLCHVuBlZx3qKuXv9o0+ERfPrOzS1voQVNNgjDsCzkyuiTKnFPORb1VNY+xvBEFk/tGSz5eEHUYzmpVqBeng8O+Z8bR90AN/LQ0CQ2HR4pe/+WEGqV+a68OOrqDyY+/vwAbnv2ICar0E2vFbv7Uzgxlp52G/36F7sXtkVdjlAri9pOSzuVtQX7Z88exJ//97NlHdOPpDktj/pZ1MGfG+k+fueRXfj4bevL3r8lhDpqjon+8XSB2NTCR52bwYukUfjoz9bha/fvnHabUh5Uw7TKuh5qH3VN1WDiRMaAYTHSFRhcVJ2rVugy1xJPmFydLGr9ljVSSoNUxkAqU37bbBGhjuaj/n93bsGnfrnJ851ZgzSnysrLNvHw83jaQCo9vT/YE54XwUddzvXwW8xKELKG/X06V0mLesaHmlV4F5mtl4+6MeOoMzkLWaOKQk1EtxBRPxFtKbuUKqOsrGLiEBRpUBuL2vL8bWTG0zn85On9Bf70rFncp6z7+YtZ1LkIxwsi6IVqWXnRr4hF3UBd5maCtdtZtzhq7ftGGkzMmtaMDLUoFvX/AHh92SXUADf5f5GudNZkZH3buP7tKg4mNpPr4+Ht/fjcb7di3+CE5/tchIam/1ysh2KvrVj6C9IIqMN4xnAt6swMrBaFrPBSHlaIZVttdOvdm5Sp8nHU5U6wsi3qKgo1Mz8OYKjsEmpA1JzSOaPQiqulRd0Mrg8ldH4XQtC186Nf/2LX0yizlxEU0jc6mdMsahlMrBd181F7LOrCcZJKuT42HBrBJV+4v6wFSjKGCYuDDY0otIaPOqLrI6i7XYs46mZyfai3vv+lEiXuWX9PFuug5NxBwdKuSdDLeHQqp/moZ25Rq+dahLo0qhH1sfbAcNF7Gp7mtLKujyPDUzAsLhr9FEQm5LmKSsWEmog+RERriGjNwMBApQ4biajrHhoWI2fU3qI2auD6WLmzH7/beHTGx1GuIb2bxsyRfNQeizqCjxoo/ZoEvSzG0nmhnqqoj3rGh5pVeAcTZ87oVA7v+v5T+M36I0XLjZFTruu24oovDZc3uEo/nmsAlen+qJhQM/NNzLyCmVf09fVV6rCRiLqSeNaw3Mkx+X2rH/WRrYHr43+f2o//enTPjI8T1KByAeIdhDd7XjFRL9OiDtg+a1ge18fIZBYjZUxPVzTDFPJP3LEBt6zaV+9qeAiKYZ4JExkDFtsv4mnLBRB3lFq5PjwJoir0bM9EbDPuGIq4PoqG5wW5PqK6TXQGU5ny6ldFoc4aMwv/UQS5aYK+yxoWblm1z/OdV6gjllPqYGLA9lnT8rg+/u+vNuGTP99Y0nF13DjqxtVpPL3nJNYfGql3NTx41yuc+fHcsZ1i4sYAEXnqUI2Uu9kZuDDV2E/VLGoiug3A0wDOJ6LDRPTBskqqIlHdFznTCnV9RI36WHtgCFd96aGSEtaX280vBd2qnOlxAO+bP6jLd+vqA/jX32/D/zy53/2ulIfDfXmV2HCDHpKsNtCZzpkYTGVLfpnqNMNSXBnDRKYKmQNnQqV9+lEtWIbm+gioSxTXx+q9J/Hx29ZP24tSbWw6q/i+LccxNFHYm8vkqmxRM/N1zHwaMyeZeSkz/3dZJVUR1R0u5r4IGhArdfbSsdE0LAb6x6MLwUzexKWUMZPwH0VQhEpQ/dXlOjScf2GVMuFFvRhLd30EWNSGZlE7nzMziP5ohqRMWcMq+6GvFup6EVXm2rnugiJthBmIuRZ14fMcxfXx1J6T+N3Go26Z246OFYSoqjYW1mZTGQMf/ula3LnucOG5RO0dhNAarg9L+RSnFwgVdK6/NfOCEa1hKQEo5YIrcammj9q2Kiv3cAT5qPUGOr8rCQAYnsz7D6NaMcysHbPUwcR8HXra7XUvsmZetDI507Y2Z+AGyvuoyz5E2Ww9OhrJv16pF3MlUdcrTlQRoY7q+mDoQm1/Z5ZoUefbj/33n+/chP+4d0dgfX659jBe+pWHC8ZLVIy1P6cPM4dGU0WlRYTaCvysY4tDoeVdqkWdHxSILgQ1cX1U2qLWhTpAvJ3nwiMq+vlNZ8Xo17/kOGpt3+6OhHsMfWaibmGXQz3jqP/05tX44RPTDxJalv2iq8TknkqiLlcsRhV5yUV1fXiiPtRgYgnjJUD+ec6Y9t9U2sB4xjuIqeqx8/g4jo2mCyKM3GMY/u+9Yzvl0BpCbRYKrx+1AC7gFYdSfdTZMgYFlLhUfzCxkkKdb2zZgBecsjyGNaH2WDHTCbVZvlDrVswcZVHrrg9nBthM3AL1cn0wM0anckWjHNT9aFTXRyJGFRmIzUa1qFmL+ggYTPQbDcdGpwpis/0WdcawCiZPqdBVlWK34PeQF4sItYPpsdCCW4jHijMqYVEXD1X70ZP7kDFM1yKtulD73Dp2PU386Ml9yBpW4CBH0HEAn486oP7q9+GJvKhEXeFFj84p1fWhi3y3I9QZ32Ci7goph3rFUbsCXMS/HuSeagTUdYsTVSS0Maq7oBTXh2UxXvqVRwpSjrouTfclaBaIuapPXqhDxN4IFnD7+OX1glpCqHMBrgw/QYNjQOkL40Z9SDYcGsH1d23Dk7sHZxQoH5WwWO2n9pzE9Xdtw/V3bcUrv7qyaK6CIN+xXn/1AKrz1y1qzwov0zyoM7GodZFXQp3Tw/MMExnD8nQ/c6aFO5476OawLoaywGodRx3VrRYUmdMIqNsfi1FFJrxED8/jfHieqou2i25Rq97Kwzv6PYdw3Ra5/MvSL8T+tuq//n6r3H/soN+i0hKrkJseV0bwhdDDwIJif6Nb1I7ro4jAKEGcyJhlT+4oBb3b1Z6IF9Rjz0AKqYyBsXQOnW3xwGMAwVaBV7QZbQlyf5/MmjBMC4l4zOcXLB7m5P8cBV3k2xMxJOMU6PrImQzTYsRjhA//ZC0e3tGPRCyGP75yadEy8lPIS6rajIkawhXmC60/eddHJWYDRjWKbIva/hyU30P/fNLpVXb5ngH/FO+MGeD68NUjzOKe1kc9uwcTiwuELjaelUgiZt5TuDejSAyruolTzuCWXYfaCHVQPUac6AzVbQsjcDAxQFj1xjeYsht/VIvae7zyoz7aEjEk4zFPDPmU4/pQ5zA6mXOtp6gPSb0GE/MCPH09ZzoduVqo2x+PUUVmA0Z1fdiDiV7XR5jRMFREqDM5043SKN2iDr5/uhXdUDMTtx8bK2lCyEzRBSJMcIP8q0D5PupijSftZqEzq+76sCzWrHbGu77/FH62+qBTvl326JQt1JNFVpkIEoFsgFDrv6tj69PGp3vxzWgwUbtPbYkY2hIxx0ftDPSkDfdhzRgmJrL5F1PUhE16mtNauj8iuz4afDAxXiHXR9Tz1AcTlVVvhhgNJx2jYk6b15mgDC99fKPAB+0X6lCL2j8I2aCDiYbFBcHi1USPBAjzNQeJjb591KgP3Yc1HUogJ7PVd334G8K6gyPYdmzUqYfdmJQvWReuIPITXuz9nt03hHs2HSsoSxcTFaakn950Xd8o4ZSh++oWdTyGtnjMc056xETGsDwhVFETNnmzwJVUvRmh7lWxVK3lxPLXAjc8jyoTnhcUFhpWrgoXzRgWvv3QLk8ssy7a6jlQEUMK3b/sCrWvXP8s2nSIi6PA9aG1u3LvWdV81JXIYhYVr+sjxEcdItSlWtRREyy5ro9sPuqj3Fy0UesE2EJlWuyuzp237NWLY3qh9odE/eCxPZ6Bl6AETcoP7lnhZVofdXAEThT0fZVFPaG5c8amNKHOWZ7B06i5qv3JheKg8I0rSKkWtWGxOz7QCOgWdSXcRpHD85C3qNfsH8ZPnjngxtgDXgNCuT784zR6T1ldf9Oy514knevrf+b9xlpWE/ugYwcdIypVu8PlroRQDoZvsCsIXRCCLeoSBxOLNB7dR60iFfyry1QKvS7KFz2VC471nIjo+lDX0W+BB1k56RIt6jA3VBR0C7wtYVvUut99TFvX0R9iFTU3hj6BopZ+6qi9tTC3VL1RlypRwoSX6XQiyEdtWYULGLPmo1YvbT3CJ8j14ffN6AO0+vXXy/L3iAst6hAfdSPHUdfPog4Raku/WDOIo44wMr9q1yAGnKRAU1mz7CT5UdFvvuraqQfAL06lWtT+BykoF0je9VHYUwnCO7Bbquuj0KLWF931WNRluj5MZiRi9qMRJDjffPB5PLD1eEn1jkKpg4n+z/VGCWIsokW9eu9JvOj6B9Afkog/KDf67c8dwiu+utLTY9NdH+qlrbcJ72Ci/VwWuify7V5v27qhUzhQHyzI/mPr+5UbqVM1ofbPdweA3f0pHB8tfXWEYngGscKEukgUg9pvaCKLbUfHQssqNuFldCqH992yGj99+gAA2yLNryJTfaEeUYOG2fxgpk4xi9of1eG/j/qkjI6k3XyUmEdNymR4rn/5ro/2uC3U487DSeS9L7ZFHWwdhcHMnsGpIMH50ZP7cO+Wagh1NNeHJy63gYRaz/UR5a7uG5xA1rRwLEQTgga295+cwMB4xvPSZTDijlKr9qr3svR7OOT0OMNF1gq1qP094qiDifr9ajiLOuih+OjP1uHf791e8bL0hzfcRx3s+vBb1D94bA/+7L9XAwB+u+EIHt3pDYwvtojqyVQGzMCE02DGpvINpmquD+18RhwfXNjAVFGL2tfd9Au166M2LczttBMzuRZ11Cnk2m+ldt2DwvNUd7c7YIBIf6Cj+Kj17jtQOJiYMy2Mp42iYY7lEHXWq+dlVIE1IiuFJ+ojgkWtBn4nQq5lkOtDXXd9Hz17nnLVjWemt6jD3BZZ32Qpz8SpgsHEYLFvqinkQb6nkxNZN+C8kkSZQh42mGj4rN3BVBZDE1mYFuM7j+zGLVq+ZaC4j3rYl/lMj0KojetD+agdofY1yImIMxNVrg+/sOvhefM62+wylFBHyLmiHwOwXRnMjD/5wdO4b8ux0H3c7QN81KqXMM/J6KfIGBbSzvn2diQiuT5csYkHW9Ru9Ew1hDrnnR0XRiWmJFcDdcsT8Wi5PsYd90TYS0+dm36+6rrr+zDyrg8Vfhrq+kh5DRmFx6L2jL+EjweUOoU8EaPGG0wMcn2kqmSJRFn92juFPNxHnXIyZqXSBsbTOaR8046LzZYamvBur/tMaxH1MTJlN8Qw18dksQkvvvMrtKjzPQplUU9qro+2hN2kph9M9PZuJrMmnt03hHUHR6atm9q3tyOB9kQMfT3tto/aOadT5rR7ts3k8hb1/DltkVwfls+iZt8tU7lNqiLUWm9tOos0TEjqjapzLGKujzGfm86PCgAIEmp9H2Z2XVWpACHXX7Yq5l+/hnoa0kKhDh9MDJ3wEpJVr6cj0VgTXoDCgRvTYkzlTM+brlJ4ZxoWt6iNgM9qP2WdjaVzgS+WYt3TYV+PYVw732pNeAmK+ghzfRSzqN0E56YF0+KC89TD8zrb4miLx9x7bTGjzQllmjbNqa93o66R/lKbbt+5XUk8+qlr8JZLl7ihUwCwYE6b91wM063bvK6oQp3vvuv/V6jwrmoYHGn3Ok7fVryDUw0k1M7feMTsecUt6nxbVMIfJMSMvOtD9QCVzsRj5DHGJgIMGP+4RiZkADr6FHKfgDvPYE9HMtTAGy3S9qvo+vBe/JQbNlMNi7p4FEGxOGp1DOXbGpnMYSJrFtS32PTdIb/rYyrY9bHx0AgOD1dm9uZ0UR8FFvU0Pmo9Z3fO4EBXQU5rjG2JGDqSMc9gomtRT6MfOZ+rSrmHorSNnMVIxmI4bW4nEvEY2hPTCXV+GvC8zmSJPurgnoG6vmFW4EzwC0YYlZjpVg3UyznuW20lDHXfw9pkUBiiMqT0fZjtSBMgb4govUnGCeqdp8S7uz2BdC4v/n4fcljUR0F4XsiApIpv17+PkT1tPex+PbjtROD3iqpa1PqNCnoTVgpdqE+MZXDRv9yH5/YPebbR46izARa4na+aXVfH0dEpu75pv0Vtev76KbCoM7pFnb9JH7l1Hb710K4iZ5ZnupU/PNO5HYt60rn+/gGP6aI+DC1nd9a0Ah8g3Ufdnoihsy2etwQtRtLx7U63CnmhRW3XuVgeZrWvbkW3aUJ9SnehUE/lTLQnYpjTHi/Jok6E+KiraVFnIlrK3twRjeej9ueGDmPMtaiDzyHohZT3UXtdHyruXW2n2lRSSxam2ldfj+0iC4qymS7qY7poDr1sf91Hp3LoSMbRnoiF+qjVIGcYVRPq+7eewAv/5T4cOGlPJU9p3ZxKLd+u0EVhz0AKE1kT2495Q+w8OZC1C+pfkFU9gMdGHKHOeuurbuJY2sCuE+MFdZku57PqzjIzBsYzGIi47iIz4z03PYPvP7Y38Hf95iuLj1k1uugWtX+V8aABYT3/Qlsihs5k3LW8DcuOP44XyZ7mz/WhHtgoFrVhsiuiAFxXCwAsLPBRm0hnTXQk4+hIxEsaTEyEiI16EU9kjIrnAYkadqcPIDaU66OI28iP66MuEvWhf1bP52SI60OhtmuLx9xnXLWvvm5HqAMmGPkXnZjWRz3NDET1m2FauH/rcbzsnAV2XpqQXl2xIIuqzj1N5yx85tdbAOQH6YDi+SZKJWfmfaNq0Vl3BpLaJiCOmtlOZqT2NSx2XyhHHKG2Q+202W7Ovrv7U3jTd1YViNn0Qu1YBVk7w5s/QiQM5YI57lj5foKiPgDb/eG3IsOsF/9xMoYV2L3Xw/PaEzF0JOPaFHJGLKb8guHno16ayTghZ7L7AEXJF52z2DNlOpnIP6CnBLg+pnImOpNxtCfjkVwf+cFEx9fuExv1QFlc+YE8j6U8zUulEhNeNh8exWv/87GivtEgnt5zEndvKozQUW4HNT27mD2m7nuYHgS7PgJ81JxPc6pQZSfjMfcejhdY1IUTjPy5zNPTXOuCqI8A63vV7kH0j2fwx1csRdt0FnWqjkIN2BWd8vl6K91tNC1GuzP5YmDcDp4/6etKBMVRq5up/JxZ03Ib29GRfBC+qrs+OgzYN85vFft91N462PsqqyyqUJ90ZjmGvXXDHtapnFlSHLVqRHaOZzNEqJUVYqI9EUdnW1ybmehY1EUWN1Uvzc5k3OP6iGZRW0jGdIs6n7Mh2PVhD3p2JuPI5EwMTWRx6+oDodaw62cNiaPW71ml23FU10exwcQ71x3G+5y5AGGsOTCE50+ksHcgVXI9r7v5Gfztz9YVCNWg004XOULIRaa95OOoo7k+LG0w8Jm9Q3h4+wm3HL9FrUgmqNCiduqnng3vhBTTZxnn27a/LfiXTct6/NL2fusODIMIuPaCRZjTlnDb+shkNnBsKYyaZHMZGM94GnWlIz9ypoWOZNwtCyi0bIPC81RYnxJ5feBPWdRA/oEMeijUVHGF30etSMTIfVmomzI8Ec2aUQLt7yUowt7SUzmzMI46gkU9pz2BrBns+tCnkCvXR1przDGyRW7aNKdOi+9qS8CwLM2i9raLoLwOBa6PaQcT7R5FRzJuD3rmTPx6/RF85tdbcDAkDW+Bj9r3dOrtqtjkoVKJ6vrIGJaWLa7wHn3y5xvxxK7BaUMIVc+zXzM0bly5Gz98Iti9FsSqXYOe/yuDQgnhdJ4PS3MzhtXT74qb1NrCQ9tP4IP/uwYTGQOWpac59ZKMx1w3nFqsdqHzQk8HWNTZEB91kDG05sAw/vTmZ9z/B+WdPjGWwcLudnQk41jc24H+8QyYGa/5z8dx0+N73O3r6vpQnBhPe27GA9tORFq/Lyqmxe50ZiXUg37Xh3PT4zFyP690ssKpFVH0buBRTajVWzBQqP0Wdch5dbbF8xa1lsQ/StdVCbS/l6AItah9ro/ejgRSmVyoNaleJN3tCfvBCBCirGG5PYu2uNdHnTHsgb4YFVs4QAl1HFmD3es7peXuBoCfrj6Al//HSu/go+UbTNRE+4z5XfnvHX9gOmeiM2m7aAyL3Tzp+otYp9iA2MlUtiCvRKUIsuSCyBoWutvyC/uGcTwkhwaQb7d6+/3l2sP4xZrDReupZoDe58t3MpjKoqcj4RpNqg08f2K8YDwnlc3nDQ8Nz/O8kKxAQb9ns+2CCbOo27TBxEKLunCCUcawkDVNJGKEGOWt7jBj6PnjKff4Hova2a9/PI3FvXZ5i3raMTKZw/GxNAbGM9h+PH9NiulhVYX6nL45AID+sYzHWvra/Ttxcwlvbp0Hth7HpV+439PlMExGhyO2amDKf+I500IiRmhPxJAzLPSPpfHhn64DkLeoRzX/rm5pqLqrh0K34gZSGXzqFxvxNz9Zg/F0DmNpw70xeuhYly7UWt3CIjl01Ijw0EQ2UGRVA0n4rIrJrNf1cdmy+UjnrNCJJer8utsTsDj4AcqZtl/fYvs6dLTlfdRHR6Zw2twOJLQBnCCU8KqXl9429M8bDo1gMJXxuKEMkz3nqe7FvK6kZ2ZiT7s9uWDKGUzsdMRjr5Mn/frfbcNrvvkYAOAvbnkWn/3NZgD5AbFEwIAYM+PQ8CTOWmi362J5U0ol6kogGdNCT0cicDvdwp4ur47forYsxpHhKew/OTHtYH/GMN12sfnwqOe3gVQGfd3trr9YXbp/+uUm/POdmz3b6vc5LNQxa1juSyFrWoHt8a5Nx8DMCNFpr0XtlLmwOzjqQ4XPZXL58Re/RT3H8b+r6KasaWFQS/SkvlfHPDGWwaKeDgDA4l777wbn+TsynDcW6uqjfuFpvQCAE2Ppgov8xK4BXPqF+4vGD/pZvW8IY2kDO7W3kWHlXR+Kk6lCH3UybueGyJkWdvfnfXPKoh4JGVhRN1hd/N6OvCAMjGewet8Qntk7hP2DtrV28ZK5AID5XfmueFdbosD1YX+e3v3xd7evx//91Wb3HMYC3EauwDoPr1pmyD+B5G2XLUFnMo5frg22mtSLRCVVDxpo0heSbU/E0OUM0jEzDg5N4swFcxAjKrIKuf1bRzJIqHPYeGjEPt5J+3oeGMovQpHz5V9WQr2op91d4FQdO6sNJqoe175B+77vPDGOXf0pDKYyeHbfEJ7dZ4dz+i1qXahHJnMYTxu40GnXlZ6dmDFMd2B7ulDCTM5Cj9MG/VEE+oId0wq1Y22rMZ2BVMZd3SSKJd7TnigQ9cFxu5tPyF87ZsYux6LWjQzVNpNxCr2OGV2oQyzq7cfGAqM+FMk4uQvdjk3l0J6IodeZUZvOeV0fauZgxrDQ7rzcp3ImTqYyePGXHgIAdLkLKufPRQlu1rBcbVDPSP94xjXc+py/6w+NAAAOa/uNF2lLVRXqs/u6kYwT+sczBX7pLUfGMJY28Nc/XjPtG9yyGDeu3O12WXc5ArvrRF5oTYs91itgi67h83El4mQLtcXYozVota+a1ef3d/l91LpFd3hoEoeGJzE6lcOaA/bDftHpjlDP0YU67tZHt6in6/KYFhe8yIK2zzoB9cpqXOD44NQLQVlfC7rb8dqLFuO+LccCr7k6v/mOZaq/8QF7ZF0X6jYnjvrIyBSu/fqjSGUMLDulC/FYsYUDLCTjhOUL5mDT4VGPm+nuzcfw1hufxKrdgzjg3PMDJycL9lUoN4iyWhTtyZg7M7GjzY76AIBDQ95zemznAKZyJvYNTiBnWlp4XmHUh6rPhUtsoZ7O9fGVe7bj03duCv09iEzOQm9nsKWskzUtdLTFndwRXkHXDZjpBFcN/CnhPazd6/3TrM50Ysze/iVnn1Ig6oOpDBb2tLnWrcW2UE1kTYylDY8fVj1ri3o6QqM+cqblGg1Zo9CifuFpvRgYz2BkMud5ZnXNTmq9u7G0gZ6OpNv7zg8m2n97nZmDGcN0UxQcHp7CmgPD7vH8ib+AfOBBxvD2dHKmhZMTmkXt/F1/cNi9XumcGSmoIJJQE9HriWgnEe0mon+Osg9gv3UX9XSg37GoO5PBq18/tmvA8//xdM71W248PIKv3b8TX39gJwBgt+Pr0i3inMkFFjWz11o1LMtZuomQMyzPaLeyYlSejMWOD0sJsuujDhglXr1vyO3iKVFVFteLl893t+tqi7uDmHq9glwfu/vH8cu1h7FvMFXQLfT3FID8wJ4aADvV6WKpBqBcAh2JGK4+dyGGJ3PY4xvtzxoWbly5GwCwYvkpAOyIHZ1kPOZZU04NJgLAfkdMz1zQhUQsViSO2kIiFsP7XnomUhkDq/cNud1lFfb1yI5+V0T0gT/DYo+POhHPW9Q6p/Z2YHd/CpmchU7N9eFHpSvNmYwDJycDJrzkt1VzAtT9DRtMtCzGz9ccwp3rjkRepxGw25WyyKaP+rCFpLsjgeOj3vbw3P4hdLXF0d2ewLGQcE7DtFzR7HeFOn+N950MFuqcaWGbMz/hJWctAOAV9cFUFgu72/PWLcPTzvYO5Lfd4FiVV5w5f9qoD69F7d3ulS9YCMAe29At6lO0nmxbQnd95Ow8MUlvr0WNB/R2Ju0p5M4cgcvOmIcNh0Y8Lp457YXtaOeJcagFB5S1njFMDDqZNBcpH7WyqDXX4+HhqdAgAZ2iQk1EcQA3AngDgAsBXEdEFxY9MoCu9jgW9bbjxHga45m871ZxxbJ56GlP4N7N+ZhMy2Jcd/MzuO7mZ2BZjJU7bRG/e9Mx7BlI4ajTndvVbwt21rDQP57xvOnUqK5ufeYMx/WRsF0feqNRPibloz7jFHtQam5nEkT5KBXlF9f9vvqg1FN7TuK0uR1u+csXzHF/62xLuK6Focms6+sKCuf73G+24h9/sRF3rjtS8NvR0TR+vuaQJ+Y46wziKWvxNRcuBpB/Iagsdx3JOF7siPBz+4f1w+LOdYfxiDO4evU5C9HVFseO4+NIxAgb/+W12PSF16ItHsN42tBcH/GC3seZC7oQK2pR25Ebl50xD5cvmwcg/3LZ6uQC/5XmnjmgCYfto84321Hn+vX52tYrz+vDjuPjODIyhQ5nMDGIh7bneyy7+1OFU8it/BR3Zdkrl15YTPq2Y2MYnswhY1hYd3AY4+lcpEleGcP7oIcxNmWgPRHDqy5YhAe2HncFh5nx6M4BXH3uQpxxSleo62MwlXVjj/vHvBZ1Mk7YNxAs1F+6ezs+9xt7XsRVZ9ntSIl61rAwOpXDwu52VwiPjk55njPdOHpqzyDOX9yD5Qu6MJENnjyk+6jH0jnPXAzAvscKvRkqH7R9Pt7BxJ6OhGZROyF0B0fQnojh1N4O7D85iQe3nUB7IobLl83D6FQOd2066h5PuUlv/NMr8I13vQgdyRhueHgX3nzDKgykMq5FPTqVc6+tsqRP6WpDIuZ1Cx4ZmQp9oepEWTPxKgC7mXkvABDR7QDeCmBbsR272uJY3NOB9YeG0duRdLOtuQc+awGWndKFB7adwAWr9gEADg1PYssR+2H9t7u34bGdA1i+oAtHR9N4/bceB2BHL2w7OoZbVu3D8yfGMZjK4N1XneGOQr/wtF48sWsQ33t0N160dB4AYMfxMSQTtuvj+RMpz2ChckWoaecfePlZWL1vyLU6bnhkN8bSBjYcGkFPRwJL53dh+7ExLJjThpMTWcTIFsHJrInlC+a43bWOZBxvuvQ03L3pGBb1tGM8ncO3H9qFHcfGcHZfNzYfGcXKHf0eP+Nk1sDTe08CAL736B47SF6zrj5+23oA9gj96y86FQCw+cgo2hMxqE7vmy5dgi/fs8P1u7oWdTKO5Qu6sLC7DXeuO+yx9n76zAGctXAOvv2ey3DJ0rm4fNk8PLn7JDqSccx19r/63IX41drD6HHOry0Rw47j3tH8pfO7ECfC8/3juMW5pzoM21JXx/jLq8/C+oPrsWRep/sS7mqLu774RT3tuH/rCXzijg1gZhwZmfK4PtR99Ls+rj1/Ef793h0A4PFRA/bL5MDJSfR2JJzucALjaQO/WncYW4/2AMj3pt77w9VIZQz8zSvPxlN7TmJxb7sbBrhq10DgaorrnK5tjIC/u30DUmkDFy7pxZsuOS1g6zwD4xlc7LjNHn9+IHAW24nxNLYdG8On33ABLjl9Lu5cdwSf/c0WXHhaL1IZA4eHp/CRa87Fg9uOY/ux4HtwwvFLn9PXjX2DE7hl1T48tnMAp8xpwwsWdeOONYewuLfD8xI2LAu3rj7g/v+iJb1oS8Rw35bjyOTy/uOF3e143UWL8eW7t+P6u7YiEbN7XSYz/uO+HXho+wmcOrcDT+waxPtfthxz2hNgBm5+Yq/nBQzYL67zFvdg3+AEvvvIbiyZ1wmi/CDllWfOd58P3aJe2NOGnc77NxknpDIGfvjEXuwbnMCyU7rctvDozgGMpQ38ev1hvO2y01130GTWxPMnUrjsDLtHrLveVC/1ktPnYtmCLvzDLzYCAHYP2C/50+d1YvmCLty4cg+uPte2+JUlHYsR+nracWw0jRcs6sau/hRuf/Yg9g5MoK+nHfmrW0gUoT4dwCHt/4cBvMS/ERF9CMCHAGDukrMBACvOPAWHh6Zw39bjODGWwTsuPx1vvOQ0XHz6XDy47QTetWIpjgxP4bcbj+Jff5/X/YuW9CJGhB85uaA//5YL0dfTjo/dth5tiRje/7LluOGR3e4+V5+7ANec14fT5nbg2Gga1121DBec2oMfrtqH327Ivw2vPncBOhJxd7HWz77phTg0NImXnrMQH7l1LdYdHMGinna8+oWLcf7iHixb0IWRySy2HxvHz1YfhMWMr73rUhAIf3/HBrzzyqX4weN7cfmy+ejtSGDlzgFceeZ8LJnbifldSZzTNwfvWrEUf/my5ThzwRwcODmB/3zoeQDAdVctQypj4KHt/Xhou3dxgnldSfzBeX347YajuPqcBVi9bwivu+hUPLFrEIOpDF530WI8sqPfFWLA7p2c3deNiYyBxT3t6Otpx9oDw4jHCNecvwhbjozi1N4OEBH+8ILFuGPNIY/vDQC+/Z7LcKnzYvvDCxbjyd0ncc6ibvf3L/zRRVh/4zB+8PhexAhYOr8Tb770NDyyox+vuXAx1uwfQkcyjjNO6cITuwax5Ujwu3xuZxLfes9lAIA3XHwqzl3UjavPXYh9gxOY25nEx/7wXHzijo3obk/gj69civ96dI9r7QP5Hg8AvPqFi/Hjpw/gmvNt6+qa8/uw7sAwzlvcjfMWd+P5EyksXzgHS7XQvT9ZcQZ+tvogXnPhYvzPU/tx9TkLcXjEtqQe3HYCMQKuOHMe7t92HEMTWVyxbB6+84jtFnr9RaciEY/hzAVdWLlzwO3x+XnR0rmY29WGx58fwIuWzsXO4+NY67veQVy4pBdbj47i/q0ncP/W4IH2Fy+fjw+8/CzEifDOK5fiN+uPuAPEnck4XnXBIhwfncLKnQOe50onGSe89bIl+PoDz7vbXHN+H774tovx7h88gy/dU7jAR097Alcs68XwZBaJeAyXnzEPT+waxBNOPHWMgPMWd2NBdzs+/Afn4BsP2m395ecuhGkxnt57EtuOjmHlzgEQAa+9cLE7iPble3YE1vO8xd249oJL8Nc/XoPDw1O4/o8uwu7+FH789AF0JOO4avkpWLV7EOed2oPHnrfHG15+bh+e3H0SbfEY3nHFUmw+PIov3m2fz6suWITeziTmdyVx9+ZjuHvzMbTFY/jgK87CYzsH8PCOfpw2twNXnjkf5y7qxpK5HTg6msYX33Yxth0bwyvOXYjP/XaLO7nqHVecjvUHR/D5t1yI9//oOVy0ZC7eccVS/OWPnsNtzx5Ed3sCZ56S71lftGQujo2m8ZoLF2PF8vn4+ZrDiBPhe++9Aq/5bHi7oGL5CojoXQBex8x/5fz/fQCuYuaPhe1z5ZUreO3aNQDs7thY2gDYHtSKxQptkImM4ZkgofxAExkTFMtHWUxlTZBjvY6lc26u4O6OBOIxcjNfqe5S0HGJCKm04TkuYFuyOYPR0RZDeyIOy7JDfsjJrZsx7LSf/mXmR6dymNOWP25vZ8ITfaDDzO6KL72ddhRI0KSSjjY7If7YlOGeG2B3hw3TroOqr35uiXjMCVUie7JH1kIyQehq89ZZr4ciHqeCgZKxdA5dybgnykLlAEnEyb0Wqg2p8za1qfhBdLbFPSGOlsWIOauCqGNMZAzEY4SOZByjUzk3kmUyaxb0zMLImXbUR1D70a9DT0cCJrObdF6d22TWQIzyddDbcFguFIXe1no77QlE6WzxmPko2/qfo6msmXdHOW6eoHusowaCbbeM/Z1qaznTcq+FTnvSzlaostUZptdvrLcJAO4z2t2RADNjImOitzNR8CylMoZn0QlF2LOvo/LzzHX8y+r50NuPZeXTFKjnUz0f+rVQx1NXVt3njGG6UTZ+1NJtMSedqnpW0znbZ93uc7upZ0PVYyprgsHoakuAiNYy84qgcqII9UsBfIGZX+f8/9NOBb8Sts+KFSt4zZo10x5XEARByDOdUEeJ+ngOwAuI6CwiagPwHgC/q2QFBUEQhHCK+qiZ2SCijwK4H0AcwC3MvLXqNRMEQRAARBtMBDPfA+CeKtdFEARBCKAmSZkEQRCE8hGhFgRBaHBEqAVBEBocEWpBEIQGp2gcdVkHJRoHcBzAaMgmc0N+C/u+2D5JAIMhv5VSTpTyF/rKKrfOUb9X5VX6moXt4z+/mRwryj5Rr2elznMhgFyFjhVln6C2Wel7FnY9q902VXlh17Ma51nK9axE+VGuZ6XKP5+ZewK3ZidnbCX/AVgD4KZpfg/8rdx9AKypRDlRyveXVcnzDPpelVfpazbdvavFPQsrr9rn2Qhts5rl6+VVu20Wu57VOM9Srmclyo9yPStVftizx8xVdX3cVcZv9d5ntpc/HY1c52a7zvUuv5x96l1+Ofs0Y/mBVMv1sYZDpkJWg1qW18rnJuVJeVJe/cqbrqxqWdQ3Vem4jVBeK5+blCflSXn1Ky+0rKpY1IIgCELlkPA8QRCEBkeEWhAEocEpW6iJKFV8q8pARCYRbdD+LZ9m20eJqCznPxExEf1E+3+CiAaI6PflHK+Ect/ulH1BFcuoy7lp5dWsvUQtcyZtRTtG1e9dQJmfIaKtRLTJeR4KVlyqcHlLiei3RLSLiPYQ0bedlMdh2/89EXWF/V6kLCaib2j//0ci+kI5x4pQltKVrUS0kYg+SUQNabw2ZKUCmGLmy7R/+6tUzgSAi4mo0/n/awAUrjA7DUQUKSOhj+sArIKd67uUsoJXbA1mxucmBFLWvSsXZyGPNwO4gpkvBfBqeJfKq3R5BOBOAL9h5hcAOA9AN4AvTbPb3wMoS6gBZAC8g4gWlrl/KShduQj28/BGAJ+vQbklMyOhJqJuInqYiNYR0WYieqvz/XIi2k5ENztvqwc0gagIRHQlET1GRGuJ6H4i0lcO/TMieoqIthDRVSUe+l4Ab3I+XwfgNq3Mq5zjrnf+nu98/34i+gUR3QXggRLPoxvA1QA+COdhJ6JriOhxIvo1EW0jou+rNz0RpYjoX4loNYCX1uDcniCiy7TtniSiS0ssV+17jW7BE9F3iej9zuf9RHS91pYqYqFOV2YFjh1278LO8Y1EtIOIVhHRDWX2Zk4DMMjMGQBg5kFmPhr2PDi9hm/N4Hl4FYA0M//IKc8E8AkAHyCiOUT0ded+bSKijxHRxwEsAbCSiFaWcX4G7OiHT/h/IKIzHb3Z5PxdRkRznbajno8uIjpERNHWa3Ng5n7Ya75+lGziRPQ1InrOKe9vtHr8k3POG4no38s4x5KZqUWdBvB2Zr4CwLUAvuG8gQHgBQBudN5WIwD+eAbldFLe7fFr5yZ8B8A7mflKALfA+4afw8wvA/AR57dSuB3Ae4ioA8ClAFZrv+0A8EpmvhzAvwD4svbbSwH8BTO/qsTy3gbgPmZ+HsAQEV3hfH8VgH8AcAmAcwC8w/l+DoAtzPwSZl5VYlnlnNsPAbwfAIjoPADtzLypxHKjMui0pf8C8I9VKqOSvA3B964A55r/AMAbmPnlAPrKLPMBAGcQ0fNE9D0i+oMqPw8XAVirf8HMYwAOAvgrAGcBuNyx7m9l5hsAHAVwLTNfW8b5AcCNAN5LRHN9338XwI9VWQBuYOZRABsB/IGzzVsA3M/MuVILZea9sDVxEeyX7ygzvxjAiwH8NdmrXL0B9n1/CTO/CMBXSz67Miinm65DAL5MRK8EYMFesXyx89s+Zt7gfF4LYPkMypli5svcQokuBnAxgAed90IcwDFt+9sAgJkfJ6JeIprHzCNRCmLmTWT7wK9D4WIJcwH8LxG9AADDzjugeJCZh1A61wH4lvP5duf/dwN41mk4IKLbALwcwC8BmAB+VUY55Z7bLwB8jog+BeADAP6nnLIjcqfzdy3yL6ZGJuzeBXEBgL3MvM/5/22wLbiSYOYUEV0J4BWwjaM7AHwRVXoeYD/jQTG8BOCVAL7PzIZz/HLafwHMPEZEPwbwcQBT2k8vRb5d/AR5kbwDwLsBrITds/neDIpXhuZrAVxKRO90/j8XtvH5agA/YuZJp64VOedizFSo3wvbMriSmXNEtB9Ah/NbRtvOBFBJ1wcB2MrMYV1/f8MqNVj8dwC+DuAaAAu07/8NwEpmfrsjeI9qv02UWAaIaAHsruXFRMSwHzCGLaJh55B2up/lUtK5MfMkET0I4K0A/gTATAbfDHh7cR2+31WbMTHzthm1zLKY5t79LqS84KXpy8C5/48CeJSINgP4W1TvedgKX2+YiHoBnAFgb4nHKoVvAVgH4EfTbKPK/h2ArxDRKQCuBPBIOQUS0dmw214/7Pv1MWa+37fN61G9cw5lpq6PuQD6HZG+FsCZFahTFHYC6CN7YAVElCSii7Tf3+18/3LY3ZewDFZh3ALgX5l5s+/7ucgPwL2/5FoX8k7YXbkzmXk5M58BYB9s6/kqp6sVg30+pbo5wijn3H4I4AYAz83QgjgA4EIiane6tX84g2PVu8ywe4eQ8nYAOJvyEUvvLqdQIjrf6fUoLgOwHdV7Hh4G0EVEf+4cIw7gG7B7Vg8A+DA5A+iOUALAOIDgLHARcdrZz2G7IBRPIT9o+144zwQzpwA8C+DbAH5fjiFDRH0Avg/gu2zPArwfwP9Rvm4iOo+I5sA+5w+QE9WinXNVKctqcW5MBraf6C4iWgNgA+zGWHWYOet0SW5wHoYE7DewWnR3mIieAtALu7te6vEPw77pfr4K2z3wSZT51vZxHQD/YMSvAPwfAE87v10C4HEAv65AeWWdGzOvJaIxTG/dhKLaCzMfIqKfA9gEYBeA9eUcr0HKDLt3fwpbYDzlMfMUEX0EwH1ENAhbWMqhG8B3iGge7N7CbtgulJtQheeBmZmI3g7ge0T0OdjG3T0A/h9s6/M8AJuIKAfgZth+5JsA3EtEx2bgpwbsF8JHtf9/HMAtjhtuAMBfar/dAdtNd00Jx+8kog2w3XwGbHfKN53ffgjbXbvOGXcbAPA2Zr6P7MH1NUSURf5aVJWyppAT0YsA3MzMpY4gCxEgomsA/CMzv7nOVQEAENES2F3tC5jZKmP/mreXRmyjRNTt+JgJ9oDZLmb+zyqX+SjstrSmmuUI1aVk1wcRfRj24MRnK18dodFwuryrAXymTJGueXtp4Db6144FtxW2q+kH9a2O0CxIUiZBEIQGJ5JFTUS3EFE/EW3RvnsRET1NduD3Xc5IMIjoveSd7m0R0WVkB6LfTXbA/1aqUaC4IAhCsxPJonbipFOwR7kvdr57Drbv6zEi+gCAs5j5c779LgHwW2Y+2xklfQkzryQ7T8DDAL7MzPdW+JwEQRBaikgWNTM/DsAflnU+7GgEAHgQwTMP3WnKzDzJzCudz1nYMZJLy6izIAjCrGImcdRbAPyR8/ldsAPg/bwbWj4JhRNa9BbYVrUgCIIwDTMR6g8A+FsiWgs7uD2r/0h26sVJZt7i+z4BW7xvUFOkBUEQhHDKnqbLzDtgz4dXyXre5NvkPQiwpmEHw+9i5m+VW7YgCMJsomyhJqJFzNzvTHH+LOzpl+q3GGx3yCt9+3wRdvzoX5VbriAIwmwjanjebbCnNJ9PRIeJ6IMAriOi52FPGz8K7/TiVwI4rLs2iGgpgM8AuBD2tMwNRCSCLQiCUASZ8CIIgtDgNMtSXIIgCLMWEWpBEIQGR4RaEAShwRGhFgRBaHBEqAVBEBocEWqhqSEi0wn13EpEG4nok04cv77Nb4noaefz67TMjiki2ul8/jERXUNEo77sj6+uz5kJQh4JzxOaGiJKMXO383kRgJ8BeJKZP+98Nw/AZtjZH9+orQJesPpJo62sIwgKsaiFloGZ+2GvH/hRZ7krwM7qeBeA25FfGFUQmgoRaqGlcGbDxgAscr5SqXZvcz4X4xU+18c5VaqqIESm7FwfgtDAEAAQ0WIA5wJY5aymbRDRxf6Mjj6eENeH0GiIRS20FER0NgATQD/sfOjzAewjov0AlkPcH0ITIkIttAxE1Ac7i+N32R4lvw7A65l5OTMvB3AlRKiFJkRcH0Kz00lEGwAkARgAfgLgm0S0HMAyAM+oDZl5HxGNEdFLmHl1yPFe4RxP8UVm/mVVai4IEZHwPEEQhAZHXB+CIAgNjgi1IAhCgyNCLQiC0OCIUAuCIDQ4ItSCIAgNjgi1IAhCgyNCLQiC0OCIUAuCIDQ4/x/IFZIBwVyfRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.PRCP['1972'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or one month!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DATE'>"
      ]
     },
     "execution_count": 47,
     "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.PRCP['02/01/1972':'03/01/1972'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can you plot for the month and or year you were born?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to do some big changes. (I am also showing you here that you can do bullets in the Markdown)\n",
    "### My end point is to look at the monthly data in boxplots. \n",
    "- But to get there we need to resample to monthly. \n",
    "- Then we need to make a new column that lists the months. \n",
    "- Then we can make a boxplot!   \n",
    "\n",
    "So lets start by making a new dataframe which is the data resampled by summing the monthly data. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_month=df.resample('M').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can describe the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>DASF</th>\n",
       "      <th>MDSF</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>SNOW</th>\n",
       "      <th>SNWD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1843.000000</td>\n",
       "      <td>1843.000000</td>\n",
       "      <td>1843.000000</td>\n",
       "      <td>1843.000000</td>\n",
       "      <td>1843.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.060770</td>\n",
       "      <td>0.157173</td>\n",
       "      <td>3.791150</td>\n",
       "      <td>2.245795</td>\n",
       "      <td>5.993814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.437887</td>\n",
       "      <td>1.508668</td>\n",
       "      <td>2.128407</td>\n",
       "      <td>4.847776</td>\n",
       "      <td>24.271023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.310000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.430000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.810000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>18.950000</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>346.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              DASF         MDSF         PRCP         SNOW         SNWD\n",
       "count  1843.000000  1843.000000  1843.000000  1843.000000  1843.000000\n",
       "mean      0.060770     0.157173     3.791150     2.245795     5.993814\n",
       "std       0.437887     1.508668     2.128407     4.847776    24.271023\n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000\n",
       "25%       0.000000     0.000000     2.310000     0.000000     0.000000\n",
       "50%       0.000000     0.000000     3.430000     0.000000     0.000000\n",
       "75%       0.000000     0.000000     4.810000     2.000000     0.000000\n",
       "max       6.000000    40.000000    18.950000    36.900000   346.000000"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So things appear to be working.  What does our data look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            DASF  MDSF  PRCP  SNOW  SNWD\n",
      "DATE                                    \n",
      "1869-02-28   0.0   0.0  0.00   0.0   0.0\n",
      "1869-03-31   0.0   0.0  4.61   0.8   0.0\n",
      "1869-04-30   0.0   0.0  1.39   0.0   0.0\n",
      "1869-05-31   0.0   0.0  4.15   0.0   0.0\n",
      "1869-06-30   0.0   0.0  4.40   0.0   0.0\n",
      "...          ...   ...   ...   ...   ...\n",
      "2022-04-30   0.0   0.0  4.53   0.0   0.0\n",
      "2022-05-31   0.0   0.0  4.52   0.0   0.0\n",
      "2022-06-30   0.0   0.0  2.92   0.0   0.0\n",
      "2022-07-31   0.0   0.0  4.55   0.0   0.0\n",
      "2022-08-31   0.0   0.0  1.59   0.0   0.0\n",
      "\n",
      "[1843 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "print (df_month) # a big difference between python 2 and 3 is you need parentheses on the print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make our plotting work we will need a new column that tells us just the month. So we are going to look at the index date and pull out the month and put it into a new column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_month['month']=df_month.index.month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets just look at 5 rows and see if it worked.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>DASF</th>\n",
       "      <th>MDSF</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>SNOW</th>\n",
       "      <th>SNWD</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</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>1869-02-28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869-03-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.61</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869-04-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869-05-31</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869-06-30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DASF  MDSF  PRCP  SNOW  SNWD  month\n",
       "DATE                                           \n",
       "1869-02-28   0.0   0.0  0.00   0.0   0.0      2\n",
       "1869-03-31   0.0   0.0  4.61   0.8   0.0      3\n",
       "1869-04-30   0.0   0.0  1.39   0.0   0.0      4\n",
       "1869-05-31   0.0   0.0  4.15   0.0   0.0      5\n",
       "1869-06-30   0.0   0.0  4.40   0.0   0.0      6"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month.head()  #head just gives you the first 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets make our boxplot!  you can google pandas boxplot to see how.  http://pandas.pydata.org/pandas-docs/stable/visualization.html  \n",
    "\n",
    "\n",
    "### Foreshadowing.  I am going to ask you to use this by notation in a future class!  \n",
    "this is a big hint.  remember column and by.  I AM GOING TO ASK THIS AGAIN!\n",
    "\n",
    "Also, to make it look fancy I am showing the mean values and making it have a notch.  You can make both options False or just delete them.  They are extra arguments in the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'PRCP'}, xlabel='month'>"
      ]
     },
     "execution_count": 60,
     "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_month.boxplot(column=['PRCP'],by='month',showmeans=True,notch=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the great thing about python is you can make a professional looking graph and save it and have it ready to use.  So lets do it.\n",
    "*  First we turn on fancy graphs.  We do fig,ax=plt.subplots()  this lets us control all parts of the graph in terms of the axes and the overall figure.  \n",
    "*  First we remove the title and suptitle by making them nothing.  ax.set_title('')   fig.suptitle('')   You could add titles if you wanted.\n",
    "*  We label our y-axis.  Always label axes. ax.set_ylabel('Precipitation (inches)')\n",
    "*  We make the x-axis nicer.\n",
    "*  We make the label fonts bigger\n",
    "*  We save the file.  I added some keywords to make the file nice.  the dpi is dots per inch so it has better resolution and the bbox makes sure nothing is cut off. \n",
    "*  You can decide if you want to turn off gridlines.  add grid=False\n",
    "*  for a hard one we could add month of year instead of 1-12.  I used ax.set_xticklabels(['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'])\n",
    "\n",
    "I think that is a sharp graph!  You should be able to find it on your computer and mail it to yourself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "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_month.boxplot(column=['PRCP'],by='month',showmeans=True,notch=True,ax=ax,grid=False)\n",
    "ax.set_title('')\n",
    "fig.suptitle('')\n",
    "ax.set_ylabel('Precipitation (inches)',fontsize=14)\n",
    "ax.set_xlabel('Month of Year',fontsize=14)\n",
    "ax.set_xticklabels(['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'])\n",
    "fig.savefig('Central-Park-Monthly-Precip.jpg',dpi=600,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I talk about making figure captions in the homework.  So lets make one here. Here is the definition of a [boxplot](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.boxplot.html#:~:text=A%20box%20plot%20is%20a,the%20range%20of%20the%20data)\n",
    "\n",
    "Figure 1.  Boxplot of monthly precipitation Central Park, New York City from 1876 to 2015.  The indent is the median, the triangle is the mean, the box goes from Q1 to Q3 with the whiskers showing the range of the data and circles are outliers larger than 1.5 times the interquartile range.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need to learn about how to talk about results.  We can see from the figure that precipitation does not systematically vary by month in Central Park and that the mean monthly precipitation is fairly uniform.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "to get the monthly data we can groupby month and the describe the data.  or just look at the mean. We will also only look at prcp.  As you can see rain is pretty uniform by month in NYC!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1     3.490065\n",
       "2     3.300649\n",
       "3     3.971688\n",
       "4     3.720325\n",
       "5     3.741818\n",
       "6     3.659286\n",
       "7     4.388442\n",
       "8     4.418961\n",
       "9     3.871765\n",
       "10    3.731830\n",
       "11    3.541765\n",
       "12    3.652876\n",
       "Name: PRCP, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month.groupby('month').PRCP.mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I hope you got some of these to work and are excited about what we can do.  You just analyzed 50,000 precipitation data points on your first day!  We are ready to begin learning data analysis and Python! But for next class we have to go back to the basics!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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