GEOS 505 Assignment 1.ipynb

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   "cell_type": "markdown",
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    "# GEOS 505 Assignment 1\n",
    "\n",
    "## Due: Thursday, October 11, 2018\n",
    "\n",
    "### Purpose\n",
    "In this assignment you will demonstrate your emerging research computing competencies by: (1) obtaining some dataset that is relevant to your research or research interests, (2) importing that dataset into Python, (3) creating a visualization of that dataset and describing (in text) what is being shown, and (4) saving and uploading the Jupyter Notebook you created to your Github account.\n",
    "\n",
    "### Instructions\n",
    "Using this Jupyter notebook as a template, you should use a combination of code and Markdown cells to illustrate and describe the workflow you are using to import and visualize your dataset. The prompts below can serve as a roadmap for how this workflow should unfold."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Describe the dataset \n",
    "Using words in Markdown, describe that you are importing and state why it is important to your research or research interests. This should include some details about where the data came from and the format in which it is stored"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am importing data from Dave Wilkins' Sunnyslope AVA weather station located in Polo Cove vineyard. Data in this set includes temperature (°F) readings for 2017-4-1 through 2017-10-31. The number of days where the temperature reaches 50°F or higher are the growing degree days (GDD) of the vineyard. GDD are used as a measurement to estimate the likelihood the fruit will reach full maturity before harvest. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Import the dataset and verify that it is what you expect\n",
    "You should include a detailed description of any cleaning or manipulation done to the data prior to reading it in here, why you needed to do it, and how you did it. You should also include a justification for how you read the data into Python using the command you did. Provide examples of other ways you could have read in the data and discuss why you chose not to use those methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "filename_PC = \"PC_GDD_17.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(filename_PC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4/1/17 1:00</td>\n",
       "      <td>44.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/1/17 2:00</td>\n",
       "      <td>44.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/1/17 3:00</td>\n",
       "      <td>44.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/1/17 4:00</td>\n",
       "      <td>44.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Value\n",
       "0  4/1/17 1:00  44.24\n",
       "1  4/1/17 2:00  44.78\n",
       "2  4/1/17 3:00  44.42\n",
       "3  4/1/17 4:00  44.06"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I downloaded the data as a '.csv' and, in Excel, deleted the first column of values \"1-5055\" that corresponded to the reading number. Doing this makes it easier for Python to read, and the data output is neater. I imported the data using filename_PC = \"####.csv\". I named the file 'PC' for \"Polo Cove\". I created a dataframe and read the '.csv' data in using Pandas. To check if my data was imported correctly (makes sense) I asked Python to show me the dataframe for rows 0 through 4. Values printed in Python matched values found in Excel, so I know it all worked out. I could have created a graph using matplotlib.pyplot and compared it to the graph on the webpage for the data, but printing the dataframe was easier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Create a visualization of some feature of your dataset\n",
    "Using a standard plotting library, create a visualization of your dataset. Describe, in words, why you are choosing to plot the data the way you are and what you intend to reveal through this visualization. Make sure your plot has a title and that axes are labeled and units are provided."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(x='Date',y='Value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4/1/17 1:00'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datearr = df['Date'].values\n",
    "datearr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2017-04-01 01:00:00')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datearr_dt = pd.to_datetime(datearr)\n",
    "datearr_dt[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.datetimes.DatetimeIndex"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(datearr_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['NP_Datetime'] = datearr_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Date</th>\n",
       "      <th>Value</th>\n",
       "      <th>NP_Datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4/1/17 1:00</td>\n",
       "      <td>44.24</td>\n",
       "      <td>2017-04-01 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/1/17 2:00</td>\n",
       "      <td>44.78</td>\n",
       "      <td>2017-04-01 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/1/17 3:00</td>\n",
       "      <td>44.42</td>\n",
       "      <td>2017-04-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/1/17 4:00</td>\n",
       "      <td>44.06</td>\n",
       "      <td>2017-04-01 04:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Value         NP_Datetime\n",
       "0  4/1/17 1:00  44.24 2017-04-01 01:00:00\n",
       "1  4/1/17 2:00  44.78 2017-04-01 02:00:00\n",
       "2  4/1/17 3:00  44.42 2017-04-01 03:00:00\n",
       "3  4/1/17 4:00  44.06 2017-04-01 04:00:00"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "plt.plot(df['NP_Datetime'].values,df['Value'].values)\n",
    "plt.title('Polo Cove Vineyard April 1st to October 31st')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Value [°F]')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This type of plot allows the reader to see temperature (°F) fluctuations during the growing season in Polo Cove vineyard. There are over 5000 values that needed to appear on the plot, and a graph such as this is easy to read. I had to convert the data and time stamp into a form that was human readable. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Describe the visualization you just created\n",
    "As if you were writing a paragraph in a results section of a technical report, peer-reviewed manuscript, or thesis, describe (again, in words) the central idea that this visualization conveys. Describe key features of the data that the outside reviewer should pay attention to. This might include a description of anything unanticipated that you see in the data, or a verification that the visualization shows what you expected to see. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temperature readings as displayed in the figure \"Polo Cove Vineyard April 1st to October 31st\" show an increased in temperature during the summer months. The start of the growing season (April 1st) and the end of the season (October 31st) display values that are significantly lower than readings from June and August. The weather station failed to take readings at the end of August and beginning of September possibly due to a dead battery. The increasing temperature trends during the summer months displayed in this graph are to be expected and verify that our data is accurate. Using the graph, we can conclude nearly half of the growing season reaches 50°F or greater. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Perform any additional analyses (optional)\n",
    "If you are interested or curious, go beyond this simple visualization to perform any additional statistical analyses on your data, create any additional visualizations, or compute any additional derived variables that may help you and an external reviewer better understand the dataset that you are showing. Use words to describe any additional analyses and why they are relevant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Python code goes here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Upload your Jupyter Notebook to your GitHub account\n",
    "Paste the link to your completed notebook in the class etherpad: https://etherpad.boisestate.edu/p/GEOS-505-FA18"
   ]
  }
 ],
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