02_time_series_exercise_solutions_UPDATE_NQ.ipynb

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        "<img style=\"float: left;\" src=\"https://drive.google.com/uc?export=view&id=13_P0Dq9rb1aXFFVfspMRn5hlP7t8RuwC\" width=\"200\" height=\"100\">\n",
        "\n",
        "\n",
        "\n",
        "# Earth Data Science Corps Summer 2022 - Solutions Notebook\n",
        "\n",
        "![Colored Bar](https://drive.google.com/uc?export=view&id=1WL0ur5SQc5ijCvXc6MvWUeoWOIVS_rQ9)"
      ]
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        "<div class='notice--success' markdown=\"1\">\n",
        "\n",
        "## Test Your Skills - Time Series Data In Python Using Pandas \n",
        "\n",
        "Now that you have learned how to open and manipulate time series data in **Python**, it's time to test your skills. Complete the activities below.\n",
        "</div>"
      ]
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      "source": [
        "# Import necessary packages\n",
        "%pip install earthpy\n",
        "\n",
        "import os\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.dates as mdates\n",
        "from matplotlib.dates import DateFormatter\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import earthpy as et\n",
        "\n",
        "# Handle date time conversions between pandas and matplotlib\n",
        "from pandas.plotting import register_matplotlib_converters\n",
        "register_matplotlib_converters()\n",
        "\n",
        "# Use white grid plot background from seaborn\n",
        "sns.set(font_scale=1.5, style=\"whitegrid\")"
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      "source": [
        "# Download the data\n",
        "data = et.data.get_data('colorado-flood')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading from https://ndownloader.figshare.com/files/16371473\n",
            "Extracted output to /root/earth-analytics/data/colorado-flood/.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6EEhe7C4dBtp"
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      "source": [
        "# Set working directory\n",
        "os.chdir(os.path.join(et.io.HOME,\n",
        "                      'earth-analytics',\n",
        "                      'data'))\n",
        "\n",
        "# Define relative path to file with daily discharge data\n",
        "stream_discharge_path = os.path.join(\"colorado-flood\",\n",
        "                                     \"discharge\",\n",
        "                                     \"06730200-discharge-daily-1986-2013.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UUeZkwpjdBtp"
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      "source": [
        "<div class=\"notice--warning alert alert-info\" markdown=\"1\">\n",
        "\n",
        "## <i class=\"fa fa-pencil-square-o\" aria-hidden=\"true\"></i> Challenge 1: Explore Your Data & Metadata\n",
        "\n",
        "Before you begin working with files using **python**, it can be helpful \n",
        "to look at the structure of the file itself. In some cases, text files \n",
        "have metadata at the top of the file that tell you more about the data within \n",
        "the file itself. This information at the top of the text file can help \n",
        "you make decisions about how you plan to import the data, and what cleanup \n",
        "steps you may need to take.\n",
        "\n",
        "Open the files `earth-analytics/data/colorado-flood/discharge/06730200-discharge-daily-1986-2013.csv` and `earth-analytics/data/colorado-flood/discharge/06730200-discharge-daily-1986-2013.txt` by clicking on them and review their contents. Use these files to answer the questions below:\n",
        "\n",
        "1. What is the delimiter used in `06730200-discharge-daily-1986-2013.csv`?\n",
        "2. What are the units for stream discharge in the data?\n",
        "3. Where was this data collected?\n",
        "4. What is the frequency of the data collection (day, week, month)?\n",
        "5. What does each number represent in the data (a single observation, minimum value, max value or mean value)?\n",
        "\n",
        "Write down your answers in the cell below as a comment.\n",
        "\n",
        "****\n",
        "HINT: You may also want to explore the `README_dischargeMetadata.rtf` file located in the same directory. This file contains metadata that describes in more detail the data that you are using"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "*List your answers to the above questions in this cell*\n",
        "\n",
        "1.) Since this is a .csv (comma separated variable) the delimiter would be a comma.\n",
        "\n",
        "2.) Stream discharge is measured in cubic feet per second (cfs).\n",
        "\n",
        "3.) Data was collected at: BOULDER CREEK AT NORTH 75TH ST. NEAR BOULDER, CO \n",
        "\n",
        "4.) Data was collected at daily intervals.\n",
        "\n",
        "5.) Each discharge measurement represents the daily mean.\n",
        "\n"
      ]
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    {
      "cell_type": "markdown",
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      "source": [
        "<div class=\"notice--warning alert alert-info\" markdown=\"1\">\n",
        "\n",
        "## <i class=\"fa fa-pencil-square-o\" aria-hidden=\"true\"></i> Challenge 2: Open and Plot a CSV File with Time Series Data\n",
        "\n",
        "The code above creates a path (`stream_discharge_path`) to open daily stream \n",
        "discharge measurements taken by U.S. Geological Survey from 1986 to 2013 at \n",
        "Boulder Creek in Boulder, Colorado. Using **pandas**, do the following with the data:\n",
        "\n",
        "1. Read the data into **Python** as a **pandas** `DataFrame`. \n",
        "2. Parse the dates in the `datetime` column of the **pandas** `DataFrame`.\n",
        "3. Set the `datetime` as the index for your `DataFrame`.\n",
        "4. Plot the newly opened data with **matplotlib**. Make sure your x-axis is the dates, and your y-axis is the `disValue` column from the **pandas** `DataFrame`.\n",
        "5. Give your plot a title and label the axes.\n",
        "\n",
        "If you need a refresher on how to plot time series data, check out [this lesson on working with time series data in Pandas.](https://www.earthdatascience.org/courses/use-data-open-source-python/use-time-series-data-in-python/date-time-types-in-pandas-python/)\n",
        "\n",
        "Once you have created your plot, answer the following questions.\n",
        "\n",
        "1. What is the max value for stream charge in the data? One what date did that value occur?\n",
        "2. Consider the entire dataset. Do you see any patterns of stream discharge values in the data?\n",
        "</div>\n",
        "\n",
        "Your final plot should look like the plot below. "
      ]
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      "source": [
        "# Import data using pandas\n",
        "boulder_discharge_1986_2013 = pd.read_csv(stream_discharge_path,\n",
        "                                       # Make sure the dates import in datetime format\n",
        "                                       parse_dates=['datetime'],\n",
        "                                       # Set DATE as the index so you can subset data by time period\n",
        "                                       index_col=['datetime'],\n",
        "                                       # Mask no data values so they are not plotted / used in analysis\n",
        "                                       na_values=['999.99'])\n",
        "boulder_discharge_1986_2013.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "           agency_cd  site_no  disValue qualCode\n",
              "datetime                                        \n",
              "1986-10-01      USGS  6730200      30.0        A\n",
              "1986-10-02      USGS  6730200      30.0        A\n",
              "1986-10-03      USGS  6730200      30.0        A\n",
              "1986-10-04      USGS  6730200      30.0        A\n",
              "1986-10-05      USGS  6730200      30.0        A"
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-003bc3d3-4ee3-4690-961f-f4108d90ae7f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-003bc3d3-4ee3-4690-961f-f4108d90ae7f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "6eMmaPwhdBtt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "6e1139b9-7fe3-49eb-f056-a2af1ea0dcfa"
      },
      "source": [
        "# Build plot from pandas DataFrame\n",
        "f, ax = plt.subplots(figsize=(10,5))\n",
        "ax.scatter(x=boulder_discharge_1986_2013.index.values,\n",
        "           y=boulder_discharge_1986_2013[\"disValue\"],\n",
        "           color=\"springgreen\")\n",
        "\n",
        "ax.set(title=\"Discharge of Boulder Creek; Boulder, CO (1986-2013)\",\n",
        "       xlabel=\"Date\",\n",
        "       ylabel=\"Discharge (cfs)\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KT0-zHCidBtu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25dad708-9ffd-4908-c3f2-8be6cf07dcf8"
      },
      "source": [
        "# Put your answers for the text questions here\n",
        "\n",
        "# 1.) Maximum discharge value of 4770 cfs occurred sometime in 2013 \n",
        "#    (can't say when for sure without going into the Excel file and cheating -- 9/12/13!)\n",
        "\n",
        "# 2.) Seasonal variability in discharge with peaks happening during springtime snowmelt.\n",
        "\n",
        "\n",
        "boulder_discharge_1986_2013.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "agency_cd       USGS\n",
              "site_no      6730200\n",
              "disValue      4770.0\n",
              "qualCode         A:e\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NV6ICEoadBtv"
      },
      "source": [
        "<div class=\"notice--warning alert alert-info\" markdown=\"1\">\n",
        "\n",
        "## <i class=\"fa fa-pencil-square-o\" aria-hidden=\"true\"></i> Challenge 3: Subset the Data\n",
        "\n",
        "The 2013 Colorado Flood occurred in 2013 (as the name implies). The plot above shows all of the stream discharge data over several decades. In this challenge you will subset the data to just the year and months during which the flood event occurred. \n",
        "\n",
        "Do the following: \n",
        "1. Subset the data to include only discharge data from August 1st, 2013 through October 31, 2013\n",
        "2. Plot the newly subset data with **matplotlib**. Make sure your x-axis contains dates,  and your y-axis is contains the `disValue` column from your **pandas** `DataFrame`.\n",
        "3. Give your plot a title and label the axes.\n",
        "4. Format the dates on the x-axis so they only show the month and the day. Additionally, you can angle the dates using the line of code `fig.autofmt_xdate()`. \n",
        "5. Make the x-axis week ticks only show up for every other week. \n",
        "\n",
        "The lessons below should help you complete this challenge:\n",
        "\n",
        "* [More on subsetting time series data using **Pandas**](https://www.earthdatascience.org/courses/use-data-open-source-python/use-time-series-data-in-python/date-time-types-in-pandas-python/subset-time-series-data-python/) \n",
        "* [More on customizing date labels on time series plots in **matplotlib**](https://www.earthdatascience.org/courses/use-data-open-source-python/use-time-series-data-in-python/date-time-types-in-pandas-python/customize-dates-matplotlib-plots-python/). \n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wsk5RC8cdBtv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "5d97abbe-3749-492f-99e1-98b548194b8d"
      },
      "source": [
        "# Subset the data\n",
        "boulder_creek_Q_aug_oct_2013 = boulder_discharge_1986_2013['2013-8-01':'2013-10-31']\n",
        "\n",
        "boulder_creek_Q_aug_oct_2013.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           agency_cd  site_no  disValue qualCode\n",
              "datetime                                        \n",
              "2013-08-01      USGS  6730200      64.0        A\n",
              "2013-08-02      USGS  6730200      68.0        A\n",
              "2013-08-03      USGS  6730200      65.0        A\n",
              "2013-08-04      USGS  6730200      65.0        A\n",
              "2013-08-05      USGS  6730200      62.0        A"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5d7eaf63-3147-4cf5-815f-d520ec774fe9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>agency_cd</th>\n",
              "      <th>site_no</th>\n",
              "      <th>disValue</th>\n",
              "      <th>qualCode</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-08-01</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>64.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-08-02</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>68.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-08-03</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>65.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-08-04</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>65.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-08-05</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>62.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d7eaf63-3147-4cf5-815f-d520ec774fe9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5d7eaf63-3147-4cf5-815f-d520ec774fe9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5d7eaf63-3147-4cf5-815f-d520ec774fe9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "gTflOJpNdBtv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "4d51226b-5d61-4131-e78b-925a54f8cffb"
      },
      "source": [
        "# Plot the data as a bar plot\n",
        "f, ax = plt.subplots(figsize=(10,5))\n",
        "ax.bar(boulder_creek_Q_aug_oct_2013.index.values,\n",
        "       boulder_creek_Q_aug_oct_2013[\"disValue\"],\n",
        "           color = \"springgreen\")\n",
        "\n",
        "ax.set(title = \"Discharge - Boulder Creek - Boulder, CO (Aug. - Oct., 2013)\",\n",
        "       xlabel = \"Date\",\n",
        "       ylabel = \"Discharge (cfs)\")\n",
        "\n",
        "# Rotate tick marks on x-axis\n",
        "#plt.setp(ax.get_xticklabels(), rotation=45)\n",
        "\n",
        "# Define the date format\n",
        "date_form = DateFormatter(\"%m-%d\")\n",
        "ax.xaxis.set_major_formatter(date_form)\n",
        "\n",
        "\n",
        "# Ensure a major tick for every other week using (interval=2) \n",
        "#ax.xaxis.set_major_locator(mdates.WeekdayLocator(interval=2))\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BL74nNDCdBtw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "b69f4258-021e-4ae8-f85d-57ea18af8ed2"
      },
      "source": [
        "# Plot the data as a scatter plot\n",
        "f, ax = plt.subplots(figsize=(10,5))\n",
        "ax.scatter(boulder_creek_Q_aug_oct_2013.index.values,\n",
        "       boulder_creek_Q_aug_oct_2013[\"disValue\"],\n",
        "           color = \"springgreen\")\n",
        "\n",
        "ax.set(title = \"Discharge - Boulder Creek - Boulder, CO (Aug. - Oct., 2013)\",\n",
        "       xlabel = \"Date\",\n",
        "       ylabel = \"Discharge (cfs)\")\n",
        "\n",
        "# Rotate tick marks on x-axis\n",
        "plt.setp(ax.get_xticklabels(), rotation=45)\n",
        "\n",
        "# Define the date format\n",
        "date_form = DateFormatter(\"%m-%d\")\n",
        "ax.xaxis.set_major_formatter(date_form)\n",
        "\n",
        "\n",
        "# Ensure a major tick for every other week using (interval=2) \n",
        "ax.xaxis.set_major_locator(mdates.WeekdayLocator(interval=2))\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O9e_PnfBdBtw"
      },
      "source": [
        "<div class=\"notice--warning alert alert-info\" markdown=\"1\">\n",
        "\n",
        "## <i class=\"fa fa-pencil-square-o\" aria-hidden=\"true\"></i> Challenge 4: Resample the Data\n",
        "\n",
        "Next, summarize the stream discharge data by week. Additionally, you will \n",
        "clean up the format of the date labels on the x-axis. Do the following: \n",
        "\n",
        "1. Resample the `DataFrame` that you made above for August - October, 2013 to represent the maximum stream discharge value for each week. \n",
        "2. Plot the newly resampled data as a scatterplot (`ax.scatter()`)using `matplotlib`. Give your plot a title and label the x and y axes.\n",
        "3. Format the dates on the x-axis so they only show the month and the day. Additionally, you can angle the dates using the line of code `fig.autofmt_xdate()`. \n",
        "4. Adjust the x-axis ticks and labels so you have one label and tick for **every other week**. \n",
        "\n",
        "****\n",
        "HINT: The lessons below might help you complete this challenge:\n",
        "\n",
        "* [Check out the Pandas time series data resampling lesson to help you resample your data.](https://www.earthdatascience.org/courses/use-data-open-source-python/use-time-series-data-in-python/date-time-types-in-pandas-python/resample-time-series-data-pandas-python/)\n",
        "* [Check out the customize time series plots lesson to help with adjusting the x axis ticks.](https://www.earthdatascience.org/courses/use-data-open-source-python/use-time-series-data-in-python/date-time-types-in-pandas-python/customize-dates-matplotlib-plots-python/)\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "guyilfUydBtx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "addd967d-b350-446c-bf9a-a85f30f8946d"
      },
      "source": [
        "# Resample by weekly max values\n",
        "boulder_creek_Qmax_aug_oct_2013 = boulder_creek_Q_aug_oct_2013.resample(\"W\").max()\n",
        "boulder_creek_Qmax_aug_oct_2013"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           agency_cd  site_no  disValue qualCode\n",
              "datetime                                        \n",
              "2013-08-04      USGS  6730200      68.0        A\n",
              "2013-08-11      USGS  6730200      62.0        A\n",
              "2013-08-18      USGS  6730200      53.0        A\n",
              "2013-08-25      USGS  6730200     108.0        A\n",
              "2013-09-01      USGS  6730200      79.0        A\n",
              "2013-09-08      USGS  6730200      72.0        A\n",
              "2013-09-15      USGS  6730200    4770.0        A\n",
              "2013-09-22      USGS  6730200    1770.0        A\n",
              "2013-09-29      USGS  6730200     795.0        A\n",
              "2013-10-06      USGS  6730200     424.0        A\n",
              "2013-10-13      USGS  6730200     265.0        A\n",
              "2013-10-20      USGS  6730200     223.0        A\n",
              "2013-10-27      USGS  6730200     209.0        A\n",
              "2013-11-03      USGS  6730200     148.0        A"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b63c812b-4d2f-4b50-b865-aa8c20d1e281\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>agency_cd</th>\n",
              "      <th>site_no</th>\n",
              "      <th>disValue</th>\n",
              "      <th>qualCode</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>datetime</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2013-08-04</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>68.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-08-11</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>62.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-08-18</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>53.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-08-25</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>108.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-09-01</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>79.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-09-08</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>72.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-09-15</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>4770.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-09-22</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>1770.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-09-29</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>795.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-06</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>424.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-13</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>265.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-20</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>223.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-10-27</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>209.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013-11-03</th>\n",
              "      <td>USGS</td>\n",
              "      <td>6730200</td>\n",
              "      <td>148.0</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b63c812b-4d2f-4b50-b865-aa8c20d1e281')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b63c812b-4d2f-4b50-b865-aa8c20d1e281 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b63c812b-4d2f-4b50-b865-aa8c20d1e281');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0SSnIVTdBty",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "06c5536c-8b9b-4f07-cc77-c166cc0bfb74"
      },
      "source": [
        "# Plot the resampled data\n",
        "f, ax = plt.subplots(figsize=(10,5))\n",
        "ax.scatter(boulder_creek_Qmax_aug_oct_2013.index.values,\n",
        "       boulder_creek_Qmax_aug_oct_2013[\"disValue\"],\n",
        "           color = \"springgreen\")\n",
        "\n",
        "ax.set(title = \"Maximum Discharge - Boulder Creek - Boulder, CO (Aug. - Oct., 2013)\",\n",
        "       xlabel = \"Date\",\n",
        "       ylabel = \"Maximum Discharge (cfs)\")\n",
        "\n",
        "# Rotate tick marks on x-axis\n",
        "plt.setp(ax.get_xticklabels(), rotation=45)\n",
        "\n",
        "# Define the date format\n",
        "date_form = DateFormatter(\"%m-%d\")\n",
        "ax.xaxis.set_major_formatter(date_form)\n",
        "\n",
        "\n",
        "# Ensure a major tick for every other week using (interval=2) \n",
        "ax.xaxis.set_major_locator(mdates.WeekdayLocator(interval=2))\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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          },
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKxhJG00dBtz"
      },
      "source": [
        "<div class=\"notice--warning alert alert-info\" markdown=\"1\">\n",
        "\n",
        "## <i class=\"fa fa-pencil-square-o\" aria-hidden=\"true\"></i> Challenge 5: Compare Two Months Side by Side\n",
        "\n",
        "In this next challenge, you will compare daily max stream discharge for two time periods. Create a plot comparing stream discharge in  levels to the levels seen 10 years ago during the same months. Do the following: \n",
        "\n",
        "1. Create a data subset for the time periods:\n",
        "    * August 1st, 2003 - October 31st, 2003 \n",
        "    * August 1st, 2013 - October 31st, 2013 \n",
        "2. Plot the data from 2003 on a plot above of the data from 2013 using `matplotlib`. \n",
        "3. Add titles to each plot and and label the x and y axes.\n",
        "4. Use `fig.suptitle(\"title-here\")` to add a title to your figure.\n",
        "5. Modify the y limits of both plots to range from 0 to the max value found in the 2013 data subset. \n",
        "    * Hint: you can use `round(data-frame-name[\"disValue\"].max(), -3)` to get the max value from your 2013 data. \n",
        "    * use `ax.set_ylim(min-value, max-value)` to set the limits\n",
        "6. Format the dates on the x-axis as follows:\n",
        "    * Make sure date ticks only show the month and the day - example: `Aug-06`.\n",
        "    * Make the x-axis week ticks only display for every other week. \n",
        "7. Use `plt.tight_layout()` to ensure your plots don't overlap each other. \n",
        "\n",
        "OPTIONAL: You may have noticed empty space on either side of the x-axis in your previous plot. Use `ax.set_xlim()` to set the x limits of your plot to the minimum and maximum date values in each of your subset datasets. \n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hhyL5j51dBtz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "96e58127-a7e4-45b3-eb73-35f0ca35e821"
      },
      "source": [
        "# Subset the 2003 data\n",
        "boulder_creek_Q_aug_oct_2003 = boulder_discharge_1986_2013['2003-8-01':'2003-10-31']\n",
        "\n",
        "boulder_creek_Q_aug_oct_2003.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           agency_cd  site_no  disValue qualCode\n",
              "datetime                                        \n",
              "2003-08-01      USGS  6730200      59.0        A\n",
              "2003-08-02      USGS  6730200      40.0        A\n",
              "2003-08-03      USGS  6730200      39.0        A\n",
              "2003-08-04      USGS  6730200      47.0        A\n",
              "2003-08-05      USGS  6730200      71.0        A"
            ],
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b0yo880kdBt0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "aca70349-8cb9-4ecd-f858-b46bc4c409f6"
      },
      "source": [
        "# Plot the 2003 and 2013 data side by side\n",
        "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,5))\n",
        "\n",
        "ax1.scatter(boulder_creek_Q_aug_oct_2003.index.values,\n",
        "       boulder_creek_Q_aug_oct_2003[\"disValue\"],\n",
        "           color = \"springgreen\")\n",
        "\n",
        "ax1.set(title = \"Aug. - Oct., 2003\",\n",
        "       ylabel = \"Discharge (cfs)\")\n",
        "\n",
        "ax2.scatter(boulder_creek_Q_aug_oct_2013.index.values,\n",
        "       boulder_creek_Q_aug_oct_2013[\"disValue\"],\n",
        "           color = \"slateblue\")\n",
        "\n",
        "ax2.set(title = \"Aug. - Oct., 2013\",\n",
        "       xlabel = \"Date\",\n",
        "       ylabel = \"Discharge (cfs)\")\n",
        "\n",
        "# Rotate tick marks on x-axis\n",
        "plt.setp(ax2.get_xticklabels(), rotation=45)\n",
        "\n",
        "# Define the date format\n",
        "date_form = DateFormatter(\"%m-%d\")\n",
        "ax1.xaxis.set_major_formatter(date_form)\n",
        "ax2.xaxis.set_major_formatter(date_form)\n",
        "\n",
        "# Set limits for vertical axes\n",
        "ax1.set_ylim(0, 5000)\n",
        "ax2.set_ylim(0, 5000)\n",
        "\n",
        "# Add a suptitle to the whole plot\n",
        "fig.suptitle(\"Discharge - Boulder Creek - Boulder, CO\")\n",
        "\n",
        "# Add some padding between the two subplots\n",
        "plt.subplots_adjust(hspace=0.5)\n",
        "\n",
        "# Turn off the axis labels for the top plot\n",
        "ax1.set_xticklabels([])\n",
        "\n",
        "# Ensure a major tick for every other week using (interval=2) \n",
        "ax1.xaxis.set_major_locator(mdates.WeekdayLocator(interval=2))\n",
        "\n",
        "#plt.tight_layout()\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9C-hKa0LdBt0"
      },
      "source": [
        "<div class=\"notice--warning alert alert-info\" markdown=\"1\">\n",
        "\n",
        "\n",
        "## <i class=\"fa fa-pencil-square-o\" aria-hidden=\"true\"></i> Bonus Challenge: Get Data from Hydrofunctions\n",
        "\n",
        "There are many ways to get data into **python**. So far you have used \n",
        "`et.data.get_data()` to download your data. However you can also access \n",
        "data directly using open source tools that access API's (automated tools \n",
        "that directly access and downoad data from the data servers). \n",
        "\n",
        "**hydrofunctions** is an open source Python package that allows you to \n",
        "download hydrologic data from the U.S. Geological Survey. For the bonus \n",
        "challenge, you'll use **hydrofunctions** to download stream discharge data \n",
        "and plot it much like you did above. To get the data using **hydrofunctions**, \n",
        "run the code below.\n",
        "\n",
        "```\n",
        "import hydrofunctions as hf\n",
        "\n",
        "# Define the site number and start and end dates that you are interested in\n",
        "site = \"06730500\"\n",
        "start = '1946-05-10'\n",
        "end = '2018-08-29'\n",
        "\n",
        "# Request data for that site and time period\n",
        "longmont_resp = hf.NWIS(site, 'dv', start, end, parameterCd=\"00060\")\n",
        "\n",
        "# Turn above request into a time series dataframe\n",
        "longmont_disc = longmont_resp.df()\n",
        "\n",
        "```\n",
        "\n",
        "Once you have imported the  data into a **pandas** `DataFrame` using the code \n",
        "above, perform the following tasks: \n",
        "\n",
        "1. Rename the columns (USGS:06730500:00060:00003, USGS:06730500:00060:00003_qualifiers)) `discharge` and `flags`. This will make the data a bit easier to work with. \n",
        "2. Subset the data to the time period: `1970` through the present.\n",
        "3. Resample the data to calculate the annual maximum stream discharge value for each year. \n",
        "4. Plot the data using `matplotlib`. Format the x and y axis so the labels are easy to read. Add a title to your plot.\n",
        "\n",
        "****\n",
        "HINT: if you don't know how to rename a dataframe column, try looking it up \n",
        "using a Google search!\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Copy and paste the code from above, than modify the pandas DataFrame as instructed\n",
        "%pip install hydrofunctions\n",
        "import hydrofunctions as hf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XH-ERxEDlojR",
        "outputId": "45fbd4dd-1bb3-4be7-8aaf-72a6fdf691fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting hydrofunctions\n",
            "  Downloading hydrofunctions-0.2.3-py3-none-any.whl (40 kB)\n",
            "\u001b[K     |████████████████████████████████| 40 kB 1.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from hydrofunctions) (1.21.6)\n",
            "Requirement already satisfied: IPython in /usr/local/lib/python3.7/dist-packages (from hydrofunctions) (5.5.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from hydrofunctions) (2.23.0)\n",
            "Requirement already satisfied: pyarrow in /usr/local/lib/python3.7/dist-packages (from hydrofunctions) (6.0.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from hydrofunctions) (1.3.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from hydrofunctions) (3.2.2)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from hydrofunctions) (4.11.4)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->hydrofunctions) (4.1.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->hydrofunctions) (3.8.0)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from IPython->hydrofunctions) (1.0.18)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from IPython->hydrofunctions) (0.7.5)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from IPython->hydrofunctions) (4.8.0)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from IPython->hydrofunctions) (0.8.1)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from IPython->hydrofunctions) (4.4.2)\n",
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            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->hydrofunctions) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->hydrofunctions) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->hydrofunctions) (2022.6.15)\n",
            "Installing collected packages: hydrofunctions\n",
            "Successfully installed hydrofunctions-0.2.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##################################################\n",
        "## HERE IS THE OLD VERSION BEFORE UPDATES TO HF ##\n",
        "##################################################\n",
        "\n",
        "# Define the site number and start and end dates that you are interested in\n",
        "site = \"06730500\"\n",
        "start = '1946-05-10'\n",
        "end = '2021-04-14'\n",
        "\n",
        "# Request data for that site and time period\n",
        "longmont_resp = hf.get_nwis(site, 'dv', start, end)\n",
        "\n",
        "# Convert the response to a json in order to use the extract_nwis_df function\n",
        "longmont_resp = longmont_resp.json()\n",
        "\n",
        "longmont_discharge = hf.extract_nwis_df(longmont_resp)\n",
        "\n",
        "# Renaming the columns\n",
        "longmont_discharge.rename(columns = {'USGS:06730500:00060:00003':'discharge', \n",
        "                                     'USGS:06730500:00060:00003_qualifiers':'flags'}, \n",
        "                                      inplace = True)\n",
        "\n",
        "# Subsetting the data from 1970 - 2021\n",
        "longmont_discharge_1970_2021 = longmont_discharge['1970-01-01':'2021-04-14']\n",
        "\n",
        "\n",
        "# Resampling the data to calculate annual max discharge\n",
        "longmont_max_discharge_1970_2021 = longmont_discharge_1970_2021.resample(\"Y\").max()\n",
        "\n",
        "longmont_max_discharge_1970_2021.head()"
      ],
      "metadata": {
        "id": "74zFQna0pSVS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "5f7f556b-f19b-4431-b83f-5dba47904734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requesting data from https://waterservices.usgs.gov/nwis/dv/?...\rRequested data from https://waterservices.usgs.gov/nwis/dv/?format=json%2C1.1&sites=06730500&startDT=1946-05-10&endDT=2021-04-14\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-294324b9d20c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# Renaming the columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m longmont_discharge.rename(columns = {'USGS:06730500:00060:00003':'discharge', \n\u001b[0m\u001b[1;32m     20\u001b[0m                                      'USGS:06730500:00060:00003_qualifiers':'flags'}, \n\u001b[1;32m     21\u001b[0m                                       inplace = True)\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'rename'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SChKezv5dBt0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "outputId": "e57fb8ea-24a7-48e6-b8c2-3731b77d5aba"
      },
      "source": [
        "#################################################\n",
        "## HERE IS THE NEW VERSION AFTER UPDATES TO HF ##\n",
        "#################################################\n",
        "\n",
        "\n",
        "# Define the site number and start and end dates that you are interested in\n",
        "site = \"06730500\"\n",
        "start = '1946-05-10'\n",
        "end = '2021-04-14'\n",
        "\n",
        "# Request discharge data (daily values) for that site and time period\n",
        "longmont_resp = hf.NWIS(site, 'dv', start, end, parameterCd=\"00060\")\n",
        "longmont_resp\n",
        "\n",
        "# Turn above request into a time series dataframe\n",
        "longmont_disc = longmont_resp.df()\n",
        "longmont_disc\n",
        "\n",
        "# Renaming the columns\n",
        "longmont_disc.columns = [\"discharge\", \"flag\"]\n",
        "\n",
        "# Extract sitename from json file of your response data and add as column\n",
        "#longmont_disc[\"name\"] = hf.get_nwis_property(longmont_resp.json, \"siteName\")[0]\n",
        "\n",
        "# Subsetting the data from 1970 - 2021\n",
        "longmont_discharge_1970_2021 = longmont_disc['1970-01-01':'2021-04-14']\n",
        "\n",
        "# Resampling the data to calculate annual max discharge\n",
        "longmont_max_discharge_1970_2021 = longmont_discharge_1970_2021.resample(\"Y\").max()\n",
        "\n",
        "longmont_max_discharge_1970_2021.tail(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requesting data from https://waterservices.usgs.gov/nwis/dv/?...\rRequested data from https://waterservices.usgs.gov/nwis/dv/?format=json%2C1.1&sites=06730500&parameterCd=00060&startDT=1946-05-10&endDT=2021-04-14\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           discharge flag\n",
              "datetimeUTC                              \n",
              "2012-12-31 00:00:00+00:00      446.0  A,e\n",
              "2013-12-31 00:00:00+00:00     8910.0  A,e\n",
              "2014-12-31 00:00:00+00:00     1220.0  A,e\n",
              "2015-12-31 00:00:00+00:00     2020.0  A,e\n",
              "2016-12-31 00:00:00+00:00      720.0  A,e\n",
              "2017-12-31 00:00:00+00:00     1140.0  A,e\n",
              "2018-12-31 00:00:00+00:00      583.0  A,e\n",
              "2019-12-31 00:00:00+00:00      597.0  A,e\n",
              "2020-12-31 00:00:00+00:00      395.0  A,e\n",
              "2021-12-31 00:00:00+00:00      151.0  A,e"
            ],
            "text/html": [
              "\n",
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38c9AMLLdBt1"
      },
      "source": [
        "# Plot the data\n",
        "fig, ax1 = plt.subplots(figsize = (10, 5))\n",
        "\n",
        "# Plot the points\n",
        "ax1.scatter(longmont_max_discharge_1970_2021.index.values,\n",
        "       longmont_max_discharge_1970_2021[\"discharge\"],\n",
        "       color = \"springgreen\")\n",
        "\n",
        "# Connect the points\n",
        "ax1.plot(longmont_max_discharge_1970_2021.index.values,\n",
        "       longmont_max_discharge_1970_2021[\"discharge\"],\n",
        "       color = \"springgreen\")\n",
        "\n",
        "# Set plot title and axes labels\n",
        "ax1.set(title = \"Discharge - Mouth of Boulder Creek - \\nLongmont, CO - 1978-2021\",\n",
        "       xlabel = \"Date\",\n",
        "       ylabel = \"Discharge (cfs)\")\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mTREE6BydBt1"
      },
      "source": [
        "<div class=\"notice--warning alert alert-info\" markdown=\"1\">\n",
        "\n",
        "## <i class=\"fa fa-pencil-square-o\" aria-hidden=\"true\"></i> Bonus Challenge 2: Plot Precipitation and Stream Discharge In One Figure \n",
        "\n",
        "For this challenge, you will open up the precipitation dataset also \n",
        "found in the `colorado-flood` download, and plot it side by side with \n",
        "discharge to see how they interact. For this challenge, you need to:\n",
        "\n",
        "### Precipitation Data Processing\n",
        "\n",
        "1. Open the precipitation data that you used previously (`colorado-flood/precipitation/805325-precip-daily-2003-2013.csv`) using **Pandas**.\n",
        "  * Make sure the date column (\"DATE\") is set as the index.\n",
        "  * Set the `na_values` to `999.99`. \n",
        "2. Subset the precipitation data to the time period 2013. \n",
        "3. Resample the precipitation data to provide a weekly `sum()` of all values instead of hourly. \n",
        "\n",
        "### Stream Discharge Data Processing\n",
        "\n",
        "1. Subset the stream discharge data to the time period 2013.\n",
        "2. Resample the discharge data provide a the weekly maximum values instead of daily. \n",
        "\n",
        "*******\n",
        "### Plot Your Data In One Figure\n",
        "\n",
        "* Plot the precipitation data and the discharge data as **scatter plots** stacked one on top of each other so you can compare the two visually. \n",
        "*  Format your plots  with titles, x and y axis labels. Make sure the dates are easy to read.\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YYQ8aZlDdBt1"
      },
      "source": [
        "# Define relative path to file with daily precipitation data\n",
        "precipitation_path = os.path.join(\"colorado-flood\",\n",
        "                                  \"precipitation\",\n",
        "                                  \"805325-precip-daily-2003-2013.csv\")\n",
        "\n",
        "# Open the data with pandas and modify it as instructed\n",
        "boulder_precip_2003_2013 = pd.read_csv(precipitation_path,\n",
        "                                       # Make sure the dates import in datetime format\n",
        "                                       parse_dates=[\"DATE\"],\n",
        "                                       # Set DATE as the index so you can subset data by time period\n",
        "                                       index_col=[\"DATE\"],\n",
        "                                       # Mask no data values so they are not plotted / used in analysis\n",
        "                                       na_values=['999.99'])\n",
        "\n",
        "# Subset the precipitation data to 2013\n",
        "boulder_precip_2013 = boulder_precip_2003_2013[\"2013\"]\n",
        "\n",
        "# Resample the 2013 precipitation data to weekly sum\n",
        "boulder_precip_2013_weekly_sum = boulder_precip_2013.resample(\"W\").sum()\n",
        "\n",
        "# Subset the discharge data to 2013\n",
        "boulder_discharge_2013 = boulder_discharge_1986_2013[\"2013\"]\n",
        "\n",
        "# Resample the 2013 discharge data to weekly max\n",
        "boulder_discharge_2013_weekly_max = boulder_discharge_2013.resample(\"W\").max()\n",
        "\n",
        "print(boulder_precip_2013_weekly_sum.head())\n",
        "print(boulder_discharge_2013_weekly_max.head())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ixMm9sMdBt2"
      },
      "source": [
        "# Plot the data\n",
        "fig, (ax1, ax2) = plt.subplots(2, 1, figsize = (10, 8))\n",
        "\n",
        "# Plot the precipitation values\n",
        "ax1.scatter(boulder_precip_2013_weekly_sum.index.values,\n",
        "            boulder_precip_2013_weekly_sum[\"HPCP\"],\n",
        "            color = \"springgreen\")\n",
        "\n",
        "# Set the title and axis labels for precipitation plot\n",
        "ax1.set(title = \"Total Weekly Preciptation (inches) - Boulder, CO - 2013\",\n",
        "        xlabel = \"Date\",\n",
        "        ylabel = \"Precip. (inches)\")\n",
        "\n",
        "# Plot the discharge values \n",
        "ax2.scatter(boulder_discharge_2013_weekly_max.index.values,\n",
        "            boulder_discharge_2013_weekly_max[\"disValue\"],\n",
        "            color = \"slateblue\")\n",
        "\n",
        "# Set title and axis labels for discharge plot\n",
        "ax2.set(title = \"Weekly Max. Discharge (cfs) - Boulder Creek - Boulder, CO - 2013\",\n",
        "        xlabel = \"Date\",\n",
        "        ylabel = \"Discharge (cfs)\")\n",
        "\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "igbqEdyZdBt2"
      },
      "source": [
        "<div class=\"notice--warning alert alert-info\" markdown=\"1\">\n",
        "\n",
        "## <i class=\"fa fa-pencil-square-o\" aria-hidden=\"true\"></i> Bonus Challenge 2b: Explore the Data \n",
        "\n",
        "Look at the two plots above. Do you notice any patterns between the max precipitation\n",
        "values and the max stream discharge values?\n",
        "\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VhashLQbdBt2"
      },
      "source": [
        "*List your answers to the above questions in this cell*"
      ]
    }
  ]
}