{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "c4IvRKVyvJwu",
        "oE_a6wVuMVmu",
        "mfL77XTyMrqZ",
        "1KkMPJ0TG3lx"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **MATE Floats! Coding Notebook** - Day 3\n",
        "\n",
        "Created by Ethan C. Campbell for NCAT/MATE/GO-BGC Marine Technology Summer Program\n",
        "\n",
        "Wednesday, August 23, 2023"
      ],
      "metadata": {
        "id": "OxvLAQ1SWpeR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-9O6SthNqtT8"
      },
      "outputs": [],
      "source": [
        "import numpy as np                         # NumPy is an array and math library\n",
        "import matplotlib.pyplot as plt            # Matplotlib is a visualization (plotting) library\n",
        "import pandas as pd                        # Pandas lets us work with spreadsheet (.csv) data\n",
        "from datetime import datetime, timedelta   # Datetime helps us work with dates and times"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Day 3, Part 1: `datetime` objects"
      ],
      "metadata": {
        "id": "c4IvRKVyvJwu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**How do we track the passage of time in a data set?**\n",
        "\n",
        "One option is to count the **time elapsed** since some starting time. For example, we might count the number of seconds, minutes, hours, or days. Instead of only using whole numbers (e.g., 1 hour, 2 hours, 3 hours, 4 hours, etc.), we usually use **fractional times** (units with decimals, like 0.75 hours, 1.0 hours, 1.25 hours, 1.5 hours, etc.).\n",
        "\n",
        "As an alternative, we may want to simply track the dates and times themselves. After all, it is important to know what date and what time of day a measurement was taken.\n",
        "\n",
        "For this, we use the **`datetime`** package in Python. We have already imported it above using:\n",
        "\n",
        "> **`from datetime import datetime, timedelta`**"
      ],
      "metadata": {
        "id": "r7LbUikrvUvF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`datetime` allows us to create a new type of variable called a **`datetime` object**. To do this, we use the following function syntax:\n",
        "\n",
        "> **`datetime(YEAR,MONTH,DAY,HOUR,MINUTE,SECOND,MICROSECOND)`**\n",
        "\n",
        "For example:"
      ],
      "metadata": {
        "id": "zRtGbU7iwlWs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "current_dt = datetime(2023,8,23,12,0,0,0)  # This is 8/23/23 at 12:00:00.0p\n",
        "current_dt = datetime(2023,8,23,12)        # Note: this gives the same result\n",
        "\n",
        "print(current_dt)"
      ],
      "metadata": {
        "id": "2f19URekvNpt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "880fed6a-cf4e-416c-c539-3b7b4e67f805"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-08-23 12:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To retrieve part of a datetime from a `datetime` object called `dt`, you can use the following syntax:\n",
        "\n",
        "```\n",
        "dt.year\n",
        "dt.month\n",
        "dt.day\n",
        "dt.hour\n",
        "dt.minute\n",
        "dt.second\n",
        "dt.microsecond\n",
        "```\n",
        "\n",
        "For example:"
      ],
      "metadata": {
        "id": "y34p-U51xrsg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(current_dt.year)"
      ],
      "metadata": {
        "id": "Rni5VdTKyIWm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3e5ac63-0cb9-42fd-8ce6-8969f0e39299"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Try creating your own datetime object. What happens when you subtract one datetime from another?***"
      ],
      "metadata": {
        "id": "UyvOBvlUycq1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code here:\n",
        "new_dt = datetime(2023,8,24,current_dt.hour,0)\n",
        "print(new_dt - current_dt)"
      ],
      "metadata": {
        "id": "tx1cYRfcyhUg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4850608-a927-4bdf-8ac3-5b2e711f9b34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 day, 0:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The great thing about `datetime` objects is that you can use them just like numbers:\n",
        "* You can add and subtract them.\n",
        "* You can put them in lists and arrays.\n",
        "* `Matplotlib` knows to treat datetimes like numbers in plots."
      ],
      "metadata": {
        "id": "aCycWJMFyKyE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Day 3, Part 2: Loading sensor time series data"
      ],
      "metadata": {
        "id": "oE_a6wVuMVmu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Up until now, we've been using data that we've typed directly into Python. However, most real-world data is stored in files that we'd like to open using Python.\n",
        "\n",
        "The most common type of data file is a **spreadsheet**, which has rows and columns. Generally, the columns will have column labels.\n",
        "\n",
        "Spreadsheets are often stored in **comma-separated value (CSV)** format, with the file extension being `.csv`. Data files in this format can be opened using Microsoft Excel or Google Sheets, as well as Python.\n",
        "\n",
        "In Python, we use the `pandas` package to work with spreadsheet data. We imported the package earlier using:\n",
        "\n",
        "> `import pandas as pd`\n",
        "\n",
        "Just like NumPy has arrays, Pandas has two types of objects: `Series` and `DataFrame`. This is what they look like:\n",
        "![Pandas example.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "-ds8ZIbDL1zy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "For now, we'll just be applying simple operations to read spreadsheet data using `pandas`. But if you would like to learn more, check out these [lesson slides](https://ethan-campbell.github.io/OCEAN_215/materials/lessons/lesson_9.pdf)."
      ],
      "metadata": {
        "id": "jMxncH-WL1z1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see how we can visualize data from the sensors that you built.\n",
        "\n",
        "***First, download two sample data files from Google Drive here:*** https://drive.google.com/drive/folders/18c42CtHgthenSEoPP9WKHEVrC1jn1Dr8?usp=drive_link. They should be named:\n",
        "* `temp_data_0m.csv` (data from 0 meters depth)\n",
        "* `temp_data_5m.csv` (data from 5 meters depth)\n",
        "\n",
        "Next, we can upload the files to this Google Colab notebook. ***Click the sidebar folder icon on the left, then use the page-with-arrow icon at the top to select the files and upload them.*** NOTE: uploaded files will be deleted from Google Colab when you refresh this notebook!\n",
        "\n",
        "We will specify each **filepath** using string variables:"
      ],
      "metadata": {
        "id": "vqsRjtMRu6h_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filepath_0m = '/content/temp_data_0m.csv'\n",
        "filepath_5m = '/content/temp_data_5m.csv'"
      ],
      "metadata": {
        "id": "KPV_riICMNLU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, we can load the files using `pandas`:\n",
        "\n",
        "> **`pd.read_csv(FILEPATH, ARGUMENTS...)`**\n",
        "\n",
        "This function is very customizable using the many optional `ARGUMENTS`, which allow it to handle almost any file. You can find documentation about the arguments [at this link](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html).\n",
        "\n",
        "***Let's first take a look at the data file using a simple text editor. Notice the long header. What argument can we use to exclude the header from being loaded?***\n",
        "\n",
        "Below, we'll load a data file using ``pd.read_csv()`` and store the data into a new variable.\n",
        "\n",
        "We can look at the data using **`display()`** (which is a fancy version of `print()` for DataFrames):"
      ],
      "metadata": {
        "id": "4a4_4izzMJbi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_0m = pd.read_csv(filepath_0m)\n",
        "\n",
        "display(data_0m)"
      ],
      "metadata": {
        "id": "rE3uBvQx2Fly",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "c976929b-c91e-438a-8ba7-0b2f717896f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     7/28/2023 11:47  1  28af1394   21.375\n",
              "0    7/28/2023 11:48  1  28af1394  21.3750\n",
              "1    7/28/2023 11:49  1  28af1394  21.3750\n",
              "2    7/28/2023 11:50  1  28af1394  21.3750\n",
              "3    7/28/2023 11:51  1  28af1394  21.5000\n",
              "4    7/28/2023 11:52  1  28af1394  21.4375\n",
              "..               ... ..       ...      ...\n",
              "105  7/28/2023 13:38  1  28af1394  21.8750\n",
              "106  7/28/2023 13:39  1  28af1394  21.8750\n",
              "107  7/28/2023 13:40  1  28af1394  21.9375\n",
              "108  7/28/2023 13:41  1  28af1394  21.8750\n",
              "109  7/28/2023 13:42  1  28af1394  21.8750\n",
              "\n",
              "[110 rows x 4 columns]"
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>7/28/2023 11:47</th>\n",
              "      <th>1</th>\n",
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              "      <th>21.375</th>\n",
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              "      <th>0</th>\n",
              "      <td>7/28/2023 11:48</td>\n",
              "      <td>1</td>\n",
              "      <td>28af1394</td>\n",
              "      <td>21.3750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7/28/2023 11:49</td>\n",
              "      <td>1</td>\n",
              "      <td>28af1394</td>\n",
              "      <td>21.3750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7/28/2023 11:50</td>\n",
              "      <td>1</td>\n",
              "      <td>28af1394</td>\n",
              "      <td>21.3750</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>7/28/2023 11:51</td>\n",
              "      <td>1</td>\n",
              "      <td>28af1394</td>\n",
              "      <td>21.5000</td>\n",
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              "      <td>7/28/2023 11:52</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>28af1394</td>\n",
              "      <td>21.8750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>7/28/2023 13:39</td>\n",
              "      <td>1</td>\n",
              "      <td>28af1394</td>\n",
              "      <td>21.8750</td>\n",
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              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>7/28/2023 13:40</td>\n",
              "      <td>1</td>\n",
              "      <td>28af1394</td>\n",
              "      <td>21.9375</td>\n",
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              "      <th>108</th>\n",
              "      <td>7/28/2023 13:41</td>\n",
              "      <td>1</td>\n",
              "      <td>28af1394</td>\n",
              "      <td>21.8750</td>\n",
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              "      <th>109</th>\n",
              "      <td>7/28/2023 13:42</td>\n",
              "      <td>1</td>\n",
              "      <td>28af1394</td>\n",
              "      <td>21.8750</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0a19ffcb-3646-45c5-b212-5f8134e9f91e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***What do you notice?***\n",
        "\n",
        "It appears that we'll need to specify a few additional arguments within `pd.read_csv()`.\n",
        "\n",
        "* `index_col`: this argument accepts an integer (e.g., 0) and tells `pandas` to convert that column into an index\n",
        "* `header`: this argument specifies which line of the file to use for column labels (default: `header=0`), or use `header=None` if there are no labels\n",
        "* `names`: to specify column labels, give a list of strings with each label\n",
        "* `parse_dates`: to tell Python to translate certain column(s) into `datetime` objects, give it those column names or indices inside a list\n",
        "\n",
        "Remember that you can consult the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html) if you need more information.\n",
        "\n",
        "Below, we will load in the two data files using `pd.read_csv()` with the function arguments above:"
      ],
      "metadata": {
        "id": "4tw33xD32AB0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_0m = pd.read_csv(filepath_0m,index_col=0,header=None,names=['Datetime','ID','Serial','Temp'],parse_dates=['Datetime'])\n",
        "data_5m = pd.read_csv(filepath_5m,index_col=0,header=None,names=['Datetime','ID','Serial','Temp'],parse_dates=['Datetime'])\n",
        "\n",
        "display(data_5m)"
      ],
      "metadata": {
        "id": "xzlsdqo4MbHp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1199b0e7-69b5-44c0-a862-bf84574dd826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                     ID    Serial     Temp\n",
              "Datetime                                  \n",
              "2023-07-28 10:48:00   1  28fc3097  19.8125\n",
              "2023-07-28 11:06:00   1  28fc3097  19.5625\n",
              "2023-07-28 11:08:00   1  28fc3097  19.5625\n",
              "2023-07-28 11:09:00   1  28fc3097  19.5000\n",
              "2023-07-28 11:10:00   1  28fc3097  19.5000\n",
              "2023-07-28 11:11:00   1  28fc3097  19.5625\n",
              "2023-07-28 11:14:00   1  28fc3097  19.6250\n",
              "2023-07-28 11:19:00   1  28fc3097  20.3750\n",
              "2023-07-28 11:31:00   1  28fc3097  19.7500\n",
              "2023-07-28 11:36:00   1  28fc3097  19.5000\n",
              "2023-07-28 11:40:00   1  28fc3097  19.6250\n",
              "2023-07-28 11:45:00   1  28fc3097  20.3125\n",
              "2023-07-28 11:50:00   1  28fc3097  20.9375\n",
              "2023-07-28 11:55:00   1  28fc3097  21.0000\n",
              "2023-07-28 12:00:00   1  28fc3097  21.0000\n",
              "2023-07-28 12:05:00   1  28fc3097  21.0000\n",
              "2023-07-28 12:10:00   1  28fc3097  20.9375\n",
              "2023-07-28 12:15:00   1  28fc3097  21.2500\n",
              "2023-07-28 12:20:00   1  28fc3097  21.1875\n",
              "2023-07-28 12:25:00   1  28fc3097  21.0000\n",
              "2023-07-28 12:30:00   1  28fc3097  21.1875\n",
              "2023-07-28 12:35:00   1  28fc3097  20.8750\n",
              "2023-07-28 12:40:00   1  28fc3097  21.1250\n",
              "2023-07-28 12:45:00   1  28fc3097  20.9375\n",
              "2023-07-28 12:50:00   1  28fc3097  21.1250\n",
              "2023-07-28 12:54:00   1  28fc3097  21.2500\n",
              "2023-07-28 12:59:00   1  28fc3097  21.2500\n",
              "2023-07-28 13:04:00   1  28fc3097  21.2500\n",
              "2023-07-28 13:09:00   1  28fc3097  21.1875\n",
              "2023-07-28 13:14:00   1  28fc3097  21.1875\n",
              "2023-07-28 13:19:00   1  28fc3097  21.1875\n",
              "2023-07-28 13:24:00   1  28fc3097  21.1875\n",
              "2023-07-28 13:29:00   1  28fc3097  21.1875\n",
              "2023-07-28 13:34:00   1  28fc3097  21.1875\n",
              "2023-07-28 13:39:00   1  28fc3097  21.2500\n",
              "2023-07-28 13:44:00   1  28fc3097  21.2500\n",
              "2023-07-28 13:49:00   1  28fc3097  22.7500"
            ],
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-1179e411-cff6-4f17-9123-c338785f5fe3 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-1179e411-cff6-4f17-9123-c338785f5fe3');\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",
              "\n",
              "\n",
              "<div id=\"df-948a1c2b-3f92-4e24-a14c-3a28a6e9aaa1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-948a1c2b-3f92-4e24-a14c-3a28a6e9aaa1')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-948a1c2b-3f92-4e24-a14c-3a28a6e9aaa1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Day 3, Part 3: Plotting sensor time series data"
      ],
      "metadata": {
        "id": "mfL77XTyMrqZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data in a `pandas` DataFrame is similar to a NumPy 2-D array, except we use **column labels** to refer to columns and **index** values to refer to rows.\n",
        "\n",
        "To retrieve a specific column, we use bracket notation: **`data_frame[COLUMN_LABEL]`**."
      ],
      "metadata": {
        "id": "4HpB3WGDMaUu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# For example:\n",
        "data_0m['Temp']"
      ],
      "metadata": {
        "id": "DXx1Vu-KMaUv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0ada16a-24aa-4ec6-e073-f44cda054162"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Datetime\n",
              "2023-07-28 11:47:00    21.3750\n",
              "2023-07-28 11:48:00    21.3750\n",
              "2023-07-28 11:49:00    21.3750\n",
              "2023-07-28 11:50:00    21.3750\n",
              "2023-07-28 11:51:00    21.5000\n",
              "                        ...   \n",
              "2023-07-28 13:38:00    21.8750\n",
              "2023-07-28 13:39:00    21.8750\n",
              "2023-07-28 13:40:00    21.9375\n",
              "2023-07-28 13:41:00    21.8750\n",
              "2023-07-28 13:42:00    21.8750\n",
              "Name: Temp, Length: 111, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_0m.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8p6GWywCVs1v",
        "outputId": "f0b52ad0-7161-451a-e819-eee04215b694"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2023-07-28 11:47:00', '2023-07-28 11:48:00',\n",
              "               '2023-07-28 11:49:00', '2023-07-28 11:50:00',\n",
              "               '2023-07-28 11:51:00', '2023-07-28 11:52:00',\n",
              "               '2023-07-28 11:53:00', '2023-07-28 11:54:00',\n",
              "               '2023-07-28 11:55:00', '2023-07-28 11:56:00',\n",
              "               ...\n",
              "               '2023-07-28 13:33:00', '2023-07-28 13:34:00',\n",
              "               '2023-07-28 13:35:00', '2023-07-28 13:36:00',\n",
              "               '2023-07-28 13:37:00', '2023-07-28 13:38:00',\n",
              "               '2023-07-28 13:39:00', '2023-07-28 13:40:00',\n",
              "               '2023-07-28 13:41:00', '2023-07-28 13:42:00'],\n",
              "              dtype='datetime64[ns]', name='Datetime', length=111, freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Using the data variables for each of the two files, try plotting the two time series (time vs. temperature) using `plt.plot()`.***\n",
        "\n",
        "IMPORTANT: instead of referencing the datetime column using `data_0m['Datetime']`, we'll have to reference it using `data_0m.index` since we designated it as the index."
      ],
      "metadata": {
        "id": "cXGIFkQx4jQq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code below:\n",
        "plt.plot(data_0m.index,data_0m['Temp'])"
      ],
      "metadata": {
        "id": "toiPuz2sM3xi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "2e3b6497-0289-4b12-ab9e-46a20cca0b5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ca1c4161810>]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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gQZg0aRL+/e9/49ixY8jPz/f7+AMGDECvXr1w8uRJ2ed1Oh3i4uLcHkRdjedaYACHwIiI/BXQW0VBEPDYY48hPz8fW7ZsQXp6ul+fIwiCW8amNefPn0dlZSWSk5MDOT2iLsVZA6SRtokzw4iIqGUBZYBycnLwwQcfYNWqVdDr9SgvL0d5eTkaGhoA2Ieu8vLysGfPHpw9exbbt2/HXXfdhejoaNx6663ScTIzM6WMUG1tLZ544gns3LkTJSUl2LhxI+bMmYOMjAxMnz69DS+VqHNxLoXhnPVlYQREROSXgAKg5cuXw2g0Ijs7G8nJydLj448/BgBERUXhu+++w6233oqMjAzcc8890Ov12L59OwwGg3ScoqIiaQaZRqPBgQMHMHv2bAwePBgPPfQQRo0ahe+++w46na4NL5WocxGnwWtdAiD2AiIi8k/AQ2AtSUlJwZdffhnQcaKjo7F+/fpAToOI4MwAadRqaNUqWGwCh8CIiPzEtcCIFEpcC0yjUkGttmeBmAEiIvIPAyAihRJ7/qjV9iAIAKxWBkBERP5gAESkUOI0eI1aBQ0zQEREAWEARKRQYg2Q1jUAYh8gIiK/MAAiUigx2FGrnAGQjRkgIiK/MAAiUijnLDAV1I4aIAtrgIiI/MIAiEihxHofewbIvo0ZICIi/zAAIlIoseePVqOCVm2/lVkDRETkHwZARAolZoDsfYDctxERUcsYABEpkCAIziJotcrZB4gZICIivzAAIlIg1zjHrRM0AyAiIr8wACJSINdAR6NRQStOg2cARETkFwZARArkOttLo3JOg2cNEBGRfxgAESmQxTUD5NIJ2sIMEBGRXxgAESmQ6xCYWydoBkBERH5hAESkQK6BDtcCIyIKHAMgIgVyrfVxnQbPTtBERP5hAESkQK7rgAGQpsGzBoiIyD8MgIgUSAqAHJkfNkIkIgoMAyAiBfLMAGk1HAIjIgoEAyAiBRIDHWkITMoAheyUiIgUhQEQkQKJtT6O+MdlFhgjICIifzAAIlIgm2cRNDNAREQBYQBEpEBWaQjMfguLa4FxKQwiIv8wACJSIGcRNBz/shM0EVEgGAARKZDnNHj2ASIiCgwDICIFEgMgMfBxzIJnBoiIyE8MgIgUSJwGL9b+iLVArAEiIvIPAyAiBRJne0kZILW4nQEQEZE/GAARKZDF0e9HWgqDq8ETEQWEARCRAon9Dr37ADEAIiLyBwMgIgWyeiyFIdYCcS0wIiL/MAAiUiCvTtAcAiMiCggDICIFcq4FJk6DZwBERBQIBkBECmT1yACxCJqIKDAMgIgUyCb4CIBYA0RE5BcGQEQK5LkUBtcCIyIKDAMgIgXyHAITa4G4FhgRkX8YABEpkNdaYJwGT0QUEAZARApk9VoLjEXQRESBYABEpEA2z2nwUgAUslMiIlIUBkBECmSRaoDsHzv7ADECIiLyBwMgIgXynAYvdYLmCBgRkV8CCoDy8vIwevRo6PV6GAwGzJ07F0VFRW77PPzwwxg4cCCio6PRu3dvzJkzB8eOHWvxuIIg4Nlnn0VycjKio6MxdepUnDhxIvCrIeoinLPA7LewltPgiYgCElAAVFBQgJycHOzcuRMbNmxAc3Mzpk2bhrq6OmmfUaNGYcWKFTh69CjWr18PQRAwbdo0WK1Wn8d9+eWX8cYbb+Dtt99GYWEhYmNjMX36dDQ2NgZ/ZUSdmLMPkP1jrgVGRBQYbSA7r1u3zu3jlStXwmAwYM+ePZg0aRIAYOHChdLz/fv3x4svvoiRI0eipKQEAwcO9DqmIAh4/fXXsWTJEsyZMwcA8P777yMxMRGrV6/G/PnzA74oos7Oaxp8B/UBulxrRmOzFak9Ytr16xARtberqgEyGo0AgISEBNnn6+rqsGLFCqSnpyMtLU12n+LiYpSXl2Pq1KnStvj4eIwdOxY7duyQ/Ryz2QyTyeT2IOpKxGnwzk7Q9u3t3Qfojre2Y/pr36K+ydKuX4eIqL0FHQDZbDYsWrQIEyZMQFZWlttzb731Frp164Zu3brhq6++woYNGxAZGSl7nPLycgBAYmKi2/bExETpOU95eXmIj4+XHr6CK6LOSqz10WrEAMh+K7fnEJjNJuBsVT3qmqyorG1qt69DRNQRgg6AcnJycOjQIXz00Udez91///3Yt28fCgoKMHjwYNx9991tWs+zePFiGI1G6XHu3Lk2OzaREoj9ftQdmAFqcmkyZLZwuj0RKVtANUCi3NxcrF27Ft9++y1SU1O9nhczM4MGDcKNN96IHj16ID8/H/fee6/XvklJSQCAixcvIjk5Wdp+8eJFXHvttbJfX6fTQafTBXPqRJ2C2O/Hay2wdpwH7xr0NDEAIiKFCygDJAgCcnNzkZ+fj02bNiE9Pd2vzxEEAWazWfb59PR0JCUlYePGjdI2k8mEwsJCjBs3LpDTI+oyxBogr07Q7ZgBMlussv8nIlKigAKgnJwcfPDBB1i1ahX0ej3Ky8tRXl6OhoYGAMDp06eRl5eHPXv24OzZs9i+fTvuuusuREdH49Zbb5WOk5mZifz8fACASqXCokWL8OKLL2LNmjU4ePAgHnzwQaSkpGDu3Lltd6VEnYg4GqXxmAXWnn2AzM3MABFR5xHQENjy5csBANnZ2W7bV6xYgQULFiAqKgrfffcdXn/9dVy5cgWJiYmYNGkStm/fDoPBIO1fVFQkzSADgCeffBJ1dXVYuHAhqqurMXHiRKxbtw5RUVFXcWlEnZfN12Ko7ZoBYg0QEXUeAQVAQit/XFNSUvDll18GfByVSoVly5Zh2bJlgZwOUZcl1vqoO3A1eNdhL2aAiEjpuBYYkQLZPPoAdUQnaGaAiKgzYQBEpEC+OkG3awDkWgPUwtI2RERKwACISIGsHjVA0mKoHdUHqJkZICJSNgZARApktYqrwbsPgbXnWmDmZpcaICsDICJSNgZARArkqw9Qu06DtzADRESdBwMgIgUSAx1xCQwxEOqoafDMABGR0jEAIlIgaTV4xyKoUg1QO8Ylbp2gm1kETUTKxgCISIHEWh/HYvDSEJilHSMg12EvMzNARKRwDICIFMg5BOa+GGp7xiWsASKizoQBEJECefUB6ohp8KwBIqJOhAEQkQL5XAuMS2EQEfmFARCRAok1QJ7T4LkUBhGRfxgAESmQ1aMGqEOWwnDLAHEWGBEpGwMgIgWSFkOVOkHbt7drH6BmZoCIqPNgAESkQJ4ZIK0jAuqoTtCsASIipWMARKRAUgCkcs8AtetaYK6NEBkAEZHCMQAiUiCvafCOQAhovywQM0BE1JkwACJSIMdi8FLgIw6F2Z9rnwCoyW0WGIugiUjZGAARKZDUCVojEwAxA0RE1CoGQEQKZPGoAeqYAIg1QETUeTAAIlIgX2uBAe03BOY6DZ4ZICJSOgZARAokBjmenaCBjimCZgaIiJSOARCRAolBjlbjPQusI4bAmAEiIqVjAESkQJ5rganVKogxUIcUQVtt7dp0kYiovTEAIlIgz07QgMt6YB0wDR6wB0FERErFAIioFUfLTHhm9SFcqjGH+lQk0lpgLkNf6nZeEd6z7ocBEBEpGQMgola8u7UY/7fzDD7/oTTUpyKRywBpHf+3tUNcYrHavAIr11lhRERKwwCIqBV1Zovbv+GgpSEwSztEQHKzvpgBIiIlYwBE1IrGZvvsp8YwWv5BrPPRuNzB4hCYrR1qgFwDoG46rX1bc/h8P4iIAsUAiKgV4ot/OA35WD1mgQHObFB7JGbEKfARGhWiIjQAmAEiImVjAETUCikACqPeN1IfILXzFta0YxG0GPzptBrotGq3bURESsQAiKgV0hBYGA35SH2AXO5gaRp8OwRAYrYnUquWAiBmgIhIyRgAEbUiLDNAgkwRtLr9+gA5M0BqRDIDRESdAAMgolaI9S/mcCqCtsn1AXJ/ri2J165zywCFz/eDiChQDICIWiFmOhrDJOMhCALEGMe9D5D9dm7PWWA6rYYZICLqFBgAEbVCrP0JlwyQa4bHNQAS/2uxtmMGKEINnZazwIhI+RgAEbUi3GqAXGt81DI1QO2SAWINEBF1MgyAiFogCELY9QFybfTsVgPUjrPA3IbAHN0XzcwAEZGCMQAiaoFr1idcOkG7ZoDcaoA07TcLTFwJPlKrhi5CzACFx/eDiCgYDICIWuAaAIVLBshXDZDUB6g9a4C0aikDxBogIlIyBkBELXAtfA6bGiDXAMhtGnw79gGyOGuAxAxQU5h8P4iIgsEAiKgFrlmfcBnycQ2A1DIZIFu71wBp3LYRESlRQAFQXl4eRo8eDb1eD4PBgLlz56KoqEh6vqqqCo899hiGDBmC6Oho9O3bF7/+9a9hNBpbPO6CBQugUqncHjNmzAjuiojaUDhmgMRZXlqX4Ado707QLtPgmQEiok4goACooKAAOTk52LlzJzZs2IDm5mZMmzYNdXV1AIDS0lKUlpbilVdewaFDh7By5UqsW7cODz30UKvHnjFjBsrKyqTHhx9+GNwVEbUh1+aHTVZbu2RXAiWtBO8rAGrXDJCzBihc+iIREQVDG8jO69atc/t45cqVMBgM2LNnDyZNmoSsrCx8+umn0vMDBw7EH/7wBzzwwAOwWCzQan1/OZ1Oh6SkpABPn6h9eWZ9zBYboiM1ITobO7llMICOCoA0zAARUadwVTVA4tBWQkJCi/vExcW1GPwAwJYtW2AwGDBkyBA8+uijqKysvJpTI2oTnnU/4ZD1kAIgjwxQ+/YB8p4FFi5DgkREwQgoA+TKZrNh0aJFmDBhArKysmT3uXz5Ml544QUsXLiwxWPNmDEDd9xxB9LT03Hq1Cn8/ve/x8yZM7Fjxw5oNN7vts1mM8xms/SxyWQK9jKIWiSXAQo1q8xK8ICzJqg91wKz9wFyLIURBt8LIqJgBR0A5eTk4NChQ9i6davs8yaTCbfddhuGDh2KpUuXtnis+fPnS/8fPnw4RowYgYEDB2LLli2YMmWK1/55eXl4/vnngz11Ir95ZnzCoReQzVcGSBoCa/uv6TYNnhkgIuoEghoCy83Nxdq1a7F582akpqZ6PV9TU4MZM2ZAr9cjPz8fERERAR1/wIAB6NWrF06ePCn7/OLFi2E0GqXHuXPngrkMolZ5rgAfDt2gLWIRtGcNkDQE1vaBibQWWARrgIiocwgoAyQIAh577DHk5+djy5YtSE9P99rHZDJh+vTp0Ol0WLNmDaKiogI+qfPnz6OyshLJycmyz+t0Ouh0uoCPSxSocMwAOWuA3Le3bxG0XA1Q6INBIqJgBZQBysnJwQcffIBVq1ZBr9ejvLwc5eXlaGhoAGAPfsRp8e+++y5MJpO0j9Xq/GOZmZmJ/Px8AEBtbS2eeOIJ7Ny5EyUlJdi4cSPmzJmDjIwMTJ8+vQ0vlShw3jVAoX/Rd/YBcr99nX2A2v5rchYYEXU2AWWAli9fDgDIzs52275ixQosWLAAe/fuRWFhIQAgIyPDbZ/i4mL0798fAFBUVCTNINNoNDhw4AD+93//F9XV1UhJScG0adPwwgsvMMtDIeeZ8fEcEgsFZx8g9+1iANS+naDV7ARNRJ1CwENgLcnOzm51H8/jREdHY/369YGcBlGHaQznafAq+WnwlvYIgFw6QUdqmQEiIuXjWmBELQjLafA+O0Hb/22PafDiyu86rQY6LWeBEZHyMQAiaoFnxsczIxQKVp9rgdlv53Ypgm529gGKZABERJ0AAyCiFnjW/ITDi744y91rGrzjbm7vtcB00hBY6INBIqJgMQAiaoH3NPjQv+hbHBGQZyNETUcthcEMEBF1AgyAiFoQjjVANh9LYUidoNtxKQxdhAY6rWMpDKvNr0kPREThiAEQUQvE2hdxtCk8psHb//W5FlgbZ4AEQZBmfLlmgAQBaG6PpkNERB2AARBRC8SlL/Q6e8eIsJ4G306doF2zXq41QIBzdhgRkdIwACJqgZgBiou2r2cXDkNgPqfBt1MfINcgR6fVSEthAOFRE0VEFAwGQEQtEDM+cVH2ACicpsF7ZoCkTtBtXJfj2g07QqOCWq2SgiBmgIhIqRgAEbVArPmJD6MMkFjjo9XIB0BtPwTmnAGmcgRd0kywMKiJIiIKBgMgohaIL/7hFABJQ2CeGSBVO2WAXAqgRVIvIGaAiEihGAARtUB88Y+LthdBh8UQmK3lafCWNp6ZJWZ5dBEaaRszQESkdAyAiFoQjkNgYg2Qdyfo9ukD5DoEJnJmgEIfEBIRBYMBEFELvIbAwigD5LkWWHv1AZIbAmMGiIiUjgEQUQucQ2DhkwHy2QlaXAqjjXsTOgMgmSEw1gARkUIxACLywbUDspgBCocaILHGx6sPkDQLrG2DEqkLdITrEJg9GGIGiIiUigEQkQ+u2R4xA9QUThkg9/inHTtB24M+1waI7ANERErHAIjIB9fshtgIMRyGwJyzwNxvX60UALXt15ObBSZmg8IhICQiCgYDICIfxMyHWgXE6uwv/uEwBCZ1gva4ezuyD5CYAQqHtdGIiILBAIjIB3EKfFSEBlFizUsYZDys1lb6ALVjJ2iRmA1iBoiIlIoBEJEPri/8UREat22h5LsPkP3f9psG7zILTMoAMQAiImViAETkg+sLv5j9aLYKbV5kHCibr07QqnYqgm6WmQXGGiAiUjgGQEQ+SBmgCLXbi3+os0BWH32AtI6i6LbuBC12e2YNEBF1JgyAiHyQaoC0Grfhn1D3vhFrfDQ+hsDaKwMUqWUGiIg6DwZARD64ZoA0ahUiHI13GkOc9ejwITCZGiAda4CISOEYABH5INW+ODIf4dL9WOzz46sTdNtPg+csMCLqfBgAEfkgZnrEGWBiABDqrIcY4Hguhqppt07QLfUBYgBERMrEAIjIB88MULhMhbc41vryngbf3rPA2AmaiDoPBkBEPnjWvoiBUGOYDIF51gBp2q0GiLPAiKjzYQBE5IO47IWY7YjUhseLvs8iaDED1ObT4L2HwMTvCYfAiEipGAAR+eCZAZKGwEKdAfLZB8hRBN1eQ2BunaDDZ2kQIqJgMAAi8sFz6EcaAgt1I0QffYDaby0wmSJoLWuAiEjZGAAR+WB2WQwVcBYBhzwD5AhwvKbBq9opAyQ3DZ4BEBEpHAMgIh8aPV74o8JkGrw0BOYe/zhngbV5HyDvtcDCpR6KiChYDICIfPBcBFTMAInF0aEiFUFr3G9f5zT4tv16cjVAUgaorb8YEVEHYQBE5IOvafChzgD5XgtMDIDa9vxkp8GL34sQDwcSEQWLARCRD2KmJyrCvQg61MM+zmnw7ts7dC0wx/+ZASIipWIAROSDz2nwYVID5KsTdBvHP1Khs2sNkI4ZICJSOAZARD74nAYf4hogMcOj1cj3AWrLDJDFapOG3GRngTEDREQKxQCIyAcx0+NcDDVMMkA2+QyQuh0CINcAJ1KmBshqE2AJ0yDoZEUtvjlyMdSnQURhigEQkQ+NXouhhsewj9XHUhjSWmBtOA3e9VojNa4ZIGc9ULhmgXJX7cUv3t+N/eeqQ30qRBSGGAAR+RCunaBtgq9O0PZ/2zIDJGa7tGoVtBrvDBAQ+oBQjqmxGcfKawAAe85cCfHZEFE4YgBE5EO4d4L2XgvMeTu3VTdouSnw4tcWa47CMQN0pNQk/f/wBWMIz4SIwlVAAVBeXh5Gjx4NvV4Pg8GAuXPnoqioSHq+qqoKjz32GIYMGYLo6Gj07dsXv/71r2E0tvwHSBAEPPvss0hOTkZ0dDSmTp2KEydOBHdFRG1EevGP8BgCC5e1wHwMgQFttx6Yswu0xuu5cO4FdMgl6DlUygCIiLwFFAAVFBQgJycHO3fuxIYNG9Dc3Ixp06ahrq4OAFBaWorS0lK88sorOHToEFauXIl169bhoYceavG4L7/8Mt544w28/fbbKCwsRGxsLKZPn47Gxsbgr4zoKnl2QJaKoEOdARLk1wJzSQBJw2RXq0lmIVSRcyZY+C2HcdglA3SyohYNTeF3jkQUWtpAdl63bp3bxytXroTBYMCePXswadIkZGVl4dNPP5WeHzhwIP7whz/ggQcegMVigVbr/eUEQcDrr7+OJUuWYM6cOQCA999/H4mJiVi9ejXmz58fzHURXTXnLLDwaoQojjj56gRt36d9h8AAZwaoMcwzQDYBOFpuwvV9e4TwjIgo3FxVDZA4tJWQkNDiPnFxcbLBDwAUFxejvLwcU6dOlbbFx8dj7Nix2LFjh+znmM1mmEwmtwdRW7LaBKm2xSsDFOJp8GJ9j9ZzCMw1AGqjDJCY7YpsIQAKtxqghiYrTl2qBQBckxwHwD0jREQEXEUAZLPZsGjRIkyYMAFZWVmy+1y+fBkvvPACFi5c6PM45eXlAIDExES37YmJidJznvLy8hAfHy890tLSgrwKInlNLkGO1zT4kK8FZv/6nkNgrhkhq7WNa4C03jVA0nIYIf5+eDpaboJNAHp10+GWzN4AWAhNRN6CDoBycnJw6NAhfPTRR7LPm0wm3HbbbRg6dCiWLl0a7JeRtXjxYhiNRulx7ty5Nj0+keswl3MafJisBu+IbbyKoNsjA9TSEJgmPAJCT2Kwk9UnDlkp8QBYCE1E3oIKgHJzc7F27Vps3rwZqampXs/X1NRgxowZ0Ov1yM/PR0REhM9jJSUlAQAuXnTv2Hrx4kXpOU86nQ5xcXFuD6K2JNf/RhcmGSBfnaBVKhXETW03Dd57HTCRuC3cMkCHLtiHu7JS4pHVxx4AFZXXhN15ElFoBRQACYKA3Nxc5OfnY9OmTUhPT/fax2QyYdq0aYiMjMSaNWsQFRXV4jHT09ORlJSEjRs3uh2jsLAQ48aNC+T0iNqMmOVxzXxESbPAQl0ELV8D5LqtrWuA5IbAnBmg8JphJWZ7svrEIbVHNOKitGi2CjhRURPiMyOicBJQAJSTk4MPPvgAq1atgl6vR3l5OcrLy9HQ0ADAGfzU1dXh3XffhclkkvaxukyVzczMRH5+PgD7u9ZFixbhxRdfxJo1a3Dw4EE8+OCDSElJwdy5c9vuSokCINf/Rsx4NIZJBshzCAxwZoUsbVUDZG1hGnxE+NUANVlsOH7RHugMS4mHSqXCMMcw2OELLIQmIqeApsEvX74cAJCdne22fcWKFViwYAH27t2LwsJCAEBGRobbPsXFxejfvz8AoKioyK054pNPPom6ujosXLgQ1dXVmDhxItatW9dq9oiovUhdoGVWQBcXAHVdGqIjSX2AVN4BkBgUtVUfILNMJkwUjjVAxy/WoNkqID46Aqk9ogHYM0E7TlfiUKkRd4MTJojILqAASGjlj2p2dnar+8gdR6VSYdmyZVi2bFkgp0PUbhqlLtAuGSCXYSCzJXQBkK2FDJC0IGpb1wDJzQILwxqgw47hr2EpcVA5vhdiHdAhzgQjkmWx2vDAu4W4aDLjs0fHo0dsZKhPqUNwLTAiGeZm76Ef1/+HciaYmAGSDYA0bZwBsvjuA6QLwxogqQDaEfQAkIbAjpbVtOlCsUSdxapdZ7HzdBWKL9fhzxuOh/p0OgwDICIZZpkMkFqtCothH7HHT0sZoLZbC8z3EFi4Z4BE6b1iEROpQUOzFcWXa0N1akRh6UpdE1792hn0/LPwjNtiwp0ZAyAiGWYfa2A5l8MIYQAkZoBkaoDE5ohtNgTW7HsafDgEg66sNgFHyrwzQBq1SuoIfYiF0ERuXt1QBGNDMzKT9JgxLAk2AXj+88N+lbMoHQMgIhly0+ABZ0YolMM+Uh8gmbtXDIpsbRSTtFwDFF6zwE5fqkVjsw2xkRqk94x1ey4rRQyAWAdEJDpSasKqwrMAgOdmDcOS26+BTqtGYXEVvjhYFuKza38MgIhkOBdCdX/h14XBAqBifY9WJgLStHEfoJZWgw+3DJDY/+ea5DivZUKGiYXQ7AhNBMA+Gen5zw/DJgC3DU/GuIE9kdojBo9mDwQA/PGLo2hoCp/6vvYQ0Cwwoq7C1/RvqRt0CIugLS1lgKQhsLYJSlqsAQqD4UBXcgXQInFJjMOlJgiCIM0QI+oqKmvN+L6kCuJ7oxMVtSgsrkJUhBqLb82U9nt40kD8a/d5XKhuwLK1hzFpUG/pOZVKhRlZ8is0KBEDICIZjT6GfqJCvCK8IAjSHzC5GiBnANQ2X0+uIaQoUhtes8DEws2hLgXQokGJ3RCpVaOm0YJ956pxfd8eHX16RCFjsdpw/z8Kcazcuxv6I5MHIrVHjPRxdKQGv7/1GuSs2osPd53Dh7uca21GatU4/uLMDjnnjsAAiEiGr+JfqRt0iDJArsXN8p2gvfe7GtI0eJmeR2IAFC41QKcu2Wd4DU7Uez0XoVHj9uHJ+GzfBby49gj+/ch4r2Eyos7qn4Vncay8Bt10WgxNdr5BSO0RjUcmD/Ta/9bhSXh48gDsO1Pttj1C27nuGQZARDLErEaUVr4GKFQZINfaHrkASKwLavNO0HKLoWrDpwi6zmxBRY0ZALwKoEVPzczEusPl2Hu2Gqv3X8Ad13sv5EzU2VTVNeHVr4sAAE/PzMQDN/Zr9XNUKhUWz7ymvU8t5FgETSSj0VcGKMRDYK1mgNRt3QeohcVQw6gGqKSyDgCQEBuJ+JgI2X0S46KQe4t9iZ4/fXUMtWZLh50fUai8+nURTI0WXJMch3vH9A316YQVBkBEMnwV/0aF0RCY/Fpg9n9tbR4A+S6CDocMUPFlewDUv2dMi/s9NDEd/XrGoKLGjDc3n+yIUyMKmcOlRqzaZZ/mvnTWUNk3TV0ZAyAiGb6nwYc2A+Q6uatj1gJrYTHUMCqCLhEDoF7yw18inVaDJbcNBQC8+12x9HlEnY0gCHh+zREIAnD7iGSMHdAz1KcUdlgDRCSj9U7QIcoAudYAtTQLzLHfuap6fPz9OdwzOg1pCS1nRwBg+8nL+PxAqTTTrMJkr6uRmwUmZYB8TDmz2gQs33IS5680SNtUKhXmXpsi+8fYWN+MFduLMe+6PugnU8dTWt2Af+0+j4duSkc3nfufruLL9QCAAa0EQAAw9RoDJg3ujW+PX8KLXxzBP346Wna/j3adRZ8e0bjJZRowUVuxWG34+3fFGJkaj/EZvbyer2lsxjvfnsbtI1IwJMm7sN/T8Ys1eH9HCSyOpXKMDc3YVWKf5v77Wzt/PU8wGAARyXB2gvaYBu8IBELVCNHiSAGpVJCdxaTxWArjzc0n8dH35/DhrrP4+09vaHH69+VaMx7+YA9qGr1rY3rKrA4tZYB8fC9WFZ7BK197L6y47lAZNv9XNrrHuB9zyX8O4fMfSnHqUh3+cu91Xp/36tfH8ene8wCA30wd5PacuMZXaxkgwB6EPXv7UEx7rQDfHK1AubERSfFRbvscKTXh6c8OIjZSg73P/ki2BoroaqzcXoKX1h1DN50Wm/5rMgx699/BP3xxFB99fw6f/1CK9b+d1OLvYLPVhtxVe3H8ovdad49OzkBK9+g2P//OgAEQkQznEFh4ZYDEITC57A/grAsSA6AD5+2djyvrmnDvOzvx57uvxW0jkmU/95X1RahptGBwYjfMHpkibc8wdJPNHkmzwGQyQFfqmqTg587rU5Hey/75n+27gNOX6vDahuN4fk6WtH/h6Up8/kMpAODg+WrZ8/vBsf2AzPMllfYMUH8fM8A8ZRi6YXCiHsfKa3DogtErABK/Rl2TFYWnqzBpMLNA1HYu1ZjxP9+cAADUmi14eV0RXrlrpPT8wfNGfLzb3n+npLIe720tkTo0y/lg5xkcv1iLHjEReGhiurQ9PiYS80entdNVKB8DICIZZh8ZIGcn6NBOg/fVw0bMANkEAWaLFccv2hufjUlPwK7iKuSs2ouSyiH4VfZAt27Irn9w/zhvOG7on9DquehayAD9ecNxaYHFl+4cDq2jOvv6vj1w3z8K8UHhWdw3th+GJOlhtQlY+vkR6XNLKuthamxGXJRzNld9k0Xq8+O5nIWxoRlVdU0A7Cu/+2tYSrw9ACo1YurQRLfnXL/GpmMVDICoTf2/9cdQY7agb0IMzlbV4997zuOBG/vh2rTuEAQBSz8/DEEA+vWMwZnKevx10wnceX0fGOKivI5VWWvGaxvsbzb+a/oQ3D+29WnuZMciaCIZjT5rgEJdBC2uA9ZyAGS1CTheXguLTUD3mAh8+Msb8bMJ/QEA/299EZ769IA0e8v1D+7ca1P8Cn4A3zVAR0pN+GfhGQD2BRa1Lk0Ux2f0wsysJFhtgrTi9Ie7zuJomQlxUVr06qaTjuHqaJlJqku6aDKjoqZRek4sZDbodYjV+f+eLquP7xXiXbdtKarw+5hErfnhXDU+2W0fyn19/rW409GP6rk1h2GzCfjP/lLsOXMFMZEafLTwRlyb1h11TVb8ad0x2eO9uuE4TI0WDE2Ow/zRnOYeCAZARDLEDJDnLLCoEK8FJvb38TUEpnHpAyRmMbJS4qFRq/DcrGFYNmcY1Crgk93nsWDFLhjrm93+4D4dQPMzZw2Q83sht8Cip9/fal9xevupSnz8/Tm84mjS9viPBmNUv+4AvFdt9wxSDrsESMV+zgDzNExaH8z9a1msNhwtcx6/pLIepy9511YQBcpms7/ZAIA7ruuD6/v2wFMzhiA2UoMfzlXj/3aeQd5XRwEAOTdnIDk+GktnDwMAfLb3AvaeveJ2vEMXjPhQnOY+exinuQeIARCRDGkV9DBthOhzCMwRGNlsgvTCPqyPs/X9g+P6492fjkZspAbbT1XijuXb3P7getbCtESuBuiLg2UoLK6CTuu+wKKrtIQYPDxpAADg6c8Oorq+GYMTu+GBG/u5BCXuAY9nQHREJgDyZwaYK3HNsDJjIyprzdL2U5fqYLbYEBupwXhHALfpGLNAdPXy913AvrPViI3U4KmZ9vvDEBeFx6bYi/qfW3MYF01m9E2IkWp5rk3rjrtG2bNEzzuyRIDzzYYgALNGpmBMun+ZW3JiDRCRjFBNgzdbrPj1h/swvE88cm8Z5PW8uMSFr3d6apdp8NLq6Cnuq6PfnGnAvx4Zj4f+93ucumQPHlz/4PpLzAA1WwWMz9sIAKiqt9fieC6w6OmR7IH4157zKDPah7LEoTJxWMozKyMGRKP798D3JVfcAiKxC3SgGaBuOi0G9IrF6ct1OFxqkup8xK89NCUOt2QasP1UJTYXVeAXNw0I6PjU+bz6dRE+23sBQpBLzVQ6atVybxmERJd6np9N6I+Pdp2VivmX3HaNW/b5iRlD8NWhcvxw3ogb8zZCq1bBKgi4aDLbV3OfKf9mg1rGDBCRjFBNg995ugrrD1/EG5tOollmdpWYAfIVAIm1QU0W5zBOVp94r/2GpsRhdc4EDO8TD61ahednD/Ma7mtNXJQWyY6MUamxEaXGRjQ229CvZ4zsAouuYiK1eG7WUKhUwOyRKZjg6IMiBmsnK2rR0GT/GbgWc999g31Gi2uRsrMLdGABEAAMc3xvXI8nBo7DUuJxS6YBALCruIpLZ3Rxe85cwV82ncSF6gbp9z3Qh9liQ4ahG34+sb/bsXVaDZ6fkwWNWoWp1xjwI4+ifIM+Cr+bNhgAUFFjRqmxERcdPbp+PWUQp7kHiRkgIhmhmgYvZh+aLDaculSLzKQ4t+etrdQAiRmg4xdrYbbY0E2nRT8fDRAT46Lwn5wJuFLfhJ6O4uNAaDVqfP3bSShxNCEU9e8Vg+jI1oOpGVnJ2PH0FPTq5uwHZIiLQm+9DpdqzDhabsL1fXu4FXOLLwznqhpgrG9GXLTWOQTWO/AAKCslDp//UIrDLjVGUu1Un3gM6N0N/XvGoKSyHltPXMaMrKSAvwYpn81RtA8Ac65NwS8mBp8NHNA7Vranz+TBvbH96VvQIybSbYamaMH4/rhpUC80NDnfGEVFqJFh6Bb0uXR1DICIZDiXgPAxDb6daoDcXogvmHwHQK3UAIl9bIYmx/msFwLsAVMwwY9IHxWB4aneGSZ/ydUcDUuJw5aiSzh8wYjr+/ZwK+buHhOJtIRonKtqwOEyI4Yk6lHTaIFKZR/GC1SWRwbIZhOk+iJxOC57iAErt5dg87EKBkBd1L/3nMeB80bodVosuW0oeuuDv2dakigzzV2kUqmQYWi9IzT5j0NgRB6sNgHNjnbyXouhasUhsPbJALkPxRi9nnf2AZL/fDEwKiq3Dxm5FkArhTgMJg5Fid8H8VrE5w9fMEnZn5T46ICH8AB7sAUAZxy9h85U1aPWbIFOq0ZGb/s7a3EYbHNRRdC1H6RcpsZmvLzePgX9N1MHtVvwQx2PARCRB9fhLa/FUNsxA2RsaMaZSudwkmchMODaB0j+1nWdBg94F0ArgVQIXWa/frEAWpwhJgYth0qNUgAUSANEV91jItHHUT9xpNQkfc8zk/RS/6KxAxIQE6lBRY3Za3YadX5vfHMCl2ubMKB3LB4c1z/Up0NtiAFQiF1xzApQOkEQYGpsbrfjNzZb2y3rAtjf5Ynv7l07G0f6aoToso/FamuTAllx6CXS8cJ7pNQkBTzS1xKnwfsY1fKsDVJiBkgMdIrKa9DQZHUWczsCH6lw+YLRZQZY4MNfImdDRKOzANqlcFyn1UhF2psDmA4fyP1gswmorlfm3wKbTXBrI+DP/i3dL3VmC+qbwqPg/GRFDVZuLwEAPHv7UK+/B6Rs/GmG0JofSnHdCxuwYltxqE/lqn1+oAwjln6Nj78/2+bHttoEzH1zG6b+uaBdgqBvjlzEiKVf471tJQCc2Z0Ijcqr1kauCPrJTw9g1AsbcMIxUylYYvZh0uBeiIpQo67JKr3Ai2yt1AC51vu4DuMoSWqPaMRHR6DZKuCrQ2VSTx5xlpeY1RKnrwPBzQATZbn0HjrsUm/k6uYh9mGwLw+VewWlnixWG55ZfQgjln6N17/xXgxWzlOfHsD1L2zAnjNVgZ5+yD3/+WHc8IdvsPZAaav7nqyowc2vbsHElzbJNpesqmvCLa9uwY/+/G27vqHyhyAIWLb2KCw2AVMyDch2/A5Q58EAKIS2n7xs//dUZYjP5Optcbwz/nDXuTY/9v5zV3CsvAbnrzTgSFnbD0GsP1wOAPjI0VFVDLKiZGZqeE6DrzNbsPYH+4v0f/a3/gLQEvHFfERqd6n4+ZDHkItUA+SzE7Tz/5nJcW7LUCiFSqWShrk+/t7++zQsJV4K7nrrdUiM00EQgG2OeyiYGWCiLJeMklhvlOWROZs+LBGxkRocLTMhf98Fn8eqaWzGQ/+7G/+3074UyF83ncTJipa7SO84VYl/7TkPm2Dv9qskP5yrxvs7z0AQgGWfH2kxs7P95GXMe2s7zlTWo7q+GS+sPeK1z6tfF+GiyYwL1Q14w7FYaKhsPFqBb49fQqRGjSW3Dw3puVD7UN5fx05ErF8Q1zJSsmJHpuKH89UBpcP94dqF97BMYfDVEoOMExW1OFdV72yCGOF9e7hmgARBwNaTl6VOyFfbLdj1xVeqg/G4XnEWmFbjqw+Q85zFISMlEoOSwmJ7RsRzKE/M0IjF6leTARKDrRMVtbhS3wytWoXBie6zbXp200ndev+07pjsC/2F6gbc9fYOFBy/hOgIDYYmx8FiE/DC2iM+i6ctVps0vRqwD7EppdBaXNZBPN2KGjP+uumk7L6ffH8OD763CzWNFoxIjUeERoXNRZew6dhFaZ/DpUas2uXMIK/cXoKTFVeXVQ2W2WLFC1/YA7SfT0wPusaMwhsDoBAShzfOVNVLL2xKJQZxggBsKbrUpsfedMx5PLmFK69GY7PVbehqc1GFzynwrttsgr0ex3WhzCNlJpQbG70+xx8NTVZptfOslHi3YRlXrfYBctku1wBRKYZ5BG/DPIakXJ/XqFVIC2IKvEjsPSTKMHSTnVH2swn90b9nDC7VmPGXTe7ZiYPnjZj75jYcK69Bb70OHz98I966/3pEatQoOH7JZ3D84a6zOFZeg/joCOi0apQaG1F0lUOpHWX1fvuyDjGRGvxhXhYA4L2txW5v6Gw2AS+vO4YnPz0Ai03ArJEp+OThcfj5BHvX8RfWHpXeTDy/5ggEAbh9RDKmZBpgsdmHoEIREL67tRhnKuth0OuQe0tGh3996hgMgEKkzmyROnk2WWworW4I8RkFr7q+CVfqneP1m9tw9exyY6PbwpSHZGZGXY3jF2ukwmLAnsURh7dkM0Au2xqardjsCM5iHI3/gl05/Gi5CTbBPrxjiIty60/j+gLQ6lpgLqfsGUQoiWfA4zkk5VqknNYjGhFXOdTnmi3zFTjqtBo8O8s+FPLe1mIpg/v14XLc/bcduFRjRmaSHqtzJmBEanf07xWLnzuWF1m29ohX88zq+ia8usFeI/S7aYOldcc2H2vbNxDtodZswZ++sk8Nz70lA/eN6YtJg3ujyWrDi47MSWOzFY99tA9vbTkFAHjslgz8zz3XIipCg9xbMtBbr0Px5Tqs2FaCtQfKsKukClERavz+1mvwzO1DEalR49vjl7DxaMeuw1ZubJQyWU/PzEQ3HdvldVYMgELEs7jV82MlEV8IxNfkb49fgkVmGYdgiMGUOFX5+MUaaaHStiBmWMTj7zhVKc3Gkc8AOW+Z/WerUW5qRHSEBj+b0B9A8MNg4lCXGLQMSuyGCI0K1fXNuOASHEtrgbXSCVpuGEdJ0nvFSkGlXDG3a5AS6BpgclyP19LQ4S2Zicge0hvNVvvQ1j++O42HP9iDhmYrJg/ujX89Mk76XQLswYFBr8OZynq8t7XE7Vh/3nAc1fXNyEzS474xfZ39hhSw8Oqbm0+iosaMfj3ta8ipVCo8e/tQaNUqfHO0Ap/tPY/7/r4TXxwoQ4RGhVfuGonfTRsi/X7qoyLw1Az7+lV/2XgCf/jCviDvr7IzkNI92i14fOEL7+CxPb207hjqm6y4vm93zL22T4d9Xep4DG1DxHP5gJLLdbhpUO+AjnG2sh4nKmpwS6ZBtnV6RxGDtxv6JeBERQ2u1Ddjz5krGDug51UfWwwo7hmdhve2FaO6vhnHL9YENLwjCAI2Hq3AsD5xSI53XzNHrLu5fWQyPt9filJjI7Yct78D92yCCNgLdCO1ajRZbFjnKJ6ekNETM4Yl483Np7D15GWYLVa34OlIqQnGhmaMG+j7++G5cKlOq8Eggx5Hykw4dMEkLSwqxpWtrQU2KFEfVGPAcKFRqzA0OQ67z1yRLeZOiY9Cj5gIXKlvvqr6H5Frxqm1361nbh+KrSe+xaZjFdLv5/1j++L52cO8zrObTounZ2bi8U9+wF82nYBGbR+mbGy24gNHobS4EKx9ltFh7Dl7Bcb6ZsTHRPg8h0MXjNh52n3yRFafeNzo4577+nA5zlbVyz4XqGargHe/s89cfea2odLveoahGxaM749/bC3G45/8AMC+XtzffnKD7O/+Hdf1wf/tPIMfzlWjrsmK1B7RWDjJucRE7i0Z+GzveZyprMfiTw9iqJ8ZTbVKhZnDk7zudcCeucrfe95nH6+aRgvy912ASgUsnT2sxS7qpHwMgEKk+LL7zJDTQRRCP/LBHhwpM+GVu0bix6NS2+rUAlbsWFF8oCEWKd2jsHp/KTYXXbrqAMhssUqzfG7JNKCwuBLbTlbicKkxoADom6MV+OX7uzGmfwI+eWSc23NiAXRWSjxqGy34Z+FZfO0IbOQCIACIcgRA4n43ZxowLCVOWsPq++IrmDjI3jemzNiAH7+9HY3NVmz6XbbPbIVz/SnXoZg4HCkz4UipUVqCQfy90fkIbmIi7bf0cAX2//E0IrU7dp+5ghEyP2uVSoXhqd3x7fFLGNgGayGJy3lo1Spck9zy925g72742YT++Pt3xVCpgP++9RopCyJn7rX2F/p9Z6vxxy+PuT136/AkKThIS4jBIEM3nKioxbcnLmHWyBTZ4zVZbLj/H4UwNrhPE9eoVfji1xO9lk/ZePQiFv7fnhavKRiTBvfGlGvcp4b/euogrN5/AZdrm9A3IQYrfjYaA320YlCrVVg6ayjmvbUdgP376Bq0uwaPn+27gM9amH3n6dO957Emd6LXG4Ul+Qex2o/ZmneNSsWI1O5+fz1SJgZAIVLsyAClxEeh1NgY8Eywc1X10pTwP311DNOHJUIf5fsdY3sqdnQv7t8zFknxjgDoWAWenpl5VcfdVVyF+iYrDHodhqXEISslHttOVuLQBRPuGe3/cdYdsgcq35+pQmWtWVr7ymK14ZjLiukxkRr8s/AsLtfah8B8ZVB0ERqg0SLtd/MQA9RqFW4e0huf7D6PTccqpADoT1/Z0+kA8M3Ri/jFTd6LKDZZbNJq556ZiE92n5eCtIqaRrxdcBoAMGtEsuy5/XhUKowNzXhgbD9/vz1h61c3D0SkVu21crboyelDkJmkx5xr5QOFQPTpHo2XfzwCsZFaxPpR8/H4j4agmy4C1/fr3mrmVq1W4bW7r8XyLafchnJidFr8dupgt31vyTTgREUtNh+r8BkAfV9SBWNDM/RRWkxxDJsdK6/BsfIaPL/mCFb9cqwUjJktVixzTDe/oV8PpPZom1XDoyM1eOyWQV5BX1xUBN5bMBpfH76In03o3+o6c9f17YFX7xqJ6oZm2XXW5l7bB6XVDa22EnC18WgFDpea8PH353Df2L7S9t0lVVi9vxQqFXD7iBT4mEiJuOgI/O5HQ/z+eqRcDIBCRHwnf3OmAf8sPIuSysDS066Fxpdr7dNPF996TZueo79KXJYjGJOeALUKKLpYgwvVDW71EIEShxeyh/S294bxWLjSHzabgILj9uOIM9TudGTLTl2qc1sxPTFOJw1vAb4zQK7bM5P0SHFc481DDPhk93lsLqrAs7OGYndJlVtvoM1FFbIB0PGLNWi2CoiPjnB7gRomrYllv96X1xWh1mzByLTuuPN6+YxfYlwUfh+i34O21qubrsUgOqtPfJvOdLv7hjS/942O1OA3Uwf5vX//XrF46ccjWt3v5kwD/vbtaWw5fglWmyA71CneF9OHJeGVu0YCsL8hmvrnAuw4XYl1h8oxc7g9QH5va4k0m2nlz8d0SEHviNTuAWVP7mwhe61Wq5B7i//fZ8BeoL5s7RG88nURbhuejPiYCFgdU/YB4J4b0vCnO1v/WVDnxyLoEBEDHrHw8WxVPZoDKBwWCyUnOlr0v7etWLazansTBMFtPabuMZG4vm8PAFffF0ecTi9+j8Ti1KNlJr/bBhy4YJQyNYB74CgGFkNT7Cumx0RqMc5l2M7XMJNrAHRzpnMIYOKgXojQqFB8uQ6nLtXiuTX2P7jiz2hXcZVs/xix+/CwlDi3d9TXJOuhUtn7q2w4chH/3nMeALB01lDWJnRSo/r1gD5Ki6q6Jvxwvlp2H/F3+BaX3720hBg87KifefGLo2hstuKiqVGart+VZjP9ZFw/DDJ0Q1VdE17faJ9l98nuczh0wQR9lBb/NZ3ZHbJjABQCxvpmVDnWABuTnoCoCDWsNgHnr/g3Fb6hySp1j37m9qG42WVWSke7XNuEWrMFKhWkXixiULDlKgKg4st1KL5chwiNChMdQwz9e8YiNlKDxmab38GeGCj2dZyb6wy1Q6XuM68A9xeVKF81QC6Bkev++qgIjO6fAAB4/OP9OFxq/4P7P/OvRb+eMWi2Cth64rLX8aQCaI9sRkykVqqfePyT/QCAO69PxXWOAJM6nwiNGpMcv+9y98+ZyjqcvlQHrVolDbOKHs3OQEp8FC5UN+BvBael4dfruthspgiNWmpX8P6OM9hdUoX/t74IALBo6mD0amVYjroOBkAhIHZNNuh10EdFSLNYPAujfdlx+jLMFhv6dI/G4MRueOb2obKdVTuCOAOsT/doKTAQg4Jtpy4HvXaXmD0ak54gvXNVq1XSTBB/h8HEd8u/yh6IHjERMDVasOfMFQDOKfCu6z7d7LLej1wfIMCZAYqPjsB1ad3dnhOv/Yfz9vNbNHUwenbTSceVm+IsF4iJxKxXTaMFsZEaPDWD7147O/ENxCaZnlLifTG6fwLiPGr+oiM10jD4m1tOSkt2LJ3V9WYz3TSoN6YNTYTVJuD+fxSiqq4JGYZueHCc8mvjqO0wAAoBsWZGnBEktlkv9pgav+/sFTzxrx+8uguLjdJuzrTXxgzo3U3qrPrkvw/gJ+8WSo8/fHGk1cUbAXtTtv/OP+j2uT99b5e0TpYv4gww11bxmUl6JMdHobHZhh1BrnMmNhS82WMBQmddTOsdoStqGnHAEYjcco0Bkwfb31lvLroEm02QVl93zbz07RmDgY51peT6ALlunzS4t9e0Z9cFE13/4Eo9XorclzqwWG3O1c5l6llctz02ZRAMcVGtXjcpm/h7euiCCRdNHvd+kfPel3P7iGSMSU+Q6tjuviEVIz2C9K5iyW321dvFKe/PzRp61Q0zqXPhb0MISDUzjsyPGAh5zgTL++oY/rXnPJasPihtEwRBehfoGhyIDdcu1zbhuxOXpcffvyvGv/eeb/F8Si7X4Y63tuOfhWfdPrfg+CU895/DLbaiF7NZrgGQSqWSpse+XXAq4Fb2dWYLCk/b14ByrbEB3BeubE2B48VieJ94GPRR0rE2H6vAmap61Jot0GnVUsAjutVRQJocLx9siNtnysxaGdg7FkMcDQhd/+COHZCA6AgNKmrMbstbfLb3AhqbbYiPjpB+H1yNTbfXJA3oHSs1W6TOrbdeh+v6dgcA/M0x6w8A6pssUu+fWzzuC5FKpcLSWcOgVaugj9LiielXNxNTyfr2jMEjjrqo6cMSA+6zRp1f16iKCzNSANTbMwPkDICMjmaCgL2PzZaiCmQPsU+RvVDdAJ1WjfEDnTUA+qgIfProeOw+UyVt23umGv+38wxeXncMM7KSvFLmgH1K7cL3d+NKfTP6dI/Gb6YMQoRWBUEAfp9/EOWmRhwtq/HZhEzKZnm8eD8yeSD+tfs8Cour8OXBctzmY9q2HHGB0X49YzDAo2+O2CfnSKkJNpvQYmpfHP4SA5/Jg3tLM9TEHj7XyDTZy7k5A2PSE6Tgw9Nzs4fhzlGp0tIFrlQqFf7352NwqcYs9ZYB7FmjCRm98M3Ri9h8rAJZfeJhamzGy+uPOb7mQNlrGZ4aj89+NR79EmJ8ZqSo8/nt1MF48L1deH9HCe4dk4ZBiXpsO1mJJosNaQnRPnvrAPai/jW5ExETqXFb46wrWjR1MG7onyDV5hG5YgYoBMS6GTFokAuAvj1xyW2m07K1R9BksUk1JOMG9kR0pPsLYlpCDOZdlyo9nrl9KAb0jsXl2ib8ZaP74o0A8J/9F3D/3wtxpb4ZI1LjkZ8zHnePTsO861Jxx/WpmOAIsFpa28t1Bpir1B4xeGTyQADAH744goYm/2uBNrtkuDz7jAzs3Q2RWjVqzBacu+K7dUCz1Ybvjl92HMf+zs91htrfHZ1s5epuoiI0uGlQb0T6KIKOj47AhIxePhvfJcVHuQU/ItdhMMC+BMDl2iYM6BWLBePTfV7L9X17tNpPhTqXSYN740dDEx0LgtpXk99c5Pu+8DQ0Ja5NlghROrVahUmDe3v9rSQCAgyA8vLyMHr0aOj1ehgMBsydOxdFRUVu+7zzzjvIzs5GXJx9Sm91dXWrx126dClUKpXbIzOzc6ZuPaeNu/5bamyQiobFP3b3jklDr26ROH2pDu/vKJEd/vIlUqvGs7fbZ0Os2FYiNRMTBAFvbDyB33y0H01WG6YPS8THC8fBoHcf8rm5lbWJbDZBCuY8AyDAngUSGz2+XXCq1fMVz80zc+MqQqPGNUn2IaaW6oB2l1xBjdmCnrGRGOnSk0Q85uVa+0K0Hbliuli3se9cNXaXVGHFthIAwDOzhvoMtqjrWnLbNYjUqPHdicvYcOSi842Bj+EvIgpMQH91CwoKkJOTg507d2LDhg1obm7GtGnTUFfnzFzU19djxowZ+P3vfx/QiQwbNgxlZWXSY+vWrQF9vlJU1jWhptE+bbxfT/vU7J6xkdDrtBAEez8gm02Q6ldmj+yDJx3j+K9/cwK7HcNivmoAPGUPMWBKpkF6J2m2WPG7f/2APztWoV44aQCW3z9K9h2S+Id279kruFLX5PX8xZpGNDbboFWrZDvMRkdq8N+32QOwtwtO4XwLGRvRkTITLprMiI7QYGy6fNran4aIYhA1eUhvt6Elz8AxK6XjAqDk+GhckxwHQQB+8f5uWGwCpmQa/Apmqevp1zMWv5xkzww+9ekBlBkbERWhdutVRUTBC6gGaN26dW4fr1y5EgaDAXv27MGkSZMAAIsWLQIAbNmyJbAT0WqRlORdVNrZiDUzKfHOaeMqlQr9e8Xi4AUjii/Xoc5sQWVdE/Q6LW7o3wNj0xPwQeEZaUZThqGb1HPHH0tuH4pvT1zCt8cv4bY3tuJkRS00ahWWzRmG+1tYMqFP92gMSdSj6GINvj1xCXM8eomIM8DSEmK86mhEtw5Pwtj0BBQWV+GPXx7Fm/dd7/a8ZypffJc7IaOnz6Uoslw6JPsqsBYzZZ6B4jXJeiTFRaHc1GhfMT3p6teRCsTNQ3rjaJkJ1fXNiNCosMSRoSOS86vsDPx7z3lcNNkzlhMG9lL0IrdE4eSq8u5Go/0FOSHh6gvMTpw4gZSUFAwYMAD3338/zp4963Nfs9kMk8nk9ghHxZfrcOMfN0rZFnEb4D1klO4yE0wMAm4a3AsRGjXUahWemzVM2lesafFXeq9YPDTRPhviZEUtuum0eG/B6BaDH+lrtTAMJjcDzJNKpbKvqqwCvjxYjvTFX0qP61/YgGPl7j875zRf31kRsRD6uxOX3Y7n+hCDPM+ZHyqVShqKGpyo7/DCYteA7OcT01v83hHF6rRYPNO5tEk2h7+I2kzQAZDNZsOiRYswYcIEZGVlXdVJjB07FitXrsS6deuwfPlyFBcX46abbkJNTY3s/nl5eYiPj5ceaWn+r+HTkf695xzKHe3oxWnbxVIPIPcMTn+XQmgpCHAZGhnVrwcWjO+P6AhNi2vn+JJ7SwYyk/QY0DsWnz46Xuo10hox2Co4fslr+QlfM8A8XZMch19O8l4D60p9M551mWZ/pa4J+85ecXxd33/oM5PiMKB364HDbcOTER/tPfPt3jF9EROpaZNFNAN1Xd8eyOoTh0GGbngswDWOqGuac20KpmQa0KubDjOGdf4sOVFHCXoafE5ODg4dOtQmtTozZ86U/j9ixAiMHTsW/fr1wyeffIKHHnrIa//Fixfj8ccflz42mUxhGQRtcjQsFATg+c8P45OHx3nNABOlOwKi70uqcMoxtJTtEQQ8N2sonrl9qOwCia3pptPii1/fBLXKe9ipJaP69UBclBZX6pux/1w1RvVzLsPgzGa1Phy3eOY1+FV2hhREXa41Y/Zft2JXcRXWHijDrJEpKDh+CTbBfYFROZFaNTb8djKMDc0+91EB6B7jHfwA9sUajyyb0eo5tweNWoXPcyfCJiConyN1PSqVCn9/8AaoArx3iahlQWWAcnNzsXbtWmzevBmpqYFnI1rTvXt3DB48GCdPnpR9XqfTIS4uzu0RbsqMDThaZoJKBURFqPF9yRV8fqBM6vbsmcFI72WvRRGDnxGp8V49PFQq1VW9aGrUqoD/gGo1akwSOyh7DIM5AyD/6mjioyOQEBuJhNhIDE7U49HJGQCAvC+PoqHJ2uLsL08atUo6ltyjR2xk2L5YXO3PkboedRD3LhG1LKAASBAE5ObmIj8/H5s2bUJ6uu/eJVejtrYWp06dQnKy/83zwo24kvm1ad3xq2znC72vYSPPLsDhNDNIPBfX1d2tNgHnquyLt3oO5/nr4ckD0Kd7NEqNjXhz80kUHHdf/Z2IiKi9BBQA5eTk4IMPPsCqVaug1+tRXl6O8vJyNDQ4VzEvLy/H/v37pezNwYMHsX//flRVOTsUT5kyBX/961+lj//rv/4LBQUFKCkpwfbt2zFv3jxoNBrce++9V3t9ISPNQhpiwMJJA5DaIxplxkY0NFuhUau8ZnHFx0Sgh8uQTTgFAZOH9IZKZZ+iLq5LVlrdgCarDZFaNVLifQ9XtSQqQoMltzkXb6yub5ZdYJSIiKitBRQALV++HEajEdnZ2UhOTpYeH3/8sbTP22+/jeuuuw6//OUvAQCTJk3CddddhzVr1kj7nDp1CpcvX5Y+Pn/+PO69914MGTIEd999N3r27ImdO3eid29lrt1itlix7aSjC3Gmwe2FHgDSekTLLsonzgjq1S0SwzuwQV9renXTYYSjmeCK7cXYXFSBzw+UAgD694y5qpWmZ2QlYdyAnhBns8stMEpERNTWAiqC9mdRy6VLl2Lp0qUt7lNSUuL28UcffRTIaYS9XcVVqG+ywqDXSUstTB+WhPEDe2L7qUqfLerTe3XD3rPVmDzYcFVBRXu4ZYgBP5yrxt8KTrst0NjaDLDWqFQqPDd7KG79n+9gE4BbfKxyTURE1Ja4GGo72CSzlpVKpcKf7hiBF784ggU+VvX++cT+MDU2I/eWjI46Vb/dOzYNu89U4Uq9syN0pEaNn0+8+jqwzKQ4LJuThe9LqjAzS7l1X0REpBwqwZ+0TpgzmUyIj4+H0WgMixlh2f9vM0oq6/H2A6MwI4t9O4iIiOSE8vWbxRZtrPhyHUoq6xGhUWHioF6hPh0iIiKSwQCojYnDX2PSE9BNxxFGIiKicMQAqI1tdqn/ISIiovDEFEULLFYb/vDl0YA+Z1exvd+RP92MiYiIKDQYALXAJgArtpUE/HnpvWIxgKt8ExERhS0GQC1Qq4CcmwcG+DkqzMhK4ro9REREYYwBUAu0GjWemJ4Z6tMgIiKiNsYiaCIiIupyGAARERFRl8MAiIiIiLocBkBERETU5TAAIiIioi6HARARERF1OQyAiIiIqMthAERERERdDgMgIiIi6nIYABEREVGXwwCIiIiIuhwGQERERNTlMAAiIiKiLqdTrAYvCAIAwGQyhfhMiIiIyF/i67b4Ot6ROkUAVFNTAwBIS0sL8ZkQERFRoGpqahAfH9+hX1MlhCLsamM2mw2lpaXQ6/VQqVQBfa7JZEJaWhrOnTuHuLi4djrD8MBr7Zy6yrV2lesEeK2dFa/VmyAIqKmpQUpKCtTqjq3K6RQZILVajdTU1Ks6RlxcXKf/hRTxWjunrnKtXeU6AV5rZ8VrddfRmR8Ri6CJiIioy2EARERERF1Olw+AdDodnnvuOeh0ulCfSrvjtXZOXeVau8p1ArzWzorXGl46RRE0ERERUSC6fAaIiIiIuh4GQERERNTlMAAiIiKiLocBEBEREXU5YRUA5eXlYfTo0dDr9TAYDJg7dy6Kiorc9ikvL8dPfvITJCUlITY2Ftdffz0+/fTTFo/77bffYtasWUhJSYFKpcLq1au99vnss88wbdo09OzZEyqVCvv37w/o3E+ePAm9Xo/u3bu7bV+5ciVUKpXbIyoqKmTX2tzcjKeeegrDhw9HbGwsUlJS8OCDD6K0tLTVa9yyZQuuv/566HQ6ZGRkYOXKlV77vPnmm+jfvz+ioqIwduxY7Nq1S3HXWlZWhvvuuw+DBw+GWq3GokWLvPbpLD/XrVu3YsKECejZsyeio6ORmZmJ1157zWu/zvBzdbVt2zZotVpce+21btuXLl3q9XPt3bt3yP4uLV26FJmZmYiNjUWPHj0wdepUFBYWtnp9SrtXg71WJd6rwV6rEu/VYK/VVSD3amZmpt/HBcIsACooKEBOTg527tyJDRs2oLm5GdOmTUNdXZ20z4MPPoiioiKsWbMGBw8exB133IG7774b+/bt83ncuro6jBw5Em+++WaL+0ycOBEvvfRSwOfd3NyMe++9FzfddJPs83FxcSgrK5MeZ86cCdm11tfXY+/evXjmmWewd+9efPbZZygqKsLs2bNbvMbi4mLcdtttuPnmm7F//34sWrQIv/jFL7B+/Xppn48//hiPP/44nnvuOezduxcjR47E9OnTsWHDBkVdq9lsRu/evbFkyRKMHDnS536d4ecaGxuL3NxcfPvttzh69CiWLFmCJUuW4J133pH26Sw/V1F1dTUefPBBTJkyRfb5YcOGuf1chw8fHrK/S4MHD8Zf//pXHDx4EFu3bkX//v0xbdo0XLp0yefnKPFeDfZalXivBnutSrxXg71WUaD36tatW1s9phshjFVUVAgAhIKCAmlbbGys8P7777vtl5CQIPz973/365gAhPz8fJ/PFxcXCwCEffv2+X2eTz75pPDAAw8IK1asEOLj492ek9smJxTXKtq1a5cAQDhz5ozPfZ588klh2LBhbtvuueceYfr06dLHY8aMEXJycqSPrVarkJKSIuTl5bl9Xrhfq6vJkycLv/nNb7y2d5afq5x58+YJDzzwgPRxZ/u53nPPPcKSJUuE5557Thg5cqTbc3LbPIXyOo1GowBA+Oabb3zu01nuVX+u1ZWS79VAr1WkxHs1kGu92nu1NWGVAfJkNBoBAAkJCdK28ePH4+OPP0ZVVRVsNhs++ugjNDY2Ijs7u93PZ8GCBV5fZ9OmTfjXv/7VYgRcW1uLfv36IS0tDXPmzMHhw4e99gnltRqNRqhUKrfhu+zsbCxYsED6eMeOHZg6darb502fPh07duwAADQ1NWHPnj1u+6jVakydOlXax/XrAeF7rf7qDD9XT/v27cP27dsxefJkAJ3v57pixQqcPn0azz33nM9jnThxAikpKRgwYADuv/9+nD171utrAR1/nU1NTXjnnXcQHx/vlu3ojPeqv9fqr3C+V4O9ViXeq4Fca1vcq60J28VQbTYbFi1ahAkTJiArK0va/sknn+Cee+5Bz549odVqERMTg/z8fGRkZLT7OSUnJ8Nms0kfV1ZWYsGCBfjggw98LvY2ZMgQvPfeexgxYgSMRiNeeeUVjB8/HocPH5YWcA3ltTY2NuKpp57Cvffe63YNffv2RXJysvRxeXk5EhMT3T43MTERJpMJDQ0NuHLlCqxWq+w+x44dkz5WwrX6o7P8XEWpqam4dOkSLBYLli5dil/84hcAgMuXL3ean+uJEyfw9NNP47vvvoNWK/+nb+zYsVi5ciWGDBmCsrIyPP/887jppptw6NAh6PX6kFzn2rVrMX/+fNTX1yM5ORkbNmxAr169fF6nku/VQK/VH+F6rwZ7rUq8VwO91ra4V/1yVfmjdvTII48I/fr1E86dO+e2PTc3VxgzZozwzTffCPv37xeWLl0qxMfHCwcOHPDruGjDIbB58+YJTz31lPSxP6nWpqYmYeDAgcKSJUukbaG61qamJmHWrFnCddddJxiNxhaPNWjQIOGPf/yj27YvvvhCACDU19cLFy5cEAAI27dvd9vniSeeEMaMGaOoa3XlK60ud3wl/lxFp0+fFg4cOCC88847QkJCgrBq1SpBEIRO83O1WCzCDTfcICxfvlza5k8K/cqVK0JcXJzwj3/8QxCE0FxnbW2tcOLECWHHjh3Cz3/+c6F///7CxYsXfR5LyfdqoNfqSmn3arDXqsR7NZBrbat71a9z9nvPDpSTkyOkpqYKp0+fdtt+8uRJAYBw6NAht+1TpkwRHn74Yb+O3ZYBUHx8vKDRaKSHWq0WAAgajUZ49913fX7ej3/8Y2H+/PmCIITuWpuamoS5c+cKI0aMEC5fvtzqsW666SavPy7vvfeeEBcXJwiCIJjNZkGj0Xh9vQcffFCYPXu2IAjKuVZX/v5RFQRl/lzlvPDCC8LgwYMFQeg8P9crV65I96b4UKlU0raNGzf6/NwbbrhBePrpp0P6d8lVRkaGV4DjSsn3qqfWrtWV0u5VT4Fcq0gp96qnlq61Le5Vf4VVDZAgCMjNzUV+fj42bdqE9PR0t+fr6+sB2Mc1XWk0GrehqY6yY8cO7N+/X3osW7YMer0e+/fvx7x582Q/x2q14uDBg0hKSgrZtTY3N+Puu+/GiRMn8M0336Bnz56tfs64ceOwceNGt20bNmzAuHHjAACRkZEYNWqU2z42mw0bN27EjTfeqKhrDYZSf65ybDYbzGYzgM7zc42Li8PBgwfd7tdHHnkEQ4YMwf79+zF27FjZz6utrcXJkyexbdu2sPm75PrzkaPUe1VOa9cajHC4V+UEc61KuFdbO29PV3Ovnjp1KrAhUr9DpQ7w6KOPCvHx8cKWLVuEsrIy6VFfXy8Igv0dX0ZGhnDTTTcJhYWFwsmTJ4VXXnlFUKlUwhdffOHzuDU1NcK+ffuEffv2CQCEP//5z8K+ffvcZoxUVlYK+/btk1LFH330kbBv3z6hrKxM2ufpp58WfvKTn/j8OnJDYM8//7ywfv164dSpU8KePXuE+fPnC1FRUcI999wTkmttamoSZs+eLaSmpgr79+93+9pms1k6zk9+8hO3SPr06dNCTEyM8MQTTwhHjx4V3nzzTUGj0Qjr1q2T9vnoo48EnU4nrFy5Ujhy5IiwcOFCoXv37sJPf/pTRV2rIAjScUeNGiXcd999wr59+4TDhw93up/rX//6V2HNmjXC8ePHhePHjwv/+Mc/BL1eL/z3f/93p/y5upJLq//ud78TtmzZIhQXFwvbtm0Tpk6dKkRFRQlxcXEdfp21tbXC4sWLhR07dgglJSXC7t27hZ/97GeCTqdze6feGe7VYK9VEJR3rwZ7rUq8V6/m5+rK33u1V69eQkVFhc/jeAqrAAiA7GPFihXSPsePHxfuuOMOwWAwCDExMcKIESO8pu552rx5s+xxf/rTn0r7rFixQnaf5557Ttrnpz/9qTB58mSfX0cuAFq0aJHQt29fITIyUkhMTBRuvfVWYe/evSG7VnGIT+6xefNm6TiTJ092+/6Ix7722muFyMhIYcCAAW7nKvrLX/4iXe+YMWOEnTt3KvJa5T6nX79+0vOd5ef6xhtvCMOGDRNiYmKEuLg44brrrhPeeustwWq1un39zvJzdSX3R/Wee+4RkpOThcjISKFPnz7CPffcE7LrbGhoEObNmyekpKQIkZGRQnJysjB79mxh165dbsfpDPfq1Vyr0u7VYK9Viffq1fxcXfl7r548ebLF8/WkEgRBABEREVEXElY1QEREREQdgQEQERERdTkMgIiIiKjLYQBEREREXQ4DICIiIupyGAARERFRl8MAiIiIiLocBkBERETU5TAAIiIioi6HARARERF1OQyAiIiIqMthAERERERdzv8HWhNx/GefD9kAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You may want to use two additional Matplotlib functions:\n",
        "\n",
        "* **`plt.gcf().autofmt_xdate()`**: this formats the x-axis datetimes more neatly\n",
        "* **`plt.xlim([START,END])`**: this sets the lower and upper limits of the x-axis; `START` and `END` can be specified as `datetime` objects (or can be `None`)\n",
        "\n",
        "***Try repeating the plot above using these functions:***"
      ],
      "metadata": {
        "id": "mCjTNWcK5Mdi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code below:\n",
        "plt.plot(data_0m.index,data_0m['Temp'])\n",
        "plt.gcf().autofmt_xdate()"
      ],
      "metadata": {
        "id": "LpXuceDK5fWG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "4b0a250a-cb74-40be-c5f7-6a303cd020f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Day 3, Part 4: Other useful `pandas` functions"
      ],
      "metadata": {
        "id": "1KkMPJ0TG3lx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To select a certain index value of a column, use `.loc[]`:\n",
        "\n",
        "> **`data[COLUMN_NAME].loc[INDEX_VALUE]`**\n",
        "\n",
        "To select multiple index values, use slicing:\n",
        "\n",
        "> **`data[COLUMN_NAME].loc[INDEX_START:INDEX_END]`**\n",
        "\n",
        "For example:\n"
      ],
      "metadata": {
        "id": "goG6rxo3G-gb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Temperature only, convered to a NumPy array:\n",
        "data_0m['Temp']"
      ],
      "metadata": {
        "id": "dG-3mgvqGWx8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select a single value of temperature\n",
        "data_0m['Temp'].loc[datetime(2023,7,28,11,49)]"
      ],
      "metadata": {
        "id": "fni0Q0dPHbdS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Select a range of temperature measurements\n",
        "data_0m['Temp'].loc[datetime(2023,7,28,11,49):datetime(2023,7,28,11,51)]"
      ],
      "metadata": {
        "id": "5iNjrVc_GY6E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To convert a column from `pandas` format to a NumPy array, use **`.values`**:"
      ],
      "metadata": {
        "id": "4M7kkUrYHicu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pandas format, including the Datetime index:\n",
        "data_0m['Temp']"
      ],
      "metadata": {
        "id": "UJkZHT9dHqEJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Temperature only, convered to a NumPy array:\n",
        "data_0m['Temp'].values"
      ],
      "metadata": {
        "id": "2K9s2ztHHuoo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}