Quickstart_Example.ipynb

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      "name": "Activity 0 Python Quickstart.ipynb",
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      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRPHAjb-E-9Q",
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      "source": [
        "# Activity 0 - Python Quickstart\n",
        "**2020 Data Labs REU**\n",
        "\n",
        "*Written by Sage Lichtenwalner, Rutgers University, June 8, 2020*\n",
        "\n",
        "This notebook is designed as a kickoff activity to our 3-day Intro to Oceanographic Data Processing in Python series.  The commands here are simple and provided mostly without context (which will be discussed in later notebooks) in order to highlight some of the advantages of using Python in Research Computing.\n",
        "\n",
        "The code from this example was adapted from [A Quick Introduction to Python](https://github.com/ooi-data-lab/data-lab-workshops/blob/master/Summer_Examples/Python_Introduction.ipynb) develoed as part of our 2019 Summer workshop series."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c5kbjL5UGBia",
        "colab_type": "text"
      },
      "source": [
        "## Notebook and Python Basics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upDBJahnE4LV",
        "colab_type": "code",
        "outputId": "83de2152-68a4-4dbb-b88e-f84658f42a17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "2+2"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZ680o6bFuIR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = 42"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzdOionBFuNg",
        "colab_type": "code",
        "outputId": "b18db6c3-daf0-48de-be0a-e0296dd21e61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "a"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "42"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzKT_UMHFuSq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "b = 'Life, The Universe, and Everything'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOHMWqmzFuWq",
        "colab_type": "code",
        "outputId": "22204f80-6a89-461a-d747-61c8825d5921",
        "colab": {
          "base_uri": "https://localhost:8080/",
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        }
      },
      "source": [
        "b"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Life, The Universe, and Everything'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4MoZJRaGFMX",
        "colab_type": "text"
      },
      "source": [
        "## A Quick Data Example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9tzAxI7Fua3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "woP9UcXUGLOk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "url = 'https://marine.rutgers.edu/~sage/OOI_Data_Workshops/CP02PMUO_R9_ar24011.csv'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fk3vEZakGMRD",
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        "outputId": "a437514a-b67b-4157-ca71-ed57c647dfdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "data = pd.read_csv(url)\n",
        "data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Depth</th>\n",
              "      <th>Pressure</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Salinity</th>\n",
              "      <th>Density</th>\n",
              "      <th>Fluorescence</th>\n",
              "      <th>Turbidity</th>\n",
              "      <th>Oxygen</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14</td>\n",
              "      <td>14.109</td>\n",
              "      <td>20.0267</td>\n",
              "      <td>34.7221</td>\n",
              "      <td>1521.47</td>\n",
              "      <td>0.0072</td>\n",
              "      <td>0.3726</td>\n",
              "      <td>34.7187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15</td>\n",
              "      <td>15.116</td>\n",
              "      <td>20.0913</td>\n",
              "      <td>34.7772</td>\n",
              "      <td>1521.73</td>\n",
              "      <td>-0.0245</td>\n",
              "      <td>0.3700</td>\n",
              "      <td>34.7693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16</td>\n",
              "      <td>16.124</td>\n",
              "      <td>20.2141</td>\n",
              "      <td>34.8684</td>\n",
              "      <td>1522.19</td>\n",
              "      <td>-0.0361</td>\n",
              "      <td>0.3785</td>\n",
              "      <td>34.8652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>17</td>\n",
              "      <td>17.132</td>\n",
              "      <td>20.3139</td>\n",
              "      <td>34.9335</td>\n",
              "      <td>1522.55</td>\n",
              "      <td>-0.0401</td>\n",
              "      <td>0.3759</td>\n",
              "      <td>34.9386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>18</td>\n",
              "      <td>18.140</td>\n",
              "      <td>20.4459</td>\n",
              "      <td>35.0250</td>\n",
              "      <td>1523.03</td>\n",
              "      <td>-0.0509</td>\n",
              "      <td>0.3744</td>\n",
              "      <td>35.0229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>866</th>\n",
              "      <td>6</td>\n",
              "      <td>6.046</td>\n",
              "      <td>19.9109</td>\n",
              "      <td>34.6342</td>\n",
              "      <td>1520.92</td>\n",
              "      <td>-0.0185</td>\n",
              "      <td>0.3842</td>\n",
              "      <td>34.6310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>867</th>\n",
              "      <td>5</td>\n",
              "      <td>5.039</td>\n",
              "      <td>19.9138</td>\n",
              "      <td>34.6359</td>\n",
              "      <td>1520.91</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>0.3816</td>\n",
              "      <td>34.6319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>868</th>\n",
              "      <td>4</td>\n",
              "      <td>4.031</td>\n",
              "      <td>19.9145</td>\n",
              "      <td>34.6371</td>\n",
              "      <td>1520.90</td>\n",
              "      <td>-0.0236</td>\n",
              "      <td>0.3874</td>\n",
              "      <td>34.6333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>869</th>\n",
              "      <td>3</td>\n",
              "      <td>3.023</td>\n",
              "      <td>19.9165</td>\n",
              "      <td>34.6385</td>\n",
              "      <td>1520.89</td>\n",
              "      <td>-0.0218</td>\n",
              "      <td>0.3842</td>\n",
              "      <td>34.6347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>870</th>\n",
              "      <td>2</td>\n",
              "      <td>2.015</td>\n",
              "      <td>19.9122</td>\n",
              "      <td>34.6354</td>\n",
              "      <td>1520.86</td>\n",
              "      <td>0.0187</td>\n",
              "      <td>0.4047</td>\n",
              "      <td>34.6308</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>871 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Depth  Pressure  Temperature  ...  Fluorescence  Turbidity   Oxygen\n",
              "0       14    14.109      20.0267  ...        0.0072     0.3726  34.7187\n",
              "1       15    15.116      20.0913  ...       -0.0245     0.3700  34.7693\n",
              "2       16    16.124      20.2141  ...       -0.0361     0.3785  34.8652\n",
              "3       17    17.132      20.3139  ...       -0.0401     0.3759  34.9386\n",
              "4       18    18.140      20.4459  ...       -0.0509     0.3744  35.0229\n",
              "..     ...       ...          ...  ...           ...        ...      ...\n",
              "866      6     6.046      19.9109  ...       -0.0185     0.3842  34.6310\n",
              "867      5     5.039      19.9138  ...       -0.0323     0.3816  34.6319\n",
              "868      4     4.031      19.9145  ...       -0.0236     0.3874  34.6333\n",
              "869      3     3.023      19.9165  ...       -0.0218     0.3842  34.6347\n",
              "870      2     2.015      19.9122  ...        0.0187     0.4047  34.6308\n",
              "\n",
              "[871 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5uja3oJrGQWF",
        "colab_type": "code",
        "outputId": "06b3603b-629b-4aa7-ba2c-a2bae651c045",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Depth</th>\n",
              "      <th>Pressure</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Salinity</th>\n",
              "      <th>Density</th>\n",
              "      <th>Fluorescence</th>\n",
              "      <th>Turbidity</th>\n",
              "      <th>Oxygen</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>871.000000</td>\n",
              "      <td>871.000000</td>\n",
              "      <td>871.000000</td>\n",
              "      <td>871.000000</td>\n",
              "      <td>871.000000</td>\n",
              "      <td>871.000000</td>\n",
              "      <td>871.000000</td>\n",
              "      <td>871.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>225.208955</td>\n",
              "      <td>227.110232</td>\n",
              "      <td>11.493936</td>\n",
              "      <td>35.353968</td>\n",
              "      <td>1498.607773</td>\n",
              "      <td>-0.259318</td>\n",
              "      <td>0.361451</td>\n",
              "      <td>35.350359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>125.862021</td>\n",
              "      <td>126.972414</td>\n",
              "      <td>3.870060</td>\n",
              "      <td>0.255037</td>\n",
              "      <td>11.098248</td>\n",
              "      <td>0.159814</td>\n",
              "      <td>0.027444</td>\n",
              "      <td>0.254881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.015000</td>\n",
              "      <td>6.252900</td>\n",
              "      <td>34.634200</td>\n",
              "      <td>1483.100000</td>\n",
              "      <td>-0.375900</td>\n",
              "      <td>0.328600</td>\n",
              "      <td>34.630800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>116.500000</td>\n",
              "      <td>117.432000</td>\n",
              "      <td>7.907650</td>\n",
              "      <td>35.123850</td>\n",
              "      <td>1487.855000</td>\n",
              "      <td>-0.335200</td>\n",
              "      <td>0.346150</td>\n",
              "      <td>35.120850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>225.000000</td>\n",
              "      <td>226.861000</td>\n",
              "      <td>11.021400</td>\n",
              "      <td>35.288300</td>\n",
              "      <td>1497.670000</td>\n",
              "      <td>-0.335000</td>\n",
              "      <td>0.352200</td>\n",
              "      <td>35.285600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>334.000000</td>\n",
              "      <td>336.851000</td>\n",
              "      <td>13.775450</td>\n",
              "      <td>35.607250</td>\n",
              "      <td>1505.530000</td>\n",
              "      <td>-0.296100</td>\n",
              "      <td>0.367750</td>\n",
              "      <td>35.603200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>443.000000</td>\n",
              "      <td>446.900000</td>\n",
              "      <td>20.648000</td>\n",
              "      <td>35.793200</td>\n",
              "      <td>1523.790000</td>\n",
              "      <td>0.327700</td>\n",
              "      <td>0.501700</td>\n",
              "      <td>35.790500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Depth    Pressure  ...   Turbidity      Oxygen\n",
              "count  871.000000  871.000000  ...  871.000000  871.000000\n",
              "mean   225.208955  227.110232  ...    0.361451   35.350359\n",
              "std    125.862021  126.972414  ...    0.027444    0.254881\n",
              "min      2.000000    2.015000  ...    0.328600   34.630800\n",
              "25%    116.500000  117.432000  ...    0.346150   35.120850\n",
              "50%    225.000000  226.861000  ...    0.352200   35.285600\n",
              "75%    334.000000  336.851000  ...    0.367750   35.603200\n",
              "max    443.000000  446.900000  ...    0.501700   35.790500\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siL32Li5GRBd",
        "colab_type": "code",
        "outputId": "a2997234-7958-490e-e093-a371cc2c5cf1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# Let's plot the depth column\n",
        "data['Depth'].plot();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yo6eAZOXGWyL",
        "colab_type": "code",
        "outputId": "47e5ae1f-6768-4ada-97e9-384ded995ffa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# Now let's plot temperature\n",
        "data['Temperature'].plot();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEBlWEjhGYDB",
        "colab_type": "code",
        "outputId": "10612c9b-e9d3-4629-8e9c-41461f60ce2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "# Temperature vs. Depth\n",
        "ax = data.plot(x='Temperature',y='Depth');\n",
        "ax.invert_yaxis(); # Flip the y-axis"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CtPO2OwG5Nc7L89q+Fe5mNgGYANCli9bGkMi3On8XM1YXcNdbywA4fUB7XpqTS0VVNd3TkxnVoy3H92zL947tRIxuDC2N7IgH+ZxzzszqcSfG0PdNBiYD5OTk1Pv7RcJJwe5yTn3gEwDatkhg++4K3l60iTMHtOfWcb3pmZHic4XS1Bzua8KtZtYBwPua77VvBDrXOC/TaxOJavd5c9UvHdGF47q3AeCi4Z35+8VDFOzii8MN97eAK7ztK4A3a7RfbkEjgCKNt0u0+/P7X/Li7A0A/HtuHv9dvpXLR3blrnP7a/hFfFOXqZAvEnzztK2Z5QF3AfcCr5jZ1cB64ELv9HcJToNcTXAq5FWNULNI2PjbtFX8Y/qa0P6wrDTuPm8A3dom+1iVSN1my1x0gEOn1HKuA64/0qJEIsGcdTt4YGpwOGZAp5ZcPiKL84dmqrcuYUGfmhCpp4qqap79Yj13v7McgO8M6sjfLjrW56pE9qVwF6mHRbmF3PzyQtZu3xNqu+Psfj5WJFI7fYJCpI5KKwLc8so3wd6qeTwvXHsc7XQjDQlD6rmL1EFxWSX/9/pS1mwLBntqs3im3XIybVok+lyZSO0U7iIHUV4V4LdvLee1eXlUBKoBGNWzDb85J1vBLmFN4S5yEJM/Xhuaww7QMTWJe847RlMdJewp3EUOYHd5FfdP/eYuSRcMzeQ352bTMinex6pE6kbhLnIAk2oE+3s3nUi/Di19rEakfjRbRuQAnvjsawAevvhYBbtEHIW7SC3eWPDNendnH9PBx0pEDo+GZURq+GDZFn787LzQ/uTLhuouSRKRFO4iwNbiMv747greWLgp1PbqdSPJyUrzsSqRw6dwFwGO+8O0ffan/7/RZGm6o0Qwhbs0ebk7SkLbz149nBN6ttVQjEQ8hbs0eRc+NhOAO8/J5sRe6T5XI9IwNFtGmrTKQDWbi8oAuHxkV5+rEWk4Cndp0t6q8QZqXKyeDhI99NssTdbyTcXc+u9FADx66VCfqxFpWAp3aZJKKwKc9bdPQ/vjstv5WI1Iw1O4S5N02qSPQ9sL7zxN9z2VqKPZMtLkPDB1JXk7SwFYOvF0WiTqaSDRRz13aVK27y7nb9NWAfDZr8Yo2CVqKdylScm5578ATDipO5mtm/tcjUjjUbhLk7F9d3lo+/az+vlYiUjjU7hLk1AZqA712i8boQ8rSfRTuEvU+3jlNnrd8V5o/+7zBvhYjcjRoXeTJKp99GU+Vz09J7S/6M5xPlYjcvSo5y5R7ZHpa0LbT181jNTmurm1NA0Kd4laeTtLmL1uBwBPXTWM0X0yfK5I5OhRuEtUWphbyAl/+giAQZmpjFGwSxOjMXeJGluKynhh9gamrdjKsk3FofZnfjTcx6pE/KFwl6iwq6yScx76bJ+57BAcZ2/VPMGnqkT8o3CXiFcVqGbI3VOpDDgA2rZIZFTPNlx3cg/6dWjpc3Ui/lC4S8Qbfd/0ULC/eO0IjuuWplUepclTuEtEe2VubmiFx2UTTydZC4GJAJotIxFsUW4hv3x1MQBPXpmjYBep4ZDhbmadzewjM1tuZsvM7CavPc3MpprZKu9ra6/dzOxvZrbazBab2ZDGfhDS9Pzi34sY//cZAJw9sANj++pOSiI11aXnXgXc6pzLBkYA15tZNnAbMM051wuY5u0DnAn08v5MAB5p8Kqlyfp8zXbOf+Rz/j0vD4B7zhvAwxcd63NVIuHnkK9jnXObgc3e9i4zWwF0AsYDo73TngGmA7/y2v/lnHPAF2bWysw6eNcROSzbdpUz6b8reWHWhlDbI5cM4cxjOvhYlUj4qtcgpZllAccCs4B2NQJ7C7D3dXEnILfGt+V5bfuEu5lNINizp0uXLvUsW5qKzUWlnPHXTykqrdynfdqtJ9MjvYVPVYmEvzqHu5m1AF4Dfu6cKzb7ZqqZc86ZmavPX+ycmwxMBsjJyanX90p0KiqtZENBCbk7S1i7bTf3fbhyn+Mn907n4uO6cFKvdJolxPpUpUhkqFO4m1k8wWB/3jk3xWveune4xcw6APle+0agc41vz/TaRA4od0cJJ/75o1qP/WhUN35zTj9qdihE5OAOGe4WfEY9Aaxwzj1Q49BbwBXAvd7XN2u032BmLwHHAUUab5cDqa52zPp6B7O/3rFP+/CsNH46pgcn9UrXB5JEDkNdeu6jgMuAJWa20Gu7nWCov2JmVwPrgQu9Y+8CZwGrgRLgqgatWKLGyq27+NHTc0IfQtrrzetHMahzK5+qEokOdZkt8xlwoK7TKbWc74Drj7AuiVJ5O0uYu24nv3tnOTv2VITa2yQncOe52XxnUEcNv4g0AH2kT46Kddv38L1HPt8n0AGGdGnFr87oy/BuaQp1kQakcJdGt6e8iutfmL9PsN96Wm8uH5ml296JNBKFuzSqD5dtYcKz80L7SfExvHPjifTM0Bx1kcakcJdGsbGwlDH3TaeiqjrU9qfvH8P5QzsTq9kvIo1O4S4Norra8daiTUxdvpX/LNl35uvd5w3gBzmdSYjTIqQiR4vCXY7YR1/mc9XTc77VfsOYnvzslF4KdREfKNzliMxcU7BPsF8xsisn90nnpF7pxMUq1EX8onCXw/bUjK+Z+PZyAHK6tubf143UdEaRMKGulRyWz1dvDwV797bJvDRhhIJdJIyo5y51VlhSwYPTVvHUjHWhtvOHZnL3+AEaghEJMwp3OaSyygB/fHcFr8zNo7QyEGqf9INBfPfYTB8rE5EDUbjLIf3h3RX8a+b60H7zhFhevHaEFvcSCWMKdzmoj1duCwV7jMGZx3TgznOyadcyyefKRORgFO5yQEs3FnHFk7MBuGBoJj87pRed05r7XJWI1IXCXWr17pLN/PT5+QCkNovnLxcM8rkiEakPhbvsY1FuIX/54Cs+W70dgMS4GF6aMMLnqkSkvhTuEpK7o4Tz/jEDV+N25TNuG0vbFon+FSUih0XhLgAUlVTuc4PqvXdGUrCLRCaFu1BYUsFPnpsf2n/qymEc37MNiXGxPlYlIkdC4d6ErS/Yw+OfruW5LzaE2m4c25MxfTN8rEpEGoLCvYnZUFDC87PXM21FPqvzd+9z7Paz+nLNCd19qkxEGpLCvYkoqahi0tSVPP7p19869tcfDGb84I5a+Eskiijcm4C123Yz9v6P92lLSYrj+0MyuXREF3pmpPhUmYg0FoV7FCuvCnDLK4v4z+Jvbnt362m9GdM3g77tU7SSo0gUU7hHod3lVby5cCN3vL401HbpiC785pxszYARaSIU7lFm+lf5XP/8fPZUfLM079KJp9MiUf/UIk2JnvFRoipQzYPTVvHQ/1bv0/7rM/sq2EWaID3ro8QZD366z9TGX5zeh1P7taNPe71ZKtIUKdyjwJ1vLg0F+6zbT9Fa6yKiG2RHurnrdoRupvHYZUMV7CICKNwjWklFFec/OhOAf1wyhNP7t/e5IhEJFwr3CLV8UzHjJn0S2j/rmA4+ViMi4UZj7hFoS1EZZ/3t09D+C9ce52M1IhKOFO4RxjnHLa8sDO1Pu/VkeqS38LEiEQlHGpaJMC/PyeXzNQUAPH55joJdRGqlcI8w//dGcEmBH43qxmnZ7XyuRkTC1SHD3cySzGy2mS0ys2VmNtFr72Zms8xstZm9bGYJXnuit7/aO57VuA+haagMVPPcF+upqg7e4PSqUVn+FiQiYa0uPfdyYKxzbhAwGDjDzEYAfwImOed6AjuBq73zrwZ2eu2TvPPkCF38+BehXnvr5vF0Tmvuc0UiEs4OGe4uaO/n2uO9Pw4YC7zqtT8DnOdtj/f28Y6fYroLxBF5YdYG5qzbCcDI7m1496YTfa5IRMJdncbczSzWzBYC+cBUYA1Q6Jyr8k7JAzp5252AXADveBHQppZrTjCzuWY2d9u2bUf2KKLUzj0VTJmfx+2vLwm1PfjDwXRIbeZjVSISCeo0FdI5FwAGm1kr4HWg75H+xc65ycBkgJycHHek14smuTtKOO/vMyjYU7FP+8MXH0uGlhcQkTqo1zx351yhmX0EjARamVmc1zvPBDZ6p20EOgN5ZhYHpAIFDVhzVFucV8h3Hp6xT9s1J3TjwmGd6d1OKzyKSN0cMtzNLB2o9IK9GXAawTdJPwLOB14CrgDe9L7lLW9/pnf8f8459cwP4d0lm/nbtFV8uWVXqO2xy4YyLrudblwtIvVWl557B+AZM4slOEb/inPuHTNbDrxkZvcAC4AnvPOfAJ41s9XADuCHjVB3RAtUO9Zu283a7Xv4evseJn+ylh37DcFceXyWFgITkcN2yHB3zi0Gjq2lfS0wvJb2MuCCBqkuylRXO15fsJGJby+juKyq1nOuPD6LsX0zOLFX26NcnYhEE60tc5Q89vEa/vjel99qH9WzDecO7Mj3hmSSEKcPDItIw1C4N6LV+bt4+H+reWPhpn3au7VN5q5zszmxVzqxMRpPF5GGp3BvBBsLSxl17/++1d49PZlJFw5mUOdWPlQlIk2Jwr0RXP30nND27Wf15ZLjupKcqB+1iBw9SpwGtmNPRWg642/PzebKUd18rkhEmiK9g9fAZqzeHtoeqOEXEfGJeu4NJFDtePzTtdzrzYjpmJrEoEyFu4j4Qz33BvLavLxQsAM8ddVwzYQREd+o594AnHO8MHsDAEnxMbx/00lktU32uSoRacrUc28A01bkszC3EIAfDuuiYBcR36nnfgSKSiu574OveH7W+lDbpSO6+liRiEiQwv0wbS4q5eqn57J8c3Go7apRWfTMaOFjVSIiQQr3w5C/q4xT7/+YPRWBUNuVx2dx5znZPlYlIvINhfth+M/izaFgj40xzhjQnt+ck61110UkbOgN1Xp6f+lmJr69PLQ/pk8Gf/r+QE17FJGwop57PWzfXc51z80HYGjX1vzl/IF0T9cYu4iEH4V7PSzdWBTafvW6kRqGEZGwpWGZepi/ITiX/cwB7RXsIhLW1HOvg7LKADe+uICpy7cCMLJHG58rEhE5OPXc6+DD5VtDwQ5wXDeFu4iEN4V7Hby3ZHNo+/+N602f9ik+ViMicmgK90P4evueUK+9e3oy153cw+eKREQOTWPuh3DqAx8TqHYA3H5mP+Ji9f+hiIQ/hftB7CqrDAX789ccx6iebX2uSESkbtQNPYi563aGthXsIhJJ1HOvxY49FRx/7zTKKqsBOKN/e58rEhGpH4V7LYbcPTW0fcHQTP5ywSAfqxERqT+F+35mrN4e2l77h7OI0YJgIhKBNOa+n0v+OQuAhy46VsEuIhFL4V5D7o6S0Pa5gzr6WImIyJFRuNcw+ZO1APxsbE+fKxEROTIK9xrWbNsNwKDOrXyuRETkyCjca/h8TQEAQ7q09rkSEZEjo3D3OOdC262TE3ysRETkyCncPdNW5PtdgohIg6lzuJtZrJktMLN3vP1uZjbLzFab2ctmluC1J3r7q73jWY1TesPZtqucO99cCsCVx2f5W4yISAOoT8/9JmBFjf0/AZOccz2BncDVXvvVwE6vfZJ3XthaX7CH8Q9/xqaiMgCGdNV4u4hEvjqFu5llAmcD//T2DRgLvOqd8gxwnrc93tvHO36KheENR7/evocbX1zAyX+ZHgr2C3MyOXOA1pERkchX1+UH/gr8Eth7C6I2QKFzrsrbzwM6edudgFwA51yVmRV553/zuX6flVUGGHPf9NB+57RmPHrpUPp3TPWvKBGRBnTIcDezc4B859w8MxvdUH+xmU0AJgB06dKloS5bJ4vzikLbT105jNF90gnDFxciIoetLsMyo4DvmNk64CWCwzEPAq3MbO9/DpnARm97I9AZwDueChTsf1Hn3GTnXI5zLic9Pf2IHkR9OOe48LGZANxxVj/G9M1QsItI1DlkuDvnfu2cy3TOZQE/BP7nnLsE+Ag43zvtCuBNb/stbx/v+P9czUnkPluzbU9o+wrNjBGRKHUk89x/BdxiZqsJjqk/4bU/AbTx2m8BbjuyEhvW9K+C89nTkhNIiNM0fxGJTvVaz905Nx2Y7m2vBYbXck4ZcEED1NYoXl8QHD26ePjRHecXETmamlTXtaikkmWbigEYrMXBRCSKNZlwd85x8T+/CO2f2Fs3vBaR6NVkwn3Ntj2hXn4DvesAAAiHSURBVPuNY3uSGBfrc0UiIo2nyYT7szPXAdCtbTI/O6WXr7WIiDS2JhHury/I45mZ6wG489xs4mObxMMWkSYs6lPuxdkbuPnlRQB0atWM0b2P3gemRET8EtXhvmNPBb+esiS0//vvDtCnUUWkSajXPPdIULC7nCUbi1i2qZhX5+WF2p/50XBOVq9dRJqIqAj3BRt28twXG5i5Znto+d6ahnZtzYk9NfVRRJqOiA/3h6at4v6pK2s9lhgXw/eGZHLnOdnExGg4RkSajogO9/xdZfsEe0pSHGcN6EC/Din0bpfCMZmppCTF+1ihiIg/IjrcZ63dEdo+uXc6k34wmLTkBB8rEhEJDxEd7iUVVaHthy4+lpbqpYuIABE+FbKsshqAH+R0VrCLiNQQ0T33cwd1JG9nCT8/tbffpYiIhJWIDve05ATuODvb7zJERMJORA/LiIhI7RTuIiJRSOEuIhKFFO4iIlFI4S4iEoUU7iIiUUjhLiIShRTuIiJRyJxzfteAmW0D1tfh1LbA9kYupyFESp0QObWqzoYVKXVC5NTqR51dnXO13oUoLMK9rsxsrnMux+86DiVS6oTIqVV1NqxIqRMip9Zwq1PDMiIiUUjhLiIShSIt3Cf7XUAdRUqdEDm1qs6GFSl1QuTUGlZ1RtSYu4iI1E2k9dxFRKQOFO4iIlEoYsLdzFqZ2atm9qWZrTCzkX7XVBszu9nMlpnZUjN70cyS/K4JwMyeNLN8M1taoy3NzKaa2Srva2s/a9zrALX+xfu3X2xmr5tZKz9r9Gr6Vp01jt1qZs7M2vpR23611Fqnmd3o/UyXmdmf/aqvRj21/bsPNrMvzGyhmc01s+F+1ujV1NnMPjKz5d7P7iavPayeTxET7sCDwPvOub7AIGCFz/V8i5l1An4G5DjnBgCxwA/9rSrkaeCM/dpuA6Y553oB07z9cPA03651KjDAOTcQWAn8+mgXVYun+XadmFlnYByw4WgXdABPs1+dZjYGGA8Mcs71B+7zoa79Pc23f55/BiY65wYDd3r7fqsCbnXOZQMjgOvNLJswez5FRLibWSpwEvAEgHOuwjlX6G9VBxQHNDOzOKA5sMnnegBwzn0C7NiveTzwjLf9DHDeUS3qAGqr1Tn3oXOuytv9Asg86oXt5wA/U4BJwC+BsJitcIA6fwLc65wr987JP+qF7ecAdTqgpbedShg8n5xzm51z873tXQQ7mp0Is+dTRIQ70A3YBjxlZgvM7J9mlux3Uftzzm0k2APaAGwGipxzH/pb1UG1c85t9ra3AO38LKYefgS853cRtTGz8cBG59wiv2s5hN7AiWY2y8w+NrNhfhd0AD8H/mJmuQSfW+Hwii3EzLKAY4FZhNnzKVLCPQ4YAjzinDsW2EP4DCGEeGNs4wn+Z9QRSDazS/2tqm5ccE5sWPQ0D8bM7iD4svh5v2vZn5k1B24nOHwQ7uKANILDCr8AXjEz87ekWv0EuNk51xm4Ge/VezgwsxbAa8DPnXPFNY+Fw/MpUsI9D8hzzs3y9l8lGPbh5lTga+fcNudcJTAFON7nmg5mq5l1APC++v7S/GDM7ErgHOASF54f0OhB8D/2RWa2juDQ0Xwza+9rVbXLA6a4oNlANcGFr8LNFQSfRwD/Bnx/QxXAzOIJBvvzzrm99YXV8ykiwt05twXINbM+XtMpwHIfSzqQDcAIM2vu9YJOIQzf+K3hLYJPHryvb/pYy0GZ2RkEx7G/45wr8bue2jjnljjnMpxzWc65LIIBOsT7/Q03bwBjAMysN5BAeK68uAk42dseC6zysRYAvOf2E8AK59wDNQ6F1/PJORcRf4DBwFxgMcFfzNZ+13SAOicCXwJLgWeBRL9r8up6keD7AJUEQ+dqoA3Bd/VXAf8F0vyu8yC1rgZygYXen0fDsc79jq8D2oZjnQTD/Dnv93Q+MDZM6zwBmAcsIjiuPTQM6jyB4JDL4hq/j2eF2/NJyw+IiEShiBiWERGR+lG4i4hEIYW7iEgUUriLiEQhhbuISBSK87sAkUMxs71TzADaAwGCy1EADHfOVfhSWC3MbDRQ4Zz73O9apGlTuEvYc84VEPycA2b2W2C3c863VQzNLM59s4jZ/kYDu4E6h/shridyWDQsIxHJzIZ6C17NM7MPanzse7qZTfLW/l5hZsPMbIq3xvY93jlZ3jrmz3vnvOqtC3Oo6/7VzOYCN5nZud6iWwvM7L9m1s5bROo64GZv/fETzexpMzu/Rt27va+jzexTM3sLWG5msd6a9XO8Net/fDR/nhJ9FO4SiQx4CDjfOTcUeBL4fY3jFc65HOBRgh8Bvx4YAFzpDfEA9AH+4ZzrBxQDP/XWCznYdROccznOufuBz4ARLriQ3UvAL51z67y/c5JzbrBz7tNDPI4hwE3Oud4EP41Z5JwbBgwDrjWzbvX/0YgEaVhGIlEiwbCe6i1kGEvwY+t7veV9XQIsc94yrGa2FugMFAK5zrkZ3nnPEbzJyvuHuO7LNbYzgZe9nn0C8PVhPI7Zzrm93zcOGFijl58K9DrM64oo3CUiGcHQPtCtFsu9r9U1tvfu7/2d33/dDVeH6+6psf0Q8IBz7i3vTdTfHuB7qvBeIZtZDMH/CGq7ngE3Ouc+OMB1ROpFwzISicqBdPPuo2tm8WbWv57X6GLf3If3YoLDLF/V47qpwEZv+4oa7buAlBr764Ch3vZ3gPgDXO8D4Cfe0BBm1jscb0gjkUPhLpGoGjgf+JOZLSK4Kl99183/iuC9L1cArQneCKaiHtf9LfBvM5vHvkvlvg18d+8bqsDjwMne9Uayb2+9pn8SXMZ6vgVvEP0YemUtR0CrQkqT481qeccFb2IuEpXUcxcRiULquYuIRCH13EVEopDCXUQkCincRUSikMJdRCQKKdxFRKLQ/we+F96wNLWgUQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gfQT6GBSGma8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data.describe().to_csv('ctd_summary.csv');"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}