Copy_of_Python4Scientists_Lesson3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Python4Scientists_Lesson3.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOkmII1vKNNQDyQCmlJJmD7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cordmaur/38Cloud-Medium/blob/master/Copy_of_Python4Scientists_Lesson3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ml4Ml4nmw5u"
      },
      "source": [
        "from Lesson 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7xfcJ6pmzP6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0892e97f-6b9e-4dda-cc97-4fef64375a3c"
      },
      "source": [
        "# Suppose 3 students, with its ages:\n",
        "\n",
        "lst = [('A', 11), ('B', 9), ('C', 10)]\n",
        "lst"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('A', 11), ('B', 9), ('C', 10)]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHgaiX0MablB"
      },
      "source": [
        "# Introduction to Python for Scientists - Lesson 3"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LdU4ssmaEar4"
      },
      "source": [
        "### Dictionaries\n",
        "Similar to lists, but with a key to identify each item<br>\n",
        "{key: value, key: value, key:value, ....}<br>\n",
        "Keys are imutables <br>\n",
        "<b>Note:</b> As of Python version 3.7, dictionaries are ordered. In Python 3.6 and earlier, dictionaries are unordered.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ljXEj8TaV1fN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11f1767e-e8ce-4720-8c8c-64475144b0a3"
      },
      "source": [
        "# if we received the structure proposed before, how could we get the age of student named C\n",
        "# solution: go through the list, check the first item of the tuple, if it is C, get the second item of the tuple...\n",
        "desired_student = 'C'\n",
        "\n",
        "for student in lst:\n",
        "    if student[0] == desired_student:   \n",
        "        print(f'Student {desired_student} has {student[1]} years old')"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Student C has 10 years old\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SPPttmCAV1b7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b5e2452-06b3-4902-990f-0f155de1673b"
      },
      "source": [
        "# if data had been passed by 2 lists... we needed to use the common index to locate the age\n",
        "names = ['A', 'B', 'C']\n",
        "ages = [11, 9, 10]\n",
        "\n",
        "desired_student == 'C'\n",
        "\n",
        "idx = names.index(desired_student)\n",
        "print(ages[idx])"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQAseaC4V1Pt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1813052c-fb7a-4709-9cc2-db1f2234ca36"
      },
      "source": [
        "# using dictionaries\n",
        "students = {'A': 11, 'B': 9, 'C': 10}\n",
        "students"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'A': 11, 'B': 9, 'C': 10}"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JOn_fErFk-EU",
        "outputId": "44b20d8c-b8b8-43f3-95de-f7644f89a9d3"
      },
      "source": [
        "students['C']"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kCGDvR7ZXiq-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc7106f8-0266-456e-911d-0f4130b8e956"
      },
      "source": [
        "# nothing prevent us to create inner dictionaries:\n",
        "students = {'A': {'age': 11, 'Height': 145},\n",
        "            'B': {'age': 9, 'Height': 144.2},\n",
        "            'C': {'age': 10, 'Height': 147},\n",
        "            }\n",
        "\n",
        "desired_student = 'C'\n",
        "students[desired_student]"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Height': 147, 'age': 10}"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1O4K8ykYtRj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b21c028-9b60-4702-dbf6-14df77a33cf4"
      },
      "source": [
        "students[desired_student]['Height']"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "147"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZlPTKw8X6PT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cfefbf0-147c-4e8e-ba7c-48277f2a19d9"
      },
      "source": [
        "desired_student = 'B'\n",
        "print(f\"Student {desired_student} has {students[desired_student]['age']} and measures {students[desired_student]['Height']}\")"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Student B has 9 and measures 144.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aTgztBnDwQLT",
        "outputId": "19138161-6daa-422a-c66c-661148d1a34c"
      },
      "source": [
        "# updating the Dictionary\n",
        "students['A']['age'] = 12\n",
        "students"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'A': {'Height': 145, 'age': 12},\n",
              " 'B': {'Height': 144.2, 'age': 9},\n",
              " 'C': {'Height': 147, 'age': 10}}"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dJydkXQUwaqi",
        "outputId": "68006e78-a985-4b8a-fcab-0b34bb76d8da"
      },
      "source": [
        "students.update({'D': {'Height': 146, 'age': 11}})\n",
        "students"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'A': {'Height': 145, 'age': 12},\n",
              " 'B': {'Height': 144.2, 'age': 9},\n",
              " 'C': {'Height': 147, 'age': 10},\n",
              " 'D': {'Height': 146, 'age': 11}}"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CizhCC36wo1k",
        "outputId": "71ec41c3-6992-48ba-f0eb-d5dbc73223f1"
      },
      "source": [
        "students['E'] = 'no information'\n",
        "students"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'A': {'Height': 145, 'age': 12},\n",
              " 'B': {'Height': 144.2, 'age': 9},\n",
              " 'C': {'Height': 147, 'age': 10},\n",
              " 'D': {'Height': 146, 'age': 11},\n",
              " 'E': 'no information'}"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJ4O7WKZ79Sq",
        "outputId": "c28c4066-eba1-4e81-c7b3-8750813abb44"
      },
      "source": [
        "students['D']['age']"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r9JvYOMyXh6d"
      },
      "source": [
        "Back to our hydrological example"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hzwf42N1r6Tn",
        "outputId": "55692801-6820-41d2-a48a-9dfc41781df5"
      },
      "source": [
        "# from Lesson 2\n",
        "\n",
        "import random\n",
        "# create the days and daily rain\n",
        "random.seed(1)\n",
        "\n",
        "daily_rain = []\n",
        "day_of_month = []\n",
        "\n",
        "for i in range(1, 32, 1):\n",
        "    day_of_month.append(i)\n",
        "    daily_rain.append(random.randint(0, 100))\n",
        "\n",
        "str(day_of_month), str(daily_rain)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]',\n",
              " '[17, 72, 97, 8, 32, 15, 63, 97, 57, 60, 83, 48, 100, 26, 12, 62, 3, 49, 55, 77, 97, 98, 0, 89, 57, 34, 92, 29, 75, 13, 40]')"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0Zelq4Hyer5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63a0d00c-d677-4c09-8247-230372fba1b3"
      },
      "source": [
        "# what happened on day X \n",
        "day = 15\n",
        "idx = day_of_month.index(day)\n",
        "print(f'Index for day {day} is idx:{idx}' )\n",
        "daily_rain[idx]"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index for day 15 is idx:14\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D0oTyaYLsaXu",
        "outputId": "fbc11b6a-58bd-47c4-8f8f-dab8ebc0bfd9"
      },
      "source": [
        "rain_dict = {}\n",
        "for idx, day in enumerate(day_of_month):\n",
        "    # rain_dict[day] = daily_rain[idx]\n",
        "    rain_dict.update({day: daily_rain[idx]})\n",
        "\n",
        "rain_dict"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1: 17,\n",
              " 2: 72,\n",
              " 3: 97,\n",
              " 4: 8,\n",
              " 5: 32,\n",
              " 6: 15,\n",
              " 7: 63,\n",
              " 8: 97,\n",
              " 9: 57,\n",
              " 10: 60,\n",
              " 11: 83,\n",
              " 12: 48,\n",
              " 13: 100,\n",
              " 14: 26,\n",
              " 15: 12,\n",
              " 16: 62,\n",
              " 17: 3,\n",
              " 18: 49,\n",
              " 19: 55,\n",
              " 20: 77,\n",
              " 21: 97,\n",
              " 22: 98,\n",
              " 23: 0,\n",
              " 24: 89,\n",
              " 25: 57,\n",
              " 26: 34,\n",
              " 27: 92,\n",
              " 28: 29,\n",
              " 29: 75,\n",
              " 30: 13,\n",
              " 31: 40}"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R0oM5Uv-ziE7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "901d3ec8-0545-4dd2-fef0-3ffce2a3ffa9"
      },
      "source": [
        "rain_dict[29]"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "75"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8wz098ikz1se"
      },
      "source": [
        "What if I wanted to create a complete time series?\n",
        "\n",
        "Let's test it with another data (river stage = river level)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjhYTjPUzu6I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "3590996a-af1e-4db9-d82e-28b4cb00419d"
      },
      "source": [
        "# Now I will create a random walk (non-stationary) to represent river stage\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "random.seed(2)\n",
        "\n",
        "river_stage = [100]\n",
        "\n",
        "for _ in range(365):\n",
        "    river_stage.append(river_stage[-1] + random.randint(-2, 2))    \n",
        "\n",
        "plt.plot(river_stage)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fddb0592b90>]"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uvHI2wotta1-"
      },
      "source": [
        "what happened on 1st of July? We could count, but it depends on the number of days that happen in february... :(\n",
        "\n",
        "\n",
        "We could create a dictionary with sequential keys (1, 2, 3...) but that would not be helpful either... "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8tmHvZgwy4p5"
      },
      "source": [
        "## Datetime Basics\n",
        "\n",
        "**datetime** class represents a static date/time <br>\n",
        "**timedelta** represents a period measured in days or months or minutes, etc\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1tCtVqhFzVY8"
      },
      "source": [
        "from datetime import datetime, timedelta"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAMzEsmDYR34",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c9a2202-b66e-4b8d-ca0b-d258d39bab43"
      },
      "source": [
        "datetime.now()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2021, 12, 7, 17, 42, 14, 287797)"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1fQ3rXetsyZ"
      },
      "source": [
        "datetime?"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nEeBEYLJy-IN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc871f9a-25da-483c-fe2d-5735e9ab02c6"
      },
      "source": [
        "dt1 = datetime(2021, 1, 1)\n",
        "dt1"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2021, 1, 1, 0, 0)"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KJLQCN0fYNSi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87adb0bf-ac8c-49f6-9a86-6243f0b59e4a"
      },
      "source": [
        "dt2 = dt1 + timedelta(days=1)\n",
        "dt2"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.datetime(2021, 1, 2, 0, 0)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e2NWjY2kYfTV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63163a3f-b65f-4fa3-da7e-8e1ceada769a"
      },
      "source": [
        "dt2 - dt1"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime.timedelta(days=1)"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmCTSl4aypuO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "569a7b1a-0964-4d25-d683-d6653686df43"
      },
      "source": [
        "dt2.day, dt2.month, dt2.year, dt2.hour, dt2.minute, dt2.second"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 1, 2021, 0, 0, 0)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bdzfas5Zu1ZZ"
      },
      "source": [
        "Now we have the river stage in a list and we know how to create dates. How can we create a dictionary to store stages into the correct dates?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QU2hel_g2SnD"
      },
      "source": [
        "## Dictionary comprehension"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XusqB1iiBcl5",
        "outputId": "0c3fb5d6-2744-45ec-a244-a4f1131d5ca5"
      },
      "source": [
        "stages = {}\n",
        "\n",
        "for idx, stage in enumerate(river_stage):\n",
        "    stages[first_day + timedelta(days=idx)] = stage\n",
        "\n",
        "stages"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{datetime.datetime(2020, 1, 1, 0, 0): 100,\n",
              " datetime.datetime(2020, 1, 2, 0, 0): 98,\n",
              " datetime.datetime(2020, 1, 3, 0, 0): 96,\n",
              " datetime.datetime(2020, 1, 4, 0, 0): 94,\n",
              " datetime.datetime(2020, 1, 5, 0, 0): 94,\n",
              " datetime.datetime(2020, 1, 6, 0, 0): 93,\n",
              " datetime.datetime(2020, 1, 7, 0, 0): 93,\n",
              " datetime.datetime(2020, 1, 8, 0, 0): 93,\n",
              " datetime.datetime(2020, 1, 9, 0, 0): 95,\n",
              " datetime.datetime(2020, 1, 10, 0, 0): 94,\n",
              " datetime.datetime(2020, 1, 11, 0, 0): 96,\n",
              " datetime.datetime(2020, 1, 12, 0, 0): 94,\n",
              " datetime.datetime(2020, 1, 13, 0, 0): 96,\n",
              " datetime.datetime(2020, 1, 14, 0, 0): 95,\n",
              " datetime.datetime(2020, 1, 15, 0, 0): 96,\n",
              " datetime.datetime(2020, 1, 16, 0, 0): 97,\n",
              " datetime.datetime(2020, 1, 17, 0, 0): 99,\n",
              " datetime.datetime(2020, 1, 18, 0, 0): 99,\n",
              " datetime.datetime(2020, 1, 19, 0, 0): 101,\n",
              " datetime.datetime(2020, 1, 20, 0, 0): 102,\n",
              " datetime.datetime(2020, 1, 21, 0, 0): 104,\n",
              " datetime.datetime(2020, 1, 22, 0, 0): 104,\n",
              " datetime.datetime(2020, 1, 23, 0, 0): 102,\n",
              " datetime.datetime(2020, 1, 24, 0, 0): 100,\n",
              " datetime.datetime(2020, 1, 25, 0, 0): 100,\n",
              " datetime.datetime(2020, 1, 26, 0, 0): 101,\n",
              " datetime.datetime(2020, 1, 27, 0, 0): 101,\n",
              " datetime.datetime(2020, 1, 28, 0, 0): 102,\n",
              " datetime.datetime(2020, 1, 29, 0, 0): 103,\n",
              " datetime.datetime(2020, 1, 30, 0, 0): 105,\n",
              " datetime.datetime(2020, 1, 31, 0, 0): 104,\n",
              " datetime.datetime(2020, 2, 1, 0, 0): 106,\n",
              " datetime.datetime(2020, 2, 2, 0, 0): 105,\n",
              " datetime.datetime(2020, 2, 3, 0, 0): 104,\n",
              " datetime.datetime(2020, 2, 4, 0, 0): 103,\n",
              " datetime.datetime(2020, 2, 5, 0, 0): 101,\n",
              " datetime.datetime(2020, 2, 6, 0, 0): 100,\n",
              " datetime.datetime(2020, 2, 7, 0, 0): 100,\n",
              " datetime.datetime(2020, 2, 8, 0, 0): 99,\n",
              " datetime.datetime(2020, 2, 9, 0, 0): 98,\n",
              " datetime.datetime(2020, 2, 10, 0, 0): 100,\n",
              " datetime.datetime(2020, 2, 11, 0, 0): 102,\n",
              " datetime.datetime(2020, 2, 12, 0, 0): 102,\n",
              " datetime.datetime(2020, 2, 13, 0, 0): 104,\n",
              " datetime.datetime(2020, 2, 14, 0, 0): 106,\n",
              " datetime.datetime(2020, 2, 15, 0, 0): 105,\n",
              " datetime.datetime(2020, 2, 16, 0, 0): 106,\n",
              " datetime.datetime(2020, 2, 17, 0, 0): 107,\n",
              " datetime.datetime(2020, 2, 18, 0, 0): 109,\n",
              " datetime.datetime(2020, 2, 19, 0, 0): 109,\n",
              " datetime.datetime(2020, 2, 20, 0, 0): 111,\n",
              " datetime.datetime(2020, 2, 21, 0, 0): 111,\n",
              " datetime.datetime(2020, 2, 22, 0, 0): 111,\n",
              " datetime.datetime(2020, 2, 23, 0, 0): 112,\n",
              " datetime.datetime(2020, 2, 24, 0, 0): 111,\n",
              " datetime.datetime(2020, 2, 25, 0, 0): 112,\n",
              " datetime.datetime(2020, 2, 26, 0, 0): 113,\n",
              " datetime.datetime(2020, 2, 27, 0, 0): 115,\n",
              " datetime.datetime(2020, 2, 28, 0, 0): 114,\n",
              " datetime.datetime(2020, 2, 29, 0, 0): 115,\n",
              " datetime.datetime(2020, 3, 1, 0, 0): 115,\n",
              " datetime.datetime(2020, 3, 2, 0, 0): 116,\n",
              " datetime.datetime(2020, 3, 3, 0, 0): 118,\n",
              " datetime.datetime(2020, 3, 4, 0, 0): 120,\n",
              " datetime.datetime(2020, 3, 5, 0, 0): 120,\n",
              " datetime.datetime(2020, 3, 6, 0, 0): 121,\n",
              " datetime.datetime(2020, 3, 7, 0, 0): 122,\n",
              " datetime.datetime(2020, 3, 8, 0, 0): 122,\n",
              " datetime.datetime(2020, 3, 9, 0, 0): 124,\n",
              " datetime.datetime(2020, 3, 10, 0, 0): 126,\n",
              " datetime.datetime(2020, 3, 11, 0, 0): 127,\n",
              " datetime.datetime(2020, 3, 12, 0, 0): 128,\n",
              " datetime.datetime(2020, 3, 13, 0, 0): 127,\n",
              " datetime.datetime(2020, 3, 14, 0, 0): 127,\n",
              " datetime.datetime(2020, 3, 15, 0, 0): 126,\n",
              " datetime.datetime(2020, 3, 16, 0, 0): 128,\n",
              " datetime.datetime(2020, 3, 17, 0, 0): 128,\n",
              " datetime.datetime(2020, 3, 18, 0, 0): 129,\n",
              " datetime.datetime(2020, 3, 19, 0, 0): 129,\n",
              " datetime.datetime(2020, 3, 20, 0, 0): 129,\n",
              " datetime.datetime(2020, 3, 21, 0, 0): 131,\n",
              " datetime.datetime(2020, 3, 22, 0, 0): 133,\n",
              " datetime.datetime(2020, 3, 23, 0, 0): 135,\n",
              " datetime.datetime(2020, 3, 24, 0, 0): 137,\n",
              " datetime.datetime(2020, 3, 25, 0, 0): 139,\n",
              " datetime.datetime(2020, 3, 26, 0, 0): 141,\n",
              " datetime.datetime(2020, 3, 27, 0, 0): 142,\n",
              " datetime.datetime(2020, 3, 28, 0, 0): 142,\n",
              " datetime.datetime(2020, 3, 29, 0, 0): 141,\n",
              " datetime.datetime(2020, 3, 30, 0, 0): 142,\n",
              " datetime.datetime(2020, 3, 31, 0, 0): 144,\n",
              " datetime.datetime(2020, 4, 1, 0, 0): 144,\n",
              " datetime.datetime(2020, 4, 2, 0, 0): 146,\n",
              " datetime.datetime(2020, 4, 3, 0, 0): 144,\n",
              " datetime.datetime(2020, 4, 4, 0, 0): 144,\n",
              " datetime.datetime(2020, 4, 5, 0, 0): 142,\n",
              " datetime.datetime(2020, 4, 6, 0, 0): 141,\n",
              " datetime.datetime(2020, 4, 7, 0, 0): 139,\n",
              " datetime.datetime(2020, 4, 8, 0, 0): 137,\n",
              " datetime.datetime(2020, 4, 9, 0, 0): 139,\n",
              " datetime.datetime(2020, 4, 10, 0, 0): 137,\n",
              " datetime.datetime(2020, 4, 11, 0, 0): 137,\n",
              " datetime.datetime(2020, 4, 12, 0, 0): 139,\n",
              " datetime.datetime(2020, 4, 13, 0, 0): 138,\n",
              " datetime.datetime(2020, 4, 14, 0, 0): 136,\n",
              " datetime.datetime(2020, 4, 15, 0, 0): 138,\n",
              " datetime.datetime(2020, 4, 16, 0, 0): 137,\n",
              " datetime.datetime(2020, 4, 17, 0, 0): 137,\n",
              " datetime.datetime(2020, 4, 18, 0, 0): 136,\n",
              " datetime.datetime(2020, 4, 19, 0, 0): 135,\n",
              " datetime.datetime(2020, 4, 20, 0, 0): 133,\n",
              " datetime.datetime(2020, 4, 21, 0, 0): 134,\n",
              " datetime.datetime(2020, 4, 22, 0, 0): 132,\n",
              " datetime.datetime(2020, 4, 23, 0, 0): 130,\n",
              " datetime.datetime(2020, 4, 24, 0, 0): 130,\n",
              " datetime.datetime(2020, 4, 25, 0, 0): 130,\n",
              " datetime.datetime(2020, 4, 26, 0, 0): 129,\n",
              " datetime.datetime(2020, 4, 27, 0, 0): 128,\n",
              " datetime.datetime(2020, 4, 28, 0, 0): 126,\n",
              " datetime.datetime(2020, 4, 29, 0, 0): 124,\n",
              " datetime.datetime(2020, 4, 30, 0, 0): 122,\n",
              " datetime.datetime(2020, 5, 1, 0, 0): 120,\n",
              " datetime.datetime(2020, 5, 2, 0, 0): 118,\n",
              " datetime.datetime(2020, 5, 3, 0, 0): 116,\n",
              " datetime.datetime(2020, 5, 4, 0, 0): 114,\n",
              " datetime.datetime(2020, 5, 5, 0, 0): 114,\n",
              " datetime.datetime(2020, 5, 6, 0, 0): 114,\n",
              " datetime.datetime(2020, 5, 7, 0, 0): 113,\n",
              " datetime.datetime(2020, 5, 8, 0, 0): 112,\n",
              " datetime.datetime(2020, 5, 9, 0, 0): 111,\n",
              " datetime.datetime(2020, 5, 10, 0, 0): 113,\n",
              " datetime.datetime(2020, 5, 11, 0, 0): 111,\n",
              " datetime.datetime(2020, 5, 12, 0, 0): 112,\n",
              " datetime.datetime(2020, 5, 13, 0, 0): 114,\n",
              " datetime.datetime(2020, 5, 14, 0, 0): 112,\n",
              " datetime.datetime(2020, 5, 15, 0, 0): 111,\n",
              " datetime.datetime(2020, 5, 16, 0, 0): 110,\n",
              " datetime.datetime(2020, 5, 17, 0, 0): 108,\n",
              " datetime.datetime(2020, 5, 18, 0, 0): 106,\n",
              " datetime.datetime(2020, 5, 19, 0, 0): 106,\n",
              " datetime.datetime(2020, 5, 20, 0, 0): 108,\n",
              " datetime.datetime(2020, 5, 21, 0, 0): 106,\n",
              " datetime.datetime(2020, 5, 22, 0, 0): 106,\n",
              " datetime.datetime(2020, 5, 23, 0, 0): 106,\n",
              " datetime.datetime(2020, 5, 24, 0, 0): 107,\n",
              " datetime.datetime(2020, 5, 25, 0, 0): 105,\n",
              " datetime.datetime(2020, 5, 26, 0, 0): 105,\n",
              " datetime.datetime(2020, 5, 27, 0, 0): 106,\n",
              " datetime.datetime(2020, 5, 28, 0, 0): 108,\n",
              " datetime.datetime(2020, 5, 29, 0, 0): 110,\n",
              " datetime.datetime(2020, 5, 30, 0, 0): 108,\n",
              " datetime.datetime(2020, 5, 31, 0, 0): 108,\n",
              " datetime.datetime(2020, 6, 1, 0, 0): 109,\n",
              " datetime.datetime(2020, 6, 2, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 3, 0, 0): 110,\n",
              " datetime.datetime(2020, 6, 4, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 5, 0, 0): 110,\n",
              " datetime.datetime(2020, 6, 6, 0, 0): 108,\n",
              " datetime.datetime(2020, 6, 7, 0, 0): 108,\n",
              " datetime.datetime(2020, 6, 8, 0, 0): 106,\n",
              " datetime.datetime(2020, 6, 9, 0, 0): 104,\n",
              " datetime.datetime(2020, 6, 10, 0, 0): 105,\n",
              " datetime.datetime(2020, 6, 11, 0, 0): 104,\n",
              " datetime.datetime(2020, 6, 12, 0, 0): 106,\n",
              " datetime.datetime(2020, 6, 13, 0, 0): 108,\n",
              " datetime.datetime(2020, 6, 14, 0, 0): 109,\n",
              " datetime.datetime(2020, 6, 15, 0, 0): 110,\n",
              " datetime.datetime(2020, 6, 16, 0, 0): 112,\n",
              " datetime.datetime(2020, 6, 17, 0, 0): 112,\n",
              " datetime.datetime(2020, 6, 18, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 19, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 20, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 21, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 22, 0, 0): 113,\n",
              " datetime.datetime(2020, 6, 23, 0, 0): 114,\n",
              " datetime.datetime(2020, 6, 24, 0, 0): 112,\n",
              " datetime.datetime(2020, 6, 25, 0, 0): 114,\n",
              " datetime.datetime(2020, 6, 26, 0, 0): 113,\n",
              " datetime.datetime(2020, 6, 27, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 28, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 29, 0, 0): 109,\n",
              " datetime.datetime(2020, 6, 30, 0, 0): 108,\n",
              " datetime.datetime(2020, 7, 1, 0, 0): 107,\n",
              " datetime.datetime(2020, 7, 2, 0, 0): 106,\n",
              " datetime.datetime(2020, 7, 3, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 4, 0, 0): 105,\n",
              " datetime.datetime(2020, 7, 5, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 6, 0, 0): 106,\n",
              " datetime.datetime(2020, 7, 7, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 8, 0, 0): 103,\n",
              " datetime.datetime(2020, 7, 9, 0, 0): 102,\n",
              " datetime.datetime(2020, 7, 10, 0, 0): 103,\n",
              " datetime.datetime(2020, 7, 11, 0, 0): 101,\n",
              " datetime.datetime(2020, 7, 12, 0, 0): 101,\n",
              " datetime.datetime(2020, 7, 13, 0, 0): 99,\n",
              " datetime.datetime(2020, 7, 14, 0, 0): 101,\n",
              " datetime.datetime(2020, 7, 15, 0, 0): 100,\n",
              " datetime.datetime(2020, 7, 16, 0, 0): 102,\n",
              " datetime.datetime(2020, 7, 17, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 18, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 19, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 20, 0, 0): 105,\n",
              " datetime.datetime(2020, 7, 21, 0, 0): 105,\n",
              " datetime.datetime(2020, 7, 22, 0, 0): 107,\n",
              " datetime.datetime(2020, 7, 23, 0, 0): 105,\n",
              " datetime.datetime(2020, 7, 24, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 25, 0, 0): 102,\n",
              " datetime.datetime(2020, 7, 26, 0, 0): 103,\n",
              " datetime.datetime(2020, 7, 27, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 28, 0, 0): 103,\n",
              " datetime.datetime(2020, 7, 29, 0, 0): 101,\n",
              " datetime.datetime(2020, 7, 30, 0, 0): 103,\n",
              " datetime.datetime(2020, 7, 31, 0, 0): 101,\n",
              " datetime.datetime(2020, 8, 1, 0, 0): 100,\n",
              " datetime.datetime(2020, 8, 2, 0, 0): 98,\n",
              " datetime.datetime(2020, 8, 3, 0, 0): 96,\n",
              " datetime.datetime(2020, 8, 4, 0, 0): 94,\n",
              " datetime.datetime(2020, 8, 5, 0, 0): 93,\n",
              " datetime.datetime(2020, 8, 6, 0, 0): 92,\n",
              " datetime.datetime(2020, 8, 7, 0, 0): 90,\n",
              " datetime.datetime(2020, 8, 8, 0, 0): 89,\n",
              " datetime.datetime(2020, 8, 9, 0, 0): 87,\n",
              " datetime.datetime(2020, 8, 10, 0, 0): 89,\n",
              " datetime.datetime(2020, 8, 11, 0, 0): 90,\n",
              " datetime.datetime(2020, 8, 12, 0, 0): 91,\n",
              " datetime.datetime(2020, 8, 13, 0, 0): 91,\n",
              " datetime.datetime(2020, 8, 14, 0, 0): 93,\n",
              " datetime.datetime(2020, 8, 15, 0, 0): 94,\n",
              " datetime.datetime(2020, 8, 16, 0, 0): 93,\n",
              " datetime.datetime(2020, 8, 17, 0, 0): 92,\n",
              " datetime.datetime(2020, 8, 18, 0, 0): 93,\n",
              " datetime.datetime(2020, 8, 19, 0, 0): 94,\n",
              " datetime.datetime(2020, 8, 20, 0, 0): 96,\n",
              " datetime.datetime(2020, 8, 21, 0, 0): 94,\n",
              " datetime.datetime(2020, 8, 22, 0, 0): 96,\n",
              " datetime.datetime(2020, 8, 23, 0, 0): 98,\n",
              " datetime.datetime(2020, 8, 24, 0, 0): 96,\n",
              " datetime.datetime(2020, 8, 25, 0, 0): 97,\n",
              " datetime.datetime(2020, 8, 26, 0, 0): 99,\n",
              " datetime.datetime(2020, 8, 27, 0, 0): 101,\n",
              " datetime.datetime(2020, 8, 28, 0, 0): 100,\n",
              " datetime.datetime(2020, 8, 29, 0, 0): 98,\n",
              " datetime.datetime(2020, 8, 30, 0, 0): 99,\n",
              " datetime.datetime(2020, 8, 31, 0, 0): 99,\n",
              " datetime.datetime(2020, 9, 1, 0, 0): 97,\n",
              " datetime.datetime(2020, 9, 2, 0, 0): 99,\n",
              " datetime.datetime(2020, 9, 3, 0, 0): 97,\n",
              " datetime.datetime(2020, 9, 4, 0, 0): 99,\n",
              " datetime.datetime(2020, 9, 5, 0, 0): 99,\n",
              " datetime.datetime(2020, 9, 6, 0, 0): 99,\n",
              " datetime.datetime(2020, 9, 7, 0, 0): 99,\n",
              " datetime.datetime(2020, 9, 8, 0, 0): 99,\n",
              " datetime.datetime(2020, 9, 9, 0, 0): 97,\n",
              " datetime.datetime(2020, 9, 10, 0, 0): 98,\n",
              " datetime.datetime(2020, 9, 11, 0, 0): 96,\n",
              " datetime.datetime(2020, 9, 12, 0, 0): 94,\n",
              " datetime.datetime(2020, 9, 13, 0, 0): 94,\n",
              " datetime.datetime(2020, 9, 14, 0, 0): 93,\n",
              " datetime.datetime(2020, 9, 15, 0, 0): 91,\n",
              " datetime.datetime(2020, 9, 16, 0, 0): 92,\n",
              " datetime.datetime(2020, 9, 17, 0, 0): 90,\n",
              " datetime.datetime(2020, 9, 18, 0, 0): 91,\n",
              " datetime.datetime(2020, 9, 19, 0, 0): 92,\n",
              " datetime.datetime(2020, 9, 20, 0, 0): 93,\n",
              " datetime.datetime(2020, 9, 21, 0, 0): 92,\n",
              " datetime.datetime(2020, 9, 22, 0, 0): 94,\n",
              " datetime.datetime(2020, 9, 23, 0, 0): 96,\n",
              " datetime.datetime(2020, 9, 24, 0, 0): 94,\n",
              " datetime.datetime(2020, 9, 25, 0, 0): 92,\n",
              " datetime.datetime(2020, 9, 26, 0, 0): 92,\n",
              " datetime.datetime(2020, 9, 27, 0, 0): 90,\n",
              " datetime.datetime(2020, 9, 28, 0, 0): 90,\n",
              " datetime.datetime(2020, 9, 29, 0, 0): 90,\n",
              " datetime.datetime(2020, 9, 30, 0, 0): 88,\n",
              " datetime.datetime(2020, 10, 1, 0, 0): 87,\n",
              " datetime.datetime(2020, 10, 2, 0, 0): 88,\n",
              " datetime.datetime(2020, 10, 3, 0, 0): 87,\n",
              " datetime.datetime(2020, 10, 4, 0, 0): 85,\n",
              " datetime.datetime(2020, 10, 5, 0, 0): 87,\n",
              " datetime.datetime(2020, 10, 6, 0, 0): 87,\n",
              " datetime.datetime(2020, 10, 7, 0, 0): 88,\n",
              " datetime.datetime(2020, 10, 8, 0, 0): 89,\n",
              " datetime.datetime(2020, 10, 9, 0, 0): 88,\n",
              " datetime.datetime(2020, 10, 10, 0, 0): 88,\n",
              " datetime.datetime(2020, 10, 11, 0, 0): 89,\n",
              " datetime.datetime(2020, 10, 12, 0, 0): 87,\n",
              " datetime.datetime(2020, 10, 13, 0, 0): 87,\n",
              " datetime.datetime(2020, 10, 14, 0, 0): 85,\n",
              " datetime.datetime(2020, 10, 15, 0, 0): 83,\n",
              " datetime.datetime(2020, 10, 16, 0, 0): 81,\n",
              " datetime.datetime(2020, 10, 17, 0, 0): 83,\n",
              " datetime.datetime(2020, 10, 18, 0, 0): 83,\n",
              " datetime.datetime(2020, 10, 19, 0, 0): 84,\n",
              " datetime.datetime(2020, 10, 20, 0, 0): 83,\n",
              " datetime.datetime(2020, 10, 21, 0, 0): 81,\n",
              " datetime.datetime(2020, 10, 22, 0, 0): 79,\n",
              " datetime.datetime(2020, 10, 23, 0, 0): 81,\n",
              " datetime.datetime(2020, 10, 24, 0, 0): 82,\n",
              " datetime.datetime(2020, 10, 25, 0, 0): 80,\n",
              " datetime.datetime(2020, 10, 26, 0, 0): 81,\n",
              " datetime.datetime(2020, 10, 27, 0, 0): 81,\n",
              " datetime.datetime(2020, 10, 28, 0, 0): 81,\n",
              " datetime.datetime(2020, 10, 29, 0, 0): 82,\n",
              " datetime.datetime(2020, 10, 30, 0, 0): 81,\n",
              " datetime.datetime(2020, 10, 31, 0, 0): 81,\n",
              " datetime.datetime(2020, 11, 1, 0, 0): 81,\n",
              " datetime.datetime(2020, 11, 2, 0, 0): 82,\n",
              " datetime.datetime(2020, 11, 3, 0, 0): 84,\n",
              " datetime.datetime(2020, 11, 4, 0, 0): 85,\n",
              " datetime.datetime(2020, 11, 5, 0, 0): 86,\n",
              " datetime.datetime(2020, 11, 6, 0, 0): 87,\n",
              " datetime.datetime(2020, 11, 7, 0, 0): 87,\n",
              " datetime.datetime(2020, 11, 8, 0, 0): 88,\n",
              " datetime.datetime(2020, 11, 9, 0, 0): 87,\n",
              " datetime.datetime(2020, 11, 10, 0, 0): 86,\n",
              " datetime.datetime(2020, 11, 11, 0, 0): 87,\n",
              " datetime.datetime(2020, 11, 12, 0, 0): 89,\n",
              " datetime.datetime(2020, 11, 13, 0, 0): 89,\n",
              " datetime.datetime(2020, 11, 14, 0, 0): 91,\n",
              " datetime.datetime(2020, 11, 15, 0, 0): 92,\n",
              " datetime.datetime(2020, 11, 16, 0, 0): 90,\n",
              " datetime.datetime(2020, 11, 17, 0, 0): 92,\n",
              " datetime.datetime(2020, 11, 18, 0, 0): 94,\n",
              " datetime.datetime(2020, 11, 19, 0, 0): 92,\n",
              " datetime.datetime(2020, 11, 20, 0, 0): 90,\n",
              " datetime.datetime(2020, 11, 21, 0, 0): 90,\n",
              " datetime.datetime(2020, 11, 22, 0, 0): 89,\n",
              " datetime.datetime(2020, 11, 23, 0, 0): 91,\n",
              " datetime.datetime(2020, 11, 24, 0, 0): 90,\n",
              " datetime.datetime(2020, 11, 25, 0, 0): 91,\n",
              " datetime.datetime(2020, 11, 26, 0, 0): 89,\n",
              " datetime.datetime(2020, 11, 27, 0, 0): 87,\n",
              " datetime.datetime(2020, 11, 28, 0, 0): 85,\n",
              " datetime.datetime(2020, 11, 29, 0, 0): 84,\n",
              " datetime.datetime(2020, 11, 30, 0, 0): 84,\n",
              " datetime.datetime(2020, 12, 1, 0, 0): 85,\n",
              " datetime.datetime(2020, 12, 2, 0, 0): 84,\n",
              " datetime.datetime(2020, 12, 3, 0, 0): 84,\n",
              " datetime.datetime(2020, 12, 4, 0, 0): 85,\n",
              " datetime.datetime(2020, 12, 5, 0, 0): 84,\n",
              " datetime.datetime(2020, 12, 6, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 7, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 8, 0, 0): 84,\n",
              " datetime.datetime(2020, 12, 9, 0, 0): 83,\n",
              " datetime.datetime(2020, 12, 10, 0, 0): 85,\n",
              " datetime.datetime(2020, 12, 11, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 12, 0, 0): 84,\n",
              " datetime.datetime(2020, 12, 13, 0, 0): 84,\n",
              " datetime.datetime(2020, 12, 14, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 15, 0, 0): 85,\n",
              " datetime.datetime(2020, 12, 16, 0, 0): 87,\n",
              " datetime.datetime(2020, 12, 17, 0, 0): 87,\n",
              " datetime.datetime(2020, 12, 18, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 19, 0, 0): 85,\n",
              " datetime.datetime(2020, 12, 20, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 21, 0, 0): 85,\n",
              " datetime.datetime(2020, 12, 22, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 23, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 24, 0, 0): 88,\n",
              " datetime.datetime(2020, 12, 25, 0, 0): 87,\n",
              " datetime.datetime(2020, 12, 26, 0, 0): 88,\n",
              " datetime.datetime(2020, 12, 27, 0, 0): 89,\n",
              " datetime.datetime(2020, 12, 28, 0, 0): 87,\n",
              " datetime.datetime(2020, 12, 29, 0, 0): 89,\n",
              " datetime.datetime(2020, 12, 30, 0, 0): 90,\n",
              " datetime.datetime(2020, 12, 31, 0, 0): 89}"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inZDUOY8yemz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e082641-33b0-4ab4-f357-5707201fd3de"
      },
      "source": [
        "first_day = datetime(2020, 1, 1)\n",
        "\n",
        "stages = {first_day + timedelta(days=i): stage for i, stage in enumerate(river_stage)}\n",
        "\n",
        "stages"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{datetime.datetime(2020, 1, 1, 0, 0): 100,\n",
              " datetime.datetime(2020, 1, 2, 0, 0): 98,\n",
              " datetime.datetime(2020, 1, 3, 0, 0): 96,\n",
              " datetime.datetime(2020, 1, 4, 0, 0): 94,\n",
              " datetime.datetime(2020, 1, 5, 0, 0): 94,\n",
              " datetime.datetime(2020, 1, 6, 0, 0): 93,\n",
              " datetime.datetime(2020, 1, 7, 0, 0): 93,\n",
              " datetime.datetime(2020, 1, 8, 0, 0): 93,\n",
              " datetime.datetime(2020, 1, 9, 0, 0): 95,\n",
              " datetime.datetime(2020, 1, 10, 0, 0): 94,\n",
              " datetime.datetime(2020, 1, 11, 0, 0): 96,\n",
              " datetime.datetime(2020, 1, 12, 0, 0): 94,\n",
              " datetime.datetime(2020, 1, 13, 0, 0): 96,\n",
              " datetime.datetime(2020, 1, 14, 0, 0): 95,\n",
              " datetime.datetime(2020, 1, 15, 0, 0): 96,\n",
              " datetime.datetime(2020, 1, 16, 0, 0): 97,\n",
              " datetime.datetime(2020, 1, 17, 0, 0): 99,\n",
              " datetime.datetime(2020, 1, 18, 0, 0): 99,\n",
              " datetime.datetime(2020, 1, 19, 0, 0): 101,\n",
              " datetime.datetime(2020, 1, 20, 0, 0): 102,\n",
              " datetime.datetime(2020, 1, 21, 0, 0): 104,\n",
              " datetime.datetime(2020, 1, 22, 0, 0): 104,\n",
              " datetime.datetime(2020, 1, 23, 0, 0): 102,\n",
              " datetime.datetime(2020, 1, 24, 0, 0): 100,\n",
              " datetime.datetime(2020, 1, 25, 0, 0): 100,\n",
              " datetime.datetime(2020, 1, 26, 0, 0): 101,\n",
              " datetime.datetime(2020, 1, 27, 0, 0): 101,\n",
              " datetime.datetime(2020, 1, 28, 0, 0): 102,\n",
              " datetime.datetime(2020, 1, 29, 0, 0): 103,\n",
              " datetime.datetime(2020, 1, 30, 0, 0): 105,\n",
              " datetime.datetime(2020, 1, 31, 0, 0): 104,\n",
              " datetime.datetime(2020, 2, 1, 0, 0): 106,\n",
              " datetime.datetime(2020, 2, 2, 0, 0): 105,\n",
              " datetime.datetime(2020, 2, 3, 0, 0): 104,\n",
              " datetime.datetime(2020, 2, 4, 0, 0): 103,\n",
              " datetime.datetime(2020, 2, 5, 0, 0): 101,\n",
              " datetime.datetime(2020, 2, 6, 0, 0): 100,\n",
              " datetime.datetime(2020, 2, 7, 0, 0): 100,\n",
              " datetime.datetime(2020, 2, 8, 0, 0): 99,\n",
              " datetime.datetime(2020, 2, 9, 0, 0): 98,\n",
              " datetime.datetime(2020, 2, 10, 0, 0): 100,\n",
              " datetime.datetime(2020, 2, 11, 0, 0): 102,\n",
              " datetime.datetime(2020, 2, 12, 0, 0): 102,\n",
              " datetime.datetime(2020, 2, 13, 0, 0): 104,\n",
              " datetime.datetime(2020, 2, 14, 0, 0): 106,\n",
              " datetime.datetime(2020, 2, 15, 0, 0): 105,\n",
              " datetime.datetime(2020, 2, 16, 0, 0): 106,\n",
              " datetime.datetime(2020, 2, 17, 0, 0): 107,\n",
              " datetime.datetime(2020, 2, 18, 0, 0): 109,\n",
              " datetime.datetime(2020, 2, 19, 0, 0): 109,\n",
              " datetime.datetime(2020, 2, 20, 0, 0): 111,\n",
              " datetime.datetime(2020, 2, 21, 0, 0): 111,\n",
              " datetime.datetime(2020, 2, 22, 0, 0): 111,\n",
              " datetime.datetime(2020, 2, 23, 0, 0): 112,\n",
              " datetime.datetime(2020, 2, 24, 0, 0): 111,\n",
              " datetime.datetime(2020, 2, 25, 0, 0): 112,\n",
              " datetime.datetime(2020, 2, 26, 0, 0): 113,\n",
              " datetime.datetime(2020, 2, 27, 0, 0): 115,\n",
              " datetime.datetime(2020, 2, 28, 0, 0): 114,\n",
              " datetime.datetime(2020, 2, 29, 0, 0): 115,\n",
              " datetime.datetime(2020, 3, 1, 0, 0): 115,\n",
              " datetime.datetime(2020, 3, 2, 0, 0): 116,\n",
              " datetime.datetime(2020, 3, 3, 0, 0): 118,\n",
              " datetime.datetime(2020, 3, 4, 0, 0): 120,\n",
              " datetime.datetime(2020, 3, 5, 0, 0): 120,\n",
              " datetime.datetime(2020, 3, 6, 0, 0): 121,\n",
              " datetime.datetime(2020, 3, 7, 0, 0): 122,\n",
              " datetime.datetime(2020, 3, 8, 0, 0): 122,\n",
              " datetime.datetime(2020, 3, 9, 0, 0): 124,\n",
              " datetime.datetime(2020, 3, 10, 0, 0): 126,\n",
              " datetime.datetime(2020, 3, 11, 0, 0): 127,\n",
              " datetime.datetime(2020, 3, 12, 0, 0): 128,\n",
              " datetime.datetime(2020, 3, 13, 0, 0): 127,\n",
              " datetime.datetime(2020, 3, 14, 0, 0): 127,\n",
              " datetime.datetime(2020, 3, 15, 0, 0): 126,\n",
              " datetime.datetime(2020, 3, 16, 0, 0): 128,\n",
              " datetime.datetime(2020, 3, 17, 0, 0): 128,\n",
              " datetime.datetime(2020, 3, 18, 0, 0): 129,\n",
              " datetime.datetime(2020, 3, 19, 0, 0): 129,\n",
              " datetime.datetime(2020, 3, 20, 0, 0): 129,\n",
              " datetime.datetime(2020, 3, 21, 0, 0): 131,\n",
              " datetime.datetime(2020, 3, 22, 0, 0): 133,\n",
              " datetime.datetime(2020, 3, 23, 0, 0): 135,\n",
              " datetime.datetime(2020, 3, 24, 0, 0): 137,\n",
              " datetime.datetime(2020, 3, 25, 0, 0): 139,\n",
              " datetime.datetime(2020, 3, 26, 0, 0): 141,\n",
              " datetime.datetime(2020, 3, 27, 0, 0): 142,\n",
              " datetime.datetime(2020, 3, 28, 0, 0): 142,\n",
              " datetime.datetime(2020, 3, 29, 0, 0): 141,\n",
              " datetime.datetime(2020, 3, 30, 0, 0): 142,\n",
              " datetime.datetime(2020, 3, 31, 0, 0): 144,\n",
              " datetime.datetime(2020, 4, 1, 0, 0): 144,\n",
              " datetime.datetime(2020, 4, 2, 0, 0): 146,\n",
              " datetime.datetime(2020, 4, 3, 0, 0): 144,\n",
              " datetime.datetime(2020, 4, 4, 0, 0): 144,\n",
              " datetime.datetime(2020, 4, 5, 0, 0): 142,\n",
              " datetime.datetime(2020, 4, 6, 0, 0): 141,\n",
              " datetime.datetime(2020, 4, 7, 0, 0): 139,\n",
              " datetime.datetime(2020, 4, 8, 0, 0): 137,\n",
              " datetime.datetime(2020, 4, 9, 0, 0): 139,\n",
              " datetime.datetime(2020, 4, 10, 0, 0): 137,\n",
              " datetime.datetime(2020, 4, 11, 0, 0): 137,\n",
              " datetime.datetime(2020, 4, 12, 0, 0): 139,\n",
              " datetime.datetime(2020, 4, 13, 0, 0): 138,\n",
              " datetime.datetime(2020, 4, 14, 0, 0): 136,\n",
              " datetime.datetime(2020, 4, 15, 0, 0): 138,\n",
              " datetime.datetime(2020, 4, 16, 0, 0): 137,\n",
              " datetime.datetime(2020, 4, 17, 0, 0): 137,\n",
              " datetime.datetime(2020, 4, 18, 0, 0): 136,\n",
              " datetime.datetime(2020, 4, 19, 0, 0): 135,\n",
              " datetime.datetime(2020, 4, 20, 0, 0): 133,\n",
              " datetime.datetime(2020, 4, 21, 0, 0): 134,\n",
              " datetime.datetime(2020, 4, 22, 0, 0): 132,\n",
              " datetime.datetime(2020, 4, 23, 0, 0): 130,\n",
              " datetime.datetime(2020, 4, 24, 0, 0): 130,\n",
              " datetime.datetime(2020, 4, 25, 0, 0): 130,\n",
              " datetime.datetime(2020, 4, 26, 0, 0): 129,\n",
              " datetime.datetime(2020, 4, 27, 0, 0): 128,\n",
              " datetime.datetime(2020, 4, 28, 0, 0): 126,\n",
              " datetime.datetime(2020, 4, 29, 0, 0): 124,\n",
              " datetime.datetime(2020, 4, 30, 0, 0): 122,\n",
              " datetime.datetime(2020, 5, 1, 0, 0): 120,\n",
              " datetime.datetime(2020, 5, 2, 0, 0): 118,\n",
              " datetime.datetime(2020, 5, 3, 0, 0): 116,\n",
              " datetime.datetime(2020, 5, 4, 0, 0): 114,\n",
              " datetime.datetime(2020, 5, 5, 0, 0): 114,\n",
              " datetime.datetime(2020, 5, 6, 0, 0): 114,\n",
              " datetime.datetime(2020, 5, 7, 0, 0): 113,\n",
              " datetime.datetime(2020, 5, 8, 0, 0): 112,\n",
              " datetime.datetime(2020, 5, 9, 0, 0): 111,\n",
              " datetime.datetime(2020, 5, 10, 0, 0): 113,\n",
              " datetime.datetime(2020, 5, 11, 0, 0): 111,\n",
              " datetime.datetime(2020, 5, 12, 0, 0): 112,\n",
              " datetime.datetime(2020, 5, 13, 0, 0): 114,\n",
              " datetime.datetime(2020, 5, 14, 0, 0): 112,\n",
              " datetime.datetime(2020, 5, 15, 0, 0): 111,\n",
              " datetime.datetime(2020, 5, 16, 0, 0): 110,\n",
              " datetime.datetime(2020, 5, 17, 0, 0): 108,\n",
              " datetime.datetime(2020, 5, 18, 0, 0): 106,\n",
              " datetime.datetime(2020, 5, 19, 0, 0): 106,\n",
              " datetime.datetime(2020, 5, 20, 0, 0): 108,\n",
              " datetime.datetime(2020, 5, 21, 0, 0): 106,\n",
              " datetime.datetime(2020, 5, 22, 0, 0): 106,\n",
              " datetime.datetime(2020, 5, 23, 0, 0): 106,\n",
              " datetime.datetime(2020, 5, 24, 0, 0): 107,\n",
              " datetime.datetime(2020, 5, 25, 0, 0): 105,\n",
              " datetime.datetime(2020, 5, 26, 0, 0): 105,\n",
              " datetime.datetime(2020, 5, 27, 0, 0): 106,\n",
              " datetime.datetime(2020, 5, 28, 0, 0): 108,\n",
              " datetime.datetime(2020, 5, 29, 0, 0): 110,\n",
              " datetime.datetime(2020, 5, 30, 0, 0): 108,\n",
              " datetime.datetime(2020, 5, 31, 0, 0): 108,\n",
              " datetime.datetime(2020, 6, 1, 0, 0): 109,\n",
              " datetime.datetime(2020, 6, 2, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 3, 0, 0): 110,\n",
              " datetime.datetime(2020, 6, 4, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 5, 0, 0): 110,\n",
              " datetime.datetime(2020, 6, 6, 0, 0): 108,\n",
              " datetime.datetime(2020, 6, 7, 0, 0): 108,\n",
              " datetime.datetime(2020, 6, 8, 0, 0): 106,\n",
              " datetime.datetime(2020, 6, 9, 0, 0): 104,\n",
              " datetime.datetime(2020, 6, 10, 0, 0): 105,\n",
              " datetime.datetime(2020, 6, 11, 0, 0): 104,\n",
              " datetime.datetime(2020, 6, 12, 0, 0): 106,\n",
              " datetime.datetime(2020, 6, 13, 0, 0): 108,\n",
              " datetime.datetime(2020, 6, 14, 0, 0): 109,\n",
              " datetime.datetime(2020, 6, 15, 0, 0): 110,\n",
              " datetime.datetime(2020, 6, 16, 0, 0): 112,\n",
              " datetime.datetime(2020, 6, 17, 0, 0): 112,\n",
              " datetime.datetime(2020, 6, 18, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 19, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 20, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 21, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 22, 0, 0): 113,\n",
              " datetime.datetime(2020, 6, 23, 0, 0): 114,\n",
              " datetime.datetime(2020, 6, 24, 0, 0): 112,\n",
              " datetime.datetime(2020, 6, 25, 0, 0): 114,\n",
              " datetime.datetime(2020, 6, 26, 0, 0): 113,\n",
              " datetime.datetime(2020, 6, 27, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 28, 0, 0): 111,\n",
              " datetime.datetime(2020, 6, 29, 0, 0): 109,\n",
              " datetime.datetime(2020, 6, 30, 0, 0): 108,\n",
              " datetime.datetime(2020, 7, 1, 0, 0): 107,\n",
              " datetime.datetime(2020, 7, 2, 0, 0): 106,\n",
              " datetime.datetime(2020, 7, 3, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 4, 0, 0): 105,\n",
              " datetime.datetime(2020, 7, 5, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 6, 0, 0): 106,\n",
              " datetime.datetime(2020, 7, 7, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 8, 0, 0): 103,\n",
              " datetime.datetime(2020, 7, 9, 0, 0): 102,\n",
              " datetime.datetime(2020, 7, 10, 0, 0): 103,\n",
              " datetime.datetime(2020, 7, 11, 0, 0): 101,\n",
              " datetime.datetime(2020, 7, 12, 0, 0): 101,\n",
              " datetime.datetime(2020, 7, 13, 0, 0): 99,\n",
              " datetime.datetime(2020, 7, 14, 0, 0): 101,\n",
              " datetime.datetime(2020, 7, 15, 0, 0): 100,\n",
              " datetime.datetime(2020, 7, 16, 0, 0): 102,\n",
              " datetime.datetime(2020, 7, 17, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 18, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 19, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 20, 0, 0): 105,\n",
              " datetime.datetime(2020, 7, 21, 0, 0): 105,\n",
              " datetime.datetime(2020, 7, 22, 0, 0): 107,\n",
              " datetime.datetime(2020, 7, 23, 0, 0): 105,\n",
              " datetime.datetime(2020, 7, 24, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 25, 0, 0): 102,\n",
              " datetime.datetime(2020, 7, 26, 0, 0): 103,\n",
              " datetime.datetime(2020, 7, 27, 0, 0): 104,\n",
              " datetime.datetime(2020, 7, 28, 0, 0): 103,\n",
              " datetime.datetime(2020, 7, 29, 0, 0): 101,\n",
              " datetime.datetime(2020, 7, 30, 0, 0): 103,\n",
              " datetime.datetime(2020, 7, 31, 0, 0): 101,\n",
              " datetime.datetime(2020, 8, 1, 0, 0): 100,\n",
              " datetime.datetime(2020, 8, 2, 0, 0): 98,\n",
              " datetime.datetime(2020, 8, 3, 0, 0): 96,\n",
              " datetime.datetime(2020, 8, 4, 0, 0): 94,\n",
              " datetime.datetime(2020, 8, 5, 0, 0): 93,\n",
              " datetime.datetime(2020, 8, 6, 0, 0): 92,\n",
              " datetime.datetime(2020, 8, 7, 0, 0): 90,\n",
              " datetime.datetime(2020, 8, 8, 0, 0): 89,\n",
              " datetime.datetime(2020, 8, 9, 0, 0): 87,\n",
              " datetime.datetime(2020, 8, 10, 0, 0): 89,\n",
              " datetime.datetime(2020, 8, 11, 0, 0): 90,\n",
              " datetime.datetime(2020, 8, 12, 0, 0): 91,\n",
              " datetime.datetime(2020, 8, 13, 0, 0): 91,\n",
              " datetime.datetime(2020, 8, 14, 0, 0): 93,\n",
              " datetime.datetime(2020, 8, 15, 0, 0): 94,\n",
              " datetime.datetime(2020, 8, 16, 0, 0): 93,\n",
              " datetime.datetime(2020, 8, 17, 0, 0): 92,\n",
              " datetime.datetime(2020, 8, 18, 0, 0): 93,\n",
              " datetime.datetime(2020, 8, 19, 0, 0): 94,\n",
              " datetime.datetime(2020, 8, 20, 0, 0): 96,\n",
              " datetime.datetime(2020, 8, 21, 0, 0): 94,\n",
              " datetime.datetime(2020, 8, 22, 0, 0): 96,\n",
              " datetime.datetime(2020, 8, 23, 0, 0): 98,\n",
              " datetime.datetime(2020, 8, 24, 0, 0): 96,\n",
              " datetime.datetime(2020, 8, 25, 0, 0): 97,\n",
              " datetime.datetime(2020, 8, 26, 0, 0): 99,\n",
              " datetime.datetime(2020, 8, 27, 0, 0): 101,\n",
              " datetime.datetime(2020, 8, 28, 0, 0): 100,\n",
              " datetime.datetime(2020, 8, 29, 0, 0): 98,\n",
              " datetime.datetime(2020, 8, 30, 0, 0): 99,\n",
              " datetime.datetime(2020, 8, 31, 0, 0): 99,\n",
              " datetime.datetime(2020, 9, 1, 0, 0): 97,\n",
              " datetime.datetime(2020, 9, 2, 0, 0): 99,\n",
              " datetime.datetime(2020, 9, 3, 0, 0): 97,\n",
              " datetime.datetime(2020, 9, 4, 0, 0): 99,\n",
              " datetime.datetime(2020, 9, 5, 0, 0): 99,\n",
              " datetime.datetime(2020, 9, 6, 0, 0): 99,\n",
              " datetime.datetime(2020, 9, 7, 0, 0): 99,\n",
              " datetime.datetime(2020, 9, 8, 0, 0): 99,\n",
              " datetime.datetime(2020, 9, 9, 0, 0): 97,\n",
              " datetime.datetime(2020, 9, 10, 0, 0): 98,\n",
              " datetime.datetime(2020, 9, 11, 0, 0): 96,\n",
              " datetime.datetime(2020, 9, 12, 0, 0): 94,\n",
              " datetime.datetime(2020, 9, 13, 0, 0): 94,\n",
              " datetime.datetime(2020, 9, 14, 0, 0): 93,\n",
              " datetime.datetime(2020, 9, 15, 0, 0): 91,\n",
              " datetime.datetime(2020, 9, 16, 0, 0): 92,\n",
              " datetime.datetime(2020, 9, 17, 0, 0): 90,\n",
              " datetime.datetime(2020, 9, 18, 0, 0): 91,\n",
              " datetime.datetime(2020, 9, 19, 0, 0): 92,\n",
              " datetime.datetime(2020, 9, 20, 0, 0): 93,\n",
              " datetime.datetime(2020, 9, 21, 0, 0): 92,\n",
              " datetime.datetime(2020, 9, 22, 0, 0): 94,\n",
              " datetime.datetime(2020, 9, 23, 0, 0): 96,\n",
              " datetime.datetime(2020, 9, 24, 0, 0): 94,\n",
              " datetime.datetime(2020, 9, 25, 0, 0): 92,\n",
              " datetime.datetime(2020, 9, 26, 0, 0): 92,\n",
              " datetime.datetime(2020, 9, 27, 0, 0): 90,\n",
              " datetime.datetime(2020, 9, 28, 0, 0): 90,\n",
              " datetime.datetime(2020, 9, 29, 0, 0): 90,\n",
              " datetime.datetime(2020, 9, 30, 0, 0): 88,\n",
              " datetime.datetime(2020, 10, 1, 0, 0): 87,\n",
              " datetime.datetime(2020, 10, 2, 0, 0): 88,\n",
              " datetime.datetime(2020, 10, 3, 0, 0): 87,\n",
              " datetime.datetime(2020, 10, 4, 0, 0): 85,\n",
              " datetime.datetime(2020, 10, 5, 0, 0): 87,\n",
              " datetime.datetime(2020, 10, 6, 0, 0): 87,\n",
              " datetime.datetime(2020, 10, 7, 0, 0): 88,\n",
              " datetime.datetime(2020, 10, 8, 0, 0): 89,\n",
              " datetime.datetime(2020, 10, 9, 0, 0): 88,\n",
              " datetime.datetime(2020, 10, 10, 0, 0): 88,\n",
              " datetime.datetime(2020, 10, 11, 0, 0): 89,\n",
              " datetime.datetime(2020, 10, 12, 0, 0): 87,\n",
              " datetime.datetime(2020, 10, 13, 0, 0): 87,\n",
              " datetime.datetime(2020, 10, 14, 0, 0): 85,\n",
              " datetime.datetime(2020, 10, 15, 0, 0): 83,\n",
              " datetime.datetime(2020, 10, 16, 0, 0): 81,\n",
              " datetime.datetime(2020, 10, 17, 0, 0): 83,\n",
              " datetime.datetime(2020, 10, 18, 0, 0): 83,\n",
              " datetime.datetime(2020, 10, 19, 0, 0): 84,\n",
              " datetime.datetime(2020, 10, 20, 0, 0): 83,\n",
              " datetime.datetime(2020, 10, 21, 0, 0): 81,\n",
              " datetime.datetime(2020, 10, 22, 0, 0): 79,\n",
              " datetime.datetime(2020, 10, 23, 0, 0): 81,\n",
              " datetime.datetime(2020, 10, 24, 0, 0): 82,\n",
              " datetime.datetime(2020, 10, 25, 0, 0): 80,\n",
              " datetime.datetime(2020, 10, 26, 0, 0): 81,\n",
              " datetime.datetime(2020, 10, 27, 0, 0): 81,\n",
              " datetime.datetime(2020, 10, 28, 0, 0): 81,\n",
              " datetime.datetime(2020, 10, 29, 0, 0): 82,\n",
              " datetime.datetime(2020, 10, 30, 0, 0): 81,\n",
              " datetime.datetime(2020, 10, 31, 0, 0): 81,\n",
              " datetime.datetime(2020, 11, 1, 0, 0): 81,\n",
              " datetime.datetime(2020, 11, 2, 0, 0): 82,\n",
              " datetime.datetime(2020, 11, 3, 0, 0): 84,\n",
              " datetime.datetime(2020, 11, 4, 0, 0): 85,\n",
              " datetime.datetime(2020, 11, 5, 0, 0): 86,\n",
              " datetime.datetime(2020, 11, 6, 0, 0): 87,\n",
              " datetime.datetime(2020, 11, 7, 0, 0): 87,\n",
              " datetime.datetime(2020, 11, 8, 0, 0): 88,\n",
              " datetime.datetime(2020, 11, 9, 0, 0): 87,\n",
              " datetime.datetime(2020, 11, 10, 0, 0): 86,\n",
              " datetime.datetime(2020, 11, 11, 0, 0): 87,\n",
              " datetime.datetime(2020, 11, 12, 0, 0): 89,\n",
              " datetime.datetime(2020, 11, 13, 0, 0): 89,\n",
              " datetime.datetime(2020, 11, 14, 0, 0): 91,\n",
              " datetime.datetime(2020, 11, 15, 0, 0): 92,\n",
              " datetime.datetime(2020, 11, 16, 0, 0): 90,\n",
              " datetime.datetime(2020, 11, 17, 0, 0): 92,\n",
              " datetime.datetime(2020, 11, 18, 0, 0): 94,\n",
              " datetime.datetime(2020, 11, 19, 0, 0): 92,\n",
              " datetime.datetime(2020, 11, 20, 0, 0): 90,\n",
              " datetime.datetime(2020, 11, 21, 0, 0): 90,\n",
              " datetime.datetime(2020, 11, 22, 0, 0): 89,\n",
              " datetime.datetime(2020, 11, 23, 0, 0): 91,\n",
              " datetime.datetime(2020, 11, 24, 0, 0): 90,\n",
              " datetime.datetime(2020, 11, 25, 0, 0): 91,\n",
              " datetime.datetime(2020, 11, 26, 0, 0): 89,\n",
              " datetime.datetime(2020, 11, 27, 0, 0): 87,\n",
              " datetime.datetime(2020, 11, 28, 0, 0): 85,\n",
              " datetime.datetime(2020, 11, 29, 0, 0): 84,\n",
              " datetime.datetime(2020, 11, 30, 0, 0): 84,\n",
              " datetime.datetime(2020, 12, 1, 0, 0): 85,\n",
              " datetime.datetime(2020, 12, 2, 0, 0): 84,\n",
              " datetime.datetime(2020, 12, 3, 0, 0): 84,\n",
              " datetime.datetime(2020, 12, 4, 0, 0): 85,\n",
              " datetime.datetime(2020, 12, 5, 0, 0): 84,\n",
              " datetime.datetime(2020, 12, 6, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 7, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 8, 0, 0): 84,\n",
              " datetime.datetime(2020, 12, 9, 0, 0): 83,\n",
              " datetime.datetime(2020, 12, 10, 0, 0): 85,\n",
              " datetime.datetime(2020, 12, 11, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 12, 0, 0): 84,\n",
              " datetime.datetime(2020, 12, 13, 0, 0): 84,\n",
              " datetime.datetime(2020, 12, 14, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 15, 0, 0): 85,\n",
              " datetime.datetime(2020, 12, 16, 0, 0): 87,\n",
              " datetime.datetime(2020, 12, 17, 0, 0): 87,\n",
              " datetime.datetime(2020, 12, 18, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 19, 0, 0): 85,\n",
              " datetime.datetime(2020, 12, 20, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 21, 0, 0): 85,\n",
              " datetime.datetime(2020, 12, 22, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 23, 0, 0): 86,\n",
              " datetime.datetime(2020, 12, 24, 0, 0): 88,\n",
              " datetime.datetime(2020, 12, 25, 0, 0): 87,\n",
              " datetime.datetime(2020, 12, 26, 0, 0): 88,\n",
              " datetime.datetime(2020, 12, 27, 0, 0): 89,\n",
              " datetime.datetime(2020, 12, 28, 0, 0): 87,\n",
              " datetime.datetime(2020, 12, 29, 0, 0): 89,\n",
              " datetime.datetime(2020, 12, 30, 0, 0): 90,\n",
              " datetime.datetime(2020, 12, 31, 0, 0): 89}"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASoB1JP3yeka",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfa8912b-fdc3-406f-80b5-29a215ff7daa"
      },
      "source": [
        "desired_day = datetime(2020, 7, 1)\n",
        "\n",
        "stages[desired_day]"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "107"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zkOOYJ-byeh4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "479eecd7-acea-43e6-ea95-6ba8a1128678"
      },
      "source": [
        "plt.figure(figsize=(15, 5))\n",
        "plt.plot(list(stages.keys()), list(stages.values()))"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fddb05e6b90>]"
            ]
          },
          "metadata": {},
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "viUoZ1Ei4H8H"
      },
      "source": [
        "## Example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RjvAMQPD36_z"
      },
      "source": [
        "What if we want to store the river stages for 3 different stations? Are dictionaries a viable solution?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3d7-Cucd27N1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14c0ae4c-1499-4606-ca52-1d20b4a1bed1"
      },
      "source": [
        "# create 3 stations\n",
        "stations = [f'Station {i}' for i in range(1, 4)]\n",
        "stations"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Station 1', 'Station 2', 'Station 3']"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5sUjqJc3zen",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12294905-11ea-47c3-a549-09caba1faf81"
      },
      "source": [
        "random.seed(4)\n",
        "\n",
        "stage_data = {}\n",
        "first_day = datetime(2020, 1, 1)\n",
        "\n",
        "for station in stations:\n",
        "\n",
        "    print(f'Creating stage data for station {station}')\n",
        "\n",
        "    # create a random series\n",
        "    stages_list = [random.randint(50, 150)]\n",
        "    for _ in range(364):\n",
        "        stages_list.append(stages_list[-1] + random.randint(-2, 2))\n",
        "\n",
        "    # create the stages dictionary with the dates and assign it to a specific station\n",
        "    stage_data[station] = {first_day + timedelta(days=i): stage for i, stage in enumerate(stages_list)}\n",
        "\n"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating stage data for station Station 1\n",
            "Creating stage data for station Station 2\n",
            "Creating stage data for station Station 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qakiwJQ93zcT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "outputId": "1af1a9f2-b554-4cf2-fe7d-3bed842501ed"
      },
      "source": [
        "str(stage_data)"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"{'Station 1': {datetime.datetime(2020, 1, 1, 0, 0): 80, datetime.datetime(2020, 1, 2, 0, 0): 80, datetime.datetime(2020, 1, 3, 0, 0): 78, datetime.datetime(2020, 1, 4, 0, 0): 79, datetime.datetime(2020, 1, 5, 0, 0): 80, datetime.datetime(2020, 1, 6, 0, 0): 79, datetime.datetime(2020, 1, 7, 0, 0): 77, datetime.datetime(2020, 1, 8, 0, 0): 75, datetime.datetime(2020, 1, 9, 0, 0): 73, datetime.datetime(2020, 1, 10, 0, 0): 74, datetime.datetime(2020, 1, 11, 0, 0): 76, datetime.datetime(2020, 1, 12, 0, 0): 76, datetime.datetime(2020, 1, 13, 0, 0): 74, datetime.datetime(2020, 1, 14, 0, 0): 73, datetime.datetime(2020, 1, 15, 0, 0): 75, datetime.datetime(2020, 1, 16, 0, 0): 77, datetime.datetime(2020, 1, 17, 0, 0): 77, datetime.datetime(2020, 1, 18, 0, 0): 77, datetime.datetime(2020, 1, 19, 0, 0): 76, datetime.datetime(2020, 1, 20, 0, 0): 74, datetime.datetime(2020, 1, 21, 0, 0): 74, datetime.datetime(2020, 1, 22, 0, 0): 73, datetime.datetime(2020, 1, 23, 0, 0): 71, datetime.datetime(2020, 1, 24, 0, 0): 71, datetime.datetime(2020, 1, 25, 0, 0): 71, datetime.datetime(2020, 1, 26, 0, 0): 70, datetime.datetime(2020, 1, 27, 0, 0): 69, datetime.datetime(2020, 1, 28, 0, 0): 69, datetime.datetime(2020, 1, 29, 0, 0): 69, datetime.datetime(2020, 1, 30, 0, 0): 69, datetime.datetime(2020, 1, 31, 0, 0): 67, datetime.datetime(2020, 2, 1, 0, 0): 69, datetime.datetime(2020, 2, 2, 0, 0): 69, datetime.datetime(2020, 2, 3, 0, 0): 70, datetime.datetime(2020, 2, 4, 0, 0): 72, datetime.datetime(2020, 2, 5, 0, 0): 71, datetime.datetime(2020, 2, 6, 0, 0): 70, datetime.datetime(2020, 2, 7, 0, 0): 69, datetime.datetime(2020, 2, 8, 0, 0): 70, datetime.datetime(2020, 2, 9, 0, 0): 70, datetime.datetime(2020, 2, 10, 0, 0): 68, datetime.datetime(2020, 2, 11, 0, 0): 70, datetime.datetime(2020, 2, 12, 0, 0): 70, datetime.datetime(2020, 2, 13, 0, 0): 68, datetime.datetime(2020, 2, 14, 0, 0): 68, datetime.datetime(2020, 2, 15, 0, 0): 70, datetime.datetime(2020, 2, 16, 0, 0): 70, datetime.datetime(2020, 2, 17, 0, 0): 72, datetime.datetime(2020, 2, 18, 0, 0): 71, datetime.datetime(2020, 2, 19, 0, 0): 72, datetime.datetime(2020, 2, 20, 0, 0): 73, datetime.datetime(2020, 2, 21, 0, 0): 75, datetime.datetime(2020, 2, 22, 0, 0): 75, datetime.datetime(2020, 2, 23, 0, 0): 76, datetime.datetime(2020, 2, 24, 0, 0): 77, datetime.datetime(2020, 2, 25, 0, 0): 76, datetime.datetime(2020, 2, 26, 0, 0): 75, datetime.datetime(2020, 2, 27, 0, 0): 75, datetime.datetime(2020, 2, 28, 0, 0): 75, datetime.datetime(2020, 2, 29, 0, 0): 73, datetime.datetime(2020, 3, 1, 0, 0): 71, datetime.datetime(2020, 3, 2, 0, 0): 69, datetime.datetime(2020, 3, 3, 0, 0): 70, datetime.datetime(2020, 3, 4, 0, 0): 70, datetime.datetime(2020, 3, 5, 0, 0): 72, datetime.datetime(2020, 3, 6, 0, 0): 74, datetime.datetime(2020, 3, 7, 0, 0): 75, datetime.datetime(2020, 3, 8, 0, 0): 75, datetime.datetime(2020, 3, 9, 0, 0): 74, datetime.datetime(2020, 3, 10, 0, 0): 73, datetime.datetime(2020, 3, 11, 0, 0): 71, datetime.datetime(2020, 3, 12, 0, 0): 72, datetime.datetime(2020, 3, 13, 0, 0): 71, datetime.datetime(2020, 3, 14, 0, 0): 72, datetime.datetime(2020, 3, 15, 0, 0): 72, datetime.datetime(2020, 3, 16, 0, 0): 71, datetime.datetime(2020, 3, 17, 0, 0): 71, datetime.datetime(2020, 3, 18, 0, 0): 72, datetime.datetime(2020, 3, 19, 0, 0): 74, datetime.datetime(2020, 3, 20, 0, 0): 74, datetime.datetime(2020, 3, 21, 0, 0): 76, datetime.datetime(2020, 3, 22, 0, 0): 75, datetime.datetime(2020, 3, 23, 0, 0): 75, datetime.datetime(2020, 3, 24, 0, 0): 73, datetime.datetime(2020, 3, 25, 0, 0): 71, datetime.datetime(2020, 3, 26, 0, 0): 70, datetime.datetime(2020, 3, 27, 0, 0): 70, datetime.datetime(2020, 3, 28, 0, 0): 72, datetime.datetime(2020, 3, 29, 0, 0): 74, datetime.datetime(2020, 3, 30, 0, 0): 73, datetime.datetime(2020, 3, 31, 0, 0): 71, datetime.datetime(2020, 4, 1, 0, 0): 71, datetime.datetime(2020, 4, 2, 0, 0): 70, datetime.datetime(2020, 4, 3, 0, 0): 70, datetime.datetime(2020, 4, 4, 0, 0): 71, datetime.datetime(2020, 4, 5, 0, 0): 69, datetime.datetime(2020, 4, 6, 0, 0): 67, datetime.datetime(2020, 4, 7, 0, 0): 67, datetime.datetime(2020, 4, 8, 0, 0): 65, datetime.datetime(2020, 4, 9, 0, 0): 65, datetime.datetime(2020, 4, 10, 0, 0): 65, datetime.datetime(2020, 4, 11, 0, 0): 63, datetime.datetime(2020, 4, 12, 0, 0): 63, datetime.datetime(2020, 4, 13, 0, 0): 63, datetime.datetime(2020, 4, 14, 0, 0): 63, datetime.datetime(2020, 4, 15, 0, 0): 62, datetime.datetime(2020, 4, 16, 0, 0): 63, datetime.datetime(2020, 4, 17, 0, 0): 65, datetime.datetime(2020, 4, 18, 0, 0): 63, datetime.datetime(2020, 4, 19, 0, 0): 63, datetime.datetime(2020, 4, 20, 0, 0): 65, datetime.datetime(2020, 4, 21, 0, 0): 64, datetime.datetime(2020, 4, 22, 0, 0): 65, datetime.datetime(2020, 4, 23, 0, 0): 65, datetime.datetime(2020, 4, 24, 0, 0): 64, datetime.datetime(2020, 4, 25, 0, 0): 64, datetime.datetime(2020, 4, 26, 0, 0): 65, datetime.datetime(2020, 4, 27, 0, 0): 67, datetime.datetime(2020, 4, 28, 0, 0): 66, datetime.datetime(2020, 4, 29, 0, 0): 66, datetime.datetime(2020, 4, 30, 0, 0): 68, datetime.datetime(2020, 5, 1, 0, 0): 66, datetime.datetime(2020, 5, 2, 0, 0): 66, datetime.datetime(2020, 5, 3, 0, 0): 64, datetime.datetime(2020, 5, 4, 0, 0): 65, datetime.datetime(2020, 5, 5, 0, 0): 64, datetime.datetime(2020, 5, 6, 0, 0): 64, datetime.datetime(2020, 5, 7, 0, 0): 64, datetime.datetime(2020, 5, 8, 0, 0): 64, datetime.datetime(2020, 5, 9, 0, 0): 66, datetime.datetime(2020, 5, 10, 0, 0): 64, datetime.datetime(2020, 5, 11, 0, 0): 65, datetime.datetime(2020, 5, 12, 0, 0): 64, datetime.datetime(2020, 5, 13, 0, 0): 65, datetime.datetime(2020, 5, 14, 0, 0): 64, datetime.datetime(2020, 5, 15, 0, 0): 62, datetime.datetime(2020, 5, 16, 0, 0): 60, datetime.datetime(2020, 5, 17, 0, 0): 58, datetime.datetime(2020, 5, 18, 0, 0): 56, datetime.datetime(2020, 5, 19, 0, 0): 55, datetime.datetime(2020, 5, 20, 0, 0): 57, datetime.datetime(2020, 5, 21, 0, 0): 56, datetime.datetime(2020, 5, 22, 0, 0): 58, datetime.datetime(2020, 5, 23, 0, 0): 56, datetime.datetime(2020, 5, 24, 0, 0): 58, datetime.datetime(2020, 5, 25, 0, 0): 59, datetime.datetime(2020, 5, 26, 0, 0): 61, datetime.datetime(2020, 5, 27, 0, 0): 60, datetime.datetime(2020, 5, 28, 0, 0): 60, datetime.datetime(2020, 5, 29, 0, 0): 58, datetime.datetime(2020, 5, 30, 0, 0): 56, datetime.datetime(2020, 5, 31, 0, 0): 58, datetime.datetime(2020, 6, 1, 0, 0): 58, datetime.datetime(2020, 6, 2, 0, 0): 59, datetime.datetime(2020, 6, 3, 0, 0): 58, datetime.datetime(2020, 6, 4, 0, 0): 59, datetime.datetime(2020, 6, 5, 0, 0): 58, datetime.datetime(2020, 6, 6, 0, 0): 57, datetime.datetime(2020, 6, 7, 0, 0): 58, datetime.datetime(2020, 6, 8, 0, 0): 59, datetime.datetime(2020, 6, 9, 0, 0): 60, datetime.datetime(2020, 6, 10, 0, 0): 58, datetime.datetime(2020, 6, 11, 0, 0): 57, datetime.datetime(2020, 6, 12, 0, 0): 58, datetime.datetime(2020, 6, 13, 0, 0): 59, datetime.datetime(2020, 6, 14, 0, 0): 58, datetime.datetime(2020, 6, 15, 0, 0): 59, datetime.datetime(2020, 6, 16, 0, 0): 58, datetime.datetime(2020, 6, 17, 0, 0): 59, datetime.datetime(2020, 6, 18, 0, 0): 58, datetime.datetime(2020, 6, 19, 0, 0): 56, datetime.datetime(2020, 6, 20, 0, 0): 54, datetime.datetime(2020, 6, 21, 0, 0): 54, datetime.datetime(2020, 6, 22, 0, 0): 54, datetime.datetime(2020, 6, 23, 0, 0): 53, datetime.datetime(2020, 6, 24, 0, 0): 55, datetime.datetime(2020, 6, 25, 0, 0): 54, datetime.datetime(2020, 6, 26, 0, 0): 53, datetime.datetime(2020, 6, 27, 0, 0): 54, datetime.datetime(2020, 6, 28, 0, 0): 54, datetime.datetime(2020, 6, 29, 0, 0): 53, datetime.datetime(2020, 6, 30, 0, 0): 53, datetime.datetime(2020, 7, 1, 0, 0): 51, datetime.datetime(2020, 7, 2, 0, 0): 51, datetime.datetime(2020, 7, 3, 0, 0): 53, datetime.datetime(2020, 7, 4, 0, 0): 51, datetime.datetime(2020, 7, 5, 0, 0): 53, datetime.datetime(2020, 7, 6, 0, 0): 54, datetime.datetime(2020, 7, 7, 0, 0): 52, datetime.datetime(2020, 7, 8, 0, 0): 53, datetime.datetime(2020, 7, 9, 0, 0): 54, datetime.datetime(2020, 7, 10, 0, 0): 52, datetime.datetime(2020, 7, 11, 0, 0): 53, datetime.datetime(2020, 7, 12, 0, 0): 52, datetime.datetime(2020, 7, 13, 0, 0): 54, datetime.datetime(2020, 7, 14, 0, 0): 53, datetime.datetime(2020, 7, 15, 0, 0): 53, datetime.datetime(2020, 7, 16, 0, 0): 53, datetime.datetime(2020, 7, 17, 0, 0): 54, datetime.datetime(2020, 7, 18, 0, 0): 54, datetime.datetime(2020, 7, 19, 0, 0): 55, datetime.datetime(2020, 7, 20, 0, 0): 57, datetime.datetime(2020, 7, 21, 0, 0): 56, datetime.datetime(2020, 7, 22, 0, 0): 56, datetime.datetime(2020, 7, 23, 0, 0): 56, datetime.datetime(2020, 7, 24, 0, 0): 57, datetime.datetime(2020, 7, 25, 0, 0): 58, datetime.datetime(2020, 7, 26, 0, 0): 56, datetime.datetime(2020, 7, 27, 0, 0): 56, datetime.datetime(2020, 7, 28, 0, 0): 55, datetime.datetime(2020, 7, 29, 0, 0): 53, datetime.datetime(2020, 7, 30, 0, 0): 54, datetime.datetime(2020, 7, 31, 0, 0): 56, datetime.datetime(2020, 8, 1, 0, 0): 55, datetime.datetime(2020, 8, 2, 0, 0): 55, datetime.datetime(2020, 8, 3, 0, 0): 53, datetime.datetime(2020, 8, 4, 0, 0): 52, datetime.datetime(2020, 8, 5, 0, 0): 53, datetime.datetime(2020, 8, 6, 0, 0): 55, datetime.datetime(2020, 8, 7, 0, 0): 56, datetime.datetime(2020, 8, 8, 0, 0): 57, datetime.datetime(2020, 8, 9, 0, 0): 58, datetime.datetime(2020, 8, 10, 0, 0): 57, datetime.datetime(2020, 8, 11, 0, 0): 55, datetime.datetime(2020, 8, 12, 0, 0): 54, datetime.datetime(2020, 8, 13, 0, 0): 53, datetime.datetime(2020, 8, 14, 0, 0): 51, datetime.datetime(2020, 8, 15, 0, 0): 53, datetime.datetime(2020, 8, 16, 0, 0): 53, datetime.datetime(2020, 8, 17, 0, 0): 51, datetime.datetime(2020, 8, 18, 0, 0): 52, datetime.datetime(2020, 8, 19, 0, 0): 53, datetime.datetime(2020, 8, 20, 0, 0): 52, datetime.datetime(2020, 8, 21, 0, 0): 54, datetime.datetime(2020, 8, 22, 0, 0): 52, datetime.datetime(2020, 8, 23, 0, 0): 51, datetime.datetime(2020, 8, 24, 0, 0): 50, datetime.datetime(2020, 8, 25, 0, 0): 52, datetime.datetime(2020, 8, 26, 0, 0): 52, datetime.datetime(2020, 8, 27, 0, 0): 54, datetime.datetime(2020, 8, 28, 0, 0): 55, datetime.datetime(2020, 8, 29, 0, 0): 57, datetime.datetime(2020, 8, 30, 0, 0): 58, datetime.datetime(2020, 8, 31, 0, 0): 56, datetime.datetime(2020, 9, 1, 0, 0): 54, datetime.datetime(2020, 9, 2, 0, 0): 52, datetime.datetime(2020, 9, 3, 0, 0): 54, datetime.datetime(2020, 9, 4, 0, 0): 52, datetime.datetime(2020, 9, 5, 0, 0): 53, datetime.datetime(2020, 9, 6, 0, 0): 55, datetime.datetime(2020, 9, 7, 0, 0): 55, datetime.datetime(2020, 9, 8, 0, 0): 57, datetime.datetime(2020, 9, 9, 0, 0): 56, datetime.datetime(2020, 9, 10, 0, 0): 54, datetime.datetime(2020, 9, 11, 0, 0): 54, datetime.datetime(2020, 9, 12, 0, 0): 52, datetime.datetime(2020, 9, 13, 0, 0): 54, datetime.datetime(2020, 9, 14, 0, 0): 52, datetime.datetime(2020, 9, 15, 0, 0): 52, datetime.datetime(2020, 9, 16, 0, 0): 52, datetime.datetime(2020, 9, 17, 0, 0): 50, datetime.datetime(2020, 9, 18, 0, 0): 48, datetime.datetime(2020, 9, 19, 0, 0): 50, datetime.datetime(2020, 9, 20, 0, 0): 51, datetime.datetime(2020, 9, 21, 0, 0): 52, datetime.datetime(2020, 9, 22, 0, 0): 51, datetime.datetime(2020, 9, 23, 0, 0): 51, datetime.datetime(2020, 9, 24, 0, 0): 52, datetime.datetime(2020, 9, 25, 0, 0): 51, datetime.datetime(2020, 9, 26, 0, 0): 52, datetime.datetime(2020, 9, 27, 0, 0): 53, datetime.datetime(2020, 9, 28, 0, 0): 51, datetime.datetime(2020, 9, 29, 0, 0): 49, datetime.datetime(2020, 9, 30, 0, 0): 47, datetime.datetime(2020, 10, 1, 0, 0): 49, datetime.datetime(2020, 10, 2, 0, 0): 49, datetime.datetime(2020, 10, 3, 0, 0): 51, datetime.datetime(2020, 10, 4, 0, 0): 52, datetime.datetime(2020, 10, 5, 0, 0): 53, datetime.datetime(2020, 10, 6, 0, 0): 54, datetime.datetime(2020, 10, 7, 0, 0): 52, datetime.datetime(2020, 10, 8, 0, 0): 51, datetime.datetime(2020, 10, 9, 0, 0): 51, datetime.datetime(2020, 10, 10, 0, 0): 52, datetime.datetime(2020, 10, 11, 0, 0): 53, datetime.datetime(2020, 10, 12, 0, 0): 51, datetime.datetime(2020, 10, 13, 0, 0): 53, datetime.datetime(2020, 10, 14, 0, 0): 52, datetime.datetime(2020, 10, 15, 0, 0): 52, datetime.datetime(2020, 10, 16, 0, 0): 51, datetime.datetime(2020, 10, 17, 0, 0): 50, datetime.datetime(2020, 10, 18, 0, 0): 49, datetime.datetime(2020, 10, 19, 0, 0): 49, datetime.datetime(2020, 10, 20, 0, 0): 50, datetime.datetime(2020, 10, 21, 0, 0): 50, datetime.datetime(2020, 10, 22, 0, 0): 50, datetime.datetime(2020, 10, 23, 0, 0): 52, datetime.datetime(2020, 10, 24, 0, 0): 50, datetime.datetime(2020, 10, 25, 0, 0): 49, datetime.datetime(2020, 10, 26, 0, 0): 47, datetime.datetime(2020, 10, 27, 0, 0): 47, datetime.datetime(2020, 10, 28, 0, 0): 45, datetime.datetime(2020, 10, 29, 0, 0): 47, datetime.datetime(2020, 10, 30, 0, 0): 45, datetime.datetime(2020, 10, 31, 0, 0): 46, datetime.datetime(2020, 11, 1, 0, 0): 48, datetime.datetime(2020, 11, 2, 0, 0): 49, datetime.datetime(2020, 11, 3, 0, 0): 51, datetime.datetime(2020, 11, 4, 0, 0): 49, datetime.datetime(2020, 11, 5, 0, 0): 51, datetime.datetime(2020, 11, 6, 0, 0): 50, datetime.datetime(2020, 11, 7, 0, 0): 51, datetime.datetime(2020, 11, 8, 0, 0): 51, datetime.datetime(2020, 11, 9, 0, 0): 51, datetime.datetime(2020, 11, 10, 0, 0): 50, datetime.datetime(2020, 11, 11, 0, 0): 49, datetime.datetime(2020, 11, 12, 0, 0): 47, datetime.datetime(2020, 11, 13, 0, 0): 45, datetime.datetime(2020, 11, 14, 0, 0): 47, datetime.datetime(2020, 11, 15, 0, 0): 47, datetime.datetime(2020, 11, 16, 0, 0): 49, datetime.datetime(2020, 11, 17, 0, 0): 50, datetime.datetime(2020, 11, 18, 0, 0): 52, datetime.datetime(2020, 11, 19, 0, 0): 52, datetime.datetime(2020, 11, 20, 0, 0): 54, datetime.datetime(2020, 11, 21, 0, 0): 55, datetime.datetime(2020, 11, 22, 0, 0): 57, datetime.datetime(2020, 11, 23, 0, 0): 59, datetime.datetime(2020, 11, 24, 0, 0): 60, datetime.datetime(2020, 11, 25, 0, 0): 61, datetime.datetime(2020, 11, 26, 0, 0): 60, datetime.datetime(2020, 11, 27, 0, 0): 60, datetime.datetime(2020, 11, 28, 0, 0): 62, datetime.datetime(2020, 11, 29, 0, 0): 62, datetime.datetime(2020, 11, 30, 0, 0): 62, datetime.datetime(2020, 12, 1, 0, 0): 61, datetime.datetime(2020, 12, 2, 0, 0): 62, datetime.datetime(2020, 12, 3, 0, 0): 60, datetime.datetime(2020, 12, 4, 0, 0): 62, datetime.datetime(2020, 12, 5, 0, 0): 61, datetime.datetime(2020, 12, 6, 0, 0): 63, datetime.datetime(2020, 12, 7, 0, 0): 62, datetime.datetime(2020, 12, 8, 0, 0): 62, datetime.datetime(2020, 12, 9, 0, 0): 62, datetime.datetime(2020, 12, 10, 0, 0): 61, datetime.datetime(2020, 12, 11, 0, 0): 63, datetime.datetime(2020, 12, 12, 0, 0): 63, datetime.datetime(2020, 12, 13, 0, 0): 65, datetime.datetime(2020, 12, 14, 0, 0): 63, datetime.datetime(2020, 12, 15, 0, 0): 61, datetime.datetime(2020, 12, 16, 0, 0): 62, datetime.datetime(2020, 12, 17, 0, 0): 61, datetime.datetime(2020, 12, 18, 0, 0): 61, datetime.datetime(2020, 12, 19, 0, 0): 61, datetime.datetime(2020, 12, 20, 0, 0): 61, datetime.datetime(2020, 12, 21, 0, 0): 60, datetime.datetime(2020, 12, 22, 0, 0): 60, datetime.datetime(2020, 12, 23, 0, 0): 58, datetime.datetime(2020, 12, 24, 0, 0): 60, datetime.datetime(2020, 12, 25, 0, 0): 58, datetime.datetime(2020, 12, 26, 0, 0): 60, datetime.datetime(2020, 12, 27, 0, 0): 58, datetime.datetime(2020, 12, 28, 0, 0): 58, datetime.datetime(2020, 12, 29, 0, 0): 56, datetime.datetime(2020, 12, 30, 0, 0): 55}, 'Station 2': {datetime.datetime(2020, 1, 1, 0, 0): 73, datetime.datetime(2020, 1, 2, 0, 0): 75, datetime.datetime(2020, 1, 3, 0, 0): 76, datetime.datetime(2020, 1, 4, 0, 0): 78, datetime.datetime(2020, 1, 5, 0, 0): 76, datetime.datetime(2020, 1, 6, 0, 0): 74, datetime.datetime(2020, 1, 7, 0, 0): 73, datetime.datetime(2020, 1, 8, 0, 0): 74, datetime.datetime(2020, 1, 9, 0, 0): 73, datetime.datetime(2020, 1, 10, 0, 0): 75, datetime.datetime(2020, 1, 11, 0, 0): 75, datetime.datetime(2020, 1, 12, 0, 0): 76, datetime.datetime(2020, 1, 13, 0, 0): 78, datetime.datetime(2020, 1, 14, 0, 0): 77, datetime.datetime(2020, 1, 15, 0, 0): 78, datetime.datetime(2020, 1, 16, 0, 0): 77, datetime.datetime(2020, 1, 17, 0, 0): 77, datetime.datetime(2020, 1, 18, 0, 0): 77, datetime.datetime(2020, 1, 19, 0, 0): 76, datetime.datetime(2020, 1, 20, 0, 0): 76, datetime.datetime(2020, 1, 21, 0, 0): 78, datetime.datetime(2020, 1, 22, 0, 0): 80, datetime.datetime(2020, 1, 23, 0, 0): 81, datetime.datetime(2020, 1, 24, 0, 0): 82, datetime.datetime(2020, 1, 25, 0, 0): 84, datetime.datetime(2020, 1, 26, 0, 0): 85, datetime.datetime(2020, 1, 27, 0, 0): 85, datetime.datetime(2020, 1, 28, 0, 0): 85, datetime.datetime(2020, 1, 29, 0, 0): 86, datetime.datetime(2020, 1, 30, 0, 0): 87, datetime.datetime(2020, 1, 31, 0, 0): 89, datetime.datetime(2020, 2, 1, 0, 0): 87, datetime.datetime(2020, 2, 2, 0, 0): 87, datetime.datetime(2020, 2, 3, 0, 0): 86, datetime.datetime(2020, 2, 4, 0, 0): 88, datetime.datetime(2020, 2, 5, 0, 0): 89, datetime.datetime(2020, 2, 6, 0, 0): 91, datetime.datetime(2020, 2, 7, 0, 0): 93, datetime.datetime(2020, 2, 8, 0, 0): 93, datetime.datetime(2020, 2, 9, 0, 0): 94, datetime.datetime(2020, 2, 10, 0, 0): 95, datetime.datetime(2020, 2, 11, 0, 0): 97, datetime.datetime(2020, 2, 12, 0, 0): 95, datetime.datetime(2020, 2, 13, 0, 0): 94, datetime.datetime(2020, 2, 14, 0, 0): 96, datetime.datetime(2020, 2, 15, 0, 0): 94, datetime.datetime(2020, 2, 16, 0, 0): 95, datetime.datetime(2020, 2, 17, 0, 0): 95, datetime.datetime(2020, 2, 18, 0, 0): 97, datetime.datetime(2020, 2, 19, 0, 0): 97, datetime.datetime(2020, 2, 20, 0, 0): 95, datetime.datetime(2020, 2, 21, 0, 0): 95, datetime.datetime(2020, 2, 22, 0, 0): 96, datetime.datetime(2020, 2, 23, 0, 0): 95, datetime.datetime(2020, 2, 24, 0, 0): 96, datetime.datetime(2020, 2, 25, 0, 0): 98, datetime.datetime(2020, 2, 26, 0, 0): 96, datetime.datetime(2020, 2, 27, 0, 0): 95, datetime.datetime(2020, 2, 28, 0, 0): 94, datetime.datetime(2020, 2, 29, 0, 0): 92, datetime.datetime(2020, 3, 1, 0, 0): 92, datetime.datetime(2020, 3, 2, 0, 0): 91, datetime.datetime(2020, 3, 3, 0, 0): 89, datetime.datetime(2020, 3, 4, 0, 0): 88, datetime.datetime(2020, 3, 5, 0, 0): 89, datetime.datetime(2020, 3, 6, 0, 0): 91, datetime.datetime(2020, 3, 7, 0, 0): 93, datetime.datetime(2020, 3, 8, 0, 0): 95, datetime.datetime(2020, 3, 9, 0, 0): 97, datetime.datetime(2020, 3, 10, 0, 0): 97, datetime.datetime(2020, 3, 11, 0, 0): 95, datetime.datetime(2020, 3, 12, 0, 0): 97, datetime.datetime(2020, 3, 13, 0, 0): 96, datetime.datetime(2020, 3, 14, 0, 0): 95, datetime.datetime(2020, 3, 15, 0, 0): 93, datetime.datetime(2020, 3, 16, 0, 0): 92, datetime.datetime(2020, 3, 17, 0, 0): 90, datetime.datetime(2020, 3, 18, 0, 0): 90, datetime.datetime(2020, 3, 19, 0, 0): 88, datetime.datetime(2020, 3, 20, 0, 0): 86, datetime.datetime(2020, 3, 21, 0, 0): 86, datetime.datetime(2020, 3, 22, 0, 0): 84, datetime.datetime(2020, 3, 23, 0, 0): 84, datetime.datetime(2020, 3, 24, 0, 0): 86, datetime.datetime(2020, 3, 25, 0, 0): 85, datetime.datetime(2020, 3, 26, 0, 0): 84, datetime.datetime(2020, 3, 27, 0, 0): 85, datetime.datetime(2020, 3, 28, 0, 0): 84, datetime.datetime(2020, 3, 29, 0, 0): 82, datetime.datetime(2020, 3, 30, 0, 0): 84, datetime.datetime(2020, 3, 31, 0, 0): 84, datetime.datetime(2020, 4, 1, 0, 0): 82, datetime.datetime(2020, 4, 2, 0, 0): 84, datetime.datetime(2020, 4, 3, 0, 0): 83, datetime.datetime(2020, 4, 4, 0, 0): 85, datetime.datetime(2020, 4, 5, 0, 0): 86, datetime.datetime(2020, 4, 6, 0, 0): 85, datetime.datetime(2020, 4, 7, 0, 0): 84, datetime.datetime(2020, 4, 8, 0, 0): 84, datetime.datetime(2020, 4, 9, 0, 0): 85, datetime.datetime(2020, 4, 10, 0, 0): 87, datetime.datetime(2020, 4, 11, 0, 0): 85, datetime.datetime(2020, 4, 12, 0, 0): 86, datetime.datetime(2020, 4, 13, 0, 0): 85, datetime.datetime(2020, 4, 14, 0, 0): 84, datetime.datetime(2020, 4, 15, 0, 0): 84, datetime.datetime(2020, 4, 16, 0, 0): 86, datetime.datetime(2020, 4, 17, 0, 0): 87, datetime.datetime(2020, 4, 18, 0, 0): 89, datetime.datetime(2020, 4, 19, 0, 0): 89, datetime.datetime(2020, 4, 20, 0, 0): 90, datetime.datetime(2020, 4, 21, 0, 0): 90, datetime.datetime(2020, 4, 22, 0, 0): 89, datetime.datetime(2020, 4, 23, 0, 0): 90, datetime.datetime(2020, 4, 24, 0, 0): 88, datetime.datetime(2020, 4, 25, 0, 0): 89, datetime.datetime(2020, 4, 26, 0, 0): 87, datetime.datetime(2020, 4, 27, 0, 0): 87, datetime.datetime(2020, 4, 28, 0, 0): 85, datetime.datetime(2020, 4, 29, 0, 0): 85, datetime.datetime(2020, 4, 30, 0, 0): 84, datetime.datetime(2020, 5, 1, 0, 0): 82, datetime.datetime(2020, 5, 2, 0, 0): 81, datetime.datetime(2020, 5, 3, 0, 0): 79, datetime.datetime(2020, 5, 4, 0, 0): 81, datetime.datetime(2020, 5, 5, 0, 0): 79, datetime.datetime(2020, 5, 6, 0, 0): 78, datetime.datetime(2020, 5, 7, 0, 0): 77, datetime.datetime(2020, 5, 8, 0, 0): 79, datetime.datetime(2020, 5, 9, 0, 0): 77, datetime.datetime(2020, 5, 10, 0, 0): 78, datetime.datetime(2020, 5, 11, 0, 0): 80, datetime.datetime(2020, 5, 12, 0, 0): 79, datetime.datetime(2020, 5, 13, 0, 0): 80, datetime.datetime(2020, 5, 14, 0, 0): 81, datetime.datetime(2020, 5, 15, 0, 0): 81, datetime.datetime(2020, 5, 16, 0, 0): 80, datetime.datetime(2020, 5, 17, 0, 0): 82, datetime.datetime(2020, 5, 18, 0, 0): 84, datetime.datetime(2020, 5, 19, 0, 0): 83, datetime.datetime(2020, 5, 20, 0, 0): 81, datetime.datetime(2020, 5, 21, 0, 0): 82, datetime.datetime(2020, 5, 22, 0, 0): 84, datetime.datetime(2020, 5, 23, 0, 0): 84, datetime.datetime(2020, 5, 24, 0, 0): 84, datetime.datetime(2020, 5, 25, 0, 0): 85, datetime.datetime(2020, 5, 26, 0, 0): 87, datetime.datetime(2020, 5, 27, 0, 0): 85, datetime.datetime(2020, 5, 28, 0, 0): 84, datetime.datetime(2020, 5, 29, 0, 0): 84, datetime.datetime(2020, 5, 30, 0, 0): 86, datetime.datetime(2020, 5, 31, 0, 0): 88, datetime.datetime(2020, 6, 1, 0, 0): 88, datetime.datetime(2020, 6, 2, 0, 0): 89, datetime.datetime(2020, 6, 3, 0, 0): 89, datetime.datetime(2020, 6, 4, 0, 0): 87, datetime.datetime(2020, 6, 5, 0, 0): 86, datetime.datetime(2020, 6, 6, 0, 0): 86, datetime.datetime(2020, 6, 7, 0, 0): 84, datetime.datetime(2020, 6, 8, 0, 0): 82, datetime.datetime(2020, 6, 9, 0, 0): 84, datetime.datetime(2020, 6, 10, 0, 0): 84, datetime.datetime(2020, 6, 11, 0, 0): 86, datetime.datetime(2020, 6, 12, 0, 0): 87, datetime.datetime(2020, 6, 13, 0, 0): 87, datetime.datetime(2020, 6, 14, 0, 0): 85, datetime.datetime(2020, 6, 15, 0, 0): 84, datetime.datetime(2020, 6, 16, 0, 0): 82, datetime.datetime(2020, 6, 17, 0, 0): 83, datetime.datetime(2020, 6, 18, 0, 0): 85, datetime.datetime(2020, 6, 19, 0, 0): 87, datetime.datetime(2020, 6, 20, 0, 0): 89, datetime.datetime(2020, 6, 21, 0, 0): 88, datetime.datetime(2020, 6, 22, 0, 0): 86, datetime.datetime(2020, 6, 23, 0, 0): 85, datetime.datetime(2020, 6, 24, 0, 0): 83, datetime.datetime(2020, 6, 25, 0, 0): 83, datetime.datetime(2020, 6, 26, 0, 0): 85, datetime.datetime(2020, 6, 27, 0, 0): 84, datetime.datetime(2020, 6, 28, 0, 0): 84, datetime.datetime(2020, 6, 29, 0, 0): 82, datetime.datetime(2020, 6, 30, 0, 0): 84, datetime.datetime(2020, 7, 1, 0, 0): 86, datetime.datetime(2020, 7, 2, 0, 0): 84, datetime.datetime(2020, 7, 3, 0, 0): 82, datetime.datetime(2020, 7, 4, 0, 0): 81, datetime.datetime(2020, 7, 5, 0, 0): 83, datetime.datetime(2020, 7, 6, 0, 0): 83, datetime.datetime(2020, 7, 7, 0, 0): 85, datetime.datetime(2020, 7, 8, 0, 0): 83, datetime.datetime(2020, 7, 9, 0, 0): 81, datetime.datetime(2020, 7, 10, 0, 0): 79, datetime.datetime(2020, 7, 11, 0, 0): 80, datetime.datetime(2020, 7, 12, 0, 0): 79, datetime.datetime(2020, 7, 13, 0, 0): 78, datetime.datetime(2020, 7, 14, 0, 0): 78, datetime.datetime(2020, 7, 15, 0, 0): 77, datetime.datetime(2020, 7, 16, 0, 0): 77, datetime.datetime(2020, 7, 17, 0, 0): 77, datetime.datetime(2020, 7, 18, 0, 0): 78, datetime.datetime(2020, 7, 19, 0, 0): 80, datetime.datetime(2020, 7, 20, 0, 0): 81, datetime.datetime(2020, 7, 21, 0, 0): 79, datetime.datetime(2020, 7, 22, 0, 0): 78, datetime.datetime(2020, 7, 23, 0, 0): 79, datetime.datetime(2020, 7, 24, 0, 0): 80, datetime.datetime(2020, 7, 25, 0, 0): 78, datetime.datetime(2020, 7, 26, 0, 0): 80, datetime.datetime(2020, 7, 27, 0, 0): 82, datetime.datetime(2020, 7, 28, 0, 0): 82, datetime.datetime(2020, 7, 29, 0, 0): 84, datetime.datetime(2020, 7, 30, 0, 0): 84, datetime.datetime(2020, 7, 31, 0, 0): 83, datetime.datetime(2020, 8, 1, 0, 0): 84, datetime.datetime(2020, 8, 2, 0, 0): 84, datetime.datetime(2020, 8, 3, 0, 0): 83, datetime.datetime(2020, 8, 4, 0, 0): 82, datetime.datetime(2020, 8, 5, 0, 0): 80, datetime.datetime(2020, 8, 6, 0, 0): 81, datetime.datetime(2020, 8, 7, 0, 0): 82, datetime.datetime(2020, 8, 8, 0, 0): 82, datetime.datetime(2020, 8, 9, 0, 0): 82, datetime.datetime(2020, 8, 10, 0, 0): 83, datetime.datetime(2020, 8, 11, 0, 0): 84, datetime.datetime(2020, 8, 12, 0, 0): 84, datetime.datetime(2020, 8, 13, 0, 0): 86, datetime.datetime(2020, 8, 14, 0, 0): 85, datetime.datetime(2020, 8, 15, 0, 0): 85, datetime.datetime(2020, 8, 16, 0, 0): 85, datetime.datetime(2020, 8, 17, 0, 0): 87, datetime.datetime(2020, 8, 18, 0, 0): 85, datetime.datetime(2020, 8, 19, 0, 0): 83, datetime.datetime(2020, 8, 20, 0, 0): 84, datetime.datetime(2020, 8, 21, 0, 0): 84, datetime.datetime(2020, 8, 22, 0, 0): 86, datetime.datetime(2020, 8, 23, 0, 0): 86, datetime.datetime(2020, 8, 24, 0, 0): 85, datetime.datetime(2020, 8, 25, 0, 0): 87, datetime.datetime(2020, 8, 26, 0, 0): 87, datetime.datetime(2020, 8, 27, 0, 0): 85, datetime.datetime(2020, 8, 28, 0, 0): 84, datetime.datetime(2020, 8, 29, 0, 0): 86, datetime.datetime(2020, 8, 30, 0, 0): 84, datetime.datetime(2020, 8, 31, 0, 0): 83, datetime.datetime(2020, 9, 1, 0, 0): 83, datetime.datetime(2020, 9, 2, 0, 0): 82, datetime.datetime(2020, 9, 3, 0, 0): 83, datetime.datetime(2020, 9, 4, 0, 0): 81, datetime.datetime(2020, 9, 5, 0, 0): 81, datetime.datetime(2020, 9, 6, 0, 0): 79, datetime.datetime(2020, 9, 7, 0, 0): 80, datetime.datetime(2020, 9, 8, 0, 0): 82, datetime.datetime(2020, 9, 9, 0, 0): 81, datetime.datetime(2020, 9, 10, 0, 0): 80, datetime.datetime(2020, 9, 11, 0, 0): 78, datetime.datetime(2020, 9, 12, 0, 0): 77, datetime.datetime(2020, 9, 13, 0, 0): 79, datetime.datetime(2020, 9, 14, 0, 0): 77, datetime.datetime(2020, 9, 15, 0, 0): 75, datetime.datetime(2020, 9, 16, 0, 0): 75, datetime.datetime(2020, 9, 17, 0, 0): 75, datetime.datetime(2020, 9, 18, 0, 0): 77, datetime.datetime(2020, 9, 19, 0, 0): 79, datetime.datetime(2020, 9, 20, 0, 0): 78, datetime.datetime(2020, 9, 21, 0, 0): 76, datetime.datetime(2020, 9, 22, 0, 0): 75, datetime.datetime(2020, 9, 23, 0, 0): 73, datetime.datetime(2020, 9, 24, 0, 0): 72, datetime.datetime(2020, 9, 25, 0, 0): 71, datetime.datetime(2020, 9, 26, 0, 0): 71, datetime.datetime(2020, 9, 27, 0, 0): 69, datetime.datetime(2020, 9, 28, 0, 0): 71, datetime.datetime(2020, 9, 29, 0, 0): 71, datetime.datetime(2020, 9, 30, 0, 0): 71, datetime.datetime(2020, 10, 1, 0, 0): 73, datetime.datetime(2020, 10, 2, 0, 0): 73, datetime.datetime(2020, 10, 3, 0, 0): 72, datetime.datetime(2020, 10, 4, 0, 0): 71, datetime.datetime(2020, 10, 5, 0, 0): 70, datetime.datetime(2020, 10, 6, 0, 0): 68, datetime.datetime(2020, 10, 7, 0, 0): 66, datetime.datetime(2020, 10, 8, 0, 0): 65, datetime.datetime(2020, 10, 9, 0, 0): 63, datetime.datetime(2020, 10, 10, 0, 0): 63, datetime.datetime(2020, 10, 11, 0, 0): 64, datetime.datetime(2020, 10, 12, 0, 0): 62, datetime.datetime(2020, 10, 13, 0, 0): 61, datetime.datetime(2020, 10, 14, 0, 0): 60, datetime.datetime(2020, 10, 15, 0, 0): 61, datetime.datetime(2020, 10, 16, 0, 0): 62, datetime.datetime(2020, 10, 17, 0, 0): 64, datetime.datetime(2020, 10, 18, 0, 0): 66, datetime.datetime(2020, 10, 19, 0, 0): 67, datetime.datetime(2020, 10, 20, 0, 0): 69, datetime.datetime(2020, 10, 21, 0, 0): 69, datetime.datetime(2020, 10, 22, 0, 0): 69, datetime.datetime(2020, 10, 23, 0, 0): 70, datetime.datetime(2020, 10, 24, 0, 0): 71, datetime.datetime(2020, 10, 25, 0, 0): 69, datetime.datetime(2020, 10, 26, 0, 0): 67, datetime.datetime(2020, 10, 27, 0, 0): 66, datetime.datetime(2020, 10, 28, 0, 0): 65, datetime.datetime(2020, 10, 29, 0, 0): 65, datetime.datetime(2020, 10, 30, 0, 0): 64, datetime.datetime(2020, 10, 31, 0, 0): 64, datetime.datetime(2020, 11, 1, 0, 0): 63, datetime.datetime(2020, 11, 2, 0, 0): 63, datetime.datetime(2020, 11, 3, 0, 0): 65, datetime.datetime(2020, 11, 4, 0, 0): 64, datetime.datetime(2020, 11, 5, 0, 0): 66, datetime.datetime(2020, 11, 6, 0, 0): 66, datetime.datetime(2020, 11, 7, 0, 0): 67, datetime.datetime(2020, 11, 8, 0, 0): 67, datetime.datetime(2020, 11, 9, 0, 0): 69, datetime.datetime(2020, 11, 10, 0, 0): 67, datetime.datetime(2020, 11, 11, 0, 0): 66, datetime.datetime(2020, 11, 12, 0, 0): 65, datetime.datetime(2020, 11, 13, 0, 0): 65, datetime.datetime(2020, 11, 14, 0, 0): 64, datetime.datetime(2020, 11, 15, 0, 0): 66, datetime.datetime(2020, 11, 16, 0, 0): 65, datetime.datetime(2020, 11, 17, 0, 0): 63, datetime.datetime(2020, 11, 18, 0, 0): 61, datetime.datetime(2020, 11, 19, 0, 0): 59, datetime.datetime(2020, 11, 20, 0, 0): 58, datetime.datetime(2020, 11, 21, 0, 0): 58, datetime.datetime(2020, 11, 22, 0, 0): 58, datetime.datetime(2020, 11, 23, 0, 0): 60, datetime.datetime(2020, 11, 24, 0, 0): 61, datetime.datetime(2020, 11, 25, 0, 0): 59, datetime.datetime(2020, 11, 26, 0, 0): 59, datetime.datetime(2020, 11, 27, 0, 0): 60, datetime.datetime(2020, 11, 28, 0, 0): 59, datetime.datetime(2020, 11, 29, 0, 0): 58, datetime.datetime(2020, 11, 30, 0, 0): 59, datetime.datetime(2020, 12, 1, 0, 0): 61, datetime.datetime(2020, 12, 2, 0, 0): 62, datetime.datetime(2020, 12, 3, 0, 0): 62, datetime.datetime(2020, 12, 4, 0, 0): 62, datetime.datetime(2020, 12, 5, 0, 0): 60, datetime.datetime(2020, 12, 6, 0, 0): 61, datetime.datetime(2020, 12, 7, 0, 0): 60, datetime.datetime(2020, 12, 8, 0, 0): 61, datetime.datetime(2020, 12, 9, 0, 0): 59, datetime.datetime(2020, 12, 10, 0, 0): 60, datetime.datetime(2020, 12, 11, 0, 0): 59, datetime.datetime(2020, 12, 12, 0, 0): 57, datetime.datetime(2020, 12, 13, 0, 0): 57, datetime.datetime(2020, 12, 14, 0, 0): 57, datetime.datetime(2020, 12, 15, 0, 0): 59, datetime.datetime(2020, 12, 16, 0, 0): 61, datetime.datetime(2020, 12, 17, 0, 0): 59, datetime.datetime(2020, 12, 18, 0, 0): 57, datetime.datetime(2020, 12, 19, 0, 0): 58, datetime.datetime(2020, 12, 20, 0, 0): 57, datetime.datetime(2020, 12, 21, 0, 0): 59, datetime.datetime(2020, 12, 22, 0, 0): 57, datetime.datetime(2020, 12, 23, 0, 0): 56, datetime.datetime(2020, 12, 24, 0, 0): 58, datetime.datetime(2020, 12, 25, 0, 0): 57, datetime.datetime(2020, 12, 26, 0, 0): 59, datetime.datetime(2020, 12, 27, 0, 0): 59, datetime.datetime(2020, 12, 28, 0, 0): 61, datetime.datetime(2020, 12, 29, 0, 0): 59, datetime.datetime(2020, 12, 30, 0, 0): 57}, 'Station 3': {datetime.datetime(2020, 1, 1, 0, 0): 98, datetime.datetime(2020, 1, 2, 0, 0): 99, datetime.datetime(2020, 1, 3, 0, 0): 98, datetime.datetime(2020, 1, 4, 0, 0): 100, datetime.datetime(2020, 1, 5, 0, 0): 99, datetime.datetime(2020, 1, 6, 0, 0): 101, datetime.datetime(2020, 1, 7, 0, 0): 99, datetime.datetime(2020, 1, 8, 0, 0): 98, datetime.datetime(2020, 1, 9, 0, 0): 97, datetime.datetime(2020, 1, 10, 0, 0): 99, datetime.datetime(2020, 1, 11, 0, 0): 99, datetime.datetime(2020, 1, 12, 0, 0): 98, datetime.datetime(2020, 1, 13, 0, 0): 99, datetime.datetime(2020, 1, 14, 0, 0): 99, datetime.datetime(2020, 1, 15, 0, 0): 101, datetime.datetime(2020, 1, 16, 0, 0): 101, datetime.datetime(2020, 1, 17, 0, 0): 100, datetime.datetime(2020, 1, 18, 0, 0): 100, datetime.datetime(2020, 1, 19, 0, 0): 102, datetime.datetime(2020, 1, 20, 0, 0): 104, datetime.datetime(2020, 1, 21, 0, 0): 104, datetime.datetime(2020, 1, 22, 0, 0): 104, datetime.datetime(2020, 1, 23, 0, 0): 106, datetime.datetime(2020, 1, 24, 0, 0): 106, datetime.datetime(2020, 1, 25, 0, 0): 108, datetime.datetime(2020, 1, 26, 0, 0): 106, datetime.datetime(2020, 1, 27, 0, 0): 108, datetime.datetime(2020, 1, 28, 0, 0): 107, datetime.datetime(2020, 1, 29, 0, 0): 107, datetime.datetime(2020, 1, 30, 0, 0): 109, datetime.datetime(2020, 1, 31, 0, 0): 108, datetime.datetime(2020, 2, 1, 0, 0): 109, datetime.datetime(2020, 2, 2, 0, 0): 110, datetime.datetime(2020, 2, 3, 0, 0): 108, datetime.datetime(2020, 2, 4, 0, 0): 106, datetime.datetime(2020, 2, 5, 0, 0): 108, datetime.datetime(2020, 2, 6, 0, 0): 107, datetime.datetime(2020, 2, 7, 0, 0): 106, datetime.datetime(2020, 2, 8, 0, 0): 105, datetime.datetime(2020, 2, 9, 0, 0): 107, datetime.datetime(2020, 2, 10, 0, 0): 106, datetime.datetime(2020, 2, 11, 0, 0): 108, datetime.datetime(2020, 2, 12, 0, 0): 106, datetime.datetime(2020, 2, 13, 0, 0): 108, datetime.datetime(2020, 2, 14, 0, 0): 108, datetime.datetime(2020, 2, 15, 0, 0): 106, datetime.datetime(2020, 2, 16, 0, 0): 104, datetime.datetime(2020, 2, 17, 0, 0): 106, datetime.datetime(2020, 2, 18, 0, 0): 105, datetime.datetime(2020, 2, 19, 0, 0): 107, datetime.datetime(2020, 2, 20, 0, 0): 106, datetime.datetime(2020, 2, 21, 0, 0): 106, datetime.datetime(2020, 2, 22, 0, 0): 108, datetime.datetime(2020, 2, 23, 0, 0): 106, datetime.datetime(2020, 2, 24, 0, 0): 106, datetime.datetime(2020, 2, 25, 0, 0): 107, datetime.datetime(2020, 2, 26, 0, 0): 107, datetime.datetime(2020, 2, 27, 0, 0): 106, datetime.datetime(2020, 2, 28, 0, 0): 107, datetime.datetime(2020, 2, 29, 0, 0): 107, datetime.datetime(2020, 3, 1, 0, 0): 109, datetime.datetime(2020, 3, 2, 0, 0): 107, datetime.datetime(2020, 3, 3, 0, 0): 106, datetime.datetime(2020, 3, 4, 0, 0): 106, datetime.datetime(2020, 3, 5, 0, 0): 107, datetime.datetime(2020, 3, 6, 0, 0): 105, datetime.datetime(2020, 3, 7, 0, 0): 103, datetime.datetime(2020, 3, 8, 0, 0): 103, datetime.datetime(2020, 3, 9, 0, 0): 105, datetime.datetime(2020, 3, 10, 0, 0): 107, datetime.datetime(2020, 3, 11, 0, 0): 107, datetime.datetime(2020, 3, 12, 0, 0): 105, datetime.datetime(2020, 3, 13, 0, 0): 104, datetime.datetime(2020, 3, 14, 0, 0): 105, datetime.datetime(2020, 3, 15, 0, 0): 103, datetime.datetime(2020, 3, 16, 0, 0): 104, datetime.datetime(2020, 3, 17, 0, 0): 102, datetime.datetime(2020, 3, 18, 0, 0): 102, datetime.datetime(2020, 3, 19, 0, 0): 101, datetime.datetime(2020, 3, 20, 0, 0): 100, datetime.datetime(2020, 3, 21, 0, 0): 99, datetime.datetime(2020, 3, 22, 0, 0): 98, datetime.datetime(2020, 3, 23, 0, 0): 96, datetime.datetime(2020, 3, 24, 0, 0): 94, datetime.datetime(2020, 3, 25, 0, 0): 92, datetime.datetime(2020, 3, 26, 0, 0): 90, datetime.datetime(2020, 3, 27, 0, 0): 90, datetime.datetime(2020, 3, 28, 0, 0): 92, datetime.datetime(2020, 3, 29, 0, 0): 94, datetime.datetime(2020, 3, 30, 0, 0): 96, datetime.datetime(2020, 3, 31, 0, 0): 97, datetime.datetime(2020, 4, 1, 0, 0): 95, datetime.datetime(2020, 4, 2, 0, 0): 93, datetime.datetime(2020, 4, 3, 0, 0): 95, datetime.datetime(2020, 4, 4, 0, 0): 95, datetime.datetime(2020, 4, 5, 0, 0): 94, datetime.datetime(2020, 4, 6, 0, 0): 93, datetime.datetime(2020, 4, 7, 0, 0): 95, datetime.datetime(2020, 4, 8, 0, 0): 97, datetime.datetime(2020, 4, 9, 0, 0): 95, datetime.datetime(2020, 4, 10, 0, 0): 93, datetime.datetime(2020, 4, 11, 0, 0): 91, datetime.datetime(2020, 4, 12, 0, 0): 90, datetime.datetime(2020, 4, 13, 0, 0): 91, datetime.datetime(2020, 4, 14, 0, 0): 90, datetime.datetime(2020, 4, 15, 0, 0): 88, datetime.datetime(2020, 4, 16, 0, 0): 90, datetime.datetime(2020, 4, 17, 0, 0): 90, datetime.datetime(2020, 4, 18, 0, 0): 92, datetime.datetime(2020, 4, 19, 0, 0): 92, datetime.datetime(2020, 4, 20, 0, 0): 92, datetime.datetime(2020, 4, 21, 0, 0): 91, datetime.datetime(2020, 4, 22, 0, 0): 93, datetime.datetime(2020, 4, 23, 0, 0): 95, datetime.datetime(2020, 4, 24, 0, 0): 93, datetime.datetime(2020, 4, 25, 0, 0): 95, datetime.datetime(2020, 4, 26, 0, 0): 97, datetime.datetime(2020, 4, 27, 0, 0): 95, datetime.datetime(2020, 4, 28, 0, 0): 97, datetime.datetime(2020, 4, 29, 0, 0): 95, datetime.datetime(2020, 4, 30, 0, 0): 94, datetime.datetime(2020, 5, 1, 0, 0): 92, datetime.datetime(2020, 5, 2, 0, 0): 92, datetime.datetime(2020, 5, 3, 0, 0): 94, datetime.datetime(2020, 5, 4, 0, 0): 95, datetime.datetime(2020, 5, 5, 0, 0): 95, datetime.datetime(2020, 5, 6, 0, 0): 97, datetime.datetime(2020, 5, 7, 0, 0): 97, datetime.datetime(2020, 5, 8, 0, 0): 96, datetime.datetime(2020, 5, 9, 0, 0): 98, datetime.datetime(2020, 5, 10, 0, 0): 98, datetime.datetime(2020, 5, 11, 0, 0): 100, datetime.datetime(2020, 5, 12, 0, 0): 101, datetime.datetime(2020, 5, 13, 0, 0): 100, datetime.datetime(2020, 5, 14, 0, 0): 102, datetime.datetime(2020, 5, 15, 0, 0): 103, datetime.datetime(2020, 5, 16, 0, 0): 102, datetime.datetime(2020, 5, 17, 0, 0): 103, datetime.datetime(2020, 5, 18, 0, 0): 105, datetime.datetime(2020, 5, 19, 0, 0): 107, datetime.datetime(2020, 5, 20, 0, 0): 105, datetime.datetime(2020, 5, 21, 0, 0): 107, datetime.datetime(2020, 5, 22, 0, 0): 109, datetime.datetime(2020, 5, 23, 0, 0): 111, datetime.datetime(2020, 5, 24, 0, 0): 110, datetime.datetime(2020, 5, 25, 0, 0): 110, datetime.datetime(2020, 5, 26, 0, 0): 112, datetime.datetime(2020, 5, 27, 0, 0): 114, datetime.datetime(2020, 5, 28, 0, 0): 116, datetime.datetime(2020, 5, 29, 0, 0): 117, datetime.datetime(2020, 5, 30, 0, 0): 117, datetime.datetime(2020, 5, 31, 0, 0): 116, datetime.datetime(2020, 6, 1, 0, 0): 116, datetime.datetime(2020, 6, 2, 0, 0): 115, datetime.datetime(2020, 6, 3, 0, 0): 115, datetime.datetime(2020, 6, 4, 0, 0): 115, datetime.datetime(2020, 6, 5, 0, 0): 115, datetime.datetime(2020, 6, 6, 0, 0): 114, datetime.datetime(2020, 6, 7, 0, 0): 116, datetime.datetime(2020, 6, 8, 0, 0): 117, datetime.datetime(2020, 6, 9, 0, 0): 119, datetime.datetime(2020, 6, 10, 0, 0): 119, datetime.datetime(2020, 6, 11, 0, 0): 119, datetime.datetime(2020, 6, 12, 0, 0): 119, datetime.datetime(2020, 6, 13, 0, 0): 118, datetime.datetime(2020, 6, 14, 0, 0): 118, datetime.datetime(2020, 6, 15, 0, 0): 116, datetime.datetime(2020, 6, 16, 0, 0): 115, datetime.datetime(2020, 6, 17, 0, 0): 115, datetime.datetime(2020, 6, 18, 0, 0): 114, datetime.datetime(2020, 6, 19, 0, 0): 113, datetime.datetime(2020, 6, 20, 0, 0): 114, datetime.datetime(2020, 6, 21, 0, 0): 112, datetime.datetime(2020, 6, 22, 0, 0): 112, datetime.datetime(2020, 6, 23, 0, 0): 110, datetime.datetime(2020, 6, 24, 0, 0): 112, datetime.datetime(2020, 6, 25, 0, 0): 110, datetime.datetime(2020, 6, 26, 0, 0): 108, datetime.datetime(2020, 6, 27, 0, 0): 106, datetime.datetime(2020, 6, 28, 0, 0): 108, datetime.datetime(2020, 6, 29, 0, 0): 107, datetime.datetime(2020, 6, 30, 0, 0): 106, datetime.datetime(2020, 7, 1, 0, 0): 105, datetime.datetime(2020, 7, 2, 0, 0): 105, datetime.datetime(2020, 7, 3, 0, 0): 106, datetime.datetime(2020, 7, 4, 0, 0): 107, datetime.datetime(2020, 7, 5, 0, 0): 106, datetime.datetime(2020, 7, 6, 0, 0): 107, datetime.datetime(2020, 7, 7, 0, 0): 108, datetime.datetime(2020, 7, 8, 0, 0): 108, datetime.datetime(2020, 7, 9, 0, 0): 110, datetime.datetime(2020, 7, 10, 0, 0): 108, datetime.datetime(2020, 7, 11, 0, 0): 108, datetime.datetime(2020, 7, 12, 0, 0): 106, datetime.datetime(2020, 7, 13, 0, 0): 104, datetime.datetime(2020, 7, 14, 0, 0): 103, datetime.datetime(2020, 7, 15, 0, 0): 101, datetime.datetime(2020, 7, 16, 0, 0): 102, datetime.datetime(2020, 7, 17, 0, 0): 100, datetime.datetime(2020, 7, 18, 0, 0): 101, datetime.datetime(2020, 7, 19, 0, 0): 100, datetime.datetime(2020, 7, 20, 0, 0): 101, datetime.datetime(2020, 7, 21, 0, 0): 99, datetime.datetime(2020, 7, 22, 0, 0): 101, datetime.datetime(2020, 7, 23, 0, 0): 100, datetime.datetime(2020, 7, 24, 0, 0): 100, datetime.datetime(2020, 7, 25, 0, 0): 100, datetime.datetime(2020, 7, 26, 0, 0): 102, datetime.datetime(2020, 7, 27, 0, 0): 102, datetime.datetime(2020, 7, 28, 0, 0): 101, datetime.datetime(2020, 7, 29, 0, 0): 99, datetime.datetime(2020, 7, 30, 0, 0): 99, datetime.datetime(2020, 7, 31, 0, 0): 99, datetime.datetime(2020, 8, 1, 0, 0): 97, datetime.datetime(2020, 8, 2, 0, 0): 98, datetime.datetime(2020, 8, 3, 0, 0): 100, datetime.datetime(2020, 8, 4, 0, 0): 99, datetime.datetime(2020, 8, 5, 0, 0): 100, datetime.datetime(2020, 8, 6, 0, 0): 100, datetime.datetime(2020, 8, 7, 0, 0): 102, datetime.datetime(2020, 8, 8, 0, 0): 103, datetime.datetime(2020, 8, 9, 0, 0): 105, datetime.datetime(2020, 8, 10, 0, 0): 103, datetime.datetime(2020, 8, 11, 0, 0): 104, datetime.datetime(2020, 8, 12, 0, 0): 103, datetime.datetime(2020, 8, 13, 0, 0): 101, datetime.datetime(2020, 8, 14, 0, 0): 102, datetime.datetime(2020, 8, 15, 0, 0): 103, datetime.datetime(2020, 8, 16, 0, 0): 105, datetime.datetime(2020, 8, 17, 0, 0): 104, datetime.datetime(2020, 8, 18, 0, 0): 102, datetime.datetime(2020, 8, 19, 0, 0): 100, datetime.datetime(2020, 8, 20, 0, 0): 100, datetime.datetime(2020, 8, 21, 0, 0): 98, datetime.datetime(2020, 8, 22, 0, 0): 97, datetime.datetime(2020, 8, 23, 0, 0): 96, datetime.datetime(2020, 8, 24, 0, 0): 97, datetime.datetime(2020, 8, 25, 0, 0): 98, datetime.datetime(2020, 8, 26, 0, 0): 98, datetime.datetime(2020, 8, 27, 0, 0): 99, datetime.datetime(2020, 8, 28, 0, 0): 99, datetime.datetime(2020, 8, 29, 0, 0): 100, datetime.datetime(2020, 8, 30, 0, 0): 100, datetime.datetime(2020, 8, 31, 0, 0): 101, datetime.datetime(2020, 9, 1, 0, 0): 99, datetime.datetime(2020, 9, 2, 0, 0): 99, datetime.datetime(2020, 9, 3, 0, 0): 100, datetime.datetime(2020, 9, 4, 0, 0): 102, datetime.datetime(2020, 9, 5, 0, 0): 100, datetime.datetime(2020, 9, 6, 0, 0): 101, datetime.datetime(2020, 9, 7, 0, 0): 102, datetime.datetime(2020, 9, 8, 0, 0): 102, datetime.datetime(2020, 9, 9, 0, 0): 101, datetime.datetime(2020, 9, 10, 0, 0): 103, datetime.datetime(2020, 9, 11, 0, 0): 105, datetime.datetime(2020, 9, 12, 0, 0): 104, datetime.datetime(2020, 9, 13, 0, 0): 105, datetime.datetime(2020, 9, 14, 0, 0): 105, datetime.datetime(2020, 9, 15, 0, 0): 103, datetime.datetime(2020, 9, 16, 0, 0): 103, datetime.datetime(2020, 9, 17, 0, 0): 105, datetime.datetime(2020, 9, 18, 0, 0): 103, datetime.datetime(2020, 9, 19, 0, 0): 105, datetime.datetime(2020, 9, 20, 0, 0): 104, datetime.datetime(2020, 9, 21, 0, 0): 106, datetime.datetime(2020, 9, 22, 0, 0): 105, datetime.datetime(2020, 9, 23, 0, 0): 106, datetime.datetime(2020, 9, 24, 0, 0): 105, datetime.datetime(2020, 9, 25, 0, 0): 107, datetime.datetime(2020, 9, 26, 0, 0): 107, datetime.datetime(2020, 9, 27, 0, 0): 108, datetime.datetime(2020, 9, 28, 0, 0): 109, datetime.datetime(2020, 9, 29, 0, 0): 110, datetime.datetime(2020, 9, 30, 0, 0): 112, datetime.datetime(2020, 10, 1, 0, 0): 110, datetime.datetime(2020, 10, 2, 0, 0): 111, datetime.datetime(2020, 10, 3, 0, 0): 109, datetime.datetime(2020, 10, 4, 0, 0): 110, datetime.datetime(2020, 10, 5, 0, 0): 110, datetime.datetime(2020, 10, 6, 0, 0): 111, datetime.datetime(2020, 10, 7, 0, 0): 111, datetime.datetime(2020, 10, 8, 0, 0): 110, datetime.datetime(2020, 10, 9, 0, 0): 109, datetime.datetime(2020, 10, 10, 0, 0): 109, datetime.datetime(2020, 10, 11, 0, 0): 109, datetime.datetime(2020, 10, 12, 0, 0): 108, datetime.datetime(2020, 10, 13, 0, 0): 108, datetime.datetime(2020, 10, 14, 0, 0): 110, datetime.datetime(2020, 10, 15, 0, 0): 112, datetime.datetime(2020, 10, 16, 0, 0): 113, datetime.datetime(2020, 10, 17, 0, 0): 115, datetime.datetime(2020, 10, 18, 0, 0): 114, datetime.datetime(2020, 10, 19, 0, 0): 115, datetime.datetime(2020, 10, 20, 0, 0): 116, datetime.datetime(2020, 10, 21, 0, 0): 117, datetime.datetime(2020, 10, 22, 0, 0): 117, datetime.datetime(2020, 10, 23, 0, 0): 119, datetime.datetime(2020, 10, 24, 0, 0): 121, datetime.datetime(2020, 10, 25, 0, 0): 119, datetime.datetime(2020, 10, 26, 0, 0): 120, datetime.datetime(2020, 10, 27, 0, 0): 120, datetime.datetime(2020, 10, 28, 0, 0): 122, datetime.datetime(2020, 10, 29, 0, 0): 124, datetime.datetime(2020, 10, 30, 0, 0): 123, datetime.datetime(2020, 10, 31, 0, 0): 125, datetime.datetime(2020, 11, 1, 0, 0): 124, datetime.datetime(2020, 11, 2, 0, 0): 122, datetime.datetime(2020, 11, 3, 0, 0): 123, datetime.datetime(2020, 11, 4, 0, 0): 125, datetime.datetime(2020, 11, 5, 0, 0): 125, datetime.datetime(2020, 11, 6, 0, 0): 124, datetime.datetime(2020, 11, 7, 0, 0): 122, datetime.datetime(2020, 11, 8, 0, 0): 120, datetime.datetime(2020, 11, 9, 0, 0): 119, datetime.datetime(2020, 11, 10, 0, 0): 119, datetime.datetime(2020, 11, 11, 0, 0): 118, datetime.datetime(2020, 11, 12, 0, 0): 118, datetime.datetime(2020, 11, 13, 0, 0): 118, datetime.datetime(2020, 11, 14, 0, 0): 119, datetime.datetime(2020, 11, 15, 0, 0): 121, datetime.datetime(2020, 11, 16, 0, 0): 120, datetime.datetime(2020, 11, 17, 0, 0): 120, datetime.datetime(2020, 11, 18, 0, 0): 118, datetime.datetime(2020, 11, 19, 0, 0): 116, datetime.datetime(2020, 11, 20, 0, 0): 114, datetime.datetime(2020, 11, 21, 0, 0): 115, datetime.datetime(2020, 11, 22, 0, 0): 113, datetime.datetime(2020, 11, 23, 0, 0): 112, datetime.datetime(2020, 11, 24, 0, 0): 111, datetime.datetime(2020, 11, 25, 0, 0): 112, datetime.datetime(2020, 11, 26, 0, 0): 114, datetime.datetime(2020, 11, 27, 0, 0): 112, datetime.datetime(2020, 11, 28, 0, 0): 114, datetime.datetime(2020, 11, 29, 0, 0): 116, datetime.datetime(2020, 11, 30, 0, 0): 115, datetime.datetime(2020, 12, 1, 0, 0): 114, datetime.datetime(2020, 12, 2, 0, 0): 113, datetime.datetime(2020, 12, 3, 0, 0): 111, datetime.datetime(2020, 12, 4, 0, 0): 110, datetime.datetime(2020, 12, 5, 0, 0): 112, datetime.datetime(2020, 12, 6, 0, 0): 111, datetime.datetime(2020, 12, 7, 0, 0): 110, datetime.datetime(2020, 12, 8, 0, 0): 108, datetime.datetime(2020, 12, 9, 0, 0): 110, datetime.datetime(2020, 12, 10, 0, 0): 111, datetime.datetime(2020, 12, 11, 0, 0): 111, datetime.datetime(2020, 12, 12, 0, 0): 111, datetime.datetime(2020, 12, 13, 0, 0): 111, datetime.datetime(2020, 12, 14, 0, 0): 113, datetime.datetime(2020, 12, 15, 0, 0): 114, datetime.datetime(2020, 12, 16, 0, 0): 116, datetime.datetime(2020, 12, 17, 0, 0): 115, datetime.datetime(2020, 12, 18, 0, 0): 115, datetime.datetime(2020, 12, 19, 0, 0): 113, datetime.datetime(2020, 12, 20, 0, 0): 115, datetime.datetime(2020, 12, 21, 0, 0): 115, datetime.datetime(2020, 12, 22, 0, 0): 115, datetime.datetime(2020, 12, 23, 0, 0): 113, datetime.datetime(2020, 12, 24, 0, 0): 111, datetime.datetime(2020, 12, 25, 0, 0): 111, datetime.datetime(2020, 12, 26, 0, 0): 109, datetime.datetime(2020, 12, 27, 0, 0): 110, datetime.datetime(2020, 12, 28, 0, 0): 108, datetime.datetime(2020, 12, 29, 0, 0): 107, datetime.datetime(2020, 12, 30, 0, 0): 106}}\""
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_4ny5bs5-wZO"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SF4196Md3zaD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85d25f69-5767-4c5c-9110-1199630fe4a0"
      },
      "source": [
        "stage_data['Station 3'][datetime(2020, 1, 1)]"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "98"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QTb7yMUX3zXF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "8b194334-5f76-497b-a06d-a619ae03c9ef"
      },
      "source": [
        "plt.figure(figsize=(15, 5))\n",
        "\n",
        "for station in stations:\n",
        "    plt.plot(list(stage_data[station].keys()), list(stage_data[station].values()), label=station)\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fddb047bfd0>"
            ]
          },
          "metadata": {},
          "execution_count": 101
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "miYQKMiT-0SN"
      },
      "source": [
        "Does it seem ackward? Dictionary inside another dictionary?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7PeqxQ-v27LT"
      },
      "source": [
        "# the indices are perfectly aligned\n",
        "import pandas as pd\n",
        "df = pd.DataFrame(stage_data)"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "HjDQJjxQEYQ7",
        "outputId": "1b1e35db-9066-464f-e125-5a96d8ac67c9"
      },
      "source": [
        "df"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Station 1</th>\n",
              "      <th>Station 2</th>\n",
              "      <th>Station 3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>80</td>\n",
              "      <td>73</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-02</th>\n",
              "      <td>80</td>\n",
              "      <td>75</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>78</td>\n",
              "      <td>76</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-04</th>\n",
              "      <td>79</td>\n",
              "      <td>78</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-05</th>\n",
              "      <td>80</td>\n",
              "      <td>76</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-26</th>\n",
              "      <td>60</td>\n",
              "      <td>59</td>\n",
              "      <td>109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-27</th>\n",
              "      <td>58</td>\n",
              "      <td>59</td>\n",
              "      <td>110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-28</th>\n",
              "      <td>58</td>\n",
              "      <td>61</td>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-29</th>\n",
              "      <td>56</td>\n",
              "      <td>59</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-12-30</th>\n",
              "      <td>55</td>\n",
              "      <td>57</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>365 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Station 1  Station 2  Station 3\n",
              "2020-01-01         80         73         98\n",
              "2020-01-02         80         75         99\n",
              "2020-01-03         78         76         98\n",
              "2020-01-04         79         78        100\n",
              "2020-01-05         80         76         99\n",
              "...               ...        ...        ...\n",
              "2020-12-26         60         59        109\n",
              "2020-12-27         58         59        110\n",
              "2020-12-28         58         61        108\n",
              "2020-12-29         56         59        107\n",
              "2020-12-30         55         57        106\n",
              "\n",
              "[365 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsmhboexGBYK"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zfb_BagxAfa7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "aa1565de-afe0-4ce6-8c15-1a801745ed5f"
      },
      "source": [
        "df.plot(figsize=(15, 5))"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-FL_heyE178"
      },
      "source": [
        "df.to_csv('stages.csv')"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_9A6I2G0HIpx",
        "outputId": "cb6d76fd-e5c3-49cf-cd5c-51fbef9c4742"
      },
      "source": [
        "stage_data['Station 1']"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{datetime.datetime(2020, 1, 1, 0, 0): 80,\n",
              " datetime.datetime(2020, 1, 2, 0, 0): 80,\n",
              " datetime.datetime(2020, 1, 3, 0, 0): 78,\n",
              " datetime.datetime(2020, 1, 4, 0, 0): 79,\n",
              " datetime.datetime(2020, 1, 5, 0, 0): 80,\n",
              " datetime.datetime(2020, 1, 6, 0, 0): 79,\n",
              " datetime.datetime(2020, 1, 7, 0, 0): 77,\n",
              " datetime.datetime(2020, 1, 8, 0, 0): 75,\n",
              " datetime.datetime(2020, 1, 9, 0, 0): 73,\n",
              " datetime.datetime(2020, 1, 10, 0, 0): 74,\n",
              " datetime.datetime(2020, 1, 11, 0, 0): 76,\n",
              " datetime.datetime(2020, 1, 12, 0, 0): 76,\n",
              " datetime.datetime(2020, 1, 13, 0, 0): 74,\n",
              " datetime.datetime(2020, 1, 14, 0, 0): 73,\n",
              " datetime.datetime(2020, 1, 15, 0, 0): 75,\n",
              " datetime.datetime(2020, 1, 16, 0, 0): 77,\n",
              " datetime.datetime(2020, 1, 17, 0, 0): 77,\n",
              " datetime.datetime(2020, 1, 18, 0, 0): 77,\n",
              " datetime.datetime(2020, 1, 19, 0, 0): 76,\n",
              " datetime.datetime(2020, 1, 20, 0, 0): 74,\n",
              " datetime.datetime(2020, 1, 21, 0, 0): 74,\n",
              " datetime.datetime(2020, 1, 22, 0, 0): 73,\n",
              " datetime.datetime(2020, 1, 23, 0, 0): 71,\n",
              " datetime.datetime(2020, 1, 24, 0, 0): 71,\n",
              " datetime.datetime(2020, 1, 25, 0, 0): 71,\n",
              " datetime.datetime(2020, 1, 26, 0, 0): 70,\n",
              " datetime.datetime(2020, 1, 27, 0, 0): 69,\n",
              " datetime.datetime(2020, 1, 28, 0, 0): 69,\n",
              " datetime.datetime(2020, 1, 29, 0, 0): 69,\n",
              " datetime.datetime(2020, 1, 30, 0, 0): 69,\n",
              " datetime.datetime(2020, 1, 31, 0, 0): 67,\n",
              " datetime.datetime(2020, 2, 1, 0, 0): 69,\n",
              " datetime.datetime(2020, 2, 2, 0, 0): 69,\n",
              " datetime.datetime(2020, 2, 3, 0, 0): 70,\n",
              " datetime.datetime(2020, 2, 4, 0, 0): 72,\n",
              " datetime.datetime(2020, 2, 5, 0, 0): 71,\n",
              " datetime.datetime(2020, 2, 6, 0, 0): 70,\n",
              " datetime.datetime(2020, 2, 7, 0, 0): 69,\n",
              " datetime.datetime(2020, 2, 8, 0, 0): 70,\n",
              " datetime.datetime(2020, 2, 9, 0, 0): 70,\n",
              " datetime.datetime(2020, 2, 10, 0, 0): 68,\n",
              " datetime.datetime(2020, 2, 11, 0, 0): 70,\n",
              " datetime.datetime(2020, 2, 12, 0, 0): 70,\n",
              " datetime.datetime(2020, 2, 13, 0, 0): 68,\n",
              " datetime.datetime(2020, 2, 14, 0, 0): 68,\n",
              " datetime.datetime(2020, 2, 15, 0, 0): 70,\n",
              " datetime.datetime(2020, 2, 16, 0, 0): 70,\n",
              " datetime.datetime(2020, 2, 17, 0, 0): 72,\n",
              " datetime.datetime(2020, 2, 18, 0, 0): 71,\n",
              " datetime.datetime(2020, 2, 19, 0, 0): 72,\n",
              " datetime.datetime(2020, 2, 20, 0, 0): 73,\n",
              " datetime.datetime(2020, 2, 21, 0, 0): 75,\n",
              " datetime.datetime(2020, 2, 22, 0, 0): 75,\n",
              " datetime.datetime(2020, 2, 23, 0, 0): 76,\n",
              " datetime.datetime(2020, 2, 24, 0, 0): 77,\n",
              " datetime.datetime(2020, 2, 25, 0, 0): 76,\n",
              " datetime.datetime(2020, 2, 26, 0, 0): 75,\n",
              " datetime.datetime(2020, 2, 27, 0, 0): 75,\n",
              " datetime.datetime(2020, 2, 28, 0, 0): 75,\n",
              " datetime.datetime(2020, 2, 29, 0, 0): 73,\n",
              " datetime.datetime(2020, 3, 1, 0, 0): 71,\n",
              " datetime.datetime(2020, 3, 2, 0, 0): 69,\n",
              " datetime.datetime(2020, 3, 3, 0, 0): 70,\n",
              " datetime.datetime(2020, 3, 4, 0, 0): 70,\n",
              " datetime.datetime(2020, 3, 5, 0, 0): 72,\n",
              " datetime.datetime(2020, 3, 6, 0, 0): 74,\n",
              " datetime.datetime(2020, 3, 7, 0, 0): 75,\n",
              " datetime.datetime(2020, 3, 8, 0, 0): 75,\n",
              " datetime.datetime(2020, 3, 9, 0, 0): 74,\n",
              " datetime.datetime(2020, 3, 10, 0, 0): 73,\n",
              " datetime.datetime(2020, 3, 11, 0, 0): 71,\n",
              " datetime.datetime(2020, 3, 12, 0, 0): 72,\n",
              " datetime.datetime(2020, 3, 13, 0, 0): 71,\n",
              " datetime.datetime(2020, 3, 14, 0, 0): 72,\n",
              " datetime.datetime(2020, 3, 15, 0, 0): 72,\n",
              " datetime.datetime(2020, 3, 16, 0, 0): 71,\n",
              " datetime.datetime(2020, 3, 17, 0, 0): 71,\n",
              " datetime.datetime(2020, 3, 18, 0, 0): 72,\n",
              " datetime.datetime(2020, 3, 19, 0, 0): 74,\n",
              " datetime.datetime(2020, 3, 20, 0, 0): 74,\n",
              " datetime.datetime(2020, 3, 21, 0, 0): 76,\n",
              " datetime.datetime(2020, 3, 22, 0, 0): 75,\n",
              " datetime.datetime(2020, 3, 23, 0, 0): 75,\n",
              " datetime.datetime(2020, 3, 24, 0, 0): 73,\n",
              " datetime.datetime(2020, 3, 25, 0, 0): 71,\n",
              " datetime.datetime(2020, 3, 26, 0, 0): 70,\n",
              " datetime.datetime(2020, 3, 27, 0, 0): 70,\n",
              " datetime.datetime(2020, 3, 28, 0, 0): 72,\n",
              " datetime.datetime(2020, 3, 29, 0, 0): 74,\n",
              " datetime.datetime(2020, 3, 30, 0, 0): 73,\n",
              " datetime.datetime(2020, 3, 31, 0, 0): 71,\n",
              " datetime.datetime(2020, 4, 1, 0, 0): 71,\n",
              " datetime.datetime(2020, 4, 2, 0, 0): 70,\n",
              " datetime.datetime(2020, 4, 3, 0, 0): 70,\n",
              " datetime.datetime(2020, 4, 4, 0, 0): 71,\n",
              " datetime.datetime(2020, 4, 5, 0, 0): 69,\n",
              " datetime.datetime(2020, 4, 6, 0, 0): 67,\n",
              " datetime.datetime(2020, 4, 7, 0, 0): 67,\n",
              " datetime.datetime(2020, 4, 8, 0, 0): 65,\n",
              " datetime.datetime(2020, 4, 9, 0, 0): 65,\n",
              " datetime.datetime(2020, 4, 10, 0, 0): 65,\n",
              " datetime.datetime(2020, 4, 11, 0, 0): 63,\n",
              " datetime.datetime(2020, 4, 12, 0, 0): 63,\n",
              " datetime.datetime(2020, 4, 13, 0, 0): 63,\n",
              " datetime.datetime(2020, 4, 14, 0, 0): 63,\n",
              " datetime.datetime(2020, 4, 15, 0, 0): 62,\n",
              " datetime.datetime(2020, 4, 16, 0, 0): 63,\n",
              " datetime.datetime(2020, 4, 17, 0, 0): 65,\n",
              " datetime.datetime(2020, 4, 18, 0, 0): 63,\n",
              " datetime.datetime(2020, 4, 19, 0, 0): 63,\n",
              " datetime.datetime(2020, 4, 20, 0, 0): 65,\n",
              " datetime.datetime(2020, 4, 21, 0, 0): 64,\n",
              " datetime.datetime(2020, 4, 22, 0, 0): 65,\n",
              " datetime.datetime(2020, 4, 23, 0, 0): 65,\n",
              " datetime.datetime(2020, 4, 24, 0, 0): 64,\n",
              " datetime.datetime(2020, 4, 25, 0, 0): 64,\n",
              " datetime.datetime(2020, 4, 26, 0, 0): 65,\n",
              " datetime.datetime(2020, 4, 27, 0, 0): 67,\n",
              " datetime.datetime(2020, 4, 28, 0, 0): 66,\n",
              " datetime.datetime(2020, 4, 29, 0, 0): 66,\n",
              " datetime.datetime(2020, 4, 30, 0, 0): 68,\n",
              " datetime.datetime(2020, 5, 1, 0, 0): 66,\n",
              " datetime.datetime(2020, 5, 2, 0, 0): 66,\n",
              " datetime.datetime(2020, 5, 3, 0, 0): 64,\n",
              " datetime.datetime(2020, 5, 4, 0, 0): 65,\n",
              " datetime.datetime(2020, 5, 5, 0, 0): 64,\n",
              " datetime.datetime(2020, 5, 6, 0, 0): 64,\n",
              " datetime.datetime(2020, 5, 7, 0, 0): 64,\n",
              " datetime.datetime(2020, 5, 8, 0, 0): 64,\n",
              " datetime.datetime(2020, 5, 9, 0, 0): 66,\n",
              " datetime.datetime(2020, 5, 10, 0, 0): 64,\n",
              " datetime.datetime(2020, 5, 11, 0, 0): 65,\n",
              " datetime.datetime(2020, 5, 12, 0, 0): 64,\n",
              " datetime.datetime(2020, 5, 13, 0, 0): 65,\n",
              " datetime.datetime(2020, 5, 14, 0, 0): 64,\n",
              " datetime.datetime(2020, 5, 15, 0, 0): 62,\n",
              " datetime.datetime(2020, 5, 16, 0, 0): 60,\n",
              " datetime.datetime(2020, 5, 17, 0, 0): 58,\n",
              " datetime.datetime(2020, 5, 18, 0, 0): 56,\n",
              " datetime.datetime(2020, 5, 19, 0, 0): 55,\n",
              " datetime.datetime(2020, 5, 20, 0, 0): 57,\n",
              " datetime.datetime(2020, 5, 21, 0, 0): 56,\n",
              " datetime.datetime(2020, 5, 22, 0, 0): 58,\n",
              " datetime.datetime(2020, 5, 23, 0, 0): 56,\n",
              " datetime.datetime(2020, 5, 24, 0, 0): 58,\n",
              " datetime.datetime(2020, 5, 25, 0, 0): 59,\n",
              " datetime.datetime(2020, 5, 26, 0, 0): 61,\n",
              " datetime.datetime(2020, 5, 27, 0, 0): 60,\n",
              " datetime.datetime(2020, 5, 28, 0, 0): 60,\n",
              " datetime.datetime(2020, 5, 29, 0, 0): 58,\n",
              " datetime.datetime(2020, 5, 30, 0, 0): 56,\n",
              " datetime.datetime(2020, 5, 31, 0, 0): 58,\n",
              " datetime.datetime(2020, 6, 1, 0, 0): 58,\n",
              " datetime.datetime(2020, 6, 2, 0, 0): 59,\n",
              " datetime.datetime(2020, 6, 3, 0, 0): 58,\n",
              " datetime.datetime(2020, 6, 4, 0, 0): 59,\n",
              " datetime.datetime(2020, 6, 5, 0, 0): 58,\n",
              " datetime.datetime(2020, 6, 6, 0, 0): 57,\n",
              " datetime.datetime(2020, 6, 7, 0, 0): 58,\n",
              " datetime.datetime(2020, 6, 8, 0, 0): 59,\n",
              " datetime.datetime(2020, 6, 9, 0, 0): 60,\n",
              " datetime.datetime(2020, 6, 10, 0, 0): 58,\n",
              " datetime.datetime(2020, 6, 11, 0, 0): 57,\n",
              " datetime.datetime(2020, 6, 12, 0, 0): 58,\n",
              " datetime.datetime(2020, 6, 13, 0, 0): 59,\n",
              " datetime.datetime(2020, 6, 14, 0, 0): 58,\n",
              " datetime.datetime(2020, 6, 15, 0, 0): 59,\n",
              " datetime.datetime(2020, 6, 16, 0, 0): 58,\n",
              " datetime.datetime(2020, 6, 17, 0, 0): 59,\n",
              " datetime.datetime(2020, 6, 18, 0, 0): 58,\n",
              " datetime.datetime(2020, 6, 19, 0, 0): 56,\n",
              " datetime.datetime(2020, 6, 20, 0, 0): 54,\n",
              " datetime.datetime(2020, 6, 21, 0, 0): 54,\n",
              " datetime.datetime(2020, 6, 22, 0, 0): 54,\n",
              " datetime.datetime(2020, 6, 23, 0, 0): 53,\n",
              " datetime.datetime(2020, 6, 24, 0, 0): 55,\n",
              " datetime.datetime(2020, 6, 25, 0, 0): 54,\n",
              " datetime.datetime(2020, 6, 26, 0, 0): 53,\n",
              " datetime.datetime(2020, 6, 27, 0, 0): 54,\n",
              " datetime.datetime(2020, 6, 28, 0, 0): 54,\n",
              " datetime.datetime(2020, 6, 29, 0, 0): 53,\n",
              " datetime.datetime(2020, 6, 30, 0, 0): 53,\n",
              " datetime.datetime(2020, 7, 1, 0, 0): 51,\n",
              " datetime.datetime(2020, 7, 2, 0, 0): 51,\n",
              " datetime.datetime(2020, 7, 3, 0, 0): 53,\n",
              " datetime.datetime(2020, 7, 4, 0, 0): 51,\n",
              " datetime.datetime(2020, 7, 5, 0, 0): 53,\n",
              " datetime.datetime(2020, 7, 6, 0, 0): 54,\n",
              " datetime.datetime(2020, 7, 7, 0, 0): 52,\n",
              " datetime.datetime(2020, 7, 8, 0, 0): 53,\n",
              " datetime.datetime(2020, 7, 9, 0, 0): 54,\n",
              " datetime.datetime(2020, 7, 10, 0, 0): 52,\n",
              " datetime.datetime(2020, 7, 11, 0, 0): 53,\n",
              " datetime.datetime(2020, 7, 12, 0, 0): 52,\n",
              " datetime.datetime(2020, 7, 13, 0, 0): 54,\n",
              " datetime.datetime(2020, 7, 14, 0, 0): 53,\n",
              " datetime.datetime(2020, 7, 15, 0, 0): 53,\n",
              " datetime.datetime(2020, 7, 16, 0, 0): 53,\n",
              " datetime.datetime(2020, 7, 17, 0, 0): 54,\n",
              " datetime.datetime(2020, 7, 18, 0, 0): 54,\n",
              " datetime.datetime(2020, 7, 19, 0, 0): 55,\n",
              " datetime.datetime(2020, 7, 20, 0, 0): 57,\n",
              " datetime.datetime(2020, 7, 21, 0, 0): 56,\n",
              " datetime.datetime(2020, 7, 22, 0, 0): 56,\n",
              " datetime.datetime(2020, 7, 23, 0, 0): 56,\n",
              " datetime.datetime(2020, 7, 24, 0, 0): 57,\n",
              " datetime.datetime(2020, 7, 25, 0, 0): 58,\n",
              " datetime.datetime(2020, 7, 26, 0, 0): 56,\n",
              " datetime.datetime(2020, 7, 27, 0, 0): 56,\n",
              " datetime.datetime(2020, 7, 28, 0, 0): 55,\n",
              " datetime.datetime(2020, 7, 29, 0, 0): 53,\n",
              " datetime.datetime(2020, 7, 30, 0, 0): 54,\n",
              " datetime.datetime(2020, 7, 31, 0, 0): 56,\n",
              " datetime.datetime(2020, 8, 1, 0, 0): 55,\n",
              " datetime.datetime(2020, 8, 2, 0, 0): 55,\n",
              " datetime.datetime(2020, 8, 3, 0, 0): 53,\n",
              " datetime.datetime(2020, 8, 4, 0, 0): 52,\n",
              " datetime.datetime(2020, 8, 5, 0, 0): 53,\n",
              " datetime.datetime(2020, 8, 6, 0, 0): 55,\n",
              " datetime.datetime(2020, 8, 7, 0, 0): 56,\n",
              " datetime.datetime(2020, 8, 8, 0, 0): 57,\n",
              " datetime.datetime(2020, 8, 9, 0, 0): 58,\n",
              " datetime.datetime(2020, 8, 10, 0, 0): 57,\n",
              " datetime.datetime(2020, 8, 11, 0, 0): 55,\n",
              " datetime.datetime(2020, 8, 12, 0, 0): 54,\n",
              " datetime.datetime(2020, 8, 13, 0, 0): 53,\n",
              " datetime.datetime(2020, 8, 14, 0, 0): 51,\n",
              " datetime.datetime(2020, 8, 15, 0, 0): 53,\n",
              " datetime.datetime(2020, 8, 16, 0, 0): 53,\n",
              " datetime.datetime(2020, 8, 17, 0, 0): 51,\n",
              " datetime.datetime(2020, 8, 18, 0, 0): 52,\n",
              " datetime.datetime(2020, 8, 19, 0, 0): 53,\n",
              " datetime.datetime(2020, 8, 20, 0, 0): 52,\n",
              " datetime.datetime(2020, 8, 21, 0, 0): 54,\n",
              " datetime.datetime(2020, 8, 22, 0, 0): 52,\n",
              " datetime.datetime(2020, 8, 23, 0, 0): 51,\n",
              " datetime.datetime(2020, 8, 24, 0, 0): 50,\n",
              " datetime.datetime(2020, 8, 25, 0, 0): 52,\n",
              " datetime.datetime(2020, 8, 26, 0, 0): 52,\n",
              " datetime.datetime(2020, 8, 27, 0, 0): 54,\n",
              " datetime.datetime(2020, 8, 28, 0, 0): 55,\n",
              " datetime.datetime(2020, 8, 29, 0, 0): 57,\n",
              " datetime.datetime(2020, 8, 30, 0, 0): 58,\n",
              " datetime.datetime(2020, 8, 31, 0, 0): 56,\n",
              " datetime.datetime(2020, 9, 1, 0, 0): 54,\n",
              " datetime.datetime(2020, 9, 2, 0, 0): 52,\n",
              " datetime.datetime(2020, 9, 3, 0, 0): 54,\n",
              " datetime.datetime(2020, 9, 4, 0, 0): 52,\n",
              " datetime.datetime(2020, 9, 5, 0, 0): 53,\n",
              " datetime.datetime(2020, 9, 6, 0, 0): 55,\n",
              " datetime.datetime(2020, 9, 7, 0, 0): 55,\n",
              " datetime.datetime(2020, 9, 8, 0, 0): 57,\n",
              " datetime.datetime(2020, 9, 9, 0, 0): 56,\n",
              " datetime.datetime(2020, 9, 10, 0, 0): 54,\n",
              " datetime.datetime(2020, 9, 11, 0, 0): 54,\n",
              " datetime.datetime(2020, 9, 12, 0, 0): 52,\n",
              " datetime.datetime(2020, 9, 13, 0, 0): 54,\n",
              " datetime.datetime(2020, 9, 14, 0, 0): 52,\n",
              " datetime.datetime(2020, 9, 15, 0, 0): 52,\n",
              " datetime.datetime(2020, 9, 16, 0, 0): 52,\n",
              " datetime.datetime(2020, 9, 17, 0, 0): 50,\n",
              " datetime.datetime(2020, 9, 18, 0, 0): 48,\n",
              " datetime.datetime(2020, 9, 19, 0, 0): 50,\n",
              " datetime.datetime(2020, 9, 20, 0, 0): 51,\n",
              " datetime.datetime(2020, 9, 21, 0, 0): 52,\n",
              " datetime.datetime(2020, 9, 22, 0, 0): 51,\n",
              " datetime.datetime(2020, 9, 23, 0, 0): 51,\n",
              " datetime.datetime(2020, 9, 24, 0, 0): 52,\n",
              " datetime.datetime(2020, 9, 25, 0, 0): 51,\n",
              " datetime.datetime(2020, 9, 26, 0, 0): 52,\n",
              " datetime.datetime(2020, 9, 27, 0, 0): 53,\n",
              " datetime.datetime(2020, 9, 28, 0, 0): 51,\n",
              " datetime.datetime(2020, 9, 29, 0, 0): 49,\n",
              " datetime.datetime(2020, 9, 30, 0, 0): 47,\n",
              " datetime.datetime(2020, 10, 1, 0, 0): 49,\n",
              " datetime.datetime(2020, 10, 2, 0, 0): 49,\n",
              " datetime.datetime(2020, 10, 3, 0, 0): 51,\n",
              " datetime.datetime(2020, 10, 4, 0, 0): 52,\n",
              " datetime.datetime(2020, 10, 5, 0, 0): 53,\n",
              " datetime.datetime(2020, 10, 6, 0, 0): 54,\n",
              " datetime.datetime(2020, 10, 7, 0, 0): 52,\n",
              " datetime.datetime(2020, 10, 8, 0, 0): 51,\n",
              " datetime.datetime(2020, 10, 9, 0, 0): 51,\n",
              " datetime.datetime(2020, 10, 10, 0, 0): 52,\n",
              " datetime.datetime(2020, 10, 11, 0, 0): 53,\n",
              " datetime.datetime(2020, 10, 12, 0, 0): 51,\n",
              " datetime.datetime(2020, 10, 13, 0, 0): 53,\n",
              " datetime.datetime(2020, 10, 14, 0, 0): 52,\n",
              " datetime.datetime(2020, 10, 15, 0, 0): 52,\n",
              " datetime.datetime(2020, 10, 16, 0, 0): 51,\n",
              " datetime.datetime(2020, 10, 17, 0, 0): 50,\n",
              " datetime.datetime(2020, 10, 18, 0, 0): 49,\n",
              " datetime.datetime(2020, 10, 19, 0, 0): 49,\n",
              " datetime.datetime(2020, 10, 20, 0, 0): 50,\n",
              " datetime.datetime(2020, 10, 21, 0, 0): 50,\n",
              " datetime.datetime(2020, 10, 22, 0, 0): 50,\n",
              " datetime.datetime(2020, 10, 23, 0, 0): 52,\n",
              " datetime.datetime(2020, 10, 24, 0, 0): 50,\n",
              " datetime.datetime(2020, 10, 25, 0, 0): 49,\n",
              " datetime.datetime(2020, 10, 26, 0, 0): 47,\n",
              " datetime.datetime(2020, 10, 27, 0, 0): 47,\n",
              " datetime.datetime(2020, 10, 28, 0, 0): 45,\n",
              " datetime.datetime(2020, 10, 29, 0, 0): 47,\n",
              " datetime.datetime(2020, 10, 30, 0, 0): 45,\n",
              " datetime.datetime(2020, 10, 31, 0, 0): 46,\n",
              " datetime.datetime(2020, 11, 1, 0, 0): 48,\n",
              " datetime.datetime(2020, 11, 2, 0, 0): 49,\n",
              " datetime.datetime(2020, 11, 3, 0, 0): 51,\n",
              " datetime.datetime(2020, 11, 4, 0, 0): 49,\n",
              " datetime.datetime(2020, 11, 5, 0, 0): 51,\n",
              " datetime.datetime(2020, 11, 6, 0, 0): 50,\n",
              " datetime.datetime(2020, 11, 7, 0, 0): 51,\n",
              " datetime.datetime(2020, 11, 8, 0, 0): 51,\n",
              " datetime.datetime(2020, 11, 9, 0, 0): 51,\n",
              " datetime.datetime(2020, 11, 10, 0, 0): 50,\n",
              " datetime.datetime(2020, 11, 11, 0, 0): 49,\n",
              " datetime.datetime(2020, 11, 12, 0, 0): 47,\n",
              " datetime.datetime(2020, 11, 13, 0, 0): 45,\n",
              " datetime.datetime(2020, 11, 14, 0, 0): 47,\n",
              " datetime.datetime(2020, 11, 15, 0, 0): 47,\n",
              " datetime.datetime(2020, 11, 16, 0, 0): 49,\n",
              " datetime.datetime(2020, 11, 17, 0, 0): 50,\n",
              " datetime.datetime(2020, 11, 18, 0, 0): 52,\n",
              " datetime.datetime(2020, 11, 19, 0, 0): 52,\n",
              " datetime.datetime(2020, 11, 20, 0, 0): 54,\n",
              " datetime.datetime(2020, 11, 21, 0, 0): 55,\n",
              " datetime.datetime(2020, 11, 22, 0, 0): 57,\n",
              " datetime.datetime(2020, 11, 23, 0, 0): 59,\n",
              " datetime.datetime(2020, 11, 24, 0, 0): 60,\n",
              " datetime.datetime(2020, 11, 25, 0, 0): 61,\n",
              " datetime.datetime(2020, 11, 26, 0, 0): 60,\n",
              " datetime.datetime(2020, 11, 27, 0, 0): 60,\n",
              " datetime.datetime(2020, 11, 28, 0, 0): 62,\n",
              " datetime.datetime(2020, 11, 29, 0, 0): 62,\n",
              " datetime.datetime(2020, 11, 30, 0, 0): 62,\n",
              " datetime.datetime(2020, 12, 1, 0, 0): 61,\n",
              " datetime.datetime(2020, 12, 2, 0, 0): 62,\n",
              " datetime.datetime(2020, 12, 3, 0, 0): 60,\n",
              " datetime.datetime(2020, 12, 4, 0, 0): 62,\n",
              " datetime.datetime(2020, 12, 5, 0, 0): 61,\n",
              " datetime.datetime(2020, 12, 6, 0, 0): 63,\n",
              " datetime.datetime(2020, 12, 7, 0, 0): 62,\n",
              " datetime.datetime(2020, 12, 8, 0, 0): 62,\n",
              " datetime.datetime(2020, 12, 9, 0, 0): 62,\n",
              " datetime.datetime(2020, 12, 10, 0, 0): 61,\n",
              " datetime.datetime(2020, 12, 11, 0, 0): 63,\n",
              " datetime.datetime(2020, 12, 12, 0, 0): 63,\n",
              " datetime.datetime(2020, 12, 13, 0, 0): 65,\n",
              " datetime.datetime(2020, 12, 14, 0, 0): 63,\n",
              " datetime.datetime(2020, 12, 15, 0, 0): 61,\n",
              " datetime.datetime(2020, 12, 16, 0, 0): 62,\n",
              " datetime.datetime(2020, 12, 17, 0, 0): 61,\n",
              " datetime.datetime(2020, 12, 18, 0, 0): 61,\n",
              " datetime.datetime(2020, 12, 19, 0, 0): 61,\n",
              " datetime.datetime(2020, 12, 20, 0, 0): 61,\n",
              " datetime.datetime(2020, 12, 21, 0, 0): 60,\n",
              " datetime.datetime(2020, 12, 22, 0, 0): 60,\n",
              " datetime.datetime(2020, 12, 23, 0, 0): 58,\n",
              " datetime.datetime(2020, 12, 24, 0, 0): 60,\n",
              " datetime.datetime(2020, 12, 25, 0, 0): 58,\n",
              " datetime.datetime(2020, 12, 26, 0, 0): 60,\n",
              " datetime.datetime(2020, 12, 27, 0, 0): 58,\n",
              " datetime.datetime(2020, 12, 28, 0, 0): 58,\n",
              " datetime.datetime(2020, 12, 29, 0, 0): 56,\n",
              " datetime.datetime(2020, 12, 30, 0, 0): 55}"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rugAme6YHh5q",
        "outputId": "8ff72331-0e2a-473c-a264-d1c297062c57"
      },
      "source": [
        "stage_data['Station 1'].keys()"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys([datetime.datetime(2020, 1, 1, 0, 0), datetime.datetime(2020, 1, 2, 0, 0), datetime.datetime(2020, 1, 3, 0, 0), datetime.datetime(2020, 1, 4, 0, 0), datetime.datetime(2020, 1, 5, 0, 0), datetime.datetime(2020, 1, 6, 0, 0), datetime.datetime(2020, 1, 7, 0, 0), datetime.datetime(2020, 1, 8, 0, 0), datetime.datetime(2020, 1, 9, 0, 0), datetime.datetime(2020, 1, 10, 0, 0), datetime.datetime(2020, 1, 11, 0, 0), datetime.datetime(2020, 1, 12, 0, 0), datetime.datetime(2020, 1, 13, 0, 0), datetime.datetime(2020, 1, 14, 0, 0), datetime.datetime(2020, 1, 15, 0, 0), datetime.datetime(2020, 1, 16, 0, 0), datetime.datetime(2020, 1, 17, 0, 0), datetime.datetime(2020, 1, 18, 0, 0), datetime.datetime(2020, 1, 19, 0, 0), datetime.datetime(2020, 1, 20, 0, 0), datetime.datetime(2020, 1, 21, 0, 0), datetime.datetime(2020, 1, 22, 0, 0), datetime.datetime(2020, 1, 23, 0, 0), datetime.datetime(2020, 1, 24, 0, 0), datetime.datetime(2020, 1, 25, 0, 0), datetime.datetime(2020, 1, 26, 0, 0), datetime.datetime(2020, 1, 27, 0, 0), datetime.datetime(2020, 1, 28, 0, 0), datetime.datetime(2020, 1, 29, 0, 0), datetime.datetime(2020, 1, 30, 0, 0), datetime.datetime(2020, 1, 31, 0, 0), datetime.datetime(2020, 2, 1, 0, 0), datetime.datetime(2020, 2, 2, 0, 0), datetime.datetime(2020, 2, 3, 0, 0), datetime.datetime(2020, 2, 4, 0, 0), datetime.datetime(2020, 2, 5, 0, 0), datetime.datetime(2020, 2, 6, 0, 0), datetime.datetime(2020, 2, 7, 0, 0), datetime.datetime(2020, 2, 8, 0, 0), datetime.datetime(2020, 2, 9, 0, 0), datetime.datetime(2020, 2, 10, 0, 0), datetime.datetime(2020, 2, 11, 0, 0), datetime.datetime(2020, 2, 12, 0, 0), datetime.datetime(2020, 2, 13, 0, 0), datetime.datetime(2020, 2, 14, 0, 0), datetime.datetime(2020, 2, 15, 0, 0), datetime.datetime(2020, 2, 16, 0, 0), datetime.datetime(2020, 2, 17, 0, 0), datetime.datetime(2020, 2, 18, 0, 0), datetime.datetime(2020, 2, 19, 0, 0), datetime.datetime(2020, 2, 20, 0, 0), datetime.datetime(2020, 2, 21, 0, 0), datetime.datetime(2020, 2, 22, 0, 0), datetime.datetime(2020, 2, 23, 0, 0), datetime.datetime(2020, 2, 24, 0, 0), datetime.datetime(2020, 2, 25, 0, 0), datetime.datetime(2020, 2, 26, 0, 0), datetime.datetime(2020, 2, 27, 0, 0), datetime.datetime(2020, 2, 28, 0, 0), datetime.datetime(2020, 2, 29, 0, 0), datetime.datetime(2020, 3, 1, 0, 0), datetime.datetime(2020, 3, 2, 0, 0), datetime.datetime(2020, 3, 3, 0, 0), datetime.datetime(2020, 3, 4, 0, 0), datetime.datetime(2020, 3, 5, 0, 0), datetime.datetime(2020, 3, 6, 0, 0), datetime.datetime(2020, 3, 7, 0, 0), datetime.datetime(2020, 3, 8, 0, 0), datetime.datetime(2020, 3, 9, 0, 0), datetime.datetime(2020, 3, 10, 0, 0), datetime.datetime(2020, 3, 11, 0, 0), datetime.datetime(2020, 3, 12, 0, 0), datetime.datetime(2020, 3, 13, 0, 0), datetime.datetime(2020, 3, 14, 0, 0), datetime.datetime(2020, 3, 15, 0, 0), datetime.datetime(2020, 3, 16, 0, 0), datetime.datetime(2020, 3, 17, 0, 0), datetime.datetime(2020, 3, 18, 0, 0), datetime.datetime(2020, 3, 19, 0, 0), datetime.datetime(2020, 3, 20, 0, 0), datetime.datetime(2020, 3, 21, 0, 0), datetime.datetime(2020, 3, 22, 0, 0), datetime.datetime(2020, 3, 23, 0, 0), datetime.datetime(2020, 3, 24, 0, 0), datetime.datetime(2020, 3, 25, 0, 0), datetime.datetime(2020, 3, 26, 0, 0), datetime.datetime(2020, 3, 27, 0, 0), datetime.datetime(2020, 3, 28, 0, 0), datetime.datetime(2020, 3, 29, 0, 0), datetime.datetime(2020, 3, 30, 0, 0), datetime.datetime(2020, 3, 31, 0, 0), datetime.datetime(2020, 4, 1, 0, 0), datetime.datetime(2020, 4, 2, 0, 0), datetime.datetime(2020, 4, 3, 0, 0), datetime.datetime(2020, 4, 4, 0, 0), datetime.datetime(2020, 4, 5, 0, 0), datetime.datetime(2020, 4, 6, 0, 0), datetime.datetime(2020, 4, 7, 0, 0), datetime.datetime(2020, 4, 8, 0, 0), datetime.datetime(2020, 4, 9, 0, 0), datetime.datetime(2020, 4, 10, 0, 0), datetime.datetime(2020, 4, 11, 0, 0), datetime.datetime(2020, 4, 12, 0, 0), datetime.datetime(2020, 4, 13, 0, 0), datetime.datetime(2020, 4, 14, 0, 0), datetime.datetime(2020, 4, 15, 0, 0), datetime.datetime(2020, 4, 16, 0, 0), datetime.datetime(2020, 4, 17, 0, 0), datetime.datetime(2020, 4, 18, 0, 0), datetime.datetime(2020, 4, 19, 0, 0), datetime.datetime(2020, 4, 20, 0, 0), datetime.datetime(2020, 4, 21, 0, 0), datetime.datetime(2020, 4, 22, 0, 0), datetime.datetime(2020, 4, 23, 0, 0), datetime.datetime(2020, 4, 24, 0, 0), datetime.datetime(2020, 4, 25, 0, 0), datetime.datetime(2020, 4, 26, 0, 0), datetime.datetime(2020, 4, 27, 0, 0), datetime.datetime(2020, 4, 28, 0, 0), datetime.datetime(2020, 4, 29, 0, 0), datetime.datetime(2020, 4, 30, 0, 0), datetime.datetime(2020, 5, 1, 0, 0), datetime.datetime(2020, 5, 2, 0, 0), datetime.datetime(2020, 5, 3, 0, 0), datetime.datetime(2020, 5, 4, 0, 0), datetime.datetime(2020, 5, 5, 0, 0), datetime.datetime(2020, 5, 6, 0, 0), datetime.datetime(2020, 5, 7, 0, 0), datetime.datetime(2020, 5, 8, 0, 0), datetime.datetime(2020, 5, 9, 0, 0), datetime.datetime(2020, 5, 10, 0, 0), datetime.datetime(2020, 5, 11, 0, 0), datetime.datetime(2020, 5, 12, 0, 0), datetime.datetime(2020, 5, 13, 0, 0), datetime.datetime(2020, 5, 14, 0, 0), datetime.datetime(2020, 5, 15, 0, 0), datetime.datetime(2020, 5, 16, 0, 0), datetime.datetime(2020, 5, 17, 0, 0), datetime.datetime(2020, 5, 18, 0, 0), datetime.datetime(2020, 5, 19, 0, 0), datetime.datetime(2020, 5, 20, 0, 0), datetime.datetime(2020, 5, 21, 0, 0), datetime.datetime(2020, 5, 22, 0, 0), datetime.datetime(2020, 5, 23, 0, 0), datetime.datetime(2020, 5, 24, 0, 0), datetime.datetime(2020, 5, 25, 0, 0), datetime.datetime(2020, 5, 26, 0, 0), datetime.datetime(2020, 5, 27, 0, 0), datetime.datetime(2020, 5, 28, 0, 0), datetime.datetime(2020, 5, 29, 0, 0), datetime.datetime(2020, 5, 30, 0, 0), datetime.datetime(2020, 5, 31, 0, 0), datetime.datetime(2020, 6, 1, 0, 0), datetime.datetime(2020, 6, 2, 0, 0), datetime.datetime(2020, 6, 3, 0, 0), datetime.datetime(2020, 6, 4, 0, 0), datetime.datetime(2020, 6, 5, 0, 0), datetime.datetime(2020, 6, 6, 0, 0), datetime.datetime(2020, 6, 7, 0, 0), datetime.datetime(2020, 6, 8, 0, 0), datetime.datetime(2020, 6, 9, 0, 0), datetime.datetime(2020, 6, 10, 0, 0), datetime.datetime(2020, 6, 11, 0, 0), datetime.datetime(2020, 6, 12, 0, 0), datetime.datetime(2020, 6, 13, 0, 0), datetime.datetime(2020, 6, 14, 0, 0), datetime.datetime(2020, 6, 15, 0, 0), datetime.datetime(2020, 6, 16, 0, 0), datetime.datetime(2020, 6, 17, 0, 0), datetime.datetime(2020, 6, 18, 0, 0), datetime.datetime(2020, 6, 19, 0, 0), datetime.datetime(2020, 6, 20, 0, 0), datetime.datetime(2020, 6, 21, 0, 0), datetime.datetime(2020, 6, 22, 0, 0), datetime.datetime(2020, 6, 23, 0, 0), datetime.datetime(2020, 6, 24, 0, 0), datetime.datetime(2020, 6, 25, 0, 0), datetime.datetime(2020, 6, 26, 0, 0), datetime.datetime(2020, 6, 27, 0, 0), datetime.datetime(2020, 6, 28, 0, 0), datetime.datetime(2020, 6, 29, 0, 0), datetime.datetime(2020, 6, 30, 0, 0), datetime.datetime(2020, 7, 1, 0, 0), datetime.datetime(2020, 7, 2, 0, 0), datetime.datetime(2020, 7, 3, 0, 0), datetime.datetime(2020, 7, 4, 0, 0), datetime.datetime(2020, 7, 5, 0, 0), datetime.datetime(2020, 7, 6, 0, 0), datetime.datetime(2020, 7, 7, 0, 0), datetime.datetime(2020, 7, 8, 0, 0), datetime.datetime(2020, 7, 9, 0, 0), datetime.datetime(2020, 7, 10, 0, 0), datetime.datetime(2020, 7, 11, 0, 0), datetime.datetime(2020, 7, 12, 0, 0), datetime.datetime(2020, 7, 13, 0, 0), datetime.datetime(2020, 7, 14, 0, 0), datetime.datetime(2020, 7, 15, 0, 0), datetime.datetime(2020, 7, 16, 0, 0), datetime.datetime(2020, 7, 17, 0, 0), datetime.datetime(2020, 7, 18, 0, 0), datetime.datetime(2020, 7, 19, 0, 0), datetime.datetime(2020, 7, 20, 0, 0), datetime.datetime(2020, 7, 21, 0, 0), datetime.datetime(2020, 7, 22, 0, 0), datetime.datetime(2020, 7, 23, 0, 0), datetime.datetime(2020, 7, 24, 0, 0), datetime.datetime(2020, 7, 25, 0, 0), datetime.datetime(2020, 7, 26, 0, 0), datetime.datetime(2020, 7, 27, 0, 0), datetime.datetime(2020, 7, 28, 0, 0), datetime.datetime(2020, 7, 29, 0, 0), datetime.datetime(2020, 7, 30, 0, 0), datetime.datetime(2020, 7, 31, 0, 0), datetime.datetime(2020, 8, 1, 0, 0), datetime.datetime(2020, 8, 2, 0, 0), datetime.datetime(2020, 8, 3, 0, 0), datetime.datetime(2020, 8, 4, 0, 0), datetime.datetime(2020, 8, 5, 0, 0), datetime.datetime(2020, 8, 6, 0, 0), datetime.datetime(2020, 8, 7, 0, 0), datetime.datetime(2020, 8, 8, 0, 0), datetime.datetime(2020, 8, 9, 0, 0), datetime.datetime(2020, 8, 10, 0, 0), datetime.datetime(2020, 8, 11, 0, 0), datetime.datetime(2020, 8, 12, 0, 0), datetime.datetime(2020, 8, 13, 0, 0), datetime.datetime(2020, 8, 14, 0, 0), datetime.datetime(2020, 8, 15, 0, 0), datetime.datetime(2020, 8, 16, 0, 0), datetime.datetime(2020, 8, 17, 0, 0), datetime.datetime(2020, 8, 18, 0, 0), datetime.datetime(2020, 8, 19, 0, 0), datetime.datetime(2020, 8, 20, 0, 0), datetime.datetime(2020, 8, 21, 0, 0), datetime.datetime(2020, 8, 22, 0, 0), datetime.datetime(2020, 8, 23, 0, 0), datetime.datetime(2020, 8, 24, 0, 0), datetime.datetime(2020, 8, 25, 0, 0), datetime.datetime(2020, 8, 26, 0, 0), datetime.datetime(2020, 8, 27, 0, 0), datetime.datetime(2020, 8, 28, 0, 0), datetime.datetime(2020, 8, 29, 0, 0), datetime.datetime(2020, 8, 30, 0, 0), datetime.datetime(2020, 8, 31, 0, 0), datetime.datetime(2020, 9, 1, 0, 0), datetime.datetime(2020, 9, 2, 0, 0), datetime.datetime(2020, 9, 3, 0, 0), datetime.datetime(2020, 9, 4, 0, 0), datetime.datetime(2020, 9, 5, 0, 0), datetime.datetime(2020, 9, 6, 0, 0), datetime.datetime(2020, 9, 7, 0, 0), datetime.datetime(2020, 9, 8, 0, 0), datetime.datetime(2020, 9, 9, 0, 0), datetime.datetime(2020, 9, 10, 0, 0), datetime.datetime(2020, 9, 11, 0, 0), datetime.datetime(2020, 9, 12, 0, 0), datetime.datetime(2020, 9, 13, 0, 0), datetime.datetime(2020, 9, 14, 0, 0), datetime.datetime(2020, 9, 15, 0, 0), datetime.datetime(2020, 9, 16, 0, 0), datetime.datetime(2020, 9, 17, 0, 0), datetime.datetime(2020, 9, 18, 0, 0), datetime.datetime(2020, 9, 19, 0, 0), datetime.datetime(2020, 9, 20, 0, 0), datetime.datetime(2020, 9, 21, 0, 0), datetime.datetime(2020, 9, 22, 0, 0), datetime.datetime(2020, 9, 23, 0, 0), datetime.datetime(2020, 9, 24, 0, 0), datetime.datetime(2020, 9, 25, 0, 0), datetime.datetime(2020, 9, 26, 0, 0), datetime.datetime(2020, 9, 27, 0, 0), datetime.datetime(2020, 9, 28, 0, 0), datetime.datetime(2020, 9, 29, 0, 0), datetime.datetime(2020, 9, 30, 0, 0), datetime.datetime(2020, 10, 1, 0, 0), datetime.datetime(2020, 10, 2, 0, 0), datetime.datetime(2020, 10, 3, 0, 0), datetime.datetime(2020, 10, 4, 0, 0), datetime.datetime(2020, 10, 5, 0, 0), datetime.datetime(2020, 10, 6, 0, 0), datetime.datetime(2020, 10, 7, 0, 0), datetime.datetime(2020, 10, 8, 0, 0), datetime.datetime(2020, 10, 9, 0, 0), datetime.datetime(2020, 10, 10, 0, 0), datetime.datetime(2020, 10, 11, 0, 0), datetime.datetime(2020, 10, 12, 0, 0), datetime.datetime(2020, 10, 13, 0, 0), datetime.datetime(2020, 10, 14, 0, 0), datetime.datetime(2020, 10, 15, 0, 0), datetime.datetime(2020, 10, 16, 0, 0), datetime.datetime(2020, 10, 17, 0, 0), datetime.datetime(2020, 10, 18, 0, 0), datetime.datetime(2020, 10, 19, 0, 0), datetime.datetime(2020, 10, 20, 0, 0), datetime.datetime(2020, 10, 21, 0, 0), datetime.datetime(2020, 10, 22, 0, 0), datetime.datetime(2020, 10, 23, 0, 0), datetime.datetime(2020, 10, 24, 0, 0), datetime.datetime(2020, 10, 25, 0, 0), datetime.datetime(2020, 10, 26, 0, 0), datetime.datetime(2020, 10, 27, 0, 0), datetime.datetime(2020, 10, 28, 0, 0), datetime.datetime(2020, 10, 29, 0, 0), datetime.datetime(2020, 10, 30, 0, 0), datetime.datetime(2020, 10, 31, 0, 0), datetime.datetime(2020, 11, 1, 0, 0), datetime.datetime(2020, 11, 2, 0, 0), datetime.datetime(2020, 11, 3, 0, 0), datetime.datetime(2020, 11, 4, 0, 0), datetime.datetime(2020, 11, 5, 0, 0), datetime.datetime(2020, 11, 6, 0, 0), datetime.datetime(2020, 11, 7, 0, 0), datetime.datetime(2020, 11, 8, 0, 0), datetime.datetime(2020, 11, 9, 0, 0), datetime.datetime(2020, 11, 10, 0, 0), datetime.datetime(2020, 11, 11, 0, 0), datetime.datetime(2020, 11, 12, 0, 0), datetime.datetime(2020, 11, 13, 0, 0), datetime.datetime(2020, 11, 14, 0, 0), datetime.datetime(2020, 11, 15, 0, 0), datetime.datetime(2020, 11, 16, 0, 0), datetime.datetime(2020, 11, 17, 0, 0), datetime.datetime(2020, 11, 18, 0, 0), datetime.datetime(2020, 11, 19, 0, 0), datetime.datetime(2020, 11, 20, 0, 0), datetime.datetime(2020, 11, 21, 0, 0), datetime.datetime(2020, 11, 22, 0, 0), datetime.datetime(2020, 11, 23, 0, 0), datetime.datetime(2020, 11, 24, 0, 0), datetime.datetime(2020, 11, 25, 0, 0), datetime.datetime(2020, 11, 26, 0, 0), datetime.datetime(2020, 11, 27, 0, 0), datetime.datetime(2020, 11, 28, 0, 0), datetime.datetime(2020, 11, 29, 0, 0), datetime.datetime(2020, 11, 30, 0, 0), datetime.datetime(2020, 12, 1, 0, 0), datetime.datetime(2020, 12, 2, 0, 0), datetime.datetime(2020, 12, 3, 0, 0), datetime.datetime(2020, 12, 4, 0, 0), datetime.datetime(2020, 12, 5, 0, 0), datetime.datetime(2020, 12, 6, 0, 0), datetime.datetime(2020, 12, 7, 0, 0), datetime.datetime(2020, 12, 8, 0, 0), datetime.datetime(2020, 12, 9, 0, 0), datetime.datetime(2020, 12, 10, 0, 0), datetime.datetime(2020, 12, 11, 0, 0), datetime.datetime(2020, 12, 12, 0, 0), datetime.datetime(2020, 12, 13, 0, 0), datetime.datetime(2020, 12, 14, 0, 0), datetime.datetime(2020, 12, 15, 0, 0), datetime.datetime(2020, 12, 16, 0, 0), datetime.datetime(2020, 12, 17, 0, 0), datetime.datetime(2020, 12, 18, 0, 0), datetime.datetime(2020, 12, 19, 0, 0), datetime.datetime(2020, 12, 20, 0, 0), datetime.datetime(2020, 12, 21, 0, 0), datetime.datetime(2020, 12, 22, 0, 0), datetime.datetime(2020, 12, 23, 0, 0), datetime.datetime(2020, 12, 24, 0, 0), datetime.datetime(2020, 12, 25, 0, 0), datetime.datetime(2020, 12, 26, 0, 0), datetime.datetime(2020, 12, 27, 0, 0), datetime.datetime(2020, 12, 28, 0, 0), datetime.datetime(2020, 12, 29, 0, 0), datetime.datetime(2020, 12, 30, 0, 0)])"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yLwHM_kxG56F",
        "outputId": "97221470-5f5e-4d14-eca6-7dbe830e7fc7"
      },
      "source": [
        "for idx, values in stage_data['Station 1'].items():\n",
        "    print(idx, values)"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2020-01-01 00:00:00 80\n",
            "2020-01-02 00:00:00 80\n",
            "2020-01-03 00:00:00 78\n",
            "2020-01-04 00:00:00 79\n",
            "2020-01-05 00:00:00 80\n",
            "2020-01-06 00:00:00 79\n",
            "2020-01-07 00:00:00 77\n",
            "2020-01-08 00:00:00 75\n",
            "2020-01-09 00:00:00 73\n",
            "2020-01-10 00:00:00 74\n",
            "2020-01-11 00:00:00 76\n",
            "2020-01-12 00:00:00 76\n",
            "2020-01-13 00:00:00 74\n",
            "2020-01-14 00:00:00 73\n",
            "2020-01-15 00:00:00 75\n",
            "2020-01-16 00:00:00 77\n",
            "2020-01-17 00:00:00 77\n",
            "2020-01-18 00:00:00 77\n",
            "2020-01-19 00:00:00 76\n",
            "2020-01-20 00:00:00 74\n",
            "2020-01-21 00:00:00 74\n",
            "2020-01-22 00:00:00 73\n",
            "2020-01-23 00:00:00 71\n",
            "2020-01-24 00:00:00 71\n",
            "2020-01-25 00:00:00 71\n",
            "2020-01-26 00:00:00 70\n",
            "2020-01-27 00:00:00 69\n",
            "2020-01-28 00:00:00 69\n",
            "2020-01-29 00:00:00 69\n",
            "2020-01-30 00:00:00 69\n",
            "2020-01-31 00:00:00 67\n",
            "2020-02-01 00:00:00 69\n",
            "2020-02-02 00:00:00 69\n",
            "2020-02-03 00:00:00 70\n",
            "2020-02-04 00:00:00 72\n",
            "2020-02-05 00:00:00 71\n",
            "2020-02-06 00:00:00 70\n",
            "2020-02-07 00:00:00 69\n",
            "2020-02-08 00:00:00 70\n",
            "2020-02-09 00:00:00 70\n",
            "2020-02-10 00:00:00 68\n",
            "2020-02-11 00:00:00 70\n",
            "2020-02-12 00:00:00 70\n",
            "2020-02-13 00:00:00 68\n",
            "2020-02-14 00:00:00 68\n",
            "2020-02-15 00:00:00 70\n",
            "2020-02-16 00:00:00 70\n",
            "2020-02-17 00:00:00 72\n",
            "2020-02-18 00:00:00 71\n",
            "2020-02-19 00:00:00 72\n",
            "2020-02-20 00:00:00 73\n",
            "2020-02-21 00:00:00 75\n",
            "2020-02-22 00:00:00 75\n",
            "2020-02-23 00:00:00 76\n",
            "2020-02-24 00:00:00 77\n",
            "2020-02-25 00:00:00 76\n",
            "2020-02-26 00:00:00 75\n",
            "2020-02-27 00:00:00 75\n",
            "2020-02-28 00:00:00 75\n",
            "2020-02-29 00:00:00 73\n",
            "2020-03-01 00:00:00 71\n",
            "2020-03-02 00:00:00 69\n",
            "2020-03-03 00:00:00 70\n",
            "2020-03-04 00:00:00 70\n",
            "2020-03-05 00:00:00 72\n",
            "2020-03-06 00:00:00 74\n",
            "2020-03-07 00:00:00 75\n",
            "2020-03-08 00:00:00 75\n",
            "2020-03-09 00:00:00 74\n",
            "2020-03-10 00:00:00 73\n",
            "2020-03-11 00:00:00 71\n",
            "2020-03-12 00:00:00 72\n",
            "2020-03-13 00:00:00 71\n",
            "2020-03-14 00:00:00 72\n",
            "2020-03-15 00:00:00 72\n",
            "2020-03-16 00:00:00 71\n",
            "2020-03-17 00:00:00 71\n",
            "2020-03-18 00:00:00 72\n",
            "2020-03-19 00:00:00 74\n",
            "2020-03-20 00:00:00 74\n",
            "2020-03-21 00:00:00 76\n",
            "2020-03-22 00:00:00 75\n",
            "2020-03-23 00:00:00 75\n",
            "2020-03-24 00:00:00 73\n",
            "2020-03-25 00:00:00 71\n",
            "2020-03-26 00:00:00 70\n",
            "2020-03-27 00:00:00 70\n",
            "2020-03-28 00:00:00 72\n",
            "2020-03-29 00:00:00 74\n",
            "2020-03-30 00:00:00 73\n",
            "2020-03-31 00:00:00 71\n",
            "2020-04-01 00:00:00 71\n",
            "2020-04-02 00:00:00 70\n",
            "2020-04-03 00:00:00 70\n",
            "2020-04-04 00:00:00 71\n",
            "2020-04-05 00:00:00 69\n",
            "2020-04-06 00:00:00 67\n",
            "2020-04-07 00:00:00 67\n",
            "2020-04-08 00:00:00 65\n",
            "2020-04-09 00:00:00 65\n",
            "2020-04-10 00:00:00 65\n",
            "2020-04-11 00:00:00 63\n",
            "2020-04-12 00:00:00 63\n",
            "2020-04-13 00:00:00 63\n",
            "2020-04-14 00:00:00 63\n",
            "2020-04-15 00:00:00 62\n",
            "2020-04-16 00:00:00 63\n",
            "2020-04-17 00:00:00 65\n",
            "2020-04-18 00:00:00 63\n",
            "2020-04-19 00:00:00 63\n",
            "2020-04-20 00:00:00 65\n",
            "2020-04-21 00:00:00 64\n",
            "2020-04-22 00:00:00 65\n",
            "2020-04-23 00:00:00 65\n",
            "2020-04-24 00:00:00 64\n",
            "2020-04-25 00:00:00 64\n",
            "2020-04-26 00:00:00 65\n",
            "2020-04-27 00:00:00 67\n",
            "2020-04-28 00:00:00 66\n",
            "2020-04-29 00:00:00 66\n",
            "2020-04-30 00:00:00 68\n",
            "2020-05-01 00:00:00 66\n",
            "2020-05-02 00:00:00 66\n",
            "2020-05-03 00:00:00 64\n",
            "2020-05-04 00:00:00 65\n",
            "2020-05-05 00:00:00 64\n",
            "2020-05-06 00:00:00 64\n",
            "2020-05-07 00:00:00 64\n",
            "2020-05-08 00:00:00 64\n",
            "2020-05-09 00:00:00 66\n",
            "2020-05-10 00:00:00 64\n",
            "2020-05-11 00:00:00 65\n",
            "2020-05-12 00:00:00 64\n",
            "2020-05-13 00:00:00 65\n",
            "2020-05-14 00:00:00 64\n",
            "2020-05-15 00:00:00 62\n",
            "2020-05-16 00:00:00 60\n",
            "2020-05-17 00:00:00 58\n",
            "2020-05-18 00:00:00 56\n",
            "2020-05-19 00:00:00 55\n",
            "2020-05-20 00:00:00 57\n",
            "2020-05-21 00:00:00 56\n",
            "2020-05-22 00:00:00 58\n",
            "2020-05-23 00:00:00 56\n",
            "2020-05-24 00:00:00 58\n",
            "2020-05-25 00:00:00 59\n",
            "2020-05-26 00:00:00 61\n",
            "2020-05-27 00:00:00 60\n",
            "2020-05-28 00:00:00 60\n",
            "2020-05-29 00:00:00 58\n",
            "2020-05-30 00:00:00 56\n",
            "2020-05-31 00:00:00 58\n",
            "2020-06-01 00:00:00 58\n",
            "2020-06-02 00:00:00 59\n",
            "2020-06-03 00:00:00 58\n",
            "2020-06-04 00:00:00 59\n",
            "2020-06-05 00:00:00 58\n",
            "2020-06-06 00:00:00 57\n",
            "2020-06-07 00:00:00 58\n",
            "2020-06-08 00:00:00 59\n",
            "2020-06-09 00:00:00 60\n",
            "2020-06-10 00:00:00 58\n",
            "2020-06-11 00:00:00 57\n",
            "2020-06-12 00:00:00 58\n",
            "2020-06-13 00:00:00 59\n",
            "2020-06-14 00:00:00 58\n",
            "2020-06-15 00:00:00 59\n",
            "2020-06-16 00:00:00 58\n",
            "2020-06-17 00:00:00 59\n",
            "2020-06-18 00:00:00 58\n",
            "2020-06-19 00:00:00 56\n",
            "2020-06-20 00:00:00 54\n",
            "2020-06-21 00:00:00 54\n",
            "2020-06-22 00:00:00 54\n",
            "2020-06-23 00:00:00 53\n",
            "2020-06-24 00:00:00 55\n",
            "2020-06-25 00:00:00 54\n",
            "2020-06-26 00:00:00 53\n",
            "2020-06-27 00:00:00 54\n",
            "2020-06-28 00:00:00 54\n",
            "2020-06-29 00:00:00 53\n",
            "2020-06-30 00:00:00 53\n",
            "2020-07-01 00:00:00 51\n",
            "2020-07-02 00:00:00 51\n",
            "2020-07-03 00:00:00 53\n",
            "2020-07-04 00:00:00 51\n",
            "2020-07-05 00:00:00 53\n",
            "2020-07-06 00:00:00 54\n",
            "2020-07-07 00:00:00 52\n",
            "2020-07-08 00:00:00 53\n",
            "2020-07-09 00:00:00 54\n",
            "2020-07-10 00:00:00 52\n",
            "2020-07-11 00:00:00 53\n",
            "2020-07-12 00:00:00 52\n",
            "2020-07-13 00:00:00 54\n",
            "2020-07-14 00:00:00 53\n",
            "2020-07-15 00:00:00 53\n",
            "2020-07-16 00:00:00 53\n",
            "2020-07-17 00:00:00 54\n",
            "2020-07-18 00:00:00 54\n",
            "2020-07-19 00:00:00 55\n",
            "2020-07-20 00:00:00 57\n",
            "2020-07-21 00:00:00 56\n",
            "2020-07-22 00:00:00 56\n",
            "2020-07-23 00:00:00 56\n",
            "2020-07-24 00:00:00 57\n",
            "2020-07-25 00:00:00 58\n",
            "2020-07-26 00:00:00 56\n",
            "2020-07-27 00:00:00 56\n",
            "2020-07-28 00:00:00 55\n",
            "2020-07-29 00:00:00 53\n",
            "2020-07-30 00:00:00 54\n",
            "2020-07-31 00:00:00 56\n",
            "2020-08-01 00:00:00 55\n",
            "2020-08-02 00:00:00 55\n",
            "2020-08-03 00:00:00 53\n",
            "2020-08-04 00:00:00 52\n",
            "2020-08-05 00:00:00 53\n",
            "2020-08-06 00:00:00 55\n",
            "2020-08-07 00:00:00 56\n",
            "2020-08-08 00:00:00 57\n",
            "2020-08-09 00:00:00 58\n",
            "2020-08-10 00:00:00 57\n",
            "2020-08-11 00:00:00 55\n",
            "2020-08-12 00:00:00 54\n",
            "2020-08-13 00:00:00 53\n",
            "2020-08-14 00:00:00 51\n",
            "2020-08-15 00:00:00 53\n",
            "2020-08-16 00:00:00 53\n",
            "2020-08-17 00:00:00 51\n",
            "2020-08-18 00:00:00 52\n",
            "2020-08-19 00:00:00 53\n",
            "2020-08-20 00:00:00 52\n",
            "2020-08-21 00:00:00 54\n",
            "2020-08-22 00:00:00 52\n",
            "2020-08-23 00:00:00 51\n",
            "2020-08-24 00:00:00 50\n",
            "2020-08-25 00:00:00 52\n",
            "2020-08-26 00:00:00 52\n",
            "2020-08-27 00:00:00 54\n",
            "2020-08-28 00:00:00 55\n",
            "2020-08-29 00:00:00 57\n",
            "2020-08-30 00:00:00 58\n",
            "2020-08-31 00:00:00 56\n",
            "2020-09-01 00:00:00 54\n",
            "2020-09-02 00:00:00 52\n",
            "2020-09-03 00:00:00 54\n",
            "2020-09-04 00:00:00 52\n",
            "2020-09-05 00:00:00 53\n",
            "2020-09-06 00:00:00 55\n",
            "2020-09-07 00:00:00 55\n",
            "2020-09-08 00:00:00 57\n",
            "2020-09-09 00:00:00 56\n",
            "2020-09-10 00:00:00 54\n",
            "2020-09-11 00:00:00 54\n",
            "2020-09-12 00:00:00 52\n",
            "2020-09-13 00:00:00 54\n",
            "2020-09-14 00:00:00 52\n",
            "2020-09-15 00:00:00 52\n",
            "2020-09-16 00:00:00 52\n",
            "2020-09-17 00:00:00 50\n",
            "2020-09-18 00:00:00 48\n",
            "2020-09-19 00:00:00 50\n",
            "2020-09-20 00:00:00 51\n",
            "2020-09-21 00:00:00 52\n",
            "2020-09-22 00:00:00 51\n",
            "2020-09-23 00:00:00 51\n",
            "2020-09-24 00:00:00 52\n",
            "2020-09-25 00:00:00 51\n",
            "2020-09-26 00:00:00 52\n",
            "2020-09-27 00:00:00 53\n",
            "2020-09-28 00:00:00 51\n",
            "2020-09-29 00:00:00 49\n",
            "2020-09-30 00:00:00 47\n",
            "2020-10-01 00:00:00 49\n",
            "2020-10-02 00:00:00 49\n",
            "2020-10-03 00:00:00 51\n",
            "2020-10-04 00:00:00 52\n",
            "2020-10-05 00:00:00 53\n",
            "2020-10-06 00:00:00 54\n",
            "2020-10-07 00:00:00 52\n",
            "2020-10-08 00:00:00 51\n",
            "2020-10-09 00:00:00 51\n",
            "2020-10-10 00:00:00 52\n",
            "2020-10-11 00:00:00 53\n",
            "2020-10-12 00:00:00 51\n",
            "2020-10-13 00:00:00 53\n",
            "2020-10-14 00:00:00 52\n",
            "2020-10-15 00:00:00 52\n",
            "2020-10-16 00:00:00 51\n",
            "2020-10-17 00:00:00 50\n",
            "2020-10-18 00:00:00 49\n",
            "2020-10-19 00:00:00 49\n",
            "2020-10-20 00:00:00 50\n",
            "2020-10-21 00:00:00 50\n",
            "2020-10-22 00:00:00 50\n",
            "2020-10-23 00:00:00 52\n",
            "2020-10-24 00:00:00 50\n",
            "2020-10-25 00:00:00 49\n",
            "2020-10-26 00:00:00 47\n",
            "2020-10-27 00:00:00 47\n",
            "2020-10-28 00:00:00 45\n",
            "2020-10-29 00:00:00 47\n",
            "2020-10-30 00:00:00 45\n",
            "2020-10-31 00:00:00 46\n",
            "2020-11-01 00:00:00 48\n",
            "2020-11-02 00:00:00 49\n",
            "2020-11-03 00:00:00 51\n",
            "2020-11-04 00:00:00 49\n",
            "2020-11-05 00:00:00 51\n",
            "2020-11-06 00:00:00 50\n",
            "2020-11-07 00:00:00 51\n",
            "2020-11-08 00:00:00 51\n",
            "2020-11-09 00:00:00 51\n",
            "2020-11-10 00:00:00 50\n",
            "2020-11-11 00:00:00 49\n",
            "2020-11-12 00:00:00 47\n",
            "2020-11-13 00:00:00 45\n",
            "2020-11-14 00:00:00 47\n",
            "2020-11-15 00:00:00 47\n",
            "2020-11-16 00:00:00 49\n",
            "2020-11-17 00:00:00 50\n",
            "2020-11-18 00:00:00 52\n",
            "2020-11-19 00:00:00 52\n",
            "2020-11-20 00:00:00 54\n",
            "2020-11-21 00:00:00 55\n",
            "2020-11-22 00:00:00 57\n",
            "2020-11-23 00:00:00 59\n",
            "2020-11-24 00:00:00 60\n",
            "2020-11-25 00:00:00 61\n",
            "2020-11-26 00:00:00 60\n",
            "2020-11-27 00:00:00 60\n",
            "2020-11-28 00:00:00 62\n",
            "2020-11-29 00:00:00 62\n",
            "2020-11-30 00:00:00 62\n",
            "2020-12-01 00:00:00 61\n",
            "2020-12-02 00:00:00 62\n",
            "2020-12-03 00:00:00 60\n",
            "2020-12-04 00:00:00 62\n",
            "2020-12-05 00:00:00 61\n",
            "2020-12-06 00:00:00 63\n",
            "2020-12-07 00:00:00 62\n",
            "2020-12-08 00:00:00 62\n",
            "2020-12-09 00:00:00 62\n",
            "2020-12-10 00:00:00 61\n",
            "2020-12-11 00:00:00 63\n",
            "2020-12-12 00:00:00 63\n",
            "2020-12-13 00:00:00 65\n",
            "2020-12-14 00:00:00 63\n",
            "2020-12-15 00:00:00 61\n",
            "2020-12-16 00:00:00 62\n",
            "2020-12-17 00:00:00 61\n",
            "2020-12-18 00:00:00 61\n",
            "2020-12-19 00:00:00 61\n",
            "2020-12-20 00:00:00 61\n",
            "2020-12-21 00:00:00 60\n",
            "2020-12-22 00:00:00 60\n",
            "2020-12-23 00:00:00 58\n",
            "2020-12-24 00:00:00 60\n",
            "2020-12-25 00:00:00 58\n",
            "2020-12-26 00:00:00 60\n",
            "2020-12-27 00:00:00 58\n",
            "2020-12-28 00:00:00 58\n",
            "2020-12-29 00:00:00 56\n",
            "2020-12-30 00:00:00 55\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cpZnTd95IHpB",
        "outputId": "dbbcbf1e-1c47-476a-fa6d-93134fb1a37e"
      },
      "source": [
        "df['Station 1']"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2020-01-01    80\n",
              "2020-01-02    80\n",
              "2020-01-03    78\n",
              "2020-01-04    79\n",
              "2020-01-05    80\n",
              "              ..\n",
              "2020-12-26    60\n",
              "2020-12-27    58\n",
              "2020-12-28    58\n",
              "2020-12-29    56\n",
              "2020-12-30    55\n",
              "Name: Station 1, Length: 365, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_cKXw5mrCyg-"
      },
      "source": [
        "## Exercise"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PH-pNYQiC1NS"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nz0dcyplC7Tx"
      },
      "source": [
        "* The daily discharge on the main river?\n",
        "* The mean discharge on the main river?\n",
        "* The monthly mean discharge on the main river?\n",
        "* The days of maximum and minimum discharge?\n",
        "\n",
        "Using the pandas dataframe or not???"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cjh2U9cniS0v"
      },
      "source": [
        "## Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSdVTHjbiUiH"
      },
      "source": [
        "A function is a block of code which only runs when it is called.\n",
        "\n",
        "You can pass data, known as parameters, into a function.\n",
        "\n",
        "A function can return data as a result."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEL4n6PffRV9"
      },
      "source": [
        "def square(x):\n",
        "    return x**2\n",
        "\n",
        "square(11)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kVyTqodhfRSe"
      },
      "source": [
        "def power(base, expoent):\n",
        "    return base**expoent\n",
        "\n",
        "power(2, 8)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQyzfe8gfROn"
      },
      "source": [
        "# base and expoent are required arguments\n",
        "# power(2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RlPfMMqfRLJ"
      },
      "source": [
        "power(expoent=8, base=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMmGbun_fRHy"
      },
      "source": [
        "# b is an optional (default) parameter\n",
        "# def y = a*x + b\n",
        "def linear(x, a, b=0):\n",
        "    y = a * x + b\n",
        "    return y\n",
        "\n",
        "linear(2, 3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGywimlifREY"
      },
      "source": [
        "# nomenclature 2 is a positional argument\n",
        "# -10 and 3 are keyword arguments\n",
        "linear(2, b=-10, a=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ChH4gniPk5ZS"
      },
      "source": [
        "# y = a*x^2 + b*x + c\n",
        "def quadratic(x, a, b=1, c=0):\n",
        "    y = a * power(x, 2) + b*x + c\n",
        "    return y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61ykMdGJk5Vw"
      },
      "source": [
        "quadratic(a=1, x=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6wrA0UwXsGly"
      },
      "source": [
        "Explain the scope. Variables have specific scope inside the function..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnNzVXFAk5Sf"
      },
      "source": [
        "import math"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-sHnb0_k5Pb"
      },
      "source": [
        "dir(math)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DkFG6WTCyJrv"
      },
      "source": [
        "## The value <b>None</b>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_3NvN-46yQmz"
      },
      "source": [
        "\n",
        "The value **None** is used to define a null value. **It is not the same as an empty string, False, or a zero.** It is a data type of the class NoneType object."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SSpOUxywk5Mb"
      },
      "source": [
        "None == \"\", None == False, None == 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJlgy6PQoe6D"
      },
      "source": [
        "# correct way of testing (is -> identity operator)\n",
        "x = 0\n",
        "x is None, x is not None"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FcEs2UaCqibC"
      },
      "source": [
        "bool(None), bool(0), bool(False), bool(123)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QpT8tF1Ap-42"
      },
      "source": [
        "# None is absence of value, so we cannot implement this...\n",
        "def check_value(x):\n",
        "    if x:\n",
        "        print(f'{x} has a value')\n",
        "    else:\n",
        "        print(f'{x} has no value at all')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JzQXAoYYpDBU"
      },
      "source": [
        "check_value(None), check_value(0), check_value(False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vjc9wc7pvwj"
      },
      "source": [
        "# Why is that... that's because if x: is testing the BOOLEAN result of x.\n",
        "# To test it correctly we should do:\n",
        "\n",
        "def check_value(x):\n",
        "    if  x is None:\n",
        "        print(f'{x} has no value at all')\n",
        "    else:\n",
        "        print(f'{x} has a value')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j3ss1ajeqzRZ"
      },
      "source": [
        "check_value(None), check_value(0), check_value(False)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}