Data_Science_Notebook_Lehrende.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# **Herzlich willkommen im Kurs Data Science für Klimadaten!** ⭐\n",
        "\n",
        "\n",
        "Heute werden wir gemeinsam Klimadaten mit der Programmiersprache Python analysieren, auswerten und veranschaulichen.\n",
        "\n",
        "Viel Spaß!"
      ],
      "metadata": {
        "id": "BlCF_tYdSaVJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Theoretischer Hintergrund zu Data Science 🧠⏭\n",
        "\n",
        "**Was ist Data Science?**\n",
        "\n",
        "Data Science bezeichnet die Wissenschaft zur Gewinnung von Informationen aus großer Datenmengen. Sie stellt die Schnittstelle zwischen Mathematik/Statistik, Informatik/Programmieren und Fachwissen dar. Bei der Analyse von Daten wird häuffig versucht, Muster zu erkennen und daraus Vorhersagen für zukünftige Ereignisse zu treffen.\n",
        "\n",
        "\n",
        "\n",
        "**Sind Daten immer objektiv?**\n",
        "\n",
        "Eigentlich sollte man meinen, dass gerade Daten besonders objektiv sind, aber tatsächlich kann es sehr schnell passieren, dass Daten verzerrt sind oder eine Voreingenommenheit widerspiegeln. Dabei kann sich eine Verzerrung oder Voreingenommenheit auf ganz unterschiedlichen Wegen in Daten einschleichen. So kann die Verzerrung schon bei der Erhebung der Daten entstehen, aber auch erst bei der Interpretation der Daten.\n",
        "\n",
        "Ein Beispiel für eine Verzerrung bei der Erhebung der Daten wäre, wenn man eine bestimmte Gruppe von Menschen z.B. gar nicht befragt und somit dann natürlich auch nicht in den Daten abbildet. Das nennt sich dann Selection Bias, was so viel bedeutet wie eine Verzerrung, die durch die Auswahl von z.B. Teilnehmer*innen einer Befragung entsteht.\n",
        "Ein anderes Beispiel für eine Voreingenommenheit von Daten bei der Interpretation der Daten nennt man Confirmation Bias. Hierbei geht es darum, dass die Verzerrung erst später, also der bei der Interpretation der Daten durch den Menschen entsteht. Der Grund für die Verzerrung ist dabei meistens, dass die Einstellungen oder Vorurteile des Menschen einfach auf dei Daten übertragen werden, damit diese dann quasi die eigenen Vorurteile bestätigen.\n",
        "\n",
        "\n",
        "Man sollte sich daher also immer fragen, woher die Daten kommen und wie sie gemessen wurden.\n"
      ],
      "metadata": {
        "id": "83LuTMImLi3l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Einführung in Python und Notebooks 💻 📔\n",
        "\n",
        "\n",
        "\n",
        "Python ist einer der am meisten benutzten Programmiersprachen. Sie wird in vielen Bereichen, z.B. auch in Data Science, eingesetzt. Man kann Python-Code in verschiedenen Entwicklungsumgebungen schreiben. Für unseren Kurs benutzen wir Notebooks wie dieses hier. In Notebooks wird der Code und auch Text in Blöcken geschrieben, was sehr übersichtlich ist. Jetzt wollen wir uns erstmal mit Python und Notebooks vertraut machen."
      ],
      "metadata": {
        "id": "oIt-ivVyLAsZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='sky blue'>__Aufgabe:__  <br>\n",
        " Füge einen neuen Codeblock und einen neuen Textblock hinzu. Dafür musst du oben in der Leiste auf `+ Code` und `+ Text` klicken. Du kannst versuchen, den Inhalt zu bearbeiten, die Reihenfolge zu verändern und die Blöcke auch wieder zu löschen.</font>"
      ],
      "metadata": {
        "id": "c6MdiS0pK8D6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vielleicht hast du den folgenden Code so oder so ähnlich schon einmal gesehen. Mit dem Befehl `print` kannst du den Inhalt der Klammern ausgeben. Du kannst den Code ausführen, indem du auf den Playbutton auf der linken Seite des Codeblocks klickst."
      ],
      "metadata": {
        "id": "VB0XhmuGMPrx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Hello World\")"
      ],
      "metadata": {
        "id": "6Gv9OL2eMRBr",
        "outputId": "fbf08ed9-dac9-4917-9eb7-b01167eff42a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello World\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Codezeilen mit einem Hashtag (#) davor werden nicht ausgeführt. Man nennt sie Kommentare, denn man kann damit seinen Code mit Erkärungen kommentieren, sodass andere ihn verstehen können. Wenn man einen Hashtag hinter eine Codezeile schreibt, wird der Rest der Zeile auch ignoriert.\n",
        "Hier ein Beispiel:"
      ],
      "metadata": {
        "id": "68S5Va9gMTSM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Das ist ein Kommentar."
      ],
      "metadata": {
        "id": "wxrhlnJxMY1H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Diese Zeile wird ausgeführt\")  # aber nur bist zum \"#\""
      ],
      "metadata": {
        "id": "ZNmLCL6lpQxc",
        "outputId": "e80f3808-d98a-4e25-ec34-72b462b84686",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Diese Zeile wird ausgeführt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Der Computer ist wie ein großer Taschenrechner. In den folgenden Codeblöcken findest du ein paar weitere Code-Beispiele.\n",
        "\n",
        "\n",
        "\n",
        "<font color='sky blue'>__Aufgabe:__  <br>\n",
        " Führe die folgenden Codezeilen aus und ändere dabei die Zahlen!</font>"
      ],
      "metadata": {
        "id": "eFeQFK5LMeL3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "3+7"
      ],
      "metadata": {
        "id": "EEXKZS-cMnkL",
        "outputId": "0f2541dc-fb6e-44bb-9ce0-f6475dae40c8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = 6\n",
        "b = 2\n",
        "\n",
        "print(a)"
      ],
      "metadata": {
        "id": "xphCBXQlMpet",
        "outputId": "452bfe33-0e6f-4f19-d16e-3cf779e76984",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Summe\n",
        "a+b"
      ],
      "metadata": {
        "id": "rbrZlJ7pMrQN",
        "outputId": "9c66dfdf-3e93-407a-c937-5e9c301ca316",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Differenz\n",
        "a-b"
      ],
      "metadata": {
        "id": "wH-O1d7SMsz3",
        "outputId": "dcb4c1d3-9cb3-4d24-eec8-c762312a6a8d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Produkt\n",
        "a*b"
      ],
      "metadata": {
        "id": "09iaXCZXMuaM",
        "outputId": "b99a84d8-aa97-4520-d8ed-adf4857131de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Quotient\n",
        "a/b"
      ],
      "metadata": {
        "id": "VxUH-SvMMwZc",
        "outputId": "40108d10-fbe2-463a-9a6b-09dda2001d1b",
        "colab": {
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        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Science für Klimadaten 🌡 📈\n",
        "\n",
        "Nachdem wir uns jetzt ein bisschen mit Python und Notebooks vertraut gemacht haben, wollen wir jetzt anfangen, einen Datensatz zu analysieren. Dafür müssen wir erst einmal ein paar Vorbereitungen treffen."
      ],
      "metadata": {
        "id": "j5Yw9kTYLL9i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Zuerst müssen wir ein paar Tools von Python importieren, mit denen wir Daten analyisieren können. Besonders wichtig für Data Science ist das Paket `pandas`. Damit können wir Datensätzen einlesen, bearbeiten und Berechnungen durchführen.\n",
        "Für Visualisierung benötigen wir noch die Pakete `matplotlib` und `plotly`. Um die Namen dieser Pakete nicht immer ausschreiben zu müssen, ist es üblich, Abkürzungen dafür zu benutzen.\n",
        "\n",
        "\n",
        "Führe dazu den folgenden Code einmal aus."
      ],
      "metadata": {
        "id": "aD6jzxYt-jep"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "!pip install gdown\n",
        "import gdown"
      ],
      "metadata": {
        "id": "7OJRhN9dDnBj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63b34328-24d7-424b-b11e-13bb4e95f94b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (4.6.6)\n",
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            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from gdown) (1.16.0)\n",
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            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.11.2)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.4.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.4)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hier laden wir unseren Datensatz herunter, mit dem wir heute arbeiten, und lesen die Daten ein. Der Datensatz stammt übrigens vom Berkeley Earth Surface Temperature-Projekt („BEST“).\n",
        "\n",
        "Auch diesen Code müssen wir einmal ausführen."
      ],
      "metadata": {
        "id": "RGzN8Ls_ci7b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lade die Daten herunter (von einem Google Drive)\n",
        "file = 'yearly_global_temperature.csv'\n",
        "filelink = 'https://drive.google.com/uc?id=1-f20UDGQbDpYOElV7_l83vE6tuDhYTQP'\n",
        "gdown.download(filelink, file)\n",
        "# Lese die Daten ein\n",
        "yearly_global_temperatures = pd.read_csv(file)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E1SUS84IcsvD",
        "outputId": "9cac2d2d-e3f4-4c35-876c-70b183d76282"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1-f20UDGQbDpYOElV7_l83vE6tuDhYTQP\n",
            "To: /content/yearly_global_temperature.csv\n",
            "100%|██████████| 23.8k/23.8k [00:00<00:00, 41.0MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Jetzt schauen wir uns die Daten an. Diese sind in einem `DataFrame` gespeichert, der wie eine Tabelle funktioniert. Welche Werte wurden gemessen und in welchen Spalten enthalten sind sie enthalten? In welchem Zeitraum wurden die Temperaturen gemessen?"
      ],
      "metadata": {
        "id": "fQzt36GTef6z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "yearly_global_temperatures"
      ],
      "metadata": {
        "id": "VLiQD2XMeI3Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "outputId": "cd441c00-400a-42bc-8742-f82cea6f3552"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Date  LandAverageTemperature  LandAverageTemperatureUncertainty  \\\n",
              "0    1850                7.900667                           0.876417   \n",
              "1    1851                8.178583                           0.881917   \n",
              "2    1852                8.100167                           0.918250   \n",
              "3    1853                8.041833                           0.835000   \n",
              "4    1854                8.210500                           0.825667   \n",
              "..    ...                     ...                                ...   \n",
              "161  2011                9.516000                           0.082000   \n",
              "162  2012                9.507333                           0.083417   \n",
              "163  2013                9.606500                           0.097667   \n",
              "164  2014                9.570667                           0.090167   \n",
              "165  2015                9.831000                           0.092167   \n",
              "\n",
              "     LandMaxTemperature  LandMaxTemperatureUncertainty  LandMinTemperature  \\\n",
              "0             13.476667                       2.394833            1.964333   \n",
              "1             13.081000                       2.397250            2.203917   \n",
              "2             13.397333                       2.619250            2.337000   \n",
              "3             13.886583                       2.095083            1.892500   \n",
              "4             13.977417                       1.783333            1.762167   \n",
              "..                  ...                            ...                 ...   \n",
              "161           15.284833                       0.114333            3.827667   \n",
              "162           15.332833                       0.107333            3.756167   \n",
              "163           15.373833                       0.115500            3.911333   \n",
              "164           15.313583                       0.111667            3.877750   \n",
              "165           15.572667                       0.106500            4.148833   \n",
              "\n",
              "     LandMinTemperatureUncertainty  LandAndOceanAverageTemperature  \\\n",
              "0                         1.571167                       14.867167   \n",
              "1                         1.632417                       14.991833   \n",
              "2                         1.382917                       15.006500   \n",
              "3                         1.355583                       14.955167   \n",
              "4                         1.357000                       14.991000   \n",
              "..                             ...                             ...   \n",
              "161                       0.136583                       15.769500   \n",
              "162                       0.145333                       15.802333   \n",
              "163                       0.149833                       15.854417   \n",
              "164                       0.139000                       15.913000   \n",
              "165                       0.141750                       16.058583   \n",
              "\n",
              "     LandAndOceanAverageTemperatureUncertainty  \n",
              "0                                     0.308167  \n",
              "1                                     0.312083  \n",
              "2                                     0.316417  \n",
              "3                                     0.283833  \n",
              "4                                     0.276417  \n",
              "..                                         ...  \n",
              "161                                   0.059000  \n",
              "162                                   0.061500  \n",
              "163                                   0.064667  \n",
              "164                                   0.063167  \n",
              "165                                   0.060833  \n",
              "\n",
              "[166 rows x 9 columns]"
            ],
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>LandAverageTemperature</th>\n",
              "      <th>LandAverageTemperatureUncertainty</th>\n",
              "      <th>LandMaxTemperature</th>\n",
              "      <th>LandMaxTemperatureUncertainty</th>\n",
              "      <th>LandMinTemperature</th>\n",
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              "      <th>0</th>\n",
              "      <td>1850</td>\n",
              "      <td>7.900667</td>\n",
              "      <td>0.876417</td>\n",
              "      <td>13.476667</td>\n",
              "      <td>2.394833</td>\n",
              "      <td>1.964333</td>\n",
              "      <td>1.571167</td>\n",
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              "      <th>1</th>\n",
              "      <td>1851</td>\n",
              "      <td>8.178583</td>\n",
              "      <td>0.881917</td>\n",
              "      <td>13.081000</td>\n",
              "      <td>2.397250</td>\n",
              "      <td>2.203917</td>\n",
              "      <td>1.632417</td>\n",
              "      <td>14.991833</td>\n",
              "      <td>0.312083</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1852</td>\n",
              "      <td>8.100167</td>\n",
              "      <td>0.918250</td>\n",
              "      <td>13.397333</td>\n",
              "      <td>2.619250</td>\n",
              "      <td>2.337000</td>\n",
              "      <td>1.382917</td>\n",
              "      <td>15.006500</td>\n",
              "      <td>0.316417</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1853</td>\n",
              "      <td>8.041833</td>\n",
              "      <td>0.835000</td>\n",
              "      <td>13.886583</td>\n",
              "      <td>2.095083</td>\n",
              "      <td>1.892500</td>\n",
              "      <td>1.355583</td>\n",
              "      <td>14.955167</td>\n",
              "      <td>0.283833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1854</td>\n",
              "      <td>8.210500</td>\n",
              "      <td>0.825667</td>\n",
              "      <td>13.977417</td>\n",
              "      <td>1.783333</td>\n",
              "      <td>1.762167</td>\n",
              "      <td>1.357000</td>\n",
              "      <td>14.991000</td>\n",
              "      <td>0.276417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>2011</td>\n",
              "      <td>9.516000</td>\n",
              "      <td>0.082000</td>\n",
              "      <td>15.284833</td>\n",
              "      <td>0.114333</td>\n",
              "      <td>3.827667</td>\n",
              "      <td>0.136583</td>\n",
              "      <td>15.769500</td>\n",
              "      <td>0.059000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>2012</td>\n",
              "      <td>9.507333</td>\n",
              "      <td>0.083417</td>\n",
              "      <td>15.332833</td>\n",
              "      <td>0.107333</td>\n",
              "      <td>3.756167</td>\n",
              "      <td>0.145333</td>\n",
              "      <td>15.802333</td>\n",
              "      <td>0.061500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>2013</td>\n",
              "      <td>9.606500</td>\n",
              "      <td>0.097667</td>\n",
              "      <td>15.373833</td>\n",
              "      <td>0.115500</td>\n",
              "      <td>3.911333</td>\n",
              "      <td>0.149833</td>\n",
              "      <td>15.854417</td>\n",
              "      <td>0.064667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>2014</td>\n",
              "      <td>9.570667</td>\n",
              "      <td>0.090167</td>\n",
              "      <td>15.313583</td>\n",
              "      <td>0.111667</td>\n",
              "      <td>3.877750</td>\n",
              "      <td>0.139000</td>\n",
              "      <td>15.913000</td>\n",
              "      <td>0.063167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>2015</td>\n",
              "      <td>9.831000</td>\n",
              "      <td>0.092167</td>\n",
              "      <td>15.572667</td>\n",
              "      <td>0.106500</td>\n",
              "      <td>4.148833</td>\n",
              "      <td>0.141750</td>\n",
              "      <td>16.058583</td>\n",
              "      <td>0.060833</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>166 rows × 9 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Minimum, Maximum und Durchschnitt**\n",
        "\n",
        "Diesen Datensatz wollen wir etwas genauer unter die Lupe nehmen. Wir beschränken uns dafür auf die ersten beiden Spalten: Die Jahreszahlen und die an Land gemessenen Durchschnittstemperaturen. Die Spalten des `DataFrames` heißen auch `Series`. Wir können sie folgendermaßen aus dem Datensatz extrahieren:"
      ],
      "metadata": {
        "id": "2Fo5fd6vgN9-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "yearly_global_temperatures.set_index(\"Date\", inplace=True)\n",
        "Jahre = yearly_global_temperatures.index\n",
        "temperaturen_global = yearly_global_temperatures['LandAverageTemperature']\n",
        "\n",
        "temperaturen_global"
      ],
      "metadata": {
        "id": "xoD7ze2mgO-1",
        "outputId": "66541074-eb40-43d3-dcb4-3ba632b40e78",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "1850    7.900667\n",
              "1851    8.178583\n",
              "1852    8.100167\n",
              "1853    8.041833\n",
              "1854    8.210500\n",
              "          ...   \n",
              "2011    9.516000\n",
              "2012    9.507333\n",
              "2013    9.606500\n",
              "2014    9.570667\n",
              "2015    9.831000\n",
              "Name: LandAverageTemperature, Length: 166, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Jetz fangen wir an, die Daten zu analysieren. Dafür bestimmen wir erst einmal einfache statistische Werte, wie den größten und kleinsten Wert sowie den Durchschnittswert.\n",
        "\n",
        "\n",
        "<font color='sky blue'>__Aufgabe:__  <br>\n",
        "Versuche folgende Fragen zu beantworten:\n",
        "*   Wie hoch war die jährliche Durchschnittstemperatur an Land im heißesten Jahr zwischen 1850 und 2015? Wie war hoch war sie im kältesten Jahr?\n",
        "*   Welches Jahr war das heißeste und welches das kälteste Jahr zwischen 1850 und 2015?\n",
        "*   Was war der Durchschnitt  aller Temperaturen von 1850 bis 2015?\n",
        "\n",
        "Tipp: Auf deinem Cheat Sheet findest du ein Beispiel mit den entsprechenden Befehlen!\n",
        "</font>"
      ],
      "metadata": {
        "id": "PXt2TEWHg5eh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dein Code hier...\n",
        "\n",
        "# maximale jährliche Durchschnittstemperatur\n",
        "max_temperatur = temperaturen_global.max()\n",
        "# Lösung: 9.830999999999998 Grad\n",
        "\n",
        "# minimale jährliche Durchschnittstemperatur\n",
        "min_temperatur = temperaturen_global.min()\n",
        "# Lösung: 7.558583333333334 Grad\n",
        "\n",
        "\n",
        "# Jahr mit maximaler Durchnittstemperatur\n",
        "temperaturen_global.idxmax()\n",
        "# Lösung: 2015\n",
        "\n",
        "# Jahr mit minimaler Durchnittstemperatur\n",
        "temperaturen_global.idxmin()\n",
        "# Lösung: 1862\n",
        "\n",
        "\n",
        "# Durchschnitt der Temperaturen  von 1850 bis 2015:\n",
        "temperaturen_global.mean()\n",
        "# Lösung: 8.5715828313253 Grad"
      ],
      "metadata": {
        "id": "en4Q0rInkc8o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Grafiken erstellen**\n",
        "\n",
        "Nur mit Minimum, Maximum und Durchschnitt können wir noch nicht so viel\n",
        "anfangen. Nun wollen wir unsere Daten visualisieren.\n",
        "\n",
        "<font color='sky blue'>__Aufgabe:__  <br>\n",
        "Erstelle ein Liniendiagramm, das den Verlauf der Temperaturen über die Jahre darstellt.\n",
        "\n",
        "Tipp: Auf deinem Cheat Sheet findest du wieder ein Beispiel mit den entsprechenden Befehlen!\n",
        "</font>"
      ],
      "metadata": {
        "id": "GI7_mtC5kn0_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Der Begriff plt.plot(x,y) erstellt einen Graphen der Werte x und y\n",
        "plt.plot(Jahre, temperaturen_global)\n",
        "# Um den Plot anzeigen zu lassen, verwenden wir plt.show()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "nhe_Pwt1g80Z",
        "outputId": "79d57d6a-d7c7-4baf-c988-99fe42a38eb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Nun können wir den Plot noch etwas verschönern, indem wir z.B. eine Überschrift\n",
        "und Achsenbeschriftungen hinzufügen. Außerdem können wir auch die Farbe und den\n",
        "Style der Linie ändern.\n",
        "\n",
        "\n",
        "\n",
        "<font color='sky blue'>__Aufgabe:__  <br>\n",
        "\n",
        "*   Füge einen Titel zu deiner Grafik hinzu.\n",
        "*   Füge Beschriftungen für die x- und y-Achse hinzu.\n",
        "*   Ändere die Farbe der Linie zu orange.\n",
        "*   Probiere weitere Farben und Linienarten aus.\n",
        "\n",
        "Tipp: Auf deinem Cheat Sheet findest du wieder ein Beispiel mit den entsprechenden Befehlen!\n",
        "</font>"
      ],
      "metadata": {
        "id": "tlPxy04jlGvh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dein Code hier...\n",
        "\n",
        "#Lösung:\n",
        "\n",
        "plt.plot(Jahre, temperaturen_global, color = 'orange', linestyle = 'solid')\n",
        "plt.title('Globale Durchschnittstemperatur auf dem Land von 1850-2015 in °C')\n",
        "plt.xlabel('Jahre')\n",
        "plt.ylabel('Durchschnittstemperaturen')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "7M8GTRPliI2_",
        "outputId": "d3a26733-dead-4488-be66-0c7f0d6db97a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sehr schön! Man kann klar erkennen, dass die Temperatur seit 1850 deutlich angestiegen ist, also bestätigen unsere Daten eigentlich genau das, was wir aus der Schule oder den Medien schon wissen."
      ],
      "metadata": {
        "id": "Av5y411kQTaC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Vorhersagen machen**\n",
        "\n",
        "\n",
        "Das eigentlich coole an Data Science ist, aus den Daten etwas ableiten zu\n",
        "können und Vorhersagungen zu machen. In unserem Beispiel wollen wir z.B.\n",
        "vorhersagen, wie hoch die globale Durchschnittstemperatur in der Zukunft sein\n",
        "wird (falls wir den CO2-Ausstoß nicht reduzieren).\n",
        "Hierzu nutzen wir ein sehr einfaches Modell und nehmen an, dass die Temperatur\n",
        "ab 1850 linear ansteigt, d.h. wir versuchen die \"beste\" Gerade durch die\n",
        "verschiedenen Datenpunkte zu ziehen. Das kennt ihr vielleicht aus dem\n",
        "Physikunterricht. Diese Methode nennt man auch \"Lineare Regression\".\n",
        "Ein einfaches Beispiel zur Veranschaulichung findest du hier:\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "HoFg0iA_lfnz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        " <font color='sky blue'>__Aufgabe:__  <br>\n",
        "Erstelle das lineare Regressionsmodell für unsere Daten!\n",
        "\n",
        "Tipp: Wie immer findest du ein entsprechendes Beispiel auf deinem Cheat Sheet.\n",
        "</font>"
      ],
      "metadata": {
        "id": "fBR18yqeE7rU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dein Code hier...\n",
        "\n",
        "\n",
        "# Lösung:\n",
        "\n",
        "# 1)  hier treffen wir einige Vorbereitungen\n",
        "X = Jahre.array.reshape(-1, 1)\n",
        "y = temperaturen_global.array.reshape(-1, 1)\n",
        "\n",
        "# 2) Erstelle das lineare Modell\n",
        "reg = LinearRegression()\n",
        "\n",
        "# 3) Lasse das Model die Rechnungen ausführen, um das Model zu erstellen\n",
        "reg.fit(X, y)\n",
        "\n",
        "# 4) Berechne Vorhersagewerte anhand des Models\n",
        "model = reg.predict(X)"
      ],
      "metadata": {
        "id": "lr3jUiyjl-By"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        " <font color='sky blue'>__Aufgabe:__  <br>\n",
        "\n",
        "*   Füge die \"beste Gerade\", d.h. die \"vorhergesagten Werte\" in\n",
        "deine Grafik\n",
        "      von vorhin hinzu. Hier kannst du auch wieder nach Belieben die Linienart\n",
        "      und -farbe anpassen.\n",
        "*   Sage die globale Durchschnittstemperatur (laut unserem Modell) für die\n",
        "      Jahre 2030 und 2050 vorher.\n",
        "\n",
        "*   Das 2-Grad-Ziel bezieht sich auf einen Temperaturanstieg im Vergleich\n",
        "      zum vorindustriellen Zeitalter (bis ca. 1850). In welchem Jahr wird das\n",
        "      2-Grad-Ziel nach unserem Modell überschritten?\n",
        "      Tipp: Berechne zuerst die \"Vorhersage\" unseres Modells für 1850 (wie eben).\n",
        "      Somit kannst du ganz einfach sehen, welcher Wert nicht überschritten werden darf.\n",
        "\n",
        "\n",
        "</font>\n"
      ],
      "metadata": {
        "id": "7QHH1bwql8_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dein Code hier\n",
        "\n",
        "# Lösung Plot\n",
        "plt.plot(Jahre, temperaturen_global, color = \"orange\")\n",
        "plt.plot(Jahre, model, color = \"grey\", linestyle = \"dotted\")\n",
        "plt.title(' Globale Durchschnittstemperatur auf dem Land von 1850-2015 in °C')\n",
        "plt.xlabel('Jahre')\n",
        "plt.ylabel('Durchschnittstemperaturen')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "g1jl960dnbiH",
        "outputId": "9d672809-e24e-4eda-a26e-e8d3996a8e50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dein Code hier....\n",
        "\n",
        "# Lösung Vorhersage\n",
        "Jahr = 2084 # gebe hier das Jahr ein, für das du die Temperatur vorhersagen willst\n",
        "jahr = [[Jahr]] # oder np.array([[Jahr]])\n",
        "reg.predict(jahr)\n",
        "\n",
        "# 2030: 9.40340929 Grad\n",
        "# 2050: 9.57404036 Grad\n",
        "\n",
        "# 1850: 7.86772967 Grad -> die Grenze ist 7.86772967 + 2 = 9.86772967 Grad\n",
        "# durch Ausprobieren: im Jahr 2085 wird das 2-Grad-Ziel überschritten\n",
        "# 2085: 9.87264473 Grad\n",
        "# das kann man auch anders lösen, aber wir wollen es nicht zu kompliziert machen ;)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "79hsjhqOn7Ib",
        "outputId": "be8ae0a4-0edd-488d-b1e7-4f9f11aaf5c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.86411318]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bei diesem Ergebnis kann man sich natürlich einige Fragen stellen:\n",
        "- Ist es realistisch, dass die 2 Grad in diesem Jahr erreicht werden?\n",
        "- Welche Annahmen haben wir gemacht um dieses Jahr zu berechnen, und warum könnten diese falsch sein?\n",
        "- Wie könnten wir unser Modell verbessern?"
      ],
      "metadata": {
        "id": "I3IeseqYnrD2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Korrelationen**\n",
        "\n",
        "Der Anstieg von Temperaturen ist nur eine Folge des Klimawandels. Wir wollen jetzt statistische Zusammenhänge zwischen verschiedenen Größen untersuchen. Hier kommt die Korrelation ins Spiel!\n",
        "\n",
        "Die Korrelation zwischen zwei Größen ist eine Zahl zwischen -1 und 1 und gibt an, wie stark ihr linearer Zusammenhang ist. Je näher die Zahl bei 1 liegt, desto stärker ist der Zusammenhang. Anschaulich bedeutet das, dass man wie in vorangegangenen Beispiel eine \"gute\" Gerade durch die Datenpunkte zeichnen kann.\n",
        "\n",
        "Korrelation darf allerdings nicht mit Kausalität (Ursache-Wirkungs-Beziehung) verwechselt werden! Eine starke Korrelation kann zwar darauf hindeuten, dass ein kausaler Zusammenhang besteht, ob und in welche Richtung geht allerdings nicht daraus hervor. Ein kleines Beispiel: Statistische Daten über Jahrzehnte hinweg zeigen eine eindeutige positive Korrelation zwischen der Storchenpopulation und der menschlichen Geburtenrate. Trotzdem gibt es keinen offensichtlichen kausalen Zusammenhang.\n",
        "\n",
        "\n",
        "Wir benutzen nun einen neuen Datensatz. In diesem sind für die Jahre 1900 bis 2013 die Durchschnittstemperaturen an Land, die jährlichen CO2-Emissionen, der Meeresspeiegelanstieg, die Anzahl von Naturkatastrophen sowie die Weltbevölkerung zusammengetragen."
      ],
      "metadata": {
        "id": "o0ZE1Qu2ZBWg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lade den neuen Datensatz herunter\n",
        "file = 'klimadaten.csv'\n",
        "filelink = 'https://drive.google.com/uc?id=17_uNRMgcDjPPLyXeCXpOHHD7-UBLXQXf'\n",
        "# Lese die Daten ein\n",
        "gdown.download(filelink, file)\n",
        "klimadaten = pd.read_csv(file)\n",
        "klimadaten.rename( columns={'Unnamed: 0':'Jahre'}, inplace=True )\n",
        "klimadaten"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "rYDRbOdhZApF",
        "outputId": "f6b9e3eb-2acf-498f-b175-3f150d5d671c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=17_uNRMgcDjPPLyXeCXpOHHD7-UBLXQXf\n",
            "To: /content/klimadaten.csv\n",
            "100%|██████████| 7.56k/7.56k [00:00<00:00, 18.0MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "     Jahre  LandAverageTemperature  Annual CO₂ emissions  Sea Level  \\\n",
              "0     1900                8.501583          1.952210e+09   0.000000   \n",
              "1     1901                8.541917          2.016744e+09  -0.015748   \n",
              "2     1902                8.304417          2.067684e+09   0.165354   \n",
              "3     1903                8.220167          2.254095e+09   0.480315   \n",
              "4     1904                8.090917          2.279749e+09   0.074803   \n",
              "..     ...                     ...                   ...        ...   \n",
              "109   2009                9.505250          3.156403e+10   7.460630   \n",
              "110   2010                9.703083          3.336435e+10   7.775591   \n",
              "111   2011                9.516000          3.448701e+10   7.838583   \n",
              "112   2012                9.507333          3.500627e+10   8.200787   \n",
              "113   2013                9.606500          3.531920e+10   7.854331   \n",
              "\n",
              "     Number of natural disasters  World Population  \n",
              "0                              5      1.647405e+09  \n",
              "1                              2      1.658786e+09  \n",
              "2                              9      1.672564e+09  \n",
              "3                              8      1.687013e+09  \n",
              "4                              2      1.701647e+09  \n",
              "..                           ...               ...  \n",
              "109                          344      6.898306e+09  \n",
              "110                          393      6.985603e+09  \n",
              "111                          334      7.073125e+09  \n",
              "112                          346      7.161698e+09  \n",
              "113                          332      7.250593e+09  \n",
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              "      <td>9.606500</td>\n",
              "      <td>3.531920e+10</td>\n",
              "      <td>7.854331</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font color='sky blue'>__Aufgabe:__  <br>\n",
        "Bestimme die Korrelationen zwischen den Spalten des neuen Datensatzes! Zwischen welchen Größen sind die Zusammenhänge besonders stark und zwischen welchen nicht so sehr vorhanden? Woran kann das liegen?\n",
        "\n",
        "Tipp: Auf deinem Cheat Sheet findest du wieder ein Beispiel mit dem entsprechenden Befehl!\n",
        "</font>"
      ],
      "metadata": {
        "id": "bR07Js7HfQ6y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lösung\n",
        "\n",
        "klimadaten.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "VI4nauqvdwBr",
        "outputId": "08a01346-b712-497f-fc52-5b043bc3e7bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                Jahre  LandAverageTemperature  \\\n",
              "Jahre                        1.000000                0.839991   \n",
              "LandAverageTemperature       0.839991                1.000000   \n",
              "Annual CO₂ emissions         0.945997                0.845154   \n",
              "Sea Level                    0.984028                0.859977   \n",
              "Number of natural disasters  0.858474                0.870910   \n",
              "World Population             0.954593                0.869529   \n",
              "\n",
              "                             Annual CO₂ emissions  Sea Level  \\\n",
              "Jahre                                    0.945997   0.984028   \n",
              "LandAverageTemperature                   0.845154   0.859977   \n",
              "Annual CO₂ emissions                     1.000000   0.972788   \n",
              "Sea Level                                0.972788   1.000000   \n",
              "Number of natural disasters              0.940532   0.897252   \n",
              "World Population                         0.994266   0.976027   \n",
              "\n",
              "                             Number of natural disasters  World Population  \n",
              "Jahre                                           0.858474          0.954593  \n",
              "LandAverageTemperature                          0.870910          0.869529  \n",
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              "Sea Level                                       0.897252          0.976027  \n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Das Handwerkszeug zur Analyse von Datensätzen haben wir gelernt!\n",
        "Jetzt kannst du gerne den neuen Datensatz etwas mehr unter die Lupe nehmen und beispielsweise wieder Plots erstellen."
      ],
      "metadata": {
        "id": "lJv1DDusgUr7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# hier ist Platz für deinen Code"
      ],
      "metadata": {
        "id": "r2LXvDIAgtny"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ausblick 🌤 🌍\n",
        "Bisher haben wir die Temperaturen nur mit Liniendiagrammen visualisiert. Hier wollen wir euch noch eine andere coole Art der Visualisierung unserer Ergebnisse zeigen, nämlich einen interaktiven Globus! Diesen könnt ihr selbst mit eurer Maus drehen und dabei für jedes beliebige Land die durchschnittliche Temperatur sehen!"
      ],
      "metadata": {
        "id": "XeiN0RB-L1TF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Für diese Visualisierung brauchen wir das Paket plotly\n",
        "import plotly.express as px\n",
        "\n",
        "# Hierfür brauchen wir wieder einen neuen Datensatz, der Temperaturen pro Land enthält\n",
        "gdown.download(\"https://drive.google.com/uc?id=1A5ptCQlqJz4sQ2Y6WW2BchGMsrUKuHtW\", \"GlobalLandTemperaturesByCountry.csv\")\n",
        "GlobalLandTemperaturesByCountry = pd.read_csv(\"GlobalLandTemperaturesByCountry.csv\")\n",
        "Länder_Durchschnittstemperaturen = GlobalLandTemperaturesByCountry.groupby(by='Country', as_index=False).mean()\n"
      ],
      "metadata": {
        "id": "ai6nQvl-oOcI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d30ca9d5-831c-4225-c61d-323810fdddf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1A5ptCQlqJz4sQ2Y6WW2BchGMsrUKuHtW\n",
            "To: /content/GlobalLandTemperaturesByCountry.csv\n",
            "100%|██████████| 22.7M/22.7M [00:00<00:00, 56.1MB/s]\n",
            "<ipython-input-21-1af00ce7a75d>:7: FutureWarning:\n",
            "\n",
            "The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.choropleth(\n",
        "        # Als ersten Funktionsparameter übergeben wir unsere Tabellenwerte von oben\n",
        "        Länder_Durchschnittstemperaturen,\n",
        "\n",
        "        # Danach müssen wir erklären wie genau unsere Tabellenspalten aufgebaut sind\n",
        "\n",
        "        # Hier müssen wir den Namen unserer Tabellenspalte eintragen, in der die Ländernamen stehen\n",
        "        locations = 'Country',\n",
        "        # Hier legen wir fest, dass in dieser Zeile ganz ausgeschriebene Ländernamen stehen. Eine andere Option wären Abkürzungen wie USA\n",
        "        locationmode = 'country names',\n",
        "        # Die Farbe soll unsere berechnete Spalte \"AverageTemperature\" mit den Durchschnittstemperaturen darstellen\n",
        "        color = 'AverageTemperature',\n",
        "        # Die Projection lässt unseren \"Blickwinkel\" auf die Erde einstellen: orthographic erstellt einen runden Globus, ihr könnt aber auch mal 'natural earth' versuchen\n",
        "        projection = 'orthographic', # 'natural earth'\n",
        "\n",
        "        # Zuletzt wählen wir den Text der als Legende / Label für unsere Tabellenspalten angezeigt werden soll: Durchschnittstemperatur und Land\n",
        "        labels = {\n",
        "            'AverageTemperature': 'Durchschnittstemperatur in °C',\n",
        "            'Country': 'Land'},\n",
        "            # die Farbskala wollen wir von einem dunklem blau Ton (283747) bis zu einem dunklem rot Ton (A93226)\n",
        "            color_continuous_scale=('#283747', '#2874A6', '#3498DB', '#F5B041', '#E67E22', '#A93226'),\n",
        "        )\n",
        "\n",
        "# Nachdem die Bibliothek nun alle Details kennt, können wir die Visualisierung anzeigen lassen und erkunden\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "L6usiloWLLAH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Zusammenfassung\n",
        "\n",
        "Was haben wir heute gelernt?\n",
        "\n",
        "*   Maximale, minimale und Durchschnittswerte von Daten bestimmen\n",
        "\n",
        "*   Daten grafisch darstellen\n",
        "\n",
        "*   mit einem Modell Daten für die Zukunft vorhersagen\n",
        "\n",
        "*   Korrelationen bzw. Zusammenhänge zwischen Größen aufzeigen\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "cNW5dQ7ENAs4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hintergrund ⏮🖱\n",
        "\n",
        "Vielleicht hast du dich gefragt, was hinter Befehlen wie `min`, `max` oder `mean` steckt. Ganz einfach: Jemand hat ein Programm dafür geschrieben. Wenn wir Pakete wie `pandas` oder `numpy` importieren, dann können wir diese Programme einfach nutzen. Wie sie eigentlich funktionieren, schauen wir uns jetzt an."
      ],
      "metadata": {
        "id": "vQi-aCjPQ2cA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Schau dir einmal das folgende Programm an, welches das Minimum einer Liste von Zahlen ausgibt. Was passiert in dem Programm?"
      ],
      "metadata": {
        "id": "tpaEh2OXSFCL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "liste = [3,6,2,7,8,5]\n",
        "\n",
        "minimum_temporär = liste[0]\n",
        "for eintrag in liste:\n",
        "  if eintrag < minimum_temporär:\n",
        "    minimum_temporär = eintrag\n",
        "minimum = minimum_temporär\n",
        "\n",
        "print(minimum)\n",
        "\n",
        "\n",
        "# Lösung:\n",
        "# Wir setzen den ersten Eintrag der Liste als temporäres Minimum.\n",
        "# Dann gehen wir die Liste von Anfang bis Ende durch.\n",
        "# Für jeden Listeneintrag vergleichen wir den Eintrag mit dem aktuellen Minimum.\n",
        "# Falls der betrachtete Eintrag kleiner ist, als das temporäre Minimum, setzen\n",
        "# wir den aktuellen Eintrag als temporäres Minimum und gehen weiter durch die Liste."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jvYRGL4ESWRo",
        "outputId": "7a615d97-fbe3-4e32-a08b-4182c52abfbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Etwas übersichtlicher können wir das Programm als Funktion schreiben:"
      ],
      "metadata": {
        "id": "k8WXTCGPTlmu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def minimum(liste):\n",
        "\n",
        "  minimum_temporär = liste[0]\n",
        "  for eintrag in liste:\n",
        "    if eintrag < minimum_temporär:\n",
        "      minimum_temporär = eintrag\n",
        "  minimum = minimum_temporär\n",
        "\n",
        "  return(minimum)"
      ],
      "metadata": {
        "id": "ZHSiZ3czTtiI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Wir können die Funktion nun für verschiedene Listen aufrufen."
      ],
      "metadata": {
        "id": "uEdVviHVT75Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "liste1 = [300, 5, 30, -80, 204, 47]\n",
        "minimum(liste1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8bHcY8QUD7X",
        "outputId": "96e4b58d-34d5-47d9-b303-12427efbcbba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-80"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "liste2 = [0.5, 0.2, 0.6, 0.7]\n",
        "minimum(liste2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DGn0Vx16UZ82",
        "outputId": "e849cfd3-aa78-49fa-b6af-d5ce8ad6fd66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "<font color='sky blue'>__Aufgabe:__  <br>\n",
        "Versuche eine ähnliche Funktion zu schreiben, die\n",
        "das Maximum einer Liste ausgibt!\n",
        "</font>"
      ],
      "metadata": {
        "id": "gPG8KNDwUiyw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def maximum(liste):\n",
        "\n",
        "  # hier kommt dein Code hin\n",
        "\n",
        "  return maximum"
      ],
      "metadata": {
        "id": "0TJm767pVWF8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Jetzt noch der Durchschnitt! Das ist vielleicht erstmal nicht so einfach. Zur Hilfe zeigt das folgende Beispiel, wie die Summe aller Zahlen in einer Liste berechnet werden kann."
      ],
      "metadata": {
        "id": "jdrkLbS1Vfgk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def summe(liste):\n",
        "\n",
        "  summe_temporär = 0\n",
        "  for eintrag in liste:\n",
        "    summe_temporär = summe_temporär + eintrag\n",
        "  summe = summe_temporär\n",
        "\n",
        "  return summe"
      ],
      "metadata": {
        "id": "_K7d82QsVemx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Und jetzt du!\n",
        "\n",
        "<font color='sky blue'>__Aufgabe:__  <br>\n",
        "Schreibe eine Funktion, die\n",
        "den Durchschnitt einer Liste berechnet!\n",
        "\n",
        "Tipp: Die Länge der Liste (= Anzahl der Einträge) wird durch den Befehl `len(liste)` ausgegeben.\n",
        "</font>"
      ],
      "metadata": {
        "id": "-53pH9yH50O-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def durchschnitt(liste):\n",
        "\n",
        "  # hier kommt dein Code hin\n",
        "\n",
        "  return durchschnitt"
      ],
      "metadata": {
        "id": "ETripTls5i6s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Lösung\n",
        "\n",
        "def durchschnitt(liste):\n",
        "\n",
        "  durchschnitt = summe(liste)/len(liste)\n",
        "\n",
        "  return durchschnitt\n",
        "\n",
        "\n",
        "\n",
        "# andere Variante:\n",
        "\n",
        "# Lösung\n",
        "\n",
        "def durchschnitt(liste):\n",
        "\n",
        "  summe_temporär = 0\n",
        "  for eintrag in liste:\n",
        "    summe_temporär = summe_temporär + eintrag\n",
        "  summe = summe_temporär\n",
        "  durchschnitt = summe/len(liste)\n",
        "\n",
        "  return durchschnitt"
      ],
      "metadata": {
        "id": "up0n5BNb52RK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}