13_italy_population.ipynb

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        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/alesaccoia/IULM_DDM2324_Notebooks/blob/main/13_italy_population.ipynb)"
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      "source": [
        "##Esercizio sulla popolazione italiana"
      ],
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            "--2024-04-09 14:45:37--  https://github.com/LeoLin72/IULM_DDM2324_Notebooks/raw/main/data/italy_population_administrative_layers.xlsx\n",
            "Resolving github.com (github.com)... 140.82.114.3\n",
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            "--2024-04-09 14:45:37--  https://raw.githubusercontent.com/LeoLin72/IULM_DDM2324_Notebooks/main/data/italy_population_administrative_layers.xlsx\n",
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            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 45407919 (43M) [application/octet-stream]\n",
            "Saving to: ‘italy_population_administrative_layers.xlsx’\n",
            "\n",
            "italy_population_ad 100%[===================>]  43.30M   145MB/s    in 0.3s    \n",
            "\n",
            "2024-04-09 14:45:38 (145 MB/s) - ‘italy_population_administrative_layers.xlsx’ saved [45407919/45407919]\n",
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              "  COUNTRY  A_ID_0 A_LBL_0  A_ID_1   A_LBL_1  A_ID_2   A_LBL_2  A_ID_3  \\\n",
              "0     ITA       0  Italia      20  Sardegna      92  Cagliari   92009   \n",
              "1     ITA       0  Italia      20  Sardegna      92  Cagliari   92009   \n",
              "2     ITA       0  Italia      20  Sardegna      92  Cagliari   92009   \n",
              "3     ITA       0  Italia      20  Sardegna      92  Cagliari   92009   \n",
              "4     ITA       0  Italia      20  Sardegna      92  Cagliari   92009   \n",
              "\n",
              "    A_LBL_3  NIELSEN_A_ID  Eta  Totale Maschi  Totale Femmine  Totale  \n",
              "0  Cagliari             3    0            313             327     640  \n",
              "1  Cagliari             3    1            348             367     715  \n",
              "2  Cagliari             3    2            395             403     798  \n",
              "3  Cagliari             3    3            451             388     839  \n",
              "4  Cagliari             3    4            468             422     890  "
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      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "!wget \"https://github.com/LeoLin72/IULM_DDM2324_Notebooks/raw/main/data/italy_population_administrative_layers.xlsx\"\n",
        "# a.1\n",
        "# Carica il foglio \"Raw Data\" dal file fornito\n",
        "df = pd.read_excel(\"italy_population_administrative_layers.xlsx\", sheet_name=\"Raw Data\")\n",
        "\n",
        "# Mostra le prime righe del dataset per comprendere la struttura dei dati\n",
        "# a.2\n",
        "df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "ktblvwcvUTZa"
      },
      "outputs": [],
      "source": [
        "#a.3\n",
        "df = df[df['Eta'] != 999]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYGLPt-MUTZa",
        "outputId": "3c395895-b5ca-4171-cd7f-257eddc1aafe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sono presenti 7912 codici comune unici\n",
            "Il numero totale di maschi in Italia é 29041830\n",
            "Il numero totale di femmine in Italia é 30582625\n"
          ]
        }
      ],
      "source": [
        "# b.1\n",
        "num_comuni = df['A_ID_3'].nunique()\n",
        "print(f\"Sono presenti {num_comuni} codici comune unici\")\n",
        "\n",
        "# b.2\n",
        "total_males = df['Totale Maschi'].sum()\n",
        "total_females = df['Totale Femmine'].sum()\n",
        "print(f\"Il numero totale di maschi in Italia é {total_males}\")\n",
        "print(f\"Il numero totale di femmine in Italia é {total_females}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
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        },
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        "outputId": "263073a2-161a-4a68-a68f-a0535b79e852"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Il comune più popoloso in Italia è (58, 'Roma', 58091, 'Roma') con una popolazione di 2808293\n",
            "Il comune meno popoloso in Italia è (97, 'Lecco', 97055, 'Morterone') con una popolazione di 30\n"
          ]
        }
      ],
      "source": [
        "# b.3\n",
        "grouped = df.groupby(['A_ID_2', 'A_LBL_2', 'A_ID_3', 'A_LBL_3'])['Totale'].sum()\n",
        "highest_population_comune = grouped.idxmax()\n",
        "lowest_population_comune = grouped.idxmin()\n",
        "\n",
        "\n",
        "highest_population_value = grouped[highest_population_comune]\n",
        "lowest_population_value = grouped[lowest_population_comune]\n",
        "\n",
        "print(f\"Il comune più popoloso in Italia è {highest_population_comune} con una popolazione di {highest_population_value}\")\n",
        "print(f\"Il comune meno popoloso in Italia è {lowest_population_comune} con una popolazione di {lowest_population_value}\")\n",
        "\n"
      ]
    },
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# c.2\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Raggruppiamo per 'Eta' e sommiamo i valori per 'Totale Maschi' e 'Totale Femmine'\n",
        "grouped_by_age = df.groupby('Eta').agg({'Totale Maschi': 'sum', 'Totale Femmine': 'sum'}).reset_index()\n",
        "\n",
        "# Creiamo il grafico a linee\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(grouped_by_age['Eta'], grouped_by_age['Totale Maschi'], label='Maschi', color='blue')\n",
        "plt.plot(grouped_by_age['Eta'], grouped_by_age['Totale Femmine'], label='Femmine', color='red')\n",
        "plt.xlabel('Età')\n",
        "plt.ylabel('Popolazione')\n",
        "plt.title('Popolazione maschile e femminile per età in Italia')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cs4XIvdZUTZc"
      },
      "outputs": [],
      "source": [
        "# c.3\n",
        "max_males_age = grouped_by_age['Totale Maschi'].idxmax()\n",
        "max_females_age = grouped_by_age['Totale Femmine'].idxmax()\n",
        "\n",
        "max_males_age, max_females_age\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YywomPQDUTZc",
        "outputId": "b8bb1536-757c-4988-bbbe-1818b89eca27"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A_LBL_1\n",
              "Liguria                  0.274369\n",
              "Friuli Venezia Giulia    0.252694\n",
              "Umbria                   0.246992\n",
              "Piemonte                 0.246165\n",
              "Toscana                  0.244920\n",
              "Marche                   0.239530\n",
              "Molise                   0.238686\n",
              "Sardegna                 0.230304\n",
              "Emilia-Romagna           0.229966\n",
              "Abruzzo                  0.229912\n",
              "Valle d'Aosta            0.229674\n",
              "Basilicata               0.221607\n",
              "Veneto                   0.220693\n",
              "Lombardia                0.217394\n",
              "Puglia                   0.213222\n",
              "Lazio                    0.209779\n",
              "Calabria                 0.208752\n",
              "Sicilia                  0.204117\n",
              "Trentino-Alto Adige      0.200361\n",
              "Campania                 0.180579\n",
              "Name: Totale, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Supponendo l'esistenza del DataFrame df\n",
        "\n",
        "# Calcola la popolazione totale per ogni regione\n",
        "total_population_by_region = df.groupby('A_LBL_1')['Totale'].sum()\n",
        "\n",
        "# Calcola la popolazione degli anziani (età > 65) per ogni regione\n",
        "elderly_population_by_region = df[df['Eta'] > 65].groupby('A_LBL_1')['Totale'].sum()\n",
        "\n",
        "# Calcola la proporzione del numero di anziani rispetto al totale della popolazione per ogni regione\n",
        "proportion_elderly_by_region = elderly_population_by_region / total_population_by_region\n",
        "\n",
        "proportion_elderly_by_region.sort_values(ascending=False)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9x09wW8UTZd"
      },
      "source": [
        "# Calcola l'età media pesata per ciascuna regione (esercizio 3 capitolo 4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JeWNS1yJUTZd",
        "outputId": "d673176f-2077-4a46-c5d9-7fe58e2c978c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A_LBL_1\n",
              "Liguria                  48.719653\n",
              "Friuli Venezia Giulia    47.306784\n",
              "Piemonte                 46.829720\n",
              "Toscana                  46.817073\n",
              "Umbria                   46.805737\n",
              "Sardegna                 46.779786\n",
              "Molise                   46.698432\n",
              "Marche                   46.427492\n",
              "Abruzzo                  46.013706\n",
              "Valle d'Aosta            45.971216\n",
              "Emilia-Romagna           45.931917\n",
              "Basilicata               45.662961\n",
              "Veneto                   45.442861\n",
              "Lombardia                45.020239\n",
              "Lazio                    44.964875\n",
              "Puglia                   44.610702\n",
              "Calabria                 44.363277\n",
              "Sicilia                  43.874251\n",
              "Trentino-Alto Adige      43.482566\n",
              "Campania                 42.492762\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Moltiplica l'età per la popolazione totale per ottenere il peso totale per ogni età\n",
        "df['weighted_age'] = df['Eta'] * df['Totale']\n",
        "\n",
        "# Calcola la somma dei pesi e la popolazione totale per ciascuna regione\n",
        "sum_weights = df.groupby('A_LBL_1')['weighted_age'].sum()\n",
        "sum_population = df.groupby('A_LBL_1')['Totale'].sum()\n",
        "\n",
        "# Calcola l'età media pesata dividendo la somma dei pesi per la popolazione totale\n",
        "average_weighted_age = sum_weights / sum_population\n",
        "average_weighted_age.sort_values(ascending=False)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Calcolo del P-value e F value"
      ],
      "metadata": {
        "id": "AWnvq3sWZfyy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8QQQs3diUTZd",
        "outputId": "2e87ec49-4047-4469-dc95-dd513157763e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F statistic: 231.53431897915394\n",
            "P-value: 0.0\n"
          ]
        }
      ],
      "source": [
        "import scipy.stats as stats\n",
        "\n",
        "# Crea una lista di età per ciascuna regione\n",
        "ages_by_region = [df['weighted_age'][df['A_LBL_1'] == region] for region in df['A_LBL_1'].unique()]\n",
        "\n",
        "# Esegui il test ANOVA\n",
        "f_statistic, p_value = stats.f_oneway(*ages_by_region)\n",
        "\n",
        "print(f\"F statistic: {f_statistic}\")\n",
        "print(f\"P-value: {p_value}\")\n"
      ]
    }
  ],
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        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.17"
    },
    "colab": {
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