LinkPrediction_EDA04_03.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "#***EDA***"
      ],
      "metadata": {
        "id": "GsB7fKGij8HQ"
      }
    },
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        {
          "output_type": "stream",
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          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "rerVssLiiuXe"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Microbiome**"
      ],
      "metadata": {
        "id": "hkrpaob4kAHG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/LinkPredictionProject/Data/microbiome.csv', encoding='latin1')\n",
        "df.head()"
      ],
      "metadata": {
        "id": "17ML3304j63C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "outputId": "5248a147-ab82-4194-db16-2b17b6a04949"
      },
      "execution_count": 50,
      "outputs": [
        {
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              "   host_ID     ASV_1  ASV_10  ASV_100  ASV_1001  ASV_1002  ASV_1004  ASV_1005  \\\n",
              "0     2010  0.090395     0.0      0.0       0.0       0.0       0.0       0.0   \n",
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    {
      "cell_type": "code",
      "source": [
        "#Check that each row sums to 1\n",
        "row_sums = df.iloc[:, 1:].sum(axis=1)\n",
        "print(f'Row sums: {row_sums}')\n",
        "print(f'All rows sum to 1: {row_sums.all()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2B7qSpODkJTk",
        "outputId": "d8d8f5fb-7fef-4c3a-f7dd-14d738ea9b2d"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Row sums: 0      1.0\n",
            "1      1.0\n",
            "2      1.0\n",
            "3      1.0\n",
            "4      1.0\n",
            "      ... \n",
            "864    1.0\n",
            "865    1.0\n",
            "866    1.0\n",
            "867    1.0\n",
            "868    1.0\n",
            "Length: 869, dtype: float64\n",
            "All rows sum to 1: True\n"
          ]
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    },
    {
      "cell_type": "markdown",
      "source": [
        "This plot describes the mean percentage of the presence of each microbiome in hosts  - the top 5"
      ],
      "metadata": {
        "id": "sq2mgVZC315Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the top 5 microbiomes by mean percentage across hosts\n",
        "top_microbiomes = df.iloc[:, 1:].mean().sort_values(ascending=False).head(5)\n",
        "# Plot a bar plot for the top 5 microbiomes\n",
        "plt.figure(figsize=(4, 4))\n",
        "sns.barplot(x=top_microbiomes.index, y=top_microbiomes.values)\n",
        "plt.title('Top 5 Microbiomes')\n",
        "plt.xlabel('Microbiome')\n",
        "plt.ylabel('Mean Percentage')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "iw3kRYkBlfRU",
        "outputId": "53c56262-f1f6-40a6-9a6f-29a2a1e30111"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can observe that the five microbiomes that make up a high percentage of all the microbiomes found in the host are:<br> AVS_1, AVS_2, AVS_3, AVS_5 and AVS_4"
      ],
      "metadata": {
        "id": "9X7C6UyAbriX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hosts_count = df.iloc[:, 1:].gt(0).sum()\n",
        "\n",
        "# Plotting\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "#filter the microbiomes\n",
        "filter_hosts = hosts_count[hosts_count > 30]\n",
        "new_xtick_labels = [x.split('_')[-1] for x in filter_hosts.index]\n",
        "\n",
        "filter_hosts.plot(kind='bar', color='skyblue')\n",
        "\n",
        "plt.title('Count of hosts for each microbiome (min 30)')\n",
        "plt.xlabel('Microbiomes')\n",
        "plt.ylabel('Hosts')\n",
        "plt.yticks(range(0, max(filter_hosts)+1, 50))\n",
        "plt.xticks(range(len(filter_hosts)), new_xtick_labels, fontsize=6, rotation=90, ha='right')\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "399d319b-51ac-4c30-fa64-e5f073cb8f83",
        "id": "nrbIQenA-D5s"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "According to the graph we can observe that AVS_2 microbiome is the most common among the hosts, followed by AVS_4 microbiome and then AVS_1 and so on"
      ],
      "metadata": {
        "id": "qC6ygaV3c33S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **host_metadata**"
      ],
      "metadata": {
        "id": "jLkgaIydszUr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/LinkPredictionProject/Data/host_metadata.csv', encoding='latin1')\n",
        "df = df.drop(columns=['museum_number','animal_id','final_identification', 'field_identification',  'mammae_condition', 'mammae_number_ax', 'mammae_number_ab',\n",
        "       'mammae_number_ing', 'embryos_r', 'age_repro', 'age_dental', 'habitat_description', 'testicle_length', 'testicle_width', 'testicle_position','pitfall_position', 'tl', 'tv', 'hf', 'ear', 'notes', 'dist_village_center', 'vagina', 'embryos_l', 'embryos_l', 'embryos_cr', 'placental_scars_l', 'placental_scars_r'])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "DQrNGha6xzTo",
        "outputId": "810ba56f-c50c-463f-9794-532720bef4b9"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   host_ID  day  month  year  season  preparation  \\\n",
              "0     2010    2     10  2019       1  skull, tail   \n",
              "1     2011    2     10  2019       1  skull, tail   \n",
              "2     2013    2     10  2019       1  skull, tail   \n",
              "3     2014    2     10  2019       1  skull, tail   \n",
              "4     2015    2     10  2019       1  skull, tail   \n",
              "\n",
              "                                            locality  village grid_name  \\\n",
              "0  Madagascar, Région SAVA, District Sambava, Com...  Mandena    Savoka   \n",
              "1  Madagascar, Région SAVA, District Sambava, Com...  Mandena    Savoka   \n",
              "2  Madagascar, Région SAVA, District Sambava, Com...  Mandena    Savoka   \n",
              "3  Madagascar, Région SAVA, District Sambava, Com...  Mandena    Savoka   \n",
              "4  Madagascar, Région SAVA, District Sambava, Com...  Mandena    Savoka   \n",
              "\n",
              "   grid_number     habitat_type trap_type trap_number   latitude  longitude  \\\n",
              "0            1  brushy_regrowth   Sherman           1 -14.466052  49.799352   \n",
              "1            1  brushy_regrowth   Sherman          15 -14.466166  49.798864   \n",
              "2            1  brushy_regrowth   Sherman          65 -14.465687  49.799079   \n",
              "3            1  brushy_regrowth   Sherman          90 -14.465359  49.799012   \n",
              "4            1  brushy_regrowth   Sherman         105 -14.465503  49.798734   \n",
              "\n",
              "   elevation.obs     sex  mass   hb  epididym  \n",
              "0     144.218796  female  80.0  153       NaN  \n",
              "1     145.284958    male  51.0  134  not con.  \n",
              "2     118.188316  female  88.0  157       NaN  \n",
              "3     133.551437    male  77.0  148  not con.  \n",
              "4     145.232788  female  65.0  134       NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-458869de-3821-4ebe-a4c3-47201a1377d1\" class=\"colab-df-container\">\n",
              "    <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>host_ID</th>\n",
              "      <th>day</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>season</th>\n",
              "      <th>preparation</th>\n",
              "      <th>locality</th>\n",
              "      <th>village</th>\n",
              "      <th>grid_name</th>\n",
              "      <th>grid_number</th>\n",
              "      <th>habitat_type</th>\n",
              "      <th>trap_type</th>\n",
              "      <th>trap_number</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>elevation.obs</th>\n",
              "      <th>sex</th>\n",
              "      <th>mass</th>\n",
              "      <th>hb</th>\n",
              "      <th>epididym</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>skull, tail</td>\n",
              "      <td>Madagascar, Région SAVA, District Sambava, Com...</td>\n",
              "      <td>Mandena</td>\n",
              "      <td>Savoka</td>\n",
              "      <td>1</td>\n",
              "      <td>brushy_regrowth</td>\n",
              "      <td>Sherman</td>\n",
              "      <td>1</td>\n",
              "      <td>-14.466052</td>\n",
              "      <td>49.799352</td>\n",
              "      <td>144.218796</td>\n",
              "      <td>female</td>\n",
              "      <td>80.0</td>\n",
              "      <td>153</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>skull, tail</td>\n",
              "      <td>Madagascar, Région SAVA, District Sambava, Com...</td>\n",
              "      <td>Mandena</td>\n",
              "      <td>Savoka</td>\n",
              "      <td>1</td>\n",
              "      <td>brushy_regrowth</td>\n",
              "      <td>Sherman</td>\n",
              "      <td>15</td>\n",
              "      <td>-14.466166</td>\n",
              "      <td>49.798864</td>\n",
              "      <td>145.284958</td>\n",
              "      <td>male</td>\n",
              "      <td>51.0</td>\n",
              "      <td>134</td>\n",
              "      <td>not con.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>skull, tail</td>\n",
              "      <td>Madagascar, Région SAVA, District Sambava, Com...</td>\n",
              "      <td>Mandena</td>\n",
              "      <td>Savoka</td>\n",
              "      <td>1</td>\n",
              "      <td>brushy_regrowth</td>\n",
              "      <td>Sherman</td>\n",
              "      <td>65</td>\n",
              "      <td>-14.465687</td>\n",
              "      <td>49.799079</td>\n",
              "      <td>118.188316</td>\n",
              "      <td>female</td>\n",
              "      <td>88.0</td>\n",
              "      <td>157</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>skull, tail</td>\n",
              "      <td>Madagascar, Région SAVA, District Sambava, Com...</td>\n",
              "      <td>Mandena</td>\n",
              "      <td>Savoka</td>\n",
              "      <td>1</td>\n",
              "      <td>brushy_regrowth</td>\n",
              "      <td>Sherman</td>\n",
              "      <td>90</td>\n",
              "      <td>-14.465359</td>\n",
              "      <td>49.799012</td>\n",
              "      <td>133.551437</td>\n",
              "      <td>male</td>\n",
              "      <td>77.0</td>\n",
              "      <td>148</td>\n",
              "      <td>not con.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>skull, tail</td>\n",
              "      <td>Madagascar, Région SAVA, District Sambava, Com...</td>\n",
              "      <td>Mandena</td>\n",
              "      <td>Savoka</td>\n",
              "      <td>1</td>\n",
              "      <td>brushy_regrowth</td>\n",
              "      <td>Sherman</td>\n",
              "      <td>105</td>\n",
              "      <td>-14.465503</td>\n",
              "      <td>49.798734</td>\n",
              "      <td>145.232788</td>\n",
              "      <td>female</td>\n",
              "      <td>65.0</td>\n",
              "      <td>134</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 880,\n  \"fields\": [\n    {\n      \"column\": \"host_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 585,\n        \"min\": 2010,\n        \"max\": 4037,\n        \"num_unique_values\": 880,\n        \"samples\": [\n          2749,\n          2607,\n          3885\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 1,\n        \"max\": 31,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          27,\n          23,\n          29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 3,\n        \"max\": 12,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          5,\n          11,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2019,\n        \"max\": 2022,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2020,\n          2022,\n          2019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"season\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"preparation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"pickle\",\n          \"skull\",\n          \"skull, tail\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"locality\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Madagascar, R\\u00e9gion SAVA, District Sambava, Commune Rurale Maroambihy, Fokontany Mandena\",\n          \"Madagascar, R\\u00e9gion SAVA, Distict Andapa, Commune Rurale Marovato, Fokontany Sarahandrano\",\n          \"Madagascar, R\\u00e9gion SAVA, District Sambava, Commune Rurale Andratamarina, Fokontany Andatsakala\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"village\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Mandena\",\n          \"Sarahandrano\",\n          \"Andatsakala\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"grid_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Vanilla, coffee, banana fields\",\n          \"Sugar cane\",\n          \"Rice field\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"grid_number\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 7,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          2,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"habitat_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"brushy_regrowth\",\n          \"agriculture\",\n          \"flooded_rice\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trap_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Sherman\",\n          \"Tomahawk\",\n          \"Pitfall\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trap_number\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 183,\n        \"samples\": [\n          \"80\",\n          \"67\",\n          \"house6, trap2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08157324706162576,\n        \"min\": -14.60889,\n        \"max\": -14.393162978813,\n        \"num_unique_values\": 567,\n        \"samples\": [\n          -14.3961200304329,\n          -14.3961870018392,\n          -14.47999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08819613746465284,\n        \"min\": 49.64405,\n        \"max\": 49.8865909595042,\n        \"num_unique_values\": 576,\n        \"samples\": [\n          49.8109700158238,\n          49.8106499947607,\n          49.65177\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"elevation.obs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 252.20772479247614,\n        \"min\": 68.0,\n        \"max\": 800.5,\n        \"num_unique_values\": 608,\n        \"samples\": [\n          126.341843,\n          141.379288,\n          121.885634999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37.50768768098439,\n        \"min\": 12.0,\n        \"max\": 203.0,\n        \"num_unique_values\": 189,\n        \"samples\": [\n          22.0,\n          184.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hb\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 75,\n        \"max\": 190,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          116,\n          153\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"epididym\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"not con.\",\n          \"con. x not con.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "source": [
        "# @title Hosts count for each habitat type\n",
        "plt.figure(figsize=(5, 4))\n",
        "df.groupby('habitat_type').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
        "plt.xlabel('Number of hosts')\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "qhKm-D_-ncds",
        "outputId": "ca5be8de-5a91-4914-ab57-bbe854d6c448"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the exploratory data analysis, the distribution of hosts across various habitat types reveals notable differences in abundance.<br>\n",
        "Agriculture and agroforest habitats exhibit the highest counts with ~160 and ~170 hosts respectively, indicating their significance in hosting diverse populations.<br> Conversely, semi-intact forest stands out with the lowest count at ~20 hosts, suggesting its comparatively limited suitability for hosting microbiomes or its conservation status restricting host populations within this habitat.<br>\n",
        "These findings underscore the importance of habitat type in shaping host populations and potentially influencing microbiome transmission dynamics within ecosystems."
      ],
      "metadata": {
        "id": "eyArVg0g7b5K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Village\n",
        "hosts_per_village = df['village'].value_counts()\n",
        "colors = plt.cm.tab20.colors[:len(hosts_per_village)]\n",
        "\n",
        "plt.figure(figsize=(4, 4))\n",
        "plt.pie(hosts_per_village, labels=hosts_per_village.index, autopct='%1.1f%%', startangle=90, colors=colors)\n",
        "plt.title('Distribution of Hosts Across Villages', pad=20)\n",
        "plt.axis('equal')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "gEXsf2ZE8E2E",
        "outputId": "097f7110-7b66-4aac-c4ad-7d3954036bd2"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hosts_per_village = df['village'].value_counts()\n",
        "for village, count in hosts_per_village.items():\n",
        "    print(f\"{village}: {count}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sFIaFGN7680c",
        "outputId": "cafa776a-47d4-4a00-ab76-b596e6a70df7"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mandena: 345\n",
            "Sarahandrano: 301\n",
            "Andatsakala: 234\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The differences in host distribution across Mandena, Sarahandrano, and Andatsakala appear relatively close, with Mandena having a slightly higher proportion of hosts compared to the other two villages.<br>While the differences may not be dramatic, they still indicate varying host populations across the villages,<br> which could be influenced by ecological, social, or geographic factors."
      ],
      "metadata": {
        "id": "F0TJlb6A9eSR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Temporal trends\n"
      ],
      "metadata": {
        "id": "42LpVb17-FJ6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "per year:"
      ],
      "metadata": {
        "id": "nWb1rljb-XB7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Group the data by year and count the number of hosts examined each year\n",
        "hosts_per_year = df.groupby('year')['host_ID'].count()\n",
        "\n",
        "# Plot\n",
        "plt.figure(figsize=(5, 4))\n",
        "hosts_per_year.plot(kind='bar', color='skyblue')\n",
        "plt.title('Number of Hosts Examined per Year')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Number of Hosts')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "-OfzHcAw9FYK",
        "outputId": "c2ed57ad-f816-432e-d7e8-6815c755830f"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hosts_per_year = df['year'].value_counts()\n",
        "for year, count in hosts_per_year.items():\n",
        "    print(f\"{year}: {count}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MrAgUFkq-Q1v",
        "outputId": "6a49360c-cb1c-422b-fb22-edd190bbbb7d"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2020: 344\n",
            "2021: 275\n",
            "2022: 167\n",
            "2019: 94\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The bar chart depicts the number of hosts examined each year, revealing a notable fluctuation in examination counts over time. <br>The year 2020 stands out with the highest examination count at 344 hosts, followed by 2021 with 275 hosts. <br>Conversely, the examination counts for 2019 and 2022 are notably lower, at 94 and 167 hosts respectively.<br> This variation suggests potential shifts in research focus, sampling efforts, or data collection methodologies across different years"
      ],
      "metadata": {
        "id": "Gpu8MUs_-wuz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check months for each season\n",
        "for season_value in sorted(df['season'].unique()):\n",
        "    season_df = df[df['season'] == season_value]\n",
        "    print(f\"Season {season_value}: {sorted(season_df['month'].unique())}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8O5JOfCHYfZ",
        "outputId": "910c8d25-a967-497b-e129-3d01dcc23e7e"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Season 1: [10, 11, 12]\n",
            "Season 2: [3, 4, 5]\n",
            "Season 3: [6, 7, 8, 9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "per season:"
      ],
      "metadata": {
        "id": "L-7aZpsE-ZTy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "season_labels = {\n",
        "    1: 'October - December',\n",
        "    2: 'March - May',\n",
        "    3: 'June - September'\n",
        "}\n",
        "df['season_label'] = df['season'].map(season_labels)\n",
        "hosts_per_season = df['season_label'].value_counts()\n",
        "ordered_months = ['October - December', 'March - May', 'June - September']\n",
        "\n",
        "# Plot\n",
        "plt.figure(figsize=(4, 5))\n",
        "hosts_per_season[ordered_months].plot(kind='bar', color='skyblue')\n",
        "plt.title('Number of Hosts Examined per season')\n",
        "plt.xlabel('Months')\n",
        "plt.ylabel('Number of Hosts')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "0cOwWB67-a5m",
        "outputId": "8f3cae60-8932-4fd4-8010-43a36a75708b"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The bar chart illustrates the distribution of host examinations across different seasons.<br>June to September emerges as the period with the highest number of host examinations.<br>\n",
        "This distribution suggests seasonal variations in sampling efforts or ecological factors influencing host populations, highlighting the importance of considering seasonal dynamics in understanding disease ecology and transmission patterns."
      ],
      "metadata": {
        "id": "zJKlODUuAfjx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Pathogens**"
      ],
      "metadata": {
        "id": "PEq9OMCZzVz8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/LinkPredictionProject/Data/pathogen.csv', encoding='latin1')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "bRwW2z9Xxcyo",
        "outputId": "8140eb31-9b8c-4a69-87e4-85cf9da8933f"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   host_ID  OTU_ID_1  OTU_ID_2  OTU_ID_3  OTU_ID_4  OTU_ID_5  OTU_ID_6  \\\n",
              "0     2010       0.0  0.907808       0.0       0.0       0.0       0.0   \n",
              "1     2011       0.0  0.000000       0.0       0.0       0.0       0.0   \n",
              "2     2013       0.0  1.000000       0.0       0.0       0.0       0.0   \n",
              "3     2014       0.0  1.000000       0.0       0.0       0.0       0.0   \n",
              "4     2015       0.0  0.000000       0.0       0.0       0.0       0.0   \n",
              "\n",
              "   OTU_ID_7  OTU_ID_8  OTU_ID_9  ...  OTU_ID_105  OTU_ID_106  OTU_ID_107  \\\n",
              "0       0.0       0.0       0.0  ...         0.0           0           0   \n",
              "1       0.0       0.0       0.0  ...         0.0           0           0   \n",
              "2       0.0       0.0       0.0  ...         0.0           0           0   \n",
              "3       0.0       0.0       0.0  ...         0.0           0           0   \n",
              "4       0.0       0.0       0.0  ...         0.0           0           0   \n",
              "\n",
              "   OTU_ID_108  OTU_ID_109  OTU_ID_110  OTU_ID_111  OTU_ID_112  OTU_ID_113  \\\n",
              "0         0.0         0.0           0         0.0         0.0         0.0   \n",
              "1         0.0         0.0           0         0.0         0.0         0.0   \n",
              "2         0.0         0.0           0         0.0         0.0         0.0   \n",
              "3         0.0         0.0           0         0.0         0.0         0.0   \n",
              "4         0.0         0.0           0         0.0         0.0         0.0   \n",
              "\n",
              "   OTU_ID_114  \n",
              "0         0.0  \n",
              "1         0.0  \n",
              "2         0.0  \n",
              "3         0.0  \n",
              "4         0.0  \n",
              "\n",
              "[5 rows x 115 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-463019df-a772-45a8-b7fc-28713e77e1fd\" class=\"colab-df-container\">\n",
              "    <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>host_ID</th>\n",
              "      <th>OTU_ID_1</th>\n",
              "      <th>OTU_ID_2</th>\n",
              "      <th>OTU_ID_3</th>\n",
              "      <th>OTU_ID_4</th>\n",
              "      <th>OTU_ID_5</th>\n",
              "      <th>OTU_ID_6</th>\n",
              "      <th>OTU_ID_7</th>\n",
              "      <th>OTU_ID_8</th>\n",
              "      <th>OTU_ID_9</th>\n",
              "      <th>...</th>\n",
              "      <th>OTU_ID_105</th>\n",
              "      <th>OTU_ID_106</th>\n",
              "      <th>OTU_ID_107</th>\n",
              "      <th>OTU_ID_108</th>\n",
              "      <th>OTU_ID_109</th>\n",
              "      <th>OTU_ID_110</th>\n",
              "      <th>OTU_ID_111</th>\n",
              "      <th>OTU_ID_112</th>\n",
              "      <th>OTU_ID_113</th>\n",
              "      <th>OTU_ID_114</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.907808</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2013</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 115 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-463019df-a772-45a8-b7fc-28713e77e1fd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
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              "        document.querySelector('#df-463019df-a772-45a8-b7fc-28713e77e1fd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-463019df-a772-45a8-b7fc-28713e77e1fd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-35c46bab-9566-45b3-bedb-0dfcde942c9a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-35c46bab-9566-45b3-bedb-0dfcde942c9a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check that each row sums to 1\n",
        "row_sums = df.iloc[:, 1:].sum(axis=1)\n",
        "print(f'Row sums: {row_sums}')\n",
        "print(f'All rows sum to 1: {row_sums.all()}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BGqy7DKvy3b0",
        "outputId": "7456f4cb-2fff-4c80-c22b-91dd3155c46a"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Row sums: 0      2.0\n",
            "1      1.0\n",
            "2      1.0\n",
            "3      1.0\n",
            "4      1.0\n",
            "      ... \n",
            "749    1.0\n",
            "750    1.0\n",
            "751    1.0\n",
            "752    1.0\n",
            "753    1.0\n",
            "Length: 754, dtype: float64\n",
            "All rows sum to 1: True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This plot describes the mean percentage of the presence of each pathogen in hosts  - the top 5"
      ],
      "metadata": {
        "id": "-iL82f822Ci9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the top 5 pathogens by mean percentage across hosts\n",
        "top_pathogens = df.iloc[:, 1:].mean().sort_values(ascending=False).head(5)\n",
        "# Plot a bar plot for the top 5 pathogens\n",
        "plt.figure(figsize=(4, 4))\n",
        "sns.barplot(x=top_pathogens.index, y=top_pathogens.values)\n",
        "plt.title('Top 5 pathogens')\n",
        "plt.xlabel('Pathogens')\n",
        "plt.ylabel('Mean Percentage')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "HzkAbrUey_HR",
        "outputId": "d3909be3-96a2-4f20-b12e-aee04b806d82"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " We observed the top 5 pathogens by mean percentage across hosts.<br>OTU_ID_2 emerges as the most prevalent pathogen, constituting 75% on average."
      ],
      "metadata": {
        "id": "zhOOCD7CqYFI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hosts_count = df.iloc[:, 1:].gt(0).sum()\n",
        "\n",
        "plt.figure(figsize=(11, 5))\n",
        "\n",
        "hosts_count.plot(kind='bar', color='skyblue')\n",
        "new_xtick_labels = [x.split('_')[-1] for x in hosts_count.index]\n",
        "\n",
        "plt.title('Counts of hosts for each pathogen')\n",
        "plt.xlabel('Pathogens')\n",
        "plt.ylabel('Hosts')\n",
        "plt.xticks(range(len(hosts_count)), new_xtick_labels, rotation=90, ha='right', fontsize=7)\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "nWctb7rd6GE5",
        "outputId": "a9e3e542-6d29-4f64-c9e1-8b3d8de3fd67"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to minimize noise and enhance clarity, we selectively filtered pathogens on the graph,<br>retaining only those with a count of hosts exceeding 10.<br>\n",
        "This adjustment allows for a more focused analysis of notable changes between the different pathogens."
      ],
      "metadata": {
        "id": "fekoWpngqlnB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hosts_count = df.iloc[:, 1:].gt(0).sum()\n",
        "\n",
        "# Plotting\n",
        "plt.figure(figsize=(7, 5))\n",
        "\n",
        "filter_hosts = hosts_count[hosts_count > 10]\n",
        "filter_hosts.plot(kind='bar', color='skyblue')\n",
        "\n",
        "plt.title('Filter counts of hosts for each pathogen (min 10)')\n",
        "plt.xlabel('Pathogens')\n",
        "plt.ylabel('Hosts')\n",
        "plt.xticks(fontsize=8, rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "D-SRpJWQ4xVF",
        "outputId": "d314a7d8-5661-49c8-9482-0bf17b25e90d"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "In this plot, we can now observe the variations between the pathogens clearly.\n",
        "<br>The graph illustrates the 22 pathogens with the highest frequency among hosts.\n",
        "<br>Pathogen number 2 stands out with the highest examination count- exceeding 600 hosts."
      ],
      "metadata": {
        "id": "GvIymdUfsOE7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Pathogen_metadata**"
      ],
      "metadata": {
        "id": "TFKK5FIMzh_u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/LinkPredictionProject/Data/pathogen_metadata.csv', encoding='latin1')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "p1RXiH55zpWx",
        "outputId": "e6e3c9ed-d3ab-4f45-9b2b-22d6588e561b"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   OTU_ID pathogen_type       otu_Order         otu_Family  \\\n",
              "0      89      Protozoa  Incertae_Sedis       Blastocystis   \n",
              "1      97      Protozoa             NaN                NaN   \n",
              "2      76      Protozoa     Parabasalia   Tritrichomonadea   \n",
              "3      79      Protozoa     Parabasalia   Tritrichomonadea   \n",
              "4      84      Protozoa     Parabasalia  Hypotrichomonadea   \n",
              "\n",
              "                    otu_Genus  \n",
              "0  Blastocystis_sp._subtype_3  \n",
              "1                         NaN  \n",
              "2              Tritrichomonas  \n",
              "3              Tritrichomonas  \n",
              "4             Hypotrichomonas  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3bf69d89-ac83-4872-bf3e-194b894ca087\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>OTU_ID</th>\n",
              "      <th>pathogen_type</th>\n",
              "      <th>otu_Order</th>\n",
              "      <th>otu_Family</th>\n",
              "      <th>otu_Genus</th>\n",
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              "      <th>0</th>\n",
              "      <td>89</td>\n",
              "      <td>Protozoa</td>\n",
              "      <td>Incertae_Sedis</td>\n",
              "      <td>Blastocystis</td>\n",
              "      <td>Blastocystis_sp._subtype_3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>97</td>\n",
              "      <td>Protozoa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>76</td>\n",
              "      <td>Protozoa</td>\n",
              "      <td>Parabasalia</td>\n",
              "      <td>Tritrichomonadea</td>\n",
              "      <td>Tritrichomonas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>79</td>\n",
              "      <td>Protozoa</td>\n",
              "      <td>Parabasalia</td>\n",
              "      <td>Tritrichomonadea</td>\n",
              "      <td>Tritrichomonas</td>\n",
              "    </tr>\n",
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              "      <td>84</td>\n",
              "      <td>Protozoa</td>\n",
              "      <td>Parabasalia</td>\n",
              "      <td>Hypotrichomonadea</td>\n",
              "      <td>Hypotrichomonas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3bf69d89-ac83-4872-bf3e-194b894ca087 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3bf69d89-ac83-4872-bf3e-194b894ca087');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-578c0b1d-cf7e-45ec-9c7c-62747aece214\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-578c0b1d-cf7e-45ec-9c7c-62747aece214')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-578c0b1d-cf7e-45ec-9c7c-62747aece214 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 114,\n  \"fields\": [\n    {\n      \"column\": \"OTU_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33,\n        \"min\": 1,\n        \"max\": 114,\n        \"num_unique_values\": 114,\n        \"samples\": [\n          72,\n          84,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pathogen_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Nematoda\",\n          \"Protozoa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"otu_Order\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Parabasalia\",\n          \"Rhabditida\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"otu_Family\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"Angiostrongylidae\",\n          \"Rhabditidae\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"otu_Genus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"Blastocystis_sp._subtype_3\",\n          \"Tritrichomonas\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pathogen_counts = df['pathogen_type'].value_counts()\n",
        "pathogen_counts = pathogen_counts.astype(int)\n",
        "\n",
        "plt.figure(figsize=(4, 4))\n",
        "plt.pie(pathogen_counts, labels=pathogen_counts.index, autopct='%1.1f%%', colors=plt.cm.tab20.colors)\n",
        "plt.title('Distribution of Pathogen Types')\n",
        "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "urjms2QgZCEu",
        "outputId": "0192a8fb-0ac2-4c4c-f72a-eed109fb727b"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The pie chart reveals a notable predominance of Nematoda, comprising 64.9% of the pathogen types,<br>while Protozoa accounts for 35.1%.<br>\n",
        "This distribution sheds light on the ecological dynamics within the studied ecosystem, indicating a higher prevalence of nematode-related infections compared to protozoan pathogens."
      ],
      "metadata": {
        "id": "m0sZHSw2xn4P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 4))\n",
        "sns.countplot(x='otu_Family', data=df)\n",
        "plt.title('Count of OTU Families')\n",
        "plt.xlabel('OTU Family')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "QIwSpFYDbbze",
        "outputId": "2ec82973-2f3f-420d-a847-c445d3ed3b96"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparing with the next analysis of genera, families such as Hypotrichomonadea and Trichostrongylidae<br>exhibit substantial representation, indicating their significance across different taxonomic levels within the dataset<br>and potentially pointing towards their ecological importance or host associations."
      ],
      "metadata": {
        "id": "-xHLLAqM0uKW"
      }
    },
    {
      "source": [
        "df.groupby('otu_Genus').size().plot(kind='barh')\n",
        "plt.xlabel('Number of pathogens')\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "ZxSF2YD2WR0W",
        "outputId": "a1a29c7f-7cdf-4e01-e89f-41b68a45ac22"
      },
      "execution_count": 70,
      "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": [
        "These findings suggest varying levels of prevalence and distribution among the identified genera.<br>\n",
        "Hypotrichomonas emerges as the most prevalent genus with a count of 8,<br>followed by Viannaia with 5 occurrences.<br>Tritrichomonas, Nippostrongylus, and Cryptosporidium share the next highest count at 4 each."
      ],
      "metadata": {
        "id": "nPbqO8r-uxQ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ct = pd.crosstab(df['otu_Order'], df['otu_Family'])\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(ct, cmap='YlGnBu', annot=True, fmt='d')\n",
        "plt.title('Heatmap of OTU Orders vs OTU Families')\n",
        "plt.xlabel('OTU Family')\n",
        "plt.ylabel('OTU Order')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.yticks(rotation='horizontal')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 814
        },
        "id": "jSJdSZ2Te0-A",
        "outputId": "55c48bf7-ed9f-43be-dd6b-f010e4c8bf5b"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The heatmap visualization of OTU Orders versus OTU Families illustrates several notable associations.<br>For instance, within the Parabasalia order, Hypotrichomonadea and Trichomonadea families each account for 8 and 5 occurrences respectively, indicating a significant presence of these families within this order.<br>Similarly, the Strongylida order exhibits a prominent relationship with Trichostrongylidae, with 7 instances recorded."
      ],
      "metadata": {
        "id": "Lk3sIfBJ3S3l"
      }
    }
  ]
}