{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f6c02db11f41edaa",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/harmonize-tools/socio4health/main/docs/source/_static/image.png\" alt=\"image info\" height=\"100\" width=\"100\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9976af9489dc682e",
   "metadata": {},
   "source": [
    "# Hands-on with socio4health: socioeconomic and demographic variables on dengue incidence in Colombia\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35b4ab37a734f323",
   "metadata": {},
   "source": [
    "**Run the tutorial via free cloud platforms:** [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/harmonize-tools/socio4health/HEAD?urlpath=%2Fdoc%2Ftree%2Fdocs%2Fsource%2Fnotebooks%2Fexample_colombia.ipynb) <a target=\"_blank\" href=\"https://colab.research.google.com/github/harmonize-tools/socio4health/blob/main/docs/source/notebooks/example_colombia.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "372d389479a3fa29",
   "metadata": {},
   "source": [
    "This notebook provides you with a real world example on how to use **socio4health** to **retrieve**, **harmonize** and **analyze** **socioeconomic and demographic** variables related to **dengue** incidence in Colombia and recreate the dataset used in the publication *Exploring Dengue Dynamics: A Multi-Scale Analysis of Spatio-Temporal Trends in Ibagué, Colombia* by Otelo et al., published in *Virus* in 2024 ([DOI](https://doi.org/10.3390/v16060906)). This tutorial assumes you have an **intermediate** or **advanced** understanding of **Python** and data manipulation.\n",
    "\n",
    "## Setting up the environment\n",
    "\n",
    "To run this notebook, you need to have the following prerequisites:\n",
    "\n",
    "- **Python 3.10+**\n",
    "\n",
    "Additionally, you need to install the `socio4health` and `pandas` package, which can be done using ``pip``:\n",
    "\n"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T14:49:06.719174Z",
     "start_time": "2025-09-23T14:49:03.443254Z"
    }
   },
   "cell_type": "code",
   "source": "!pip install socio4health pandas -q",
   "id": "1c66e4be789eb9ca",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 25.1.1 -> 25.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "In case you want to run this notebook in **Google Colab**, you also need to run the following command to use your files stored in **Google Drive**:",
   "id": "8572ca66825b64db"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ],
   "id": "37fe947f351b28ad"
  },
  {
   "cell_type": "markdown",
   "id": "fb366db39d507af0",
   "metadata": {},
   "source": [
    "## Import Libraries\n",
    "\n",
    "To perform the data extraction, the `socio4health` library provides the `Extractor` class for data extraction, and the `Harmonizer` class for data harmonization of the retrieved date. `pandas` will be used for data manipulation. Additionally, we will use some utility functions from the `socio4health.utils.harmonizer_utils` module to standardize and translate the dictionary.\n"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T14:49:34.438354Z",
     "start_time": "2025-09-23T14:49:17.741559Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import datetime\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from socio4health import Extractor\n",
    "from socio4health.harmonizer import Harmonizer\n",
    "from socio4health.utils import harmonizer_utils"
   ],
   "id": "5378f5ed0fe6a719",
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "id": "69f52ae88db42a0f",
   "metadata": {},
   "source": [
    "## 1. Extract data from Colombia\n",
    "\n",
    "To extract data from Colombia, use the `Extractor` class from the `socio4health` library. As in the publication, extract the Colombian National Population and Housing Census 2018 (**CNPV 2018**) dataset from the Colombian Nacional Administration of Statistics (**DANE**) website. The dataset is available at: https://microdatos.dane.gov.co/index.php/catalog/643/related-materials.\n",
    "\n",
    "The `Extractor` class requires the following parameters:\n",
    "- `input_path`: The `URL` or local path to the data source.\n",
    "- `down_ext`: A list of file extensions to download. This can include `.CSV`, `.csv`, `.zip`, etc.\n",
    "- `output_path`: The local path where the extracted data will be saved.\n",
    "- `key_words`: A list of keywords to filter the files to be downloaded. In this case, we are only interested in the file `14045.zip`, which contains the data at the desired level of granularity (census block level or \"Manzana\").\n",
    "- `depth`: The depth of the directory structure to traverse when downloading files. A depth of `0` means only the files in the specified directory will be downloaded.\n",
    "\n"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T14:51:31.155788Z",
     "start_time": "2025-09-23T14:49:52.219994Z"
    }
   },
   "cell_type": "code",
   "source": [
    "col_online_extractor = Extractor(input_path=\"https://microdatos.dane.gov.co/index.php/catalog/643/related-materials\",\n",
    "                                 down_ext=['.cpg', '.dbf', '.prj','.sbn', '.sbx', '.shx', '.shp', '.zip'],\n",
    "                                 output_path=\"../CNVP2018\",\n",
    "                                 key_words=[\"14045.zip\"],\n",
    "                                 depth=0)\n",
    "col_CNPV = col_online_extractor.s4h_extract()"
   ],
   "id": "99cb294462133ad7",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-23 09:49:52,233 - INFO - ----------------------\n",
      "2025-09-23 09:49:52,235 - INFO - Starting data extraction...\n",
      "2025-09-23 09:49:52,236 - INFO - Extracting data in online mode...\n",
      "2025-09-23 09:49:52,236 - INFO - Scraping URL: https://microdatos.dane.gov.co/index.php/catalog/643/related-materials with depth 0\n",
      "2025-09-23 09:49:56,948 - INFO - Spider completed successfully for URL: https://microdatos.dane.gov.co/index.php/catalog/643/related-materials\n",
      "2025-09-23 09:49:56,950 - INFO - Downloading files to: ../CNVP2018\n",
      "Downloading files:   0%|          | 0/1 [00:00<?, ?it/s]2025-09-23 09:50:07,974 - INFO - Successfully downloaded: 14045.zip\n",
      "Downloading files: 100%|██████████| 1/1 [00:11<00:00, 11.02s/it]\n",
      "2025-09-23 09:50:07,986 - INFO - Processing (depth 0): 14045.zip\n",
      "2025-09-23 09:50:15,101 - INFO - Extracted: 383d6920_MGN_NivelManzana_Integrado_CNPV_MGN_ANM_MANZANA.cpg\n",
      "2025-09-23 09:50:17,199 - INFO - Extracted: 383d6920_MGN_NivelManzana_Integrado_CNPV_MGN_ANM_MANZANA.dbf\n",
      "2025-09-23 09:50:17,204 - INFO - Extracted: 383d6920_MGN_NivelManzana_Integrado_CNPV_MGN_ANM_MANZANA.prj\n",
      "2025-09-23 09:50:17,219 - INFO - Extracted: 383d6920_MGN_NivelManzana_Integrado_CNPV_MGN_ANM_MANZANA.sbn\n",
      "2025-09-23 09:50:17,227 - INFO - Extracted: 383d6920_MGN_NivelManzana_Integrado_CNPV_MGN_ANM_MANZANA.sbx\n",
      "2025-09-23 09:50:17,862 - INFO - Extracted: 383d6920_MGN_NivelManzana_Integrado_CNPV_MGN_ANM_MANZANA.shp\n",
      "2025-09-23 09:50:17,886 - INFO - Extracted: 383d6920_MGN_NivelManzana_Integrado_CNPV_MGN_ANM_MANZANA.shx\n",
      "Processing files:   0%|          | 0/7 [00:00<?, ?it/s]2025-09-23 09:50:17,904 - WARNING - Unsupported extension: .cpg\n",
      "2025-09-23 09:50:17,906 - WARNING - Unsupported extension: .shx\n",
      "2025-09-23 09:50:17,909 - WARNING - Unsupported extension: .dbf\n",
      "2025-09-23 09:50:17,917 - WARNING - Unsupported extension: .prj\n",
      "Processing files:  71%|███████▏  | 5/7 [01:13<00:29, 14.60s/it]2025-09-23 09:51:31,124 - WARNING - Unsupported extension: .sbn\n",
      "Processing files:  86%|████████▌ | 6/7 [01:13<00:11, 11.41s/it]2025-09-23 09:51:31,132 - WARNING - Unsupported extension: .sbx\n",
      "Processing files: 100%|██████████| 7/7 [01:13<00:00, 10.46s/it]\n",
      "2025-09-23 09:51:31,136 - INFO - Successfully processed 7/7 files\n",
      "2025-09-23 09:51:31,141 - INFO - Extraction completed successfully.\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T14:51:49.753740Z",
     "start_time": "2025-09-23T14:51:44.728006Z"
    }
   },
   "cell_type": "code",
   "source": "col_CNPV[0]",
   "id": "ad028a6de2823c88",
   "outputs": [
    {
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       "                                                 filename  \n",
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       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.001338</td>\n",
       "      <td>8.776894e-08</td>\n",
       "      <td>050021990000000000010105</td>\n",
       "      <td>POLYGON ((-75.4291 5.79393, -75.4291 5.79393, ...</td>\n",
       "      <td>383d6920_MGN_NivelManzana_Integrado_CNPV_MGN_A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>504991</th>\n",
       "      <td>9977320030102700010102</td>\n",
       "      <td>99</td>\n",
       "      <td>773</td>\n",
       "      <td>99773</td>\n",
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       "      <td>003</td>\n",
       "      <td>997732003</td>\n",
       "      <td>01</td>\n",
       "      <td>99773200301</td>\n",
       "      <td>027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.002270</td>\n",
       "      <td>2.451366e-07</td>\n",
       "      <td>997732990030102700010102</td>\n",
       "      <td>POLYGON ((-69.85155 4.33427, -69.85149 4.33427...</td>\n",
       "      <td>383d6920_MGN_NivelManzana_Integrado_CNPV_MGN_A...</td>\n",
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       "      <td>99</td>\n",
       "      <td>773</td>\n",
       "      <td>99773</td>\n",
       "      <td>2</td>\n",
       "      <td>003</td>\n",
       "      <td>997732003</td>\n",
       "      <td>01</td>\n",
       "      <td>99773200301</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.002919</td>\n",
       "      <td>5.196303e-07</td>\n",
       "      <td>997732990030102700010103</td>\n",
       "      <td>POLYGON ((-69.85227 4.33365, -69.85257 4.3337,...</td>\n",
       "      <td>383d6920_MGN_NivelManzana_Integrado_CNPV_MGN_A...</td>\n",
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       "    <tr>\n",
       "      <th>504993</th>\n",
       "      <td>9977320030102700010104</td>\n",
       "      <td>99</td>\n",
       "      <td>773</td>\n",
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       "      <td>99773200301</td>\n",
       "      <td>027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.002938</td>\n",
       "      <td>3.280837e-07</td>\n",
       "      <td>997732990030102700010104</td>\n",
       "      <td>POLYGON ((-69.85276 4.33338, -69.85274 4.3336,...</td>\n",
       "      <td>383d6920_MGN_NivelManzana_Integrado_CNPV_MGN_A...</td>\n",
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       "    <tr>\n",
       "      <th>504994</th>\n",
       "      <td>9977320030102700010105</td>\n",
       "      <td>99</td>\n",
       "      <td>773</td>\n",
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       "      <td>003</td>\n",
       "      <td>997732003</td>\n",
       "      <td>01</td>\n",
       "      <td>99773200301</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.002514</td>\n",
       "      <td>3.375903e-07</td>\n",
       "      <td>997732990030102700010105</td>\n",
       "      <td>POLYGON ((-69.85313 4.33348, -69.85311 4.33368...</td>\n",
       "      <td>383d6920_MGN_NivelManzana_Integrado_CNPV_MGN_A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504995</th>\n",
       "      <td>9977320030102700010106</td>\n",
       "      <td>99</td>\n",
       "      <td>773</td>\n",
       "      <td>99773</td>\n",
       "      <td>2</td>\n",
       "      <td>003</td>\n",
       "      <td>997732003</td>\n",
       "      <td>01</td>\n",
       "      <td>99773200301</td>\n",
       "      <td>027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.002535</td>\n",
       "      <td>1.992453e-07</td>\n",
       "      <td>997732990030102700010106</td>\n",
       "      <td>POLYGON ((-69.85425 4.33397, -69.8541 4.33396,...</td>\n",
       "      <td>383d6920_MGN_NivelManzana_Integrado_CNPV_MGN_A...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>504996 rows × 111 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 2. Load and standardize the dictionary\n",
    "To harmonize the data, provide a dictionary that describes the variables in the dataset. The CNPV 2018 dictionary is available at [here](https://microdatos.dane.gov.co/index.php/catalog/643/download/14045). Follow the steps in the tutorial [\"How to Create a Raw Dictionary for Data Harmonization\"](https://harmonize-tools.github.io/socio4health/dictionary.html) to create a raw dictionary in Excel format.\n",
    "\n"
   ],
   "id": "5c273d94496b0eee"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T14:51:52.536971Z",
     "start_time": "2025-09-23T14:51:49.959090Z"
    }
   },
   "cell_type": "code",
   "source": [
    "raw_dic = pd.read_excel(\"raw_dic_mzn.xlsx\")\n",
    "raw_dic"
   ],
   "id": "8e6eb631c8ecb8fd",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    variable_name    type  size  \\\n",
       "0      COD_DANE_A    Text  22.0   \n",
       "1      DPTO_CCDGO    Text   2.0   \n",
       "2      MPIO_CCDGO    Text   3.0   \n",
       "3      MPIO_CDPMP    Text   5.0   \n",
       "4      CLAS_CCDGO    Text   1.0   \n",
       "..            ...     ...   ...   \n",
       "101    TP51_13_ED  Double   NaN   \n",
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       "103      CD_LC_CM    Text  10.0   \n",
       "104     NMB_LC_CM    Text  50.0   \n",
       "105      TP_LC_CM    Text  20.0   \n",
       "\n",
       "                                              question      description  value  \n",
       "0    Código de manzana concatenado (departamento, m...              NaN    NaN  \n",
       "1                              Código del departamento              NaN    NaN  \n",
       "2                                 Código del municipio              NaN    NaN  \n",
       "3       Código concatenado que identifica al municipio              NaN    NaN  \n",
       "4    Código de la clase 1 cabecera municipal, 2 cen...              NaN    NaN  \n",
       "..                                                 ...              ...    ...  \n",
       "101  Conteo de personas donde el nivel educativo de...          Ninguno    NaN  \n",
       "102  Conteo de personas donde el nivel educativo de...  Sin información    NaN  \n",
       "103                    Código de la localidad o comuna              NaN    NaN  \n",
       "104                    Nombre de la localidad o comuna              NaN    NaN  \n",
       "105  Descripción de tipo localidad o comuna o corre...              NaN    NaN  \n",
       "\n",
       "[106 rows x 6 columns]"
      ],
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       "      <td>NaN</td>\n",
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "id": "84bcbfd71d0a79b0",
   "metadata": {},
   "source": "Standardize the dictionary using the `s4h_standardize_dict` function from the `harmonizer_utils` module. Then, translate it to English using the `s4h_translate_column` function from the same module (**Note**: This function depends on your internet connection and Google's deep_translator extension, so it may take a few minutes). Finally, classify the variables into categories using a pre-trained **BERT model**."
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T14:55:24.723613Z",
     "start_time": "2025-09-23T14:53:09.299708Z"
    }
   },
   "cell_type": "code",
   "source": [
    "dic = harmonizer_utils.s4h_standardize_dict(raw_dic)\n",
    "dic = harmonizer_utils.s4h_translate_column(dic, \"question\", language=\"en\")\n",
    "dic = harmonizer_utils.s4h_translate_column(dic, \"description\", language=\"en\")\n",
    "dic = harmonizer_utils.s4h_translate_column(dic, \"possible_answers\", language=\"en\")\n"
   ],
   "id": "6a5c0527b974e23f",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\isabe\\PycharmProjects\\socio4health\\src\\socio4health\\utils\\harmonizer_utils.py:98: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  .apply(_process_group, include_groups=True)\\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "question translated\n",
      "description translated\n",
      "possible_answers translated\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-24T14:49:26.396680Z",
     "start_time": "2025-09-24T14:48:16.546320Z"
    }
   },
   "cell_type": "code",
   "source": [
    "dic = harmonizer_utils.s4h_classify_rows(dic, \"question_en\", \"description_en\", \"possible_answers_en\",\n",
    "                                        new_column_name=\"category\",\n",
    "                                        MODEL_PATH=\"files/bert_finetuned_classifier\")"
   ],
   "id": "31ec0f6f2857a226",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Device set to use cpu\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "This is how the standardized and translated dictionary looks:",
   "id": "4750ab08d0bc55cd"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-24T15:02:34.096364Z",
     "start_time": "2025-09-24T15:02:34.029261Z"
    }
   },
   "cell_type": "code",
   "source": "dic",
   "id": "509e22f04f5176a1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    variable_name          type  size  \\\n",
       "0         VERSION  Long Integer   NaN   \n",
       "1      CTNENCUEST        Double   NaN   \n",
       "2        TP3_1_SI        Double   NaN   \n",
       "3         TP3A_RI        Double   NaN   \n",
       "4        TP3B_TCN        Double   NaN   \n",
       "..            ...           ...   ...   \n",
       "101      DATO_ANM          Text  50.0   \n",
       "102     NMB_LC_CM          Text  50.0   \n",
       "103    TP27_PERSO        Double   NaN   \n",
       "104      DENSIDAD        Double   NaN   \n",
       "105          AREA        Double   NaN   \n",
       "\n",
       "                                              question         description  \\\n",
       "0                     año de la información geográfica                 NaN   \n",
       "1                      cantidad de encuestas cnpv 2018                 NaN   \n",
       "2    cantidad de encuestas que reportaron estar en ...                 NaN   \n",
       "3    cantidad de encuestas que reportaron estar en ...  resguardo indígena   \n",
       "4    cantidad de encuestas que reportaron estar en ...                tccn   \n",
       "..                                                 ...                 ...   \n",
       "101                            nombre capa anonimizada                 NaN   \n",
       "102                    nombre de la localidad o comuna                 NaN   \n",
       "103                                 número de personas                 NaN   \n",
       "104  número promedio de habitantes en la manzana qu...                 NaN   \n",
       "105  área de la manzana en metros cuadrados (sistem...                 NaN   \n",
       "\n",
       "    value possible_answers                                        question_en  \\\n",
       "0     NaN              NaN                   year of geographical information   \n",
       "1     NaN              NaN                        Number of CNPV 2018 surveys   \n",
       "2     NaN              NaN  number of surveys that reported to be in ethni...   \n",
       "3     NaN              NaN  number of surveys that reported to be in ethni...   \n",
       "4     NaN              NaN  number of surveys that reported to be in ethni...   \n",
       "..    ...              ...                                                ...   \n",
       "101   NaN              NaN                               Anonymity layer name   \n",
       "102   NaN              NaN                           locality or commune name   \n",
       "103   NaN              NaN                                   number of people   \n",
       "104   NaN              NaN  Average number of inhabitants in the apple liv...   \n",
       "105   NaN              NaN  Apple area in square meters (Magna_Colombia_bo...   \n",
       "\n",
       "         description_en possible_answers_en        category  \n",
       "0                   NaN                 NaN  Identification  \n",
       "1                   NaN                 NaN  Identification  \n",
       "2                   NaN                 NaN  Identification  \n",
       "3    Indigenous shelter                 NaN  Identification  \n",
       "4                  TCCN                 NaN  Identification  \n",
       "..                  ...                 ...             ...  \n",
       "101                 NaN                 NaN  Identification  \n",
       "102                 NaN                 NaN  Identification  \n",
       "103                 NaN                 NaN  Identification  \n",
       "104                 NaN                 NaN  Identification  \n",
       "105                 NaN                 NaN  Identification  \n",
       "\n",
       "[106 rows x 11 columns]"
      ],
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VERSION</td>\n",
       "      <td>Long Integer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>año de la información geográfica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>year of geographical information</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Identification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CTNENCUEST</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cantidad de encuestas cnpv 2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Number of CNPV 2018 surveys</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Identification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TP3_1_SI</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cantidad de encuestas que reportaron estar en ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>number of surveys that reported to be in ethni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Identification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TP3A_RI</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cantidad de encuestas que reportaron estar en ...</td>\n",
       "      <td>resguardo indígena</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>number of surveys that reported to be in ethni...</td>\n",
       "      <td>Indigenous shelter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Identification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TP3B_TCN</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cantidad de encuestas que reportaron estar en ...</td>\n",
       "      <td>tccn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>number of surveys that reported to be in ethni...</td>\n",
       "      <td>TCCN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Identification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>DATO_ANM</td>\n",
       "      <td>Text</td>\n",
       "      <td>50.0</td>\n",
       "      <td>nombre capa anonimizada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Anonymity layer name</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Identification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>NMB_LC_CM</td>\n",
       "      <td>Text</td>\n",
       "      <td>50.0</td>\n",
       "      <td>nombre de la localidad o comuna</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>locality or commune name</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Identification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>TP27_PERSO</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>número de personas</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>number of people</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Identification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>DENSIDAD</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>número promedio de habitantes en la manzana qu...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Average number of inhabitants in the apple liv...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Identification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>AREA</td>\n",
       "      <td>Double</td>\n",
       "      <td>NaN</td>\n",
       "      <td>área de la manzana en metros cuadrados (sistem...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apple area in square meters (Magna_Colombia_bo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Identification</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106 rows × 11 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 3. Harmonize the data\n",
    "\n",
    "Use the Harmonizer class from the **socio4health** library to harmonize the data. First, set the similarity threshold to `0.9`, meaning that only variables with a similarity score of `0.9` or higher will be considered for harmonization. Next, set the `nan_threshold` to `1`, meaning that **only variables with no missing values** will be considered for harmonization.\n",
    "\n",
    "The available columns in the dataset can be checked using the `s4h_get_available_columns` method. This method takes a list of DataFrames as input and **returns a list of column names that are present in the DataFrames**."
   ],
   "id": "aab5518d435f8f42"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-24T14:51:19.103848Z",
     "start_time": "2025-09-24T14:51:18.862254Z"
    }
   },
   "cell_type": "code",
   "source": [
    "har = Harmonizer()\n",
    "har.similarity_threshold = 0.9\n",
    "har.nan_threshold = 1\n",
    "available_columns = har.s4h_get_available_columns(col_CNPV)\n",
    "available_columns"
   ],
   "id": "440a56525e6911f9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AG_CCDGO',\n",
       " 'AREA',\n",
       " 'CD_LC_CM',\n",
       " 'CLAS_CCDGO',\n",
       " 'COD_DANE_A',\n",
       " 'COD_RDTM',\n",
       " 'CTNENCUEST',\n",
       " 'DATO_ANM',\n",
       " 'DENSIDAD',\n",
       " 'DPTO_CCDGO',\n",
       " 'LATITUD',\n",
       " 'LONGITUD',\n",
       " 'MANZ_CCDGO',\n",
       " 'MPIO_CCDGO',\n",
       " 'MPIO_CDPMP',\n",
       " 'NMB_LC_CM',\n",
       " 'PERSONAS_L',\n",
       " 'PERSONAS_S',\n",
       " 'SECR_CCDGO',\n",
       " 'SECR_CCNCT',\n",
       " 'SECU_CCDGO',\n",
       " 'SECU_CCNCT',\n",
       " 'SETR_CCDGO',\n",
       " 'SETR_CCNCT',\n",
       " 'SETU_CCDGO',\n",
       " 'SETU_CCNCT',\n",
       " 'Shape_Area',\n",
       " 'Shape_Leng',\n",
       " 'TP14_1_TIP',\n",
       " 'TP14_2_TIP',\n",
       " 'TP14_3_TIP',\n",
       " 'TP14_4_TIP',\n",
       " 'TP14_5_TIP',\n",
       " 'TP14_6_TIP',\n",
       " 'TP15_1_OCU',\n",
       " 'TP15_2_OCU',\n",
       " 'TP15_3_OCU',\n",
       " 'TP15_4_OCU',\n",
       " 'TP16_HOG',\n",
       " 'TP19_ACU_1',\n",
       " 'TP19_ACU_2',\n",
       " 'TP19_ALC_1',\n",
       " 'TP19_ALC_2',\n",
       " 'TP19_EE_1',\n",
       " 'TP19_EE_2',\n",
       " 'TP19_EE_E1',\n",
       " 'TP19_EE_E2',\n",
       " 'TP19_EE_E3',\n",
       " 'TP19_EE_E4',\n",
       " 'TP19_EE_E5',\n",
       " 'TP19_EE_E6',\n",
       " 'TP19_EE_E9',\n",
       " 'TP19_GAS_1',\n",
       " 'TP19_GAS_2',\n",
       " 'TP19_GAS_9',\n",
       " 'TP19_INTE1',\n",
       " 'TP19_INTE2',\n",
       " 'TP19_INTE9',\n",
       " 'TP19_RECB1',\n",
       " 'TP19_RECB2',\n",
       " 'TP27_PERSO',\n",
       " 'TP32_1_SEX',\n",
       " 'TP32_2_SEX',\n",
       " 'TP34_1_EDA',\n",
       " 'TP34_2_EDA',\n",
       " 'TP34_3_EDA',\n",
       " 'TP34_4_EDA',\n",
       " 'TP34_5_EDA',\n",
       " 'TP34_6_EDA',\n",
       " 'TP34_7_EDA',\n",
       " 'TP34_8_EDA',\n",
       " 'TP34_9_EDA',\n",
       " 'TP3A_RI',\n",
       " 'TP3B_TCN',\n",
       " 'TP3_1_SI',\n",
       " 'TP3_2_NO',\n",
       " 'TP4_1_SI',\n",
       " 'TP4_2_NO',\n",
       " 'TP51POSTGR',\n",
       " 'TP51PRIMAR',\n",
       " 'TP51SECUND',\n",
       " 'TP51SUPERI',\n",
       " 'TP51_13_ED',\n",
       " 'TP51_99_ED',\n",
       " 'TP9_1_USO',\n",
       " 'TP9_2_1_MI',\n",
       " 'TP9_2_2_MI',\n",
       " 'TP9_2_3_MI',\n",
       " 'TP9_2_4_MI',\n",
       " 'TP9_2_9_MI',\n",
       " 'TP9_2_USO',\n",
       " 'TP9_3_10_N',\n",
       " 'TP9_3_1_NO',\n",
       " 'TP9_3_2_NO',\n",
       " 'TP9_3_3_NO',\n",
       " 'TP9_3_4_NO',\n",
       " 'TP9_3_5_NO',\n",
       " 'TP9_3_6_NO',\n",
       " 'TP9_3_7_NO',\n",
       " 'TP9_3_8_NO',\n",
       " 'TP9_3_99_N',\n",
       " 'TP9_3_9_NO',\n",
       " 'TP9_3_USO',\n",
       " 'TP9_4_USO',\n",
       " 'TP_LC_CM',\n",
       " 'TVIVIENDA',\n",
       " 'VERSION',\n",
       " 'ZU_CCDGO',\n",
       " 'ZU_CDIVI',\n",
       " 'filename',\n",
       " 'geometry']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Configure the dictionary, categories, additional columns, key column, and key values to prepare for the harmonization process. Set the `dict_df` parameter to the standardized and translated dictionary. Set the `categories` parameter to \"Housing\" to ensure that only housing-related variables are considered for harmonization. Set the `extra_cols` and `key_col`parameter to `MPIO_CDPMP`, which stands for the municipality code. This allows for **filtering the data** to include only records from a specific municipality. In this case, we are interested in the municipality with the code `73001`, which corresponds to Ibague. Finally, use the `s4h_data_selector` method to **filter the data based on the specified key column and key values**. This method returns a list of DataFrames that match the filtering criteria.",
   "id": "5cb3e3657ca2673c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-24T14:51:23.555915Z",
     "start_time": "2025-09-24T14:51:23.502668Z"
    }
   },
   "cell_type": "code",
   "source": "col_CNPV[0].shape",
   "id": "8da06d242d4cd3ec",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(504996, 111)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-24T14:51:27.885420Z",
     "start_time": "2025-09-24T14:51:26.361511Z"
    }
   },
   "cell_type": "code",
   "source": [
    "har.dict_df = dic\n",
    "har.categories = [\"Housing\"]\n",
    "har.extra_cols = ['MPIO_CDPMP', 'GEOMETRY']\n",
    "har.key_col = 'MPIO_CDPMP'\n",
    "har.key_val = ['73001']\n",
    "filtered_ddfs = har.s4h_data_selector(col_CNPV)"
   ],
   "id": "3f8090a7f1b77ea9",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "The method `s4h_compare_with_dict` can be used to compare the available columns in the dataset with the variables in the dictionary. This method helps to identify which variables from the dictionary are present in the dataset and can be harmonized.\n",
    "\n",
    "Finally, display the filtered DataFrames to see the harmonized data for the specified municipality. If needed, the harmonized data can be exported to a CSV file using the `to_csv` method."
   ],
   "id": "8c7c10a51d70c875"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-24T14:51:30.991076Z",
     "start_time": "2025-09-24T14:51:30.930167Z"
    }
   },
   "cell_type": "code",
   "source": "har.s4h_compare_with_dict(col_CNPV)",
   "id": "8f7bd187dd8a3f2b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matches with dict_df: 106 (95.50%)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "  Unmatched ddfs variable Unmatched dict_df variables\n",
       "0                COD_RDTM                        None\n",
       "1                FILENAME                        None\n",
       "2                GEOMETRY                        None\n",
       "3              SHAPE_AREA                        None\n",
       "4              SHAPE_LENG                        None"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unmatched ddfs variable</th>\n",
       "      <th>Unmatched dict_df variables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COD_RDTM</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FILENAME</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GEOMETRY</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SHAPE_AREA</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SHAPE_LENG</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Finally, display the filtered DataFrames to see the harmonized data for the specified municipality. If needed, the harmonized data can be **exported to a CSV file** using the `to_csv` method or **visualized** using `matplotlib`, `geopandas`, and `numpy` as shown below:\n",
   "id": "d6cc455138796d4d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-24T14:51:36.705371Z",
     "start_time": "2025-09-24T14:51:36.645691Z"
    }
   },
   "cell_type": "code",
   "source": "filtered_ddfs",
   "id": "41fa7e6c1c78d1aa",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[       MPIO_CDPMP  TP9_4_USO  TP9_2_USO  TP9_3_USO  TP9_1_USO  TP9_2_9_MI  \\\n",
       " 422086      73001        0.0        1.0        1.0       48.0         0.0   \n",
       " 422087      73001        0.0        0.0        2.0       67.0         0.0   \n",
       " 422088      73001        0.0        0.0        0.0        0.0         0.0   \n",
       " 422089      73001        0.0        2.0        3.0      110.0         0.0   \n",
       " 422090      73001        0.0        0.0        1.0        0.0         0.0   \n",
       " ...           ...        ...        ...        ...        ...         ...   \n",
       " 427978      73001        0.0        1.0        0.0        9.0         0.0   \n",
       " 427979      73001        0.0        1.0        1.0        3.0         0.0   \n",
       " 427980      73001        0.0        1.0        0.0        9.0         0.0   \n",
       " 427981      73001        0.0        0.0        0.0        8.0         0.0   \n",
       " 427982      73001        0.0        0.0        0.0        1.0         0.0   \n",
       " \n",
       "         TP9_3_4_NO  TP9_3_7_NO  TP9_3_3_NO  TP9_3_99_N  ...  TP19_INTE2  \\\n",
       " 422086         0.0         0.0         0.0         0.0  ...        38.0   \n",
       " 422087         0.0         0.0         0.0         0.0  ...         8.0   \n",
       " 422088         0.0         0.0         0.0         0.0  ...         0.0   \n",
       " 422089         0.0         0.0         1.0         0.0  ...        63.0   \n",
       " 422090         0.0         0.0         0.0         0.0  ...         0.0   \n",
       " ...            ...         ...         ...         ...  ...         ...   \n",
       " 427978         0.0         0.0         0.0         0.0  ...         5.0   \n",
       " 427979         0.0         0.0         0.0         0.0  ...         0.0   \n",
       " 427980         0.0         0.0         0.0         0.0  ...         8.0   \n",
       " 427981         0.0         0.0         0.0         0.0  ...         7.0   \n",
       " 427982         0.0         0.0         0.0         0.0  ...         0.0   \n",
       " \n",
       "         TP19_RECB2  TP14_2_TIP  TP14_1_TIP  TP14_6_TIP  TP14_3_TIP  \\\n",
       " 422086         0.0        10.0        22.0         1.0        16.0   \n",
       " 422087         1.0        57.0        10.0         0.0         0.0   \n",
       " 422088         0.0         0.0         0.0         0.0         0.0   \n",
       " 422089         3.0        81.0        20.0         0.0        11.0   \n",
       " 422090         0.0         0.0         0.0         0.0         0.0   \n",
       " ...            ...         ...         ...         ...         ...   \n",
       " 427978         4.0         1.0         9.0         0.0         0.0   \n",
       " 427979         0.0         0.0         4.0         0.0         0.0   \n",
       " 427980         4.0         0.0        10.0         0.0         0.0   \n",
       " 427981         1.0         0.0         8.0         0.0         0.0   \n",
       " 427982         0.0         0.0         1.0         0.0         0.0   \n",
       " \n",
       "         TP14_4_TIP  TP14_5_TIP  TP15_3_OCU  \\\n",
       " 422086         0.0         0.0         1.0   \n",
       " 422087         0.0         0.0         2.0   \n",
       " 422088         0.0         0.0         0.0   \n",
       " 422089         0.0         0.0         1.0   \n",
       " 422090         0.0         0.0         0.0   \n",
       " ...            ...         ...         ...   \n",
       " 427978         0.0         0.0         2.0   \n",
       " 427979         0.0         0.0         1.0   \n",
       " 427980         0.0         0.0         2.0   \n",
       " 427981         0.0         0.0         1.0   \n",
       " 427982         0.0         0.0         1.0   \n",
       " \n",
       "                                                  GEOMETRY  \n",
       " 422086  POLYGON ((-75.25204 4.45339, -75.25203 4.45339...  \n",
       " 422087  POLYGON ((-75.24798 4.44987, -75.24797 4.44985...  \n",
       " 422088  POLYGON ((-75.24873 4.44883, -75.24877 4.44879...  \n",
       " 422089  POLYGON ((-75.24897 4.44924, -75.24893 4.44919...  \n",
       " 422090  POLYGON ((-75.25099 4.45195, -75.25111 4.45187...  \n",
       " ...                                                   ...  \n",
       " 427978  POLYGON ((-75.19101 4.35893, -75.191 4.35893, ...  \n",
       " 427979  POLYGON ((-75.18923 4.35806, -75.18929 4.35799...  \n",
       " 427980  POLYGON ((-75.19102 4.35539, -75.19116 4.35513...  \n",
       " 427981  POLYGON ((-75.19008 4.35431, -75.18991 4.35423...  \n",
       " 427982  POLYGON ((-75.19121 4.35258, -75.19168 4.35285...  \n",
       " \n",
       " [5897 rows x 42 columns]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-24T14:51:41.186867Z",
     "start_time": "2025-09-24T14:51:41.070467Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "df = filtered_ddfs[0]\n",
    "\n",
    "# Create a GeoDataFrame\n",
    "gdf = gpd.GeoDataFrame(df, geometry='GEOMETRY', crs=\"EPSG:4326\")\n",
    "\n",
    "gdf['TP19_GAS_1_Log'] =np.log(gdf['TP19_GAS_1'])"
   ],
   "id": "21c8cb24131c9612",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\isabe\\PycharmProjects\\socio4health\\.venv\\Lib\\site-packages\\pandas\\core\\arraylike.py:399: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-24T14:51:48.005792Z",
     "start_time": "2025-09-24T14:51:44.054935Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "# plot without automatic colorbar\n",
    "gdf.plot(column='TP19_GAS_1_Log', cmap='viridis', ax=ax)\n",
    "\n",
    "# Plot with custom colorbar\n",
    "sm = plt.cm.ScalarMappable(cmap='viridis')\n",
    "sm.set_array(gdf['TP19_GAS_1_Log'])\n",
    "cbar = fig.colorbar(sm, ax=ax, fraction=0.03, pad=0.02)  # fraction controls the size of the colorbar\n",
    "cbar.set_label(\"Gas line connection (log)\")\n",
    "\n",
    "ax.set_title(\"Gas line connection map\", fontsize=14)\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ],
   "id": "ae3fcf6a040d44cb",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\isabe\\PycharmProjects\\socio4health\\.venv\\Lib\\site-packages\\matplotlib\\colors.py:2294: RuntimeWarning: invalid value encountered in subtract\n",
      "  resdat -= vmin\n",
      "C:\\Users\\isabe\\PycharmProjects\\socio4health\\.venv\\Lib\\site-packages\\matplotlib\\colors.py:2295: RuntimeWarning: invalid value encountered in divide\n",
      "  resdat /= (vmax - vmin)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "39c54fb4d71cb424"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "social4health",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}