{ "cells": [ { "cell_type": "markdown", "id": "161e30de", "metadata": {}, "source": [ "\"image\n", "\n", "\n", "# Hands-on with socio4health: effects of hydrometeorologigcal hazards and urbanization on dengue risk in Brazil \n", "\n" ] }, { "cell_type": "markdown", "id": "0696ab2e", "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_brazil.ipynb) \n", " \"Open\n", "" ] }, { "cell_type": "markdown", "id": "7d0056d2", "metadata": {}, "source": [ "This notebook provides a real-world example of how to use **socio4health** to **retrieve**, **harmonize** and **analyze** **socioeconomic and demographic** variables, such as the level of urbanization and access to water supply in Brazil, to recreate the dataset used in the publication *Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study* by Lowe et al., published in *The Lancet Planetary Health* in 2021 ([DOI](https://doi.org/10.1016/S2542-5196(20)30292-8)). The study evaluated how the association between hydrometeorological events and **dengue** risk varies with these variables. This tutorial assumes 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" ] }, { "cell_type": "code", "execution_count": 1, "id": "936cbd3c", "metadata": { "ExecuteTime": { "end_time": "2025-09-24T15:47:04.783758Z", "start_time": "2025-09-24T15:47:00.244317Z" } }, "outputs": [], "source": [ "!pip install socio4health pandas -q" ] }, { "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": "a6bf4e15a2607598" }, { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "id": "5e413e59ab16bedc" }, { "cell_type": "markdown", "id": "83710eb4", "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" ] }, { "cell_type": "code", "execution_count": 2, "id": "ce028ae1", "metadata": { "ExecuteTime": { "end_time": "2025-09-24T15:47:34.053624Z", "start_time": "2025-09-24T15:47:10.194253Z" } }, "outputs": [], "source": [ "import re\n", "import pandas as pd\n", "import dask.dataframe as dd\n", "import matplotlib.pyplot as plt\n", "from matplotlib.ticker import FuncFormatter\n", "from socio4health import Extractor\n", "from socio4health.harmonizer import Harmonizer\n", "from socio4health.utils import harmonizer_utils, extractor_utils" ] }, { "cell_type": "markdown", "id": "877441c8", "metadata": {}, "source": [ "## 1. Load and standardize the dictionary\n", "To harmonize the data, provide a dictionary that describes the variables in the dataset. The study retrieved data from the 2010 census, from Instituto Brasileiro de Geografia e Estatística (**IBGE**). The dictionary for the census data can be found [here](https://ftp.ibge.gov.br/Censos/Censo_Demografico_2010/Resultados_Gerais_da_Amostra/Microdados/Documentacao.zip). 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", "This dictionary must be standardized and translated to English. The `socio4health.utils.harmonizer_utils` module provides utility functions to perform these tasks. Additionally, the `socio4health.utils.extractor_utils` module provides utility functions to parse fixed-width file (FWF) dictionaries, which is the format used in the **IBGE** census data.\n" ] }, { "cell_type": "markdown", "id": "b58f709ed499f61f", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 3, "id": "d2b84f67", "metadata": { "ExecuteTime": { "end_time": "2025-09-24T15:47:40.379186Z", "start_time": "2025-09-24T15:47:39.366302Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\Juan\\anaconda3\\envs\\social4health\\Lib\\site-packages\\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" ] } ], "source": [ "raw_dic = pd.read_excel(\"raw_dictionary_br_2010.xlsx\")\n", "dic=harmonizer_utils.s4h_standardize_dict(raw_dic)\n", "colnames, colspecs =extractor_utils.s4h_parse_fwf_dict(dic)\n" ] }, { "cell_type": "markdown", "id": "9ae230acce982f32", "metadata": {}, "source": [ "This is how the standardized dictionary looks:" ] }, { "cell_type": "code", "execution_count": 4, "id": "21a8b4b0d057b20b", "metadata": { "ExecuteTime": { "end_time": "2025-09-24T15:47:57.294782Z", "start_time": "2025-09-24T15:47:57.253131Z" } }, "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "variable_name", "rawType": "object", "type": "string" }, { "name": "question", "rawType": "object", "type": "string" }, { "name": "description", "rawType": "float64", "type": "float" }, { "name": "value", "rawType": "object", "type": "unknown" }, { "name": "initial_position", "rawType": "object", "type": "unknown" }, { "name": "final_position", "rawType": "float64", "type": "float" }, { "name": "size", "rawType": "object", "type": "unknown" }, { "name": "dec", "rawType": "float64", "type": "float" }, { "name": "type", "rawType": "object", "type": "string" }, { "name": "possible_answers", "rawType": "object", "type": "unknown" } ], "ref": "db429a7c-65ff-4cce-89d6-f580cb8fdab0", "rows": [ [ "0", "V0402", "a responsabilidade pelo domicílio é de:", null, "1.0; 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0V0402a responsabilidade pelo domicílio é de:NaN1.0; 2.0; 9.0107.0107.01.0NaNCapenas um morador; mais de um morador; ignorado
1V0209abastecimento de água, canalização:NaN1.0; 2.0; 3.090.090.01.0NaNCsim, em pelo menos um cômodo; sim, só na propr...
2V0208abastecimento de água, forma:NaN1.0; 2.0; 3.0; 4.0; 5.0; 6.0; 7.0; 8.0; 9.0; 10.088.089.02.0NaNCrede geral de distribuição; poço ou nascente n...
3V6210adequação da moradiaNaN1.0; 2.0; 3.0144.0144.01.0NaNCadequada; semi-adequada; inadequada
4V0301alguma pessoa que morava com você(s) estava mo...NaN1.0; 2.0104.0104.01.0NaNCsim; não
.................................
71V0214televisão, existência:NaN1.0; 2.095.095.01.0NaNCsim; não
72V4002tipo de espécie:NaN11.0; 12.0; 13.0; 14.0; 15.0; 51.0; 52.0; 53.0...56.057.02.0NaNC\\ncasa; casa de vila ou em condomínio; apartamen...
73V0001unidade da federação:NaN11.0; 12.0; 13.0; 14.0; 15.0; 16.0; 17.0; 21.0...1.02.02.0NaNArondônia; acre; amazonas; roraima; pará; amapá...
74V2011valor do aluguel (em reais)NaNNaN59.064.06.0NaNNNaN
75V0011área de ponderaçãoNaNNaN8.020.013.0NaNANaN
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" ], "text/plain": [ " variable_name question \\\n", "0 V0402 a responsabilidade pelo domicílio é de: \n", "1 V0209 abastecimento de água, canalização: \n", "2 V0208 abastecimento de água, forma: \n", "3 V6210 adequação da moradia \n", "4 V0301 alguma pessoa que morava com você(s) estava mo... \n", ".. ... ... \n", "71 V0214 televisão, existência: \n", "72 V4002 tipo de espécie: \n", "73 V0001 unidade da federação: \n", "74 V2011 valor do aluguel (em reais) \n", "75 V0011 área de ponderação \n", "\n", " description value \\\n", "0 NaN 1.0; 2.0; 9.0 \n", "1 NaN 1.0; 2.0; 3.0 \n", "2 NaN 1.0; 2.0; 3.0; 4.0; 5.0; 6.0; 7.0; 8.0; 9.0; 10.0 \n", "3 NaN 1.0; 2.0; 3.0 \n", "4 NaN 1.0; 2.0 \n", ".. ... ... \n", "71 NaN 1.0; 2.0 \n", "72 NaN 11.0; 12.0; 13.0; 14.0; 15.0; 51.0; 52.0; 53.0... \n", "73 NaN 11.0; 12.0; 13.0; 14.0; 15.0; 16.0; 17.0; 21.0... \n", "74 NaN NaN \n", "75 NaN NaN \n", "\n", " initial_position final_position size dec type \\\n", "0 107.0 107.0 1.0 NaN C \n", "1 90.0 90.0 1.0 NaN C \n", "2 88.0 89.0 2.0 NaN C \n", "3 144.0 144.0 1.0 NaN C \n", "4 104.0 104.0 1.0 NaN C \n", ".. ... ... ... ... ... \n", "71 95.0 95.0 1.0 NaN C \n", "72 56.0 57.0 2.0 NaN C\\n \n", "73 1.0 2.0 2.0 NaN A \n", "74 59.0 64.0 6.0 NaN N \n", "75 8.0 20.0 13.0 NaN A \n", "\n", " possible_answers \n", "0 apenas um morador; mais de um morador; ignorado \n", "1 sim, em pelo menos um cômodo; sim, só na propr... \n", "2 rede geral de distribuição; poço ou nascente n... \n", "3 adequada; semi-adequada; inadequada \n", "4 sim; não \n", ".. ... \n", "71 sim; não \n", "72 casa; casa de vila ou em condomínio; apartamen... \n", "73 rondônia; acre; amazonas; roraima; pará; amapá... \n", "74 NaN \n", "75 NaN \n", "\n", "[76 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dic" ] }, { "cell_type": "markdown", "id": "aaf1ab4a643c5fdd", "metadata": {}, "source": [ "The classification model used in this tutorial is a **BERT model** fine-tuned for the task of classifying survey questions into categories. You can use your own model by providing the path to the model in the `MODEL_PATH` parameter of the `harmonizer_utils.s4h_classify_rows` function." ] }, { "cell_type": "code", "execution_count": 5, "id": "6efd8bb3", "metadata": { "ExecuteTime": { "end_time": "2025-09-24T15:49:40.042305Z", "start_time": "2025-09-24T15:48:02.185799Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "question translated\n", "description translated\n", "possible_answers translated\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Device set to use cpu\n" ] }, { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { "columns": [ { "name": "index", "rawType": "int64", "type": "integer" }, { "name": "variable_name", "rawType": "object", "type": "string" }, { "name": "question", "rawType": "object", "type": "string" }, { "name": "description", "rawType": "float64", "type": "float" }, { "name": "value", "rawType": "object", "type": "unknown" }, { "name": "initial_position", "rawType": "object", "type": "unknown" }, { "name": "final_position", "rawType": "float64", "type": "float" }, { "name": "size", "rawType": "object", "type": "unknown" }, { "name": "dec", "rawType": "float64", "type": "float" }, { "name": "type", "rawType": "object", "type": "string" }, { "name": "possible_answers", "rawType": "object", "type": "unknown" }, { "name": "question_en", "rawType": "object", "type": "string" }, { "name": "description_en", "rawType": "float64", "type": "float" }, { "name": "possible_answers_en", "rawType": "object", "type": "unknown" }, { "name": "category", "rawType": "object", "type": "string" } ], "ref": "93fedbf9-80e6-407a-b829-7bff4db714b4", "rows": [ [ "0", "V0402", "a responsabilidade pelo domicílio é de:", null, "1.0; 2.0; 9.0", "107.0", "107.0", "1.0", null, "C", "apenas um morador; mais de um morador; ignorado", "Responsibility for the home is:", null, "just one resident; more than one resident; ignored", "Housing" ], [ "1", "V0209", "abastecimento de água, canalização:", null, "1.0; 2.0; 3.0", "90.0", "90.0", "1.0", null, "C", "sim, em pelo menos um cômodo; sim, só na propriedade ou terreno; não", "water supply, plumbing:", null, "yes, in at least one room; yes, only on the property or land; no", "Housing" ], [ "2", "V0208", "abastecimento de água, forma:", null, "1.0; 2.0; 3.0; 4.0; 5.0; 6.0; 7.0; 8.0; 9.0; 10.0", "88.0", "89.0", "2.0", null, "C", "rede geral de distribuição; poço ou nascente na propriedade; poço ou nascente fora da propriedade; carro-pipa; água da chuva armazenada em cisterna; água da chuva armazenada de outra forma; rios, açudes, lagos e igarapés; outra; poço ou nascente na aldeia; poço ou nascente fora da aldeia", "water supply, form:", null, "general distribution network; well or spring on the property; well or spring outside the property; water car; rainwater stored in a cistern; rainwater stored in another way; rivers, reservoirs, lakes and streams; other; well or spring in the village; well or spring outside the village", "Business" ], [ "3", "V6210", "adequação da moradia", null, "1.0; 2.0; 3.0", "144.0", "144.0", "1.0", null, "C", "adequada; semi-adequada; inadequada", "suitability of housing", null, "adequate; semi-adequate; inappropriate", "Housing" ], [ "4", "V0301", "alguma pessoa que morava com você(s) estava morando em outro país em 31 de julho de 2010:", null, "1.0; 2.0", "104.0", "104.0", "1.0", null, "C", "sim; não", "someone who lived with you was living in another country on July 31, 2010:", null, "Yes; no", "Business" ], [ "5", "V2012", "aluguel em nº de salários mínimos", null, null, "65.0", "73.0", "4.0", "5.0", "N", null, "rent in number of minimum wages", null, null, "Business" ], [ "6", "V0222", "automóvel para uso particular, existência:", null, "1.0; 2.0", "103.0", "103.0", "1.0", null, "C", "sim; não", "car for private use, existence:", null, "Yes; no", "Business" ], [ "7", "V0205", "banheiros de uso exclusivo, número:", null, "0.0; 1.0; 2.0; 3.0; 4.0; 5.0; 6.0; 7.0; 8.0; 9.0", "85.0", "85.0", "1.0", null, "C\n", "zero banheiros; um banheiro; dois banheiros; três banheiros; quatro banheiros; cinco banheiros; seis banheiros; sete banheiros; oito banheiros; nove ou mais banheiros", "exclusive use bathrooms, number:", null, "zero bathrooms; a bathroom; two bathrooms; three bathrooms; four bathrooms; five bathrooms; six bathrooms; seven bathrooms; eight bathrooms; nine or more bathrooms", "Housing" ], [ "8", "V0300", "controle", null, null, "21.0", "28.0", "8.0", null, "N", null, "control", null, null, "Identification" ], [ "9", "V1002", "código da mesorregião:", null, null, "46.0", "47.0", "2.0", null, "A", null, "mesoregion code:", null, null, "Identification" ], [ "10", "V1003", "código da microrregião:", null, null, "48.0", "50.0", "3.0", null, "A", null, "microregion code:", null, null, "Identification" ], [ "11", "V1004", "código da região metropolitana:", null, null, "51.0", "52.0", "2.0", null, "A", null, "metropolitan region code:", null, null, "Identification" ], [ "12", "V0002", "código do município", null, null, "3.0", "7.0", "5.0", null, "A", null, "municipality code", null, null, "Identification" ], [ "13", "V0204", "cômodos como dormitório, número:", null, null, "80.0", "81.0", "2.0", null, "N", null, "rooms such as dormitory, number:", null, null, "Housing" ], [ "14", "V0203", "cômodos, número:", null, null, "75.0", "76.0", "2.0", null, "N", null, "rooms, number:", null, null, "Housing" ], [ "15", "V0701", "de agosto de 2009 a julho de 2010, faleceu alguma pessoa que morava com você(s) (inclusive crianças recém-nascidas e idosos):", null, "1.0; 2.0", "108.0", "108.0", "1.0", null, "C", "sim; não", "From August 2009 to July 2010, did anyone who lived with you die (including newborn children and the elderly):", null, "Yes; no", "Business" ], [ "16", "V6204", "densidade de morador / dormitório", null, null, "82.0", "84.0", "2.0", "1.0", "N", null, "resident/dorm density", null, null, "Housing" ], [ "17", "V6203", "densidade de morador/cômodo", null, null, "77.0", "79.0", "2.0", "1.0", "N", null, "resident/room density", null, null, "Housing" ], [ "18", "V0201", "domicílio, condição de ocupação:", null, "1.0; 2.0; 3.0; 4.0; 5.0; 6.0", "58.0", "58.0", "1.0", null, "C\n", "próprio de algum morador - já pago; próprio de algum morador - ainda pagando; alugado; cedido por empregador; cedido de outra forma; outra condição", "domicile, occupation condition:", null, "owned by a resident - already paid; owned by a resident - still paying; rented; provided by employer; otherwise assigned; other condition", "Housing" ], [ "19", "V0211", "energia elétrica, existência:", null, "1.0; 2.0; 3.0", "92.0", "92.0", "1.0", null, "C", "sim, de companhia distribuidora; sim, de outras fontes; não existe energia elétrica", "electrical energy, existence:", null, "yes, from a distribution company; yes, from other sources; there is no electricity", "Business" ], [ "20", "V0207", "esgotamento sanitário, tipo:", null, "1.0; 2.0; 3.0; 4.0; 5.0; 6.0", "87.0", "87.0", "1.0", null, "C\n", "rede geral de esgoto ou pluvial; fossa séptica; fossa rudimentar; vala; rio, lago ou mar; outro", "sanitary sewage, type:", null, "general sewage or rainwater network; septic tank; rudimentary septic tank; ditch; river, lake or sea; other", "Business" ], [ "21", "V6600", "espécie da unidade doméstica", null, "1.0; 2.0; 3.0; 4.0", "143.0", "143.0", "1.0", null, "C", "unipessoal; nuclear; estendida; composta", "type of domestic unit", null, "single-person; nuclear; extended; composite", "Housing" ], [ "22", "V4001", "espécie de unidade visitada:", null, "1.0; 2.0; 5.0; 6.0", "54.0", "55.0", "2.0", null, "C\n", "domicílio particular permanente ocupado; domicílio particular permanente ocupado sem entrevista realizada; domicílio particular improvisado ocupado; domicílio coletivo com morador", "type of unit visited:", null, "occupied permanent private home; occupied permanent private home without an interview carried out; occupied improvised private home; collective home with resident", "Housing" ], [ "23", "V0212", "existência de medidor ou relógio, energia elétrica, companhia distribuidora:", null, "1.0; 2.0; 3.0", "93.0", "93.0", "1.0", null, "C\n", "sim, de uso exclusivo; sim, de uso comum; não tem medidor ou relógio", "existence of a meter or clock, electricity, distribution company:", null, "yes, for exclusive use; yes, in common use; There is no meter or clock", "Business" ], [ "24", "V0216", "geladeira, existência:", null, "1.0; 2.0", "97.0", "97.0", "1.0", null, "C", "sim; não", "refrigerator, existence:", null, "Yes; no", "Identification" ], [ "25", "V0210", "lixo, destino:", null, "1.0; 2.0; 3.0; 4.0; 5.0; 6.0; 7.0", "91.0", "91.0", "1.0", null, "C\n", "coletado diretamente por serviço de limpeza; colocado em caçamba de serviço de limpeza; queimado (na propriedade); enterrado (na propriedade); jogado em terreno baldio ou logradouro; jogado em rio, lago ou mar; tem outro destino", "garbage, destination:", null, "collected directly by cleaning service; placed in cleaning service bucket; burned (on property); buried (on the property); played on vacant land or in a public place; thrown into a river, lake or sea; has another destiny", "Nonstandard job" ], [ "26", "M0201", "marca de imputação na v0201:", null, "1.0; 2.0", "145.0", "145.0", "1.0", null, "C", "sim; não", "imputation mark in v0201:", null, "Yes; no", "Business" ], [ "27", "M0202", "marca de imputação na v0202:", null, "1.0; 2.0", "147.0", "147.0", "1.0", null, "C", "sim; não", "imputation mark in v0202:", null, "Yes; no", "Business" ], [ "28", "M0203", "marca de imputação na v0203:", null, "1.0; 2.0", "148.0", "148.0", "1.0", null, "C", "sim; não", "imputation mark in v0203:", null, "Yes; no", "Business" ], [ "29", "M0204", "marca de imputação na v0204:", null, "1.0; 2.0", "149.0", "149.0", "1.0", null, "C", "sim; não", "imputation mark in v0204:", null, "Yes; no", "Business" ], [ "30", "M0205", "marca de imputação na v0205:", null, "1.0; 2.0", "150.0", "150.0", "1.0", null, "C", "sim; não", "imputation mark in v0205:", null, "Yes; no", "Business" ], [ "31", "M0206", "marca de imputação na v0206:", null, "1.0; 2.0", "151.0", "151.0", "1.0", null, "C", "sim; não", "imputation mark in v0206:", null, "Yes; no", "Business" ], [ "32", "M0207", "marca de imputação na v0207:", null, "1.0; 2.0", "152.0", "152.0", "1.0", null, "C", "sim; não", "imputation mark in v0207:", null, "Yes; no", "Business" ], [ "33", "M0208", "marca de imputação na v0208:", null, "1.0; 2.0", "153.0", "153.0", "1.0", null, "C", "sim; não", "imputation mark in v0208:", null, "Yes; no", "Business" ], [ "34", "M0209", "marca de imputação na v0209:", null, "1.0; 2.0", "154.0", "154.0", "1.0", null, "C", "sim; não", "imputation mark in v0209:", null, "Yes; no", "Business" ], [ "35", "M0210", "marca de imputação na v0210:", null, "1.0; 2.0", "155.0", "155.0", "1.0", null, "C", "sim; não", "imputation mark in v0210:", null, "Yes; no", "Business" ], [ "36", "M0211", "marca de imputação na v0211:", null, "1.0; 2.0", "156.0", "156.0", "1.0", null, "C", "sim; não", "imputation mark in v0211:", null, "Yes; no", "Business" ], [ "37", "M0212", "marca de imputação na v0212:", null, "1.0; 2.0", "157.0", "157.0", "1.0", null, "C", "sim; não", "imputation mark in v0212:", null, "Yes; no", "Business" ], [ "38", "M0213", "marca de imputação na v0213:", null, "1.0; 2.0", "158.0", "158.0", "1.0", null, "C", "sim; não", "imputation mark in v0213:", null, "Yes; no", "Business" ], [ "39", "M0214", "marca de imputação na v0214:", null, "1.0; 2.0", "159.0", "159.0", "1.0", null, "C", "sim; não", "imputation mark in v0214:", null, "Yes; no", "Business" ], [ "40", "M0215", "marca de imputação na v0215:", null, "1.0; 2.0", "160.0", "160.0", "1.0", null, "C", "sim; não", "imputation mark in v0215:", null, "Yes; no", "Business" ], [ "41", "M0216", "marca de imputação na v0216:", null, "1.0; 2.0", "161.0", "161.0", "1.0", null, "C", "sim; não", "imputation mark in v0216:", null, "Yes; no", "Business" ], [ "42", "M0217", "marca de imputação na v0217:", null, "1.0; 2.0", "162.0", "162.0", "1.0", null, "C", "sim; não", "imputation mark in v0217:", null, "Yes; no", "Business" ], [ "43", "M0218", "marca de imputação na v0218:", null, "1.0; 2.0", "163.0", "163.0", "1.0", null, "C", "sim; não", "imputation mark in v0218:", null, "Yes; no", "Business" ], [ "44", "M0219", "marca de imputação na v0219:", null, "1.0; 2.0", "164.0", "164.0", "1.0", null, "C", "sim; não", "imputation mark in v0219:", null, "Yes; no", "Business" ], [ "45", "M0220", "marca de imputação na v0220:", null, "1.0; 2.0", "165.0", "165.0", "1.0", null, "C", "sim; não", "imputation mark in v0220:", null, "Yes; no", "Business" ], [ "46", "M0221", "marca de imputação na v0221:", null, "1.0; 2.0", "166.0", "166.0", "1.0", null, "C", "sim; não", "imputation mark in v0221:", null, "Yes; no", "Business" ], [ "47", "M0222", "marca de imputação na v0222:", null, "1.0; 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variable_namequestiondescriptionvalueinitial_positionfinal_positionsizedectypepossible_answersquestion_endescription_enpossible_answers_encategory
0V0402a responsabilidade pelo domicílio é de:NaN1.0; 2.0; 9.0107.0107.01.0NaNCapenas um morador; mais de um morador; ignoradoResponsibility for the home is:NaNjust one resident; more than one resident; ign...Housing
1V0209abastecimento de água, canalização:NaN1.0; 2.0; 3.090.090.01.0NaNCsim, em pelo menos um cômodo; sim, só na propr...water supply, plumbing:NaNyes, in at least one room; yes, only on the pr...Housing
2V0208abastecimento de água, forma:NaN1.0; 2.0; 3.0; 4.0; 5.0; 6.0; 7.0; 8.0; 9.0; 10.088.089.02.0NaNCrede geral de distribuição; poço ou nascente n...water supply, form:NaNgeneral distribution network; well or spring o...Business
3V6210adequação da moradiaNaN1.0; 2.0; 3.0144.0144.01.0NaNCadequada; semi-adequada; inadequadasuitability of housingNaNadequate; semi-adequate; inappropriateHousing
4V0301alguma pessoa que morava com você(s) estava mo...NaN1.0; 2.0104.0104.01.0NaNCsim; nãosomeone who lived with you was living in anoth...NaNYes; noBusiness
.............................................
71V0214televisão, existência:NaN1.0; 2.095.095.01.0NaNCsim; nãotelevision, existence:NaNYes; noIdentification
72V4002tipo de espécie:NaN11.0; 12.0; 13.0; 14.0; 15.0; 51.0; 52.0; 53.0...56.057.02.0NaNC\\ncasa; casa de vila ou em condomínio; apartamen...species type:NaNhome; town house or condominium; apartment; ho...Housing
73V0001unidade da federação:NaN11.0; 12.0; 13.0; 14.0; 15.0; 16.0; 17.0; 21.0...1.02.02.0NaNArondônia; acre; amazonas; roraima; pará; amapá...federation unit:NaNRondônia; acre; Amazons; roraima; to; amapá; t...Business
74V2011valor do aluguel (em reais)NaNNaN59.064.06.0NaNNNaNrental value (in reais)NaNNaNBusiness
75V0011área de ponderaçãoNaNNaN8.020.013.0NaNANaNweighting areaNaNNaNHousing
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76 rows × 14 columns

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" ], "text/plain": [ " variable_name question \\\n", "0 V0402 a responsabilidade pelo domicílio é de: \n", "1 V0209 abastecimento de água, canalização: \n", "2 V0208 abastecimento de água, forma: \n", "3 V6210 adequação da moradia \n", "4 V0301 alguma pessoa que morava com você(s) estava mo... \n", ".. ... ... \n", "71 V0214 televisão, existência: \n", "72 V4002 tipo de espécie: \n", "73 V0001 unidade da federação: \n", "74 V2011 valor do aluguel (em reais) \n", "75 V0011 área de ponderação \n", "\n", " description value \\\n", "0 NaN 1.0; 2.0; 9.0 \n", "1 NaN 1.0; 2.0; 3.0 \n", "2 NaN 1.0; 2.0; 3.0; 4.0; 5.0; 6.0; 7.0; 8.0; 9.0; 10.0 \n", "3 NaN 1.0; 2.0; 3.0 \n", "4 NaN 1.0; 2.0 \n", ".. ... ... \n", "71 NaN 1.0; 2.0 \n", "72 NaN 11.0; 12.0; 13.0; 14.0; 15.0; 51.0; 52.0; 53.0... \n", "73 NaN 11.0; 12.0; 13.0; 14.0; 15.0; 16.0; 17.0; 21.0... \n", "74 NaN NaN \n", "75 NaN NaN \n", "\n", " initial_position final_position size dec type \\\n", "0 107.0 107.0 1.0 NaN C \n", "1 90.0 90.0 1.0 NaN C \n", "2 88.0 89.0 2.0 NaN C \n", "3 144.0 144.0 1.0 NaN C \n", "4 104.0 104.0 1.0 NaN C \n", ".. ... ... ... ... ... \n", "71 95.0 95.0 1.0 NaN C \n", "72 56.0 57.0 2.0 NaN C\\n \n", "73 1.0 2.0 2.0 NaN A \n", "74 59.0 64.0 6.0 NaN N \n", "75 8.0 20.0 13.0 NaN A \n", "\n", " possible_answers \\\n", "0 apenas um morador; mais de um morador; ignorado \n", "1 sim, em pelo menos um cômodo; sim, só na propr... \n", "2 rede geral de distribuição; poço ou nascente n... \n", "3 adequada; semi-adequada; inadequada \n", "4 sim; não \n", ".. ... \n", "71 sim; não \n", "72 casa; casa de vila ou em condomínio; apartamen... \n", "73 rondônia; acre; amazonas; roraima; pará; amapá... \n", "74 NaN \n", "75 NaN \n", "\n", " question_en description_en \\\n", "0 Responsibility for the home is: NaN \n", "1 water supply, plumbing: NaN \n", "2 water supply, form: NaN \n", "3 suitability of housing NaN \n", "4 someone who lived with you was living in anoth... NaN \n", ".. ... ... \n", "71 television, existence: NaN \n", "72 species type: NaN \n", "73 federation unit: NaN \n", "74 rental value (in reais) NaN \n", "75 weighting area NaN \n", "\n", " possible_answers_en category \n", "0 just one resident; more than one resident; ign... Housing \n", "1 yes, in at least one room; yes, only on the pr... Housing \n", "2 general distribution network; well or spring o... Business \n", "3 adequate; semi-adequate; inappropriate Housing \n", "4 Yes; no Business \n", ".. ... ... \n", "71 Yes; no Identification \n", "72 home; town house or condominium; apartment; ho... Housing \n", "73 Rondônia; acre; Amazons; roraima; to; amapá; t... Business \n", "74 NaN Business \n", "75 NaN Housing \n", "\n", "[76 rows x 14 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "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", "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\")\n", "dic" ] }, { "cell_type": "markdown", "id": "f42e3eeb4cef7cc", "metadata": {}, "source": [ "## 2. Extract data from Brazil Census 2010\n", "\n", "To extract data, use the `Extractor` class from the `socio4health` library. As in the publication, extract the Brazil Census 2010 dataset from the Brazilian Institute of Geography and Statistics (**IBGE**) website or from a local copy. The dataset is available [here](https://www.ibge.gov.br/estatisticas/sociais/saude/9662-censo-demografico-2010.html?=&t=microdados).\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 `.txt`,`.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, a regular expression is used to select only the files with a `.zip` extension that contain uppercase letters in their names.\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", "- `is_fwf`: A boolean indicating whether the files are in fixed-width format (FWF). In this case, the files are in FWF format, so this parameter is set to `True`.\n", "- `colnames`: A list of column names for the FWF files, extracted from the standardized dictionary.\n", "- `colspecs`: A list of tuples indicating the start and end positions of each column in the FWF files, extracted from the standardized dictionary.\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "f582cde8", "metadata": { "ExecuteTime": { "end_time": "2025-09-24T16:16:26.216106Z", "start_time": "2025-09-24T15:49:42.518992Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<>:4: SyntaxWarning: invalid escape sequence '\\.'\n", "<>:4: SyntaxWarning: invalid escape sequence '\\.'\n", "C:\\Users\\Juan\\AppData\\Local\\Temp\\ipykernel_16272\\2648841082.py:4: SyntaxWarning: invalid escape sequence '\\.'\n", " key_words=[\"^[A-Z]+\\.zip$\"],\n", "2025-10-23 16:24:42,329 - INFO - ----------------------\n", "2025-10-23 16:24:42,329 - INFO - Starting data extraction...\n", "2025-10-23 16:24:42,329 - INFO - Extracting data in online mode...\n", "2025-10-23 16:24:42,329 - INFO - Scraping URL: https://www.ibge.gov.br/estatisticas/sociais/saude/9662-censo-demografico-2010.html?=&t=microdados with depth 0\n", "2025-10-23 16:26:48,218 - INFO - Spider completed successfully for URL: https://www.ibge.gov.br/estatisticas/sociais/saude/9662-censo-demografico-2010.html?=&t=microdados\n", "2025-10-23 16:26:48,302 - INFO - Downloading files to: ../../../../Socio4HealthData/input/IBGE_2010_\n", "Downloading files: 0%| | 0/27 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
V0001V0208V0301V2012V0222V0701V0211V0207V0212M0201...V0202V0221V0401V6531V6532V6530V6529V0206V1005V2011
01105001100030830303<NA>...6030<NA><NA>53208382020<NA>001001
11101000100003010500<NA>...1010<NA><NA>04309491570<NA>001001
21100000300002520303<NA>...0010<NA><NA>25108938100<NA>001001
31102000200007410607<NA>...0020<NA><NA>18712508100<NA>001001
41102000200009510205<NA>...3030<NA><NA>18511325140<NA>001001
..................................................................
6000413501000200003510303<NA>...8020<NA><NA>79605969650<NA>001001
6000423502000400009420205<NA>...5020<NA><NA>68304008640<NA>001001
6000433501001100007220107<NA>...7040<NA><NA>83207888610<NA>001001
6000443506000300018920000<NA>...3010<NA><NA>01709953610<NA>001001
6000453501020200006730000<NA>...0020<NA><NA>68306928030<NA>001001
\n", "

32004235 rows × 46 columns

\n", "" ], "text/plain": [ " V0001 V0208 V0301 V2012 V0222 V0701 V0211 V0207 V0212 M0201 ... \\\n", "0 11 05 0 011000308 3 0 3 0 3 ... \n", "1 11 01 0 001000030 1 0 5 0 0 ... \n", "2 11 00 0 003000025 2 0 3 0 3 ... \n", "3 11 02 0 002000074 1 0 6 0 7 ... \n", "4 11 02 0 002000095 1 0 2 0 5 ... \n", "... ... ... ... ... ... ... ... ... ... ... ... \n", "600041 35 01 0 002000035 1 0 3 0 3 ... \n", "600042 35 02 0 004000094 2 0 2 0 5 ... \n", "600043 35 01 0 011000072 2 0 1 0 7 ... \n", "600044 35 06 0 003000189 2 0 0 0 0 ... \n", "600045 35 01 0 202000067 3 0 0 0 0 ... \n", "\n", " V0202 V0221 V0401 V6531 V6532 V6530 V6529 V0206 V1005 V2011 \n", "0 6 0 30 532 0838202 0 001001 \n", "1 1 0 10 043 0949157 0 001001 \n", "2 0 0 10 251 0893810 0 001001 \n", "3 0 0 20 187 1250810 0 001001 \n", "4 3 0 30 185 1132514 0 001001 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "600041 8 0 20 796 0596965 0 001001 \n", "600042 5 0 20 683 0400864 0 001001 \n", "600043 7 0 40 832 0788861 0 001001 \n", "600044 3 0 10 017 0995361 0 001001 \n", "600045 0 0 20 683 0692803 0 001001 \n", "\n", "[32004235 rows x 46 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filtered_ddfs[0].compute()" ] }, { "cell_type": "markdown", "id": "b109534ea806104d", "metadata": {}, "source": [ "Finally, we can perform some **analysis** on the harmonized data. In this case, we will calculate the total population by state (`V0001`) using the variable `V0401`, which represents the total population in each census tract. We will then create a horizontal bar plot to visualize the population distribution across states using `matplotlib`." ] }, { "cell_type": "code", "execution_count": 11, "id": "a31ec791", "metadata": { "ExecuteTime": { "end_time": "2025-09-16T13:28:26.646075Z", "start_time": "2025-09-16T13:28:26.592145Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\Juan\\anaconda3\\envs\\social4health\\Lib\\site-packages\\dask\\dataframe\\dask_expr\\_groupby.py:1562: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", " self._meta = self.obj._meta.groupby(\n" ] } ], "source": [ "ddf = filtered_ddfs[0][[\"V0001\", \"V0401\"]]\n", "\n", "ddf = ddf.assign(\n", " V0001 = ddf[\"V0001\"].astype(\"category\"),\n", " V0401 = dd.to_numeric(ddf[\"V0401\"], errors=\"coerce\").astype(\"float64\").fillna(0.0)\n", ").categorize(columns=[\"V0001\"])\n", "\n", "pop = ddf.groupby(\"V0001\")[\"V0401\"].sum(split_out=8).compute()" ] }, { "cell_type": "code", "execution_count": 17, "id": "7aa45736", "metadata": {}, "outputs": [], "source": [ "pop = pop[pop>0]\n", "row = dic.loc[dic[\"variable_name\"]==\"V0001\", [\"value\",\"possible_answers\"]].iloc[0]\n", "\n", "vals = [s for s in re.split(r\"\\s*;\\s*\", str(row[\"value\"]).strip(\" ;\")) if s]\n", "labs = [s for s in re.split(r\"\\s*;\\s*\", str(row[\"possible_answers\"]).strip(\" ;\")) if s]\n", "\n", "idx = pop.index\n", "if pd.api.types.is_integer_dtype(idx):\n", " keys = [int(float(v)) for v in vals]\n", "elif pd.api.types.is_float_dtype(idx):\n", " keys = [float(v) for v in vals]\n", "else:\n", " keys = [str(int(float(v))) for v in vals]\n", "\n", "if len(keys) != len(labs):\n", " raise ValueError(f\"Misalignment: {len(keys)} codes vs {len(labs)} names\")\n", "code2name = dict(zip(keys, labs))\n", "\n", "pop_named = pop.rename(index=code2name)\n" ] }, { "cell_type": "code", "execution_count": 18, "id": "2d972c64", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "top = pop_named.sort_values()\n", "top_titled = top.copy()\n", "top_titled.index = [str(s).title() for s in top.index]\n", "\n", "fig, ax = plt.subplots(figsize=(11,7), dpi=130)\n", "ax.barh(top_titled.index, top_titled.values)\n", "\n", "ax.spines[\"top\"].set_visible(False)\n", "ax.spines[\"right\"].set_visible(False)\n", "ax.set_title(f\"Population by State\", pad=10)\n", "ax.set_xlabel(\"Population\")\n", "ax.set_ylabel(\"State\")\n", "ax.xaxis.set_major_formatter(FuncFormatter(lambda x, p: f\"{x/1e6:.1f} M\"))\n", "\n", "total = pop_named.sum()\n", "for i, v in enumerate(top_titled.values):\n", " ax.text(v, i, f\"{v/1e6:.1f} M ({v/total:.1%})\", va=\"center\", ha=\"left\", fontsize=9)\n", "\n", "ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.3)\n", "plt.margins(x=0.03)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "d8b0f790", "metadata": {}, "outputs": [], "source": [] } ], "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 }