{"id":27040053,"url":"https://github.com/cuadernin/regex_importance","last_synced_at":"2025-04-05T03:27:40.928Z","repository":{"id":250959563,"uuid":"835967034","full_name":"Cuadernin/Regex_Importance","owner":"Cuadernin","description":"Un simple ensayo sobre expresiones regulares","archived":false,"fork":false,"pushed_at":"2024-08-08T22:09:02.000Z","size":11,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-08-10T00:26:19.066Z","etag":null,"topics":["clean-code","data-analysis","data-mining","data-science","python","r","regex"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Cuadernin.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-07-30T22:22:35.000Z","updated_at":"2024-08-08T22:09:05.000Z","dependencies_parsed_at":"2024-08-09T00:25:54.383Z","dependency_job_id":"533275ce-371f-4ed9-8179-700341ca9ba8","html_url":"https://github.com/Cuadernin/Regex_Importance","commit_stats":null,"previous_names":["cuadernin/regex_importance"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Cuadernin%2FRegex_Importance","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Cuadernin%2FRegex_Importance/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Cuadernin%2FRegex_Importance/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Cuadernin%2FRegex_Importance/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Cuadernin","download_url":"https://codeload.github.com/Cuadernin/Regex_Importance/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247284932,"owners_count":20913690,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["clean-code","data-analysis","data-mining","data-science","python","r","regex"],"created_at":"2025-04-05T03:27:40.450Z","updated_at":"2025-04-05T03:27:40.911Z","avatar_url":"https://github.com/Cuadernin.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Importancia de las expresiones regulares en la limpieza de datos\n\nCreo que muchos analistas y científicos de datos no dimensionan la importancia que tienen las expresiones regulares. No solo\npara identificar patrones y crear condiciones basadas en ellos (como en el código que hice algunos años [Clasificación de correos y teléfonos usando Python](https://github.com/Cuadernin/class_tel_email/tree/main)) sino que simplifican muchísimo el arduo trabajo\nde limpieza de datos.\n\nEstás están presentes en un gran número de lenguajes de programación como _Python, R, SQL, Javascript_, entre otros, debido a que es una sintaxis universal para identificar carácteres en una cadena de texto.\n\nA continuación presento dos casos de uso donde el saber expresiones regulares simplifica muchísimo el trabajo:\n\n## Ejemplo 1 (Python):\nSupongamos que tenemos los siguientes datos:\n```py\nCP = [5010,5060,5280,7500,5601,8905]\nDireccion = ['Calle Allende 7 Queretaro 7500', 'Boulevard 7 8905 Zacatenco', 'Ignacio 8 Zacatecas', '5060Franciso I Madero 87',\n            'Emeterio Arenas 5800 9 Toluca']\ndf = pd.DataFrame({'ID':[i for i in range(len(Direccion))],\"Direccion\":Direccion})\n```\n\n| ID      | Direccion |\n| ----------- | ----------- |\n| 0      | Calle Allende 7 Queretaro 7500       |\n| 1   | Boulevard 7 8905 Zacatenco        |\n| 2     | Ignacio 8 Zacatecas       |\n| 3   | 5060Franciso I Madero 87        |\n| 4      | Emeterio Arenas 5800 9 Toluca       |\n\nY se nos pide crear una nueva columna que contenga el CP de cada domicilio que siga lo siguiente:\n- Si la dirección no tiene CP, la celda debe quedar vacía\n- Si la dirección tiene un CP ajeno a los CP existentes (lista CP), la celda debe quedar vacía\n\n| ID      | Direccion | CP  |\n| ----------- | ----------- | ----------- |\n| 0      | Calle Allende 7 Queretaro 7500       | 7500\n| 1   | Boulevard 7 8905 Zacatenco        | 8905\n| 2     | Ignacio 8 Zacatecas       | NaN\n| 3   | 5060Franciso I Madero 87        |  5060\n| 4      | Emeterio Arenas 5800 9 Toluca       | NaN\n\n\n\u003e :bulb: **Solución.** Hay muchas soluciones posibles, pero usando expresiones regulares queda de la sig. manera:\n``` py\ndf['CP'] = df.Direccion.str.extract(r\"(\\d{4})\").astype(str)\ndf.loc[~df.CP.astype(float).isin(CP),'CP'] = np.nan\n```\n\n\n## Ejemplo 2 (R):\nTenemos el siguiente dataset con muchas frases:\n\n| Sent      \n| ----------- | \n| The birch canoe slid on the smooth planks.       | \n| Glue the sheet to the dark blue background.   | \n| It's easy to tell the depth of a well.     | \n| These days a chicken leg is a rare dish.   | \n| Rice is often served in round bowls.      | \n| The juice of lemons makes fine punch. |\n| The box was thrown beside the parked truck. |\n\nY deseamos conocer la distribución del total de **palabras** que existen \n\n\u003e :bulb: **Solución.** Usando expresiones regulares queda de la sig. manera:\n \n``` R\nhead(sents,7)\n\n( sents_count \u003c- sents %\u003e% mutate(Sent = str_to_lower(str_remove_all(Sent,\"[:punct:]\"))) %\u003e% \n    mutate(pals = str_count(Sent,\"\\\\s+\")+1) %\u003e% count(pals) %\u003e% arrange(desc(n)) )\n```\n  | pals |     n |\n  | ----------- |  ----------- | \n| 8  | 245 |\n|       7 |  188 |\n|       9 |  150 |\n|       6 |   56 |\n|      10  |  54 |\n|       11 |   15 |\n|        5 |   10 |\n|     12 |    2 |\n\nEn el último ejemplo abuse de la sintaxis de R usando [POSIX Character Classes](https://www.gastonsanchez.com/r4strings/character-sets.html) pero podría usarse la expresión [^\\w\\s]. No obstante, el punto es ver como el \ncódigo tanto en Python como R es más limpio, legible y mucho más rápido en ejecución. \n\n**Cuanto más sepas usar expresiones regulares más fácil y exhaustiva será la limpieza de datos 🥰.**\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcuadernin%2Fregex_importance","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcuadernin%2Fregex_importance","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcuadernin%2Fregex_importance/lists"}