155 lines
4.6 KiB
Plaintext
155 lines
4.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "47114285-22af-469e-81f3-560261036208",
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"metadata": {},
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"outputs": [],
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"source": [
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"from functools import cache\n",
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"import pandas as pd\n",
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"\n",
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"pd.set_option(\"display.max_columns\", None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "73c5d186-84f4-4a8e-a572-566ab4936bd7",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/05/y38rqjl55hjb_hbnypxzgrsw0000gn/T/ipykernel_93262/3018518299.py:3: DtypeWarning: Columns (20) have mixed types. Specify dtype option on import or set low_memory=False.\n",
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" pd.read_csv(\"2010-2016.csv\"),\n"
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]
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}
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],
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"source": [
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"df = pd.concat(\n",
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" [\n",
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" pd.read_csv(\"2010-2016.csv\"),\n",
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" pd.read_csv(\"2017-2019.csv\"),\n",
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" ]\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "194a6cbd-0bd7-4c24-8eb4-f895457ecfed",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.to_csv(\"2010-2019.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "3cfe3c4f-91ea-41e5-a0b9-557183080871",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 20918084 entries, 0 to 5873376\n",
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"Data columns (total 32 columns):\n",
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" # Column Dtype \n",
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"--- ------ ----- \n",
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" 0 Unnamed: 0 int64 \n",
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" 1 edo_captura object\n",
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" 2 edo_nac_madre object\n",
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" 3 fecha_nac_madre object\n",
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" 4 edad_madre int64 \n",
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" 5 estado_conyugal object\n",
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" 6 entidad_residencia_madre object\n",
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" 7 numero_embarazos int64 \n",
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" 8 hijos_nacidos_muertos int64 \n",
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" 9 hijos_nacidos_vivos int64 \n",
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" 10 hijos_sobrevivientes int64 \n",
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" 11 el_hijo_anterior_nacio object\n",
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" 12 vive_aun_hijo_anterior object\n",
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" 13 orden_nacimiento int64 \n",
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" 14 recibio_atencion_prenatal object\n",
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" 15 trimestre_recibio_primera_consulta object\n",
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" 16 total_consultas_recibidas int64 \n",
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" 17 madre_sobrevivio_al_parto object\n",
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" 18 escolaridad_madre object\n",
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" 19 ocupacion_habitual_madre object\n",
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" 20 trabaja_actualmente object\n",
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" 21 fecha_nacimiento_nac_vivo object\n",
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" 22 hora_nacimiento_nac_vivo object\n",
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" 23 sexo_nac_vivo object\n",
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" 24 semanas_gestacion_nac_vivo int64 \n",
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" 25 talla_nac_vivo int64 \n",
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" 26 peso_nac_vivo int64 \n",
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" 27 valoracion_apgar_nac_vivo int64 \n",
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" 28 valoracion_silverman_nac_vivo int64 \n",
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" 29 producto_de_un_embarazo object\n",
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" 30 codigo_anomalia object\n",
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" 31 entidad_certifico object\n",
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"dtypes: int64(13), object(19)\n",
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"memory usage: 5.1+ GB\n"
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]
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}
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],
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"source": [
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"df.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "f302574e-65d3-4b4e-9fad-c4a93b1ebba7",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"16777232 80891859 -rw-r--r-- 1 miguel.salgado staff 0 4854454496 \"Feb 25 01:57:13 2024\" \"Feb 25 01:59:10 2024\" \"Feb 25 01:59:10 2024\" \"Feb 25 01:57:10 2024\" 4096 9504688 0 2010-2019.csv\n"
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]
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}
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],
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"source": [
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"! stat 2010-2019.csv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6f6673b8-7e8d-42fc-af10-4302cee2b37d",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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