1857 lines
229 KiB
Plaintext
1857 lines
229 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 177,
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"id": "043492dd-e09f-440f-ad35-e2e741860bba",
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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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"import numpy as np\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": 2,
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"id": "558043e1-1724-4bf6-8acf-e85c18b0150e",
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"metadata": {},
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"outputs": [],
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"source": [
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"estados_mexicanos = {\n",
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" \"AGUASCALIENTES\",\n",
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" \"BAJA CALIFORNIA\",\n",
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" \"BAJA CALIFORNIA SUR\",\n",
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" \"CAMPECHE\",\n",
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" \"CHIAPAS\",\n",
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" \"CHIHUAHUA\",\n",
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" \"COAHUILA DE ZARAGOZA\",\n",
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" \"COLIMA\",\n",
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" \"DISTRITO FEDERAL\",\n",
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" \"DURANGO\",\n",
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" \"GUANAJUATO\",\n",
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" \"GUERRERO\",\n",
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" \"HIDALGO\",\n",
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" \"JALISCO\",\n",
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" \"MEXICO\",\n",
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" \"MICHOACAN DE OCAMPO\",\n",
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" \"MORELOS\",\n",
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" \"NAYARIT\",\n",
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" \"NUEVO LEON\",\n",
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" \"OAXACA\",\n",
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" \"PUEBLA\",\n",
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" \"QUERETARO DE ARTEAGA\",\n",
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" \"QUINTANA ROO\",\n",
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" \"SAN LUIS POTOSI\",\n",
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" \"SINALOA\",\n",
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" \"SONORA\",\n",
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" \"TABASCO\",\n",
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" \"TAMAULIPAS\",\n",
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" \"TLAXCALA\",\n",
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" \"VERACRUZ DE IGNACIO DE LA LLAVE\",\n",
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" \"YUCATAN\",\n",
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" \"ZACATECAS\",\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": 3,
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"id": "6b047178-2902-4eb2-9a34-0b7d7beb277e",
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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_93495/3168623387.py:1: DtypeWarning: Columns (21) have mixed types. Specify dtype option on import or set low_memory=False.\n",
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" df = pd.read_csv(\"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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"df = pd.read_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": 4,
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"id": "61675b16-391b-4821-8376-f92ec4b5b916",
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"metadata": {},
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"outputs": [],
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"source": [
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"def _ano_nacimiento_vivo_func(str_date):\n",
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" try:\n",
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" return str_date.split(\"/\")[-1]\n",
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" except:\n",
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" return \"\"\n",
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"\n",
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"\n",
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"df[\"año_de_nacimiento_vivo\"] = df[\"fecha_nacimiento_nac_vivo\"].apply(\n",
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" _ano_nacimiento_vivo_func\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": 19,
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"id": "87a086d4-bab8-43a8-a121-8aaf3554e672",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = df[(5 < df[\"edad_madre\"]) & (df[\"edad_madre\"] < 90)]"
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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": 20,
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"id": "f8eff617-7273-435f-a09a-8db4ec005ee0",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_trisomias = df[df[\"codigo_anomalia\"].apply(lambda x: \"Q9\" in str(x))]"
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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": 87,
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"id": "1ff41e12-b6cd-41db-bd1b-47c2aa21c45e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th colspan=\"8\" halign=\"left\">edad_madre</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th>count</th>\n",
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" <th>mean</th>\n",
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" <th>std</th>\n",
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" <th>min</th>\n",
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" <th>max</th>\n",
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" <th>median</th>\n",
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" <th>Q1</th>\n",
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" <th>Q3</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>año_de_nacimiento_vivo</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>2010</th>\n",
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" <td>930</td>\n",
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" <td>30.546237</td>\n",
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" <td>8.244939</td>\n",
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" <td>10</td>\n",
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" <td>48</td>\n",
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" <td>31.0</td>\n",
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" <td>23.0</td>\n",
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" <td>37.75</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2011</th>\n",
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" <td>1051</td>\n",
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" <td>31.010466</td>\n",
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" <td>8.193777</td>\n",
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" <td>12</td>\n",
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" <td>49</td>\n",
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" <td>32.0</td>\n",
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" <td>24.0</td>\n",
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" <td>38.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2012</th>\n",
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" <td>961</td>\n",
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" <td>30.462019</td>\n",
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" <td>8.310565</td>\n",
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" <td>13</td>\n",
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" <td>47</td>\n",
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" <td>31.0</td>\n",
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" <td>23.0</td>\n",
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" <td>38.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2013</th>\n",
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" <td>1055</td>\n",
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" <td>31.182938</td>\n",
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" <td>8.247919</td>\n",
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" <td>11</td>\n",
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" <td>51</td>\n",
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" <td>32.0</td>\n",
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" <td>24.0</td>\n",
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" <td>38.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2014</th>\n",
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" <td>1031</td>\n",
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" <td>31.018429</td>\n",
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" <td>8.356304</td>\n",
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" <td>13</td>\n",
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" <td>50</td>\n",
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" <td>32.0</td>\n",
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" <td>24.0</td>\n",
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" <td>38.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2015</th>\n",
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" <td>1016</td>\n",
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" <td>31.500984</td>\n",
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" <td>8.295052</td>\n",
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" <td>14</td>\n",
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" <td>52</td>\n",
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" <td>32.0</td>\n",
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" <td>24.0</td>\n",
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" <td>39.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2016</th>\n",
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" <td>1044</td>\n",
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" <td>31.453065</td>\n",
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" <td>8.147413</td>\n",
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|
" <td>14</td>\n",
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|
" <td>47</td>\n",
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|
" <td>32.0</td>\n",
|
|
" <td>24.0</td>\n",
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|
" <td>39.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2017</th>\n",
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" <td>1043</td>\n",
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" <td>31.410355</td>\n",
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" <td>8.174581</td>\n",
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|
" <td>13</td>\n",
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|
" <td>47</td>\n",
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|
" <td>33.0</td>\n",
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" <td>24.0</td>\n",
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" <td>38.50</td>\n",
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" </tr>\n",
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" <tr>\n",
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|
" <th>2018</th>\n",
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" <td>1059</td>\n",
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" <td>31.064212</td>\n",
|
|
" <td>8.173198</td>\n",
|
|
" <td>13</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>32.0</td>\n",
|
|
" <td>24.0</td>\n",
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" <td>38.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2019</th>\n",
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" <td>941</td>\n",
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|
" <td>32.018066</td>\n",
|
|
" <td>8.195918</td>\n",
|
|
" <td>13</td>\n",
|
|
" <td>47</td>\n",
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|
" <td>34.0</td>\n",
|
|
" <td>25.0</td>\n",
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|
" <td>39.00</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" edad_madre \\\n",
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" count mean std min max median Q1 \n",
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"año_de_nacimiento_vivo \n",
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"2010 930 30.546237 8.244939 10 48 31.0 23.0 \n",
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"2011 1051 31.010466 8.193777 12 49 32.0 24.0 \n",
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"2012 961 30.462019 8.310565 13 47 31.0 23.0 \n",
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"2013 1055 31.182938 8.247919 11 51 32.0 24.0 \n",
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"2014 1031 31.018429 8.356304 13 50 32.0 24.0 \n",
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"2015 1016 31.500984 8.295052 14 52 32.0 24.0 \n",
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"2016 1044 31.453065 8.147413 14 47 32.0 24.0 \n",
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"2017 1043 31.410355 8.174581 13 47 33.0 24.0 \n",
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"2018 1059 31.064212 8.173198 13 48 32.0 24.0 \n",
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"2019 941 32.018066 8.195918 13 47 34.0 25.0 \n",
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"\n",
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" \n",
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" Q3 \n",
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"año_de_nacimiento_vivo \n",
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"2010 37.75 \n",
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"2011 38.00 \n",
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"2012 38.00 \n",
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"2013 38.00 \n",
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"2014 38.00 \n",
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"2015 39.00 \n",
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"2016 39.00 \n",
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"2017 38.50 \n",
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"2018 38.00 \n",
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"2019 39.00 "
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]
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},
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"execution_count": 87,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"consulta_trisomias = df_trisomias.groupby([\"año_de_nacimiento_vivo\"]).agg(\n",
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" {\n",
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" \"edad_madre\": [\n",
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" \"count\",\n",
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" \"mean\",\n",
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" \"std\",\n",
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" \"min\",\n",
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" \"max\",\n",
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" \"median\",\n",
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" lambda x: x.quantile(0.25), # For Q1\n",
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" lambda x: x.quantile(0.75), # For Q3\n",
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" ],\n",
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" }\n",
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")\n",
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"consulta_trisomias = consulta_trisomias.rename(\n",
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" columns={\n",
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" \"<lambda_0>\": \"Q1\",\n",
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" \"<lambda_1>\": \"Q3\",\n",
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" }\n",
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")\n",
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"consulta_trisomias"
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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": 243,
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"id": "a1642d2b-ecf7-4358-9dd6-c020fde27b95",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"año_de_nacimiento_vivo\n",
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"2010 930\n",
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"2011 1051\n",
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"2012 961\n",
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"2013 1055\n",
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"2014 1031\n",
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"2015 1016\n",
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"2016 1044\n",
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"2017 1043\n",
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"2018 1059\n",
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"2019 941\n",
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"Name: (edad_madre, count), dtype: int64"
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]
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},
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"execution_count": 243,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 88,
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"id": "942da486-5c14-4d37-a775-009151c68f29",
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"metadata": {},
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"outputs": [
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{
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"data": {
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" <thead>\n",
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" <tr>\n",
|
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" <th></th>\n",
|
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" <th colspan=\"8\" halign=\"left\">edad_madre</th>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th></th>\n",
|
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" <th>count</th>\n",
|
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" <th>mean</th>\n",
|
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" <th>std</th>\n",
|
|
" <th>min</th>\n",
|
|
" <th>max</th>\n",
|
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" <th>median</th>\n",
|
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" <th>Q1</th>\n",
|
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" <th>Q3</th>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>año_de_nacimiento_vivo</th>\n",
|
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" <th></th>\n",
|
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
|
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" <tr>\n",
|
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" <th>2010</th>\n",
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" <td>2063533</td>\n",
|
|
" <td>25.253220</td>\n",
|
|
" <td>6.319567</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>58</td>\n",
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" <td>24.0</td>\n",
|
|
" <td>20.0</td>\n",
|
|
" <td>30.0</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>2011</th>\n",
|
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" <td>2156751</td>\n",
|
|
" <td>25.234223</td>\n",
|
|
" <td>6.331894</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>58</td>\n",
|
|
" <td>24.0</td>\n",
|
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" <td>20.0</td>\n",
|
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" <td>30.0</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>2012</th>\n",
|
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" <td>2197327</td>\n",
|
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" <td>25.195768</td>\n",
|
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" <td>6.321840</td>\n",
|
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" <td>9</td>\n",
|
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" <td>58</td>\n",
|
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" <td>24.0</td>\n",
|
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" <td>20.0</td>\n",
|
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" <td>30.0</td>\n",
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" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2013</th>\n",
|
|
" <td>2189257</td>\n",
|
|
" <td>25.198235</td>\n",
|
|
" <td>6.322081</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>59</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" <td>20.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2014</th>\n",
|
|
" <td>2173773</td>\n",
|
|
" <td>25.276009</td>\n",
|
|
" <td>6.322130</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>58</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" <td>20.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2015</th>\n",
|
|
" <td>2143345</td>\n",
|
|
" <td>25.367835</td>\n",
|
|
" <td>6.296604</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>59</td>\n",
|
|
" <td>25.0</td>\n",
|
|
" <td>20.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2016</th>\n",
|
|
" <td>2079251</td>\n",
|
|
" <td>25.468008</td>\n",
|
|
" <td>6.292815</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>59</td>\n",
|
|
" <td>25.0</td>\n",
|
|
" <td>20.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2017</th>\n",
|
|
" <td>2037647</td>\n",
|
|
" <td>25.510821</td>\n",
|
|
" <td>6.305873</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>62</td>\n",
|
|
" <td>25.0</td>\n",
|
|
" <td>21.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2018</th>\n",
|
|
" <td>1940338</td>\n",
|
|
" <td>25.678051</td>\n",
|
|
" <td>6.328369</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>25.0</td>\n",
|
|
" <td>21.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2019</th>\n",
|
|
" <td>1867693</td>\n",
|
|
" <td>25.840630</td>\n",
|
|
" <td>6.342544</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>58</td>\n",
|
|
" <td>25.0</td>\n",
|
|
" <td>21.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" edad_madre \\\n",
|
|
" count mean std min max median Q1 \n",
|
|
"año_de_nacimiento_vivo \n",
|
|
"2010 2063533 25.253220 6.319567 9 58 24.0 20.0 \n",
|
|
"2011 2156751 25.234223 6.331894 9 58 24.0 20.0 \n",
|
|
"2012 2197327 25.195768 6.321840 9 58 24.0 20.0 \n",
|
|
"2013 2189257 25.198235 6.322081 9 59 24.0 20.0 \n",
|
|
"2014 2173773 25.276009 6.322130 9 58 24.0 20.0 \n",
|
|
"2015 2143345 25.367835 6.296604 9 59 25.0 20.0 \n",
|
|
"2016 2079251 25.468008 6.292815 9 59 25.0 20.0 \n",
|
|
"2017 2037647 25.510821 6.305873 9 62 25.0 21.0 \n",
|
|
"2018 1940338 25.678051 6.328369 9 60 25.0 21.0 \n",
|
|
"2019 1867693 25.840630 6.342544 9 58 25.0 21.0 \n",
|
|
"\n",
|
|
" \n",
|
|
" Q3 \n",
|
|
"año_de_nacimiento_vivo \n",
|
|
"2010 30.0 \n",
|
|
"2011 30.0 \n",
|
|
"2012 30.0 \n",
|
|
"2013 30.0 \n",
|
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"2014 30.0 \n",
|
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"2015 30.0 \n",
|
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"2016 30.0 \n",
|
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"2017 30.0 \n",
|
|
"2018 30.0 \n",
|
|
"2019 30.0 "
|
|
]
|
|
},
|
|
"execution_count": 88,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Edades de madres\n",
|
|
"consulta_total = df.groupby([\"año_de_nacimiento_vivo\"]).agg(\n",
|
|
" {\n",
|
|
" \"edad_madre\": [\n",
|
|
" \"count\",\n",
|
|
" \"mean\",\n",
|
|
" \"std\",\n",
|
|
" \"min\",\n",
|
|
" \"max\",\n",
|
|
" \"median\",\n",
|
|
" lambda x: x.quantile(0.25), # For Q1\n",
|
|
" lambda x: x.quantile(0.75), # For Q3\n",
|
|
" ],\n",
|
|
" }\n",
|
|
")\n",
|
|
"consulta_total = consulta_total.rename(\n",
|
|
" columns={\n",
|
|
" \"<lambda_0>\": \"Q1\",\n",
|
|
" \"<lambda_1>\": \"Q3\",\n",
|
|
" }\n",
|
|
")\n",
|
|
"consulta_total"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 89,
|
|
"id": "5290532e-d470-49b6-bd68-07eab1b86e4c",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"consulta = consulta_total.join(\n",
|
|
" consulta_trisomias, rsuffix=\"_trisomias\", lsuffix=\"_general\"\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 90,
|
|
"id": "7a171ccc-139d-4fd3-b438-0475dd43e27b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"consulta[\"porcentaje\"] = (\n",
|
|
" consulta[(\"edad_madre_trisomias\", \"count\")]\n",
|
|
" / consulta[(\"edad_madre_general\", \"count\")]\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 91,
|
|
"id": "2d932831-b2ce-46e4-a531-edd08d4d5ecb",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
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|
"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe thead tr th {\n",
|
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" text-align: left;\n",
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" }\n",
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"\n",
|
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" .dataframe thead tr:last-of-type th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <th></th>\n",
|
|
" <th colspan=\"8\" halign=\"left\">edad_madre_general</th>\n",
|
|
" <th colspan=\"8\" halign=\"left\">edad_madre_trisomias</th>\n",
|
|
" <th>porcentaje</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th></th>\n",
|
|
" <th>count</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std</th>\n",
|
|
" <th>min</th>\n",
|
|
" <th>max</th>\n",
|
|
" <th>median</th>\n",
|
|
" <th>Q1</th>\n",
|
|
" <th>Q3</th>\n",
|
|
" <th>count</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std</th>\n",
|
|
" <th>min</th>\n",
|
|
" <th>max</th>\n",
|
|
" <th>median</th>\n",
|
|
" <th>Q1</th>\n",
|
|
" <th>Q3</th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>año_de_nacimiento_vivo</th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>2010</th>\n",
|
|
" <td>2063533</td>\n",
|
|
" <td>25.253220</td>\n",
|
|
" <td>6.319567</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>58</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" <td>20.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" <td>930</td>\n",
|
|
" <td>30.546237</td>\n",
|
|
" <td>8.244939</td>\n",
|
|
" <td>10</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>31.0</td>\n",
|
|
" <td>23.0</td>\n",
|
|
" <td>37.75</td>\n",
|
|
" <td>0.000451</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2011</th>\n",
|
|
" <td>2156751</td>\n",
|
|
" <td>25.234223</td>\n",
|
|
" <td>6.331894</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>58</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" <td>20.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" <td>1051</td>\n",
|
|
" <td>31.010466</td>\n",
|
|
" <td>8.193777</td>\n",
|
|
" <td>12</td>\n",
|
|
" <td>49</td>\n",
|
|
" <td>32.0</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" <td>38.00</td>\n",
|
|
" <td>0.000487</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2012</th>\n",
|
|
" <td>2197327</td>\n",
|
|
" <td>25.195768</td>\n",
|
|
" <td>6.321840</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>58</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" <td>20.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" <td>961</td>\n",
|
|
" <td>30.462019</td>\n",
|
|
" <td>8.310565</td>\n",
|
|
" <td>13</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>31.0</td>\n",
|
|
" <td>23.0</td>\n",
|
|
" <td>38.00</td>\n",
|
|
" <td>0.000437</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2013</th>\n",
|
|
" <td>2189257</td>\n",
|
|
" <td>25.198235</td>\n",
|
|
" <td>6.322081</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>59</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" <td>20.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" <td>1055</td>\n",
|
|
" <td>31.182938</td>\n",
|
|
" <td>8.247919</td>\n",
|
|
" <td>11</td>\n",
|
|
" <td>51</td>\n",
|
|
" <td>32.0</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" <td>38.00</td>\n",
|
|
" <td>0.000482</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2014</th>\n",
|
|
" <td>2173773</td>\n",
|
|
" <td>25.276009</td>\n",
|
|
" <td>6.322130</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>58</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" <td>20.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" <td>1031</td>\n",
|
|
" <td>31.018429</td>\n",
|
|
" <td>8.356304</td>\n",
|
|
" <td>13</td>\n",
|
|
" <td>50</td>\n",
|
|
" <td>32.0</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" <td>38.00</td>\n",
|
|
" <td>0.000474</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2015</th>\n",
|
|
" <td>2143345</td>\n",
|
|
" <td>25.367835</td>\n",
|
|
" <td>6.296604</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>59</td>\n",
|
|
" <td>25.0</td>\n",
|
|
" <td>20.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" <td>1016</td>\n",
|
|
" <td>31.500984</td>\n",
|
|
" <td>8.295052</td>\n",
|
|
" <td>14</td>\n",
|
|
" <td>52</td>\n",
|
|
" <td>32.0</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" <td>39.00</td>\n",
|
|
" <td>0.000474</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2016</th>\n",
|
|
" <td>2079251</td>\n",
|
|
" <td>25.468008</td>\n",
|
|
" <td>6.292815</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>59</td>\n",
|
|
" <td>25.0</td>\n",
|
|
" <td>20.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" <td>1044</td>\n",
|
|
" <td>31.453065</td>\n",
|
|
" <td>8.147413</td>\n",
|
|
" <td>14</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>32.0</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" <td>39.00</td>\n",
|
|
" <td>0.000502</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2017</th>\n",
|
|
" <td>2037647</td>\n",
|
|
" <td>25.510821</td>\n",
|
|
" <td>6.305873</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>62</td>\n",
|
|
" <td>25.0</td>\n",
|
|
" <td>21.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" <td>1043</td>\n",
|
|
" <td>31.410355</td>\n",
|
|
" <td>8.174581</td>\n",
|
|
" <td>13</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>33.0</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" <td>38.50</td>\n",
|
|
" <td>0.000512</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2018</th>\n",
|
|
" <td>1940338</td>\n",
|
|
" <td>25.678051</td>\n",
|
|
" <td>6.328369</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>25.0</td>\n",
|
|
" <td>21.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" <td>1059</td>\n",
|
|
" <td>31.064212</td>\n",
|
|
" <td>8.173198</td>\n",
|
|
" <td>13</td>\n",
|
|
" <td>48</td>\n",
|
|
" <td>32.0</td>\n",
|
|
" <td>24.0</td>\n",
|
|
" <td>38.00</td>\n",
|
|
" <td>0.000546</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2019</th>\n",
|
|
" <td>1867693</td>\n",
|
|
" <td>25.840630</td>\n",
|
|
" <td>6.342544</td>\n",
|
|
" <td>9</td>\n",
|
|
" <td>58</td>\n",
|
|
" <td>25.0</td>\n",
|
|
" <td>21.0</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" <td>941</td>\n",
|
|
" <td>32.018066</td>\n",
|
|
" <td>8.195918</td>\n",
|
|
" <td>13</td>\n",
|
|
" <td>47</td>\n",
|
|
" <td>34.0</td>\n",
|
|
" <td>25.0</td>\n",
|
|
" <td>39.00</td>\n",
|
|
" <td>0.000504</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
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"</div>"
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],
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"text/plain": [
|
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" edad_madre_general \\\n",
|
|
" count mean std min max median \n",
|
|
"año_de_nacimiento_vivo \n",
|
|
"2010 2063533 25.253220 6.319567 9 58 24.0 \n",
|
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"2011 2156751 25.234223 6.331894 9 58 24.0 \n",
|
|
"2012 2197327 25.195768 6.321840 9 58 24.0 \n",
|
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"2013 2189257 25.198235 6.322081 9 59 24.0 \n",
|
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"2014 2173773 25.276009 6.322130 9 58 24.0 \n",
|
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"2015 2143345 25.367835 6.296604 9 59 25.0 \n",
|
|
"2016 2079251 25.468008 6.292815 9 59 25.0 \n",
|
|
"2017 2037647 25.510821 6.305873 9 62 25.0 \n",
|
|
"2018 1940338 25.678051 6.328369 9 60 25.0 \n",
|
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"2019 1867693 25.840630 6.342544 9 58 25.0 \n",
|
|
"\n",
|
|
" edad_madre_trisomias \\\n",
|
|
" Q1 Q3 count mean std \n",
|
|
"año_de_nacimiento_vivo \n",
|
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"2010 20.0 30.0 930 30.546237 8.244939 \n",
|
|
"2011 20.0 30.0 1051 31.010466 8.193777 \n",
|
|
"2012 20.0 30.0 961 30.462019 8.310565 \n",
|
|
"2013 20.0 30.0 1055 31.182938 8.247919 \n",
|
|
"2014 20.0 30.0 1031 31.018429 8.356304 \n",
|
|
"2015 20.0 30.0 1016 31.500984 8.295052 \n",
|
|
"2016 20.0 30.0 1044 31.453065 8.147413 \n",
|
|
"2017 21.0 30.0 1043 31.410355 8.174581 \n",
|
|
"2018 21.0 30.0 1059 31.064212 8.173198 \n",
|
|
"2019 21.0 30.0 941 32.018066 8.195918 \n",
|
|
"\n",
|
|
" porcentaje \n",
|
|
" min max median Q1 Q3 \n",
|
|
"año_de_nacimiento_vivo \n",
|
|
"2010 10 48 31.0 23.0 37.75 0.000451 \n",
|
|
"2011 12 49 32.0 24.0 38.00 0.000487 \n",
|
|
"2012 13 47 31.0 23.0 38.00 0.000437 \n",
|
|
"2013 11 51 32.0 24.0 38.00 0.000482 \n",
|
|
"2014 13 50 32.0 24.0 38.00 0.000474 \n",
|
|
"2015 14 52 32.0 24.0 39.00 0.000474 \n",
|
|
"2016 14 47 32.0 24.0 39.00 0.000502 \n",
|
|
"2017 13 47 33.0 24.0 38.50 0.000512 \n",
|
|
"2018 13 48 32.0 24.0 38.00 0.000546 \n",
|
|
"2019 13 47 34.0 25.0 39.00 0.000504 "
|
|
]
|
|
},
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"execution_count": 91,
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"metadata": {},
|
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"output_type": "execute_result"
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}
|
|
],
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"source": [
|
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"consulta"
|
|
]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "b6a970d1-1150-43fb-9068-71cc46374d79",
|
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"metadata": {},
|
|
"outputs": [],
|
|
"source": []
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},
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{
|
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"cell_type": "markdown",
|
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"id": "a3a83bc5-b01f-4bee-a17a-6ab5ba8458ad",
|
|
"metadata": {},
|
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"source": [
|
|
"# Pendiente\n",
|
|
"\n",
|
|
"Generar gráfica de cajas con edades de las madres con hijos de trisomias.\n",
|
|
"\n",
|
|
"https://stackoverflow.com/a/66565512"
|
|
]
|
|
},
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{
|
|
"cell_type": "code",
|
|
"execution_count": 227,
|
|
"id": "ac7dbb16-de49-4550-b88b-4608ed6e17ae",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"stats = []\n",
|
|
"for i, x in consulta_trisomias.iterrows():\n",
|
|
" stat = dict(\n",
|
|
" label=i,\n",
|
|
" mean=x[(\"edad_madre\", \"mean\")],\n",
|
|
" count=x[(\"edad_madre\", \"count\")],\n",
|
|
" std=x[(\"edad_madre\", \"std\")],\n",
|
|
" whislo=x[(\"edad_madre\", \"min\")],\n",
|
|
" whishi=x[(\"edad_madre\", \"max\")],\n",
|
|
" med=x[(\"edad_madre\", \"median\")],\n",
|
|
" q1=x[(\"edad_madre\", \"Q1\")],\n",
|
|
" q3=x[(\"edad_madre\", \"Q3\")],\n",
|
|
" )\n",
|
|
" stats.append(stat)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 345,
|
|
"id": "fc564e07-8ada-44cf-91aa-d18012495e09",
|
|
"metadata": {},
|
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"outputs": [
|
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{
|
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"data": {
|
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"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead tr th {\n",
|
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" text-align: left;\n",
|
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <th></th>\n",
|
|
" <th colspan=\"8\" halign=\"left\">edad_madre</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th></th>\n",
|
|
" <th>count</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std</th>\n",
|
|
" <th>min</th>\n",
|
|
" <th>max</th>\n",
|
|
" <th>median</th>\n",
|
|
" <th>Q1</th>\n",
|
|
" <th>Q3</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>count</th>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td>1013.100000</td>\n",
|
|
" <td>31.166677</td>\n",
|
|
" <td>8.233967</td>\n",
|
|
" <td>12.600000</td>\n",
|
|
" <td>48.600000</td>\n",
|
|
" <td>32.100000</td>\n",
|
|
" <td>23.900000</td>\n",
|
|
" <td>38.325000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>49.771589</td>\n",
|
|
" <td>0.461478</td>\n",
|
|
" <td>0.068843</td>\n",
|
|
" <td>1.264911</td>\n",
|
|
" <td>1.837873</td>\n",
|
|
" <td>0.875595</td>\n",
|
|
" <td>0.567646</td>\n",
|
|
" <td>0.500694</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>930.000000</td>\n",
|
|
" <td>30.462019</td>\n",
|
|
" <td>8.147413</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>47.000000</td>\n",
|
|
" <td>31.000000</td>\n",
|
|
" <td>23.000000</td>\n",
|
|
" <td>37.750000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>974.750000</td>\n",
|
|
" <td>31.012457</td>\n",
|
|
" <td>8.179380</td>\n",
|
|
" <td>12.250000</td>\n",
|
|
" <td>47.000000</td>\n",
|
|
" <td>32.000000</td>\n",
|
|
" <td>24.000000</td>\n",
|
|
" <td>38.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>1037.000000</td>\n",
|
|
" <td>31.123575</td>\n",
|
|
" <td>8.220429</td>\n",
|
|
" <td>13.000000</td>\n",
|
|
" <td>48.000000</td>\n",
|
|
" <td>32.000000</td>\n",
|
|
" <td>24.000000</td>\n",
|
|
" <td>38.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>1049.250000</td>\n",
|
|
" <td>31.442388</td>\n",
|
|
" <td>8.283268</td>\n",
|
|
" <td>13.000000</td>\n",
|
|
" <td>49.750000</td>\n",
|
|
" <td>32.000000</td>\n",
|
|
" <td>24.000000</td>\n",
|
|
" <td>38.875000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>1059.000000</td>\n",
|
|
" <td>32.018066</td>\n",
|
|
" <td>8.356304</td>\n",
|
|
" <td>14.000000</td>\n",
|
|
" <td>52.000000</td>\n",
|
|
" <td>34.000000</td>\n",
|
|
" <td>25.000000</td>\n",
|
|
" <td>39.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" edad_madre \\\n",
|
|
" count mean std min max median \n",
|
|
"count 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000 \n",
|
|
"mean 1013.100000 31.166677 8.233967 12.600000 48.600000 32.100000 \n",
|
|
"std 49.771589 0.461478 0.068843 1.264911 1.837873 0.875595 \n",
|
|
"min 930.000000 30.462019 8.147413 10.000000 47.000000 31.000000 \n",
|
|
"25% 974.750000 31.012457 8.179380 12.250000 47.000000 32.000000 \n",
|
|
"50% 1037.000000 31.123575 8.220429 13.000000 48.000000 32.000000 \n",
|
|
"75% 1049.250000 31.442388 8.283268 13.000000 49.750000 32.000000 \n",
|
|
"max 1059.000000 32.018066 8.356304 14.000000 52.000000 34.000000 \n",
|
|
"\n",
|
|
" \n",
|
|
" Q1 Q3 \n",
|
|
"count 10.000000 10.000000 \n",
|
|
"mean 23.900000 38.325000 \n",
|
|
"std 0.567646 0.500694 \n",
|
|
"min 23.000000 37.750000 \n",
|
|
"25% 24.000000 38.000000 \n",
|
|
"50% 24.000000 38.000000 \n",
|
|
"75% 24.000000 38.875000 \n",
|
|
"max 25.000000 39.000000 "
|
|
]
|
|
},
|
|
"execution_count": 345,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"consulta_trisomias.describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 228,
|
|
"id": "46753c90-fca5-4b92-9f64-165460b03bd5",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"execution_count": 228,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"%matplotlib notebook\n",
|
|
"from matplotlib.figure import Figure\n",
|
|
"from matplotlib.ticker import AutoMinorLocator\n",
|
|
"\n",
|
|
"fig = Figure()\n",
|
|
"ax = fig.add_subplot()\n",
|
|
"ax.bxp(\n",
|
|
" stats,\n",
|
|
" showfliers=False,\n",
|
|
" showmeans=True,\n",
|
|
")\n",
|
|
"ax.set_ylabel(\"Edad de la Madre\")\n",
|
|
"ax.set_xlabel(\"Año de Registro\")\n",
|
|
"ax.set_title(\"Distribuciones de Edad de las Madres\")\n",
|
|
"ax.yaxis.set_minor_locator(AutoMinorLocator())\n",
|
|
"\n",
|
|
"ax.grid(visible=True, which=\"both\", axis=\"y\", linewidth=1, alpha=0.2)\n",
|
|
"fig"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "25258b7f-2f7d-4ee3-86e3-23cdeebb5046",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 238,
|
|
"id": "fddb5f2d-2cdd-42b5-a8df-227645e2e6ea",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/var/folders/05/y38rqjl55hjb_hbnypxzgrsw0000gn/T/ipykernel_93495/1078194369.py:30: SettingWithCopyWarning: \n",
|
|
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
|
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
|
"\n",
|
|
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
|
" df_trisomias[\"Trisomía\"] = df_trisomias.codigo_anomalia.apply(\n",
|
|
"/var/folders/05/y38rqjl55hjb_hbnypxzgrsw0000gn/T/ipykernel_93495/1078194369.py:33: SettingWithCopyWarning: \n",
|
|
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
|
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
|
"\n",
|
|
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
|
" df_trisomias[\"Síndrome de Down\"] = (df_trisomias[\"Trisomía\"] == \"Down\")\n",
|
|
"/var/folders/05/y38rqjl55hjb_hbnypxzgrsw0000gn/T/ipykernel_93495/1078194369.py:34: SettingWithCopyWarning: \n",
|
|
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
|
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
|
"\n",
|
|
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
|
" df_trisomias[\"Síndrome de Edwards\"] = (df_trisomias[\"Trisomía\"] == \"Edwards\")\n",
|
|
"/var/folders/05/y38rqjl55hjb_hbnypxzgrsw0000gn/T/ipykernel_93495/1078194369.py:35: SettingWithCopyWarning: \n",
|
|
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
|
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
|
"\n",
|
|
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
|
" df_trisomias[\"Síndrome de Patau\"] = (df_trisomias[\"Trisomía\"] == \"Patau\")\n",
|
|
"/var/folders/05/y38rqjl55hjb_hbnypxzgrsw0000gn/T/ipykernel_93495/1078194369.py:36: SettingWithCopyWarning: \n",
|
|
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
|
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
|
"\n",
|
|
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
|
" df_trisomias[\"Otro Síndrome\"] = (df_trisomias[\"Trisomía\"] == \"Otra\")\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"def _anomalias_filtradas(anomalia):\n",
|
|
" splitted = [x for x in anomalia.split(\",\") if len(x) == 4 and x[:2] == \"Q9\"]\n",
|
|
" if len(splitted) < 2:\n",
|
|
" return \",\".join(splitted)\n",
|
|
" if splitted[0] == splitted[1]:\n",
|
|
" return splitted[0]\n",
|
|
" return \",\".join(splitted)\n",
|
|
"\n",
|
|
"\n",
|
|
"def _clasificador(codigos):\n",
|
|
"\n",
|
|
" if \"Q910\" in codigos or \"Q911\" in codigos or \"Q912\" in codigos or \"Q913\" in codigos:\n",
|
|
" return \"Edwards\"\n",
|
|
" if \"Q914\" in codigos or \"Q915\" in codigos or \"Q916\" in codigos or \"Q917\" in codigos:\n",
|
|
" return \"Patau\"\n",
|
|
" if \"Q90\" in codigos:\n",
|
|
" return \"Down\"\n",
|
|
" return \"Otra\"\n",
|
|
"\n",
|
|
"\n",
|
|
"df_trisomias[\"Trisomía\"] = df_trisomias.codigo_anomalia.apply(\n",
|
|
" _anomalias_filtradas\n",
|
|
").apply(_clasificador)\n",
|
|
"df_trisomias[\"Síndrome de Down\"] = df_trisomias[\"Trisomía\"] == \"Down\"\n",
|
|
"df_trisomias[\"Síndrome de Edwards\"] = df_trisomias[\"Trisomía\"] == \"Edwards\"\n",
|
|
"df_trisomias[\"Síndrome de Patau\"] = df_trisomias[\"Trisomía\"] == \"Patau\"\n",
|
|
"df_trisomias[\"Otro Síndrome\"] = df_trisomias[\"Trisomía\"] == \"Otra\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 239,
|
|
"id": "f395f66a-58aa-4fcd-bd7b-6cb4aecf1d0e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Index(['Unnamed: 0.1', 'Unnamed: 0', 'edo_captura', 'edo_nac_madre',\n",
|
|
" 'fecha_nac_madre', 'edad_madre', 'estado_conyugal',\n",
|
|
" 'entidad_residencia_madre', 'numero_embarazos', 'hijos_nacidos_muertos',\n",
|
|
" 'hijos_nacidos_vivos', 'hijos_sobrevivientes', 'el_hijo_anterior_nacio',\n",
|
|
" 'vive_aun_hijo_anterior', 'orden_nacimiento',\n",
|
|
" 'recibio_atencion_prenatal', 'trimestre_recibio_primera_consulta',\n",
|
|
" 'total_consultas_recibidas', 'madre_sobrevivio_al_parto',\n",
|
|
" 'escolaridad_madre', 'ocupacion_habitual_madre', 'trabaja_actualmente',\n",
|
|
" 'fecha_nacimiento_nac_vivo', 'hora_nacimiento_nac_vivo',\n",
|
|
" 'sexo_nac_vivo', 'semanas_gestacion_nac_vivo', 'talla_nac_vivo',\n",
|
|
" 'peso_nac_vivo', 'valoracion_apgar_nac_vivo',\n",
|
|
" 'valoracion_silverman_nac_vivo', 'producto_de_un_embarazo',\n",
|
|
" 'codigo_anomalia', 'entidad_certifico', 'año_de_nacimiento_vivo',\n",
|
|
" 'Trisomia', 'Sindrome de Down', 'Sindrome de Edwards',\n",
|
|
" 'Sindrome de Patau', 'Otro Sindrome', 'Trisomía', 'Síndrome de Down',\n",
|
|
" 'Síndrome de Edwards', 'Síndrome de Patau', 'Otro Síndrome'],\n",
|
|
" dtype='object')"
|
|
]
|
|
},
|
|
"execution_count": 239,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df_trisomias.columns"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 341,
|
|
"id": "07c757bc-0af3-4225-b63c-ddf07c48c88a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"execution_count": 341,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib.ticker as mtick\n",
|
|
"\n",
|
|
"fig = Figure()\n",
|
|
"ax = fig.add_subplot()\n",
|
|
"_to_plot = df_trisomias.groupby(\"año_de_nacimiento_vivo\").agg(\n",
|
|
" {\n",
|
|
" \"Síndrome de Down\": [\"sum\"],\n",
|
|
" \"Síndrome de Edwards\": [\"sum\"],\n",
|
|
" \"Síndrome de Patau\": [\"sum\"],\n",
|
|
" \"Otro Síndrome\": [\"sum\"],\n",
|
|
" }\n",
|
|
")\n",
|
|
"_index = _to_plot.index.to_list()\n",
|
|
"_records = _to_plot.to_dict(\"records\")\n",
|
|
"_labels = [x[0] for x in _records[0].keys()]\n",
|
|
"_data = np.array([[*x.values()] for x in _records], dtype=\"float64\")\n",
|
|
"_totals = (_data @ np.ones(_data.shape[1])) / 100\n",
|
|
"bottom = _data[:, 0] * 0\n",
|
|
"for i, label in enumerate(_labels):\n",
|
|
" data = _data[:, i] / _totals\n",
|
|
" ax.bar(_index, data, 0.7, label=label, bottom=bottom)\n",
|
|
" bottom += data\n",
|
|
"ax.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
|
|
"ax.set_title(\"Distribución de trisomias y otros síndromes\")\n",
|
|
"ax.legend()\n",
|
|
"fig"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 343,
|
|
"id": "bf7242c1-f708-42ca-91fa-3fc1e577414a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"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>Down</th>\n",
|
|
" <th>Edwards</th>\n",
|
|
" <th>Patau</th>\n",
|
|
" <th>Otros</th>\n",
|
|
" <th>Total</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>count</th>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td>0.846123</td>\n",
|
|
" <td>0.020646</td>\n",
|
|
" <td>0.007997</td>\n",
|
|
" <td>0.125234</td>\n",
|
|
" <td>1013.100000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>0.040421</td>\n",
|
|
" <td>0.006969</td>\n",
|
|
" <td>0.003110</td>\n",
|
|
" <td>0.038154</td>\n",
|
|
" <td>49.771589</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>0.797699</td>\n",
|
|
" <td>0.010627</td>\n",
|
|
" <td>0.004162</td>\n",
|
|
" <td>0.065156</td>\n",
|
|
" <td>930.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>0.813188</td>\n",
|
|
" <td>0.016582</td>\n",
|
|
" <td>0.005697</td>\n",
|
|
" <td>0.105395</td>\n",
|
|
" <td>974.750000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>0.846609</td>\n",
|
|
" <td>0.019526</td>\n",
|
|
" <td>0.007144</td>\n",
|
|
" <td>0.126721</td>\n",
|
|
" <td>1037.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>0.861515</td>\n",
|
|
" <td>0.023294</td>\n",
|
|
" <td>0.010124</td>\n",
|
|
" <td>0.154212</td>\n",
|
|
" <td>1049.250000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>0.912181</td>\n",
|
|
" <td>0.036433</td>\n",
|
|
" <td>0.012752</td>\n",
|
|
" <td>0.180645</td>\n",
|
|
" <td>1059.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Down Edwards Patau Otros Total\n",
|
|
"count 10.000000 10.000000 10.000000 10.000000 10.000000\n",
|
|
"mean 0.846123 0.020646 0.007997 0.125234 1013.100000\n",
|
|
"std 0.040421 0.006969 0.003110 0.038154 49.771589\n",
|
|
"min 0.797699 0.010627 0.004162 0.065156 930.000000\n",
|
|
"25% 0.813188 0.016582 0.005697 0.105395 974.750000\n",
|
|
"50% 0.846609 0.019526 0.007144 0.126721 1037.000000\n",
|
|
"75% 0.861515 0.023294 0.010124 0.154212 1049.250000\n",
|
|
"max 0.912181 0.036433 0.012752 0.180645 1059.000000"
|
|
]
|
|
},
|
|
"execution_count": 343,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"_to_plot = df_trisomias.groupby(\"año_de_nacimiento_vivo\").agg(\n",
|
|
" {\n",
|
|
" \"Síndrome de Down\": [\"sum\"],\n",
|
|
" \"Síndrome de Edwards\": [\"sum\"],\n",
|
|
" \"Síndrome de Patau\": [\"sum\"],\n",
|
|
" \"Otro Síndrome\": [\"sum\"],\n",
|
|
" }\n",
|
|
")\n",
|
|
"_to_plot.columns = [\"Down\", \"Edwards\", \"Patau\", \"Otros\"]\n",
|
|
"_to_plot[\"Total\"] = _to_plot.apply(sum, axis=1)\n",
|
|
"for col in [\"Down\", \"Edwards\", \"Patau\", \"Otros\"]:\n",
|
|
" _to_plot[col] = _to_plot[col] / _to_plot[\"Total\"]\n",
|
|
"_to_plot.describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 325,
|
|
"id": "7dca1382-4073-48b7-8cca-02327919a448",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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"text/plain": [
|
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"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"execution_count": 325,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig = Figure()\n",
|
|
"ax = fig.add_subplot()\n",
|
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"_x = [int(x) for x in (consulta.index.to_list())]\n",
|
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"_z = consulta[(\"edad_madre_trisomias\", \"count\")].to_list()\n",
|
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"_y = (consulta.porcentaje * 100).to_list()\n",
|
|
"ax.plot(_x, _y, \"--\")\n",
|
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"ax.scatter(_x, _y, label=\"Porcentaje y cantidad de nacidos con malformación\")\n",
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"\n",
|
|
"ax.annotate(f\"{int(_z[0])}\", (_x[0] - 0.075, _y[0] - 0.001))\n",
|
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"ax.annotate(f\"{int(_z[1])}\", (_x[1] - 0.075, _y[1] + 0.0005))\n",
|
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"ax.annotate(f\"{int(_z[2])}\", (_x[2] - 0.075, _y[2] - 0.001))\n",
|
|
"ax.annotate(f\"{int(_z[3])}\", (_x[3] - 0.075, _y[3] + 0.0005))\n",
|
|
"ax.annotate(f\"{int(_z[4])}\", (_x[4] - 0.075, _y[4] - 0.001))\n",
|
|
"ax.annotate(f\"{int(_z[5])}\", (_x[5] - 0.075, _y[5] - 0.001))\n",
|
|
"ax.annotate(f\"{int(_z[6])}\", (_x[6] - 0.075, _y[6] - 0.001))\n",
|
|
"ax.annotate(f\"{int(_z[7])}\", (_x[7] - 0.075, _y[7] - 0.001))\n",
|
|
"ax.annotate(f\"{int(_z[8])}\", (_x[8] + 0.2, _y[8] - 0.00025))\n",
|
|
"ax.annotate(f\"{int(_z[9])}\", (_x[9] - 0.075, _y[9] - 0.001))\n",
|
|
"\n",
|
|
"ax.set_ylim(0.04, 0.055)\n",
|
|
"ax.xaxis.set_ticks(_x)\n",
|
|
"ax.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
|
|
"ax.set_title(\"Distribución de nacimiento con malformación\")\n",
|
|
"ax.set_xlabel(\"Año\")\n",
|
|
"ax.set_ylabel(\"Porcentaje\")\n",
|
|
"ax.yaxis.set_minor_locator(AutoMinorLocator())\n",
|
|
"ax.grid(alpha=0.1, which=\"both\")\n",
|
|
"ax.legend()\n",
|
|
"fig"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 326,
|
|
"id": "c257ab12-1d75-446b-9610-449543634671",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"count 10.000000\n",
|
|
"mean 0.000487\n",
|
|
"std 0.000031\n",
|
|
"min 0.000437\n",
|
|
"25% 0.000474\n",
|
|
"50% 0.000485\n",
|
|
"75% 0.000503\n",
|
|
"max 0.000546\n",
|
|
"Name: porcentaje, dtype: float64"
|
|
]
|
|
},
|
|
"execution_count": 326,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"consulta.porcentaje.describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 328,
|
|
"id": "a3789b37-2029-4794-8cc6-f551bfeb0dc4",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"stats = []\n",
|
|
"for i, x in consulta_total.iterrows():\n",
|
|
" stat = dict(\n",
|
|
" label=i,\n",
|
|
" mean=x[(\"edad_madre\", \"mean\")],\n",
|
|
" count=x[(\"edad_madre\", \"count\")],\n",
|
|
" std=x[(\"edad_madre\", \"std\")],\n",
|
|
" whislo=x[(\"edad_madre\", \"min\")],\n",
|
|
" whishi=x[(\"edad_madre\", \"max\")],\n",
|
|
" med=x[(\"edad_madre\", \"median\")],\n",
|
|
" q1=x[(\"edad_madre\", \"Q1\")],\n",
|
|
" q3=x[(\"edad_madre\", \"Q3\")],\n",
|
|
" )\n",
|
|
" stats.append(stat)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 332,
|
|
"id": "6aed222a-8753-4ddc-afec-f3d7769a98ba",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"execution_count": 332,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"%matplotlib notebook\n",
|
|
"from matplotlib.figure import Figure\n",
|
|
"from matplotlib.ticker import AutoMinorLocator\n",
|
|
"\n",
|
|
"fig = Figure()\n",
|
|
"ax = fig.add_subplot()\n",
|
|
"ax.bxp(\n",
|
|
" stats,\n",
|
|
" showfliers=False,\n",
|
|
" showmeans=True,\n",
|
|
")\n",
|
|
"ax.set_ylabel(\"Edad de la Madre\")\n",
|
|
"ax.set_xlabel(\"Año de Registro\")\n",
|
|
"ax.set_title(\"Distribuciones Poblacionales de Edad de las Madres\")\n",
|
|
"ax.yaxis.set_minor_locator(AutoMinorLocator())\n",
|
|
"\n",
|
|
"ax.grid(visible=True, which=\"both\", axis=\"y\", linewidth=1, alpha=0.2)\n",
|
|
"fig"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 350,
|
|
"id": "35ee2a09-00fa-458b-b978-734591ce021c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"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 tr th {\n",
|
|
" text-align: left;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <th></th>\n",
|
|
" <th colspan=\"8\" halign=\"left\">edad_madre</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th></th>\n",
|
|
" <th>count</th>\n",
|
|
" <th>mean</th>\n",
|
|
" <th>std</th>\n",
|
|
" <th>min</th>\n",
|
|
" <th>max</th>\n",
|
|
" <th>median</th>\n",
|
|
" <th>Q1</th>\n",
|
|
" <th>Q3</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>count</th>\n",
|
|
" <td>1.000000e+01</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.0</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.000000</td>\n",
|
|
" <td>10.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td>2.084892e+06</td>\n",
|
|
" <td>25.402280</td>\n",
|
|
" <td>6.318372</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>58.900000</td>\n",
|
|
" <td>24.500000</td>\n",
|
|
" <td>20.300000</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>1.109581e+05</td>\n",
|
|
" <td>0.220145</td>\n",
|
|
" <td>0.015605</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1.286684</td>\n",
|
|
" <td>0.527046</td>\n",
|
|
" <td>0.483046</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>1.867693e+06</td>\n",
|
|
" <td>25.195768</td>\n",
|
|
" <td>6.292815</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>58.000000</td>\n",
|
|
" <td>24.000000</td>\n",
|
|
" <td>20.000000</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>2.044118e+06</td>\n",
|
|
" <td>25.238972</td>\n",
|
|
" <td>6.309296</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>58.000000</td>\n",
|
|
" <td>24.000000</td>\n",
|
|
" <td>20.000000</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>2.111298e+06</td>\n",
|
|
" <td>25.321922</td>\n",
|
|
" <td>6.321961</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>58.500000</td>\n",
|
|
" <td>24.500000</td>\n",
|
|
" <td>20.000000</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>2.169518e+06</td>\n",
|
|
" <td>25.500118</td>\n",
|
|
" <td>6.326809</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>59.000000</td>\n",
|
|
" <td>25.000000</td>\n",
|
|
" <td>20.750000</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>2.197327e+06</td>\n",
|
|
" <td>25.840630</td>\n",
|
|
" <td>6.342544</td>\n",
|
|
" <td>9.0</td>\n",
|
|
" <td>62.000000</td>\n",
|
|
" <td>25.000000</td>\n",
|
|
" <td>21.000000</td>\n",
|
|
" <td>30.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" edad_madre \\\n",
|
|
" count mean std min max median \n",
|
|
"count 1.000000e+01 10.000000 10.000000 10.0 10.000000 10.000000 \n",
|
|
"mean 2.084892e+06 25.402280 6.318372 9.0 58.900000 24.500000 \n",
|
|
"std 1.109581e+05 0.220145 0.015605 0.0 1.286684 0.527046 \n",
|
|
"min 1.867693e+06 25.195768 6.292815 9.0 58.000000 24.000000 \n",
|
|
"25% 2.044118e+06 25.238972 6.309296 9.0 58.000000 24.000000 \n",
|
|
"50% 2.111298e+06 25.321922 6.321961 9.0 58.500000 24.500000 \n",
|
|
"75% 2.169518e+06 25.500118 6.326809 9.0 59.000000 25.000000 \n",
|
|
"max 2.197327e+06 25.840630 6.342544 9.0 62.000000 25.000000 \n",
|
|
"\n",
|
|
" \n",
|
|
" Q1 Q3 \n",
|
|
"count 10.000000 10.0 \n",
|
|
"mean 20.300000 30.0 \n",
|
|
"std 0.483046 0.0 \n",
|
|
"min 20.000000 30.0 \n",
|
|
"25% 20.000000 30.0 \n",
|
|
"50% 20.000000 30.0 \n",
|
|
"75% 20.750000 30.0 \n",
|
|
"max 21.000000 30.0 "
|
|
]
|
|
},
|
|
"execution_count": 350,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"consulta_total.describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "9a8fbacb-61fb-40f2-b434-ab49ac1f819b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.11.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|