{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "AVfznQKR-I--" }, "source": [ "# Visualizacion del Coronavirus (COVID19) Mundial con plotly\n", "por: Jose R. Zapata - https://joserzapata.github.io/\n", "\n", "\n", "Link: https://joserzapata.github.io/post/covid19-visualizacion/\n", "\n", "He visto en las redes sociales varias visualizaciones de los datos del COVID 19 y queria realizarlos en Python para tener la actualizacion de las graficas\n", "actualizadas cada dia, y ademas practicar el uso de [plotly](https://plotly.com/) para visualizacion interactiva.\n", "\n", "Las Graficas se actualizaran diariamente con los nuevos datos!\n", "\n", "Informacion extraida de 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE\n", "\n", "https://github.com/CSSEGISandData/COVID-19\n", "\n", "**Actualizaciones**\n", "- 25/May/2020 agregar datos de personas recuperadas\n", "- 29/May/2020 actualizacion a plotly 0.48\n", "- 25/Sep/2020 Mapa Mundial de Confirmados por Pais con choropleth\n", "- 24/Nov/2022 Top 10 progresion paises\n", "- 21/Oct/2023 actualizar graficas y librerias" ] }, { "cell_type": "markdown", "metadata": { "id": "l8tbwHgTf71J" }, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JoseRZapata/covid_plots/blob/main/notebooks/Covid19_Visualizacion_es.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/JoseRZapata/covid_plots/HEAD?labpath=notebooks%2FCovid19_Visualizacion_es.ipynb) [![nbviewer](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/JoseRZapata/covid_plots/blob/main/notebooks/Covid19_Visualizacion_es.ipynb)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "Fx_yb96YHeOb" }, "outputs": [], "source": [ "#!pip install chart_studio -q\n", "import chart_studio\n", "import numpy as np\n", "import pandas as pd\n", "import plotly.express as px" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pandas version: 2.1.1\n", "numpy version: 1.26.0\n" ] } ], "source": [ "# print pandas, px an numpy version\n", "print(\"pandas version: \", pd.__version__)\n", "print(\"numpy version: \", np.__version__)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "Ihyj4DOWzcdo", "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "api_key loaded!\n" ] } ], "source": [ "# Leer archivo con los datos de acceso a chart_studio\n", "# si no esta, dejar los campos vacios\n", "try:\n", " with open(\"../../info_chart.csv\") as f:\n", " info_user = f.read().split(\";\")\n", " username = info_user[0] # your username\n", " api_key = info_user[1] # your api key\n", " print(\"api_key loaded!\")\n", "except FileNotFoundError:\n", " username = \"\" # your username\n", " api_key = \"\"\n", "\n", "if api_key:\n", " chart_studio.tools.set_credentials_file(username=username, api_key=api_key)\n", "import chart_studio.plotly as py" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "lT7Dgoqy-YQA" }, "outputs": [], "source": [ "# Leer datos\n", "\n", "# los datos de personas recuperadas no eran confiables entonces\n", "# ya solo se tienen los datos de confirmados y muertos\n", "\n", "confirmed = pd.read_csv(\n", " \"https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\"\n", ")\n", "death = pd.read_csv(\n", " \"https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\"\n", ")\n", "recovered = pd.read_csv(\n", " \"https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv\"\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "1nocx9xeA5UY" }, "source": [ "## Datos CSSEGISandData/COVID-19\n", "\n", "Descripcion de los datos\n", "\n", "**Province/State:** China - province name; US/Canada/Australia/ - city name, state/province name; Others - name of the event (e.g., \"Diamond Princess\" cruise ship); other countries - blank.\n", "\n", "**Country/Region:** country/region name conforming to WHO (will be updated).\n", "\n", "**Last Update:** MM/DD/YYYY HH:mm (24 hour format, in UTC).\n", "\n", "**Confirmed: **the number of confirmed cases. For Hubei Province: from Feb 13 (GMT +8), we report both clinically diagnosed and lab-confirmed cases. For lab-confirmed cases only (Before Feb 17), please refer to who_covid_19_situation_reports. For Italy, diagnosis standard might be changed since Feb 27 to \"slow the growth of new case numbers.\"\n", "\n", "**Deaths:** the number of deaths.\n", "\n", "**Recovered:** the number of recovered cases." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "fTZI2C13_rsQ" }, "outputs": [ { "data": { "text/html": [ "
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Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/20
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" ], "text/plain": [ " Province/State Country/Region Lat Long 1/22/20 1/23/20 \\\n", "0 NaN Afghanistan 33.93911 67.709953 0 0 \n", "1 NaN Albania 41.15330 20.168300 0 0 \n", "2 NaN Algeria 28.03390 1.659600 0 0 \n", "3 NaN Andorra 42.50630 1.521800 0 0 \n", "4 NaN Angola -11.20270 17.873900 0 0 \n", "\n", " 1/24/20 1/25/20 \n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "confirmed.iloc[:5, :8]" ] }, { "cell_type": "markdown", "metadata": { "id": "N2E7F_J4BqN-" }, "source": [ "## Datos Generales de cada Dataframe" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "dKDC24PCCGjq" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 289 entries, 0 to 288\n", "Columns: 1147 entries, Province/State to 3/9/23\n", "dtypes: float64(2), int64(1143), object(2)\n", "memory usage: 2.5+ MB\n" ] } ], "source": [ "confirmed.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Paises con multiples datos por Provincia/Estado" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Australia' 'Canada' 'China' 'Denmark' 'France' 'Netherlands'\n", " 'New Zealand' 'United Kingdom']\n" ] } ], "source": [ "print(\n", " confirmed.loc[confirmed[\"Country/Region\"].duplicated(keep=False), \"Country/Region\"]\n", " .drop_duplicates()\n", " .unique()\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sumar los datos de cada pais" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def sumar_datos_region(df: pd.DataFrame) -> pd.DataFrame:\n", " \"\"\"\n", " Suma los datos de cada región en el dataframe dado.\n", " Args:\n", " df (pd.DataFrame): El dataframe del cual se sumarán los datos.\n", " Returns:\n", " pd.DataFrame: Un nuevo dataframe con los datos sumados para cada región.\n", " \"\"\"\n", " df = df.groupby([\"Country/Region\"]).sum().reset_index()\n", " return df\n", "\n", "\n", "# aplicar la funcion a los tres dataframes\n", "confirmed, death, recovered = (\n", " sumar_datos_region(df) for df in (confirmed, death, recovered)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Country/Region como indice de los dataframes" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "confirmed, death, recovered = (\n", " df.set_index(\"Country/Region\") for df in (confirmed, death, recovered)\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "Vh7JPE-MChF2" }, "source": [ "### Eliminar Ubicacion\n", "\n", "Se va realizar un analisis general de los datos y No se van a tomar los datos geograficos de *latitud*, *longitud* y los datos de *Province/State* estan incompletos.\n", "\n", "Solo se realizara un analisis por pais entonces se eliminaran las columnas mencionadas anteriormenten de cada dataframe.\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "bnNhCaqKDdmC" }, "outputs": [], "source": [ "confirmed, death, recovered = (\n", " df.drop(columns=[\"Lat\", \"Long\", \"Province/State\"])\n", " for df in (confirmed, death, recovered)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Datos de Personas Recuperadas" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plotly.com" }, "data": [ { "hovertemplate": "x=%{x}
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1 ], "title": { "text": "x" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "y" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "total_recuperados_dia = recovered.loc[:, :\"8/15/21\"].sum(axis=\"index\")\n", "fig = px.line(\n", " x=total_recuperados_dia.index,\n", " y=total_recuperados_dia.values,\n", " title=\"Numero de casos recuperados por dia\",\n", ")\n", "# grabar grafica en chart-studio si se proporciona el api-key\n", "if api_key:\n", " py.plot(fig, filename=\"recuperados_por_dia\", auto_open=False)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los datos de *Recovered* estan disponibles solo hasta el 4/Agosto/2021" ] }, { "cell_type": "markdown", "metadata": { "id": "x1I4DaJLnksH" }, "source": [ "### Datos Mundiales por dia" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "column_names = [\"Fecha\", \"Confirmados\", \"Recuperados\", \"Muertos\"]\n", "world = pd.DataFrame(columns=column_names)\n", "world[\"Fecha\"] = confirmed.columns\n", "world[\"Confirmados\"] = confirmed.sum(axis=\"rows\").values\n", "world[\"Recuperados\"] = recovered.sum(axis=\"rows\").values\n", "world[\"Muertos\"] = death.sum(axis=\"rows\").values\n", "world[\"Activos\"] = world[\"Confirmados\"] - world[\"Recuperados\"] - world[\"Muertos\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "D1Pvbt3dzcdv" }, "source": [ "# Evolucion Animada de Casos Activos por Pais\n", "La gráfica animada de la evolución temporal de los casos activos por país, la he creado con la libreria [Pandas alive](https://github.com/JackMcKew/pandas_alive) y [Bar Chart Race](https://github.com/dexplo/bar_chart_race).\n", "La barra horizontal gris representa el valor promedio de casos activos a nivel mundial." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZotaompXzcdv" }, "outputs": [], "source": [ "active_evol = active_group.set_index(\"date\") # noqa\n", "active_evol.index = pd.to_datetime(active_evol.index)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zWUBWG8Tzcdw", "tags": [] }, "outputs": [], "source": [ "import warnings\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "hola = active_evol.plot_animated(\n", " filename=\"evolucion_casos_activos.mp4\",\n", " n_bars=8,\n", " n_visible=8,\n", " title=\"Evolución en el tiempo de Casos Activos COVID-19 por pais \\n https://joserzapata.github.io/\",\n", " period_label={\"x\": 0.99, \"y\": 0.25, \"ha\": \"right\", \"va\": \"center\"},\n", " period_fmt=\"%B %d, %Y\",\n", " dpi=300,\n", " period_summary_func=lambda v: {\n", " \"x\": 0.99,\n", " \"y\": 0.18,\n", " \"s\": f\"Total Activos: {v.nlargest(8).sum():,.0f}\",\n", " \"ha\": \"right\",\n", " \"size\": 9,\n", " \"family\": \"Courier New\",\n", " },\n", ");" ] }, { "cell_type": "markdown", "metadata": { "id": "Q2e5kRWeQrCs" }, "source": [ "# Visualizacion con Plotly" ] }, { "cell_type": "markdown", "metadata": { "id": "Z8B5K4RxbkEQ" }, "source": [ "## Valores Mundiales de Confirmados, Recuperados y Muertos\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plotly.com" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "color=Confirmados
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"white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Total casos COVID-19 en el mundo al 3/9/23" }, "width": 600, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Numero de Personas" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plotly bar plot of the last row of the world dataframe without Recuperados column\n", "fig = px.bar(\n", " world.iloc[-1][[\"Confirmados\", \"Muertos\"]],\n", " x=[\"Confirmados\", \"Muertos\"],\n", " color=[\"Confirmados\", \"Muertos\"],\n", " y=world.iloc[-1][[\"Confirmados\", \"Muertos\"]].values,\n", " text=world.iloc[-1][[\"Confirmados\", \"Muertos\"]].values,\n", " color_discrete_sequence=[\"navy\", \"coral\"],\n", " height=500,\n", " width=600,\n", " title=\"Total casos COVID-19 en el mundo al {}\".format(world.iloc[-1][\"Fecha\"]),\n", " labels={\"value\": \"Número de casos\", \"variable\": \"Tipo de caso\"},\n", ")\n", "fig.update_traces(textposition=\"outside\") # poner los valores de las barras fuera\n", "fig.add_annotation(\n", " x=\"Muertos\",\n", " y=world[\"Confirmados\"].max(),\n", " text=\"https://joserzapata.github.io/\",\n", " showarrow=False,\n", ")\n", "fig.layout.update(\n", " showlegend=False,\n", " yaxis={\"title\": {\"text\": \"Numero de Personas\"}}, # Cambiar texto eje y\n", " xaxis={\"title\": {\"text\": \"\"}}, # Esconder nombre eje x\n", ")\n", "# grabar grafica en chart-studio si se proporciona el api-key\n", "if api_key:\n", " py.plot(fig, filename=\"total_casos_general\", auto_open=False)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "GxS-iNEJ1Gu6" }, "source": [ "## Mapa Mundial de Confirmados por Pais" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "AWj0By9l00Ac" }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plotly.com" }, "data": [ { "coloraxis": "coloraxis", "customdata": [ [ 209451 ], [ 334457 ], [ 271496 ], [ 47890 ], [ 105288 ], [ 11 ], [ 9106 ], [ 10044957 ], [ 447308 ], [ 11399460 ], [ 5961143 ], [ 828825 ], [ 37491 ], [ 710693 ], [ 2037871 ], [ 106798 ], [ 994037 ], [ 4739365 ], [ 70757 ], [ 27999 ], [ 62627 ], [ 1194277 ], [ 401729 ], [ 329758 ], [ 37076053 ], [ 279661 ], [ 1297523 ], [ 22056 ], [ 633950 ], [ 53631 ], [ 63244 ], [ 138719 ], [ 124392 ], [ 4617095 ], [ 15368 ], [ 7679 ], [ 5192286 ], [ 4903524 ], [ 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"linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Mapa de Confirmados COVID 19 por Pais - 3/9/23" }, "width": 900 } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "conf_max = confirmed.iloc[:, -1].copy()\n", "conf_max = conf_max.to_frame().reset_index()\n", "conf_max = conf_max.rename(columns={\"3/9/23\": \"Confirmados\"})\n", "\n", "fig = px.choropleth(\n", " conf_max,\n", " locations=\"Country/Region\",\n", " locationmode=\"country names\",\n", 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}, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Muertos vs Confirmados - 3/9/23 - (15 Paises)" }, "width": 900, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Confirmados" }, "type": "log" }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Muertos" }, "type": "log" } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "max_fecha = \"3/9/23\"\n", "death_max = death.iloc[:, -1].copy()\n", "death_max = death_max.to_frame().reset_index()\n", "death_max = death_max.rename(columns={max_fecha: \"Muertos\"})\n", "\n", "maxi_y = death_max[\"Muertos\"].max()\n", "maxi_x = conf_max[\"Confirmados\"].max()\n", "\n", "full_melt_max = pd.merge(\n", " conf_max[[\"Country/Region\", \"Confirmados\"]],\n", " death_max[[\"Country/Region\", \"Muertos\"]],\n", " on=\"Country/Region\",\n", " how=\"left\",\n", ")\n", "\n", "fig = px.scatter(\n", " full_melt_max.sort_values(\"Muertos\", ascending=False).iloc[:15, :],\n", " x=\"Confirmados\",\n", " y=\"Muertos\",\n", " color=\"Country/Region\",\n", " size=\"Confirmados\",\n", " height=500,\n", " width=900,\n", " text=\"Country/Region\",\n", " log_x=True,\n", " log_y=True,\n", " title=f\"Muertos vs Confirmados - {max_fecha} - (15 Paises)\",\n", ")\n", "fig.add_annotation(\n", " x=0.5,\n", " y=1,\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " text=\"https://joserzapata.github.io/\",\n", " showarrow=False,\n", ")\n", "fig.update_traces(textposition=\"top center\")\n", "fig.layout.update(showlegend=False)\n", "\n", "# grabar grafica en chart-studio si se proporciona el api-key\n", "if api_key:\n", " py.plot(fig, filename=\"scatter_muertos_confirmados\", auto_open=False)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "3b83uuD9G0yg" }, "source": [ "## Progresion Mundial en el Tiempo del numero de casos\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "OUcnRkN9lqLk" }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plotly.com" }, "data": [ { "hovertemplate": "variable=Confirmados
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\"coral\", \"navy\"],\n", " title=f\"Total de Casos en el tiempo de COVID 19 - {fecha_datos_completos}\",\n", ")\n", "for n in list(world.columns)[1:]:\n", " fig.add_annotation(\n", " x=world.iloc[-1, 0],\n", " y=world.loc[world.index[-1], n],\n", " text=n,\n", " xref=\"x\",\n", " yref=\"y\",\n", " showarrow=True,\n", " ax=-50,\n", " ay=-20,\n", " )\n", "# Indicador de numero total de confirmados\n", "fig.add_indicator(\n", " title={\"text\": \"Confirmados\", \"font\": {\"color\": \"teal\"}},\n", " value=world[\"Confirmados\"].iloc[-1],\n", " mode=\"number+delta\",\n", " delta={\"reference\": world[\"Confirmados\"].iloc[-2], \"relative\": True},\n", " domain={\"x\": [0, 0.25], \"y\": [0.15, 0.4]},\n", ")\n", "# Indicador numero total de Activos\n", "fig.add_indicator(\n", " title={\"text\": \"Activos\", \"font\": {\"color\": \"navy\"}},\n", " value=world[\"Activos\"].iloc[-1],\n", " mode=\"number+delta\",\n", " delta={\"reference\": world[\"Activos\"].iloc[-2], \"relative\": True},\n", " domain={\"x\": [0, 0.25], \"y\": [0.6, 0.85]},\n", ")\n", "# Indicador numero total de Recuperados\n", "fig.add_indicator(\n", " title={\"text\": \"Recuperados\", \"font\": {\"color\": \"green\"}},\n", " value=world[\"Recuperados\"].iloc[-1],\n", " mode=\"number+delta\",\n", " delta={\"reference\": world[\"Recuperados\"].iloc[-2], \"relative\": True},\n", " domain={\"x\": [0.25, 0.50], \"y\": [0.6, 0.85]},\n", ")\n", "# Indicador numero total de muertos\n", "fig.add_indicator(\n", " title={\"text\": \"Muertos\", \"font\": {\"color\": \"coral\"}},\n", " value=world[\"Muertos\"].iloc[-1],\n", " mode=\"number+delta\",\n", " delta={\"reference\": world[\"Muertos\"].iloc[-2], \"relative\": True},\n", " domain={\"x\": [0.25, 0.5], \"y\": [0.15, 0.4]},\n", ")\n", "fig.add_annotation(\n", " x=400,\n", " y=world_melt[\"value\"].max(),\n", " text=\"https://joserzapata.github.io/\",\n", " showarrow=False,\n", ")\n", "fig.layout.update(\n", " showlegend=False,\n", " yaxis={\"title\": {\"text\": \"Numero de Personas\"}}, # Cambiar texto eje y\n", ")\n", "# grabar grafica en chart-studio si se proporciona el api-key\n", "if api_key:\n", " py.plot(fig, filename=\"total_casos_serie\", auto_open=False)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "o-ong5NYHCsT" }, "source": [ "## Total Casos Confirmados de COVID 19 por Pais (Top 10)\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "iSsXyRwJN8_X" }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plotly.com" }, "data": [ { "hovertemplate": "%{hovertext}

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Country/Region=Japan
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Country/Region=Italy
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Country/Region=United Kingdom
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"left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Total Casos Confirmados de COVID 19 por Pais (Top 10) - 8/4/21" }, "width": 900, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Fecha" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Numero de Personas" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df1 = confirmed.copy()\n", "fecha = confirmed.columns[-1] # obtener la fecha del ultimo dato\n", "paises = df1.iloc[:, -1].copy() # obtener la serie sin el primer dato, fecha\n", "paises.sort_values(ascending=False, inplace=True)\n", "top = 10\n", "# keep top countries\n", "df1 = df1.loc[paises[:top].index.to_list(), :]\n", "df1 = df1.T\n", "\n", "if api_key:\n", " # se toman la serie de tiempo cada 7 dias, por que las graficas\n", " # grandes no se pueden subir a chart-studio con subscripcion gratuita\n", " df1 = df1.iloc[::-7].iloc[::-1]\n", "\n", "fig = px.line(\n", " df1,\n", " x=df1.index,\n", " y=df1.columns,\n", " color=\"Country/Region\",\n", " color_discrete_sequence=px.colors.qualitative.G10,\n", " width=900,\n", " hover_name=\"Country/Region\",\n", " title=f\"Total Casos Confirmados de COVID 19 por Pais (Top 10) - {world.iloc[-1,0]}\",\n", ")\n", "# top paises mas infectados\n", "\n", "mas_infectados = []\n", "for n in range(top):\n", " fig.add_annotation(\n", " x=fecha,\n", " y=paises.iloc[n],\n", " text=paises.index[n],\n", " showarrow=True,\n", " ax=+45,\n", " xref=\"x\",\n", " yref=\"y\",\n", " )\n", " mas_infectados.append(paises.index[n])\n", "\n", "fig.layout.update(\n", " showlegend=False,\n", " yaxis={\"title\": {\"text\": \"Numero de Personas\"}}, # Cambiar texto eje y\n", " xaxis={\"title\": {\"text\": \"Fecha\"}}, # Esconder nombre eje x\n", ")\n", "fig.add_annotation(\n", " x=200, y=df1.max().max(), text=\"https://joserzapata.github.io/\", showarrow=False\n", ")\n", "# grabar grafica en chart-studio si se proporciona el api-key\n", "if api_key:\n", " py.plot(fig, filename=\"total_casos_no_china\", auto_open=False)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "gPDj3HYfKBIJ" }, "source": [ "# Animacion del Mapa de Evolucion Temporal del Codiv 19" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# confirmed data frame en formato wide\n", "confirmed = confirmed.T\n", "confirmed.reset_index(inplace=True)\n", "confirmed.rename(columns={\"index\": \"Fecha\"}, inplace=True)\n", "confirmed_melt = confirmed.melt(id_vars=\"Fecha\").copy()\n", "confirmed_melt.rename(columns={\"value\": \"Confirmados\"}, inplace=True)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "id": "AQxF9NDpKHtS" }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plotly.com" }, "data": [ { "geo": "geo", "hovertemplate": "%{hovertext}

Fecha=2020-01-24 00:00:00
size=%{marker.size}
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Confirmados=%{marker.color}", "hovertext": [ "Afghanistan", "Albania", "Algeria", "Andorra", "Angola", "Antarctica", "Antigua and Barbuda", "Argentina", "Armenia", "Australia", "Austria", "Azerbaijan", "Bahamas", "Bahrain", "Bangladesh", "Barbados", "Belarus", "Belgium", "Belize", "Benin", "Bhutan", "Bolivia", "Bosnia and Herzegovina", "Botswana", "Brazil", "Brunei", "Bulgaria", "Burkina Faso", "Burma", "Burundi", "Cabo Verde", "Cambodia", "Cameroon", "Canada", "Central African Republic", "Chad", "Chile", "China", "Colombia", "Comoros", "Congo (Brazzaville)", "Congo (Kinshasa)", "Costa Rica", "Cote d'Ivoire", "Croatia", "Cuba", "Cyprus", "Czechia", "Denmark", "Diamond Princess", "Djibouti", "Dominica", "Dominican Republic", "Ecuador", "Egypt", "El Salvador", "Equatorial Guinea", "Eritrea", "Estonia", "Eswatini", "Ethiopia", "Fiji", "Finland", "France", "Gabon", "Gambia", "Georgia", "Germany", "Ghana", "Greece", "Grenada", "Guatemala", "Guinea", "Guinea-Bissau", "Guyana", "Haiti", "Holy 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Fecha=2020-01-24 00:00:00
size=%{marker.size}
Country/Region=%{location}
Confirmados=%{marker.color}", "hovertext": [ "Afghanistan", "Albania", "Algeria", "Andorra", "Angola", "Antarctica", "Antigua and Barbuda", "Argentina", "Armenia", "Australia", "Austria", "Azerbaijan", "Bahamas", "Bahrain", "Bangladesh", "Barbados", "Belarus", "Belgium", "Belize", "Benin", "Bhutan", "Bolivia", "Bosnia and Herzegovina", "Botswana", "Brazil", "Brunei", "Bulgaria", "Burkina Faso", "Burma", "Burundi", "Cabo Verde", "Cambodia", "Cameroon", "Canada", "Central African Republic", "Chad", "Chile", "China", "Colombia", "Comoros", "Congo (Brazzaville)", "Congo (Kinshasa)", "Costa Rica", "Cote d'Ivoire", "Croatia", "Cuba", "Cyprus", "Czechia", "Denmark", "Diamond Princess", "Djibouti", "Dominica", "Dominican Republic", "Ecuador", "Egypt", "El Salvador", "Equatorial Guinea", "Eritrea", "Estonia", "Eswatini", "Ethiopia", "Fiji", "Finland", "France", "Gabon", "Gambia", "Georgia", "Germany", "Ghana", "Greece", "Grenada", "Guatemala", "Guinea", "Guinea-Bissau", "Guyana", "Haiti", "Holy 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tiempo cada 18 dias, por que las graficas\n", " # grandes no se pueden subir a chart-studio con subscripcion gratuita\n", " confirmed_melt = confirmed.iloc[::-30].iloc[::-1].melt(id_vars=\"Fecha\").copy()\n", " confirmed_melt.rename(columns={\"value\": \"Confirmados\"}, inplace=True)\n", "\n", "confirmed_melt[\"Fecha\"] = pd.to_datetime(confirmed_melt[\"Fecha\"], format=\"%m/%d/%y\")\n", "confirmed_melt[\"size\"] = confirmed_melt[\"Confirmados\"].pow(0.3)\n", "confirmed_melt.dropna(inplace=True) # eliminar filas con valores faltantes\n", "\n", "fig = px.scatter_geo(\n", " confirmed_melt,\n", " locations=\"Country/Region\",\n", " locationmode=\"country names\",\n", " color=\"Confirmados\",\n", " size=\"size\",\n", " hover_name=\"Country/Region\",\n", " range_color=[0, max(confirmed_melt[\"Confirmados\"]) + 2],\n", " projection=\"natural earth\",\n", " animation_frame=\"Fecha\",\n", " title=\"Contagiados COVID 19 en el Tiempo\",\n", ")\n", 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"" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Corona virus (COVID 19) en Colombia - 8/4/21" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Fecha" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Numero de Personas" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "column_names = [\"Fecha\", \"Confirmados\", \"Recuperados\", \"Muertos\"]\n", "colombia = pd.DataFrame(columns=column_names)\n", "colombia[\"Fecha\"] = confirmed[\"Fecha\"].values\n", "colombia[\"Confirmados\"] = confirmed[\"Colombia\"].values\n", "colombia[\"Recuperados\"] = recovered.loc[\"Colombia\"].values\n", "colombia[\"Muertos\"] = death.loc[\"Colombia\"].values\n", "colombia[\"Activos\"] = (\n", " colombia[\"Confirmados\"] - colombia[\"Recuperados\"] - colombia[\"Muertos\"]\n", ")\n", "\n", "fecha_datos_completos = \"8/4/21\"\n", "pos_final = len(colombia.set_index(\"Fecha\").loc[:fecha_datos_completos, :])\n", "colombia = colombia.iloc[:pos_final, :]\n", "\n", "\n", "df_melt3 = colombia.melt(\n", " id_vars=\"Fecha\", value_vars=list(colombia.columns)[1:], var_name=None\n", ")\n", "fig = px.line(\n", " df_melt3,\n", " x=\"Fecha\",\n", " y=\"value\",\n", " color=\"variable\",\n", " color_discrete_sequence=[\"teal\", \"green\", \"coral\", \"navy\"],\n", " title=f\"Corona virus (COVID 19) en Colombia - {colombia.iloc[-1,0]}\",\n", ")\n", "# Indicador de numero total de confirmados\n", "fig.add_indicator(\n", " title={\"text\": \"Confirmados\", \"font\": {\"color\": \"teal\"}},\n", " value=colombia[\"Confirmados\"].iloc[-1],\n", " mode=\"number+delta\",\n", " delta={\"reference\": colombia[\"Confirmados\"].iloc[-2], \"relative\": True},\n", " domain={\"x\": [0, 0.25], \"y\": [0.15, 0.4]},\n", ")\n", "# Indicador numero total de Activos\n", "fig.add_indicator(\n", " title={\"text\": \"Activos\", \"font\": {\"color\": \"navy\"}},\n", " value=colombia[\"Activos\"].iloc[-1],\n", " mode=\"number+delta\",\n", " delta={\"reference\": colombia[\"Activos\"].iloc[-2], \"relative\": True},\n", " domain={\"x\": [0, 0.25], \"y\": [0.6, 0.85]},\n", ")\n", "# Indicador numero total de Recuperados\n", "fig.add_indicator(\n", " title={\"text\": \"Recuperados\", \"font\": {\"color\": \"green\"}},\n", " value=colombia[\"Recuperados\"].iloc[-1],\n", " mode=\"number+delta\",\n", " delta={\"reference\": colombia[\"Recuperados\"].iloc[-2], \"relative\": True},\n", " domain={\"x\": [0.25, 0.50], \"y\": [0.6, 0.85]},\n", ")\n", "# Indicador numero total de muertos\n", "fig.add_indicator(\n", " title={\"text\": \"Muertos\", \"font\": {\"color\": \"coral\"}},\n", " value=colombia[\"Muertos\"].iloc[-1],\n", " mode=\"number+delta\",\n", " delta={\"reference\": colombia[\"Muertos\"].iloc[-2], \"relative\": True},\n", " domain={\"x\": [0.25, 0.5], \"y\": [0.15, 0.4]},\n", ")\n", "fig.add_annotation(\n", " x=140,\n", " y=df_melt3[\"value\"].max(),\n", " text=\"https://joserzapata.github.io/\",\n", " showarrow=False,\n", ")\n", "fig.layout.update(\n", " showlegend=False,\n", " yaxis={\"title\": {\"text\": \"Numero de Personas\"}}, # Cambiar texto eje y\n", " xaxis={\"title\": {\"text\": \"Fecha\"}},\n", ")\n", "# grabar grafica en chart-studio si se proporciona el api-key\n", "if api_key:\n", " py.plot(fig, filename=\"Colombia_general\", auto_open=False)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "P7oX7axkfJyq" }, "source": [ "# Codigo Fuente Jupyter notebook\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JoseRZapata/covid_plots/blob/main/notebooks/Covid19_Visualizacion_es.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/JoseRZapata/covid_plots/HEAD?labpath=notebooks%2FCovid19_Visualizacion_es.ipynb) [![nbviewer](https://img.shields.io/badge/render-nbviewer-orange.svg)](https://nbviewer.jupyter.org/github/JoseRZapata/covid_plots/blob/main/notebooks/Covid19_Visualizacion_es.ipynb)" ] }, { "cell_type": "markdown", "metadata": { "id": "E6mkrGmdxXI4" }, "source": [ "# Refencias\n", "Fuentes de datos, visualizaciones y analisis de datos.\n", "\n", "- https://github.com/CSSEGISandData/COVID-19\n", "- https://www.kaggle.com/imdevskp/covid-19-analysis-viz-prediction-comparisons\n", "- https://junye0798.com/post/build-a-dashboard-to-track-the-spread-of-coronavirus-using-dash/\n", "- https://github.com/Perishleaf/data-visualisation-scripts/tree/master/dash-2019-coronavirus\n", "- https://medium.com/tomas-pueyo/coronavirus-por-qu%C3%A9-debemos-actuar-ya-93079c61e200" ] } ], "metadata": { "colab": { "name": "Covid19_Visualizacion_v3.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3.8.3 ('base')", "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": "3.10.13" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { 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