{ "cells": [ { "cell_type": "markdown", "id": "915e76c4", "metadata": {}, "source": [ "# Calidad del Aire UCA — Exploración de Datos\n", "\n", "Notebook de exploración para el sitio de monitoreo de calidad de aire\n", "(`calidadaire.uca.edu.ar`). Fuente: mediciones del sensor exportadas a CSV.\n", "\n", "\n", "**Contenido**\n", "\n", "1. [Configuración e imports](#1)\n", "2. [Carga de datos](#2)\n", "3. [Preparación de variables](#3)\n", "4. [Vista general del dataset](#4)\n", "5. [Réplica de los gráficos del sitio](#5)\n", "6. [Análisis exploratorio adicional](#6)\n" ] }, { "cell_type": "markdown", "id": "c258c5ee", "metadata": {}, "source": [ "## 1. Configuración e imports " ] }, { "cell_type": "code", "execution_count": 1, "id": "4f9028e4", "metadata": {}, "outputs": [], "source": [ "import warnings\n", "from pathlib import Path\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "import plotly.io as pio\n", "from IPython.display import display\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "pd.set_option(\"display.max_columns\", None)\n", "pd.set_option(\"display.width\", 120)\n", "\n", "pio.templates.default = \"plotly_white\"\n" ] }, { "cell_type": "markdown", "id": "565765f1", "metadata": {}, "source": [ "## 2. Carga de datos \n", "\n", "Se busca la carpeta `data/` subiendo por el árbol de directorios desde el\n", "directorio actual, para que la notebook funcione sin importar desde dónde\n", "se la ejecute dentro del repo." ] }, { "cell_type": "code", "execution_count": 2, "id": "047b253f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Archivo de datos : ./datos_calidad_aire_uca_2026-04-01_2026-07-01.csv\n" ] } ], "source": [ "file_path = \"./datos_calidad_aire_uca_2026-04-01_2026-07-01.csv\"\n", "\n", "print(f\"Archivo de datos : {file_path}\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "ccf51e2d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idtemperatura_chumedadpunto_rocio_cindice_calor_caqipm25pm10fecha_reporte
0809826.8384.724.0630.4428.9880946.9571437.3857142026-04-01 00:00:00
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4810226.8984.724.1130.5624.5238085.8857145.8857142026-04-01 00:25:00
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" ], "text/plain": [ " id temperatura_c humedad punto_rocio_c indice_calor_c aqi pm25 pm10 fecha_reporte\n", "0 8098 26.83 84.7 24.06 30.44 28.988094 6.957143 7.385714 2026-04-01 00:00:00\n", "1 8099 26.83 84.9 24.06 30.39 27.499998 6.600000 7.542857 2026-04-01 00:05:00\n", "2 8100 26.83 83.9 23.89 30.33 31.944443 7.666666 9.086957 2026-04-01 00:15:00\n", "3 8101 26.89 83.7 23.89 30.33 29.166666 7.000000 7.115942 2026-04-01 00:20:00\n", "4 8102 26.89 84.7 24.11 30.56 24.523808 5.885714 5.885714 2026-04-01 00:25:00" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(file_path)\n", "df[\"fecha_reporte\"] = pd.to_datetime(df[\"fecha_reporte\"])\n", "\n", "df.head()" ] }, { "cell_type": "markdown", "id": "96d94d16", "metadata": {}, "source": [ "## 3. Preparación de variables " ] }, { "cell_type": "code", "execution_count": 4, "id": "1cd87393", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idtemperatura_chumedadpunto_rocio_cindice_calor_caqipm25pm10fecha_reportehoraaniodia_semanafranja_horaria
0809826.8384.724.0630.4428.9880946.9571437.3857142026-04-01 00:00:0002026MiércolesMadrugada
1809926.8384.924.0630.3927.4999986.6000007.5428572026-04-01 00:05:0002026MiércolesMadrugada
2810026.8383.923.8930.3331.9444437.6666669.0869572026-04-01 00:15:0002026MiércolesMadrugada
3810126.8983.723.8930.3329.1666667.0000007.1159422026-04-01 00:20:0002026MiércolesMadrugada
4810226.8984.724.1130.5624.5238085.8857145.8857142026-04-01 00:25:0002026MiércolesMadrugada
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" ], "text/plain": [ " id temperatura_c humedad punto_rocio_c indice_calor_c aqi pm25 pm10 fecha_reporte \\\n", "0 8098 26.83 84.7 24.06 30.44 28.988094 6.957143 7.385714 2026-04-01 00:00:00 \n", "1 8099 26.83 84.9 24.06 30.39 27.499998 6.600000 7.542857 2026-04-01 00:05:00 \n", "2 8100 26.83 83.9 23.89 30.33 31.944443 7.666666 9.086957 2026-04-01 00:15:00 \n", "3 8101 26.89 83.7 23.89 30.33 29.166666 7.000000 7.115942 2026-04-01 00:20:00 \n", "4 8102 26.89 84.7 24.11 30.56 24.523808 5.885714 5.885714 2026-04-01 00:25:00 \n", "\n", " hora anio dia_semana franja_horaria \n", "0 0 2026 Miércoles Madrugada \n", "1 0 2026 Miércoles Madrugada \n", "2 0 2026 Miércoles Madrugada \n", "3 0 2026 Miércoles Madrugada \n", "4 0 2026 Miércoles Madrugada " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"hora\"] = df[\"fecha_reporte\"].dt.hour\n", "df[\"anio\"] = df[\"fecha_reporte\"].dt.year\n", "\n", "dias_semana_es = {\n", " 0: \"Lunes\", 1: \"Martes\", 2: \"Miércoles\", 3: \"Jueves\",\n", " 4: \"Viernes\", 5: \"Sábado\", 6: \"Domingo\",\n", "}\n", "\n", "df[\"dia_semana\"] = df[\"fecha_reporte\"].dt.weekday.map(dias_semana_es)\n", "\n", "\n", "def clasificar_franja(hora: int) -> str:\n", " if 0 <= hora < 6:\n", " return \"Madrugada\"\n", " elif 6 <= hora < 12:\n", " return \"Mañana\"\n", " elif 12 <= hora < 14:\n", " return \"Mediodía\"\n", " elif 14 <= hora < 20:\n", " return \"Tarde\"\n", " else:\n", " return \"Noche\"\n", "\n", "\n", "df[\"franja_horaria\"] = df[\"hora\"].apply(clasificar_franja)\n", "\n", "df.head()\n" ] }, { "cell_type": "markdown", "id": "001bd801", "metadata": {}, "source": [ "## 4. Vista general del dataset " ] }, { "cell_type": "code", "execution_count": 5, "id": "ef347f04", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filas x columnas: (14488, 13)\n", "\n", "RangeIndex: 14488 entries, 0 to 14487\n", "Data columns (total 13 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 id 14488 non-null int64 \n", " 1 temperatura_c 14488 non-null float64 \n", " 2 humedad 14488 non-null float64 \n", " 3 punto_rocio_c 14488 non-null float64 \n", " 4 indice_calor_c 14488 non-null float64 \n", " 5 aqi 14488 non-null float64 \n", " 6 pm25 14488 non-null float64 \n", " 7 pm10 14488 non-null float64 \n", " 8 fecha_reporte 14488 non-null datetime64[us]\n", " 9 hora 14488 non-null int32 \n", " 10 anio 14488 non-null int32 \n", " 11 dia_semana 14488 non-null str \n", " 12 franja_horaria 14488 non-null str \n", "dtypes: datetime64[us](1), float64(7), int32(2), int64(1), str(2)\n", "memory usage: 1.3 MB\n" ] } ], "source": [ "print(f\"Filas x columnas: {df.shape}\")\n", "df.info()" ] }, { "cell_type": "code", "execution_count": 6, "id": "94026257", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No hay valores faltantes.\n" ] } ], "source": [ "faltantes = df.isna().sum().sort_values(ascending=False)\n", "faltantes = faltantes[faltantes > 0]\n", "if faltantes.empty:\n", " print(\"No hay valores faltantes.\")\n", "else:\n", " display(faltantes.to_frame(\"nulos\"))\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "32f38768", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
temperatura_c14488.016.4745794.6447326.7813.28000015.22000019.00000037.2800
humedad14488.070.19901312.60301535.4060.00000071.10000081.00000097.9000
punto_rocio_c14488.010.7855984.980854-0.567.11000010.44000013.89000025.2800
indice_calor_c14488.016.3880745.3052226.5012.83000014.89000018.94000046.5000
aqi14488.045.61207742.1424210.0011.11111131.64251170.996826574.6422
pm2514488.014.50236617.8607840.002.6666677.59420321.608696613.3919
pm1014488.016.98194720.9496340.003.5507258.88405823.564045739.5000
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" ], "text/plain": [ " count mean std min 25% 50% 75% max\n", "temperatura_c 14488.0 16.474579 4.644732 6.78 13.280000 15.220000 19.000000 37.2800\n", "humedad 14488.0 70.199013 12.603015 35.40 60.000000 71.100000 81.000000 97.9000\n", "punto_rocio_c 14488.0 10.785598 4.980854 -0.56 7.110000 10.440000 13.890000 25.2800\n", "indice_calor_c 14488.0 16.388074 5.305222 6.50 12.830000 14.890000 18.940000 46.5000\n", "aqi 14488.0 45.612077 42.142421 0.00 11.111111 31.642511 70.996826 574.6422\n", "pm25 14488.0 14.502366 17.860784 0.00 2.666667 7.594203 21.608696 613.3919\n", "pm10 14488.0 16.981947 20.949634 0.00 3.550725 8.884058 23.564045 739.5000" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols_numericas = df.select_dtypes(include=\"number\").columns.tolist()\n", "cols_numericas.remove(\"id\")\n", "cols_numericas.remove(\"hora\")\n", "cols_numericas.remove(\"anio\")\n", "\n", "df[cols_numericas].describe().T" ] }, { "cell_type": "code", "execution_count": 8, "id": "c3a156ff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rango temporal: 2026-04-01 00:00:00 → 2026-07-01 00:00:00\n", "Duración: 91 days 00:00:00\n" ] } ], "source": [ "rango_inicio = df[\"fecha_reporte\"].min()\n", "rango_fin = df[\"fecha_reporte\"].max()\n", "print(f\"Rango temporal: {rango_inicio} → {rango_fin}\")\n", "print(f\"Duración: {rango_fin - rango_inicio}\")" ] }, { "cell_type": "markdown", "id": "f62fed4f", "metadata": {}, "source": [ "## 5. Réplica de los gráficos del sitio " ] }, { "cell_type": "markdown", "id": "fad3ff25", "metadata": {}, "source": [ "### 5.1 Serie temporal de AQI (últimas 24 hs)" ] }, { "cell_type": "code", "execution_count": 9, "id": "2121654e", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "#378ADD", "width": 2 }, "mode": "lines", "name": "AQI", "type": "scatter", "x": [ "29 jun 02:25", "29 jun 02:30", "29 jun 02:35", "29 jun 02:40", "29 jun 02:45", "29 jun 02:50", "29 jun 02:55", "29 jun 03:00", "29 jun 03:05", "29 jun 03:10", "29 jun 03:15", "29 jun 03:20", "29 jun 03:25", "29 jun 03:30", "29 jun 03:35", "29 jun 03:40", "29 jun 03:45", "29 jun 03:50", "29 jun 03:55", "29 jun 04:00", "29 jun 04:05", "29 jun 04:10", "29 jun 04:15", "29 jun 04:20", "29 jun 04:25", "29 jun 04:30", "29 jun 04:35", "29 jun 04:40", "29 jun 04:45", "29 jun 04:50", "29 jun 04:55", "29 jun 05:00", "29 jun 05:05", "29 jun 05:10", "29 jun 05:15", "29 jun 05:20", "29 jun 05:25", "29 jun 05:30", "29 jun 05:35", "29 jun 05:40", "29 jun 05:45", "29 jun 05:50", "29 jun 05:55", "29 jun 06:00", "29 jun 06:05", "29 jun 06:10", "29 jun 06:15", "29 jun 06:20", "29 jun 06:25", "29 jun 06:30", "29 jun 06:35", "29 jun 06:40", "29 jun 06:45", "29 jun 06:50", "29 jun 06:55", "29 jun 07:00", "29 jun 07:05", "29 jun 07:10", "29 jun 07:15", "29 jun 07:20", "29 jun 07:25", "29 jun 07:30", "29 jun 07:35", "29 jun 07:40", "29 jun 07:45", "29 jun 07:50", "29 jun 07:55", "29 jun 08:00", "29 jun 08:05", "29 jun 08:10", "29 jun 08:15", "29 jun 08:20", "29 jun 08:25", "29 jun 08:30", "29 jun 08:35", "29 jun 08:40", "29 jun 08:45", "29 jun 08:50", "29 jun 08:55", "29 jun 09:00", "29 jun 09:05", "29 jun 09:10", "29 jun 09:15", "29 jun 09:20", "29 jun 09:25", "29 jun 09:30", "29 jun 09:35", "29 jun 09:40", "29 jun 09:45", "29 jun 09:50", "29 jun 09:55", "29 jun 10:00", "29 jun 10:05", "29 jun 10:10", "29 jun 10:15", "29 jun 10:20", "29 jun 10:25", "29 jun 10:30", "29 jun 10:35", "29 jun 10:40", "29 jun 10:45", "29 jun 10:50", "29 jun 10:55", "29 jun 11:00", "29 jun 11:05", "29 jun 11:10", "29 jun 11:15", "29 jun 11:20", "29 jun 11:25", "29 jun 11:30", "29 jun 11:35", "29 jun 11:40", "29 jun 11:45", "29 jun 11:50", "29 jun 11:55", "29 jun 12:00", "29 jun 12:05", "29 jun 12:10", "29 jun 12:15", "29 jun 12:20", "29 jun 12:25", "29 jun 12:30", "29 jun 12:35", "29 jun 12:40", "29 jun 12:45", "29 jun 12:50", "29 jun 12:55", "29 jun 13:00", "29 jun 13:05", "29 jun 13:10", "29 jun 13:15", "29 jun 13:20", "29 jun 13:25", "29 jun 13:30", "29 jun 13:35", "29 jun 13:40", "29 jun 13:45", "29 jun 13:50", "29 jun 13:55", "29 jun 14:00", "29 jun 14:05", "29 jun 14:10", "29 jun 14:15", "29 jun 14:20", "29 jun 14:25", "29 jun 14:30", "29 jun 14:35", "29 jun 14:40", "29 jun 14:45", "29 jun 14:50", "29 jun 14:55", "29 jun 15:00", "29 jun 15:05", "29 jun 15:10", "29 jun 15:15", "29 jun 15:20", "29 jun 15:25", "29 jun 15:30", "29 jun 15:35", "29 jun 15:40", "29 jun 15:45", "29 jun 15:50", "29 jun 15:55", "29 jun 16:00", "29 jun 16:05", "29 jun 16:10", "29 jun 16:15", "29 jun 16:20", "29 jun 16:25", "29 jun 16:30", "29 jun 16:35", "29 jun 16:40", "29 jun 18:32", "29 jun 19:58", "29 jun 20:51", "29 jun 21:39", "30 jun 11:56", "30 jun 14:40", "30 jun 14:45", "30 jun 14:58", "30 jun 15:05", "30 jun 15:10", "30 jun 15:15", "30 jun 15:20", "30 jun 15:25", "30 jun 15:30", "30 jun 15:35", "30 jun 15:40", "30 jun 15:45", "30 jun 15:50", "30 jun 15:55", "30 jun 16:00", "30 jun 16:05", "30 jun 16:10", "30 jun 16:15", "30 jun 16:20", "30 jun 16:25", "30 jun 16:30", "30 jun 16:35", "30 jun 16:40", "30 jun 16:45", "30 jun 16:50", "30 jun 16:55", "30 jun 17:00", "30 jun 17:05", "30 jun 17:10", "30 jun 17:15", "30 jun 17:20", "30 jun 17:25", "30 jun 17:30", "30 jun 17:35", "30 jun 17:40", "30 jun 17:45", "30 jun 17:50", "30 jun 17:55", "30 jun 18:00", "30 jun 18:05", "30 jun 18:10", "30 jun 18:15", "30 jun 18:20", "30 jun 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"30 jun 21:55", "30 jun 22:00", "30 jun 22:05", "30 jun 22:10", "30 jun 22:15", "30 jun 22:20", "30 jun 22:25", "30 jun 22:30", "30 jun 22:35", "30 jun 22:40", "30 jun 22:45", "30 jun 22:50", "30 jun 22:55", "30 jun 23:00", "30 jun 23:05", "30 jun 23:10", "30 jun 23:15", "30 jun 23:20", "30 jun 23:25", "30 jun 23:30", "30 jun 23:35", "30 jun 23:40", "30 jun 23:45", "30 jun 23:50", "30 jun 23:55", "1 jul 00:00" ], "categoryorder": "array", "showgrid": false, "tickmode": "array", "ticktext": [ "29 jun 02:25", "29 jun 04:25", "29 jun 06:25", "29 jun 08:25", "29 jun 10:25", "29 jun 12:25", "29 jun 14:25", "29 jun 16:25", "30 jun 16:05", "30 jun 18:05", "30 jun 20:05", "30 jun 22:05" ], "tickvals": [ "29 jun 02:25", "29 jun 04:25", "29 jun 06:25", "29 jun 08:25", "29 jun 10:25", "29 jun 12:25", "29 jun 14:25", "29 jun 16:25", "30 jun 16:05", "30 jun 18:05", "30 jun 20:05", "30 jun 22:05" ], "title": { "text": "Horario" } }, "yaxis": { "showgrid": false, "title": { "text": "AQI" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def grafico_aqi_serie_temporal(df):\n", " g = df.sort_values(\"fecha_reporte\").tail(24 * 12) # últimas 24hs = 288 lecturas de 5min\n", " g = g.copy()\n", " g[\"aqi_smooth\"] = g[\"aqi\"].rolling(window=10, min_periods=2).mean()\n", "\n", " meses = {\n", " 1: \"ene\", 2: \"feb\", 3: \"mar\", 4: \"abr\", 5: \"may\", 6: \"jun\",\n", " 7: \"jul\", 8: \"ago\", 9: \"sep\", 10: \"oct\", 11: \"nov\", 12: \"dic\",\n", " }\n", "\n", " g[\"fecha_str\"] = g[\"fecha_reporte\"].apply( lambda d: f\"{d.day} {meses[d.month]} {d.hour:02d}:{d.minute:02d}\" )\n", "\n", " fig = go.Figure()\n", "\n", " fig.add_trace(go.Scatter(\n", " x=g[\"fecha_str\"],\n", " y=g[\"aqi_smooth\"],\n", " mode=\"lines\",\n", " line=dict(color=\"#378ADD\", width=2),\n", " name=\"AQI\",\n", " ))\n", "\n", " fig.update_layout(\n", " xaxis_title=\"Horario\",\n", " yaxis_title=\"AQI\",\n", " template=\"plotly_white\",\n", " )\n", " fig.update_xaxes(\n", " categoryorder=\"array\",\n", " categoryarray=g[\"fecha_str\"].tolist(),\n", " tickmode=\"array\",\n", " tickvals=g[\"fecha_str\"].tolist()[::24],\n", " ticktext=g[\"fecha_str\"].tolist()[::24],\n", " showgrid=False,\n", " )\n", " fig.update_yaxes(showgrid=False)\n", "\n", " return fig\n", "\n", "\n", "grafico_aqi_serie_temporal(df).show()\n" ] }, { "cell_type": "markdown", "id": "bba9dc7f", "metadata": {}, "source": [ "### 5.2 Heatmap de AQI por día y franja horaria" ] }, { "cell_type": "code", "execution_count": 13, "id": "6135e9f4", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "hovertemplate": "dia_semana: %{x}
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"showland": true, "subunitcolor": "#C8D4E3" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "white", "polar": { "angularaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" }, "bgcolor": "white", "radialaxis": { "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "yaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" }, "zaxis": { "backgroundcolor": "white", "gridcolor": "#DFE8F3", "gridwidth": 2, "linecolor": "#EBF0F8", "showbackground": true, "ticks": "", "zerolinecolor": "#EBF0F8" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "baxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" }, "bgcolor": "white", "caxis": { "gridcolor": "#DFE8F3", "linecolor": "#A2B1C6", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Día" } }, "yaxis": { "anchor": "x", "autorange": "reversed", "domain": [ 0, 1 ], "title": { "text": "Franja horaria" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def grafico_heatmap_aqi(df):\n", " df = df.copy()\n", " df[\"hora\"] = df[\"fecha_reporte\"].dt.hour\n", " df[\"dia_semana\"] = df[\"fecha_reporte\"].dt.weekday\n", "\n", " dias_semana_es = {\n", " 0: \"Lunes\", 1: \"Martes\", 2: \"Miércoles\", 3: \"Jueves\",\n", " 4: \"Viernes\", 5: \"Sábado\", 6: \"Domingo\",\n", " }\n", " df[\"dia_semana\"] = df[\"dia_semana\"].map(dias_semana_es)\n", "\n", " def clasificar_franja(hora):\n", " if 0 <= hora < 6:\n", " return \"Madrugada\"\n", " elif 6 <= hora < 12:\n", " return \"Mañana\"\n", " elif 12 <= hora < 14:\n", " return \"Mediodía\"\n", " elif 14 <= hora < 20:\n", " return \"Tarde\"\n", " else:\n", " return \"Noche\"\n", "\n", " df[\"franja_horaria\"] = df[\"hora\"].apply(clasificar_franja)\n", "\n", " orden_dias = [\"Lunes\", \"Martes\", \"Miércoles\", \"Jueves\", \"Viernes\", \"Sábado\", \"Domingo\"]\n", " orden_franjas = [\"Madrugada\", \"Mañana\", \"Mediodía\", \"Tarde\", \"Noche\"]\n", "\n", " tabla = df.pivot_table(values=\"aqi\", index=\"franja_horaria\", columns=\"dia_semana\", aggfunc=\"mean\")\n", " tabla = tabla.reindex(index=orden_franjas, columns=orden_dias)\n", " tabla = tabla.interpolate(axis=0).interpolate(axis=1)\n", "\n", " fig = px.imshow(\n", " tabla,\n", " text_auto=\".1f\",\n", " aspect=\"auto\",\n", " color_continuous_scale=[\n", " [0, \"#d4fcd4\"],\n", " [0.33, \"#66cc66\"],\n", " [0.66, \"#006600\"],\n", " [1, \"#ffff00\"],\n", " ],\n", " )\n", "\n", " fig.update_layout(\n", " xaxis_title=\"Día\",\n", " yaxis_title=\"Franja horaria\",\n", " template=\"plotly_white\",\n", " )\n", "\n", " return fig\n", "\n", "\n", "grafico_heatmap_aqi(df).show()\n" ] }, { "cell_type": "markdown", "id": "6ae14067", "metadata": {}, "source": [ "### 5.5 Promedio por hora (función auxiliar)" ] }, { "cell_type": "code", "execution_count": 14, "id": "c36de95a", "metadata": {}, "outputs": [], "source": [ "def df_promedio_hora(df):\n", " df_hora = df.groupby(\"hora\").mean(numeric_only=True).reset_index()\n", " horas = pd.DataFrame({\"hora\": range(24)})\n", " df_h = horas.merge(df_hora, on=\"hora\", how=\"left\")\n", " return df_h.interpolate()\n" ] }, { "cell_type": "markdown", "id": "02f1f1a9", "metadata": {}, "source": [ "## 6. Análisis exploratorio adicional \n", "\n", "Gráficos pensados para explorar patrones que los gráficos del\n", "sitio no muestran: perfil horario promedio, correlación entre\n", "contaminantes, distribución del AQI por categoría y continuidad de las\n", "mediciones en el tiempo." ] }, { "cell_type": "markdown", "id": "cd941ba2", "metadata": {}, "source": [ "### 6.1 Perfil horario promedio (24 hs)\n", "\n", "Usa `df_promedio_hora` de `graficos.py`, la misma función que probablemente alimenta vistas tipo \"día típico\" del sitio." ] }, { "cell_type": "code", "execution_count": 15, "id": "7dd4d947", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "#378ADD", "width": 2 }, "mode": "lines+markers", "name": "AQI", "type": "scatter", "x": { "bdata": "AAECAwQFBgcICQoLDA0ODxAREhMUFRYX", "dtype": "i1" }, "y": { "bdata": 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"#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "Matriz de correlación" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ] }, "yaxis": { "anchor": "x", "autorange": "reversed", "domain": [ 0, 1 ] } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "corr_cols = [\"aqi\", \"pm25\", \"pm10\", \"temperatura_c\", \"humedad\",\"punto_rocio_c\",\"indice_calor_c\"]\n", "\n", "corr = df[corr_cols].corr()\n", "\n", "fig = px.imshow(\n", " corr,\n", " text_auto=\".2f\",\n", " color_continuous_scale=\"RdBu_r\",\n", " zmin=-1, zmax=1,\n", " aspect=\"auto\",\n", ")\n", "fig.update_layout(title=\"Matriz de correlación\")\n", "fig.show()\n" ] }, { "cell_type": "markdown", "id": "34d56a4e", "metadata": {}, "source": [ "### 6.3 Distribución de AQI por categoría\n", "\n", "Se usan las bandas estándar de AQI (EPA) para ver qué proporción del tiempo el aire estuvo en cada categoría." ] }, { "cell_type": "code", "execution_count": 17, "id": "e81aa06f", "metadata": {}, "outputs": [ { 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2 }, "yaxis": { "automargin": true, "gridcolor": "#EBF0F8", "linecolor": "#EBF0F8", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#EBF0F8", "zerolinewidth": 2 } } }, "title": { "text": "% del tiempo en cada categoría de AQI" }, "xaxis": { "anchor": "y", "categoryarray": [ "Buena", "Moderada", "Dañina (grupos sensibles)", "Dañina", "Muy dañina", "Peligrosa" ], "categoryorder": "array", "domain": [ 0, 1 ], "showgrid": false, "title": { "text": "Categoría" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "showgrid": false, "title": { "text": "% del tiempo" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def categorizar_aqi(valor: float) -> str:\n", " if pd.isna(valor):\n", " return np.nan\n", " if valor <= 50:\n", " return \"Buena\"\n", " elif valor <= 100:\n", " return \"Moderada\"\n", " elif valor <= 150:\n", " return \"Dañina (grupos sensibles)\"\n", " elif valor <= 200:\n", " return \"Dañina\"\n", " elif valor <= 300:\n", " return \"Muy dañina\"\n", " else:\n", " return \"Peligrosa\"\n", "\n", "\n", "orden_categorias = [\"Buena\", \"Moderada\", \"Dañina (grupos sensibles)\", \"Dañina\", \"Muy dañina\", \"Peligrosa\"]\n", "colores_categorias = {\n", " \"Buena\": \"#00e400\",\n", " \"Moderada\": \"#ffff00\",\n", " \"Dañina (grupos sensibles)\": \"#ff7e00\",\n", " \"Dañina\": \"#ff0000\",\n", " \"Muy dañina\": \"#8f3f97\",\n", " \"Peligrosa\": \"#7e0023\",\n", "}\n", "\n", "df[\"categoria_aqi\"] = df[\"aqi\"].apply(categorizar_aqi)\n", "\n", "conteo = (\n", " df[\"categoria_aqi\"]\n", " .value_counts(normalize=True)\n", " .reindex(orden_categorias)\n", " .fillna(0)\n", " .mul(100)\n", " .reset_index()\n", ")\n", "conteo.columns = [\"categoria\", \"porcentaje\"]\n", "\n", "fig = px.bar(\n", " conteo, x=\"categoria\", y=\"porcentaje\",\n", " color=\"categoria\", color_discrete_map=colores_categorias,\n", " category_orders={\"categoria\": orden_categorias},\n", ")\n", "fig.update_layout(\n", " title=\"% del tiempo en cada categoría de AQI\",\n", " xaxis_title=\"Categoría\",\n", " yaxis_title=\"% del tiempo\",\n", " showlegend=False,\n", ")\n", "fig.update_xaxes(showgrid=False)\n", "fig.update_yaxes(showgrid=False)\n", "fig.show()\n" ] }, { "cell_type": "markdown", "id": "7232e846", "metadata": {}, "source": [ "### 6.4 Distribución de AQI por día de la semana" ] }, { "cell_type": "code", "execution_count": 18, "id": "2394bf89", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "dia_semana=%{x}
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"Jueves", "Viernes", "Sábado", "Domingo" ], "categoryorder": "array", "domain": [ 0, 1 ], "showgrid": false, "title": { "text": "Día" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "showgrid": false, "title": { "text": "AQI" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "orden_dias = [\"Lunes\", \"Martes\", \"Miércoles\", \"Jueves\", \"Viernes\", \"Sábado\", \"Domingo\"]\n", "\n", "fig = px.box(\n", " df, x=\"dia_semana\", y=\"aqi\",\n", " category_orders={\"dia_semana\": orden_dias},\n", " color_discrete_sequence=[\"#378ADD\"],\n", ")\n", "fig.update_layout(\n", " title=\"Distribución de AQI por día de la semana\",\n", " xaxis_title=\"Día\",\n", " yaxis_title=\"AQI\",\n", ")\n", "fig.update_xaxes(showgrid=False)\n", "fig.update_yaxes(showgrid=False)\n", "fig.show()\n" ] } ], "metadata": { "kernelspec": { "display_name": "venv (3.12.3)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, 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