[FIX] colores y estilos

.idea/.gitignore

@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (punto_organico)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/punto_organico.iml" filepath="$PROJECT_DIR$/.idea/punto_organico.iml" />
+    </modules>
+  </component>
+</project>

.idea/punto_organico.iml

@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/venv" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+</module>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

.streamlit/config.toml

@@ -0,0 +1,6 @@
+[theme]
+primaryColor = "#FC5C9C"
+backgroundColor = "#F5FFFA"
+secondaryBackgroundColor = "#C4C754"
+textColor = "#556B2F"
+font = "sans serif"

ML/geocoding/.env

@@ -1,2 +0,0 @@
-API_KEY='AIzaSyAaqCt-qi-w7APxSjFXRgR3M5Z23Uj6NOE'
-BASE_URL='https://maps.googleapis.com/maps/api/geocode/json'

ML/geocoding/app.py

@@ -1,157 +0,0 @@
-import os
-
-import folium
-import matplotlib.pyplot as plt
-import requests
-import streamlit as st
-from dotenv import load_dotenv
-from sklearn.cluster import KMeans
-from streamlit_folium import folium_static
-
-load_dotenv()
-
-st.set_page_config(layout='wide')  # Para usar todo el ancho de la página
-
-API_KEY = os.getenv('API_KEY')
-BASE_URL = os.getenv('BASE_URL')
-
-
-def geocode_address(address):
-    api_key = API_KEY
-    base_url = BASE_URL
-    params = {
-        'address': address,
-        'key': api_key
-        }
-
-    try:
-        response = requests.get(base_url, params=params)
-        data = response.json()
-
-        if data['status'] == 'OK' and len(data['results']) > 0:
-            location = data['results'][0]['geometry']['location']
-            latitude = location['lat']
-            longitude = location['lng']
-
-            return latitude, longitude
-        else:
-            st.error(
-                    'No se encontraron resultados para la dirección especificada.'
-                    )
-    except requests.exceptions.RequestException as e:
-        st.error('Error en la solicitud:', e)
-
-
-direcciones = [
-    'San Martín y Garibaldi',
-    'Avenida Emilio Civit s/n',
-    'Parque General San Martín',
-    'Calle Sarmiento, entre las calles Patricias Mendocinas y Garibaldi',
-    'Calle Belgrano y España',
-    'Calle Las Heras 50',
-    'Plaza Independencia',
-    'Avenida España y Costanera',
-    'Calle 9 de Julio 1228',
-    'Calle Chile 1754',
-    'Avenida Arístides Villanueva',
-    'Avenida Emilio Civit y España',
-    'Calle Chile y Avenida Colón',
-    'Calle Emilio Civit y Avenida San Martín',
-    'Acceso Este y Avenida San Francisco de Asís',
-    'Calle San Martín y Avellaneda',
-    'Plaza Pedro del Castillo',
-    'Calle San Martín y Avenida España',
-    'Calle Emilio Civit y Avenida San Martín',
-    'Avenida España',
-    'Calle Avellaneda y Patricias Mendocinas',
-    'Parque General San Martín',
-    'Ruta Nacional 7',
-    'Avenida Costanera y calle Peltier',
-    'Calle Montecaseros 2625',
-    'Calle Francisco Delgado 1220',
-    'Ruta Provincial 86, s/n',
-    'Ruta 15, km 23',
-    'Calle San Martín 2044',
-    'Ruta 7 y Acceso Sur'
-    ]
-
-coordenadas = []
-for direccion in direcciones:
-    resultado = geocode_address(direccion + ', Capital, Mendoza, Argentina')
-    if resultado:
-        coordenadas.append(resultado)
-    else:
-        coordenadas.append((None, None))
-
-max_k = len(coordenadas)
-inertias = []
-for k in range(2, max_k + 1):
-    modelo = KMeans(n_clusters=k, random_state=42)
-    modelo.fit(coordenadas)
-    inertias.append(modelo.inertia_)
-
-# Titulo
-
-# Ajustar el tamaño del gráfico del codo
-col1, col2, col3 = st.columns(3)
-
-with col2:
-    col2.title('Clientes por Zonas')
-
-    fig, ax = plt.subplots(figsize=(6, 4))
-    ax.plot(range(2, max_k + 1), inertias, marker='o')
-    ax.set_xlabel('Número de zonas (k)')
-    ax.set_ylabel('Inercia')
-    ax.set_title('Método del Codo')
-    st.pyplot(fig)
-
-k_optimo = st.number_input(
-        "Ingrese el valor óptimo de k según el gráfico:", min_value=2,
-        max_value=max_k, step=1
-        )
-
-modelo = KMeans(n_clusters=k_optimo, random_state=42)
-modelo.fit(coordenadas)
-
-etiquetas = modelo.labels_
-num_zonas = len(set(etiquetas))
-
-zonas = {}
-for i, etiqueta in enumerate(etiquetas):
-    if etiqueta not in zonas:
-        zonas[etiqueta] = []
-    zonas[etiqueta].append(direcciones[i])
-
-# Crear el mapa centrado en la primera coordenada
-primer_coordenada = coordenadas[0]
-mapa = folium.Map(location=primer_coordenada, zoom_start=15)
-
-colors = ['red', 'blue', 'green', 'purple', 'orange', 'darkred', 'lightred',
-          'beige', 'darkblue', 'darkgreen', 'cadetblue',
-          'darkpurple', 'white', 'pink', 'lightblue', 'lightgreen', 'gray',
-          'black', 'lightgray']  # Colores disponibles en Folium
-
-for i, coordenada in enumerate(coordenadas):
-    zona = etiquetas[i] + 1
-    color = colors[zona % len(colors)]
-    if coordenada != (None, None):
-        folium.Marker(
-                location=coordenada, popup=f'Zona: {zona}',
-                icon=folium.Icon(color=color)
-                ).add_to(mapa)
-
-# Mostrar el mapa y la leyenda en Streamlit en dos columnas
-col1, col2, col3 = st.columns(3)
-with col1:
-    folium_static(mapa, width=800, height=600)
-with col3:
-    # Zonas ordenadas de forma descendente
-    for zona, direcciones_zona in sorted(zonas.items(), reverse=False):
-        st.markdown(
-                f"<div style='background-color:gray;'><span style='color:black;font-weight:bold;font-size:18px'>**ZONA"
-                f" {zona + 1}:**</span></div>",
-                unsafe_allow_html=True
-                )
-        for direccion in direcciones_zona:
-            st.markdown(f"<div>- {direccion}</div>", unsafe_allow_html=True)
-        st.markdown("<hr>", unsafe_allow_html=True)

ML/geocoding/doctest.py

@@ -1,5 +0,0 @@
-import janitor
-import pandas as pd
-
-diabetes_df = pd.read_csv("https://nrvis.com/data/mldata/pima-indians-diabetes.csv", names=['pregnancies', 'glucose', 'blood_pressurte', 'skin_thinckness', 'insulin', 'bmi', 'diabetes_pedigree_function', 'age', 'outcome'])
-print(diabetes_df.info())

ML/geocoding/mapa.html

@@ -1,393 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-    
-    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
-    
-        <script>
-            L_NO_TOUCH = false;
-            L_DISABLE_3D = false;
-        </script>
-    
-    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
-    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
-    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.js"></script>
-    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
-    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/js/bootstrap.bundle.min.js"></script>
-    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
-    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.9.3/dist/leaflet.css"/>
-    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.2.2/dist/css/bootstrap.min.css"/>
-    <link rel="stylesheet" href="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css"/>
-    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/fontawesome-free@6.2.0/css/all.min.css"/>
-    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
-    <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css"/>
-    
-            <meta name="viewport" content="width=device-width,
-                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
-            <style>
-                #map_a1f3ad7074dad87fc12508baa541fc65 {
-                    position: relative;
-                    width: 100.0%;
-                    height: 100.0%;
-                    left: 0.0%;
-                    top: 0.0%;
-                }
-                .leaflet-container { font-size: 1rem; }
-            </style>
-        
-</head>
-<body>
-    
-    
-            <div class="folium-map" id="map_a1f3ad7074dad87fc12508baa541fc65" ></div>
-        
-</body>
-<script>
-    
-    
-            var map_a1f3ad7074dad87fc12508baa541fc65 = L.map(
-                "map_a1f3ad7074dad87fc12508baa541fc65",
-                {
-                    center: [-31.2468127, -64.4703813],
-                    crs: L.CRS.EPSG3857,
-                    zoom: 15,
-                    zoomControl: true,
-                    preferCanvas: false,
-                }
-            );
-
-            
-
-        
-    
-            var tile_layer_d9d2b3f43262cdc24dcd2c54eeebb088 = L.tileLayer(
-                "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
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-            ).addTo(map_a1f3ad7074dad87fc12508baa541fc65);
-        
-    
-            var marker_64957d9a580d2f09d6b28627d2374f19 = L.marker(
-                [-31.2468127, -64.4703813],
-                {}
-            ).addTo(map_a1f3ad7074dad87fc12508baa541fc65);
-        
-    
-            var icon_e7fcc95ead3ff14f2e41fbbd6da350af = L.AwesomeMarkers.icon(
-                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "green", "prefix": "glyphicon"}
-            );
-            marker_64957d9a580d2f09d6b28627d2374f19.setIcon(icon_e7fcc95ead3ff14f2e41fbbd6da350af);
-        
-    
-        var popup_bf3d0b061120e3d729bed354e930e4df = L.popup({"maxWidth": "100%"});
-
-        
-            
-                var html_bd14c223170ed15e88a0f653d228e0e2 = $(`<div id="html_bd14c223170ed15e88a0f653d228e0e2" style="width: 100.0%; height: 100.0%;">Zona: 2</div>`)[0];
-                popup_bf3d0b061120e3d729bed354e930e4df.setContent(html_bd14c223170ed15e88a0f653d228e0e2);
-            
-        
-
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-        ;
-
-        
-    
-    
-            var marker_3e649aefb9e78244fb39c588fd9a3ac9 = L.marker(
-                [-31.2376351, -64.4633182],
-                {}
-            ).addTo(map_a1f3ad7074dad87fc12508baa541fc65);
-        
-    
-            var icon_c2e37e415e755d5ae2fb5d6744152223 = L.AwesomeMarkers.icon(
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-            );
-            marker_3e649aefb9e78244fb39c588fd9a3ac9.setIcon(icon_c2e37e415e755d5ae2fb5d6744152223);
-        
-    
-        var popup_07653847e4a06ae31938ebcb8d2eca37 = L.popup({"maxWidth": "100%"});
-
-        
-            
-                var html_21e5747412cad3612aafc34fd6b3be25 = $(`<div id="html_21e5747412cad3612aafc34fd6b3be25" style="width: 100.0%; height: 100.0%;">Zona: 4</div>`)[0];
-                popup_07653847e4a06ae31938ebcb8d2eca37.setContent(html_21e5747412cad3612aafc34fd6b3be25);
-            
-        
-
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-        ;
-
-        
-    
-    
-            var marker_e13999e64912723800ac3a97727dabd4 = L.marker(
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-                {}
-            ).addTo(map_a1f3ad7074dad87fc12508baa541fc65);
-        
-    
-            var icon_423d8e108dfccb44e2e748ecfbf23787 = L.AwesomeMarkers.icon(
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-            );
-            marker_e13999e64912723800ac3a97727dabd4.setIcon(icon_423d8e108dfccb44e2e748ecfbf23787);
-        
-    
-        var popup_6b92830b169df9aabdb773716653a875 = L.popup({"maxWidth": "100%"});
-
-        
-            
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-                popup_6b92830b169df9aabdb773716653a875.setContent(html_5b7bdc07ff7b072d83d0e5d219eeb336);
-            
-        
-
-        marker_e13999e64912723800ac3a97727dabd4.bindPopup(popup_6b92830b169df9aabdb773716653a875)
-        ;
-
-        
-    
-    
-            var marker_b93dbbfe159140629cbf332f09d29fb3 = L.marker(
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-        
-    
-            var icon_f3eff6f0f41d10a6d2f8b923fdd1ef19 = L.AwesomeMarkers.icon(
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-            marker_b93dbbfe159140629cbf332f09d29fb3.setIcon(icon_f3eff6f0f41d10a6d2f8b923fdd1ef19);
-        
-    
-        var popup_a842edfd2578ea5cc15a45349fe70352 = L.popup({"maxWidth": "100%"});
-
-        
-            
-                var html_5952226ea8ba9fb25833269dddf881d7 = $(`<div id="html_5952226ea8ba9fb25833269dddf881d7" style="width: 100.0%; height: 100.0%;">Zona: 2</div>`)[0];
-                popup_a842edfd2578ea5cc15a45349fe70352.setContent(html_5952226ea8ba9fb25833269dddf881d7);
-            
-        
-
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-        
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-
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-                var html_aacefb7498fe48e7b5fb3b63b9f7d7b9 = $(`<div id="html_aacefb7498fe48e7b5fb3b63b9f7d7b9" style="width: 100.0%; height: 100.0%;">Zona: 4</div>`)[0];
-                popup_951f0a1581569f022e99e6aa8e9d338c.setContent(html_aacefb7498fe48e7b5fb3b63b9f7d7b9);
-            
-        
-
-        marker_19a21bb4d7758eed979556e650fd9850.bindPopup(popup_951f0a1581569f022e99e6aa8e9d338c)
-        ;
-
-        
-    
-    
-            var marker_feaf44d2278fe7179ccd0d5e2eb15adc = L.marker(
-                [-31.2385765, -64.4657421],
-                {}
-            ).addTo(map_a1f3ad7074dad87fc12508baa541fc65);
-        
-    
-            var icon_8f40d2ca12bda04ade22e98328cdf5df = L.AwesomeMarkers.icon(
-                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "orange", "prefix": "glyphicon"}
-            );
-            marker_feaf44d2278fe7179ccd0d5e2eb15adc.setIcon(icon_8f40d2ca12bda04ade22e98328cdf5df);
-        
-    
-        var popup_8be21ff1f811e905ae07dd9c3e1cb918 = L.popup({"maxWidth": "100%"});
-
-        
-            
-                var html_91d720565690b9c0803d95e567d3a4cb = $(`<div id="html_91d720565690b9c0803d95e567d3a4cb" style="width: 100.0%; height: 100.0%;">Zona: 4</div>`)[0];
-                popup_8be21ff1f811e905ae07dd9c3e1cb918.setContent(html_91d720565690b9c0803d95e567d3a4cb);
-            
-        
-
-        marker_feaf44d2278fe7179ccd0d5e2eb15adc.bindPopup(popup_8be21ff1f811e905ae07dd9c3e1cb918)
-        ;
-
-        
-    
-    
-            var marker_42ca96bf651128ed0c4cd9b5123810e0 = L.marker(
-                [-31.2475221, -64.4670759],
-                {}
-            ).addTo(map_a1f3ad7074dad87fc12508baa541fc65);
-        
-    
-            var icon_b01aa13d507a66bc9c2635134a99fa5f = L.AwesomeMarkers.icon(
-                {"extraClasses": "fa-rotate-0", "icon": "info-sign", "iconColor": "white", "markerColor": "green", "prefix": "glyphicon"}
-            );
-            marker_42ca96bf651128ed0c4cd9b5123810e0.setIcon(icon_b01aa13d507a66bc9c2635134a99fa5f);
-        
-    
-        var popup_e72e5359945fbb012812026e5c1ba4c2 = L.popup({"maxWidth": "100%"});
-
-        
-            
-                var html_da8d62fbe3e6f3578064566e4a1cff1b = $(`<div id="html_da8d62fbe3e6f3578064566e4a1cff1b" style="width: 100.0%; height: 100.0%;">Zona: 2</div>`)[0];
-                popup_e72e5359945fbb012812026e5c1ba4c2.setContent(html_da8d62fbe3e6f3578064566e4a1cff1b);
-            
-        
-
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-        ;
-
-        
-    
-    
-            var marker_5dabd99b029782f8b9cd78ac26d62093 = L.marker(
-                [-31.234152, -64.4582696],
-                {}
-            ).addTo(map_a1f3ad7074dad87fc12508baa541fc65);
-        
-    
-            var icon_877898bc86e5c023c519334e6433aed4 = L.AwesomeMarkers.icon(
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-            );
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-        
-    
-        var popup_f4424169d52f5561070611bccf7ec318 = L.popup({"maxWidth": "100%"});
-
-        
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-                popup_f4424169d52f5561070611bccf7ec318.setContent(html_9ceeb9fa4dd64455bee680a9253ce92e);
-            
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-
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-        
-    
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-            );
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-        
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-
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-                popup_273c92c917e5b50ca34588b1aaec4851.setContent(html_be5f558e220bb41bf1ae1aa7efd96f7f);
-            
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-        ;
-
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-    
-</script>
-</html>

ML/geocoding/modelo.py

@@ -1,137 +0,0 @@
-import random
-import os
-import requests
-from dotenv import load_dotenv
-import folium
-import streamlit as st
-import joblib
-from sklearn.cluster import KMeans
-import numpy as np
-import matplotlib.pyplot as plt
-
-load_dotenv()
-
-API_KEY = os.getenv('API_KEY')
-BASE_URL = os.getenv('BASE_URL')
-
-def geocode_address(address):
-    # Aquí debes insertar tu clave de API de Google Maps
-    api_key = API_KEY
-
-    # URL base de la API de Geocodificación de Google Maps
-    base_url = BASE_URL
-
-    # Parámetros de la solicitud
-    params = {
-        'address': address,
-        'key': api_key
-    }
-
-    try:
-        # Realizar la solicitud a la API de Geocodificación
-        response = requests.get(base_url, params=params)
-        data = response.json()
-
-        if data['status'] == 'OK' and len(data['results']) > 0:
-            # Obtener las coordenadas geográficas de la primera coincidencia
-            location = data['results'][0]['geometry']['location']
-            latitude = location['lat']
-            longitude = location['lng']
-
-            return latitude, longitude
-        else:
-            print('No se encontraron resultados para la dirección especificada.')
-    except requests.exceptions.RequestException as e:
-        print('Error en la solicitud:', e)
-
-
-# Direcciones ficticias en Cosquín, Córdoba, Argentina
-direcciones = [
-    'San Martín 123',
-    'Tucumán 456',
-    'Catamarca 789',
-    'Perón 1321',
-    'Corrientes 654',
-    'Laurencena 877',
-    'San Martín 234',
-    'Tucumán 567',
-    'Catamarca 890',
-    'San Martin 1432',
-    'Avenida parana 10',
-    'Japon 23'
-]
-
-# Obtener las coordenadas geográficas de cada dirección
-coordenadas = []
-for direccion in direcciones:
-    resultado = geocode_address(direccion + ', Cosquín, Córdoba, Argentina')
-    if resultado:
-        coordenadas.append(resultado)
-    else:
-        coordenadas.append((None, None))
-
-# Calcular el número de zonas (k) utilizando el método del codo
-max_k = len(coordenadas)
-inertias = []
-for k in range(2, max_k+1):
-    modelo = KMeans(n_clusters=k, random_state=42)
-    modelo.fit(coordenadas)
-    inertias.append(modelo.inertia_)
-
-# Graficar las inercias en función de k
-plt.plot(range(2, max_k+1), inertias, marker='o')
-plt.xlabel('Número de zonas (k)')
-plt.ylabel('Inercia')
-plt.title('Método del Codo')
-plt.show()
-
-# Elegir el valor de k óptimo
-k_optimo = int(input("Ingrese el valor óptimo de k según el gráfico: "))
-
-# Crear el modelo de K-Means con k_optimo zonas
-modelo = KMeans(n_clusters=k_optimo, random_state=42)
-modelo.fit(coordenadas)
-
-# Obtener las etiquetas de las zonas
-etiquetas = modelo.labels_
-
-# Contar el número de zonas
-num_zonas = len(set(etiquetas))
-
-# Crear un diccionario para almacenar las direcciones por zona
-zonas = {}
-for i, etiqueta in enumerate(etiquetas):
-    if etiqueta not in zonas:
-        zonas[etiqueta] = []
-    zonas[etiqueta].append(direcciones[i])
-
-# Mostrar las direcciones por zona
-for zona, direcciones_zona in zonas.items():
-    print(f"Zona {zona + 1}:")
-    for direccion in direcciones_zona:
-        print(direccion)
-    print()
-
-# Lista de colores predefinidos
-colores = ['red', 'blue', 'green', 'purple', 'orange', 'gray', 'pink',
-           'cyan', 'yellow', 'brown', 'black', 'white', 'violet']
-
-# Crear un mapa centrado en la primera coordenada
-primer_coordenada = coordenadas[0]
-mapa = folium.Map(location=primer_coordenada, zoom_start=15)
-
-# Agregar marcadores para cada coordenada con su zona y color correspondiente
-for i, coordenada in enumerate(coordenadas):
-    zona = etiquetas[i] + 1  # Sumar 1 para que las zonas se muestren como números a partir de 1
-    if coordenada != (None, None):
-        color = colores[zona % len(colores)]  # Obtener el color correspondiente a la zona
-        folium.Marker(location=coordenada, popup=f'Zona: {zona}', icon=folium.Icon(color=color)).add_to(mapa)
-    else:
-        folium.Marker(location=coordenada, popup='Ubicación desconocida').add_to(mapa)
-
-# Guardar el mapa como un archivo HTML
-mapa.save('mapa.html')
-
-# Abrir el archivo HTML en el navegador para visualizar el mapa
-import webbrowser
-webbrowser.open('mapa.html')

ML/geocoding/requirements.txt

@@ -1,65 +0,0 @@
-altair==4.2.2
-args==0.1.0
-attrs==23.1.0
-blinker==1.6.2
-branca==0.6.0
-cachetools==5.3.0
-certifi==2023.5.7
-charset-normalizer==3.1.0
-click==8.1.3
-clint==0.5.1
-contourpy==1.0.7
-coverage==7.2.5
-cycler==0.11.0
-decorator==5.1.1
-entrypoints==0.4
-folium==0.14.0
-fonttools==4.39.4
-gitdb==4.0.10
-GitPython==3.1.31
-idna==3.4
-importlib-metadata==6.6.0
-Jinja2==3.1.2
-joblib==1.2.0
-jsonschema==4.17.3
-kiwisolver==1.4.4
-mamba==0.11.2
-markdown-it-py==2.2.0
-MarkupSafe==2.1.2
-matplotlib==3.7.1
-mdurl==0.1.2
-numpy==1.24.3
-packaging==23.1
-pandas==2.0.1
-Pillow==9.5.0
-plotly==5.14.1
-protobuf==3.20.3
-pyarrow==12.0.0
-pydeck==0.8.1b0
-Pygments==2.15.1
-Pympler==1.0.1
-pyparsing==3.0.9
-pyrsistent==0.19.3
-python-dateutil==2.8.2
-python-dotenv==1.0.0
-pytz==2023.3
-requests==2.31.0
-rich==13.3.5
-scikit-learn==1.2.2
-scipy==1.10.1
-six==1.16.0
-smmap==5.0.0
-streamlit==1.22.0
-streamlit-folium==0.11.1
-tenacity==8.2.2
-threadpoolctl==3.1.0
-toml==0.10.2
-toolz==0.12.0
-tornado==6.3.2
-typing_extensions==4.6.0
-tzdata==2023.3
-tzlocal==5.0.1
-urllib3==2.0.2
-validators==0.20.0
-watchdog==3.0.0
-zipp==3.15.0

boxplot_clientes.py

@@ -6,6 +6,9 @@ import streamlit as st
 # Carga del archivo CSV
 df = pd.read_csv('data/po_excel_original.csv')
 
+# Ordenar el DataFrame por la columna "Total" en orden descendente
+df = df.sort_values(by="Total", ascending=False)
+
 def boxplot():
     # Subtítulo: Boxplot de Clientes
     st.subheader("Boxplot de Clientes")
@@ -14,18 +17,18 @@ def boxplot():
     fig = go.Figure()
 
     # Agregar los boxplots
-    fig.add_trace(go.Box(y=df["Total"], name="Boxplot"))
-
-    # Configurar el posicionamiento de los nombres de los clientes
-    pos_x = [len(df) + 2] * len(df)
+    fig.add_trace(go.Box(y=df["Total"],
+                         name="Boxplot",
+                         marker_color = 'green'))  # Color del boxplot
 
     # Agregar los puntos de los clientes y sus nombres a la leyenda
-    for i, cliente in enumerate(df["Clientes"]):
-        fig.add_trace(go.Scatter(x=pos_x, y=[df.loc[i, "Total"]],
+    for i, row in df.iterrows():
+        fig.add_trace(go.Scatter(x=[len(df) + 2],
+                                 y=[row["Total"]],
                                  mode='markers',
-                                 name=cliente,
-                                 marker=dict(color='green', size=12,
-                                             symbol='circle'),
+                                 name=row["Clientes"],
+                                 marker=dict(color='#FC5C9C', size=12,
+                                             symbol='circle'),  # Color de los markers
                                  visible='legendonly'))
 
     # Configurar el diseño del boxplot y el tamaño de la figura

ventas_anuales_clientes.py

@@ -25,5 +25,8 @@ def ventas_anuales():
     # Crea el gráfico de barras
     fig = px.bar(df, x='Mes', y='Ventas', title='Ventas Anuales', labels={'Ventas': 'Ventas ($)', 'Mes': 'Mes', 'Año': 'Año'})
 
+    # Cambia el color de las barras a verde
+    fig.update_traces(marker_color='green')
+
     # Muestra el gráfico en Streamlit
     st.plotly_chart(fig, use_container_width=True)