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p4.py
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p4.py
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# -*- coding: utf-8 -*-
"""p4.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1WCFDRzrTyZbzlVL0qWK7pbuzaqnzTIsw
"""
import pandas as pd
import numpy as np
import plotly.express as px
import requests
import folium
import plotly.figure_factory as ff
import streamlit as st
from streamlit_option_menu import option_menu
from streamlit_folium import folium_static
import plotly.graph_objects as go
from PIL import Image
# Set page config to wide layout
st.set_page_config(page_title="Población de carros eléctricos en Washington", layout="wide", page_icon="https://cdn-icons-png.flaticon.com/512/1996/1996729.png")
col1, col2, col3 = st.columns(3)
col1.metric("Clean source", "70 %", "-30%")
col2.metric("Tesla population", "50%", "-5%")
col3.metric("King County", "50%", "2.4%")
bg_img = '''
<style>
[data-testid="stAppViewContainer"] {
background-image: url('https://images.rawpixel.com/image_800/cHJpdmF0ZS9sci9pbWFnZXMvd2Vic2l0ZS8yMDIyLTA1L3BmLXMxMjQtYWstMjY4MV8yLmpwZw.jpg');
background-size: cover;
background-repeat: no-repeat;
color: black;
}
</style>
'''
st.markdown(bg_img, unsafe_allow_html=True)
image = Image.open('UK.png')
# Importing Google Fonts
st.markdown(
"""
<link href='https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap' rel='stylesheet'>
<style>
.st-cc h1 {
color: black;
font-family: 'Roboto', sans-serif;
}
</style>
""",
unsafe_allow_html=True
)
# Mostrar el título in a more professional font
st.markdown('<h1 style="color: black; font-family: \'Buffalo\';">Población de carros eléctricos en Washington</h1>', unsafe_allow_html=True)
# Contenido del sidebar
selected2 = option_menu(None, ["Home", "Map", "KPIs", 'References'],
icons=['house', 'map', "chart-pie", 'book'],
menu_icon="cast", default_index=0, orientation="horizontal")
selected2
# Load data and perform preprocessing
#mapa
df = pd.read_excel('Electric_Vehicle_Population_Data.xlsx')
fips_df = pd.read_csv('FIP.csv')
df = df.dropna()
df.rename(columns={"Electric Vehicle Type": "elec", "Clean Alternative Fuel Vehicle (CAFV) Eligibility": "clean"}, inplace=True)
df.replace({'Plug-in Hybrid Electric Vehicle (PHEV)': 'PHEV', 'Battery Electric Vehicle (BEV)': 'BEV',
'Clean Alternative Fuel Vehicle Eligible': 'Clean Alt Fuel', 'Eligibility unknown as battery range has not been researched': 'Unknown'
, 'Not eligible due to low battery range': 'Not Clean'}, inplace=True)
county_counts = df['County'].value_counts().to_dict()
df['Count'] = df['County'].map(county_counts)
df3 = pd.merge(df,fips_df, on='County')
state_geo = requests.get("https://raw.githubusercontent.com/python-visualization/folium/master/tests/us-counties.json").json()
excel_url = "https://github.com/SVterry2023/al/raw/main/df3.xlsx"
state_data = pd.read_excel(excel_url, engine="openpyxl")
m = folium.Map(location=[0, 0], zoom_start=1)
#Frecuencias absolutas
tfa = pd.Series(df['clean']).value_counts()
tt= pd.DataFrame(tfa)
#Tabla de frecuencias relativas
t = df['clean'].shape[0]
tfr = tfa/t
tf = pd.DataFrame(tfr)
tf['name'] = tf.index
#Frecuencias absolutas
tfm = pd.Series(df['Make']).value_counts()
tm= pd.DataFrame(tfm)
#Tabla de frecuencias relativas
tm = df['Make'].shape[0]
tfrm = tfm/tm
tfm = pd.DataFrame(tfrm)
tfm['name'] = tfm.index
top_5_values = tfm.head(5)
#Frecuencias absolutas
tfz = pd.Series(df['County']).value_counts()
tz= pd.DataFrame(tfz)
#Tabla de frecuencias relativas
tz = df['County'].shape[0]
tfrz = tfz/tz
tfz = pd.DataFrame(tfrz)
tfz['name'] = tfz.index
top_5 = tfz.head(5)
if selected2 == "Home":
heading_text = '<h2 style="color: black;"> La situación actual</h2>'
st.markdown(heading_text, unsafe_allow_html=True)
st.markdown('<p style="font-family: \'Times New Roman\'; font-size: 18px; color: black;"> La producción de carros eléctricos ha aumentado, pero esto no necesariamente representa una mejora para el medio ambiente .</p>', unsafe_allow_html=True)
st.image("UK.png")
@st.cache_data
def compute_Sankey_chart():
# Extract unique values
elec_values = df['elec'].unique()
clean_values = df['clean'].unique()
# Create a list of unique labels
unique_labels = list(elec_values) + list(clean_values)
# Map labels to indices
label_indices = {label: idx for idx, label in enumerate(unique_labels)}
node_colors = ["rgba(0, 128, 255, 0.8)", "rgba(0, 255, 0, 0.8)", "rgba(255, 0, 0, 0.8)", "rgba(169, 169, 169, 0.8)"]
# Create Sankey diagram data
source_indices = [label_indices[elec] for elec in df['elec']]
target_indices = [label_indices[clean] for clean in df['clean']]
link_values = [1] * len(df)
# Create Sankey diagram
fig = go.Figure(data=[go.Sankey(
valueformat=".0f",
valuesuffix="TWh",
node=dict(
pad=15,
thickness=15,
line=dict(color="black", width=0.5),
label=unique_labels,
color=node_colors
),
link=dict(
source=source_indices,
target=target_indices,
value=link_values,
color="rgba(173, 216, 230, 0.5)"
))])
fig.update_layout({
'plot_bgcolor': 'rgba(0, 0, 0, 0)',
'paper_bgcolor': 'rgba(0, 0, 0, 0)',
'margin': dict(l=0, r=0, b=0, t=0, pad=0),
'height': 500, # Adjust the height as needed
'width': 800, # Adjust the width as needed
})
return fig
fig4 = compute_Sankey_chart()
heading_text11 = '<h3 style="color: black;"> Diagrama diluvial</h3>'
st.markdown(heading_text11, unsafe_allow_html=True)
st.plotly_chart(fig4, use_container_width=True)
if selected2== 'References':
heading_text1 = '<h2 style="color: black;">Referencias</h2>'
st.markdown(heading_text1, unsafe_allow_html=True)
st.markdown('<p style="font-family: \'Times New Roman\'; font-size: 18px; color: black;"> Gupta, S. (2021). Electric Vehicle Population Data. Kaggle. https://www.kaggle.com/code/shubhamgupta012/electric-vehicle-population-data .</p>', unsafe_allow_html=True)
st.markdown('<p style="font-family: \'Times New Roman\'; font-size: 18px; color: black;"> Tabuchi, H. (2021, March 2). Electric Vehicles Are Better for the Environment. The New York Times. https://www.nytimes.com/2021/03/02/climate/electric-vehicles-environment.html .</p>', unsafe_allow_html=True)
if selected2 == "Map":
folium.Choropleth(
geo_data=state_geo,
name="choropleth",
data=state_data,
columns=["FIP", "Count"],
key_on="feature.id",
fill_color="YlGn",
fill_opacity=0.7,
line_opacity=0.2,
legend_name="Cantidad de carros eléctricos",
).add_to(m)
folium.LayerControl().add_to(m)
heading_text2 = '<h2 style="color: black;"> Mapa de condados en Estados Unidos </h2>'
st.markdown(heading_text2, unsafe_allow_html=True)
folium_static(m)
st.markdown('<p style="font-family: \'Times New Roman\'; font-size: 18px; color: black;"> Análisis de los condados de Estados Unidos .</p>', unsafe_allow_html=True)
# Sunburst chart centered on the webpage
if selected2 == "KPIs":
opcion = st.sidebar.selectbox('Escoge la sección', ['¿La fuente es sustentable?', 'Distribucion de marcas y modelos', 'Distribución de condados'])
if opcion == "¿La fuente es sustentable?":
@st.cache_data
def compute_sunburst():
fig = px.sunburst(df3, path=['elec', 'clean', 'Make'], color='clean', color_discrete_sequence=px.colors.qualitative.Set1)
fig.update_layout({
'plot_bgcolor': 'rgba(0, 0, 0, 0)',
'paper_bgcolor': 'rgba(0, 0, 0, 0)',
'margin': dict(l=0, r=0, b=0, t=0, pad=0),
'height': 500, # Adjust the height as needed
'width': 800, # Adjust the width as needed
})
return fig
@st.cache_data
def compute_p2():
fig = px.pie(tf, values='clean', names='name', color_discrete_sequence=px.colors.sequential.Blugrn)
fig.update_layout({
'plot_bgcolor': 'rgba(0, 0, 0, 0)',
'paper_bgcolor': 'rgba(0, 0, 0, 0)',
'margin': dict(l=0, r=0, b=0, t=0, pad=0),
'height': 500, # Adjust the height as needed
'width': 800, # Adjust the width as needed
})
return fig
figv = compute_p2()
fig3 = compute_sunburst()
heading_text20 = '<h2 style="color: black;"> ¿La fuente es sustentable? </h2>'
st.markdown(heading_text20, unsafe_allow_html=True)
heading_text3 = '<h3 style="color: black;"> Sunburst</h3>'
st.markdown(heading_text3, unsafe_allow_html=True)
st.plotly_chart(fig3, use_container_width=True)
st.markdown('<p style="font-family: \'Times New Roman\'; font-size: 18px; color: black;"> En las siguientes gráficas se puede observar la proporción de vehículos con energía limpia .</p>', unsafe_allow_html=True)
heading_text4 = '<h3 style="color: black;"> Proporcion de carros sustentables</h3>'
st.markdown(heading_text4, unsafe_allow_html=True)
st.plotly_chart(figv, use_container_width=True)
if opcion == "Distribucion de marcas y modelos":
@st.cache_data
def compute_Treemap():
fig = px.treemap(df, path=[px.Constant("Car model proportions"), 'Model Year', 'Make', 'Model'], color='Model', color_discrete_sequence=px.colors.diverging.Portland)
fig.update_layout({
'plot_bgcolor': 'rgba(0, 0, 0, 0)',
'paper_bgcolor': 'rgba(0, 0, 0, 0)',
'margin': dict(l=0, r=0, b=0, t=0, pad=0),
'height': 500, # Adjust the height as needed
'width': 800, # Adjust the width as needed
})
return fig
@st.cache_data
def compute_p1():
fig = px.pie(top_5_values, values='Make', names='name', color_discrete_sequence=px.colors.sequential.RdBu)
fig.update_layout({
'plot_bgcolor': 'rgba(0, 0, 0, 0)',
'paper_bgcolor': 'rgba(0, 0, 0, 0)',
'margin': dict(l=0, r=0, b=0, t=0, pad=0),
'height': 500, # Adjust the height as needed
'width': 800, # Adjust the width as needed
})
return fig
figr = compute_p1()
fig7 = compute_Treemap()
heading_text21 = '<h2 style="color: black;"> Distribucion de marcas y modelos </h2>'
st.markdown(heading_text21, unsafe_allow_html=True)
heading_text5 = '<h3 style="color: black;"> Treemap</h3>'
st.markdown(heading_text5, unsafe_allow_html=True)
st.plotly_chart(fig7, use_container_width=True)
heading_text6 = '<h3 style="color: black;"> Marcas </h3>'
st.markdown(heading_text6, unsafe_allow_html=True)
st.plotly_chart(figr, use_container_width=True)
if opcion == "Distribución de condados":
@st.cache_data
def compute_p3():
fig = px.pie(top_5, values='County', names='name', color_discrete_sequence=px.colors.sequential.Burgyl)
fig.update_layout({
'plot_bgcolor': 'rgba(0, 0, 0, 0)',
'paper_bgcolor': 'rgba(0, 0, 0, 0)',
'margin': dict(l=0, r=0, b=0, t=0, pad=0),
'height': 500, # Adjust the height as needed
'width': 800, # Adjust the width as needed
})
return fig
figk = compute_p3()
heading_text22 = '<h2 style="color: black;"> Distribución de condados </h2>'
st.markdown(heading_text22, unsafe_allow_html=True)
heading_text15 = '<h3 style="color: black;"> Condados </h3>'
st.markdown(heading_text15, unsafe_allow_html=True)
st.plotly_chart(figk, use_container_width=True)