🐍 Создание интерактивных панелей с Streamlit и Python

        $ pip install streamlit
$ streamlit hello
    

        import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
from urllib.request import urlopen
import json
    

        # Оставляем только необходимые переменные
df = df[['iso_code','location','total_cases_per_million','total_deaths_per_million','people_vaccinated_per_hundred','date']]
df = df[df.location != 'World']
df["date"] = pd.to_datetime(df["date"])
# Сохраним данные в формате csv для создания приложения
df.to_csv('data.csv', index = False)
    

        # Настройка заголовка и текста 
st.title("COVID 19 IN THE WORLD DASHBOARD")
st.write("""This dashboard will present the spread of COVID-19 in the world by visualizing the timeline of the total cases and deaths. As well as the total number of vaccinated people.""")

# Настройка боковой панели
st.sidebar.title("About")
st.sidebar.info(
    """
    This app is Open Source dashboard.
    """
)
st.sidebar.info("Feel free to collaborate and comment on the work. The github link can be found "
                "[here](https://github.com/yuliianikolaenko/COVID_dashboard_proglib).")


    

        # Загружаем новые оптимизированные данные
DATA = ('data.csv')
DATE_COLUMN = 'date'
@st.cache # для оптимизации работы приложения

# Создадим функцию для загрузки данных
def load_data():
    df = pd.read_csv(DATA, parse_dates=[DATE_COLUMN])
    return df   

# Применим функцию 
df = load_data() 

# Загружаем координаты стран для карты
 with open('countries.geo.json') as json_file:
    json_locations = json.load(json_file)

    

        # Создадим функции для визуализации количества случаев заражения, смертей и вакцинированных людей.
def draw_map_cases():
    fig = px.choropleth_mapbox(df, geojson=json_locations, locations='iso_code', color='total_cases_per_million',
                               color_continuous_scale="Reds",
                               mapbox_style="carto-positron",
                               title = "COVID 19 cases per million",
                               zoom=1,
                               opacity=0.5,
                               )
    fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
    return fig

def draw_map_deaths():
    fig = px.choropleth_mapbox(df, geojson=json_locations, locations='iso_code', color='total_deaths_per_million',
                               color_continuous_scale="Greys",
                               mapbox_style="carto-positron",
                               title = "Deaths from COVID 19 per million",
                               zoom=1,
                               opacity=0.5,
                               )
    fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
    return fig

def draw_map_vaccine():
    fig = px.choropleth_mapbox(df, geojson=json_locations, locations='iso_code', color='people_vaccinated_per_hundred',
                               color_continuous_scale="BuGn",
                               mapbox_style="carto-positron",
                               title = "Vaccinations from COVID 19 per hundred",
                               zoom=1,
                               opacity=0.5,
                               )
    fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
    return fig
    

        show_data = st.sidebar.checkbox('Show raw data')
if show_data == True:
    st.subheader('Raw data')
    st.markdown(
        "#### Data on COVID-19 (coronavirus) by Our World in Data could be found [here](https://github.com/owid/covid-19-data/tree/master/public/data).")
    st.write(df)

    

        # Создадим поле выбора для даты
show_timerange = st.sidebar.checkbox("Show date range")
if show_timerange == True:
    # Вычислим даты для создания временного слайдера
    min_ts = min(df[DATE_COLUMN]).to_pydatetime()
    max_ts = max(df[DATE_COLUMN]).to_pydatetime()
    day_date = pd.to_datetime(st.sidebar.slider("Date to chose", min_value=min_ts, max_value=max_ts, value=max_ts))
    st.write(f"Data for {day_date.date()}")
    df = df[(df['date'] == day_date)]

# Создадим поле выбора для визуализации общего количества случаев, смертей или вакцинаций
select_event = st.sidebar.selectbox('Show map', ('cases per million', 'deaths per million', 'vaccinated per hundred'))
if select_event == 'cases per million':
    st.plotly_chart(draw_map_cases(), use_container_width=True)

if select_event == 'deaths per million':
    st.plotly_chart(draw_map_deaths(), use_container_width=True)

if select_event == 'vaccinated per hundred':
    st.plotly_chart(draw_map_vaccine(), use_container_width=True)

    

        # Настройка заголовка и текста 
st.title("COVID 19 IN THE WORLD DASHBOARD")
st.write("""This dashboard will present the spread of COVID-19 in the world by visualizing the timeline of the total cases and deaths. As well as the total number of vaccinated people.""")

# Настройка боковой панели
st.sidebar.title("About")
st.sidebar.info(
    """
    This app is Open Source dashboard.
    """
)
st.sidebar.info("Feel free to collaborate and comment on the work. The github link can be found "
                "[here](https://github.com/yuliianikolaenko/COVID_dashboard_proglib).")
# Загружаем новые оптимизированные данные
DATA = ('data.csv')
DATE_COLUMN = 'date'
@st.cache # для оптимизации работы приложения

# Создадим функцию для загрузки данных
def load_data():
    df = pd.read_csv(DATA, parse_dates=[DATE_COLUMN])
    return df   

# Применим функцию 
df = load_data()  


# Создадим функции для визуализации количества случаев заражения, смертей и вакцинированных людей.
def draw_map_cases():
    fig = px.choropleth(df, locations="iso_code",
                         color="total_cases",
                         hover_name="location",
                         title="Total COVID 19 cases in the world",
                         color_continuous_scale=px.colors.sequential.Redor)
    return fig

def draw_map_deaths():
    fig = px.choropleth(df, locations="iso_code",
                         color="total_deaths",
                         hover_name="location",
                         title="Total deaths from COVID 19 in the world",
                         color_continuous_scale=px.colors.sequential.Greys)
    return fig

def draw_map_vaccine():
    fig = px.choropleth(df, locations="iso_code",
                         color="total_vaccinations",
                         hover_name="location",
                         title="Total vaccinated from COVID 19 in the world",
                         color_continuous_scale=px.colors.sequential.Greens)
    return fig

show_data = st.sidebar.checkbox('Show raw data')
if show_data == True:
    st.subheader('Raw data')
    st.markdown(
        "#### Data on COVID-19 (coronavirus) by Our World in Data could be found [here](https://github.com/owid/covid-19-data/tree/master/public/data).")
    st.write(df)

# Вычислим даты для создания временного слайдера
min_ts = min(df[DATE_COLUMN]).to_pydatetime()
max_ts = max(df[DATE_COLUMN]).to_pydatetime()

# Создадим поле выбора для даты
show_timerange = st.sidebar.checkbox("Show date range")
if show_timerange:
    min_selection, max_selection = st.sidebar.slider("Timeline", min_value=min_ts, max_value=max_ts, value=[min_ts, max_ts])
    df = df[(df[DATE_COLUMN] >= min_selection) & (df[DATE_COLUMN] <= max_selection)]

# Создадим поле выбора для визуализации общего количества случаев, смертей или вакцинаций
select_event = st.sidebar.selectbox('Show map', ('total_cases', 'total_deaths', 'total_vaccinations'))
if select_event == 'total_cases':
    st.plotly_chart(draw_map_cases(), use_container_width=True)

if select_event == 'total_deaths':
    st.plotly_chart(draw_map_deaths(), use_container_width=True)

if select_event == 'total_vaccinations':
    st.plotly_chart(draw_map_vaccine(), use_container_width=True)