Update app.py

app.py

@@ -1,134 +1,132 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Apr  8 14:53:45 2025
-
-@author: mritchey
-"""
-
-
-# streamlit run "C:\Users\mritchey\.spyder-py3\Python Scripts\streamlit projects\weather api\app.py"
-import glob
-import os
-import folium
-import numpy as np
-import pandas as pd
-import plotly.express as px
-import streamlit as st
-from geopy.extra.rate_limiter import RateLimiter
-from geopy.geocoders import Nominatim
-
-
-def geocode(address):
-    try:
-        address2 = address.replace(' ', '+').replace(',', '%2C')
-        df = pd.read_json(
-            f'https://geocoding.geo.census.gov/geocoder/locations/onelineaddress?address={address2}&benchmark=2020&format=json')
-        results = df.iloc[:1, 0][0][0]['coordinates']
-        lat, lon = results['y'], results['x']
-    except:
-        geolocator = Nominatim(user_agent="GTA Lookup")
-        geocode = RateLimiter(geolocator.geocode, min_delay_seconds=1)
-        location = geolocator.geocode(address)
-        lat, lon = location.latitude, location.longitude
-    return lat, lon
-
-
-
-@st.cache_data
-def get_weather_data(lat, lon, start_date, end_date):
-
-    url = f'https://archive-api.open-meteo.com/v1/archive?latitude={lat}&longitude={lon}&start_date={start_date}&end_date={end_date}&hourly=temperature_2m,precipitation,windspeed_10m,windgusts_10m&models=best_match&temperature_unit=fahrenheit&windspeed_unit=mph&precipitation_unit=inch'
-    df = pd.read_json(url).reset_index()
-    data = pd.DataFrame({c['index']: c['hourly'] for r, c in df.iterrows()})
-    data['time'] = pd.to_datetime(data['time'])
-    data['date'] = pd.to_datetime(data['time'].dt.date)
-    data = data.query("temperature_2m==temperature_2m")
-
-    data_agg = data.groupby(['date']).agg({'temperature_2m': ['min', 'mean', 'max'],
-                                           'precipitation': ['sum'],
-                                           'windspeed_10m': ['min', 'mean', 'max'],
-                                           'windgusts_10m': ['min', 'mean', 'max']
-                                           })
-    data_agg.columns = data_agg.columns.to_series().str.join('_')
-    data_agg = data_agg.query("temperature_2m_min==temperature_2m_min")
-    return data.drop(columns=['date']), data_agg
-
-
-@st.cache_data
-def convert_df(df):
-    return df.to_csv(index=0).encode('utf-8')
-
-
-st.set_page_config(layout="wide")
-col1, col2 = st.columns((2))
-
-
-address = st.sidebar.text_input(
-    "Address", "1000 Main St, Cincinnati, OH 45202")
-start_date = st.sidebar.date_input("Start Date",  pd.Timestamp(2022, 9, 28))
-end_date = st.sidebar.date_input("End Date",  pd.Timestamp(2022, 9, 30))
-type_var = st.sidebar.selectbox(
-    'Type:', ('Gust', 'Wind', 'Temp', 'Precipitation'))
-hourly_daily = st.sidebar.radio('Aggregate Data', ('Hourly', 'Daily'))
-
-
-start_date = start_date.strftime("%Y-%m-%d")
-end_date = end_date.strftime("%Y-%m-%d")
-
-lat, lon = geocode(address)
-
-df_all, df_all_agg = get_weather_data(lat, lon, start_date, end_date)
-
-# Keys
-var_key = {'Gust': 'i10fg', 'Wind': 'wind10',
-           'Temp': 't2m', 'Precipitation': 'tp'}
-
-variable = var_key[type_var]
-
-unit_key = {'Gust': 'MPH', 'Wind': 'MPH',
-            'Temp': 'F', 'Precipitation': 'In.'}
-unit = unit_key[type_var]
-
-cols_key = {'Gust': ['windgusts_10m'], 'Wind': ['windspeed_10m'], 'Temp': ['temperature_2m'],
-            'Precipitation': ['precipitation']}
-
-cols_key_agg = {'Gust': ['windgusts_10m_min', 'windgusts_10m_mean',
-                         'windgusts_10m_max'],
-                'Wind': ['windspeed_10m_min', 'windspeed_10m_mean',
-                         'windspeed_10m_max'],
-                'Temp': ['temperature_2m_min', 'temperature_2m_mean', 'temperature_2m_max'],
-                'Precipitation': ['precipitation_sum']}
-
-if hourly_daily == 'Hourly':
-    cols = cols_key[type_var]
-else:
-    cols = cols_key_agg[type_var]
-
-if hourly_daily == 'Hourly':
-    fig = px.line(df_all, x="time", y=cols[0])
-    df_downloald = df_all
-else:
-    fig = px.line(df_all_agg.reset_index(), x="date", y=cols[0])
-    df_downloald = df_all_agg.reset_index()
-
-with col1:
-    st.title('Weather Data')
-    st.plotly_chart(fig)
-
-    csv = convert_df(df_downloald)
-
-    st.download_button(
-        label="Download data as CSV",
-        data=csv,
-        file_name=f'{start_date}.csv',
-        mime='text/csv')
-
-
-with col2:
-    st.title('')
-
-
-st.markdown(""" <style>
-#MainMenu {visibility: hidden;}
-footer {visibility: hidden;}
-</style> """, unsafe_allow_html=True)
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Apr  8 14:53:45 2025
+
+@author: mritchey
+"""
+
+# streamlit run "C:\Users\mritchey\.spyder-py3\Python Scripts\streamlit projects\weather api\app.py"
+import glob
+import os
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+from geopy.extra.rate_limiter import RateLimiter
+from geopy.geocoders import Nominatim
+
+
+def geocode(address):
+    try:
+        address2 = address.replace(' ', '+').replace(',', '%2C')
+        df = pd.read_json(
+            f'https://geocoding.geo.census.gov/geocoder/locations/onelineaddress?address={address2}&benchmark=2020&format=json')
+        results = df.iloc[:1, 0][0][0]['coordinates']
+        lat, lon = results['y'], results['x']
+    except:
+        geolocator = Nominatim(user_agent="GTA Lookup")
+        geocode = RateLimiter(geolocator.geocode, min_delay_seconds=1)
+        location = geolocator.geocode(address)
+        lat, lon = location.latitude, location.longitude
+    return lat, lon
+
+
+
+@st.cache_data
+def get_weather_data(lat, lon, start_date, end_date):
+
+    url = f'https://archive-api.open-meteo.com/v1/archive?latitude={lat}&longitude={lon}&start_date={start_date}&end_date={end_date}&hourly=temperature_2m,precipitation,windspeed_10m,windgusts_10m&models=best_match&temperature_unit=fahrenheit&windspeed_unit=mph&precipitation_unit=inch'
+    df = pd.read_json(url).reset_index()
+    data = pd.DataFrame({c['index']: c['hourly'] for r, c in df.iterrows()})
+    data['time'] = pd.to_datetime(data['time'])
+    data['date'] = pd.to_datetime(data['time'].dt.date)
+    data = data.query("temperature_2m==temperature_2m")
+
+    data_agg = data.groupby(['date']).agg({'temperature_2m': ['min', 'mean', 'max'],
+                                           'precipitation': ['sum'],
+                                           'windspeed_10m': ['min', 'mean', 'max'],
+                                           'windgusts_10m': ['min', 'mean', 'max']
+                                           })
+    data_agg.columns = data_agg.columns.to_series().str.join('_')
+    data_agg = data_agg.query("temperature_2m_min==temperature_2m_min")
+    return data.drop(columns=['date']), data_agg
+
+
+@st.cache_data
+def convert_df(df):
+    return df.to_csv(index=0).encode('utf-8')
+
+
+st.set_page_config(layout="wide")
+col1, col2 = st.columns((2))
+
+
+address = st.sidebar.text_input(
+    "Address", "1000 Main St, Cincinnati, OH 45202")
+start_date = st.sidebar.date_input("Start Date",  pd.Timestamp(2022, 9, 28))
+end_date = st.sidebar.date_input("End Date",  pd.Timestamp(2022, 9, 30))
+type_var = st.sidebar.selectbox(
+    'Type:', ('Gust', 'Wind', 'Temp', 'Precipitation'))
+hourly_daily = st.sidebar.radio('Aggregate Data', ('Hourly', 'Daily'))
+
+
+start_date = start_date.strftime("%Y-%m-%d")
+end_date = end_date.strftime("%Y-%m-%d")
+
+lat, lon = geocode(address)
+
+df_all, df_all_agg = get_weather_data(lat, lon, start_date, end_date)
+
+# Keys
+var_key = {'Gust': 'i10fg', 'Wind': 'wind10',
+           'Temp': 't2m', 'Precipitation': 'tp'}
+
+variable = var_key[type_var]
+
+unit_key = {'Gust': 'MPH', 'Wind': 'MPH',
+            'Temp': 'F', 'Precipitation': 'In.'}
+unit = unit_key[type_var]
+
+cols_key = {'Gust': ['windgusts_10m'], 'Wind': ['windspeed_10m'], 'Temp': ['temperature_2m'],
+            'Precipitation': ['precipitation']}
+
+cols_key_agg = {'Gust': ['windgusts_10m_min', 'windgusts_10m_mean',
+                         'windgusts_10m_max'],
+                'Wind': ['windspeed_10m_min', 'windspeed_10m_mean',
+                         'windspeed_10m_max'],
+                'Temp': ['temperature_2m_min', 'temperature_2m_mean', 'temperature_2m_max'],
+                'Precipitation': ['precipitation_sum']}
+
+if hourly_daily == 'Hourly':
+    cols = cols_key[type_var]
+else:
+    cols = cols_key_agg[type_var]
+
+if hourly_daily == 'Hourly':
+    fig = px.line(df_all, x="time", y=cols[0])
+    df_downloald = df_all
+else:
+    fig = px.line(df_all_agg.reset_index(), x="date", y=cols[0])
+    df_downloald = df_all_agg.reset_index()
+
+with col1:
+    st.title('Weather Data')
+    st.plotly_chart(fig)
+
+    csv = convert_df(df_downloald)
+
+    st.download_button(
+        label="Download data as CSV",
+        data=csv,
+        file_name=f'{start_date}.csv',
+        mime='text/csv')
+
+
+with col2:
+    st.title('')
+
+
+st.markdown(""" <style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style> """, unsafe_allow_html=True)