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import pandas as pd
import plotly.express as px
from geopy.geocoders import Nominatim
from geopy.extra.rate_limiter import RateLimiter
def main():
st.set_page_config(page_title="Enhanced CSV Tool", layout="wide")
st.title("📍 Enhanced CSV Tool: Mapping & Data Enrichment")
tabs = st.tabs(["Map Visualization", "Data Enrichment"])
### Map Visualization Tab
with tabs[0]:
st.write("""
## 📍 Interactive Map Visualization
Upload your CSV file, select latitude and longitude columns, customize map settings, and visualize the data points on an interactive map.
""")
uploaded_file_map = st.file_uploader("📂 Upload CSV for Mapping", type=["csv"], key="map_upload")
if uploaded_file_map is not None:
try:
df_map = pd.read_csv(uploaded_file_map)
st.success("✅ File uploaded successfully!")
st.write("### 📊 Data Preview")
st.dataframe(df_map.head())
numeric_columns_map = df_map.select_dtypes(include=['float64', 'int64']).columns.tolist()
if len(numeric_columns_map) < 2:
st.error("❌ The uploaded CSV does not contain enough numeric columns for latitude and longitude.")
else:
col1_map, col2_map = st.columns(2)
with col1_map:
lat_col_map = st.selectbox("📍 Select Latitude Column", options=numeric_columns_map, key="lat_map")
with col2_map:
lon_col_map = st.selectbox("🧭 Select Longitude Column", options=numeric_columns_map, key="lon_map")
if lat_col_map and lon_col_map:
if not df_map[lat_col_map].between(-90, 90).all():
st.warning("⚠️ Some latitude values are out of the valid range (-90 to 90).")
if not df_map[lon_col_map].between(-180, 180).all():
st.warning("⚠️ Some longitude values are out of the valid range (-180 to 180).")
valid_df_map = df_map.dropna(subset=[lat_col_map, lon_col_map])
valid_df_map = valid_df_map[
(valid_df_map[lat_col_map].between(-90, 90)) &
(valid_df_map[lon_col_map].between(-180, 180))
]
if valid_df_map.empty:
st.error("❌ No valid data points to display on the map.")
else:
st.write(f"### 📍 Displaying {len(valid_df_map)} Points on the Map")
st.sidebar.header("🗺️ Map Settings")
size_options_map = valid_df_map.select_dtypes(include=['float64', 'int64']).columns.tolist()
size_options_map.insert(0, None)
size_col_map = st.sidebar.selectbox("🔹 Select a column for bubble size (optional)", options=size_options_map, key="size_map")
hover_options_map = [col for col in df_map.columns if col not in [lat_col_map, lon_col_map]]
hover_options_map.insert(0, None)
hover_col_map = st.sidebar.selectbox("🔹 Select a column for hover name (optional)", options=hover_options_map, key="hover_map")
color_mode_map = st.sidebar.radio("🎨 Select Color Mode", options=["Single Color", "Color Scale"], index=0, key="color_mode_map")
if color_mode_map == "Single Color":
color_map = st.sidebar.color_picker("🔹 Pick a color for the points", "#FF5733", key="color_map")
color_column_map = None
else:
color_column_options_map = [col for col in df_map.columns if col not in [lat_col_map, lon_col_map]]
color_column_options_map.insert(0, None)
color_column_map = st.sidebar.selectbox("🔹 Select a column for color scale (optional)", options=color_column_options_map, key="color_column_map")
color_map = None
map_style_options = [
"open-street-map",
"carto-positron",
"carto-darkmatter",
"stamen-terrain",
"stamen-toner",
"stamen-watercolor",
"white-bg",
"basic",
"light",
"dark",
"satellite",
"satellite-streets",
"outdoors",
"traffic-day",
"traffic-night"
]
map_style_map = st.sidebar.selectbox("🌐 Select Map Style", options=map_style_options, index=0, key="map_style_map")
default_size_map = st.sidebar.slider("🔹 Default Bubble Size", min_value=5, max_value=20, value=10, key="default_size_map")
map_height_map = st.sidebar.slider("🔹 Map Height (pixels)", min_value=400, max_value=1000, value=600, key="map_height_map")
zoom_level_map = st.sidebar.slider("🔹 Initial Zoom Level", min_value=1, max_value=20, value=3, key="zoom_level_map")
if color_mode_map == "Color Scale" and not color_column_map:
st.sidebar.warning("⚠️ Please select a column for color scale.")
st.stop()
if color_mode_map == "Color Scale" and color_column_map:
color_param_map = color_column_map
color_discrete_sequence_map = None
color_continuous_scale_map = "Viridis"
elif color_mode_map == "Color Scale" and not color_column_map:
color_param_map = None
color_discrete_sequence_map = "Viridis"
color_continuous_scale_map = None
elif color_mode_map == "Single Color":
color_param_map = None
color_discrete_sequence_map = [color_map]
color_continuous_scale_map = None
if size_col_map:
fig_map = px.scatter_mapbox(
valid_df_map,
lat=lat_col_map,
lon=lon_col_map,
size=size_col_map,
size_max=15,
color=color_param_map,
color_continuous_scale=color_continuous_scale_map,
color_discrete_sequence=color_discrete_sequence_map,
hover_name=hover_col_map if hover_col_map else None,
hover_data=[col for col in valid_df_map.columns if col not in [lat_col_map, lon_col_map]],
zoom=zoom_level_map,
mapbox_style=map_style_map,
height=map_height_map,
title="📍 Interactive Map"
)
else:
fig_map = px.scatter_mapbox(
valid_df_map,
lat=lat_col_map,
lon=lon_col_map,
color=color_param_map,
color_continuous_scale=color_continuous_scale_map,
color_discrete_sequence=color_discrete_sequence_map,
hover_name=hover_col_map if hover_col_map else None,
hover_data=[col for col in valid_df_map.columns if col not in [lat_col_map, lon_col_map]],
zoom=zoom_level_map,
mapbox_style=map_style_map,
height=map_height_map,
title="📍 Interactive Map"
)
if color_mode_map == "Single Color":
fig_map.update_traces(marker=dict(color=color_map))
if not size_col_map:
fig_map.update_traces(marker=dict(size=default_size_map))
fig_map.update_layout(
margin={"r":0,"t":30,"l":0,"b":0},
title_x=0.5
)
st.plotly_chart(fig_map, use_container_width=True)
except Exception as e:
st.error(f"❌ An error occurred while processing the file: {e}")
else:
st.info("📥 Awaiting CSV file upload for mapping.")
### Data Enrichment Tab
with tabs[1]:
st.write("""
## 🔍 Data Enrichment with Geopy
Enrich your CSV data by retrieving additional geographic information based on latitude and longitude. Select the desired information to add to your dataset.
""")
uploaded_file_enrich = st.file_uploader("📂 Upload CSV for Enrichment", type=["csv"], key="enrich_upload")
if uploaded_file_enrich is not None:
try:
df_enrich = pd.read_csv(uploaded_file_enrich)
st.success("✅ File uploaded successfully!")
st.write("### 📊 Data Preview")
st.dataframe(df_enrich.head())
numeric_columns_enrich = df_enrich.select_dtypes(include=['float64', 'int64']).columns.tolist()
if len(numeric_columns_enrich) < 2:
st.error("❌ The uploaded CSV does not contain enough numeric columns for latitude and longitude.")
else:
col1_enrich, col2_enrich = st.columns(2)
with col1_enrich:
lat_col_enrich = st.selectbox("📍 Select Latitude Column", options=numeric_columns_enrich, key="lat_enrich")
with col2_enrich:
lon_col_enrich = st.selectbox("🧭 Select Longitude Column", options=numeric_columns_enrich, key="lon_enrich")
if lat_col_enrich and lon_col_enrich:
if not df_enrich[lat_col_enrich].between(-90, 90).all():
st.warning("⚠️ Some latitude values are out of the valid range (-90 to 90).")
if not df_enrich[lon_col_enrich].between(-180, 180).all():
st.warning("⚠️ Some longitude values are out of the valid range (-180 to 180).")
valid_df_enrich = df_enrich.dropna(subset=[lat_col_enrich, lon_col_enrich])
valid_df_enrich = valid_df_enrich[
(valid_df_enrich[lat_col_enrich].between(-90, 90)) &
(valid_df_enrich[lon_col_enrich].between(-180, 180))
]
if valid_df_enrich.empty:
st.error("❌ No valid data points to enrich.")
else:
st.write(f"### 🔍 Enriching {len(valid_df_enrich)} Points")
st.sidebar.header("📝 Enrichment Settings")
address_fields = [
"City",
"County",
"Region",
"Postcode",
"Country",
"Neighbourhood",
"Road",
"Suburb",
"State District",
"State",
"Town",
"Village",
"ISO3166-1 Alpha-2",
"ISO3166-1 Alpha-3"
]
selected_info = []
for field in address_fields:
if st.sidebar.checkbox(field):
selected_info.append(field)
if not selected_info:
st.sidebar.warning("⚠️ Please select at least one information to retrieve.")
enrich_button = st.button("🔄 Enrich Data")
if enrich_button:
if not selected_info:
st.warning("⚠️ Please select at least one information to retrieve before enriching.")
else:
geolocator = Nominatim(user_agent="streamlit_app_enrichment")
reverse = RateLimiter(geolocator.reverse, min_delay_seconds=1)
for info in selected_info:
if info not in df_enrich.columns:
df_enrich[info] = ""
progress_bar = st.progress(0)
status_text = st.empty()
for index, row in valid_df_enrich.iterrows():
try:
location = reverse((row[lat_col_enrich], row[lon_col_enrich]), exactly_one=True)
if location and location.raw and 'address' in location.raw:
address = location.raw['address']
for info in selected_info:
if info == "City":
df_enrich.at[index, "City"] = address.get('city', address.get('town', address.get('village', '')))
elif info == "County":
df_enrich.at[index, "County"] = address.get('county', '')
elif info == "Region":
df_enrich.at[index, "Region"] = address.get('state', '')
elif info == "Postcode":
df_enrich.at[index, "Postcode"] = address.get('postcode', '')
elif info == "Country":
df_enrich.at[index, "Country"] = address.get('country', '')
elif info == "Neighbourhood":
df_enrich.at[index, "Neighbourhood"] = address.get('neighbourhood', '')
elif info == "Road":
df_enrich.at[index, "Road"] = address.get('road', '')
elif info == "Suburb":
df_enrich.at[index, "Suburb"] = address.get('suburb', '')
elif info == "State District":
df_enrich.at[index, "State District"] = address.get('state_district', '')
elif info == "State":
df_enrich.at[index, "State"] = address.get('state', '')
elif info == "Town":
df_enrich.at[index, "Town"] = address.get('town', '')
elif info == "Village":
df_enrich.at[index, "Village"] = address.get('village', '')
elif info == "ISO3166-1 Alpha-2":
df_enrich.at[index, "ISO3166-1 Alpha-2"] = address.get('ISO3166-1_alpha-2', '')
elif info == "ISO3166-1 Alpha-3":
df_enrich.at[index, "ISO3166-1 Alpha-3"] = address.get('ISO3166-1_alpha-3', '')
else:
df_enrich.at[index, info] = address.get(info.lower(), '')
except Exception as e:
for info in selected_info:
df_enrich.at[index, info] = 'Error'
progress = (index + 1) / len(valid_df_enrich)
progress_bar.progress(min(progress, 1.0))
status_text.text(f"Processing {index + 1} of {len(valid_df_enrich)}")
progress_bar.empty()
status_text.empty()
st.success("✅ Enrichment complete!")
st.write("### 📈 Enriched Data")
st.dataframe(df_enrich.head())
csv_enriched = df_enrich.to_csv(index=False).encode('utf-8')
st.download_button(
label="📥 Download Enriched CSV",
data=csv_enriched,
file_name='enriched_data.csv',
mime='text/csv'
)
except Exception as e:
st.error(f"❌ An error occurred while processing the file: {e}")
else:
st.info("📥 Awaiting CSV file upload for enrichment.")
if __name__ == "__main__":
main()
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