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Browse files- app.py +184 -0
- preprocessed_geodata.xlsx +0 -0
- requirements.txt +6 -0
app.py
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#%%
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import pandas as pd
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import streamlit as st
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import plotly.graph_objects as go
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df = pd.read_excel("preprocessed_geodata.xlsx")
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def get_lat_lon(row):
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row = eval(row['result_dadata'])[0]
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lat = row['geo_lat']
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lon = row['geo_lon']
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adress = row['result']
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return lat, lon, adress
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def get_colors(row):
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if row > 1200:
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return 'red'
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elif row <= 1200 and row > 800:
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return 'blue'
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elif row <= 800 and row > 400:
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return 'yellow'
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elif row <= 400 and row > 200:
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return 'green'
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elif row <= 200 and row > 100:
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return 'purple'
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elif row <= 100 and row > 30:
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return 'orange'
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else:
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return 'black'
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df[['lat', 'lon', 'adress']] = df.apply(get_lat_lon, axis=1, result_type='expand')
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viz_df = df[['lpu_name','lat', 'lon', 'adress', 'Число разных элементов в столбце LETTER_ID']].copy()
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viz_df[['lat','lon']] = viz_df[['lat','lon']].astype(float)
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viz_df['colors'] = viz_df['Число разных элементов в столбце LETTER_ID'].apply(get_colors)
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text_series ="Название клиники: "+ viz_df['lpu_name'] + "<br>" + "Адрес: " + viz_df["adress"] + "<br>" + "Количество гарантийных писем:" + viz_df['Число разных элементов в столбце LETTER_ID'].astype(str)
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#%%
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fig_bubbles_coords = go.Figure(data=go.Scattermapbox(
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lat=viz_df['lat'],
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lon=viz_df['lon'],
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mode='markers',
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marker=go.scattermapbox.Marker(
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size=viz_df['Число разных элементов в столбце LETTER_ID'],
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color='red',
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sizemode='area',
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sizeref=5,
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opacity=0.6
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),
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text=text_series,
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))
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fig_bubbles_coords.update_layout(
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mapbox_style='open-street-map',
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autosize=True,
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hovermode='closest',
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showlegend=False,
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mapbox=dict(
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center=dict(lat=viz_df['lat'].mean(), lon=viz_df['lon'].mean()),
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zoom=9
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),
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width=900,
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height=900
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)
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# fig_bubbles_coords.show()
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#%%
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fig_bubbles = go.Figure(data=go.Scattermapbox(
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lat=viz_df['lat'],
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lon=viz_df['lon'],
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mode='markers',
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marker=go.scattermapbox.Marker(
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size=viz_df['Число разных элементов в столбце LETTER_ID'],
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color='red',
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sizemode='area',
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sizeref=5,
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opacity=0.6,
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),
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customdata=text_series,
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))
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fig_bubbles.update_layout(
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mapbox_style='open-street-map',
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autosize=True,
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hovermode='closest',
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showlegend=False,
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mapbox=dict(
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center=dict(lat=viz_df['lat'].mean(), lon=viz_df['lon'].mean()),
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zoom=9
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),
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width=900,
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height=900
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)
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fig_bubbles.update_traces(hovertemplate='<b>%{customdata}</b>')
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# fig_bubbles.show()
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#%%
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fig_heatbar = go.Figure(data=go.Scattermapbox(
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lat=viz_df['lat'],
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lon=viz_df['lon'],
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mode='markers',
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marker=go.scattermapbox.Marker(
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color=viz_df['Число разных элементов в столбце LETTER_ID'],
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sizemode='area',
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sizeref=5,
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opacity=1,
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colorscale='rdylbu', # Градиент цветов от светло-синего до темно-синего
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colorbar=dict(title='Число разных элементов в столбце LETTER_ID')
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),
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customdata=text_series,
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))
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fig_heatbar.update_layout(
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mapbox_style='open-street-map',
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autosize=True,
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hovermode='closest',
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showlegend=False,
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mapbox=dict(
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center=dict(lat=viz_df['lat'].mean(), lon=viz_df['lon'].mean()),
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zoom=9
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),
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width=900,
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height=900
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)
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fig_heatbar.update_traces(hovertemplate='<b>%{customdata}</b>')
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# fig_heatbar.show()
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#%%
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dct_colors = {'red':'больше 1200',
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'blue':'1200-800',
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'yellow':'800-400',
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'green':'400-200',
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'purple':'200-100',
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'orange':'100-30',
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'black':'меньше 30'}
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colors = ['red', 'blue', 'yellow', 'green','purple','orange','black']
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fig_colored = go.Figure()
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for color in colors:
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temp_df = viz_df[viz_df['colors'] == color]
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text_series_temp = "Название клиники: "+ temp_df['lpu_name'] + "<br>" + "Адрес: " + temp_df["adress"] + "<br>" + "Количество гарантийных писем:" + temp_df['Число разных элементов в столбце LETTER_ID'].astype(str)
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fig_colored.add_trace(go.Scattermapbox(
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lat=temp_df['lat'],
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lon=temp_df['lon'],
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mode='markers',
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marker=go.scattermapbox.Marker(
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color=color,
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sizemode='area',
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sizeref=5,
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opacity=1,
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),
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name=dct_colors[color],
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customdata=text_series_temp,
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))
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fig_colored.update_layout(
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mapbox_style='open-street-map',
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autosize=True,
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hovermode='closest',
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showlegend=True,
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mapbox=dict(
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center=dict(lat=viz_df['lat'].mean(), lon=viz_df['lon'].mean()),
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zoom=9
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),
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width=900,
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height=900
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)
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fig_colored.update_traces(hovertemplate='<b>%{customdata}</b>')
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# fig_colored.show()
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# %%
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viz_df = viz_df.rename(columns={"Число разных элементов в столбце LETTER_ID":"Число_ГП"})
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#%%
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st.dataframe(data=viz_df[['lpu_name', 'Число_ГП', 'adress']])
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# st.plotly_chart(fig_bubbles_coords)
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st.plotly_chart(fig_bubbles)
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st.plotly_chart(fig_heatbar)
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st.plotly_chart(fig_colored)
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# st.table(df)
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preprocessed_geodata.xlsx
ADDED
Binary file (130 kB). View file
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requirements.txt
ADDED
@@ -0,0 +1,6 @@
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|
|
|
1 |
+
numpy
|
2 |
+
pandas
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3 |
+
matplotlib
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4 |
+
seaborn
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5 |
+
plotly
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openpyxl
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