60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
import pandas as pd
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import dash
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from dash import dcc, html
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from dash.dependencies import Input, Output
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import plotly.graph_objects as go
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# Read the CSV file into a Pandas DataFrame
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file_path = "texts_by_period_and_location_saparated_by_periods_in_columns.csv"
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df = pd.read_csv(file_path)
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# Initialize the Dash app
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app = dash.Dash(__name__)
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# Define the columns to summarize
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columns_to_summarize = ["ancient", "old", "middle", "new", "late"]
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# Create the figure for the initial graph (placeholder data)
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initial_data = df.groupby("place")[columns_to_summarize].sum().reset_index()
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initial_fig = go.Figure()
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for col in columns_to_summarize:
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initial_fig.add_trace(go.Bar(name=col, x=initial_data["place"], y=initial_data[col], text=initial_data[col], textposition="auto"))
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# Define the app layout
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app.layout = html.Div([
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html.H1("Dynamic Stacked Bar Graph with Labels"),
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dcc.Dropdown(
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id="place-selector",
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options=[{"label": place, "value": place} for place in df["place"].unique()],
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multi=True,
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placeholder="Select Places",
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),
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dcc.Graph(id="bar-graph", figure=initial_fig),
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])
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# Define a callback to update the graph based on selected places
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@app.callback(
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Output("bar-graph", "figure"),
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[Input("place-selector", "value")]
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)
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def update_graph(selected_places):
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filtered_df = df[df["place"].isin(selected_places)]
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grouped = filtered_df.groupby("place")[columns_to_summarize].sum().reset_index()
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fig = go.Figure()
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for col in columns_to_summarize:
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fig.add_trace(go.Bar(name=col, x=grouped["place"], y=grouped[col], text=grouped[col], textposition="auto"))
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fig.update_layout(barmode="stack") # Set the bars to be stacked
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for index, place in enumerate(grouped["place"]):
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total = grouped[columns_to_summarize].iloc[index].sum()
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fig.add_annotation(text=f"Total: {total}", x=place, y=total + 5)
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return fig
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if __name__ == "__main__":
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app.run_server(debug=True)
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