forked from saret/DHGeography
65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
import pandas as pd
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import dash
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import dash_core_components as dcc
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import dash_html_components as 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
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fig = go.Figure()
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# Add an initial trace for demonstration
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fig.add_trace(go.Bar(x=["Place 1", "Place 2", "Place 3"],
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y=[5, 7, 4], name="ancient"))
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# Define the app layout
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app.layout = html.Div([
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html.H1("Interactive Bar Graph with Filtering"),
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dcc.Checklist(
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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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value=df["place"].unique().tolist(),
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labelStyle={'display': 'block'}
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),
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dcc.Checklist(
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id="column-selector",
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options=[{"label": col, "value": col} for col in columns_to_summarize],
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value=columns_to_summarize,
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labelStyle={'display': 'block'}
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),
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dcc.Graph(id="bar-graph", figure=fig)
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])
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# Define callback functions to update the graph
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@app.callback(
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Output("bar-graph", "figure"),
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[Input("place-selector", "value"), Input("column-selector", "value")]
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)
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def update_graph(selected_places, selected_columns):
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filtered_df = df[(df["place"].isin(selected_places)) & (df[selected_columns].sum(axis=1) > 0)]
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grouped = filtered_df.groupby("place")[selected_columns].sum().reset_index()
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fig = go.Figure()
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for col in selected_columns:
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fig.add_trace(go.Bar(x=grouped["place"], y=grouped[col], name=col, text=grouped[col], textposition='auto'))
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fig.update_layout(
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title="Summary of Categories by Place with Selection",
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xaxis_title="Place",
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yaxis_title="Total Count",
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xaxis=dict(categoryorder='total descending'),
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barmode='stack'
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)
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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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