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