keep dynamic graph

ratios.py

@@ -1,6 +1,6 @@
 import pandas as pd
-import matplotlib.pyplot as plt
+import plotly.express as px
-import numpy as np
+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"
@@ -12,24 +12,21 @@ columns_to_summarize = ["ancient", "old", "middle", "new", "late"]
 # Group the data by the "place" column and calculate sums within each group
 grouped = df.groupby("place")[columns_to_summarize].sum().reset_index()
 
-# Create a list of colors for the inner divisions
+# Create a bar chart using Plotly
-colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd"]
+fig = go.Figure()
 
-# Create the figure and axis for the bar chart
+# Add a bar trace for each category
-fig, ax = plt.subplots()
+for col in columns_to_summarize:
+    fig.add_trace(go.Bar(x=grouped["place"], y=grouped[col], name=col))
 
-# Create bars for each place with inner divisions
+# Customize the layout
-bottom = np.zeros(len(grouped))
+fig.update_layout(
-for i, col in enumerate(columns_to_summarize):
+    title="Summary of Categories by Place with Filtering",
-    ax.bar(grouped["place"], grouped[col], 0.6, bottom=bottom, color=colors[i], label=col)
+    xaxis_title="Place",
-    bottom += grouped[col]
+    yaxis_title="Total Count",
+    xaxis=dict(categoryorder='total descending'),
+    barmode='group'
+)
 
-# Customize the plot
+# Show the interactive graph
-ax.set_ylabel("Total Count")
+fig.show()
-ax.set_title("Summary of Categories by Place with Inner Divisions")
-ax.set_xticks(np.arange(len(grouped["place"])))
-ax.set_xticklabels(grouped["place"], rotation=45, ha="right")
-ax.legend(loc='upper right')
-
-# Display the plot
-plt.show()