Upload assignment_app.py

assignment_app.py

@@ -0,0 +1,77 @@
+
+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Apply the default theme and activate color codes
+sns.set_theme()
+sns.set(color_codes=True)
+
+# Import the dataset
+tips = sns.load_dataset("tips")
+tips["tip_percentage"] = tips["tip"] / tips["total_bill"] * 100
+
+# Create the title and subtitle
+st.title("How does the amount of tip / percentage of tips differ across different days of the week?")
+st.subheader("This app shows which days of the week bring in higher tip percentages and tip amounts, helping restaurants and staff adapt to customer tipping behavior and optimize their business.")
+
+# create filters/sidebars for our interactive plot
+with st.sidebar:
+    st.subheader("Filters")
+
+    # Select the day
+    all_days = sorted(tips["day"].unique())
+    selected_days = st.multiselect(
+        "Days to show",
+        options=all_days,
+        default=all_days,
+    )
+
+    # Select the x-axis
+    feature_options = {
+        "Tip": "tip",
+        "Tip Percentage": "tip_percentage"
+    }
+    feature_label = st.selectbox("Feature (x-axis)", list(feature_options.keys()))
+    x_col = feature_options[feature_label]
+
+    # enable fill options
+    fill = st.checkbox("Shade area", value=True)
+
+if not selected_days:
+    st.info("Select at least one day to display the plot.")
+else:
+    # Filter the data
+    data = tips[tips["day"].isin(selected_days)].dropna(subset=[x_col])
+
+    # Make/show the KPI
+    avg_value = data[x_col].mean()
+    unit = "$" if x_col == "tip" else "%"
+    st.metric(
+        label=f"Average {feature_label} for selected days",
+        value=f"{avg_value:.2f} {unit}"
+    )
+
+    # The plot itself
+    g = sns.displot(
+        data=data,
+        x=x_col,
+        hue="day",
+        kind="kde",
+        fill=fill
+    )
+
+    fig = g.fig
+    st.pyplot(fig)
+    plt.close(fig)
+
+    # Adding the dynamic text
+    max_day = (
+        data.groupby("day")[x_col].mean()
+        .sort_values(ascending=False)
+        .index[0]
+    )
+    st.success(
+        f"💡 On average, **{max_day}** has the highest {feature_label.lower()} among the selected days."
+    )