Update eda.py

eda.py

@@ -24,88 +24,88 @@ st.markdown(hide_streamlit_style, unsafe_allow_html=True)
 
 
 def run():
+    with st.sidebar:
+        st.title('EDA')
     
-    st.title('EDA')
-
-    d = pd.read_csv('hotel_bookings.csv')
-
-    fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(15, 10))
-
-    sns.histplot(data=d, x='lead_time', hue='is_canceled',
-                 kde=True, ax=ax[0][0], palette='Set1').set_title("distribution of Lead Time")
-    
-    sns.histplot(data=d, x='booking_changes', hue='is_canceled',
-                 ax=ax[0][1], palette='Set1').set_title("distribution of Booking Changes")
+        d = pd.read_csv('hotel_bookings.csv')
     
-    sns.histplot(data=d, x='deposit_type', hue='is_canceled',
-                 ax=ax[1][0], palette='Set1').set_title("distribution of Deposit Type")
+        fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(15, 10))
     
-    plt.tight_layout()
-    st.pyplot(fig)
-
-    booking_counts = d.groupby(['arrival_date_year', 'arrival_date_month', 'arrival_date_week_number', 'hotel']).size().reset_index(name='booking_count')
-
-    pivot_table = booking_counts.pivot_table(index=['arrival_date_month', 'arrival_date_week_number'], columns=['arrival_date_year', 'hotel'], values='booking_count', fill_value=0)
+        sns.histplot(data=d, x='lead_time', hue='is_canceled',
+                     kde=True, ax=ax[0][0], palette='Set1').set_title("distribution of Lead Time")
+        
+        sns.histplot(data=d, x='booking_changes', hue='is_canceled',
+                     ax=ax[0][1], palette='Set1').set_title("distribution of Booking Changes")
+        
+        sns.histplot(data=d, x='deposit_type', hue='is_canceled',
+                     ax=ax[1][0], palette='Set1').set_title("distribution of Deposit Type")
+        
+        plt.tight_layout()
+        st.pyplot(fig)
     
-    plt.figure(figsize=(12, 10))
-    pivot_table.plot(kind='line')
-    plt.title('Seasonal Booking Trends')
-    plt.xlabel('Month and Week Number')
-    plt.ylabel('Booking Count')
-    plt.legend(title='Hotel Type')
-    plt.xticks(rotation=45)
-    plt.tight_layout()
-    st.pyplot()
-
-    demographics_counts = d[['babies', 'adults', 'children']].sum()
+        booking_counts = d.groupby(['arrival_date_year', 'arrival_date_month', 'arrival_date_week_number', 'hotel']).size().reset_index(name='booking_count')
     
-    # creating the pie chart
-    plt.figure(figsize=(8, 8))
-    plt.pie(demographics_counts, labels=demographics_counts.index, autopct='%1.1f%%', startangle=140)
-    plt.title('Distribution of Guest Demographics')
-    plt.axis('equal')  
+        pivot_table = booking_counts.pivot_table(index=['arrival_date_month', 'arrival_date_week_number'], columns=['arrival_date_year', 'hotel'], values='booking_count', fill_value=0)
+        
+        plt.figure(figsize=(12, 10))
+        pivot_table.plot(kind='line')
+        plt.title('Seasonal Booking Trends')
+        plt.xlabel('Month and Week Number')
+        plt.ylabel('Booking Count')
+        plt.legend(title='Hotel Type')
+        plt.xticks(rotation=45)
+        plt.tight_layout()
+        st.pyplot()
     
-    st.pyplot()
-
+        demographics_counts = d[['babies', 'adults', 'children']].sum()
         
-    fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))
-
-    sns.histplot(data=d, x='lead_time', hue='hotel', multiple='stack', bins=20, ax=ax[0, 0], palette='Set1')
-    ax[0, 0].set_title("Booking Behavior by Hotel Type (Lead Time)")
-
-    sns.barplot(data=d, x='hotel', y='is_canceled', ax=ax[0, 1], palette='Set1')
-    ax[0, 1].set_title("Cancellation Rate by Hotel Type")
-
-    sns.countplot(data=d, x='booking_changes', hue='hotel', ax=ax[1, 0], palette='Set1')
-    ax[1, 0].set_title("Booking Changes by Hotel Type")
-
-    sns.countplot(data=d, x='hotel', ax=ax[1, 1], palette='Set1')
-    ax[1, 1].set_title("Total Bookings by Hotel Type")
-
-
-    plt.tight_layout()
-
-    st.pyplot(fig)
-
-    plt.figure(figsize=(12, 6))
-    sns.countplot(data=d, x='market_segment', palette='Set3')
-    plt.title('Distribution of Market Segmentation')
-    plt.xlabel('Market Segment')
-    plt.ylabel('Count')
-    plt.xticks(rotation=45, ha='right')
-    plt.tight_layout()
+        # creating the pie chart
+        plt.figure(figsize=(8, 8))
+        plt.pie(demographics_counts, labels=demographics_counts.index, autopct='%1.1f%%', startangle=140)
+        plt.title('Distribution of Guest Demographics')
+        plt.axis('equal')  
+        
+        st.pyplot()
+    
+            
+        fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(12, 10))
+    
+        sns.histplot(data=d, x='lead_time', hue='hotel', multiple='stack', bins=20, ax=ax[0, 0], palette='Set1')
+        ax[0, 0].set_title("Booking Behavior by Hotel Type (Lead Time)")
     
-    st.pyplot()
+        sns.barplot(data=d, x='hotel', y='is_canceled', ax=ax[0, 1], palette='Set1')
+        ax[0, 1].set_title("Cancellation Rate by Hotel Type")
     
-    # create a count plot for distribution channels
-    plt.figure(figsize=(10, 6))
-    sns.countplot(data=d, x='distribution_channel', palette='Set2')
-    plt.title('Distribution of Distribution Channels')
-    plt.xlabel('Distribution Channel')
-    plt.ylabel('Count')
-    plt.tight_layout()
+        sns.countplot(data=d, x='booking_changes', hue='hotel', ax=ax[1, 0], palette='Set1')
+        ax[1, 0].set_title("Booking Changes by Hotel Type")
     
-    st.pyplot()
+        sns.countplot(data=d, x='hotel', ax=ax[1, 1], palette='Set1')
+        ax[1, 1].set_title("Total Bookings by Hotel Type")
+    
+    
+        plt.tight_layout()
+    
+        st.pyplot(fig)
+    
+        plt.figure(figsize=(12, 6))
+        sns.countplot(data=d, x='market_segment', palette='Set3')
+        plt.title('Distribution of Market Segmentation')
+        plt.xlabel('Market Segment')
+        plt.ylabel('Count')
+        plt.xticks(rotation=45, ha='right')
+        plt.tight_layout()
+        
+        st.pyplot()
+        
+        # create a count plot for distribution channels
+        plt.figure(figsize=(10, 6))
+        sns.countplot(data=d, x='distribution_channel', palette='Set2')
+        plt.title('Distribution of Distribution Channels')
+        plt.xlabel('Distribution Channel')
+        plt.ylabel('Count')
+        plt.tight_layout()
+        
+        st.pyplot()
         
 
 if __name__ == '__main__':