Upload eda.py

eda.py

@@ -17,6 +17,7 @@ def run():
     # Checking Central Tendency Numerical Data
     df_describe = df.describe().T
     df_describe = df_describe.apply(lambda x: x.map('{:.2f}'.format))
+    df_describe = pd.DataFrame(df_describe)
     df_describe
 
 # Showing Explanation
@@ -53,7 +54,7 @@ def run():
 
     # Show the plot
     plt.tight_layout()
-    plt.show()
+    st.pyplot()
     # Showing Explanation
     with st.expander('Explanation'):
         st.caption('From the visualization, we can see that a large number of customers Not Churn 33881 (52.6%) and those who will be Churn 30493 (47.4%). In my opinion, this number is a pretty bad number for the company, because customers who Not Churn and those who do Churn are only a very small difference so it is necessary to improve both technically and non-technically.')
@@ -66,7 +67,7 @@ def run():
     plt.title('Age Distribution by Customer Churn')
     plt.xlabel('Age')
     plt.ylabel('Frequency')
-    plt.show()
+    st.pyplot()
 
 # Showing Explanation
     with st.expander('Explanation'):
@@ -92,7 +93,7 @@ def run():
             shadow=True, startangle=140)
     plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
     plt.title('Churn Rate by Gender')
-    plt.show()
+    st.pyplot()
 # Showing Explanation
     with st.expander('Explanation'):
         st.caption('We can see from the results of the pierchart visualization above, where the possibility of churn rates in the female gender tends to be more than men, where women 58.8% and men 41.2% of the total data, from these results we can later make improvements to maintain female gender customers.')
@@ -114,7 +115,7 @@ def run():
     for i, rate in enumerate(churn_rate_by_subscription_normalized):
         plt.text(i, rate, f'{rate:.4f}', ha='center', va='bottom')
         
-    plt.show()
+    st.pyplot()
 # Showing Explanation
     with st.expander('Explanation'):
         st.caption('For Further Analysis Since churn rates are similar, it suggests that factors other than subscription type may have a more significant impact on churn. And  for Business Strategy A churn rate approaching 50% warrants a detailed examination of customer service practices, product quality, pricing strategy, and competitive pressures. Strategies need to be implemented to enhance customer satisfaction and loyalty across all subscription types.')
@@ -138,7 +139,7 @@ def run():
     # Add labels on each bar
     for i, rate in enumerate(churn_rate_by_contract_length_normalized):
         plt.text(i, rate, f'{rate:.4f}', ha='center', va='bottom')
-    plt.show()
+    st.pyplot()
 
 # Showing Explanation
     with st.expander('Explanation'):
@@ -154,7 +155,7 @@ def run():
     plt.title('Total Spend Distribution by Customer Churn')
     plt.xlabel('Total Spend')
     plt.ylabel('Frequency')
-    plt.show()
+    st.pyplot()
 
 
 # Showing Explanation