Update app.py

app.py

@@ -530,6 +530,15 @@ def kmeans_clustering(df, k):
   df['cluster'] = kmeans.fit_predict(numeric_df)
   return df
 
+def sample_size_calculator(confidence_level, p, E):
+    Z = norm.ppf(1 - (1 - confidence_level) / 2)
+    
+    n = (Z**2 * p * (1 - p)) / (E**2)
+    
+    n = math.ceil(n)
+    
+    return n
+
 def upload_and_select_dataframe():
     st.sidebar.title("File Upload")
     uploaded_files = st.sidebar.file_uploader("Choose CSV or Excel files", type=["csv", "xlsx", "xls", "xlsb"], accept_multiple_files=True)
@@ -555,21 +564,12 @@ def upload_and_select_dataframe():
         return None
 
     if dataframes:
-        selected_file = st.sidebar.selectbox("Select a DataFrame", list(dataframes.keys()))
+        selected_file = st.sidebar.selectbox("Select a DataFrame", ["Select a dataset"] + list(dataframes.keys()))
         return dataframes[selected_file]
     else:
         st.sidebar.info("Please upload some files.")
         return None
 
-def sample_size_calculator(confidence_level, p, E):
-    Z = norm.ppf(1 - (1 - confidence_level) / 2)
-    
-    n = (Z**2 * p * (1 - p)) / (E**2)
-    
-    n = math.ceil(n)
-    
-    return n
-
 #st.markdown('[Click to register a suggestion or comment](https://docs.google.com/forms/d/e/1FAIpQLScLyP7bBbqMfGdspjL7Ij64UZ6v2KjqjKNbm8gwEsgWsFs_Qg/viewform?usp=header)')
 
 st.image("Insightzen.png", width=600)