Update app.py

app.py

@@ -113,7 +113,7 @@ def multi_answer(df):
             friquency[i] = 0
             
 
-    friquency_dataframe = pd.DataFrame({"Value": friquency.keys(), "Friquency": friquency.values(), "Percentage": np.array(list(friquency.values()))/len(df.dropna(how='all'))*100}).sort_values(by='Value')
+    friquency_dataframe = pd.DataFrame({"Value": friquency.keys(), 'Frequency': friquency.values(), "Percentage": np.array(list(friquency.values()))/len(df.dropna(how='all'))*100}).sort_values(by='Value')
     friquency_dataframe.loc[len(friquency_dataframe)] = ['Sample_size', len(df.dropna(how='all')), 1]
     return friquency_dataframe
     
@@ -236,6 +236,34 @@ def two_variable_msc(df, var1, var2):
 
     return mean_dataframe
 
+def funnel(df, dictionary):
+    friquency = {}
+    for i in dictionary.keys():
+        if dictionary[i] == "Single":
+            friquency[i] = list(single_answer(df[i])['Frequency'])[:-1]
+
+        elif dictionary[i] == "Multi":
+            matching_cols = [col for col in df.columns if is_matching_pattern(col, i)]
+            friquency[i] = list(multi_answer(df[matching_cols])['Frequency'])[:-1]
+
+        elif dictionary[i] == "Score":
+            friquency[i] = list(score_answer(df[i])['Frequency'])[:-1]
+
+    try:
+        friquency = pd.DataFrame(friquency)
+    except:
+        friquency = equalize_list_lengths(friquency)
+    
+    first = None
+    for key, value in dictionary.items():
+        if value == "Single":
+            first = key
+            break
+    
+    percentage = friquency/len(df[first])
+
+    return friquency, percentage
+
 def t_test(m1, m2, n1, n2, v1, v2):
     te = (m1 - m2) / ((v1/n1 + v2/n2)**0.5)
     p_value = 2 * (1 - t.cdf(abs(te), n1+n2-2))
@@ -364,7 +392,7 @@ def analyze_z_test(file):
 st.title("Data Analysis Application")
 
 # Main options
-main_option = st.selectbox("Please select an option:", ["Tabulation", "Hypothesis test", "Machine Learning", "Coding"])
+main_option = st.selectbox("Please select an option:", ["Tabulation", "Funnel", "Hypothesis test", "Machine Learning", "Coding"])
 
 if main_option == "Tabulation":
     st.header("Tabulation Analysis")
@@ -504,6 +532,56 @@ if main_option == "Tabulation":
         except Exception as e:
             st.error(f"❌ Error reading the Excel file: {e}")
 
+elif main_option == "Funnel":
+    st.header("Funnel")
+    
+    uploaded_file = st.file_uploader("Please upload your Excel file", type=["xlsx", "xls"])
+    if uploaded_file:
+        try:
+            #df = pd.read_excel(uploaded_file)
+            #st.subheader("Displaying the first few rows of the DataFrame")
+
+            data = {
+                'A': [10, 20, 30, np.nan, np.nan],
+                'B': [5, 10, 15, 20, 25],
+                'C': [np.nan, 100, 150, 200, 250],
+                'D': [100, np.nan, 200, 300, 400]
+            }
+            df = pd.DataFrame(data)
+
+            st.dataframe(df.head())
+            st.sidebar.header("Chart Settings")
+            bar_columns = st.sidebar.multiselect('Which columns should be displayed as bar charts?', df.columns)
+            line_columns = st.sidebar.multiselect('Which columns should be displayed as line charts?', df.columns)
+            
+            df_cleaned = df.dropna(axis=0, how='all')
+            
+            fig = go.Figure()
+            
+            for col in bar_columns:
+                df_col = df_cleaned[col]
+                fig.add_trace(go.Bar(x=df_cleaned.index, y=df_col, name=col))
+            
+            for col in line_columns:
+                df_col = df_cleaned[col]
+                fig.add_trace(go.Scatter(x=df_cleaned.index, y=df_col, mode='lines', name=col))
+            
+            fig.update_layout(
+                title="Combined Bar and Line Chart",
+                xaxis_title="Rows",
+                yaxis_title="Value",
+                template="plotly_dark",
+                barmode="group",
+                xaxis=dict(tickmode='linear')
+            )
+            
+            st.plotly_chart(fig)
+
+
+        except Exception as e:
+            st.error(f"❌ Error reading the Excel file: {e}")
+    
+
 elif main_option == "Hypothesis test":
     st.header("Hypothesis Testing")
     hypothesis_option = st.selectbox("Please select the type of hypothesis test:", ["Z test", "T test", "Chi-Square test", "ANOVA test"])