Update app.py

app.py

@@ -3,7 +3,7 @@ import pandas as pd
 import numpy as np
 import plotly.express as px
 import plotly.graph_objects as go
-from scipy.stats import norm
+from scipy.stats import norm, t
 
 def sorting(df):
     df.index = list(map(float, df.index))
@@ -233,16 +233,10 @@ def two_variable_msc(df, var1, var2):
     
     return mean_dataframe
 
-# Functions related to Z-Test
-def read_excel_sheets(file):
-    """Reads an Excel file with multiple sheets and returns a dictionary of DataFrames."""
-    try:
-        xls = pd.ExcelFile(file)
-        sheets_data = {sheet: xls.parse(sheet) for sheet in xls.sheet_names}
-        return sheets_data
-    except Exception as e:
-        st.error(f"❌ Error reading Excel file: {e}")
-        return None
+def t_test(m1, m2, n1, n2, v1, v2):
+    t = (m1 - m2) / ((v1/n1 + v2/n2)**0.5)
+    p_value = 2 * (1 - t.cdf(abs(t)))
+    return p_value
 
 def z_testes(n1, n2, p1, p2):
     p_hat = ((n1*p1) + (n2*p2)) / (n1 + n2)
@@ -268,6 +262,17 @@ def z_test_data(df):
     
     return df.style.apply(lambda _: styles, axis=None)
 
+# Functions related to Z-Test
+def read_excel_sheets(file):
+    """Reads an Excel file with multiple sheets and returns a dictionary of DataFrames."""
+    try:
+        xls = pd.ExcelFile(file)
+        sheets_data = {sheet: xls.parse(sheet) for sheet in xls.sheet_names}
+        return sheets_data
+    except Exception as e:
+        st.error(f"❌ Error reading Excel file: {e}")
+        return None
+
 def Z_test_dataframes(sheets_data):
     """Processes each sheet's DataFrame and computes new DataFrames with Z-test results."""
     result_dataframes = {}
@@ -471,7 +476,7 @@ if main_option == "Tabulation":
 
                     elif type1 == "Multiple answer" and type2 == "Score answer":
                         matching_cols1 = [col for col in df.columns if is_matching_pattern(col, var1)]
-                        if matching_cols1 and matching_cols2:
+                        if matching_cols1:
                             percentile_df, frequency_df = two_variable_mm(df[matching_cols1 + [var2]], matching_cols1, var2)
                             st.subheader("Mean Table")
                             st.write(mean_df)