QOL changes:

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 import pandas as pd
+import numpy as np
 from sklearn.metrics import roc_auc_score, precision_score, recall_score
 from pandas.tseries.offsets import BDay
 
@@ -131,8 +132,9 @@ if st.button('🌞 At Open'):
 
     # st.subheader('New Prediction')
 
+    int_labels = ['(-∞, .20]', '(.20, .40]', '(.40, .60]', '(.60, .80]', '(.80, ∞]']
     # df_probas = res1.groupby(pd.qcut(res1['Predicted'],5)).agg({'True':[np.mean,len,np.sum]})
-    df_probas = res1.groupby(pd.cut(res1['Predicted'],[-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf])).agg({'True':[np.mean,len,np.sum]})
+    df_probas = res1.groupby(pd.cut(res1['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)).agg({'True':[np.mean,len,np.sum]})
     df_probas.columns = ['PctGreen','NumObs','NumGreen']
 
     roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values)
@@ -322,8 +324,9 @@ if st.button('⌚ After 30 Mins'):
 
     # st.subheader('New Prediction')
 
+    int_labels = ['(-∞, .20]', '(.20, .40]', '(.40, .60]', '(.60, .80]', '(.80, ∞]']
     # df_probas = res1.groupby(pd.qcut(res1['Predicted'],5)).agg({'True':[np.mean,len,np.sum]})
-    df_probas = res1.groupby(pd.cut(res1['Predicted'],[-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf])).agg({'True':[np.mean,len,np.sum]})
+    df_probas = res1.groupby(pd.cut(res1['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)).agg({'True':[np.mean,len,np.sum]})
     df_probas.columns = ['PctGreen','NumObs','NumGreen']
 
     roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values)
@@ -512,9 +515,9 @@ if st.button('⏳ After 60 Mins'):
     results.columns = ['Outputs']
 
     # st.subheader('New Prediction')
-
+    int_labels = ['(-∞, .20]', '(.20, .40]', '(.40, .60]', '(.60, .80]', '(.80, ∞]']
     # df_probas = res1.groupby(pd.qcut(res1['Predicted'],5)).agg({'True':[np.mean,len,np.sum]})
-    df_probas = res1.groupby(pd.cut(res1['Predicted'],[-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf])).agg({'True':[np.mean,len,np.sum]})
+    df_probas = res1.groupby(pd.cut(res1['Predicted'], bins = [-np.inf, 0.2, 0.4, 0.6, 0.8, np.inf], labels = int_labels)).agg({'True':[np.mean,len,np.sum]})
     df_probas.columns = ['PctGreen','NumObs','NumGreen']
 
     roc_auc_score_all = roc_auc_score(res1['True'].astype(int), res1['Predicted'].values)