As you'll see from the below code, I'm creating separate data frames of a much larger data frame, then updating a column for each one. What I'm doing is looking at the second column and checking to see if it meets a certain threshold value that is associated with the brand name found within the first column that has each individual hotel's name (part of that whole hotel name contains the specific brand name so I use .contains to group each of these hotels by brand name into specific data frame). Finally, I merge all of them together to get the original data frame with the updated column "Meeting/Not Meeting" column. When I've tried making a change only to the "splice" of the data frame associated with the rows that I'm interested in applying a certain threshold comparison to, it overwrites the previous values of the column with blank values, so this was the best solution I could come up with my limited knowledge.

Another thing I want to improve is the repetitiveness of the below code. I'm rewriting so much it seems and I'm sure there is a much better way of doing the following.

import pandas as pd
from google.colab import files

# file uploaded here and name extracted
uploaded = files.upload()
filename = next(iter(uploaded))

df = pd.read_excel(filename)

# separate dfs for each brand name since they have their own threshold value for Overall Experience guest rating
hotel_core_df = df.loc[(df['Property'].str.contains('Hotel')) & ~(df['Property'].str.contains('Hotel Suites')) & ~(df['Property'].str.contains('Hotel Premium'))].copy()
hotel_core_df['Meeting /Not Meeting'] = hotel_core_df['Overall Experience'] >= 72.10

# if Extended Stay in name - treat as Extended Stay brand even if Suites or Premium property
hotel_suites_df = df.loc[df['Property'].str.contains('Hotel Suites') & ~(df['Property'].str.contains('Extended Stay'))].copy()
hotel_suites_df['Meeting /Not Meeting'] = hotel_suites_df['Overall Experience'] >= 74.65

hotel_prem_df = df.loc[df['Property'].str.contains('Hotel Premium') & ~(df['Property'].str.contains('Extended Stay'))].copy()
hotel_prem_df['Meeting /Not Meeting'] = hotel_prem_df['Overall Experience'] >= 75.71

nexus_df = df.loc[df['Property'].str.contains('Nexus')].copy()
nexus_df['Meeting /Not Meeting'] = nexus_df['Overall Experience'] >= 74.82

plaza_df = df.loc[df['Property'].str.contains('Plaza')].copy()
plaza_df['Meeting /Not Meeting'] = plaza_df['Overall Experience'] >= 73.82

extend_df = df.loc[df['Property'].str.contains('Extended Stay')].copy()
extend_df['Meeting /Not Meeting'] = extend_df['Overall Experience'] >= 74.82

majestic_df = df.loc[df['Property'].str.contains('Majestic')].copy()
majestic_df['Meeting /Not Meeting'] = majestic_df['Overall Experience'] >= 75.78

merged_df = pd.concat([hotel_core_df, hotel_suites_df, hotel_prem_df, nexus_df, plaza_df, extend_df, majestic_df])
merged_df.sort_values(by=['Overall Experience'], ascending=False, inplace=True)

merged_df['Meeting /Not Meeting'] = merged_df.apply(lambda x: 'Meeting' if x['Meeting /Not Meeting'] == True else 'Not Meeting',axis=1)
    
merged_df.to_excel('OE threshold.xlsx', index=False)