import pandas as pd

# assuming you have a pandas DataFrame called pnl
# filter for variation\_category where the value is A or B
filtered\_df = pnl[pnl['variation\_category'].isin(['A', 'B'])]

# drop rows where list\_price\_local is NaN
filtered\_df = filtered\_df.dropna(subset=['list\_price\_local'])

# convert the date column to a pandas datetime object
filtered\_df['date'] = pd.to\_datetime(filtered\_df['date'])

# filter for rows that have a date within the last 3 days
most\_recent\_date = filtered\_df['date'].max()
three\_days\_ago = most\_recent\_date - pd.DateOffset(days=2)
last\_3\_days\_mask = (filtered\_df['date'] >= three\_days\_ago) & (filtered\_df['date'] <= most\_recent\_date)
filtered\_df = filtered\_df[last\_3\_days\_mask]

# define a function that creates a new column that contains the previous day's list price for each group
def add\_previous\_list\_price(group):
 group['previous\_list\_price'] = group['list\_price\_local'].shift(1)
 group.dropna(subset=['previous\_list\_price'], inplace=True)
 return group

# group the DataFrame by asin and country code, and apply the add\_previous\_list\_price function to each group
grouped = filtered\_df.groupby(['asin', 'country\_code']).apply(add\_previous\_list\_price)

# create a new column that flags instances where the list price has changed from the previous day
grouped['price\_changed'] = grouped['list\_price\_local'] != grouped['previous\_list\_price']

# filter for rows where price\_changed is True
grouped = grouped[grouped['price\_changed'] == True]

# create a new column that concatenates all the changes into a single string for each group
grouped['price\_changes'] = grouped.apply(lambda row: f"{row['asin']} in {row['country\_code']} has changed from {row['previous\_list\_price']} to {row['list\_price\_local']}", axis=1)
Is this code ordering by date, then highlighting when a price has changed one day to the next? i.e "price\_local"
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