Cannot compare tz-naive and tz-aware timestamps

I'm finding the error below: Cannot compare tz-naive and tz-aware timestamps

How can I convert dates to fix the issue? The error appear in the end of the syntax below.

from datetime import datetime, timedelta
import pytz
# Define start and end dates
start = datetime(2024, 6, 1)
end = datetime(2024, 12, 10)
current_date = start
# List to store valid dates
valid_dates = []

while current_date <= end:
    valid_dates.append(current_date.strftime('%Y-%m-%d'))
    current_date += timedelta(days=1)

df['OpenDate'] = pd.to_datetime(df['OpenDate'], utc=True)
df['CloseDate'] = pd.to_datetime(df['CloseDate'], errors='coerce', utc=True)

date_range = pd.date_range(start=start, end=end)

results = []

for index, row in df.iterrows():
    for current_date in date_range:
        if row['OpenDate'] < current_date and (pd.isnull(row['CloseDate']) or 
row['CloseDate'] > current_date) and row['CloseType'] in [0, 1, 3, 7, 8]:
            result_row = {
                'CurrentDate': current_date,
                'User_Id': row['User_Id'],
                'OpenDate': row['OpenDate'],
                'CloseDate': row['CloseDate'],
                'CloseType': row['CloseType']
            }
            results.append(result_row)

Solution

try this to convert to tz-aware:

start = datetime(2024, 6, 1, tzinfo=pytz.UTC)
end = datetime(2024, 12, 10, tzinfo=pytz.UTC)

And to create a tz-aware time range:

date_range = pd.date_range(start=start, end=end, tz='UTC')

Complete code:

from datetime import datetime, timedelta
import pytz
import pandas as pd

# Define start and end dates (timezone-aware)
start = datetime(2024, 6, 1, tzinfo=pytz.UTC)
end = datetime(2024, 12, 10, tzinfo=pytz.UTC)
current_date = start

# List to store valid dates
valid_dates = []
while current_date <= end:
    valid_dates.append(current_date.strftime('%Y-%m-%d'))
    current_date += timedelta(days=1)

# Convert 'OpenDate' and 'CloseDate' to timezone-aware datetimes
df['OpenDate'] = pd.to_datetime(df['OpenDate'], utc=True)
df['CloseDate'] = pd.to_datetime(df['CloseDate'], errors='coerce', utc=True)

# Create a timezone-aware date range
date_range = pd.date_range(start=start, end=end, tz='UTC')

results = []
for index, row in df.iterrows():
    for current_date in date_range:
        if row['OpenDate'] < current_date and (pd.isnull(row['CloseDate']) or 
row['CloseDate'] > current_date) and row['CloseType'] in [0, 1, 3, 7, 8]:
            result_row = {
                'CurrentDate': current_date,
                'User_Id': row['User_Id'],
                'OpenDate': row['OpenDate'],
                'CloseDate': row['CloseDate'],
                'CloseType': row['CloseType']
            }
            results.append(result_row)

Hope this helps.