Hi, everyone. I’m working on processing a dataset with date ranges in various formats, such as single dates, single month ranges, cross-month ranges, and cross-year ranges. The goal is to extract and standardize these dates into “Start Date” and “End Date” columns with a consistent format (e.g., “19 Nov 2024”).
I have a sample of how it will be handled:
# Function to convert date format def convert_date(date_str): # Pattern for single date (e.g., "Feb 10, 2024") single_date_pattern = r"([A-Za-z]+ d{1,2}, d{4})" # Pattern for single month range (e.g., "Apr 22 - 28, 2024") single_month_range_pattern = r"([A-Za-z]+ d{1,2}) - (d{1,2}, (d{4}))" # Pattern for cross-month range (e.g., "Jul 28 - Aug 4, 2024") cross_month_range_pattern = r"([A-Za-z]+ d{1,2}) - ([A-Za-z]+ d{1,2}, (d{4}))" # Pattern for cross-year range (e.g., "Dec 9, 2023 - Jan 19, 2024") cross_year_range_pattern = r"([A-Za-z]+ d{1,2}, d{4}) - ([A-Za-z]+ d{1,2}, d{4})" # Handle single date format if re.match(single_date_pattern, date_str): date_obj = datetime.strptime(date_str, '%b %d, %Y') return date_obj.strftime('%d %b %Y'), date_obj.strftime('%d %b %Y') # Handle single month range (e.g., "Apr 22 - 28, 2024") elif re.match(single_month_range_pattern, date_str): start_date_str, end_date_str, year = re.findall(single_month_range_pattern, date_str)[0] # Parse the start date start_date_obj = datetime.strptime(start_date_str + f", {year}", '%b %d, %Y') # Remove the comma and split the day and year from end_date_str end_date_str = end_date_str.replace(',', '') # Remove the comma (e.g., "28, 2024" -> "28 2024") start_month = start_date_str.split()[0] # e.g., "Apr" end_date_with_month = f"{start_month} {end_date_str}" # "Apr 28 2024" # Parse the end date end_date_obj = datetime.strptime(end_date_with_month, '%b %d %Y') return start_date_obj.strftime('%d %b %Y'), end_date_obj.strftime('%d %b %Y') # Handle cross-month range (e.g., "Jul 28 - Aug 4, 2024") elif re.match(cross_month_range_pattern, date_str): start_date_str, end_date_str, year = re.findall(cross_month_range_pattern, date_str)[0] # Parse the start date start_date_obj = datetime.strptime(start_date_str + f", {year}", '%b %d, %Y') # Remove the comma from the end date string (e.g., "Aug 4, 2024" -> "Aug 4 2024") end_date_str = end_date_str.replace(',', '') # Parse the end date with its year included end_date_obj = datetime.strptime(end_date_str, '%b %d %Y') return start_date_obj.strftime('%d %b %Y'), end_date_obj.strftime('%d %b %Y') # Handle cross-year range (e.g., "Dec 9, 2023 - Jan 19, 2024") elif re.match(cross_year_range_pattern, date_str): start_date_str, end_date_str = re.findall(cross_year_range_pattern, date_str)[0] # Parse the start date start_date_obj = datetime.strptime(start_date_str, '%b %d, %Y') end_date_str = end_date_str.replace(',', '') # Parse the end date end_date_obj = datetime.strptime(end_date_str, '%b %d %Y') return start_date_obj.strftime('%d %b %Y'), end_date_obj.strftime('%d %b %Y') # If no valid format is matched, return None else: return None, None # Example dataframe with different date formats tier_1_df = pd.DataFrame({ 'Date': [ 'Feb 10, 2024', # Single Date 'Apr 22 - 28, 2024', # Single Month Range 'Jul 28 - Aug 4, 2024', # Cross-Month Range 'Dec 9, 2023 - Jan 19, 2024' # Cross-Year Range ] }) # Apply the conversion function to create new columns tier_1_df[['Start Date', 'End Date']] = tier_1_df['Date'].apply(lambda x: pd.Series(convert_date(x))) however when I run this I encounter ValueError: unconverted data remains: - Jan 19, 2024
So I figured it is related to my cross-year range format and decided to isolate that part of the code and test it:
# Function to convert date format (including handling of cross-year ranges) def convert_date(date_str): # Define pattern for cross-year range (e.g., "Dec 9, 2023 - Jan 19, 2024") cross_year_range_pattern = r"([A-Za-z]+ d{1,2}, d{4}) - ([A-Za-z]+ d{1,2}, d{4})" # Match the pattern for cross-year range if re.match(cross_year_range_pattern, date_str): start_date_str, end_date_str = re.findall(cross_year_range_pattern, date_str)[0] # Parse the start date start_date_obj = datetime.strptime(start_date_str, '%b %d, %Y') # Parse the end date end_date_obj = datetime.strptime(end_date_str, '%b %d, %Y') return start_date_obj.strftime('%d %b %Y'), end_date_obj.strftime('%d %b %Y') return None, None # Return None if format doesn't match # Test DataFrame similar to tier_1_df data = {'Date': ['Dec 9, 2023 - Jan 19, 2024', 'Feb 10, 2024', 'Apr 22 - 28, 2024']} tier_1_df = pd.DataFrame(data) # Apply the conversion function and create 'Start Date' and 'End Date' columns tier_1_df[['Start Date', 'End Date']] = tier_1_df['Date'].apply(lambda x: pd.Series(convert_date(x))) This isolated part ran successfully, so I have no idea why it doesn’t work in my initial sample.
Any help would do! Thanks!
submitted by /u/pspixsde
[link] [comments]
r/learnpython Hi, everyone. I’m working on processing a dataset with date ranges in various formats, such as single dates, single month ranges, cross-month ranges, and cross-year ranges. The goal is to extract and standardize these dates into “Start Date” and “End Date” columns with a consistent format (e.g., “19 Nov 2024”). I have a sample of how it will be handled: # Function to convert date format def convert_date(date_str): # Pattern for single date (e.g., “Feb 10, 2024″) single_date_pattern = r”([A-Za-z]+ d{1,2}, d{4})” # Pattern for single month range (e.g., “Apr 22 – 28, 2024″) single_month_range_pattern = r”([A-Za-z]+ d{1,2}) – (d{1,2}, (d{4}))” # Pattern for cross-month range (e.g., “Jul 28 – Aug 4, 2024″) cross_month_range_pattern = r”([A-Za-z]+ d{1,2}) – ([A-Za-z]+ d{1,2}, (d{4}))” # Pattern for cross-year range (e.g., “Dec 9, 2023 – Jan 19, 2024″) cross_year_range_pattern = r”([A-Za-z]+ d{1,2}, d{4}) – ([A-Za-z]+ d{1,2}, d{4})” # Handle single date format if re.match(single_date_pattern, date_str): date_obj = datetime.strptime(date_str, ‘%b %d, %Y’) return date_obj.strftime(‘%d %b %Y’), date_obj.strftime(‘%d %b %Y’) # Handle single month range (e.g., “Apr 22 – 28, 2024″) elif re.match(single_month_range_pattern, date_str): start_date_str, end_date_str, year = re.findall(single_month_range_pattern, date_str)[0] # Parse the start date start_date_obj = datetime.strptime(start_date_str + f”, {year}”, ‘%b %d, %Y’) # Remove the comma and split the day and year from end_date_str end_date_str = end_date_str.replace(‘,’, ”) # Remove the comma (e.g., “28, 2024” -> “28 2024”) start_month = start_date_str.split()[0] # e.g., “Apr” end_date_with_month = f”{start_month} {end_date_str}” # “Apr 28 2024” # Parse the end date end_date_obj = datetime.strptime(end_date_with_month, ‘%b %d %Y’) return start_date_obj.strftime(‘%d %b %Y’), end_date_obj.strftime(‘%d %b %Y’) # Handle cross-month range (e.g., “Jul 28 – Aug 4, 2024″) elif re.match(cross_month_range_pattern, date_str): start_date_str, end_date_str, year = re.findall(cross_month_range_pattern, date_str)[0] # Parse the start date start_date_obj = datetime.strptime(start_date_str + f”, {year}”, ‘%b %d, %Y’) # Remove the comma from the end date string (e.g., “Aug 4, 2024” -> “Aug 4 2024″) end_date_str = end_date_str.replace(‘,’, ”) # Parse the end date with its year included end_date_obj = datetime.strptime(end_date_str, ‘%b %d %Y’) return start_date_obj.strftime(‘%d %b %Y’), end_date_obj.strftime(‘%d %b %Y’) # Handle cross-year range (e.g., “Dec 9, 2023 – Jan 19, 2024″) elif re.match(cross_year_range_pattern, date_str): start_date_str, end_date_str = re.findall(cross_year_range_pattern, date_str)[0] # Parse the start date start_date_obj = datetime.strptime(start_date_str, ‘%b %d, %Y’) end_date_str = end_date_str.replace(‘,’, ”) # Parse the end date end_date_obj = datetime.strptime(end_date_str, ‘%b %d %Y’) return start_date_obj.strftime(‘%d %b %Y’), end_date_obj.strftime(‘%d %b %Y’) # If no valid format is matched, return None else: return None, None # Example dataframe with different date formats tier_1_df = pd.DataFrame({ ‘Date’: [ ‘Feb 10, 2024’, # Single Date ‘Apr 22 – 28, 2024’, # Single Month Range ‘Jul 28 – Aug 4, 2024’, # Cross-Month Range ‘Dec 9, 2023 – Jan 19, 2024’ # Cross-Year Range ] }) # Apply the conversion function to create new columns tier_1_df[[‘Start Date’, ‘End Date’]] = tier_1_df[‘Date’].apply(lambda x: pd.Series(convert_date(x))) however when I run this I encounter ValueError: unconverted data remains: – Jan 19, 2024 So I figured it is related to my cross-year range format and decided to isolate that part of the code and test it: # Function to convert date format (including handling of cross-year ranges) def convert_date(date_str): # Define pattern for cross-year range (e.g., “Dec 9, 2023 – Jan 19, 2024″) cross_year_range_pattern = r”([A-Za-z]+ d{1,2}, d{4}) – ([A-Za-z]+ d{1,2}, d{4})” # Match the pattern for cross-year range if re.match(cross_year_range_pattern, date_str): start_date_str, end_date_str = re.findall(cross_year_range_pattern, date_str)[0] # Parse the start date start_date_obj = datetime.strptime(start_date_str, ‘%b %d, %Y’) # Parse the end date end_date_obj = datetime.strptime(end_date_str, ‘%b %d, %Y’) return start_date_obj.strftime(‘%d %b %Y’), end_date_obj.strftime(‘%d %b %Y’) return None, None # Return None if format doesn’t match # Test DataFrame similar to tier_1_df data = {‘Date’: [‘Dec 9, 2023 – Jan 19, 2024’, ‘Feb 10, 2024’, ‘Apr 22 – 28, 2024’]} tier_1_df = pd.DataFrame(data) # Apply the conversion function and create ‘Start Date’ and ‘End Date’ columns tier_1_df[[‘Start Date’, ‘End Date’]] = tier_1_df[‘Date’].apply(lambda x: pd.Series(convert_date(x))) This isolated part ran successfully, so I have no idea why it doesn’t work in my initial sample. Any help would do! Thanks! submitted by /u/pspixsde [link] [comments]
Hi, everyone. I’m working on processing a dataset with date ranges in various formats, such as single dates, single month ranges, cross-month ranges, and cross-year ranges. The goal is to extract and standardize these dates into “Start Date” and “End Date” columns with a consistent format (e.g., “19 Nov 2024”).
I have a sample of how it will be handled:
# Function to convert date format def convert_date(date_str): # Pattern for single date (e.g., "Feb 10, 2024") single_date_pattern = r"([A-Za-z]+ d{1,2}, d{4})" # Pattern for single month range (e.g., "Apr 22 - 28, 2024") single_month_range_pattern = r"([A-Za-z]+ d{1,2}) - (d{1,2}, (d{4}))" # Pattern for cross-month range (e.g., "Jul 28 - Aug 4, 2024") cross_month_range_pattern = r"([A-Za-z]+ d{1,2}) - ([A-Za-z]+ d{1,2}, (d{4}))" # Pattern for cross-year range (e.g., "Dec 9, 2023 - Jan 19, 2024") cross_year_range_pattern = r"([A-Za-z]+ d{1,2}, d{4}) - ([A-Za-z]+ d{1,2}, d{4})" # Handle single date format if re.match(single_date_pattern, date_str): date_obj = datetime.strptime(date_str, '%b %d, %Y') return date_obj.strftime('%d %b %Y'), date_obj.strftime('%d %b %Y') # Handle single month range (e.g., "Apr 22 - 28, 2024") elif re.match(single_month_range_pattern, date_str): start_date_str, end_date_str, year = re.findall(single_month_range_pattern, date_str)[0] # Parse the start date start_date_obj = datetime.strptime(start_date_str + f", {year}", '%b %d, %Y') # Remove the comma and split the day and year from end_date_str end_date_str = end_date_str.replace(',', '') # Remove the comma (e.g., "28, 2024" -> "28 2024") start_month = start_date_str.split()[0] # e.g., "Apr" end_date_with_month = f"{start_month} {end_date_str}" # "Apr 28 2024" # Parse the end date end_date_obj = datetime.strptime(end_date_with_month, '%b %d %Y') return start_date_obj.strftime('%d %b %Y'), end_date_obj.strftime('%d %b %Y') # Handle cross-month range (e.g., "Jul 28 - Aug 4, 2024") elif re.match(cross_month_range_pattern, date_str): start_date_str, end_date_str, year = re.findall(cross_month_range_pattern, date_str)[0] # Parse the start date start_date_obj = datetime.strptime(start_date_str + f", {year}", '%b %d, %Y') # Remove the comma from the end date string (e.g., "Aug 4, 2024" -> "Aug 4 2024") end_date_str = end_date_str.replace(',', '') # Parse the end date with its year included end_date_obj = datetime.strptime(end_date_str, '%b %d %Y') return start_date_obj.strftime('%d %b %Y'), end_date_obj.strftime('%d %b %Y') # Handle cross-year range (e.g., "Dec 9, 2023 - Jan 19, 2024") elif re.match(cross_year_range_pattern, date_str): start_date_str, end_date_str = re.findall(cross_year_range_pattern, date_str)[0] # Parse the start date start_date_obj = datetime.strptime(start_date_str, '%b %d, %Y') end_date_str = end_date_str.replace(',', '') # Parse the end date end_date_obj = datetime.strptime(end_date_str, '%b %d %Y') return start_date_obj.strftime('%d %b %Y'), end_date_obj.strftime('%d %b %Y') # If no valid format is matched, return None else: return None, None # Example dataframe with different date formats tier_1_df = pd.DataFrame({ 'Date': [ 'Feb 10, 2024', # Single Date 'Apr 22 - 28, 2024', # Single Month Range 'Jul 28 - Aug 4, 2024', # Cross-Month Range 'Dec 9, 2023 - Jan 19, 2024' # Cross-Year Range ] }) # Apply the conversion function to create new columns tier_1_df[['Start Date', 'End Date']] = tier_1_df['Date'].apply(lambda x: pd.Series(convert_date(x))) however when I run this I encounter ValueError: unconverted data remains: - Jan 19, 2024
So I figured it is related to my cross-year range format and decided to isolate that part of the code and test it:
# Function to convert date format (including handling of cross-year ranges) def convert_date(date_str): # Define pattern for cross-year range (e.g., "Dec 9, 2023 - Jan 19, 2024") cross_year_range_pattern = r"([A-Za-z]+ d{1,2}, d{4}) - ([A-Za-z]+ d{1,2}, d{4})" # Match the pattern for cross-year range if re.match(cross_year_range_pattern, date_str): start_date_str, end_date_str = re.findall(cross_year_range_pattern, date_str)[0] # Parse the start date start_date_obj = datetime.strptime(start_date_str, '%b %d, %Y') # Parse the end date end_date_obj = datetime.strptime(end_date_str, '%b %d, %Y') return start_date_obj.strftime('%d %b %Y'), end_date_obj.strftime('%d %b %Y') return None, None # Return None if format doesn't match # Test DataFrame similar to tier_1_df data = {'Date': ['Dec 9, 2023 - Jan 19, 2024', 'Feb 10, 2024', 'Apr 22 - 28, 2024']} tier_1_df = pd.DataFrame(data) # Apply the conversion function and create 'Start Date' and 'End Date' columns tier_1_df[['Start Date', 'End Date']] = tier_1_df['Date'].apply(lambda x: pd.Series(convert_date(x))) This isolated part ran successfully, so I have no idea why it doesn’t work in my initial sample.
Any help would do! Thanks!
submitted by /u/pspixsde
[link] [comments]