import re

import pandas as pd

def normalize_log_data(log_file_path, date_format='%Y-%m-%d %H:%M:%S'):

"""

Normalizes data from a log file, extracting relevant fields and converting timestamps.

Args:

log_file_path (str): Path to the log file.

date_format (str): Format of the date/time string in the log file.

Returns:

pandas.DataFrame: A DataFrame containing the normalized log data. Returns an empty DataFrame if the file is not found or empty.

"""

try:

with open(log_file_path, 'r') as f:

log_lines = f.readlines()

except FileNotFoundError:

print(f"Error: Log file not found at {log_file_path}")

return pd.DataFrame()

if not log_lines:

print("Log file is empty.")

return pd.DataFrame()

data = []

for line in log_lines:

# Use regex to extract timestamp and message (adjust pattern as needed)

match = re.match(r'(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}).*?(?=\n|$)', line) #Capture timestamp until newline or end of line

if match:

timestamp_str = match.group(1)

try:

timestamp = pd.to_datetime(timestamp_str, format=date_format)

except ValueError:

timestamp = None # Handle invalid timestamps

message = match.group(0).replace(timestamp_str, '', 1).strip() #remove timestamp from message

data.append([timestamp, message])

df = pd.DataFrame(data, columns=['timestamp', 'message'])

#Basic sanity checks

if df.empty:

print("No data extracted from the log file.")

return pd.DataFrame()

#Check for timestamp validity

if df['timestamp'].isnull().any():

print("Warning: Invalid timestamps found. Check date format and log file content.")

return df

if __name__ == '__main__':

#Example usage

log_file = 'sample.log'

# Create a dummy log file for testing

with open(log_file, 'w') as f:

f.write('2023-11-15 10:00:00 This is a log message.\n')

f.write('2023-11-15 10:05:00 Another log entry.\n')

f.write('Invalid Timestamp\n')

f.write('2023-11-15 10:10:00 Yet another message.\n')

normalized_df = normalize_log_data(log_file)

print(normalized_df.head())