import pandas as pd

import os

import json

def process_batch(file_path, limit=1000):

"""Processes a single file with metadata limits.

Args:

file_path (str): Path to the file.

limit (int): Maximum number of rows to process.

"""

try:

# Read the file into a Pandas DataFrame

df = pd.read_csv(file_path)

# Check if the DataFrame exceeds the limit

if len(df) > limit:

print(f"File {file_path} exceeds processing limit ({limit}). Processing first {limit} rows.")

df = df.head(limit) #Process only the first 'limit' rows.

# Perform some operation on the DataFrame (example: calculate sum of a column)

total_value = df['value'].sum() #Assuming 'value' is a column in the dataframe

print(f"Processed {len(df)} rows from {file_path}. Total value: {total_value}")

# Save the results (optional)

with open(f"{os.path.splitext(file_path)[0]}_processed.csv", "w") as f:

df.to_csv(f, index=False)

except Exception as e:

print(f"Error processing {file_path}: {e}")

def main():

"""Main function to process multiple files with metadata limits."""

# Define the directory containing the files

input_dir = "data" # Replace with your directory

# Define the processing limit

processing_limit = 1000

# Iterate through all files in the directory

for filename in os.listdir(input_dir):

if filename.endswith(".csv"): # Process only CSV files

file_path = os.path.join(input_dir, filename)

process_batch(file_path, limit=processing_limit)

if __name__ == "__main__":

# Create the 'data' directory if it doesn't exist, and populate it with sample files

if not os.path.exists("data"):

os.makedirs("data")

#Sample CSV file creation

data1 = {'value': [1, 2, 3, 4, 5] for _ in range(2000)}

df1 = pd.DataFrame(data1)

df1.to_csv("data/file1.csv", index=False)

data2 = {'value': [6, 7, 8, 9, 10] for _ in range(500)}

df2 = pd.DataFrame(data2)

df2.to_csv("data/file2.csv", index=False)

main()