# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
"""
## Listing All Files in Directory
"""
import shutil
import os
entries = os.listdir('../input/full-quran-as-text-by-surah/Quran/')
for entry in entries:
    print(entry)
    
"""
## Reading and Printing any File
"""
def read_file(filename):
    try:
        my_file_handle=open(filename)
    except IOError:
        print("File not found or path is incorrect")
    return my_file_handle
    
file_handle = read_file("../input/full-quran-as-text-by-surah/Quran/17. al-Isra.txt")
print(file_handle.read())
"""
## Reading Line by Line
"""
file_handle = read_file("../input/full-quran-as-text-by-surah/Quran/17. al-Isra.txt")
print(file_handle.readline())
print(file_handle.readline())
print(file_handle.readline())
