File Handling

2. 📂 Opening Files

2.1 open() Function
1. The open() function is used to open a file. It returns a file object. Syntax: open(filename, mode, encoding="utf-8") Here: filename ➜ Name or path of the file mode ➜ Purpose of opening the file encoding ➜ Character encoding used for text Example: file_obj = open("sample.txt", "r", encoding="utf-8")content = file_obj.read() print(content) file_obj.close() 📌 encoding="utf-8" helps handle text characters correctly.

2.3 Why Close Files?
1. After working with a file, we should close it. Closing a file is important because: ✅ It releases system resources ✅ It saves pending changes properly ✅ It prevents file locking issues ✅ It avoids unexpected behavior Example: file_obj = open("sample.txt", "r", encoding="utf-8")content = file_obj.read() file_obj.close()
2.4 Context Manager
1. A context manager is a preferred way to work with files. It automatically closes the file after the work is done. We use with open(...) for this. Example: with open("sample.txt", "r", encoding="utf-8") as file_obj:content = file_obj.read()print(content) 📌 The file is automatically closed after the with block ends. 📌 For the rest of this note, we will use context manager for file operations.

3. 📝 Text Files

3.1 What is a Text File?
1. A text file stores human-readable data. Text files usually have .txt extension. Example: notes.txt Text files can store: - Notes - Essays - Logs - Simple records - Plain text data
3.2 Reading Text Files using read()
1. read() - reads the entire file as one string. Example: with open("sample.txt", "r", encoding="utf-8") as file_obj:content = file_obj.read()print(content) 📌 Use read() when you want to read the full content of a file.
3.3 readline() and readlines()
1. readline() - reads one line from the file. readlines() - reads all lines and returns them as a list. Example using readline(): with open("sample.txt", "r", encoding="utf-8") as file_obj:first_line = file_obj.readline()print(first_line)Example using readlines(): with open("sample.txt", "r", encoding="utf-8") as file_obj:lines = file_obj.readlines()print(lines)
3.4 Reading File Line by Line
1. We can also read a file line by line using a for loop. Example: with open("sample.txt", "r", encoding="utf-8") as file_obj:for line in file_obj:print(line) 📌 This is useful when working with large text files.
3.5 Writing Text Files using write()
1. write() is used to write a string to a file. Example: with open("output.txt", "w", encoding="utf-8") as file_obj:file_obj.write("Hello, World!\n")file_obj.write("How are you?") ⚠️ w mode overwrites old content if the file already exists.
3.6 Writing Multiple Lines using writelines()
1. writelines() is used to write a list of strings to a file. Example: data_list = ["I am Rabindra\n", "I am learning Python\n", "Bye\n"]with open("output.txt", "w", encoding="utf-8") as file_obj:file_obj.writelines(data_list) 📌 Each string should include \n if we want a new line.
3.7 Appending Text Files
1. Append mode is used to add new content at the end of a file. Example: with open("output.txt", "a", encoding="utf-8") as file_obj:file_obj.write("\nThis line is added later.") 📌 a mode does not remove old content. 📌 It adds new content at the end of the file.

4. 📊 CSV Files

4.1 What is a CSV File?
1. CSV stands for Comma-Separated Values. Imagine a table in Excel. It has rows and columns. A CSV file stores table-like data in plain text. Each line is a row. Values are usually separated by commas. Example: Name,Age,Grade Ram,20,A Sita,21,B Python provides the csv module to read and write CSV files. 📌 CSV data is plain text, but it can be opened in spreadsheet apps like Excel.
4.2 Reading CSV using csv.reader()
1. csv.reader() reads each row as a list. Example: import csvwith open("data.csv", "r", newline="", encoding="utf-8") as file_obj:csv_reader = csv.reader(file_obj)data = list(csv_reader)print(data)Output example: [["Name", "Age"], ["Rabindra", "30"], ["Hari", "25"]] 📌 csv.reader() returns rows as lists.
4.3 Reading CSV using csv.DictReader()
1. csv.DictReader() reads each row as a dictionary. Column names become dictionary keys. Example: import csvwith open("data.csv", "r", newline="", encoding="utf-8") as file_obj:csv_reader = csv.DictReader(file_obj)data = list(csv_reader)print(data)Output example: [{"Name": "Rabindra", "Age": "30"}, {"Name": "Hari", "Age": "25"}] 📌 csv.DictReader() is useful when CSV has headers.
4.4 Writing CSV using csv.writer()
1. csv.writer() writes data from a list of lists. Example: import csvdata = [["Name", "Age"],["Rabindra", 30],["Hari", 25] ]with open("output.csv", "w", newline="", encoding="utf-8") as file_obj:csv_writer = csv.writer(file_obj)csv_writer.writerows(data) 📌 newline="" helps avoid extra blank lines in CSV files.
4.5 Writing CSV using csv.DictWriter()
1. csv.DictWriter() writes data from a list of dictionaries. Example: import csvdata = [{"Name": "Rabindra", "Age": 30},{"Name": "Hari", "Age": 25} ]with open("output.csv", "w", newline="", encoding="utf-8") as file_obj:fieldnames = ["Name", "Age"]csv_writer = csv.DictWriter(file_obj, fieldnames=fieldnames)csv_writer.writeheader()csv_writer.writerows(data) 📌 fieldnames defines the column names. 📌 writeheader() writes the header row.

5. 🧾 JSON Files

5.1 What is JSON?
1. JSON stands for JavaScript Object Notation. JSON is a lightweight format used to store and exchange data. It is easy for humans to read and easy for machines to parse. JSON data often looks similar to Python dictionaries. Python provides the json module to read and write JSON data.
5.2 JSON Examples
1. Example 1: {"name": "Rabindra", "age": 30}Example 2: {"country": "Nepal", "cities": ["Kathmandu", "Pokhara", "Lalitpur"]}Example 3: {"name": "Ram", "age": 27, "is_student": false, "courses": ["Python", "JS"]} 📌 JSON uses lowercase true, false, and null. 📌 Python uses True, False, and None.
5.3 JSON and Python Data
1. JSON object is similar to Python dictionary. JSON array is similar to Python list. Examples: JSON object: {"name": "Ram", "age": 20}Python dictionary: {"name": "Ram", "age": 20}JSON array: ["Python", "JavaScript", "SQL"]Python list: ["Python", "JavaScript", "SQL"]

6. 🔄 Serialization and Deserialization

6.1 What is Serialization?
1. Serialization means converting a Python object into a JSON string. Example: import jsondata = {"name": "Rabindra", "age": 30} json_string = json.dumps(data) print(json_string) print(type(json_string)) 📌 json.dumps() converts a Python object into a JSON string.
6.2 What is Deserialization?
1. Deserialization means converting a JSON string back into a Python object. Example: import jsonjson_string = '{"name": "Rabindra", "age": 30}' data = json.loads(json_string) print(data["name"]) print(type(data)) 📌 json.loads() converts a JSON string into a Python object.
6.3 dumps(), loads(), dump(), and load()
1. json.dumps() Converts Python object into JSON string. json.loads() Converts JSON string into Python object. json.dump() Writes Python object into JSON file. json.load() Reads JSON file into Python object. 📌 dumps() and loads() work with strings. 📌 dump() and load() work with files.

7. 📖 Reading and Writing JSON Files

7.1 Reading JSON File
1. json.load() is used to read JSON data from a file. Example: import jsonwith open("data.json", "r", encoding="utf-8") as file_obj:data = json.load(file_obj)print(data) 📌 data becomes a Python object, usually a dictionary or list.
7.2 Writing JSON File
1. json.dump() is used to write Python data into a JSON file. Example: import jsondata = {"name": "Python", "duration": 30}with open("output.json", "w", encoding="utf-8") as file_obj:json.dump(data, file_obj) 📌 This writes data into output.json.
7.3 Writing Pretty JSON
1. We can use indent to make JSON easier to read. Example: import jsondata = {"name": "Python", "duration": 30}with open("output.json", "w", encoding="utf-8") as file_obj:json.dump(data, file_obj, indent=4) 📌 indent=4 formats JSON with proper spacing.