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Now, let's move to the next topic of Data Science Roadmap:

✅ Python Dictionaries 📚

Dictionaries are one of the most important data structures in Python, especially in data science and real-world datasets. They store data in key–value pairs.

🔹 1. What is a Dictionary?
A dictionary stores data in key:value format.

✅ Example:

student = { "name": "Rahul", "age": 22, "course": "Data Science" }
print(student)

Output: {'name': 'Rahul', 'age': 22, 'course': 'Data Science'}

✔ Uses curly brackets {}

🔹 2. Access Dictionary Values

Use the key to access values.

student = { "name": "Rahul", "age": 22 }
print(student["name"])

Output: Rahul

🔹 3. Add New Elements

student = { "name": "Rahul", "age": 22 }
student["city"] = "Delhi"
print(student)

Output: {'name': 'Rahul', 'age': 22, 'city': 'Delhi'}

🔹 4. Modify Values

student["age"] = 23

🔹 5. Remove Elements

student.pop("age")

🔹 6. Important Dictionary Methods
⭐

✅ Get Method:

print(student.get("name"))

Output: Rahul

✅ Keys Method:

print(student.keys())

Output: dict_keys(['name', 'age'])

✅ Values Method:

print(student.values())

Output: dict_values(['Rahul', 22])

✅ Items Method:

print(student.items())

Output: dict_items([('name', 'Rahul'), ('age', 22)])

🔹 7. Loop Through Dictionary

student = { "name": "Rahul", "age": 22 }

for key, value in student.items():
    print(key, value)

Output:
name Rahul
age 22

🎯 Today’s Goal
✔ Understand key–value pairs
✔ Access dictionary values
✔ Add or update data
✔ Loop through dictionary

👉 Dictionaries are widely used in APIs, JSON data, and machine learning datasets.

Double Tap ♥️ For More

Data Science Roadmap

✅ Python File Handling

🐍📂 File handling allows Python programs to read and write data from files.

👉 Very important in data science because most datasets come as:
✔ CSV files
✔ Text files
✔ Logs
✔ JSON files

🔹 1. Opening a File
Python uses the open() function.
Syntax: open("filename", "mode")
Example: file = open("data.txt", "r")
👉 "r" → Read mode

🔹 2. File Modes
- "r" → Read file
- "w" → Write file (overwrites existing content)
- "a" → Append file (adds to existing content)
- "r+" → Read and write

🔹 3. Reading a File
- Read Entire File: file.read()
- Read One Line: file.readline()
- Read All Lines: file.readlines()

🔹 4. Writing to a File

file = open("data.txt", "w")
file.write("Hello Data Science")
file.close()

⚠ "w" will overwrite existing content.

🔹 5. Append to File

file = open("data.txt", "a")
file.write("\nNew line added")
file.close()

✔ Adds content without deleting old data.

🔹 6. Best Practice (Very Important ⭐)
Use with statement.

with open("data.txt", "r") as file:
    content = file.read()
    print(content)

✔ Automatically closes the file.

🔹 7. Why File Handling is Important?
Used for:
✔ Reading datasets
✔ Saving results
✔ Logging machine learning models
✔ Data preprocessing

🎯 Today’s Goal
✔ Understand file modes
✔ Read files
✔ Write files
✔ Use with open()

👉 File handling is used heavily when working with CSV datasets in data science.

Double Tap ♥️ For More

Top Programming Languages for Beginners 👆