What is a NumPy array?

NumPy stands for Numerical Python.

NumPy was created by Travis Oliphant, an American data scientist and software developer.

Travis Oliphant build Numpy at 2006 by merged two older Python libraries, Numeric (1995) and Numarray (2001), to create NumPy.

The goal was to provide a single, powerful library for numerical computing in Python.

 

What is a NumPy array?

A NumPy array is like a list in Python but much faster and better for handling numbers and large datasets. It allows us to store and work with many numbers at once, in rows and columns, just like a table or spreadsheet.

 

What is the diffrence between Python List and Numpy?

Numpy is very similar to the Python List.

If we have List in Python, so why are we using Numpy?

Faster: NumPy arrays work much quicker than lists, especially when we do math with large amounts of data.

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For Example:

Let's check the calculation speed with Python List or Python Numpy.

import time

python_list = list(range(1, 1000001))

start = time.time()
sum(python_list)
print("Python list time:", time.time() - start)

Output: Python list take time: ~0.03 seconds

Python list time: 0.011348247528076172

 

Let's check the calculation speed with Python List or Python Numpy.

import numpy as np
import time

numpy_array = np.arange(1, 1000001)

start = time.time()
numpy_array.sum()
print("NumPy array time:", time.time() - start)

Output: Numpy array take time: ~0.0002 seconds

NumPy array time: 0.000274658203125

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Saves Space: NumPy arrays use less memory than lists because they store data more efficiently.

Better for Math: NumPy arrays have many built-in tools for math, like adding, multiplying, or finding averages, and they’re easier to use for calculations than lists.

Handles Big Data Easily: When you’re working with large datasets (like in data science or machine learning), NumPy makes it faster and simpler to manage the data.

 

Why NumPy is fast?

• Written in C: NumPy is mostly built in C language, which is much faster than Python.
• No Python Loops: Instead of looping through numbers one by one (like Python), NumPy does all calculations in one step.
• Stores Data Together: NumPy keeps all data in a single block of memory, making it quick to find and use the numbers.
• Same Data Type: All numbers in a NumPy array are of the same type (e.g., all integers or all floats). This makes it faster because there’s no need to check the type of each number.
• Bulk Operations: NumPy can do math (like addition or multiplication) on whole arrays at once, instead of doing it number by number.
• Pre-Built Functions: NumPy has ready-made, super-fast tools (like sum and mean) to handle big calculations quickly.

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19+ Interview questions focused on understanding NumPy and its purpose

Question 1. What is an array in simple words?

Answer : An array is like a container that stores multiple values, all of the same type, organized in rows and columns, just like a table.

 

Question 2. Python has lists, so why do we need NumPy arrays?

Answer :

• Faster calculations
• Less memory usage
• Built-in math functions
• Easier handling of large datasets

 

Question 3. What are some real-world uses of NumPy?

Answer :

• Data Analysis: Handling large datasets in data science.
• Machine Learning: Working with data for training models.
• Image Processing: Manipulating images stored as arrays.
• Scientific Computing: Solving math and engineering problems.

 

Question 4. What are the prerequisites to learn NumPy?

Answer :

• Basic Python knowledge: Lists, loops, and basic math.
• Understanding of arrays: Concept of rows and columns.

 

Question 5. How does NumPy help in data science?

Answer : NumPy makes it easier to handle, process, and calculate large datasets quickly, which is essential in data science.

 

Question 6. What is the advantage of NumPy over regular Python?

Answer : NumPy is faster, more memory-efficient, and comes with ready-made tools for complex mathematical operations.

 

Question 7. What kind of data can NumPy handle?

Answer : NumPy can handle:

• Numbers (integers, floats)
• Multidimensional data (2D tables, 3D matrices, etc.)

 

Question 8. What is broadcasting in NumPy?

Answer : Broadcasting allows NumPy to perform operations on arrays of different shapes, like adding a single number to all elements in a matrix.

 

Question 9. Why is NumPy faster than regular Python?

Answer :

• Written in C language.
• Avoids Python loops by doing bulk operations.
• Uses efficient memory storage.

 

Question 10. What is the difference between a NumPy array and a matrix?

Answer :

• NumPy Array: General-purpose; can handle 1D, 2D, or even n-dimensional data.
• Matrix: Specifically for 2D data; less flexible than arrays.

 

Question 11. How does NumPy save memory compared to Python lists?

Answer : NumPy stores all elements in one block of memory and uses fixed data types, while lists store elements as separate objects.

 

Question 12. Why is it important to learn NumPy before Pandas or other data libraries?

Answer : NumPy is the foundation for libraries like Pandas, SciPy, and TensorFlow. Understanding NumPy makes it easier to work with these tools.

 

Question 13. Can NumPy handle missing data?

Answer : No, NumPy does not have built-in support for missing data. For that, we use Pandas.

 

Question 14. What is the shape of a NumPy array? Why is it important?

Answer : The shape of a NumPy array tells how many rows and columns it has. This is important for reshaping and mathematical operations.

 

Question 15. What are NumPy's limitations?

Answer :

• Cannot handle non-numerical data directly.
• Does not support missing values.
• Limited support for advanced statistical methods (for which Pandas or SciPy are used).

 

Question 16. What are some key mathematical functions in NumPy?

Answer :

• sum(): Add all elements.
• mean(): Find the average.
• std(): Calculate standard deviation.
• dot(): Perform matrix multiplication.

 

Question 17. What is the difference between a NumPy array and a DataFrame in Pandas?

Answer :

• NumPy Array: Only handles numerical data, no labels.
• DataFrame: Can handle numerical and non-numerical data with labeled rows and columns.

 

Question 18. How does NumPy interact with other Python libraries?

Answer : NumPy is the foundation for many libraries like Pandas, Matplotlib, TensorFlow, and SciPy. These tools depend on NumPy arrays for fast and efficient computations.

 

Question 19. Why is NumPy so popular in machine learning and AI?

Answer :

• Handles large datasets efficiently.
• Supports matrix operations, which are essential for ML algorithms.
• Provides fast mathematical computations.