๐น 1. What is NumPy and why is it important?
NumPy (Numerical Python) is a powerful Python library for numerical computing. It supports fast array operations, broadcasting, linear algebra, and random number generation. Itโs the backbone of many data science libraries like Pandas and Scikit-learn.
๐น 2. Difference between Python list and NumPy array
Python lists can store mixed data types and are slower for numerical operations. NumPy arrays are faster, use less memory, and support vectorized operations, making them ideal for numerical tasks.
๐น 3. How to create a NumPy array
import numpy as np
arr = np.array([1, 2, 3])
๐น 4. What is broadcasting in NumPy?
Broadcasting lets you perform operations on arrays of different shapes. For example, adding a scalar to an array applies the operation to each element.
๐น 5. How to generate random numbers
Use
np.random.rand() for uniform distribution, np.random.randn() for normal distribution, and np.random.randint() for random integers.๐น 6. How to reshape an array
Use
.reshape() to change the shape of an array without changing its data. Example:
arr.reshape(2, 3) turns a 1D array of 6 elements into a 2x3 matrix.๐น 7. Basic statistical operations
Use functions like
mean(), std(), var(), sum(), min(), and max() to get quick stats from your data.๐น 8. Difference between zeros(), ones(), and empty()
np.zeros() creates an array filled with 0s, np.ones() with 1s, and np.empty() creates an array without initializing values (faster but unpredictable).๐น 9. Handling missing values
Use
np.nan to represent missing values and np.isnan() to detect them. Example:
arr = np.array([1, 2, np.nan])
np.isnan(arr) # Output: [False False True]
๐น 10. Element-wise operations
NumPy supports element-wise addition, subtraction, multiplication, and division.
Example:
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
a + b # Output: [5 7 9]
๐ก Pro Tip: NumPy is all about speed and efficiency. Mastering it gives you a huge edge in data manipulation and model building.
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