NumPy Arrays | Numpy Arange | Numpy Linspace | Numpy Rand | Numpy Reshape | Numpy Shape
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3. NumPy Arrays: • NumPy Arrays | Numpy Arange | Numpy Linspa...
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NumPy provides a powerful array data structure for efficient numerical computing in Python. Here are some key features and characteristics of NumPy arrays:
🔹Homogeneous data: NumPy arrays are homogeneous, which means all elements in an array must be of the same data type. This allows for efficient use of memory and vectorized operations.
🔸 Multidimensional: NumPy arrays can have any number of dimensions, which allows for efficient representation of multidimensional data such as images, sound waves, and time series.
🔹 Broadcasting: NumPy arrays allow for broadcasting, which is a way of performing operations on arrays of different shapes without having to explicitly reshape them. This can lead to more concise and efficient code.
🔸 Indexing and slicing: NumPy arrays support advanced indexing and slicing operations, which allows for efficient extraction and manipulation of data.
🔹 Mathematical operations: NumPy arrays support a wide range of mathematical operations such as addition, multiplication, exponentiation, and trigonometry functions. These operations can be performed efficiently using vectorized operations.
🔸 Fast and efficient: NumPy arrays are implemented in C and use contiguous blocks of memory, which allows for fast and efficient computation of numerical operations.
In this video you will learn about following concepts through Google Colab
➡️ NumPy is a Linear Algebra Library for Python, the reason it is so important for Data Science with Python is that almost all of the libraries in the PyData Ecosystem rely on NumPy as one of their main building blocks.
🔹 NumPy Arrays -
Numpy arrays essentially come in two flavors: vectors and matrices. Vectors are strictly 1-d arrays and matrices are 2-d (but you should note a matrix can still have only one row or one column).
➡️ Creating NumPy Arrays
-- From a Python List we can create an array by directly converting a list or list of lists.
🔸 Built-in Methods
-- Numpy Arange
➡️ Return evenly spaced values within a given interval.
🔹Numpy Linspace
-- Return evenly spaced numbers over a specified interval.
➡️ Numpy also has lots of ways to create random number arrays:
🔸 rand
-- Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1].
🔹 randn
-- Return a sample (or samples) from the "standard normal" distribution. Unlike rand which is uniform.
🔸 randint
-- Return random integers from low (inclusive) to high (exclusive).
🔹Array Attributes and Methods
-- max, min, argmax, argmin
➡️ These are useful methods for finding max or min values. Or to find their index locations using argmin or argmax
🔸 Numpy Reshape
-- Returns an array containing the same data with a new shape.
🔹 Numpy Shape
-- Shape is an attribute that arrays have (not a method).
🔸 dtype
-- You can also grab the data type of the object in the array
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