NumPy Functions in Python

NumPy is a powerful library for numerical and scientific computing in Python. It provides a wide range of functions and features for working with arrays, matrices, and numerical data. Here are some commonly used NumPy functions and features:

1. Creating Arrays:

• numpy.array(): Create a NumPy array from a list or other iterable.
• numpy.arange(): Create an array with evenly spaced values within a specified range.
• numpy.linspace(): Create an array with evenly spaced values over a specified interval.
• numpy.zeros(): Create an array filled with zeros.
• numpy.ones(): Create an array filled with ones.
• numpy.eye(): Create a 2D identity matrix.
• numpy.random.rand(): Create an array with random values from a uniform distribution.
• numpy.random.randn(): Create an array with random values from a standard normal distribution.
• numpy.random.randint(): Create an array with random integers within a specified range.

2. Array Operations:

• Arithmetic operations: NumPy arrays support element-wise arithmetic operations (+, -, *, /, **).
• numpy.dot(): Compute the dot product of two arrays (matrix multiplication).
• numpy.transpose(): Transpose an array or matrix.
• numpy.sum(), numpy.mean(), numpy.median(), numpy.min(), numpy.max(): Compute statistics on arrays.
• numpy.sort(): Sort an array.
• numpy.argmax(), numpy.argmin(): Find the indices of the maximum and minimum values.

3. Indexing and Slicing:

• NumPy arrays support indexing and slicing similar to Python lists.
• You can use integer indexing, slicing with colon notation, and boolean indexing to access and manipulate elements.

4. Reshaping and Manipulating Arrays:

• numpy.reshape(): Change the shape of an array.
• numpy.concatenate(): Join arrays along an existing axis.
• numpy.split(): Split an array into multiple sub-arrays.
• numpy.append(), numpy.insert(), numpy.delete(): Add, insert, or remove elements from an array.
• numpy.unique(): Find the unique elements of an array.

5. Mathematical and Statistical Functions:

• numpy.sin(), numpy.cos(), numpy.exp(), numpy.log(), numpy.sqrt(): Perform various mathematical operations on arrays element-wise.
• numpy.sum(), numpy.mean(), numpy.median(), numpy.var(), numpy.std(): Compute statistics on arrays.
• numpy.correlate(), numpy.cov(): Calculate the correlation or covariance between two arrays.

6. Broadcasting:

• NumPy allows operations between arrays of different shapes through broadcasting.
• Broadcasting automatically expands smaller arrays to match the shape of larger arrays for element-wise operations.

7. File I/O:

• numpy.save(): Save an array to a binary file.
• numpy.load(): Load an array from a binary file.
• numpy.savetxt(): Save an array to a text file.
• numpy.loadtxt(): Load an array from a text file.

8. Linear Algebra:

• numpy.linalg.inv(): Compute the inverse of a matrix.
• numpy.linalg.det(): Calculate the determinant of a matrix.
• numpy.linalg.eig(): Compute eigenvalues and eigenvectors of a matrix.
• numpy.linalg.solve(): Solve a linear system of equations.

These are just a few examples of the many functions and capabilities that NumPy provides. NumPy is a fundamental library for data manipulation and scientific computing in Python, and it forms the foundation for many other data science libraries and tools.