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NumPy (Numerical Python) is a fundamental library for numerical computing in Python. It provides support for multidimensional arrays, along with a collection of mathematical functions to operate on these arrays efficiently. Here are some of the key functions and features provided by NumPy:

1. Creating Arrays:

   – `numpy.array()`: Create a NumPy array from a Python list or tuple.

   – `numpy.zeros()`: Create an array filled with zeros.

   – `numpy.ones()`: Create an array filled with ones.

   – `numpy.empty()`: Create an uninitialized array.

   – `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.

2. Array Manipulation:

   – `numpy.reshape()`: Reshape an array into a specified shape.

   – `numpy.concatenate()`: Concatenate arrays along a specified axis.

   – `numpy.split()`: Split an array into multiple sub-arrays.

   – `numpy.append()`: Append values to the end of an array.

   – `numpy.delete()`: Delete elements from an array.

   – `numpy.sort()`: Sort an array.

3. Mathematical Functions:

   – `numpy.sum()`: Compute the sum of array elements.

   – `numpy.mean()`: Compute the mean of array elements.

   – `numpy.median()`: Compute the median of array elements.

   – `numpy.min()`, `numpy.max()`: Compute the minimum and maximum values in an array.

   – `numpy.abs()`: Compute the absolute values of array elements.

   – `numpy.sqrt()`: Compute the square root of array elements.

   – `numpy.exp()`: Compute the exponential of array elements.

   – `numpy.log()`, `numpy.log10()`: Compute the natural logarithm and base-10 logarithm of array elements.

   – `numpy.sin()`, `numpy.cos()`, `numpy.tan()`: Compute trigonometric functions of array elements.

4. Array Operations:

   – Element-wise operations: Addition (`+`), subtraction (`-`), multiplication (`*`), division (`/`), exponentiation (`**`), etc.

   – Dot product: `numpy.dot()`, `numpy.matmul()`

   – Transpose: `numpy.transpose()`, array attribute `T`

   – Broadcasting: Automatic alignment of arrays with different shapes during arithmetic operations.

5. Statistical Functions:

   – `numpy.mean()`, `numpy.median()`, `numpy.var()`, `numpy.std()`: Compute mean, median, variance, and standard deviation.

   – `numpy.percentile()`: Compute percentiles of array elements.

   – `numpy.histogram()`: Compute the histogram of an array.

6. Linear Algebra:

   – Matrix multiplication: `numpy.matmul()`, `@` operator.

   – Matrix inversion: `numpy.linalg.inv()`.

   – Eigenvalues and eigenvectors: `numpy.linalg.eig()`.

7. Random Number Generation:

   – `numpy.random.rand()`: Generate random samples from a uniform distribution.

   – `numpy.random.randn()`: Generate random samples from a standard normal distribution.

   – `numpy.random.randint()`: Generate random integers within a specified range.

   – `numpy.random.choice()`: Generate random samples from a given 1-D array.

These are just some of the many functions provided by NumPy for efficient numerical computing in Python. NumPy’s capabilities make it a cornerstone of many scientific and data analysis workflows.