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What is NumPy?

An overview of NumPy and its importance in scientific computing and data analysis.
Installing NumPy:

How to install NumPy using package managers like pip or conda.
NumPy Arrays (ndarray):

Understanding NumPy arrays, their attributes, and data types.

Creating arrays using functions like numpy.array(), numpy.zeros(), numpy.ones(), and numpy.arange().

Array Shape and Dimensions:

Exploring the shape and dimensions of NumPy arrays.

Reshaping arrays using numpy.reshape().

Array Indexing and Slicing:

Accessing and modifying elements within NumPy arrays.

Slicing arrays to extract specific parts.

Mathematical Operations:

Performing element-wise mathematical operations on arrays.

Broadcasting and its role in operations with arrays of different shapes.

Aggregation and Statistics:

Calculating basic statistics like mean, median, and standard deviation.

Aggregating data along specific axes using functions like numpy.sum() and numpy.mean().

Array Concatenation and Splitting:

Combining multiple arrays using numpy.concatenate() and numpy.vstack(), numpy.hstack().

Splitting arrays using numpy.split() and numpy.hsplit().

Boolean Indexing:

Filtering data based on conditions using Boolean indexing.

File Input and Output:

Loading and saving NumPy arrays to/from files using numpy.save() and numpy.load().

Random Number Generation:

Generating random numbers and random arrays with NumPy's random module.