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.