Broadcasting works by expanding smaller arrays along missing dimensions to match larger arrays. NumPy compares shapes from right to left and inserts dimensions with size 1 when needed.

Operations then run element-wise without copying actual data. This makes broadcasting extremely fast and memory-efficient. It is widely used for vectorized math operations in data science.

NumPy uses optimized C and Fortran code under the hood, eliminating Python’s loop overhead. Vectorized operations push computations to low-level routines that execute in bulk. This reduces interpretation time and improves cache efficiency.

SIMD CPU instructions further speed up execution. As a result, vectorized NumPy operations can be 10–100× faster than plain Python loops

reshape() modifies array dimensions without changing the underlying data.

ravel() returns a flattened view of the array whenever possible, meaning no copy is made.

flatten() always creates a new copy of the array. These operations use shape and stride manipulation to create new views efficiently.

Reshaping is widely used in ML preprocessing and model input formatting.

NumPy arrays are more efficient for numerical computation because they enforce a single data type and store data in packed memory blocks.

These functions help structure and clean array shapes before applying ML models or transformations.

ravel() is preferred for performance unless a true copy is required, such as for independent modifications.

Understanding axis is essential for applying sum(), mean(), concatenate(), and many other operations correctly.

dtype defines the data type of elements stored in the array. Choosing the right dtype improves memory efficiency and computation speed. Common types include int32, float64, and bool. NumPy prohibits mixed datatypes inside the same array to maintain uniform memory blocks. dtype also determines how bytes are interpreted during processing.

NumPy slicing uses the pattern start:stop:step to extract subarrays efficiently. Unlike Python lists, NumPy slices create views, not copies. This means data is not duplicated, improving performance. Slicing supports multidimensional arrays using comma-separated indices. It is heavily used in ML preprocessing tasks like selecting features or rows.

shape returns the size of each dimension in an array, while ndim returns the count of dimensions. These attributes help understand memory layout and structure. Many operations like reshaping or concatenation rely on shape awareness. ndim is useful when writing generic functions for variable-sized inputs. These properties are fundamental for array manipulation.

NumPy itself does not have native missing-value support like pandas. Missing values are often represented using NaN in float arrays. Functions like isnan() and nanmean() help detect and compute while ignoring NaNs. Masked arrays provide additional control for missing data. For large-scale missing value handling, NumPy is typically used with pandas.

Vectorization performs operations on entire arrays without explicit loops. It uses underlying C-level operations for speed. This reduces Python interpreter overhead and improves cache locality. Vectorization results in cleaner code and significant speedups. Most NumPy functions are inherently vectorized.

Broadcasting allows arithmetic operations between arrays of different shapes by expanding dimensions automatically. It removes the need for manual loops or replication. Broadcasting powers operations like scaling, normalization, and matrix arithmetic. It helps improve performance and reduce memory usage. Understanding broadcasting rules is essential for error-free NumPy coding.

NumPy provides the random module which includes distributions like uniform, normal, and binomial. Generators can produce arrays of random values efficiently. Seed functions ensure reproducibility in experiments. Random sampling is widely used for ML initialization, augmentation, and simulations. Newer versions use numpy.random.default_rng for improved performance.

append() adds elements to the end but creates a new copy, making it slower.

concatenate() joins arrays along an existing axis.

stack() adds a new dimension while joining arrays. These functions help structure data in ML workflows like batching and reshaping. Choosing the correct function avoids performance pitfalls.

NumPy implements optimized routines for mean, median, variance, and percentiles. These functions operate at C speed and avoid Python-level loops. They take advantage of contiguous memory layout for fast aggregation. Many functions accept axis arguments for dimensional control. This makes NumPy ideal for preprocessing datasets in ML.

Memory-mapping allows loading large arrays from disk without fully loading them into RAM. It is done using numpy.memmap, which accesses only required portions. This prevents memory overflow when working with large datasets. It is useful in deep learning, simulations, and large image processing. Memory-mapped arrays behave like normal NumPy arrays.

np.where() returns indices where conditions are true or can be used to select values based on conditions. It supports vectorized conditional operations, similar to SQL CASE. It is commonly used for filtering, thresholding, and label creation. Results can be shapes of arrays or multiple coordinate arrays. It is widely used in ML tasks like masking.

NumPy arrays are the backbone of pandas DataFrames and ML libraries like scikit-learn and TensorFlow. Most models accept NumPy arrays as input. Operations like preprocessing and scaling are performed using NumPy.

Data movement between libraries is efficient due to shared memory structures. NumPy’s speed and flexibility make it foundational in the entire data science ecosystem.