NumPy Fundamentals for Data Analytics in Python

Explore NumPy constants like pi, e, and Euler's gamma, plus special constants such as inf and nan, and learn how new axes enable flexible reshaping and broadcasting in arrays.

Explore numpy constants and functions, including Euler's number e, Euler's gamma, infinity checks, isinf, isnan, and isfinite, plus pi, with new axis and array shapes in Python.

Explore numpy array creation—from shape and value to lists and tuples such as record, structure, and character arrays, plus evenly spaced and logarithmic ranges—applied to data like daily sales simulations.

Explore numpy array creation with empty, empty_like, identity and I, including diagonal ones and non-square shapes, and initialize arrays with ones, zeros, and full variants.

Create numpy arrays from lists, tuples, and nested data with np.array, convert types with astype, and copy to preserve originals. Build arrays from buffers, files, strings, and iterables, including loadtxt.

Explore creating record arrays in NumPy, using structured data with labeled fields (id, name, price) and converting from tuples, arrays, records, strings, and files.

Create character arrays from a list of strings with numpy by using np.array or np.char.array, and verify the result is an array of strings.

Explore creating arrays with numerical ranges, including range, linspace, logspace, and geom space, and compare mesh grid, em grid, and o grid for plotting and broadcasting.

Harness NumPy to build and extract matrices using diag and diagonal flat, then create triangular matrices with tri variants and a Vandermonde matrix via vendor for polynomial fitting.

NumPy treats arrays and matrices. Compare np.dot array, np.mat, and MATLAB-style matrices; learn dot products with @ for arrays and build block matrices with np.bmat for vectorized calculations.

Master NumPy array manipulation by reshaping, changing dimensions, and axis concepts, then join, split, tile, rearrange, transpose, flatten, and efficiently add or remove elements for real-world data analysis.

Explore array manipulations while verifying python 3.12.2 and numpy 1.26; upgrade numpy to 2.1 in colab, and compare with the conda environment to preserve library compatibility.

Explore numpy basics by using copyto to transfer data between source and destination arrays, observe in-place type conversion from int to float, and inspect ndim, shape, size, and axis.

Explore reshaping a numpy array with np.reshape to a 2 by 3 shape, learn how ravel and flatten differ in copying versus viewing, and note element total must match.

Convert lists to arrays and explore numpy kinds like array, matrix, and Fortran. Verify contiguity and finite values with flags, and handle errors with try-except.

Explore the axis concept in numpy arrays, and learn how axis 0, 1, and 2 define depth, rows, and columns for summing 2-D and 3-D arrays with np.sum.

Explore numpy's transpose-like operations to reorder axes and shapes, including move axis, roll axis, swap axis, and transpose or permute dims, with examples on 2x3x4 and 2x2x2x3 arrays.

Explore numpy array dimension manipulation by converting inputs to at least 1d, 2d, and 3d, inspecting ndim and shape, and applying broadcast for element-wise operations, expanddims, and squeeze.

Learn to join arrays with numpy using concatenate, axis, and stacking methods such as vstack, hstack, dstack, block, zstack, and column stacking for one dimensional and two dimensional arrays.

Learn to create new arrays by tiling blocks with NumPy tile and repeat, building larger grids by repeating values across rows and columns.

Explore NumPy array splitting techniques to divide an array into equal parts with split and unequal parts with array_split. Apply depth-wise, horizontal, and vertical splits, plus unstack in NumPy versions.

Explore adding and removing elements in NumPy arrays using delete, insert, append, resize, and reshape, and learn how to trim zeros, unique, and pad to manage values and shapes.

Learn numpy array rearrangement with flip, flip r, flip ud, reshape, roll, and rotate, and distinguish these from transpose for data analysis and image processing.

Explore NumPy bitwise operations on arrays, including and, or, xor, and not, for elementwise binary data manipulation. Learn formatting outputs to visualize bits and debug data for hardware-level applications.

Explore numpy bitwise operations including and, or, xor, not (invert), and bitwise shifts on arrays, with clear binary explanations and version notes for numpy in colab.

Learn how to pack bits and unpack bits in NumPy to convert numbers like 178 into binary forms such as 10110010, and format binary output with width.

Explore how NumPy handles strings and text data with fast vectorized operations on large arrays, including concatenation, splitting, case changes, substring replacement, element-wise comparisons, and extracting length and pattern matches.

Explore numpy string operations, including concatenation with dot add, centering, decode and encode, case changes, padding, partitioning, replacing, and translating strings for data analytics.

Learn numpy comparison operations on arrays, including equal, not equal, greater, less, greater than or equal, and less than or equal, with strings and numbers and lexicographical ordering.

Explore NumPy string operations such as counting substring occurrences, endswith checks, and indexing. Learn to validate alphanumeric, digits, case, and whitespace across arrays.

Learn to handle dates, times, and timedeltas in NumPy by creating, comparing, and computing dates and time intervals, including business day functions for time-based data and time series analysis.

Explore NumPy date time support functions, converting date times to strings and back with date time 64, and examine date time data for resolution and step size.

Explore NumPy's business day functions, creating a custom holiday calendar, checking business days, applying offsets, and counting working days between dates.

Explore NumPy data types from integers and floats to strings and custom records, and learn to inspect, create, and manipulate them, with type compatibility checks and memory-efficient structured dtypes.

Explore NumPy data type routines, including can_cast, promote_types, min scalar type, result type, and common type, with practical examples of casting, promotion, and type inference.

Create numpy data types with dtype and int32, convert from floats noting loss of decimals, and use rec dot format underscore parser for structured dtypes and mixed tuples.

Explore numpy data type information with finfo and iinfo to reveal the min and max values for float32 and int32.

Explore NumPy data type testing using is_dtype for checking a specific kind and using issubdtype to confirm subtypes like int32 versus integer or float32 versus float.

Learn how numpy uses type name and type code to identify unicode, integers, booleans, strings, and complex numbers, and determine the minimum size type for mixed data.

Unlock NumPy's power with rounding, differencing, trigonometric, logarithmic, and rational tools, plus floating point accuracy, complex numbers, and fast min/max, all vectorized in one line of code.

Explore numpy’s trigonometric functions—sine, cosine, tangent, inverse functions, and arctan2—along with conversions between degrees and radians, the unwrap helper, and suppressing scientific notation for readable output.

Explore hyperbolic functions in NumPy, computing sinh with np.sin, cosh with np.cosh, and tanh with np.tanh, and access inverses via np.arcsinh, np.arccosh, and np.arctanh for array values.

Explore numpy rounding functions to control precision and integer results, including np.round, np.around, np.rint, np.fix, np.floor, np.ceil, and np.trunc, with practical examples.

Explore NumPy methods for summing, multiplying, and differencing arrays, including prod, sum, nan handling, cumulative operations, cross and dot products, and trapezoidal integration with trapz.

Explore numpy's exponent and logarithm operations, including exp, expm1, and exp2, then compute natural logs with log, log10, log2, and log1p.

Explore NumPy's special functions, including the modified Bessel function I0 and the sinc function, with practical examples and a manual derivation using a series.

Explore NumPy floating point routines, including sign bit, copy sign, frexp, ldexp, nextafter, and spacing, to manipulate and inspect floating point data efficiently.

Explore rational routines by computing lowest common multiple and greatest common divisor using example arrays. See how lcm yields the smallest common multiple and gcd finds the greatest common divisor.

Explore element-wise numpy arithmetic operations, including add, subtract, multiply, divide, reciprocal, positive and negative, power and float power, floor divide, true divide, mod, modf, and remainder.

Explore how NumPy handles complex numbers using angle, real, imag, and conjugate functions, including np.angle, np.real, np.imag, np.conj, and np.conjugate, and comparing with arc tan.

Discover extrema finding in numpy, using np.maximum for element-wise maxima and np.minimum for minima, and fmax or nanmax to handle missing values.

Explore numpy's miscellaneous operations, including convolution and convolution 2d with mode and padding, clip, sqrt, cbrt, square, absolute, sign, heaviside, nan_to_num, real_if_close, interpolate, and bitwise count.