NumPy Fundamentals for Data Analytics in Python

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.

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.

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.

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

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.

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.

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 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 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.