NumPy Data Analysis for Data Scientists with Python

Explore Python as a general purpose, high-level, object-oriented programming language with an easy syntax and powerful libraries for mathematics, AI, machine learning, data science, and web development.

Learn how to check for python in your system using cmd, verify by typing python, download from python.org, install with add to path, and set up an IDE for development.

Explore how Python variables act as containers that store values, and learn to declare and initialize them, plus the difference between undefined, global, and local variables by scope.

Explore how Python lists function as mutable, heterogeneous, ordered data structures that support nesting, concatenation, repetition, indexing, and membership checks, with practical code examples.

Explore the Python dictionary data structure, which stores data as key-value pairs, is mutable, has an order, and prohibits duplicates. Access items by key, and represent dictionaries in curly braces.

Master the elif decision making structure in Python, using it with if and else to handle multiple conditions and realistic character input, including vowel versus consonant checks.

Explore how to define and call functions in Python using the def keyword, function bodies, and parameters. See how arguments pass values, how functions improve readability and enable code reusability.

Master the for loop in Python by iterating over any iterable, like list, tuple, dictionary, set, or string, using range(start, stop, step) to generate numbers and print items.

Learn why numerical computing powers AI by converting data to numbers, using NumPy for fast, scalable array operations and preprocessing, with Pandas for data handling.

Learn how arrays power machine learning models by storing features and labels in structured data, enabling integration with NumPy, tensor, tensorflow, PyTorch, and scikit-learn to train models.

Explore how mathematical optimization improves AI models by reducing prediction error with efficient resource use. See how NumPy enables error metrics and array operations that support optimization in data analysis.

Transform raw data into insights by performing exploratory data analysis, cleaning, and preprocessing, then feed it to a NumPy-based model to inform business decisions and predictions.

Explore NumPy, the essential Python library for working with arrays and matrices, perform mathematical operations, and learn environment setup and basic array creation for data analysis in Python.

Learn NumPy, the open source numerical computing library for Python, and perform mathematical operations on one- to n-dimensional arrays for data science, machine learning, and scientific computing.

Set up the numpy environment by installing Python from python.org, adding it to your path, installing numpy with pip, and verifying the installation by importing numpy.

Verify your Python and NumPy installation, import numpy as np (alias), and create 1d and 2d arrays with np.array to confirm the ND array object.

Explore the numpy library with chapter two topics on creating arrays, indexing and slicing, array dimensions, and array objects, methods, attributes, data types, and type conversion.

Create arrays in numpy using numpy's array method, from one to n dimensions, and learn zero-based and negative indexing. Specify dtype for integers, floats, or strings and print results.

Learn how to index and slice numpy arrays, including 1d and 2d arrays, to access elements using zero-based indices and range-based slices.

Explore the ndarray object in numpy, learn to create one dimensional and multi dimensional arrays, and use attributes like shape, size, and itemsize with mean and sum.

Explore numpy data types, including integer, float, boolean, string, and complex arrays. Learn dtype usage with np, string storage via str_ vs character arrays, and date and time types.

Explore numpy data types, including time delta for date differences and date time 64 bit values, and learn to use object dtype to store mixed data in arrays.

Explains data type conversion in numpy by converting int arrays to float and float arrays back to int using the astype method, with examples and notes on data loss.

Explore chapter three of numpy, covering array attributes, array object attributes, and array creation methods—from empty array to creating arrays from existing data and ranges like arange, linspace, logspace.

Explore numpy array attributes such as shape, ndim, size, dtype, itemsize, data, strides, and flags, with examples of 1d, 2d, and 3d arrays and how these properties reveal memory layout.

Distinguish array attributes from array object attributes by applying operations to the entire array versus a specific array object instance. Explore numpy basics with np.array and size checks.

Explore common numpy array methods such as reshape, sum, mean, min, max, argmin, and argmax. Learn how axis parameter affects results on 1D and 2D arrays.

Learn how to create uninitialized numpy arrays with np.empty, specifying shape, optional dtype (default float), and memory layout (row major c or column major), then fill with actual elements.

Learn to create a zero matrix in numpy using zeros with a shape like 2x2 or 4x4. The default data type is float, and the array uses row-major order.

Learn how to create a numpy array filled with ones using the ones method, with examples for 2x2, 2x3, and 4x3 shapes, including float dtype and printing results.

Learn how to create numpy arrays from existing data using the as array method, converting lists, tuples, dictionaries, pandas series, or DataFrame objects into arrays.

Explore chapter four of the complete numpy library course, covering broadcasting rules, array iteration techniques, and updating array values for data analysis with numpy operations.

Explore numpy broadcasting to perform element-wise operations on arrays with different shapes, guided by rules that ensure shapes are compatible for addition, subtraction, and other operations.

explains numpy broadcasting rules two and three, showing how size-one dimensions stretch and scalar arrays broadcast to match shapes for elementwise operations.

Learn to convert a buffer object containing bytes into a one-dimensional numpy array with numpy.frombuffer, choosing dtype, count, and offset to read the desired elements.

Learn to create a NumPy array from any iterable with fromiter, including lists, tuples, strings, and dictionary keys or values, by specifying the iterable, dtype, and count.

Learn how to create a numpy array from a numerical range using arange, with start, end, and optional step to produce a one-dimensional array where end is exclusive.

Learn how NumPy linspace creates a one-dimensional array of evenly spaced values between start and stop with a chosen number of elements, including endpoint and dtype; compare with logspace.

Master iterating numpy arrays and other iterables with for loops, including one- and two-dimensional arrays, using nested loops and filtration to display even elements.

Discover how to iterate numpy arrays with nditer, handling one dimensional, two dimensional, and multi dimensional data using row major or column major order, without nested loops.

Learn how to iterate NumPy arrays of any dimension using enumerate, which returns both indices and values, with practical 1d and 2d examples.

Master updating NumPy array elements with the fill method and basic update techniques. See how a zero array becomes ten across all elements when applying fill.

Update numpy array elements with direct assignment and slicing, learning to modify single indices and ranges in a one-dimensional array for efficient data analysis in Python.

Update specific numpy array elements using the put method by providing indices and new values. The lecture illustrates with examples updating indices 0, 2, and 4 in code.

Explore updating numpy array elements with boolean indexing, using conditions like greater than three or equal to four. See practical examples updating values and printing the array.

Explore chapter 5 of the numpy data analysis course as you learn array manipulations and operations, including array joining, transpose, and splitting, plus other array operations.

Learn how to reshape arrays with numpy, turning a 1x6 or 1x12 array into shapes like 2x3 or 3x2 while preserving data and returning a new array.

Explore how NumPy's flat iterable object converts a multi-dimensional array into a flat sequence for element-wise iteration and for loops over all elements.

Discover numpy's flatten method, which converts a multidimensional array into a one-dimensional array and returns it without altering the original.

Learn how numpy's ravel method converts a 2d array to a 1d view without creating a new array, showing how updates propagate back to the original array.

Learn to concatenate numpy arrays along a specified axis, joining one- and two-dimensional arrays column-wise or row-wise, with practical examples using a and b.

Explore numpy's transpose operation and learn to convert rows to columns using the transpose method. See a 2x3 array becomes 3x2 and practice applying numpy.transpose with an example in code.

Apply np.split to an eight-element array to produce two equal subarrays, store them in subarrays, and iterate to access each element.

Learn to manipulate numpy arrays with insert, append, delete, resize, and unique operations, exploring one dimensional and two dimensional shapes and axis behavior for effective data analysis.

Explore chapter six of the NumPy course, focusing on five binary operations: bitwise or, bitwise and, not, left shift, and right shift, with programming examples.

Explore how the bitwise and operator works in NumPy by applying the bitwise_and function to two unsigned 8-bit arrays and translating binary results back to decimal.

Explore how the numpy bitwise or operator works on arrays, including converting values to binary, applying the operation, and converting back to decimal, with a practical example.

Apply the numpy bitwise not operator to invert array elements, turning 0 into 1 and false into true, using np.invert to produce the reciprocal bit patterns.

Learn how to perform bitwise left and right shifts on numpy arrays, specifying shift amounts, handling bit width, and interpreting results in decimal form.