NumPy, SciPy, Matplotlib & Pandas A-Z: Machine Learning

Explore creating arrays with NumPy, from 1D and 2D arrays to higher dimensional structures using np.array, printing results, and understanding dimensionality in numerical computations.

Explore numpy array indexing to access elements with positive and negative indices, work with 2D arrays, and apply boolean indexing to filter data using masks.

Master numpy array slicing by extracting data with indexing and ranges. Learn to slice 2d and multidimensional arrays by specifying rows and columns, creating and printing slices.

Explore how numpy handles array copying with views, compare copies and views, create independent copies, and see how modifying a copied array does not affect the original.

Explore numpy array shapes by creating 1d, 2d, and 3d arrays with numpy, and inspect their shapes using the shape property to understand dimensions and structure.

Master numpy array reshaping to transform data structures, learn to reshape 1d arrays to 2d, and compare row major (c order) and column major (fortran order).

Create and explore numpy arrays, including one-dimensional and two-dimensional arrays, inspect shape, size, and data type, and practice indexing, slicing, and common array operations with zeros, ones, arrange, and range.

Learn to model NumPy data distribution by generating random numbers with probabilities via the choice method. Create one dimensional and two dimensional arrays to sample 100 values from a distribution.

Explore numpy's random permutations by using shuffle and permutation methods to rearrange arrays, including multi-dimensional arrays, while keeping the original array intact.

discover seaborn, a matplotlib-based library that enhances data visualization, learn to install, import, and create plots such as distplot with and without histograms for exploring distributions.

Explore the numpy normal distribution, also known as the Gaussian distribution, and generate, visualize, and customize random samples using mean, standard deviation, and size parameters.

Explore how numpy generates binomial random data and visualizes it with histograms, highlighting the binomial distribution’s discrete nature, its relation to the normal distribution, and key parameters n and p.

Explore the NumPy Poisson distribution by generating discrete random data with a lambda rate and size, then visualize the difference between normal and Poisson distributions using Matplotlib and Seaborn.

Explore the uniform distribution with NumPy by generating equal-probability random samples, visualize results with Matplotlib and Seaborn, and learn how to create and inspect arrays of various sizes.

Explore numpy universal functions (ufuncs) and their element-wise array operations, and learn how to create your own ufuncs by defining a Python function and wrapping it with numpy.

Explore numpy universal functions for simple arithmetic on arrays, performing element-wise addition, subtraction, multiplication, division, and broadcasting.

Explore how NumPy's universal functions perform element-wise rounding on arrays. See how to use np.round with the decimals parameter to control precision.

Explore logarithmic and exponential operations with numpy ufuncs, including log, log10 (log base ten), and exp on numpy arrays, with results printed to illustrate core numerical tools.

Explore numpy universal functions for summations, using arrays to perform data manipulation and analysis with axis-based totals and cumulative sums via cumsum.

Explore numpy universal functions with the product operation, using np.product to multiply array elements, compute along an axis, and generate cumulative product results.

Learn how pandas series provide a one-dimensional labeled array, like a column, and how to create series from lists or dictionaries with a custom index.

Explore pandas dataframes as a versatile tool for Python data manipulation, learning to create dataframes from dictionaries and lists of dictionaries, with each key as a column.

Learn to read csv files with pandas using pd.read_csv, load data into a dataframe, and manage display options such as the maximum number of rows for tabular data.

Learn how to read json files into a pandas dataframe using pd.read_json, and explore json structures, dictionaries, and dataframe results.

Master pandas dataframe analysis by loading data from data.csv, viewing with head and tail, and inspecting info to identify data types and null values for cleaning.

Learn to use matplotlib in Python to create publication-quality 2d plots, customize line plots with colors, markers, labels, and titles, and explore options from simple lines to 3d visuals.

Master plotting in Python with Matplotlib and Seaborn, drawing lines and markers from x and y points, plotting multiple points, and visualizing data to craft clear visual narratives.

Learn how markers in Python plots with matplotlib highlight data points by using the marker parameter in plt.plot, exploring circle, square, triangle, and star symbols to visualize different data series.

Explore line plots in Python using Matplotlib to connect data points, customize line style and color, and plot multiple lines with labels and a title to reveal trends and relationships.

Learn to add and customize levels in matplotlib plots—axis labels, titles, legends, and styling for clear, multi-series data visualization in Python.

Learn how to use grids in Matplotlib to improve readability of plots, customize grid appearance, and apply grids to subplots for clearer data visualization.

Master creating multiple plots within a single figure using Python's Matplot library, exploring a grid of subplots, customizing colors and titles to compare data visually.

Create and customize scatter plots in Python using Matplot to visualize relationships between two numerical variables, adjusting color, markers, size, and axis labels for clear data insights.

Learn to create bar plots in Python using matplotlib to visualize and compare categorical data, customize titles, labels, and colors, and build horizontal bar plots for long category names.

Learn how to visualize data distributions using histograms in Python with Matplotlib, set up basic histograms, adjust bins, and interpret frequency and exam score distributions.

Explore creating pie charts in Python with Matplotlib and NumPy to visualize proportions, add labels and legends, adjust start angles, and customize slices for clear data insights.

Install the scientific Python stack with pip, import NumPy and SciPy, and perform basic operations like addition, exponential value, and the Bessel function.

Explore SciPy's constant module and access fundamental scientific and mathematical constants. Retrieve the speed of light, gravitational constant, Boltzmann constant, pi, Euler gamma, and the golden ratio, plus unit conversions.

Explore the SciPy optimize module to solve local and global optimization problems. Use minimize_scalar on a quadratic function and differential evolution for global optimization.

Explore SciPy sparse data handling with dense to sparse conversions, including CSR and CSC formats. Learn memory-efficient operations, non-zero counting, and zero removal for large datasets.

Explore how to build and analyze graphs with SciPy: create adjacency and csr matrices, compute connected components, shortest paths with Warshall and Bellman-Ford, and perform depth-first and breadth-first traversals.

Explore spatial data with SciPy to triangulate polygons using the deloughery triangular method, compute convex hulls, and build k-d trees for nearest-neighbor queries, with visualization via the matplot library.

Explore seamless integration of Matlab arrays into Python using scipy.io to load and save Matlab .mat files, manipulate data with NumPy, and bridge Python and Matlab workflows.

Explore statistical significance tests with SciPy, including t-test and KS test, interpret p-values against null and alternative hypotheses, and generate data descriptions with the describe function.