Numpy, Scipy, Matplotlib, Pandas, Ufunc : Machine Learning

demonstrates creating and visualizing a random data distribution in python using numpy, scipy, and matplotlib, including a normal distribution with mean zero and std deviation one, and plotting a histogram.

Explore the random normal distribution, also known as the Gaussian distribution, in Python using numpy, generate samples with a mean and std dev, and visualize the results with a histogram.

Explore how to generate binomial distributed random numbers in Python using numpy and scipy, with parameters n and p, create 1000 samples, and visualize a histogram with matplotlib.

Explore the Poisson distribution in Python by using a lambda parameter as the average rate, generate random samples with numpy, and visualize the results with a matplotlib histogram.

Explore generating random numbers from the logistic distribution in Python with NumPy, using mu and scale, sampling 1000 points, and visualizing a 30-bin histogram.

Generate 1000 random samples from a chi square distribution using numpy with the specified degrees of freedom, then plot a histogram with matplotlib.

Explore the Pareto distribution in Python by generating random samples with alpha and scale using NumPy and Matplotlib, and visualize a histogram to study heavy tails.

Create numpy ufuncs from Python functions to perform elementwise operations on arrays, including a simple add and a custom function that yields 4, 9, 16, 25, 36.

Explore the NumPy ufunc for simple arithmetic and perform element-wise addition on arrays. Learn how to use np.add to achieve vectorized operations and see the result.

Learn how to use numpy's universal function to round decimals in arrays, producing a nearest-integer rounded array and a separate rounded-down array.

Explore natural logarithm and base-10 log computations with numpy ufuncs, vectorizing logarithm operations on arrays for efficient numerical computing.

Explore numpy's universal function (ufunc) for element-wise, vectorized multiplication across two arrays using np.array and the np.multiply operation.

Learn to compute the lowest common multiple using numpy's ufuncs, first with two numbers and then with an array via lcm.reduce, highlighting practical code examples.

Demonstrate numpy's universal function (ufunc) for vectorized gcd calculations, from two numbers like 24 and 36 to reducing an array with numpy's gcd via reduce.

Learn to create and use numpy ufuncs for hyperbolic functions, computing hyperbolic sine and cosine from radians and printing the results with np.sinh and np.cosh.

Explore numpy ufuncs to perform set operations on arrays, computing unions and intersections with vectorized, pythonic logic, and display results for two example sets.

Learn to create and manipulate pandas series, a one dimensional labeled array with indexing, data alignment, and missing data handling, using pd.Series from Python lists with default and custom indices.

Learn to create pandas dataframes from a dictionary of lists, a two-dimensional labeled data structure, and print a dataframe with name, age, and city columns.

Learn how to read a csv file into a pandas data frame with read_csv, display the resulting data, and access local file paths for simple data analysis.

Learn to create and read JSON data with pandas, loading JSON files from local paths or URLs and converting them to dataframes using JSON as a lightweight data interchange format.

Create and analyze pandas dataframes to explore, manipulate, and aggregate tabular data, and compute descriptive statistics such as count, mean, std, min, max, and quartiles.

Learn how to clean data with pandas by fixing wrong scores in a student dataset, using conditional updates to divide values over 100 by ten and preserve data integrity.

Learn how to remove duplicate rows in a pandas dataframe using the drop method, illustrated with an employees dataset across departments to ensure clean, unique records.

Pandas demonstrates creating a data frame of student scores in math, science, and English and computing the correlation matrix to reveal relationships between subjects via correlation coefficients.

Create a pandas histogram to visualize the age distribution and its frequency using the data frame plot function with five bins and a clear title.

Visualize data distribution and identify outliers with pandas box plots, using a data frame of exam scores to plot and interpret central tendency and spread.

Explore pandas line plots to visualize temperature trends over time, crafting a simple data frame with daily dates and temperatures to generate a clear line chart.

Explore pandas scatter plots to visualize relationships between two variables using a blue scatter plot of height (cm) versus weight (kg) with a simple data frame example.

Discover how to use SciPy constants in Python to calculate circle circumference with pi and radius, and derive the energy equivalent from the atomic mass unit.

Explore handling sparse data with NumPy and SciPy sparse, creating and printing sparse matrices to save memory when most values are zero, using a connectivity matrix or a document matrix.

Explore SciPy graphs by building and visualizing graphs with an adjacency matrix and sparse matrices in Python, using NumPy, SciPy, and Matplotlib to analyze node relationships and connected components.

Explore spatial data analysis with SciPy, handling geographical components like points, lines, and polygons; perform triangulation to compute polygon areas and visualize results.

Learn to manipulate Matlab arrays in Python using SciPy io, including loading Matlab data, creating and combining arrays, printing results, and saving back to Matlab format.

Learn one-dimensional interpolation with scipy, generate sine data using numpy, build x and y, interpolate to new x values, and plot original and interpolated curves.

Explore statistical significance with SciPy and NumPy, performing t tests and chi-square tests on sample data to compute statistics, p-values, and degrees of freedom.

Import matplotlib.pyplot as plt and define sample x and y data to plot a line graph. Set x and y labels, add a title, and display the plot.

Explore matplotlib to create static and animated visualizations in Python, focusing on line plots with numpy arrays, plotting x and y points, and displaying results with plt.show.

Explore creating Matplotlib plots with custom markers in Python, using plt.plot with x and y data, marker styles, and dash linestyles, then label axes, title, and display the figure.

Learn to create line plots with Matplotlib in Python, including setting x and y data, markers, dash styles, colors, labels, titles, and legends for multi-line visualizations.

Learn to create a scatter plot in python with matplotlib, add x and y labels, and set a title using numpy-generated random data.

Create and customize a Matplotlib grid on plots by applying the grid function, setting x and y data, and adding a title and axis labels. Adjust grid properties with color, line style, and line width, such as a red, double dash line with width 0.5.

Create a matplotlib subplot with a basic line plot using sample data x, y, and y2, set axis labels and title, and display multiple subplots in a single figure.

Create a customized Matplotlib scatter plot in Python by generating random x and y data, configuring color, marker, and alpha, and adding labels, legend, and title.

Learn to create basic histograms in matplotlib from random data, set bins and colors, and add x and y labels with frequency for clear visualization.

Learn to create and customize pie charts with matplotlib in Python, including setting sizes and labels, applying colors, and using autopct, explode, shadow, and startangle to emphasize data proportions.