Python Data Analysis and Data Science Essentials.

Learn how to work with Python lists: create lists with square brackets, index, modify, slice, and delete elements, including nested lists and practical examples with months.

Explore Python dictionaries, key-value pairs in curly braces, and how to add, update, and access data with a dynamic department-manager example.

Learn to use Pandas series as a one dimensional data structure, create a series from a list, and access data with indices or custom labels.

Import pandas as pd to create a DataFrame from a dictionary of names, ages, and sex, then select columns and compute max, min, and mean.

Convert an Excel file to a csv and load it into a pandas data frame with read_csv, then display the full data frame using to_string.

Create and display one-, two-, and three-dimensional arrays, then determine each array’s dimensionality using its dimension attribute to reveal how many dimensions it has.

Learn to access elements in one, two, and three dimensional arrays using zero-based indexing and negative indices, retrieving the first, second, and last elements.

Explore Python array slicing using colon syntax in square brackets, specifying start, end, and step indices, noting that end is exclusive and stepping selects every nth element.

Explore data types for arrays, including integers, floats, booleans, complex, and strings, inspect array dtypes with dtype and convert types using astype.

Learn how an array copy owns its data and stays unaffected by changes to the original array. Contrast this with a view, which reflects edits to the original array.

Explore array shapes and reshaping in Python data analysis, learning to determine the number of elements per dimension and reshape a 16-element array into 1D and 2D forms.

Master for loop driven array iteration in Python data analysis by traversing one, two, and three dimensional arrays, accessing and printing each element in sequence.

Explore enumerated iteration and learn how enumeration assigns sequence numbers to elements, then see how to identify the corresponding index from a simple one-dimensional example.

Learn to split arrays with a split function—the reverse of joining—by transforming a six-element one-dimensional array into two or three parts and noting how elements are distributed.

Demonstrate searching a value in an array and returning its index with the where method, using a six-element one-dimensional array and locating the value three at index two.

Explore creating one-dimensional arrays in Python and sorting their elements with the sort function, including numeric and string data types through practical examples.

Learn to generate random numbers in Python with the random module, producing integers and floats between 0 and 20, and shaping results into arrays, including using random.choice.

Explore random data distribution using the random module to generate values that follow a probability density function, assign probabilities that sum to one, and control sample size.

Explore permutations of elements by shuffling an array and applying permutation methods to rearrange and revert the original order using a random module.

Learn to generate and visualize a normal distribution from random normal data, adjust mean, scale, and size, and explore plotting with Seaborn and Matplotlib.

Explains the binomial distribution as a discrete model with n trials and p, using a six-trial coin toss example, and demonstrates plotting the distribution with Seaborn and Matplotlib.

Explore universal functions (ufuncs) that operate on n-dimensional arrays, enabling fast broadcasting and mathematical computations. Learn how ufuncs offer accumulate methods and conditional where support.

Use zip to pair elements from two lists and add them with a universal function, producing sums like 3, 7, 11, and 15.

Define a universal function with three inputs X, Y, and Z, add it to the universal function library, and demonstrate multiplication with sample inputs and outputs.

Explore array arithmetic conditionals by defining arithmetic operations on arrays, performing addition, subtraction, multiplication, and division with import and universal functions to compute results.

Explore numpy universal functions for array arithmetic, including rounding to two decimals, floor and truncation to integers, and ceiling to the nearest integer, with practical examples.

The lecture demonstrates how to compute a cumulative sum by adding elements sequentially and retaining a running total using cumulative sum function on arrays, with axis 1 illustrating the result.

Calculate the least common multiple of a set of numbers using numpy and a reduce approach, demonstrated through a practical example with an LCM of 12.

Explore seaborn color palettes and the color palette function to beautify data visualizations. Visualize sequential and diverging palettes for data values, and apply them to a flights dataset.

Explore the foundations of scientific Python, covering essential Python concepts, common functions, and SciPy with probability distributions, optimization, and signal processing for data science.

Explore sparse data in Python using SciPy to handle zero-valued elements efficiently. Learn about compressed dispersed column for efficient arithmetic.

Explore SciPy image processing by loading, displaying, and transforming images—cropping, flipping, rotating by 45 degrees, filtering, and feature extraction using matplotlib in notebooks.

Learn to work with geometrical data in SciPy spatial data, perform triangulation of polygons to calculate areas using Delaunay methods, and visualize results with matplotlib.

Explore kd trees as efficient data structures for nearest-neighbor queries in Python, demonstrating building a kd-tree with points and performing queries to retrieve nearby neighbors.

Discover how matplotlib provides Python tools for graph plotting to visualize data, an open source library often preinstalled with Anaconda or Spyder, and how to import and use pyplot utilities.

Learn to plot coordinates with the Matplotlib plot function by supplying x and y arrays to draw a line by default, and adjust a parameter to display only points.

Plot six coordinates by pairing x and y values on a line diagram, ensuring matching elements, and display the dot plot to visualize the data.

Learn how matplotlib markers emphasize data points by using marker shapes such as stop, plus, diamond, and star, and adjust line styles and colors for plots.

Learn to plot lines in matplotlib by setting line style, color, and width, and plot multiple lines by repeatedly calling the plot function with X and Y coordinates.

Master line construction part 2 by plotting coordinates with customizable marker size and color, placing markers at chosen points, and styling plots with Matplotlib.

Set axis labels and a plot title, define coordinates for x and y, and visualize statistics with the mathematics exam score summary title and an optional grid.

learn to create multiple plots on one figure with the subplot function, specify layout by rows and columns, import plotting libraries, and label each subplot.

Create a pie chart in Matplotlib by supplying data values, assigning labels, choosing colors, and adding a legend and titles to visualize proportions.

Load csv data with pandas, view the head, inspect shape and data types, and compute Pearson correlation coefficients between GPA and gender.