Python Fundamentals For Absolute Beginner Using Google Colab

Discover Google Colab as a free Python and Jupyter notebook platform with pre-installed libraries. Use it with a Gmail account and enjoy CPU and GPU access for running code.

Learn to use Google Colab for Python coding, mount drive, access course materials, read CSV with pandas, and run code on CPU or GPU.

Learn Python arithmetic operations—addition, subtraction, multiplication, division, exponent, and modulo—performed in Google Colab without libraries. Master the order of operations, parentheses, and soft coding with variables for flexible calculations.

Explore Python comparison operators: greater than, less than, equal, not equal, greater or equal, less or equal—and the and/or logic operators, the difference between single equals and double equals, yielding true or false.

Learn to use if, else, and elif to control code execution with comparisons and logical statements. Master Python syntax with colon and indentation to write correct conditional blocks.

Learn to create 1d and 2d numpy arrays, inspect their shape, size, and data type, and find max, min, and their indices with argmax and argmin.

Generate numpy arrays with arange and linspace, noting low inclusive, high exclusive ranges. Reshape arrays, and create zeros, ones, identity matrices, and random numbers from normal, uniform, and randint.

Explore indexing and slicing in NumPy arrays using Python in Google Colab, covering 1D and 2D arrays, accessing specific elements, slicing ranges, negative indexing, reversing, and extracting submatrices and columns.

Learn to plot data using matplotlib.pyplot in Python, generate x and y with NumPy, plot multiple curves, and customize color, size, labels, legend, and title for clear visualization.

Plot a sine wave, set x and y axis limits and ticks, add labels and legends, customize with styles, markers, and grids in NumPy and Matplotlib.

Learn to create and manage subplots in matplotlib with numpy, using plt.subplots for 2x2 grids and tightlayout for even spacing, then plot continuous and discrete sine waves in multiple positions.

Learn to use Matplotlib rc parameters to control line width and style globally, set figure sizes, create 2x2 subplots, and customize x and y label sizes in plotting sine waves.

Explore how to create lists in Python, compare with NumPy arrays, and perform indexing, slicing, and common methods like append, pop, reverse, and sort, including nested lists and matrix-like structures.

Discover how for loops in python use range to generate sequences, import numpy and matplotlib to print numbers, filter with if and modulo, and accumulate results in a list.

Master nested for loops in python by exploring outer and inner loop execution. Build matrices with numpy zeros, print values, and generate a checkerboard using a formula and matplotlib.

Learn how the Python while loop works, control flow with conditions, incrementing to avoid infinite loops, and using break statements, with comparisons to for loops and nested examples.

Explore strings in Python fundamentals by creating and converting data to strings, indexing and slicing, immutability, concatenation, repetition, and essential string methods like upper, split, capitalize, count, and find.

Learn how to format strings in Python using the print function and the dot format method to fill curly bracket placeholders with values, including positional and alphabetic indices.

Explore dictionaries in Python fundamentals: use keys to index values and create key value pairs with curly brackets. Access items via d2.items, keys, and values, unlike numpy arrays.

Explore dictionaries in Python by creating key-value pairs, retrieving values through indexing, and handling nested dictionaries, including empty dictionaries and data types like lists and strings.

Import Seaborn and matplotlib, load the tips dataset, and explore numerical and categorical features. Visualize distributions of total bill, tip, and size with Seaborn's distplot to reveal frequency patterns.

Learn to explore categorical features with seaborn by plotting count plots for day, sex, and smoker. Use bar plots to compare average tips by day and by sex.

Explore how to compute and visualize correlations among numerical features in the tips dataset, using a correlation matrix, heatmaps, and scatter plots for total bill, tip, and size.