Master Python in 14 Days: Numpy, Pandas, Seaborn, RegEx, OOP

Discover Google Colab as a cloud-based Python development environment, connect to Google Compute Engine, run and debug code, access data in the cloud, and share notebooks.

Start learning Python with Google Colab, create variables like x and y, use # for comments, and print values with the print() function, noting Python 3 requires parentheses.

Explore python variables, naming rules, and case sensitivity with examples like x, y, and weight. Types: integers, floats, strings, booleans, and complex numbers, and note type conversions and dynamic typing.

Explore Python operators, including arithmetic, comparison, logical, membership, and identity operators, with examples of addition, division, modulus, exponentiation, and assignment applied to datasets.

Explore conditional statements in Python, including if, elif, and else, using comparison operators and nested conditions to control program flow.

Explore how loops work in Python, using while and for loops to repeat actions, with examples of i increments, break and continue, and nested loops over lists like fruits.

Learn how functions in Python enable reusable code with user-defined and predefined functions, import libraries like NumPy and pandas, and apply them to machine learning tasks.

Explore Python arrays and the four data structures—lists, tuples, sets, and dictionaries—covering order, duplicates, indexing, and bracket-based creation for one- and two-dimensional data.

Learn to create and modify Python lists by adding, inserting, and removing items, using index and negative index, while distinguishing between clear and delete, and practicing copy operations.

Master Python tuples by learning their immutability, indexing, and slicing; create tuples with parentheses, convert to and from lists to modify data, and join tuples using plus.

Explore sets in Python: unordered, unindexed collections that disallow duplicates. Learn creation with curly braces, membership tests, and operations like add, update, remove, discard, pop, clear, and union.

Master Python dictionaries by creating them with curly brackets, avoiding duplicates, and noting they are mutable and unordered. Practice printing and updating keys name, class, and age, adding sex: men.

Master numpy's array operations: import options, one- and two-dimensional arrays, indexing and slicing, and the copy versus view distinction for memory efficiency.

Master NumPy shape, reshape, and flattening to prepare data for analysis. Examine how shape defines dimensions, reshape converts between forms, and flattening enables model-ready arrays.

Discover NumPy array iteration techniques for multidimensional data, including row-wise processing, nested loops for conditional logic, and flat iteration with np.nditer for data cleaning.

Master the art of joining arrays in NumPy by stacking and concatenating along horizontal, vertical, and depth axes. Apply to real-world data like regional sales, sensor inputs, and time series.

Learn how NumPy splits arrays for large data sets and uneven divisions using array_split, as shown with a nine-element revenue array, while split requires exact divisibility.

Explore numpy searching and sorting with np.where and np.sort to locate values, identify odd and even numbers, and sort numeric and text arrays for clear data analysis.

Explore how pandas enables high-level data manipulation with data frames, the two-dimensional structure for storing tabular data, and how to import, create, and print data from Excel.

Master pandas data frame analysis by inspecting types, viewing samples, describing numeric statistics, sorting, selecting columns, updating values, transposing, and grouping to identify patterns.

Learn to visualize data in Python with matplotlib, creating bar and line charts, scatterplots, box plots, and 3D plots from a data frame to reveal insights and relationships.

Explore advanced data visualizations in Seaborn and Altair using an insurance multiple regression dataset. Create histograms, scatter plots with age, BMI, children, smoker status, and multi-variable comparisons.

Leverage EDA libraries like clip and sweetviz in Google Colab to perform quick univariate and bivariate analysis, visualize correlations, and inspect missing values, outliers, and imbalanced data.

Create an interactive scatter plot with bokeh using the iris dataset, mapping petal length to x and petal width to y, with hover tooltips and color-coded species.

Build an interactive Dash app with Plotly Express to visualize insurance charges by smoker status and gender, using KDE density and box-plot features.

Create a clustered heatmap with Seaborn and dendrograms to reveal correlations and clusters among income, spending score, age, savings, and investment.

Master regular expressions in Python to automate data extraction and processing, from employee numbers to invoice details, using search, find all, split, replace, and compile techniques for process automation.