Python & Introduction to Data Science

Configure the Python development environment with Anaconda to streamline setup, install Python 3.x on 64-bit systems, and use libraries such as NumPy, Pandas, and Matplotlib, with graphical notebook support.

Learn to install python libraries with Anaconda on Windows, list available packages, use pip install or conda, and search for suitable 64-bit wheel via a library site.

Open Anaconda to use the notebook interface, create and rename notebooks, write code and markdown in cells, run code to see outputs, manage cells and kernels, and save files.

Explore Python's semantic simplicity and extensive libraries for AI, data mining, and web development. Learn about its history, the hello world example, and Python Zen guidelines for readable, simplified code.

Explore Python arithmetic operations—addition, subtraction, multiplication, division, modulo, and exponentiation with **—and understand the notebook's automatic result display and order of execution.

Learn how to create variables, assign values, and convert between int, float, and string types in Python, including booleans, rounding, and min and max functions.

Learn to manipulate strings in Python with concatenation, split, join, replace, and case changes. Use strip, lstrip, rstrip, and len to trim and measure strings in Python & data science.

Discover how Python lists offer a flexible data structure, using square brackets to create and index elements (0-based), slice with colons, and add, replace, or delete items.

Explore how to work with lists in Python, including nested lists, indexing, counting elements, and common operations like append, insert, remove, pop, sort, and reverse.

Explore dictionaries in Python by creating, updating, and querying keys, values, and items; use get for safe access, plus equals for numeric updates, and del or pop to remove entries.

Explore Python sets, their uniqueness constraint, and how to create them from lists or literals. Learn union, intersection, difference, and subset operations, plus add or remove to manage unique data.

Analyze Python's assignment mechanism and memory identity using id and the is operator. See how two names can reference the same object or different objects with identical values.

Explore Python conditionals with if, elif, and else, using examples like apples and bananas to print OK based on value ranges and logical checks.

Explore Python iteration constructs, including for and while loops, range and length usage, and conditions with if-else, continue, and break, to process lists and datasets.

learn how to create custom functions in python using def, return, and parameters to avoid repetition, compare print and return, and use for loops and *args for flexible inputs.

Learn how to create script files and modules in Python, write and import them in code, and use notebook workflows and the os module to manage files.

Explore Python's error handling with try, except, and finally to manage value errors during input and calculations. Define exception classes, use raise, and implement custom messages with finally.

Learn to open, read, write, and close files in Python, handle paths and errors, and use with, try/except, and finally constructs, including the panderers library.

Create classes in Python to group common attributes like energy and age into reusable toy soldiers, using an initialization function and self to manage state.

Explore how inheritance lets you extend a parent class in Python by adding new methods to child classes, using dogs and animals as practical examples.

Import time and calendar to work with dates, print current and local times, format outputs, and display calendars with calendar.month.

Learn to generate all unique three-digit combinations from four digits using Python for loops, range constraints, and conditional checks (not equal) to enforce distinct digits, with practice on this exercise.

Learn to calculate employee bonuses across six profit levels using Python, applying tiered percentages and an input-driven program with lists and conditional logic.

Demonstrates a Python exercise that inputs three numbers, appends them to a list, and uses sort to arrange them smallest to largest, with reverse for descending.

Count characters in an input string by iterating over each character and classifying letters, spaces, numbers, and others using isalpha and isdigit, then print the results.

Convert each list element to a string, join them with a comma using the join function, and print the result as a sentence for Word or Excel.

Demonstrate creating an anonymous lambda function for a one-line addition, showing how x and y are added and printed, and discuss the advantage of brevity versus the limit of complexity.

Learn to copy NumPy arrays with the copy function, avoid shared data via assignment, and use arange for start-stop-step sequences and random for random numbers.

Explore numpy's mathematical functions by creating a two-dimensional array and applying sum, prod, min, max, mean, std, and var with axis distinctions, then clip and round results using decimals.

Learn to reorder numpy arrays with sort, argsort, and searchsorted on a two-by-three decimal array, tracing element order and insertion positions, and preview pandas in the next module.

Learn to manage numpy arrays across dimensions using shape and reshape, add axes with newaxis, and apply transpose, concatenate, vstack, hstack, ravel, and flatten.

Learn numpy array creation with arange and linspace, including dtype, to generate equidistant sequences and grids. Use meshgrid to form a 2d grid mapping x and y coordinates.

Explore numpy array creation with zeros, ones, and shape-aware copies. Learn mesh grid, R and C functions for horizontal and vertical vectors, and the identity function.

Learn how numpy performs element-wise logical operations on arrays, including equality, not equal, and greater or equal and less or equal, and fix size mismatches by adjusting array lengths.

Explore NumPy's random functions, generating decimals with rand, creating random integers within a max limit, and using shuffle and seed to enable repeatable sampling and analysis, with set_printoptions controlling precision.

Learn to read and write text files with numpy, compare a manual line-by-line approach to numpy reading, and handle delimiters and skiprows to produce arrays.

Learn how to write numpy arrays to files with the save function, verify creation by printing, and save or load single or multiple arrays in compressed formats using dictionary keys.

Swap the first two rows of a NumPy matrix, reshape a sequence into a three-by-six array, and print the result to practice NumPy operations.

learn to find a user-specified number of largest values in a numpy array by creating and shuffling the array, selecting the top elements, and printing the results.

Create a numpy array of zeros, print it, and use the item size function to determine each element's size. Multiply 9 elements by 8 bytes to yield 72 bytes.

Construct a 4x4 numpy array, print it, and apply numpy.pad with width, mode='constant', and constant_values=0 to create a framed array for graph construction.

Master numpy date handling by generating and printing every day of November using arange, setting the start month and year, and displaying days in the specified format.

Explore practical numpy exercises by generating decimal numbers with a random function, printing the array, and using the floor function to remove decimals without rounding, creating a new integer array.

Explore numpy read-only arrays by creating a protected array, test read access, and attempt edits to verify write protection in a hands-on practical exercise.

Pandas helps you read a dataset, inspect its structure, and use core functions like head, info, index, columns, values, and dtypes to analyze data quickly.

Explore basic data frame and series concepts in pandas by creating a data frame from dictionaries, loading the Titanic dataset, and performing selection, indexing, arithmetic, and descriptive statistics.

explore pandas indexing methods to select columns and rows, using iloc and loc, boolean filters, and setting a column as index, then compute aggregates like mean.

Learn to use pandas group by for aggregations like sum and mean on dataframes, with examples on sales data and Titanic by sex and survivors.

Explore creating a pandas data frame and performing mathematical operations such as sum, mean, max, min, median, and value counts to analyze data distributions.

Learn to index, replace, add, and delete elements in a pandas series. Manage its index with rename and drop operations, and use in-place changes and ignore_index to reset state.

Master indexing, editing, and deleting a data frame; add columns and merge data with the concat function, including in-place operations and handling row and column deletions.

Learn to customize Pandas display options with set and get option, controlling rows, columns, cell width, and decimal precision through practical examples.

Learn to build pivot tables in Pandas to summarize data by index, columns, and values, using mean, max, and count, with cross-tab options.

Learn to manage dates in pandas using datetime, timestamp, and to_datetime, create and format date data with date_range, perform arithmetic with time delta, and convert strings to datetime for analysis.

Explore three Pandas pre-processing techniques: sorting by a reference column, dropping duplicates with subset, and mapping a dictionary to create new columns.

Learn pandas data processing by creating new columns with assign and binning ages with cut. Evaluate nulls with isnull and any, then fill missing values to clarify data.

Explore string editing in pandas with lower, upper, and strip. Learn to replace, split with expand, and contains, applying length to strings in data frames.

Master advanced indexing in pandas: use is in for boolean checks, create and work with a two-dimensional index, and apply where and query to filter date-indexed data by conditions.

Explore practical memory management for large data in pandas by downcasting integers and floats, converting object columns to category, and using date time formatting to reduce ram usage.