Python for Data Analysis & Visualization

Explore Python objects and methods, differentiating integers and strings, and learn how functions differ from object methods with dot notation. See how to create variables and follow pep8 naming conventions.

Explore the three numeric types: integers, floating point numbers, and complex numbers, with emphasis on integers and floats, perform arithmetic, and apply Python's order of operations including modulo and exponentiation.

Explore how Python handles strings as immutable sequences, using single, double, or triple quotes, and master escaping with backslashes to include punctuation, newlines, and tabs.

Explore string indexing and slicing in Python to access specific characters and substrings. Use zero-based indices, start/stop/step parameters, and negative indices to reverse strings.

Explore Python string methods such as upper, lower, find, replace, and split, and learn about immutability, reassignment, case sensitivity, and start and stop indexes.

Learn to concatenate strings with the plus operator and manage whitespace. Use f-strings to embed variables and format dynamic text, such as a name.

Explore Python lists as mutable sequences, storing mixed types and embedded lists, using indexing and slicing (including negative indices) and applying append, extend, remove, pop, and insert to modify them.

Learn how dictionaries store data as key-value pairs, are ordered since Python 3.7, and support nesting; access, update, and inspect with square brackets and items, keys, values.

Explore tuples, immutable sequences with multiple data types and indexable elements, and sets, which store unique values, can't be indexed or sliced, and use set() to create the empty set.

Explore booleans, the true or false data type, with case-sensitive values. See how conditional statements yield true or false, such as five greater than seven returning false.

Explore Python operators, including comparison operators, equality versus assignment, and data-type effects on evaluation. Learn how logical operators and is and is not compare values.

Master Python control flow using if, elif, and else statements to execute blocks of code based on boolean conditions, including nesting, indentation rules, and practical print examples.

Learn how to use for loops to iterate over lists and nested lists, assign loop variables, and understand indentation, indexing, and unpacking in Python.

Explore for loops across tuples, lists, sets, strings, and dictionaries, using unpacking and items to access elements and key-value pairs, apply conditional filters, and build loop proficiency.

Master while loops by executing a code block while a condition holds, starting with a = 0, incrementing by one, and stopping when a >= 5, including infinite loop cautions.

Explore how break, continue, and pass control flow statements manage loops in Python, using examples that skip items, exit loops, and leave placeholders.

Explore building a Python list of characters from a string using append versus list comprehension, showing uppercase transformations and repeated elements, with notes on readability and performance.

Explore the in and not in operators across lists, sets, strings, and dictionaries, demonstrating how membership tests return true or false for elements, keys, and values.

Explore built-in Python functions, including print, type, help, range, sorted, and input, and see how they operate on lists, tuples, and sets with max, min, sum, and conversions.

Learn how to define and call user defined functions in Python, including default arguments, printing versus returning values, and using snake_case names for data analysis and visualization.

Learn to build user defined functions in Python that compute averages from lists using sum and len, support range creation from user input, and use return to output results.

Learn how to define Python functions that accept any number of positional arguments with *args and any number of keyword arguments with **kwargs, including defaults, tuples, and dictionaries.

Apply map to transform each element and return a list, using country names and character counts as examples; filter keeps only strings, showcasing how these tools support analysis with pandas.

Learn about lambda functions, the anonymous one-line Python helpers that take arguments and return expressions. Use them with map and filter to simplify code without defining functions.

Learn to handle errors in Python with try/except blocks, specify exact exceptions like zero division and type errors, and use else and finally to control flow.

Launch a Jupyter Notebook to access the course resources folder, then tackle Python data analysis challenges from control flow to functions, indexing, slicing, and list operations.

Walk through Python data analysis solutions, detailing control-flow choices, dictionary access, slicing, map and lambda usage, filtering, and robust input handling with try-except.

Explore Python modules, libraries, and packages, including built-in modules, and learn to import modules or use from statistics import mean to access the mean function.

Discover how external libraries extend Python for data analysis and visualization, with the Anaconda distribution pre-installing essentials like pandas, and learn to install, search, and import them with aliases.

Explore numpy, a numerical python library for multi-dimensional arrays. Create one-dimensional and two-dimensional arrays, understand axes and shapes, learn about homogeneous data types, and see how numpy differs from lists.

Explore indexing and slicing of one- and two-dimensional arrays, including axis-specific selections, boolean filtering, and element reassignment, with shape concepts and comparisons to lists.

Master array manipulation in Python for data analysis using Nampai, covering reshape, flatten, ravel, concatenate, transpose, and sort with axis guidance and not in place transformations.

Explore additional array creation functions in Python for data analysis, including full, range, linspace, and random arrays, with shape, data type, and reshaping options.

Explore array arithmetic on 2d arrays, demonstrate addition and broadcasting rules, and compute sum, mean, max, min, and cumulative sums along specified axes.

Learn to input and output data with numpy using load text and options, including delimiter, skip rows, and usecols, then perform statistics like max, min, mean, and standard deviation.

Launch Jupyter notebook, locate the course resources folder, and work through challenge questions on creating and slicing arrays, filtering values, and saving and combining csv files for data analysis.

Explore practical numpy and pandas techniques with challenge solutions, including creating and reshaping arrays, slicing and boolean indexing, concatenation, and sum min max across the rows.

Explore pandas, a powerful Python library for data analysis and manipulation, built on top of Nampai, with series and data frames for relational or label data.

Learn to create and use a series object with indexes in pandas, specify data and index, define data types, auto generate indexes, and access values by index.

Introduce data frames as the core pandas structure for tabular data, built from lists or arrays, with labeled rows and columns. Import data from CSV with read_csv.

Learn how to select and filter data in pandas dataframes by index and columns, including single or multiple column selection, conditional filters, and previewing with head and tail.

Use pandas isin to filter a data frame by multiple region values, yielding Asia or Americas, and invert the condition to exclude them.

Learn practical pandas data manipulation: inspect, rename, drop, perform arithmetic, clean data types, and create derived columns like population millions and country region using apply and lambda.

Learn practical data manipulation in pandas, including reading csv with read_csv, cleaning an unused column, converting to_datetime with format codes, and using sort_values, transpose, and explode.

Explore data aggregation and grouping with pandas, applying sum, mean, max, and count to the population column, using groupby, pivot tables, and pivot to shape the data.

Cleanse data with pandas to prepare for analysis and visualization. Remove leading and trailing spaces, handle missing values with drop or fill, and convert data types.

Combine multiple dataframes with pandas using concat, join, and merge. Understand axis handling, key-based joins, and inner, left, right, and outer joins with example employee and department data.

Explore windowing operations in pandas, including expanding for cumulative totals and rolling for time-based aggregates, with examples of monthly data and three- and six-month averages.

Explore pandas challenge questions built around the TfL dataset, work through sequential notebook steps, and only review the solution notebook after completing each challenge.

Explore a solutions walkthrough for Python data analysis, covering data loading, dtype conversion, datetime handling, aggregation with mean and pivot tables, and counting days classified as low, medium, or high.

Tackle the step-by-step challenge questions on the employees dataset for data analysis in python, using three csv files and two notebooks, one containing the solutions.

Walk through practical data analysis with pandas, including loading data, type casting, filtering current employees, joins, group by, and pivot table techniques to analyze salaries and hires.