Data Analytics using Python

Explore data analytics using Python to massage data, apply statistical methods, and visualize insights for business problems, using NumPy and Pandas with matplotlib and seaborn.

Learn how Python underpins data science, machine learning, and web development, highlighting its simplicity, dynamic typing, garbage collection, and powerful libraries such as NumPy, pandas, scikit-learn, and TensorFlow.

Install and set up Python in Jupyter Notebook via Anaconda, learn code chunking, markdown cells, and essential data tools like NumPy and pandas; compare with Spyder for production environment.

Explore Python built-in data types (integers, floats, booleans, and strings) and how dynamic typing lets a variable change type; use if, elif, and else with colon and indentation for conditionals.

Explore Python iteration structures, including definite and indefinite loops, and master for loops over iterables such as lists, tuples, sets, dictionaries, and strings.

Explore Python iterations with range as an iterator, including start, end, and step values. Build a custom iterator with yield, and compare break, continue, and pass with return.

Explore Python lists as mutable collection objects, learn indexing, slicing with strides, and common list operations like append, extend, pop, remove, and del to manage data.

Explore python tuples and how they differ from lists. Learn to create, nest, and convert tuples from lists, dictionaries, and strings; perform indexing, slicing, negative indexing, and enumerate.

Learn how Python dictionaries store key–value pairs with curly braces, including nested and mixed data types, and how to create, access, update, delete, and handle key errors.

Learn to use dictionary methods in Python, including items, keys, values, pop item function, set default function, and iterate over key-value pairs with a for loop.

Master Python sets through unions, intersections, differences, and symmetric differences, with practical demo of add, update, and membership checks, noting sets are unordered and unique.

Assignment or equals operator creates a new name referencing the same memory location for sets, while the copy function makes a physically separate shallow copy with distinct memory ids.

Discover NumPy's management of numeric arrays for data science, including creating shapes with zeros, ones, full, and identity, plus random, arange, reshape, dot product and transpose.

Learn how to perform numpy array operations including dot product and transpose, reshape and flatten, and apply functions like mean, max, standard deviation, and astype, with indexing and cross sections.

Learn how to iterate NumPy arrays with for loops across rows and columns, stack and split arrays horizontally and vertically, and distinguish between assignment-based views and copy-based copies.

Master pandas through series and data frames, mastering index management, missing data handling, grouping, and pivot tables for robust data analysis in Python.

Learn to access and slice a pandas series with loc, iloc, and iat, and explore attributes like dtypes, isna, size, and values.

Learn to manipulate pandas series with between, copy types, describe statistics, and arithmetic operations like add, div, and multiply, plus drop, fillna, replace, and groupby.

Learn to manipulate pandas series with rounding, std and var calculations, and string replacement. Create and access multi-index series, convert to dict or list, and export to Excel.

Explore pandas data frames, a versatile Python tool for tabular data with rows and columns, created from CSV, Excel, dictionaries, arrays, or series, and indexed by date ranges.

Use describe to summarize numeric columns with count, mean, std, min, and percentiles, note missing values; apply transpose, sort by average yearly sale descending, and subset with iloc/loc and conditions.

Manipulate data frames by adding a record with a series, updating cells with the at indexer, and creating a discounted value column as resale value times 0.8.

Explore how to merge pandas dataframes using inner, left, right, and full outer joins, illustrated with the Gapminder dataset and country code merging.

Uncover practical data merging in pandas using left outer and inner joins, then manage missing values, drop rows and columns, and fill gaps with fillna.

Replace missing values with fillna using a dictionary, filling resale value with its mean and price in thousands with 1, then group by continent for mean, size, and pivot table.

Learn how to create Python user defined functions to encapsulate reusable code, pass various arguments, and return multiple values. Discover docstrings and the global vs local scope for robust functions.

Explore Python lambda expressions as anonymous inline functions, and learn to use map and reduce to process series and data frame fields with improved readability and efficiency.

Apply filter with a user defined function or lambda to select numbers meeting a condition. Learn date-time operations: create date objects, compute deltas, parse with strptime, and use pandas date_range.

Explore Python string operations, from creating strings with quotes and triple quotes to indexing, slicing, concatenating, formatting, splitting, joining, replacing, and upper and lower case transformations for data analytics.