Data Analytics with Python

List & Tuple operations, functions, range, recursion, lists of lists, comprehension, slicing & indexing, Dictionary functions, creation, iteration, keys, values

String & numeric functions, type conversion, passing arguments, return values, none & yield, default values, scope, If, Elif, Else, For, While, Break, Continue, Nesting – if-else & loops, Examples, specific keywords – range function with ‘for’ loop

Reading data from & adding / appending data to a file, errors, exceptions, finally keyword, lambda functions map, filters, generators, __name__ variable, packages, directories

Classes, objects, class methods, class attributes, instance attributes, constructor, user defined & in-built classes, implementing methods, code best practices & guidelines

Multiple & multi-level inheritance, recursion, sets, itertools, operator overloading & overriding, data hiding, encapsulation, polymorphism, access modifiers, private class usage & variables

Search & find all, match, find & replace, group, metacharacters (dot, caret, dollar, star / asterisk, plus, optional), character class, curly braces, wildcard, Serialization with Pickle & Shelve)

Pandas & series, converting dictionaries to series, dataframes, changing column sequence, changing columns & transposing dataframe, reindexing series & dataframes, deleting rows & columns

Arithmetic operations & sorting on dataframe & series, handling duplicate values, calculating sum, max & min, dropping NaN values, loading data from, a file & analyzing the data loaded from file, unique method, aggregate functions

Creating & slicing a NumPy array, logspace & linspace, advanced indexing & slicing, broadcasting, iterating using nditer, plotting data using seaborn & matplotlib