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Course Outline

Introduction
  • Distributions – python.org, Anaconda Python
  • Shells – Python, Jupyter
  • IDEs – PyCharm, Spyder, Eclipse
  • Editors - Visual Studio Code
  • First program - ‘Hello World!’
  • Interpretation and .pyc, .pyo files
  • Python data types
  • type (), id (), sys.getsizeof()
  • Values, Variables, Identifiers & Labels
  • Python labeling system
  • Object pooling
  • Conversion functions
  • The language which knew Infinity
  • Console input, output
  • Operators in Python
  • Strings
    • Encoding
    • Define a string - Multiple quotes and
    • Multiple lines
    • String functions
    • String slicing - start, end & step
    • -ve indexing
  • Built-in functions
Control Structures
  • Execution flow of a Program
  • Conditional statements
    • If, if-else, if-elif
    • Multiple if & Nested if-else
  • Looping statements
    • while & while-else
    • for loop
    • range()
    • xrange() - Legacy
    • Containers, Generator & Iterator
    • For-else
    • break & continue
    • When to use for-else?
    • Tuple unpacking method
    • Converting List/Tuples of Tuples/Lists into Dictionary
    • Converting Dictionary to List of Tuples
    • Lambda introduction
    • Sorting List of Tuples and Dictionaries
    • Finding max(), min() in a Dictionary
    • Wherever you go, Dictionary follows you!
  • USE CASES:
    • Counting Problem
    • Grouping Problem
    • Indexing
    • Caching
    • Keep the latest
  • Counter() - simplest counting algorithm
  • DefaultDict - Always has a value
  • OrderedDict - Maintains order
  • Dequeue - Short time memory loss
  • Forzenset() – hashable set
  • namedtuple() – hashable dict
  • Heapq - efficient in-memory min-heap()
  • Importance of Hashability
  • Packing and Unpacking
    • Swapping two values
  • Iterator using iter() and next()
Data Structures
  • List
    • Introduction to List
    • Purpose of a List
    • Iterating through a List
    • List slicing, -ve indexing
    • List functions
    • List Operations
    • List of Lists
    • Comparing Lists
    • Homogeneous data
    • Built-in array.array()
    • numpy.array()
  • Tuple
    • Introduction of Tuple
    • Tuple Slicing
    • -ve indexing
    • Iterating through a Tuple
    • List of Tuples
    • Purpose of a Tuple
    • List Vs Tuple
  • List Vs Tuple
  • Set
    • Introduction of set
    • How to remove duplicates in a list
    • How Set removes duplicates?
    • Set functions
  • Sets are hashable but Lists are unhashable
  • Set Use-Cases:
    • Removing duplicates
    • Common and uncommon items
    • Lookup table
  • Dictionary (Most Popular Data Structure) Introduction of Dictionary - Associative data structure
    • Creating a Dictionary
    • Adding elements to Dictionary
    • Deleting key value pair
    • Updating / extending a Dictionary
    • Iterating through a Dictionary
    • Python Code Files
    • Importing functions from another file
    • __name __: Preventing unwanted code execution
    • Importing from a folder
    • Folders Vs Packages
    • __init__.py
    • Namespace
    • __all__
    • import *
    • Private global variables and functions
    • __builtins__
    • Recursive imports
    • Use case: Project Structure
Functions
  • Purpose of a function
  • Defining a function
  • Calling a function
  • Function parameter passing
    • Formal arguments
    • Actual arguments
    • Positional arguments
    • Keyword arguments
    • Variable arguments
    • Variable keyword arguments
    • Use case *args, **kwargs
  • Function call stack
    • locals()
    • globals()
    • Stackframe
  • Call-by-object-reference
    • Shallow copy - copy.copy()
    • Deep copy - copy.deepcopy()
Modules
  • Data filtering
  • Data Frames
    • Constructing from a dictionary with
    • values as Lists
    • Custom indexing
    • Rearranging the columns
    • ioc(), iloc(), at() & iat()
    • Assigning a column t the dataframe
    • Adding a new column
    • Deleting a column
    • Slicing
    • Indexing and Advanced indexing
    • Boolean indexing
    • Transposing
    • Sort by
  • Concatenate
  • Merge
  • Join
  • Group by Aggregation
  • Data Munging
  • Working with missing data
  • Reading Data from CSV,
  • Excel, JSON
  • Writing Data t CSV, Excel, JSON, HTML
  • Reading data from database and storing in
  • Dataframe
  • Writing dataframe t database
  • Handling PDF files - tabula-py
File – IO
  • Creating File
  • File reading & writing
  • Line by line file reading
  • Writing multiple lines
  • Reading JSON, XML, CSV & Excel
  • Binary files
  • Pickling
  • Use case - Cleaning text
NumPy
  • NumPy arrays
    • Double dimension arrays
    • Resizing, reshaping
    • Vector multiplication
    • Boolean filtering
    • Querying using where () function
    • Indexing
    • Slicing
    • Mean, Median, Standard deviation, Average
    • Transpose
    • Broadcasting
  • NumPy matrix
    • Addition, multiplication
    • Transpose, inverse
  • NumPy random module
Pandas
  • Series
    • Constructing from dictionaries
  • Custom index
  • Data Collections
  • Data Preprocessing
  • Exploratory Data Analysis
  • Profiling & Visualization

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