Don't Confuse to learn Python.

Learn This Concept to be proficient in Python.

𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻:
- Python Syntax
- Data Types
- Variables
- Operators
- Control Structures:
if-elif-else
Loops
Break and Continue
try-except block
- Functions
- Modules and Packages

𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Classes and Objects
- Inheritance
- Polymorphism
- Encapsulation
- Abstraction

𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀:
- Pandas
- Numpy

𝗣𝗮𝗻𝗱𝗮𝘀:
- What is Pandas?
- Installing Pandas
- Importing Pandas
- Pandas Data Structures (Series, DataFrame, Index)

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀:
- Creating DataFrames
- Accessing Data in DataFrames
- Filtering and Selecting Data
- Adding and Removing Columns
- Merging and Joining DataFrames
- Grouping and Aggregating Data
- Pivot Tables

𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Handling Missing Values
- Handling Duplicates
- Data Formatting
- Data Transformation
- Data Normalization

𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀:
- Handling Large Datasets with Dask
- Handling Categorical Data with Pandas
- Handling Text Data with Pandas
- Using Pandas with Scikit-learn
- Performance Optimization with Pandas

𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Lists
- Tuples
- Dictionaries
- Sets

𝗙𝗶𝗹𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Reading and Writing Text Files
- Reading and Writing Binary Files
- Working with CSV Files
- Working with JSON Files

𝗡𝘂𝗺𝗽𝘆:
- What is NumPy?
- Installing NumPy
- Importing NumPy
- NumPy Arrays

𝗡𝘂𝗺𝗣𝘆 𝗔𝗿𝗿𝗮𝘆 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
- Creating Arrays
- Accessing Array Elements
- Slicing and Indexing
- Reshaping Arrays
- Combining Arrays
- Splitting Arrays
- Arithmetic Operations
- Broadcasting

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗡𝘂𝗺𝗣𝘆:
- Reading and Writing Data with NumPy
- Filtering and Sorting Data
- Data Manipulation with NumPy
- Interpolation
- Fourier Transforms
- Window Functions

𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗡𝘂𝗺𝗣𝘆:
- Vectorization
- Memory Management
- Multithreading and Multiprocessing
- Parallel Computing