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Pandas in Python

Introduction
Setting Up Your Python Environment
Installing Python and PyCharm IDE Creating and Running Your First Python Program
Python Data Types
Numeric Types String Types Boolean Types None Type
Operators and Expressions
Arithmetic Operators Comparison Operators Logical Operators Bitwise Operators Assignment Operators Operator Precedence
Control Flow Statements
Conditional Statements Loops Break and Continue Statements Exception Handling
Functions and Modules
Defining and Calling Functions Function Arguments and Return Values Lambda Functions Modules and Packages
Python Data Structures
Lists Tuples Dictionaries Sets Arrays
ObjectOriented Programming in Python
Classes and Objects Attributes and Methods Inheritance and Polymorphism Abstract Classes and Interfaces
File Handling
Opening and Closing Files Reading and Writing Text Files Reading and Writing Binary Files CSV Files
Regular Expressions
Basic Regular Expressions and Metacharacters Anchors and Quantifiers Grouping and Capturing Lookarounds
Datetime and Timezones
Date and Time Objects Date and Time Formatting Timezone and Daylight Saving Time Handling
Debugging and Testing
Debugging Techniques Debugging Tools in PyCharm Unit Testing with PyTest
Web Scraping with Python
Requesting Data with urllib Parsing HTML with Beautiful Soup Extracting and Storing Data
Database Handling with Python
Connecting to Databases Performing CRUD Operations Database Access with ORM (ObjectRelational Mapping)
Introduction to Data Science with Python
NumPy Pandas Matplotlib ScikitLearn
GUI Programming with Python
Creating GUI Applications GUI Toolkits (Tkinter, PyQt, wxWidgets)
Network Programming with Python
Socket Programming Creating and Running a Simple Web Server Connecting to APIs
Best Practices and Tips
Python Coding Standards Code Organization Writing Efficient Code Tips and Tricks for Python Development
Conclusion and Next Steps.

Pandas

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#description

Pandas is a powerful and popular open-source data analysis library for Python. It provides high-performance, easy-to-use data structures and data analysis tools.

Pandas allows you to work with different types of data such as CSV, Excel, SQL databases, and other file formats. It is also useful in cleaning up data by filling in missing values, formatting data, and merging data from different sources.

Pandas has two main data structures: Series and DataFrame. A Series is a one-dimensional array of labeled data while a DataFrame is a two-dimensional labeled data structure with columns of potentially different types.

Pandas also offers various functions and methods like groupby, merge, pivot, and reshape to manipulate and analyze data. Additionally, it enables you to plot and visualize data using built-in functions or external libraries like Matplotlib and Seaborn.

Overall, Pandas is an essential tool for data scientists, analysts, and researchers working with tabular data in Python.

March 25, 2023

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