Today we will share Beginners Pandas Step By Step Guide on YouTube.
Topics to be covered:
1. Overview of Pandas
2. Pandas Data Structures
3. Data Preprocessing
4. Data aggregation
5. Data visualization.

This tutorial is a guide for having pandas zero experience beginners.

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This is a comprehensive beginner's guide to pandas tutorial. In this tutorial, the following topics will be covered:

1. Overview of Pandas - Understanding the basic concepts of pdans
2. Pandas Data Structures - creating and manipulating pandas series and dataframe
3. Data Preprocessing
4. Data aggregation
5. Data visualization

https://youtu.be/CU0ejeVJGKo

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Python POP QUIZ: What is the output of the following code?

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Debugging and Troubleshooting in Python: A Developer’s Essential Guide

https://medium.com/@epythonlab/debugging-and-troubleshooting-in-python-a-developers-essential-guide-b3415f53b1e0

You notice that we have started writing a blog on topics that many developers, especially beginners, find challenging.

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​​How to make box move with the python

🖥 Which are positional arguments?

➕Check out solution here:https://www.youtube.com/watch?v=K-u3vndXulg

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Why NumPy is faster than array list?

✅Size:- Numpy data structures take up less space

✅Performance:- they have a need for speed and are faster than lists

✅Functionality: - SciPy and NumPy have optimized functions such as linear algebra operations built in.

✅Memory:- The main benefits of using NumPy arrays should be smaller memory consumption and better runtime behavior. 

✅TIP

Indentation is important for python programming you never miss it.

Indentation refers to the spaces at the beginning of a code line.

Where in other programming languages the indentation in code is for readability only, the indentation in Python is very important.

Python uses indentation to indicate a block of code.
Example:
for x in range (2, 5):
print x

N.B: Python will give you an error if you skip the indentation:

EXAMPLE:
for x in 5:
print x

Python is a general-purpose programming language. It can do almost all of what other languages can do with comparable, or faster, speed. It is often chosen by Data Analysts and Data Scientists for prototyping, visualization, and execution of data analyses on datasets.

There’s an important question here. Plenty of other programming languages, like R, can be useful in the field of data science. Why are so many people choosing Python?

One major factor is Python’s versatility. There are over 125,000 third-party Python libraries. These libraries make Python more useful for specific purposes, from the traditional (e.g. web development, text processing) to the cutting edge (e.g. AI and machine learning). For example, a biologist might use the Biopython library to aid their work in genetic sequencing.

Additionally, Python has become a go-to language for data analysis. With data-focused libraries like pandas, NumPy, and Matplotlib, anyone familiar with Python’s syntax and rules can use it as a powerful tool to process, manipulate, and visualize data.

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Master MongoDB with VS Code: Step-by-Step Guide to Creating Databases, Collections, and Documents
https://youtu.be/KiQKCafwMXU

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Next we will share you how to create collections and read data. Stay tuned!!

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