Guys, those who are interested in learning ———»»» Data Wrangling «««———

using:
Pandas/Numpy/regex,

download the above data set.
Contains a set of 2000 products, quantity, category and other details.

Will post some practice tasks, which will be based on this dataset.

#python #dataset #DataWrangling

#DataWrangling : Task: 0001
🎯 »»»» FORWARD IF YOU LIKE IT «««« 🎯

Task:
Load the dataset using Pandas.

Optional Tasks:
»» Import only "Product Name, Quantity & Categories" columns
»» Load only 100 rows into the dataframe


📝 ———»»» Click here for the dataset ««« ———
««« Discuss your solution @python_programmers_club and proper solutions will be added to our Github page »»»

🔥Ready to take this challenge? 🔥

««« Click here for the Solution »»»
#python #practiceCode #samples #DataWrangling #pandas

https://towardsdatascience.com/wrangling-data-with-pandas-27ef828aff01

#python #samples #DataWrangling #pandas #tutorial

Wrangling Data with Pandas (AI Adventures) ——— By Google Cloud Platform
#python #DataWrangling #pandas #tutorial #videotutorial

🖥

AI Adventures

A series of 45 videos on Machine Learning for Beginners. These videos explain the concepts related to Machine learning in simple ways, so that it gives a starting platform for beginners, who are new to Machine learning and don't know how to proceed.
#python #DataWrangling #pandas #tutorial #videotutorial #videoSeries #machineLearning

#DataWrangling : Task: 0002 ——————————————————— Continued from 0001
🎯 »»»» FORWARD IF YOU LIKE IT «««« 🎯

Task:
Clean the column "Product Name".
There are a lot of rows with Product Name as empty or contains some junk characters. Delete all such rows.


Optional Tasks:
»» Go through the list of Product Names and clear them of any special characters that you feel is not needed.
»» Clean up the Quantity & Categories columns by removing un-necessary special characters or junk values.


📝 ———»»» Click here for the dataset ««« ———
««« Send your solution @hbabureddy and proper solutions will be added to our Github page »»»

🔥Ready to take this challenge? 🔥

««« Click here for the Solution »»»
#python #practiceCode #samples #DataWrangling #pandas

#DataWrangling : Task: 0003 ——————————————————— Continued from 0002
🎯 »»»» FORWARD IF YOU LIKE IT «««« 🎯

Task:
The categories column contains the category and a list of sub-categories. Extract them into separate columns, with names as Category suffixed by a number depending on the hierarchy.
The level of hierarchy is not the same for all, you need to handle it.


Optional Tasks:
»» Clean up the newly created Categories columns by removing additional spaces. Try using regex.


📝 ———»»» Click here for the dataset ««« ———
««« Send your solution @hbabureddy and proper solutions will be added to our Github page »»»

🔥Ready to take this challenge? 🔥

««« Click here for the Solution »»»
#python #practiceCode #samples #DataWrangling #pandas