Products_2K.csv
230.8 KB
ProductsList2K #dataset
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
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
Forwarded from Python Projects
#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
🎯 »»»» 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
Telegram
Python Resources - Basic Python, ML, DataScience, BigData
ProductsList2K #dataset
Forwarded from Python Projects
#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
🎯 »»»» 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
Telegram
Python Resources - Basic Python, ML, DataScience, BigData
ProductsList2K #dataset
Forwarded from Python Projects
#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
🎯 »»»» 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
Telegram
Python Resources - Basic Python, ML, DataScience, BigData
ProductsList2K #dataset