Covert Object type to Pure String in Python

Question

In my data frame there are two columns and they are obj type.

index	COLOUR	CATEGORY
0	blue	iphone11
1	black	iphone12

iphone_sale['COLOUR'] = iphone_sale['COLOUR'].astype(str)
iphone_sale['CATEGORY'] = iphone_sale['CATEGORY'].astype(str)

I need to create a new column using above both columns.

iphone_sales_sum["colour_category"] = iphone_sales_sum.apply(lambda x: f"{x['COLOUR']} /{x['CATEGORY']}", axis=1)

But I am getting a noisy values as the derived column.

iphone 12\nName: 0, dtype: object/... etc

But it doesn't give any error i lmbda function.This code perfectly worked for an other scenario.

@NikolayTsvetanov iphone 12\nName: 0, dtype: object/..blue\nName: 0, dtype: object/ Values in derived column are like this — jithmi97
– jithmi97, Commented Apr 13, 2022 at 18:27

Nikolay Tsvetanov · Accepted Answer · 2022-04-14 08:55:54Z

1

when i reproduce your code:

import pandas as pd
iphone_sales_sum = pd.DataFrame([[1,"blue", "iphone 12"],[2,"black","iphone12"],[3,"sad3","jkl3"]], columns=["index",'COLOUR', 'CATEGORY' ])
iphone_sales_sum['COLOUR'] = iphone_sales_sum['COLOUR'].astype(str)
iphone_sales_sum['CATEGORY'] = iphone_sales_sum['CATEGORY'].astype(str)
iphone_sales_sum['colour_category'] = iphone_sales_sum.apply(lambda x: f"{x['COLOUR']} / {x['CATEGORY']}", axis=1)

i get

colour_category
blue / iphone 12
black / iphone12
sad3 / jkl3

answered Apr 14, 2022 at 8:55

Nikolay Tsvetanov

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