Clean MESSY String Data in Pandas

Coding in #python and #pandas you can easily clean messy string columns with some built in methods. Here we show an example of cleaning address values to make them more standardized. #datascience

[ErulianADRaghath]

I love pandas for cleaning up problematic data sets before feeding them into a model. It's just so dang satisfying to see messy data turn into nice consistent points on a scatter plot :D

[Fubbel]

I would omit the " " in str.split (it defaults to split on all whitespace). Though it doesn't matter that much if you do n=1, if you have messy data, chances are there are double spaces, too, which may give you empty strings that may cause issues down the line.

[kmateti]

This content is exactly what I needed. Thank you!

[UnholyRenton]

Nice! I would also recommend using chaining to make it a bit more readable

[daironperezfrias]

I just beggin in this world, this is very helpeful for me,
Thanks

[rahul]

Nice Video 👍I don't know how to code. But I can relate this to MS excel..

[Fine_Mouche]

What the $ make in strings since regex is set to false ?

[LethalLuggage]

That data looks so clean I'm jealous. This wouldn't work on the address data I deal with

[littlepianist]

The second line of code raises an error for me (TypeError: string indices must be integers). Does anyone know why this happens? When I'm not trying to reassign the column it works just fine.

[pewster]

Any suggestions on dealing with date strings? I can’t seem to parse them into a date object to save my life. Formats all over the mmddyyyy yyyymmdd. Nightmare.

[vinikun]

Can i know what kind of software your using please

[alejandropu]

Next video: how to get time control of shorts on youtube with Python ;)

[MachineLearningPro]

Great video! Take a look at my Pandas tutorial if you want.

[rikaminski]

dict_rp = {'St.':'Stress',
'Rd':'Road'}

df_data['Address'].replace(dict_rp, regex = True)

[Xarxes]

The "ohh it looks better now" feeling after cleaning up some dogshit data.

[br]

Bro some condos and apartments are labeled by the half (ie. 354.5 urmoms lane). You just messed it all up in the matter of 10 seconds.