Pandas Trick. Convert Strings to Float in Pandas DataFrame (parsing data with RegEx)

With this video I demonstrate how to extract or convert numerical data (digits) from Pandas DataFrame to Float type values in whole data structure.

This is a very quick Pandas tip that let you to parse your data stored in Pandas DataFrame and prepare it for Data Analysis or for Data Scientist purposes. So because of this reason converting strings to numerical data (floats) is a part of data pre-processing (preparation) in your data analysis implemented in Python programming language.

For this I used Regular Expression (RegEx) applied into Pandas Replace method.

Enjoy!

Vytautas

#pandastrick
#stringstofloat
#regexpython

[ici]

i had been searching for this for over 5 hours and just for this video i am subscribing your channel. Thanks a lot.

[shauryadabas]

i had been searching for this for over an hour and just for this video i am subscribing your channel

[FelipeGonzalez-jtos]

whats mean [^\d] and this will work for others characters ??

[pavithravenkatachalapathy]

It was very useful, when I tried it did change the object type but It is converting all negative values to positive values. can you help me?

[genesebarbosa]

Thx for sharing! It’s a great ideia 💡 🇧🇷✌🏾

[googlegmail]

great, and works very well, but I hope you explain what [^ \d.] means and show that it converts negative values.

[childofchrist]

I have an error... Couldn't convert string to float

[mdsaddam-cmqi]

I am following the steps to convert object to int value

Data=data.astype(int)


There is showing error base 10:`10000`

In python

[kathirvelmuthusamy]

how to convert string to integer for train the dataset in ML

[haniefknight]

i get error ...

TypeError Traceback (most recent call last)
in <module>()
----> 1 data_ = data1('[^\d.]', '', regex=True).astype(float)

TypeError: 'DataFrame' object is not callable

[rahulranjan]

Bro I want to touch your feet. You can ask me any help if its under my capacity i will do it for u!
Thanks for such a short crisp video and not wasting time like other Youtubers do.
subscribed u! will watch whatever adds pops up!
keep making such videos!