How to Replace Values of Dataframes | Replace, Where, Mask, Update and More

[@misraturp]

👉 All you need to know about Pandas in one place! Download my Pandas Cheat Sheet (free) misraturp.gumroad.com/l/pandascs

[@himanshupandey9902]

Hey, again. You really make things simpler, so that a laymen also can understand.

[@misraturp]

Amazing to hear!

[@maxinechantel8361]

Hi Misra, thank you for your videos they are helping me to learn Pandas. You explain so simple and it is really encouraging.

[@misraturp]

That's amazing to hear, thank you Maxine!

[@message59]

hands down your videos are the best ressources I found to these kind of topics thanks a lot for making these videos✌🏻😁

[@barspinoza]

Thanks!

[@misraturp]

You're very welcome!

[@saheedoyewole9424]

Thanks a lot Misra, you're doing a wonderful job.

[@misraturp]

Thank you!

[@ArnauViaM]

pandas cut seems extremely useful! Thanks

[@misraturp]

I know right! You're welcome.

[@e.t.499]

Hey Misra, can you make a video about deciding under which conditions we should use normalization or standardization?

[@misraturp]

Great suggestion!

[@bay-bicerdover]

take that as a no

[@chastippett]

super helpful. have never seen the pd.cut function - Thank you

[@misraturp]

You're very welcome!

[@avonstar8893]

Very helpful Misra. Thank you.

[@misraturp]

Great to hear :)

[@muriloambrozio7563]

Could I use a list in the loc. Method?

[@durgav8122]

Hai Mam....Can you please come with the videos for SQL in data Analytics.....?

[@birsenbas1009]

Hi Mısra, you're really have great explanations. I have learned a lot of and useful things even a few days. I am really excited to continue listening your videos aas a new data scientist candidate. Also you make me very motivated. Thanks a lot again!

[@misraturp]

That's great to hear Birsen! Happy to hear you like the videos and that they motivate you.

[@burakceylan.92]

Crystal clear speak and to the point infos. Thank you

[@misraturp]

You're very welcome!

[@sakhachowdhury5266]

Can we use any function to replace a data.like normally we replace a data with tha mean of that column.is there any other method instead of mean.

[@PrashantVidyarthi-v5l]

Hi Misra, If we have a column where we are having a message which is returned from the device, so from this message how can we create a new column by taking keyword from the existing column and display the status of it in the new column by using python?

[@mjacfardk]

Thank you Pls video of pd.cut and filling/imputing missing values

[@misraturp]

Yes, working on that one now! Thank you for the suggestion!

[@curtisjackson3377]

Hey MIsra, thanks for another simple to understand video. Can you possibly make a similar thread on pandas dataframe Join/Merge? That would be great help. Thanks again. Cheers 🍻

[@Kidpambi]

This would be very helpful 🎉

[@misraturp]

Hey Curtis, you're very welcome. :) I actually have that video already: kzbin.info/www/bejne/bWmbfHuIeqmVjpo

[@Asma-ir8rr]

I like your explanation 👌🏻♥️

[@vrbaac1641]

Hi, Thank you for this video... it is very clear and easy to follow... I also want to ask... In case of a data frame having negative numbers, is it possible to replace these negative numbers by its corresponding absolute value, using the same routine in the video? thank you ^^

[@misraturp]

I don't know this off the top of my head but I'm sure there is a quick way to achieve this using Pandas :)

[@isha4350]

Informative video .

[@SlimPit]

Hi, i have a question that i dont know. I need to change a specific value in a specific line in my Dataframe. Example in your Dataframe in Column "Job ID" in the line (1), i want to change the value 379094 to another value, how can i do this? I tried to loc but not worked. Can someone help please, thank you. Sorry for the english, im brazilian.

[@bay-bicerdover]

watch the vid

[@kennethstephani692]

Terrific Video!!

[@misraturp]

Thank you!

[@barisbulut7193]

selam, thanks for the info. i have a probem in changing characters within the cells. in my dataframe, some of values are for instance: 12.4 and some of them are 12,4, meaning some are seperated with comma, and some are seperated with dot. how can i change all the dots with commas without changing the values. thanks

[@bay-bicerdover]

watch the vid

[@tonywillingham8109]

How does one download the chest sheet? It is unclear.

[@misraturp]

Here is the link: misraturp.gumroad.com/l/pandascs

[@marcelocarvalho4510]

Hello, I loved your video! I would like to ask a question. In this case, the intent would be data cleaning where I have a dataframe where all the information in the "data" column has characters that shouldn't be there, for example: '01012023TT00'. I would like to remove these "TT00" characters. How should I do? I'm doing it this way, but it doesn't update the dataframe.: import pandas as pd import numpy as np df = pd.read_csv('/content/HRDataset_v14.csv') df.head() df['Employee_Name'].str.replace('Anderson, Carol','Anderson') df ################ If we put the df = df['Employee_Name'].str.replace('Anderson, Carol','Anderson') It will update, but not in the dataframe.

[@bay-bicerdover]

watch the vid

[@reledata8035]

thank you Misra

[@vashisht1]

When didn't you use replace like this data[ 'Agency'].replace( { dict}) Inside dictionary I will give all the old values as key and new values a pair ..

[@syedaneesdurez8766]

Excellent

[@mehdismaeili3743]

Excellent, thanks.

[@misraturp]

You're welcome!

[@graceblomqvist5378]

Very very good! thx!

[@misraturp]

Glad you like it!

[@sachintyagi2003]

nice explain

[@learnwithgandalf]

cool mam

[@Krishnapatil1996]

as compared to data cleansing .... till the end video, I just see you ...

[@comment8767]

Misra is too distracting .... makes it difficult to concentrate on the code...

[@myselfandpesit]

Too many ways to do it makes it quite confusing for me

[@bay-bicerdover]

themore thebetter

[@arvinstatistics]

You are so pretty and smart.

[@MikeDev-Sooworr]

I'm sorry, but it's very difficult to listen to SUCH an amazingly beautiful girl! (( I only lasted one minute... But of course I gave it the like.

[@ROBINTHOMASRajagiri]

aww

[@GregThatcher]

Thanks!