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

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

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

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

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

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

pandas cut seems extremely useful! Thanks

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

Crystal clear speak and to the point infos. Thank you

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!

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?

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

Very helpful Misra. Thank you.

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

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.

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 🍻

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

I like your explanation 👌🏻♥️

Could I use a list in the loc. Method?

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.

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 ^^

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

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 ..

Informative video .

Terrific Video!!

thank you Misra

Excellent, thanks.

nice explain

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.

Very very good! thx!

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

Thanks!

cool mam

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

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

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

You are so pretty and smart.

aww

Thanks!