Hi, thank you! I have a problem while trying to convert 'missing' to int I have this error (IntCastingNaNError: Cannot convert non-finite values (NA or inf) to integer). Also my vs Code ipynb file won't recognise Int64: ..errors='coerce').astype(Int64)
When you read in the excel file you can specify the data type (dtype) of the column df = pd.read_excel('excel_file.xlsx', dtype={'col_a': float, 'col_d':float}) If your column of numbers has commas in (1,500.00) it you will have to read in the file, remove the commas and any other symbols and then convert to float. If you want two decimal places you can change it for the entire dataframe with the following pd.options.display.float_format = "{:,.2f}".format
can anyone explain at money col he replaces everything with empty string but i have loss which is indicated by - how can i keep the - sign for indicatiing loss and change its type to nu[meric ??
How to find out which of the columns are having a datatype object and convert them to string type ...i mean not specifically mentioning the column name..but by iterating and taking them automatically...PLS REPLY
You completely skipped over the only column I cared about (missing) by saying "now we should be fully equipped to deal with this on our own". Abysmal tutorial and teacher.
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The best video...i was searching in so many videos here and thanks to you I'm done:) MANY WISHESSSSS
Thank you! I've watched a lot of videos, but it was yours that helped me at a certain point👍
Explanation with rationale as to why we do it a certain way is exemplary
It's a nicely explained tutorial. 👍
nice and short..thanks
Hi, thank you! I have a problem while trying to convert 'missing' to int I have this error (IntCastingNaNError: Cannot convert non-finite values (NA or inf) to integer). Also my vs Code ipynb file won't recognise Int64: ..errors='coerce').astype(Int64)
really helpful bro... thanks for creating this video
Thank youuuuuuuuuuuuuuuuuuuu!!!!!!!!!!!!!!!!!!!!
Thank you so much for this! :)
You're Welcome!
Thanks so much it is very informative.
Hello, I just want to ask what to do if a dataframe is displaying 1,500 even if the value on the excel is 1,500.00
When you read in the excel file you can specify the data type (dtype) of the column
df = pd.read_excel('excel_file.xlsx', dtype={'col_a': float, 'col_d':float})
If your column of numbers has commas in (1,500.00) it you will have to read in the file, remove the commas and any other symbols and then convert to float.
If you want two decimal places you can change it for the entire dataframe with the following
pd.options.display.float_format = "{:,.2f}".format
can anyone explain at money col he replaces everything with empty string but i have loss which is indicated by - how can i keep the - sign for indicatiing loss and change its type to nu[meric ??
Can you share the link to the video for converting column to datetime? It's not in your descriptions
Hi Mike, thanks for letting me know! I had forgotten to add the link ruclips.net/video/f7LODKIjtaA/видео.html
@@ChartExplorers thank you. Your video was very helpful by the way
Thanks! I'm glad it helped (it makes it feel worth it when I know it actually helps out).
Thanks for this!
You're welcome! :D
How to find out which of the columns are having a datatype object and convert them to string type ...i mean not specifically mentioning the column name..but by iterating and taking them automatically...PLS REPLY
Very helpful
How to convert high and low in float
It is not working with any of your suggestions please help
Thanks alot u save my life
You're welcome, I'm glad it helped!
Thank you so much bro
Thank you!
thank you so much..!!
Did you figure out your error?
@@ChartExplorers yes.. thanks again.!
You completely skipped over the only column I cared about (missing) by saying "now we should be fully equipped to deal with this on our own".
Abysmal tutorial and teacher.
Thank you!