Great video! I process all my data in excel, and know I am learning Python but I was not sure how to use it for data processing. This gave me a better idea all the potential of python.
This is all great and great for me being an expert with excel but I realize I really need python. Basically as I see it when the dataset gets too big to house in excel you will need to house it somewhere else and that is when these "new" techniques come into play.
Awesome tutorial, thanks for putting things in perspective! Would love to see more videos related to this, such as handling common data analyst assignments when applying for jobs.
thank you very much for this video - exactly what I was looking for. I'm a big fan of excel, and even more so of python, so I'm fascinated to upskill in pandas.
Great tutorial! I do think you're missing nearly 10 years of updates embedded in MS Excel that make things very easy to scale where speed is not a problem at all even when using data with millions of rows. Please look up power query/power pivot tools. You can literally do all this once and click and drag new csv's into a designated folder and have your entire analysis updated while your steps are documented. Furthermore things like data scraping, which I've done in both platforms, take a lot more effort (to me at least) in python. I guess it's knowing enough about each subject to adequately compare the two. I certainly need to learn more python and picked up a few things from your videos. However, in excel I'm a beast. Keep up the great work man!
You’re probably right as I’m not an excel enthusiast now a day. What I’m also trying to teach people is excel is a contained eco system that is difficult to scale in the field of data science. Also there is a high dependency on the laptop you are using. Python is far more scalable, connects with 1000s libraries to do analytics and can be run on a remote machine so users aren’t limited by their device. Python also connects to backend, front end development in additional to the analytical capabilities. All three which scale and connect well with python as a language. Excel again is more self contained for analysis with the tools and ecosystem provided by Microsoft
@@SATSifaction Love the perspective you provide! I've loved my python journey so far and am learning day to day. Thanks for your words and your videos, they've helped immensely
Your are some how right, MS is doing a good job with Excel +PowerBI etc. But in many, many ways they can't compete with Python: say scalability, automation posibilities etc.
@Oscar Martinez you’ve got to look into automation tools in Excel, you’re missing out. There’s data wrangling in Python and there’s Power Query, Power Pivot, Dax, M Code etc in Excel/Power BI. Just had a conversation w someone about this. Both Excel AND Power BI have the capability to connect to ANY data source, clean, trim, prep data in any way you’d like. Steps are automatically documented (aka stored procedures) and have things refresh at designated intervals (automation) for further analysis. Where Power BI goes beyond the capabilities of excel is: >interactivity< (w drill throughs on visualizations, excel don’t do that well), >row level security< on reports you furnish and finally the ability to see already developed reports on >any devicedifferent< data warehouses, websites (web scraping as it’s known) you want to analyze over time, prep your data (one time) and you’re good to move straight to the analyzing after hitting refresh every other week/month/day/etc. You can make canned reports and disseminate however you please. I will say I still have much to learn on python, it’s super popular and used in pretty much all FANG companies so I’m leaning that way pretty hard. It’s my NFL (new found love)
One of the frustrating things about power query/Pivot is that you have M and DAX, two totally different languages, not joined up thinking on the part of MS.
It is hard to read what you are typing in the notebook. Please zoom it closer in the next clip. Great clip, good idea to show it side by side excel vs python. thanks for this clip
Thank you for the tutorial , but you missed a very basic part , which is how did you get the data from Kaggle. Appreciate if you can share the steps to get this data , this would allow us to test your code step by step on the same data you used on this tutorial. Thank you in advance .
Hi there! I can't get past the line of code that says: df['price']=df['price'].astype('int64') I get this error: ValueError: cannot convert float NaN to integer Thank you very much Sir.
There is an error 'dfpiv=pd.pivot_table(df,index=['city'], values=['price', 'price'], columns=['bedrooms'], aggfunc=['count','mean'], fill_value=0)'. Not sure if it's due to the version of pandas i'm running. It works with values=['price'] (not twice) and and aggfunc = [np.count_nonzero, np.mean].
nice video but in the barplot() u didn't specify the type of aggregation, how it figured out its average and not summed the values like what happened in excel
@@SATSifaction Thanks, another idea is if you could reduce the space that Jupyter wastes at the top of the screen. These two things should open your videos to the mobile audience.
ruclips.net/video/qOHk_a3DxKs/видео.html
Great video! I process all my data in excel, and know I am learning Python but I was not sure how to use it for data processing.
This gave me a better idea all the potential of python.
Stuff you share is CRAZY useful. Wonderful. Thanks for your efforts.
Thanks
This is all great and great for me being an expert with excel but I realize I really need python. Basically as I see it when the dataset gets too big to house in excel you will need to house it somewhere else and that is when these "new" techniques come into play.
Thats a great outlook. Excel is powerful but in combination with other tools you just build your skill set with what you can do with data
Your Python tutorials are amazing!!!! Thanks!! Sugestion: "Power BI vs. Python" or "Power BI + Python"
i can add it to my list
That would be a good comparison!
Awesome tutorial, thanks for putting things in perspective! Would love to see more videos related to this, such as handling common data analyst assignments when applying for jobs.
Thank you
Great tutorial! This is what I was looking for. Thank you.
Excellent video. You explained so well the comparison. Thanks for sharing
My pleasure
thank you very much for this video - exactly what I was looking for. I'm a big fan of excel, and even more so of python, so I'm fascinated to upskill in pandas.
Great tutorial! I do think you're missing nearly 10 years of updates embedded in MS Excel that make things very easy to scale where speed is not a problem at all even when using data with millions of rows. Please look up power query/power pivot tools. You can literally do all this once and click and drag new csv's into a designated folder and have your entire analysis updated while your steps are documented. Furthermore things like data scraping, which I've done in both platforms, take a lot more effort (to me at least) in python. I guess it's knowing enough about each subject to adequately compare the two. I certainly need to learn more python and picked up a few things from your videos. However, in excel I'm a beast. Keep up the great work man!
You’re probably right as I’m not an excel enthusiast now a day. What I’m also trying to teach people is excel is a contained eco system that is difficult to scale in the field of data science. Also there is a high dependency on the laptop you are using. Python is far more scalable, connects with 1000s libraries to do analytics and can be run on a remote machine so users aren’t limited by their device. Python also connects to backend, front end development in additional to the analytical capabilities. All three which scale and connect well with python as a language. Excel again is more self contained for analysis with the tools and ecosystem provided by Microsoft
@@SATSifaction Love the perspective you provide! I've loved my python journey so far and am learning day to day. Thanks for your words and your videos, they've helped immensely
Your are some how right, MS is doing a good job with Excel +PowerBI etc. But in many, many ways they can't compete with Python: say scalability, automation posibilities etc.
@Oscar Martinez you’ve got to look into automation tools in Excel, you’re missing out. There’s data wrangling in Python and there’s Power Query, Power Pivot, Dax, M Code etc in Excel/Power BI. Just had a conversation w someone about this. Both Excel AND Power BI have the capability to connect to ANY data source, clean, trim, prep data in any way you’d like. Steps are automatically documented (aka stored procedures) and have things refresh at designated intervals (automation) for further analysis. Where Power BI goes beyond the capabilities of excel is: >interactivity< (w drill throughs on visualizations, excel don’t do that well), >row level security< on reports you furnish and finally the ability to see already developed reports on >any devicedifferent< data warehouses, websites (web scraping as it’s known) you want to analyze over time, prep your data (one time) and you’re good to move straight to the analyzing after hitting refresh every other week/month/day/etc. You can make canned reports and disseminate however you please. I will say I still have much to learn on python, it’s super popular and used in pretty much all FANG companies so I’m leaning that way pretty hard. It’s my NFL (new found love)
One of the frustrating things about power query/Pivot is that you have M and DAX, two totally different languages, not joined up thinking on the part of MS.
Great video, I learned so much. If I may offer one suggestion, please skip the background music on future videos, I found it a little bit distracting.
Excellent presentation. Thanks
Great comparison, thank you!
Your tutorial is very useful and straighforward !! Many thanks !!
It is hard to read what you are typing in the notebook. Please zoom it closer in the next clip. Great clip, good idea to show it side by side excel vs python. thanks for this clip
Great tutorial
Nice job. Keeping up with the Excel instructions can be a challenge for someone not conversant with Excel. Please don't just shrug off details.
As usual a very nice content!
Thank you for the tutorial , but you missed a very basic part , which is how did you get the data from Kaggle.
Appreciate if you can share the steps to get this data , this would allow us to test your code step by step on the same data you used on this tutorial.
Thank you in advance .
Thanks x
How do we bring mean price and count price under a single column for each Bedroom. I mean, for Bedroom 1: mean, count ; Bedroom 2: mean, count
23:11 how do you jump from the column of sold to the equivalent column of mean in one keystroke?
Hi there! I can't get past the line of code that says:
df['price']=df['price'].astype('int64')
I get this error:
ValueError: cannot convert float NaN to integer
Thank you very much Sir.
You drop or replace Nan values
There is an error 'dfpiv=pd.pivot_table(df,index=['city'], values=['price', 'price'], columns=['bedrooms'], aggfunc=['count','mean'], fill_value=0)'. Not sure if it's due to the version of pandas i'm running. It works with values=['price'] (not twice) and and aggfunc = [np.count_nonzero, np.mean].
Love it
Thanks!
when i df.describe(), the numbers are showing up with "e", such as "4.600000e+03". what is causing this and how can i have it show normal format?
In 7:59 he says about scientific notation, which in this case is your 'e' issue. Just add that code and it will fix it
screen's not visible
nice video but in the barplot() u didn't specify the type of aggregation, how it figured out its average and not summed the values like what happened in excel
The default is average
@@SATSifaction and how to change it to other aggregation?
Background music is so irritating
I understand. I will remove them in future tutorials
Please zoom in.
Thanks for the feedback. Will definitely do so in future.
@@SATSifaction Thanks, another idea is if you could reduce the space that Jupyter wastes at the top of the screen. These two things should open your videos to the mobile audience.
4 min intro?