By the way, the output I would like to have is Intercept if we have the constant term in the regression, and all other regression regressions; R^2, and STEYX. Thanks in advance.
I am wondering if you could help me with the "Rolling Multiple Regression" method with window size held constant. Not many people show this on RUclips. Thank you in advance.
you are so gorgeous, and explaining subject so easily to all, wondaful work, keep it up, could you explain OHLCV data, apply, map functions, user defined functions to to find the trend on the High, Low columns, to get buy signals and sell signals by not using any price lagging indicators.
I've been learning many related stuff about data for the las year and as a personal challenge I decided to do it in english while my native languaje is Spanish, with you that's not a problem, your tone of voice and speed are just perfect as well everything you explain just fit into my mind right away. Looking foward to learn wit you about deep learning since I saw you have a playlist for that topic. I really like the way you transmit information in such as understandable way.
She does explain but easy to miss. You can apply. Let’s say implement to not get confused here… you can implement the apply function to either a whole df or to its series (columns). If you implement the apply function to a series it will apply the given function to each row in the series. So if you use the arg axis = 1 in the apply function itself. You are saying implement the apply function to the columns of the df. So now it will implement the function specified in the apply function arg (like np.mean) on each of the rows of the column in the df. Hope that helps
I discovered your channel yesterday and I must confess that I am very impressed with your way of explaining. Congratulations on your work and thanks for the videos! 😉
Hello Misra, Thanks a ton for uploading this video. Please help if I have to update one column for eg. the salary column based on the condition on another column (here we can take posting type column). The condition is that for all those rows whose posting type is external, the salary column should be incremented by 2000 and for the rest salary should be incremented by 1000. How can this be done in pandas using the apply function.
This is Cool Turp !! There is a space between attributes which is causing lot of issue while copying variables in script !! Great explaining with the data !! This helps 1!! cheers !!
ah great! just started learning python and rite into pandas because i need it spacifically. You have explained this better then anyone else i have found. Also you are beautifal like Eva Mendes.
Def common_word(row): ........... df['word_c'] =df.apply(common_word, axis =1) It's not creating a new column Error----> AttributeError : 'float' object has no attribute 'split'
Thank you very much.. I have been looking for this.. I have df which contains billions of raw data. I had to make formulas for calculations and KPIs. using apply helped alot in terms of processing time. I cant thank you enough.
Thank you, great content. that’s an obvious and instructive explanation. For being more comprehensive, you could state apply, map and applymap functions at the same video next time. best wishes 🙂
df['q1_num_words'] = df['question1'].apply(lambda row: len(row.split(" "))) im getting error -----> AttributeError: 'float' object has no attribute 'split' pls help me out
@@misraturp no it's totally a string , actually I solving ml problem quora question pair similarity But when I check it showing me ---> Object type So what should I do
Hi Misra, thank you for the great explanation. How come you applied data[["salary Range From","Salary Range To']].apply(np.mean, axis=1) with double brackets and you got a series at minute 4:34. You said earlier double brackets will return a dataframe. And wasn't the case here
Hello Avedis, it is because on the dataframe that is generated, I apply a numpy mean (np.mean) function. The result of this mean function is a series object.
@Misra, the point you made in the tutorial was to ilustrate the apply method over a dataframe object and at that point in time you used [[colname]] passing two numeric variables. This is no dought a dataframe object, over which you calculted the average using df.apply(mean). Type(df[[colname ]]) is a dataframe class/type
👉 All you need to know about Pandas in one place!
Download my Pandas Cheat Sheet (free)
misraturp.gumroad.com/l/pandascs
How can I get access it's asking amount
Great Content, Just Keep going.
I have to learn a lot from you.
Glad to hear that!
Nice, you are back, thanks for this great video
Yes, and there is more to come! :)
By the way, the output I would like to have is Intercept if we have the constant term in the regression, and all other regression regressions; R^2, and STEYX. Thanks in advance.
I am wondering if you could help me with the "Rolling Multiple Regression" method with window size held constant. Not many people show this on RUclips. Thank you in advance.
this video is great. Very simple explanation and easy to understand. Thanks
you are so gorgeous, and explaining subject so easily to all, wondaful work, keep it up, could you explain OHLCV data, apply, map functions, user defined functions to to find the trend on the High, Low columns, to get buy signals and sell signals by not using any price lagging indicators.
I've been learning many related stuff about data for the las year and as a personal challenge I decided to do it in english while my native languaje is Spanish, with you that's not a problem, your tone of voice and speed are just perfect as well everything you explain just fit into my mind right away. Looking foward to learn wit you about deep learning since I saw you have a playlist for that topic. I really like the way you transmit information in such as understandable way.
I love your videos. Keep up the good work! :)
Well explained, thanks ❤
I am sorry but "axis = 0" refers to rows while "axis = 1" refers to columns
She does explain but easy to miss. You can apply. Let’s say implement to not get confused here… you can implement the apply function to either a whole df or to its series (columns). If you implement the apply function to a series it will apply the given function to each row in the series.
So if you use the arg axis = 1 in the apply function itself. You are saying implement the apply function to the columns of the df. So now it will implement the function specified in the apply function arg (like np.mean) on each of the rows of the column in the df.
Hope that helps
The last bit was interesting 💯
I discovered your channel yesterday and I must confess that I am very impressed with your way of explaining. Congratulations on your work and thanks for the videos! 😉
Thank you so much!
Great video ...very simplified and easy to understand ...and look great
Thanks!
love you 🤫
Hello Misra, Thanks a ton for uploading this video. Please help if I have to update one column for eg. the salary column based on the condition on another column (here we can take posting type column). The condition is that for all those rows whose posting type is external, the salary column should be incremented by 2000 and for the rest salary should be incremented by 1000. How can this be done in pandas using the apply function.
Awesome, I able understand fully. Thanks
This is Cool Turp !! There is a space between attributes which is causing lot of issue while copying variables in script !! Great explaining with the data !! This helps 1!! cheers !!
ah great! just started learning python and rite into pandas because i need it spacifically. You have explained this better then anyone else i have found. Also you are beautifal like Eva Mendes.
Superb Explanation :)
Mam not received mail of cheatsheet 😑
Def common_word(row):
...........
df['word_c'] =df.apply(common_word, axis =1)
It's not creating a new column
Error----> AttributeError : 'float' object has no attribute 'split'
Can you creat a function and use that function as a new column that will going to help me
Happy to see you back!!! Thanks a lot for this videoooo!!! 🙏🏼🙏🏼🙏🏼
It’s good to be back at it :) you are very welcome!
Thank you very much.. I have been looking for this.. I have df which contains billions of raw data. I had to make formulas for calculations and KPIs. using apply helped alot in terms of processing time. I cant thank you enough.
That's amazing to hear! You're very welcome :)
Great video!
Thanks!
Thank you, great content.
that’s an obvious and instructive explanation. For being more comprehensive, you could state apply, map and applymap functions at the same video next time.
best wishes 🙂
That is actually my next video. :) I'll upload it in a couple of days!
Lovely! I'm learning python right as part of my data science degree, we just touched on pandas. Very informative videos. Very insightful content here!
Thank you!
Thanks, you took a long leave from you tube. Your videos helped me. So thanks.
Yes, I was on holiday for a while. :) That's great to hear Himanshu, you are very welcome. :)
Hi Misra, I have started learning pandas to work one of my project and your videos really helped me. You are a great educator! Best wishes :)
Great to hear Tejas, thank you!
Thanks a lot Misra that’s a really good content, well explained !
You're welcome!
Thanks
No problem
I am participating in a Data Science Bootcamp at the moment. The material that you provide takes so much frustration off! Thanks a lot Misra.
That's great to hear! Thanks Daniel! Good luck with the bootcamp. :)
Thank you. Really, thank you. From brazil
You are welcome!
Thanks, Misra. Really useful. You are a great educator. 🙂
Thank you! 😃
df['q1_num_words'] = df['question1'].apply(lambda row: len(row.split(" ")))
im getting error -----> AttributeError: 'float' object has no attribute 'split'
pls help me out
seems like the row you're calling is a float and not a string.
@@misraturp no it's totally a string , actually I solving ml problem quora question pair similarity
But when I check it showing me ---> Object type
So what should I do
Great information Misra! Very helpful as I am starting to learn Pandas
That's great to hear Hemant!
Nice job 👏
Hi Misra, thank you for the great explanation. How come you applied data[["salary Range From","Salary Range To']].apply(np.mean, axis=1) with double brackets and you got a series at minute 4:34. You said earlier double brackets will return a dataframe. And wasn't the case here
Hello Avedis, it is because on the dataframe that is generated, I apply a numpy mean (np.mean) function. The result of this mean function is a series object.
@Misra, the point you made in the tutorial was to ilustrate the apply method over a dataframe object and at that point in time you used [[colname]] passing two numeric variables. This is no dought a dataframe object, over which you calculted the average using df.apply(mean).
Type(df[[colname ]]) is a dataframe class/type
Now I've got it well!
Thanks a lot Misra :)
You're very welcome :)
Thanks for the great content! Keep up the good work please🥰
Thanks for watching!
I like this format, thank you! 🙂
Glad you enjoy it!
great sharing..Thank you
Thank you! Cheers!
Excellent, thanks.
You are welcome!
practical video
Thank you!
Thanks, 👍👍👍
You're welcome!