Відео

Love your enthusiasm and excitement

for step 4 my solution was: filtered_chipo = chipo[chipo['item_price'] > 10] filtered_chipo.item_name.nunique() which also gives 31. Its always good to see different solutions.

did anyone get step 5 the exact way in the series like in the exercise?

nice

Thank you, finally understand like 80% of the iid assumption, thanks!

Nice tutorial

how can i actually see the median values-show them in the plot ....so thati can actually compare them, for example in titanic dataset i want to know if median age of survivors in males is higher than in non-survivors or the median age of survivors in females is higher than in non-survivors.

Great

thank you sir

Thank you! this was useful and quick!

I could understand the whole thing very easily. Thanks :)

Thanks for your Video ! Can you please let me know if we can put those collapse and expand functionality in actually generated pivot table in excel using python ??

i got invalidargumenterror graph execution error in model.fit() using cnn model please tell me how to resolve @Data Talks

So if we use train_test split do we also need to use cross-validation?

Precisely explained. Loved the explanation !

they said: IQR the data! me: Long tails? they said: Winsorize! me: Still long tails!? they said: RobustScaler! QuantileTransformer! SciKit the data! me: Throw the data away? they: ...You're new here, aren't you? me: Yes 😭 YOU: Boxenplot to save the data! me: THANK YOU

please share how to increase fontsize of legend and that of the feature names

Good tutorial. However, the revenue is incorrectly calculated as the price should be multiplied by the quantity. Also, the number of orders should be revised as there are many items in the same order, so the number of rows in the dataset is higher than the number of orders

Hello Nathaniel, is this playlist relevant in 2023? Please advise. Thanks.

Awesome video man, if only more people saw this!

I can't find the url :( raw.githubusercontent.com/guipsamora/pandas_exercises/master/06_Stats/US_Baby_Names_right.csv

i though that cap is your signature as it was there in last 2 videos 😂

great series, concise but definitely going in-depth enough for intermediates. did you ever do a tutorial for timeseries in the end? would love to see that

thank you for posting good content from a data science undergrad

Nice tutorial. Do you have any example of React client calling parametrized publication and subscription? Thanks

Thank you for sharing this video. I am still not clear about your dataset. class1_points and class2_points have a shape of (5000, 10, 30). That means you created a dataset of 5000 people, 10 credit cards, and 30 features of credit cards?

your screen is not clear to viewers

For step 16, you're simply doing overall average price, for ALL order. The questions is PER order, so I believe it should be: chipo.groupby('order_id').agg({'item_price': 'mean'})

i want to learn machine learning, but before that I am preparing i finished learning python 40 h slow course by abdul from udemy, i watched from youtube 1 h course numpy 1 h course pandas 1 h course matplotlib and this pandas series/ playlist i wonder if i am ready to start the machine learning course from Coursea by Andrew Ng, or should i leant Linear Algebra first

This video quite usefull.. but can you please explain point 11 12 and point 4 again.

amazing explanation! Thank you

Very informative!

Great video

Love it. Simple and to the point.

no this channel is so underrated i love this channel

Hey! Any plans on covering pandas2.0 problems/exercises?

From where I can get the dataset of all the case studies that you have discussed in all the visualization videos

The title is a bit confusing. I was searching for a video on how to determine the confidence interval of the 10th-percentile. But this was about determining the confidence interval of the mean.

Zeeo to hero ,I liked that😂

Very nice

Nice

I think the resample method might give you unexpected results, to work with the actual dataset index (keeping the same info of the resulting record) I found the .tail(1) method more accurate. The dataset in the video was already filtered listing working days only, to find the last of the dates in the index by month: df.groupby([df.index.year, df.index.month]).tail(1)

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The concept you explain is really good and feels much easy.but after seeing that better bootstrap ci pdf my mind totally changed 😢😅

Thanks for this! How are you showing the info on the variable? At 4:20.

9:41 Which keys should we need to press to get the associated functions of that series?

hey, new fan here!!

lol this video could have been 1 sentence long based on the method you showed: Padding is how to Deal with Variable Length Features and Deep Learning

I really love the way to explain . Just I would like to ask if you have any vide with interpretation of the plots . For example boxplot interpretation quit answers of western about this data. Thank for all.

Very helpful, thanks for sharing! I'm wondering -- would your explanation for why standardization matters even when there's no effect modification still apply in a situation with a binary outcome variable? Since in that case the absolute values of the outcomes are constrained in the same way within each group of L?