Tutorial 9- Seaborn Tutorial- Distplot, Joinplot, Pairplot Part 1




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Welcome to the Python Crash Course. In this video we will understand about Seaborn

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Comment List

  • Krish Naik
    November 17, 2020

    Sir, In kaggle which dataset are you import ? please give the URL of that dataset called tips

  • Krish Naik
    November 17, 2020

    Thank you for clear explanantion

  • Krish Naik
    November 17, 2020

    Bro… u r the best educator.

  • Krish Naik
    November 17, 2020

    How do you analyse this graph 11:54 by looking at it?

  • Krish Naik
    November 17, 2020

    i am bit confused for the x and y labels in the command " sns.jointplot(x='tip', y ='total_bill', data=df, kind='hex') " which starts some where at 13:00. the confusion is, x label should be 'total_bill' and y should be 'tip' because tip is dependent on the bill and there are many chances when there is no tip at all, customer just pay the bill it means tip is dependent not the bill. will you please clarify this doubt?

  • Krish Naik
    November 17, 2020

    how can we know what is independent variable and which is dependent feature.i mean every time in the question we may not get the details of this thing? can you please explain for this

  • Krish Naik
    November 17, 2020

    @11:00 negative correlation mens as one feature value increase next feature value decreases as of i knew.
    Correct me sir if m wrong.

  • Krish Naik
    November 17, 2020

    Hi Krish Dada
    I am following your videos of python and machine learning
    Till now I am into seaborn tutorial
    I am from database background
    Will need your help to understand once I have hands on in the topics of
    Machine learning, how can I prepare for interviews and can jump to this new domain
    Though its very early to ask as I have just started but your response will be highly appreciated

    Thanks Dada

  • Krish Naik
    November 17, 2020

    How to understand dependent and independent variable,bacause you told in this video "tips" is dependent variable,please answer the question.

  • Krish Naik
    November 17, 2020

    which correlation is good? positive or negative?

  • Krish Naik
    November 17, 2020

    Sir ye file restaurant wali kaise read kare python me

  • Krish Naik
    November 17, 2020

    Correction: correlation is (+) means they are directly proportional
    correlation is (-) means they are inversely proportional

  • Krish Naik
    November 17, 2020

    I have a question, can you tell me how to identify the dependent or independent feature.

  • Krish Naik
    November 17, 2020

    Sir why did I get an error in this statement.. df=sns.load_dataset("tips")

  • Krish Naik
    November 17, 2020

    Hello sir while practicing sea born I'm getting this error(RuntimeError: In FT2Font: Can not load face.) please tell me to solve this error

  • Krish Naik
    November 17, 2020

    you really nailed..i was struggling to plot seaborn graphs..

  • Krish Naik
    November 17, 2020

    great explanation….everything maintains with proper sequence….please complete the deep learning playlist…..missing it

  • Krish Naik
    November 17, 2020

    Crystal clear explanation .I bet no online courses explain it so well. Thanks a lot

  • Krish Naik
    November 17, 2020

    Categorical Data

    Categorical data represents characteristics. Therefore it can represent things like a personโ€™s gender, language etc. Categorical data can also take on numerical values (Example: 1 for female and 0 for male). Note that those numbers donโ€™t have mathematical meaning.

    Numerical

    Numerical data, on the other hand, as its name suggests, represents numbers. It is further divided into two subsets: discrete and continuous.

    Discrete Data

    Discrete data can usually be counted in a finite matter.

    Examples

    Take the number of children that you want to have. Even if you donโ€™t know exactly how many, you are absolutely sure that the value will be an integer. So a number like 0, 1, 2, or even 10.

    Continuous Data

    Itโ€™s easier to understand discrete data by saying itโ€™s the opposite of continuous data. Continuous data is infinite, impossible to count, and impossible to imagine.
    refer : https://365datascience.com/numerical-categorical-data/

  • Krish Naik
    November 17, 2020

    Hi Krish. One small suggestion. You can use
    import warnings

    warnings.filterwarnings("ignore")
    to ignore the warnings that you are otherwise getting when plotting the distplot.

  • Krish Naik
    November 17, 2020

    Thanks for this video,Krish. Very well elucidated. Request you to kindly correct your notebook and change "JoinPlot" to "JointPlot"

  • Krish Naik
    November 17, 2020

    from where to get tips data set

  • Krish Naik
    November 17, 2020

    In loading this dataset it is showing error

  • Krish Naik
    November 17, 2020

    Sir i cant run this code

  • Krish Naik
    November 17, 2020

    Nice video.
    Have one question, when to use dist plots?
    joinplot & pairplot explained when to use.

  • Krish Naik
    November 17, 2020

    Negative correlation implies that if one is increasing other is decreasing (in reference to 11:01 )

  • Krish Naik
    November 17, 2020

    there are many institutions including online courses charging anything more than 1 lack but not able explain concisely and clearly the you way you do it.
    I like your explanation very much.

  • Krish Naik
    November 17, 2020

    Sir I am getting confused where I have to use brackets and square brackets, While typing code and retrieving data. where exactly we use bracket and square bracket ?

  • Krish Naik
    November 17, 2020

    Krish my question is in join plot when you have taken two features and calling it both. bivariate and univariate. so clr that one. what we have to consider and why?

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