Understanding the diff. btw 'size()' and 'count()' by implementing 'Wine-Review' example.

At first lets import the dataset, and see how it looks like

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1. the most common diff is count does not include any NaN values, but size does

2. Type and Dimension:

   the dataset contains a number of tasters,

   Question: How can we check , who are the most common reviewers in the dataset?

   ans:1. count(), 2.size()

   1.count()

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   if you see the o/p , reviews_by_count returns a dataframe, which is ndarray.

   at first we group the data by same taster_name, then for groups contains every columns except in index(taster_name)

   let's see the type:

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   as you see it returns a DataFrame as a object

   2.size()

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   as u see it didn't return any multiple columns , only one column,

   Let's check the type:

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   Well, it returns a series(1D object)

3. Usage at diff. time****

   Question: What combination of countries and varieties are most common?Create a Series whose index is a MultiIndexof {country, variety} pairs. For example, a pinot noir produced in the US should map to {"US", "Pinot Noir"}.Sort the values in the Series in descending order based on wine count

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as we see it returns a DataFrame , where it is a multi index country and variety.

as per the question, we have to sort by values

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as you see, i try to implement sort_values() in decending order, at first i did not give any column, so it

throws me error, but in 2nd time i sort respective to price column,

it shows the fundamental structure of count, bco'z it is a ndarray , it needs a specific col among all columns

but in case of size():

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as you see it returns only a column,

and we can sort it without passing by='' parameter, bco'z it has only 1 column to sort.

Pls, give a upvote if you like my explanation !!