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The data source is from Mood and Popularity through Music in Obsevable This is the modification of the Yearly Trend Music by calculating the means of features from the top 10 music every year.

Please check the dataset before preprocess from my another gist

bbbbrianna

The data source is from Mood and Popularity through Music in Obsevable

The dataset collects the informatio of top 50 music played on Spotify in 2019 from all over the world. Each abbreviatory features has different meaning and evaluation. I want to use this dataset to find how people's preference of music is different according to their country, and quantify the difference by the features of the most popular music.

Below are the questions that I am addressing to this dataset.

what's the difference of the most popular music's type accross different country in 2019?

Who is the most popular singer of 2019 in every country?

Which country's people prefer the new-released the music than the old songs? or vice versa?

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The dataset is originally from the National Park Service

This dataset contains the information of trails in national parks in United States. I've applied a deep cleaning towards the raw data to remove the missing information and other "junk records" causing error. Several categories with repeated name/groups have been combined manually.

I want to use this dataset to perform a visualization summary of the trail information in national parks, which can be used for our next traval plan. Currently, in this dataset it provides the information of the trailtype/seasonal/class which is very useful for people who are into hiking/biking like us.

Which trails of national parks closes at winter? what is the rate of open/close?

The number of developed trails that one national park has? which national park has the most developed/undeveopled trail?

Which national park is th

bbbbrianna

The data source is from Mood and Popularity through Music in Obsevable

The dataset introduces the top 50 music played in Spotify from 2010 to 2019 in United States. Each abbreviatory features has different meaning and evaluation, we are exploring the relationship of them and try to examine the change of people's taste of music.

How do people's taste of music changes since 2010?

Is there a trend of music evaluated by their factors?(e.g. bpm, dance)

Which artist became more popular and which became less popular?

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Name of products	Area	Ingredient1	Ingredient2	Ingredient3	Ingredient4	Ingredient5	Ingredient6
Acoustic Guitar	1.0x1.0	Softwood (8)	Iron Nugget (3)				
Angled Signpost	1.0x1.0	Hardwood (2)	Softwood (3)				
Axe	1.0x1.0	Flimsy Axe (1)	Wood (3)	Iron Nugget (1)			
Bamboo Basket	1.0x1.0	Bamboo Piece (7)					
Bamboo Doll	1.0x1.0	Young Spring Bamboo (6)					
Bamboo Lunch Box	1.0x1.0	Bamboo Piece (4)					
Bamboo Noodle Slide	3.0x1.0	Young Spring Bamboo (7)	Wood (3)				
Bamboo Sphere	1.0x1.0	Bamboo Piece (3)					
Bamboo Wand	1.0x1.0	Young Spring Bamboo (6)	Star Fragment (3)