Project 1

use ecommerce;

select* from users_data;

DESC users_data;

select*from users_data limit 100;

select count(distinct country) ,count(distinct language) from users_data;

6...

select count (socialNbFollowers ) from users_data group by gender ;

Select (socialNbFollowers) from users_data group by gender;

select count(*) as c , hasProfilePicture from users_data group by hasProfilePicture order by c , hasProfilePicture=True;

select count(*) as c , hasAnyApp from users_data group by hasAnyApp order by c , c=true;

select count(*) as c ,hasAndroidApp from users_data group by hasAndroidApp order by c , hasAndroidApp = 'True';

select count(*) as c ,hasIosApp from users_data group by hasIosApp order by c , hasIosApp= true;

use ecommerce;

select* from users_data;

select count( productsBought) from users_data group by productsBought order by productsBought ASC;

select count( productsSold) from users_data group by productsSold order by productsSold desc;

select max(productsPassRate) , country from users_data where productsPassRate=100 group by country limit 10;

select count country , productsPassRate from users_data group by country order by productsPassRate desc;

select count( language) , language from users_data group by language ;

select d1. productsWished , d2.socialProductsLiked, d1.gender from users_data d1,users_data d2 where d1.gender=d2.gender like 'M%';

select d1.productsBought , d2.productsSold,d2.gender from users_data d1,users_data d2 where d1.gender =d2.gender like 'F%' ;

select max(productsBought), country from users_data group by country;

select country from users_data where productsBought = 57;

select count(country) from users_data where productsSold=0 ;

select*from users_data order by socialNbFollows desc limit 110 ;

select count(gender) from users_data where daysSinceLastLogin > 100 ;

select count(gender) , country from users_data where gender like'F%' group by country ;

select count( gender) , country from users_data where gender like 'M%' group by country;

select avg(productsSold), avg(productsBought) from users_data where gender Like 'M%';