I have the following dataset. I need to calculate the number of each "job_title": how many "data scientist", "Machine learning Scientist" do I have. The problem is I need to have all the results together in the same table with MongoDB.
/* 1 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292727"),
"null" : 0,
"work_year" : 2020,
"experience_level" : "MI",
"employment_type" : "FT",
"job_title" : "Data Scientist",
"salary" : 70000,
"salary_currency" : "EUR",
"salary_in_usd" : 79833,
"employee_residence" : "DE",
"remote_ratio" : 0,
"company_location" : "DE",
"company_size" : "L"
},
/* 2 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292728"),
"null" : 1,
"work_year" : 2020,
"experience_level" : "SE",
"employment_type" : "FT",
"job_title" : "Machine Learning Scientist",
"salary" : 260000,
"salary_currency" : "USD",
"salary_in_usd" : 260000,
"employee_residence" : "JP",
"remote_ratio" : 0,
"company_location" : "JP",
"company_size" : "S"
},
/* 3 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292729"),
"null" : 2,
"work_year" : 2020,
"experience_level" : "SE",
"employment_type" : "FT",
"job_title" : "Big Data Engineer",
"salary" : 85000,
"salary_currency" : "GBP",
"salary_in_usd" : 109024,
"employee_residence" : "GB",
"remote_ratio" : 50,
"company_location" : "GB",
"company_size" : "M"
},
/* 4 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a6729272a"),
"null" : 3,
"work_year" : 2020,
"experience_level" : "MI",
"employment_type" : "FT",
"job_title" : "Product Data Analyst",
"salary" : 20000,
"salary_currency" : "USD",
"salary_in_usd" : 20000,
"employee_residence" : "HN",
"remote_ratio" : 0,
"company_location" : "HN",
"company_size" : "S"
},
/* 5 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a6729272b"),
"null" : 4,
"work_year" : 2020,
"experience_level" : "SE",
"employment_type" : "FT",
"job_title" : "Machine Learning Engineer",
"salary" : 150000,
"salary_currency" : "USD",
"salary_in_usd" : 150000,
"employee_residence" : "US",
"remote_ratio" : 50,
"company_location" : "US",
"company_size" : "L"
},
/* 6 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a6729272c"),
"null" : 5,
"work_year" : 2020,
"experience_level" : "EN",
"employment_type" : "FT",
"job_title" : "Data Analyst",
"salary" : 72000,
"salary_currency" : "USD",
"salary_in_usd" : 72000,
"employee_residence" : "US",
"remote_ratio" : 100,
"company_location" : "US",
"company_size" : "L"
},
/* 7 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a6729272d"),
"null" : 6,
"work_year" : 2020,
"experience_level" : "SE",
"employment_type" : "FT",
"job_title" : "Lead Data Scientist",
"salary" : 190000,
"salary_currency" : "USD",
"salary_in_usd" : 190000,
"employee_residence" : "US",
"remote_ratio" : 100,
"company_location" : "US",
"company_size" : "S"
},
/* 8 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a6729272e"),
"null" : 7,
"work_year" : 2020,
"experience_level" : "MI",
"employment_type" : "FT",
"job_title" : "Data Scientist",
"salary" : 11000000,
"salary_currency" : "HUF",
"salary_in_usd" : 35735,
"employee_residence" : "HU",
"remote_ratio" : 50,
"company_location" : "HU",
"company_size" : "L"
},
/* 9 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a6729272f"),
"null" : 8,
"work_year" : 2020,
"experience_level" : "MI",
"employment_type" : "FT",
"job_title" : "Business Data Analyst",
"salary" : 135000,
"salary_currency" : "USD",
"salary_in_usd" : 135000,
"employee_residence" : "US",
"remote_ratio" : 100,
"company_location" : "US",
"company_size" : "L"
},
/* 10 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292730"),
"null" : 9,
"work_year" : 2020,
"experience_level" : "SE",
"employment_type" : "FT",
"job_title" : "Lead Data Engineer",
"salary" : 125000,
"salary_currency" : "USD",
"salary_in_usd" : 125000,
"employee_residence" : "NZ",
"remote_ratio" : 50,
"company_location" : "NZ",
"company_size" : "S"
},
/* 11 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292731"),
"null" : 10,
"work_year" : 2020,
"experience_level" : "EN",
"employment_type" : "FT",
"job_title" : "Data Scientist",
"salary" : 45000,
"salary_currency" : "EUR",
"salary_in_usd" : 51321,
"employee_residence" : "FR",
"remote_ratio" : 0,
"company_location" : "FR",
"company_size" : "S"
},
/* 12 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292732"),
"null" : 11,
"work_year" : 2020,
"experience_level" : "MI",
"employment_type" : "FT",
"job_title" : "Data Scientist",
"salary" : 3000000,
"salary_currency" : "INR",
"salary_in_usd" : 40481,
"employee_residence" : "IN",
"remote_ratio" : 0,
"company_location" : "IN",
"company_size" : "L"
},
/* 13 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292733"),
"null" : 12,
"work_year" : 2020,
"experience_level" : "EN",
"employment_type" : "FT",
"job_title" : "Data Scientist",
"salary" : 35000,
"salary_currency" : "EUR",
"salary_in_usd" : 39916,
"employee_residence" : "FR",
"remote_ratio" : 0,
"company_location" : "FR",
"company_size" : "M"
},
/* 14 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292734"),
"null" : 13,
"work_year" : 2020,
"experience_level" : "MI",
"employment_type" : "FT",
"job_title" : "Lead Data Analyst",
"salary" : 87000,
"salary_currency" : "USD",
"salary_in_usd" : 87000,
"employee_residence" : "US",
"remote_ratio" : 100,
"company_location" : "US",
"company_size" : "L"
},
/* 15 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292735"),
"null" : 14,
"work_year" : 2020,
"experience_level" : "MI",
"employment_type" : "FT",
"job_title" : "Data Analyst",
"salary" : 85000,
"salary_currency" : "USD",
"salary_in_usd" : 85000,
"employee_residence" : "US",
"remote_ratio" : 100,
"company_location" : "US",
"company_size" : "L"
},
/* 16 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292736"),
"null" : 15,
"work_year" : 2020,
"experience_level" : "MI",
"employment_type" : "FT",
"job_title" : "Data Analyst",
"salary" : 8000,
"salary_currency" : "USD",
"salary_in_usd" : 8000,
"employee_residence" : "PK",
"remote_ratio" : 50,
"company_location" : "PK",
"company_size" : "L"
},
/* 17 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292737"),
"null" : 16,
"work_year" : 2020,
"experience_level" : "EN",
"employment_type" : "FT",
"job_title" : "Data Engineer",
"salary" : 4450000,
"salary_currency" : "JPY",
"salary_in_usd" : 41689,
"employee_residence" : "JP",
"remote_ratio" : 100,
"company_location" : "JP",
"company_size" : "S"
},
/* 18 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292738"),
"null" : 17,
"work_year" : 2020,
"experience_level" : "SE",
"employment_type" : "FT",
"job_title" : "Big Data Engineer",
"salary" : 100000,
"salary_currency" : "EUR",
"salary_in_usd" : 114047,
"employee_residence" : "PL",
"remote_ratio" : 100,
"company_location" : "GB",
"company_size" : "S"
},
/* 19 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292739"),
"null" : 18,
"work_year" : 2020,
"experience_level" : "EN",
"employment_type" : "FT",
"job_title" : "Data Science Consultant",
"salary" : 423000,
"salary_currency" : "INR",
"salary_in_usd" : 5707,
"employee_residence" : "IN",
"remote_ratio" : 50,
"company_location" : "IN",
"company_size" : "M"
},
/* 20 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a6729273a"),
"null" : 19,
"work_year" : 2020,
"experience_level" : "MI",
"employment_type" : "FT",
"job_title" : "Lead Data Engineer",
"salary" : 56000,
"salary_currency" : "USD",
"salary_in_usd" : 56000,
"employee_residence" : "PT",
"remote_ratio" : 100,
"company_location" : "US",
"company_size" : "M"
},
/* 21 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a6729273b"),
"null" : 20,
"work_year" : 2020,
"experience_level" : "MI",
"employment_type" : "FT",
"job_title" : "Machine Learning Engineer",
"salary" : 299000,
"salary_currency" : "CNY",
"salary_in_usd" : 43331,
"employee_residence" : "CN",
"remote_ratio" : 0,
"company_location" : "CN",
"company_size" : "M"
},
/* 22 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a6729273c"),
"null" : 21,
"work_year" : 2020,
"experience_level" : "MI",
"employment_type" : "FT",
"job_title" : "Product Data Analyst",
"salary" : 450000,
"salary_currency" : "INR",
"salary_in_usd" : 6072,
"employee_residence" : "IN",
"remote_ratio" : 100,
"company_location" : "IN",
"company_size" : "L"
},
/* 23 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a6729273d"),
"null" : 22,
"work_year" : 2020,
"experience_level" : "SE",
"employment_type" : "FT",
"job_title" : "Data Engineer",
"salary" : 42000,
"salary_currency" : "EUR",
"salary_in_usd" : 47899,
"employee_residence" : "GR",
"remote_ratio" : 50,
"company_location" : "GR",
"company_size" : "L"
},
/* 24 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a6729273e"),
"null" : 23,
"work_year" : 2020,
"experience_level" : "MI",
"employment_type" : "FT",
"job_title" : "BI Data Analyst",
"salary" : 98000,
"salary_currency" : "USD",
"salary_in_usd" : 98000,
"employee_residence" : "US",
"remote_ratio" : 0,
"company_location" : "US",
"company_size" : "M"
},
/* 25 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a6729273f"),
"null" : 24,
"work_year" : 2020,
"experience_level" : "MI",
"employment_type" : "FT",
"job_title" : "Lead Data Scientist",
"salary" : 115000,
"salary_currency" : "USD",
"salary_in_usd" : 115000,
"employee_residence" : "AE",
"remote_ratio" : 0,
"company_location" : "AE",
"company_size" : "L"
},
/* 26 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292740"),
"null" : 25,
"work_year" : 2020,
"experience_level" : "EX",
"employment_type" : "FT",
"job_title" : "Director of Data Science",
"salary" : 325000,
"salary_currency" : "USD",
"salary_in_usd" : 325000,
"employee_residence" : "US",
"remote_ratio" : 100,
"company_location" : "US",
"company_size" : "L"
},
/* 27 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292741"),
"null" : 26,
"work_year" : 2020,
"experience_level" : "EN",
"employment_type" : "FT",
"job_title" : "Research Scientist",
"salary" : 42000,
"salary_currency" : "USD",
"salary_in_usd" : 42000,
"employee_residence" : "NL",
"remote_ratio" : 50,
"company_location" : "NL",
"company_size" : "L"
},
/* 28 createdAt:12/11/2022 13:02:36*/
{
"_id" : ObjectId("636f8b5cbe2ead3a67292742"),
"null" : 27,
"work_year" : 2020,
"experience_level" : "SE",
"employment_type" : "FT",
"job_title" : "Data Engineer",
"salary" : 720000,
"salary_currency" : "MXN",
"salary_in_usd" : 33511,
"employee_residence" : "MX",
"remote_ratio" : 0,
"company_location" : "MX",
"company_size" : "S"
},
I did an account for each one, but dont know how to do it all together.
CodePudding user response：
You should use $group, on job_title field:
db.collection.aggregate([
{
"$group": {
"_id": "$job_title",
"count": {
"$sum": 1
}
}
}
])