Let's say I have a table like this
I want to calculate the frequency ( How many times that product exists in that price range ), in intervals of "50"
So eventually it will give me a table like
Interval for range will be lets pretend a fixed 50
We don't know highest and lowest price of these each products.
So I will run the query and it will give a table as shown above.
You can use arithmetic and aggregation:
select product, count(*) as frequency,
floor(price / 50)*50 as range_start, floor(price / 50)*50 + 50 as range_end
from t
group by product, floor(price / 50)
order by product, min(price)
Related
The problem statement is: I have a table (order_t) which has customer feedback (one column) and quarter number (as another column).
Using a CTE, I need to calculate the percentage of number of customer feedback in each category as well as the total number of customer feedback in each quarter.
After this happens, I need the percentage of different types of customer feedback (like good, bad, ok, very good, very bad) but using CTE.
How can I solve this statement?
I try to solve customer feedback as
WITH total_feedback AS
(
SELECT *
COUNT(CUSTOMER_FEEDBACK), QUARTER NUMBER
FROM
table1
GROUP BY
2
)
But I'm unable to calculate the first half portion, i.e. percentage of different types of customer feedback in each quarter using CTE.
How can I do that?
Find the file of the data
What you could do, and I'll keep the example as close to the code you provided as possible, is the following - using 2 CTE's:
WITH total_feedback AS (
SELECT COUNT(CUSTOMER_FEEDBACK) AS total_feedback, QUARTER_NUMBER
FROM table1
GROUP BY 2
),
category_feedback AS (
SELECT COUNT(CUSTOMER_FEEDBACK) AS feedback_count, CUSTOMER_FEEDBACK, QUARTER_NUMBER
FROM table1
GROUP BY 2, 3
)
SELECT
category_feedback.CUSTOMER_FEEDBACK,
category_feedback.QUARTER_NUMBER,
(feedback_count / total_feedback.total_feedback) * 100 AS feedback_percentage
FROM category_feedback
INNER JOIN total_feedback
ON category_feedback.QUARTER_NUMBER = total_feedback.QUARTER_NUMBER
I am trying to group a certain records by its price range. Lets say Customer A bought Product B multiple Times as shown below figure, I want to group them together. The Below customer bought products at different price points like 800,810,830,850 etc. I want to compare each price point against others price points in the tables and see if they can grouped together.
Lets say there are ten price points
800,800,850,820,830,1200,1220,1200,1250,1230.
I want to group numbers which are in 10% of its range. The first 5 numbers 800,800,850,820,830 are in one group and the other numbers are in a different group. How can I achieve this SQL Server?
If I understand correctly, you want one group of:
min + 0.1 * (max - min)
for each customer as a group. Then you want the rest in another group. You can use window functions and arithmetic for this:
select t.*,
(case when price <= 0.1 * max(price) over (partition by customer) + 0.9 * min(price) over (partition by customer)
then 1 else 2
end) as the_group
from t;
enter image description here
I need to write a query that count the number of times customers transactions exceed 250 Pounds. Adding cumulatively until the sum exceeds 250 then reset and start from the following row until it exceeds 250 and so on. This functionality can be carried out using Teradata keywords 'RESET WHEN' yet I am supposed to create a query that's only composed of ANSI SQL SYNTAX.
Can anyone help with that?
SUM(sales) OVER (
PARTITION BY region
ORDER BY day_of_calendar
RESET WHEN sales < /* preceding row */ SUM(sales) OVER (
PARTITION BY region
ORDER BY day_of_calendar
ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING)
ROWS UNBOUNDED PRECEDING
)
1: https://i.stack.imgur.com/lu4Jp.png This is a sample of the input of customer
enter image description here
And that's the output.
Every time the customer's total spent exceeds 250, I should be summing from 0 once again and find the day at which the customer exceeded 250 USD.
Without your table definitions and just a screenshot of a very limited dataset it is hard to test my answer on your data - so I'm showing it first on the dataset supplied in the match_recognize tutorial on live SQL and then with your columns:
SELECT
*
FROM
ticker MATCH_RECOGNIZE (
PARTITION BY symbol
ORDER BY tstamp
MEASURES
nvl(SUM(up.price),0) AS tot
ALL ROWS PER MATCH
PATTERN ( up* ) DEFINE
up AS SUM(up.price) - up.price <= 100
);
So on your table this would be something like
SELECT
*
FROM
your_table MATCH_RECOGNIZE (
PARTITION BY region
ORDER BY day_of_calendar
MEASURES
nvl(SUM(up.sales),0) AS tot
ALL ROWS PER MATCH
PATTERN ( up* ) DEFINE
up AS SUM(up.sales) - up.sales <= 250
);
I have a table with count by product. I am trying to add a new column that would find % as compared to sum of all rows in that column.
prod_name,count
prod_a,100
prod_b,50
prod_c,150
For example, I want to find % of prod_a as compared to the total count and so on.
Expected output:
prod_name,count,%
prod_a,100,0.33
prod_b,50,0.167
prod_c,150,0.5
Edit on SQL:
select count(*),ratio_to_report(prod_name)
over (partition by count(*))
from sales
group by prod_name;
Using window functions.
select t.*,100.0*cnt_by_prod/sum(cnt_by_prod) over() as pct
from tbl t
Edit: Based on OP's question change, To compute the counts and then percentage, use
select prod_name,100.0*count(*)/sum(count(*)) over()
from tbl
group by prod_name
I want to see if the price of a stock has changed by 5% this week. I have data that captures the price everyday. I can get the rows from the last 7 days by doing the following:
select price from data where date(capture_timestamp)>date(current_timestamp)-7;
But then how do I analyze that and see if the price has increased or decreased 5%? Is it possible to do all this with one sql statement? I would like to be able to then insert any results of it into a new table but I just want to focus on it printing out in the shell first.
Thanks.
It seems odd to have only one stock in a table called data. What you need to do is bring the two rows together for last week's and today's values, as in the following query:
select d.price
from data d cross join
data dprev
where cast(d.capture_timestamp as date = date(current_timestamp) and
cast(dprev.capture_timestamp as date) )= cast(current_timestamp as date)-7 and
d.price > dprev.price * 1.05
If the data table contains the stock ticker, the cross join would be an equijoin.
You may be able to use query from the following subquery for whatever calculations you want to do. This is assuming one record per day. The 7 preceding rows is literal.
SELECT ticker, price, capture_ts
,MIN(price) OVER (PARTITION BY ticker ORDER BY capture_ts ROWS BETWEEN 7 PRECEDING AND CURRENT ROW) AS min_prev_7_records
,MAX(price) OVER (PARTITION BY ticker ORDER BY capture_ts ROWS BETWEEN 7 PRECEDING AND CURRENT ROW) AS max_prev_7_records
FROM data