I have a data-set which includes time {hh,mm,ss} and temperature.
I want to aggregate the temperature with respect to the time.
For each minute in a specific hour there are number of temperature records and I want to calculate the average of them to have a single value for each minute.
Thanks in advance.
Use date functions ( http://www.w3schools.com/sql/ ) to get more general (less precise) time [i.e. Hour and Minute only], group by that and use Average SQL function to get your average value.
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I need to write a SQL query that helps return the highest count in a given hourly range. The problem is that in my table, it just logs orders as they come and doesn’t have a unique identifier that separates hours from hours.
So basically, I need to find the highest number of orders (on any given hour), from 7/08/2022, - 7/15/2022, have a table that does not distinguish distinct hour sets, and logs orders as they come.
I have tried to use a query that combines MAX(), COUNT(), and DATETIME(), but to no avail.
Can I please receive some help?
I've had to tackle this kind of measurement in the past..
Here's what I did for 15 minute intervals:
My datetime column is named datreg in my database log area.
cast(round(floor(cast(datreg as float(53))*24*4)/(24*4),5) as smalldatetime
I times by 4 in this formula, to get 4 intervals inside my 24 hour period.. For you it would look like this to get just hourly intervals:
cast(round(floor(cast(datreg as float(53))*24)/(24),5) as smalldatetime
This is a little piece of magic when it comes to dashboards and reports.
In my dataset as follows (two columns: DATE and RATE)
I want to get the mean for the RATE for each day (from the dataset, you can see that there are multiple rate values for the same day). I have about 1,000 rows, so that I am trying to find an easier way to calculate the mean for each day, then save the results to a data frame.
You have to group by date then aggregate
https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.aggregate.html
In your case
df.groupby('DATE').agg({'RATE': ['mean']})
You can groupby the date and perform mean operation.
new_df = df.groupby('DATE').mean()
Need to calculated the Moving Range for a set of data without using group by clause. As I am calculating the average value and the previous avg value I need to take into account only existing values. I cant not use DIFFDATE(start-end).
Another constrains is that I need to do it at row-level as I need to have it as a pre-calculated value (denominator) to calculate the AVG Moving Range.
At the moment I am using window functions to calculate the average and previous averages.
ROUND(AVG(SUMCOUNTSFT3) OVER (partition by to_date(to_char(DATETIMEOFREADING, 'DD/MM/RR'))),2) as AVG_SUMCOUNTSFT3,
ROUND(AVG(SUMCOUNTSFT3) OVER (order by to_date(to_char(DATETIMEOFREADING, 'DD/MM/RR')) RANGE between interval '1' day preceding AND interval '1' day preceding),2) as LAG_VAL
Here is some sample data, as you can see I have multiple readings from a sensor. I have calculated the average for that day and for the previous day. Then I will have the difference between data points by |Xi - Xi-1|, the denominator is the column that I am trying to calculate. In some cases we will not have reading for a day if the sensor is failing and I need to discard those days if there is no data.
I believe a ROW_NUMBER() or DENSE_RANK() will do the job with a partition clause.
I have a sample table that looks like this
I need to to a SQL script to get the Average Handling Time of a Case, I researched for suggestions but never worked with timestamps and I'm really lost on how to do it.
If you subtract one timestamp from another, you get an interval. And you can calculate the average over intervals.
select avg(close_timestamp - create_timestamp)
from the_table;
You can calculate the AVG of the difference of the timestamp.
SELECT agent, avg(close_timestamp - create_timestamp) average_timestamp
FROM your_table
GROUP BY agent
ORDER BY agent
You can format the solution for obtain it in days/hours/minutes/seconds.
I am pretty new to SQL and am working with a (what I expected to be easy) little bidding tool.
I am trying to compute average lows and highs from the same column. I have managed to figure out how to use SQL's MIN, MAX, AVG functions, but how would I go about averaging MIN and MAX?
This is the query I am using:
$query = $pdo->prepare("SELECT AVG(bid),MIN(bid),MAX(bid) FROM bidding WHERE bid_id=:bid_id GROUP BY bid_id");
Try the following query to accomplish task
SELECT ((max(bid)+min(bid))/2) as average FROM bidding WHERE bid_id=:bid_id GROUP BY bid_id
Because the predefined avg function takes only one argument that may be column from table or single value. So you have to find the average of the min and max value of bid like above
As You are saying you need to find out the Min of Avg and Max of Avg,
Now what you are doing is group on one column this means Avg(Bid) will return only one value. And the thing that you are doing will make sense only if it is done with two column,
For example you wanna know the min of Averages per day. You need to identify one more column on which base you want to find out max and min of Avg. See in my example i am using Date as second column. the query will go like.
Select Max(MAx_Bid),Min(Min_Bid),Min(Avg_Bid),Max(Avg_Bid) FROM
(SELECT AVG(bid) Avg_Bid,MIN(bid) Mix_Bid,MAX(bid) max_bid FROM bidding WHERE bid_id=:bid_id GROUP BY bid_id,Days_Date(Dummy column))A