average

I have values in an Excel file like this: QR | QR AVG | val1 | q1 5 q1 3 q1 4 q2 7 q2 9 q3 10 q3 11 q3 12 q3 11 q4 5 q5 5 q5 7 And I would like the QR AVG field to represent the average value partitioned by different QR values. In other words, I'd like to have the following values after my calculation: QR | QR AVG | val1 | q1 4 5 q1 4 3 q1 4 4 q2 8 7 q2 8 9 q3 11 10 q3 11 11 q3 11 12 q3 11 11 q4 5 5 q5 6 5 q5 6 7 Where I don't know the exact number of rows that I will have, and I will be intermittently adding rows randomly into the table. I would prefer not to write a macro to do this if

This is the plot I'm having now. It's generated from this code: ggplot(data1, aes(x=POS,y=DIFF,colour=GT)) + geom_point() + facet_grid(~ CHROM,scales="free_x",space="free_x") + theme(strip.text.x = element_text(size=40), strip.background = element_rect(color='lightblue',fill='lightblue'), legend.position="top", legend.title = element_text(size=40,colour="lightblue"), legend.text = element_text(size=40), legend.key.size = unit(2.5, "cm")) + guides(fill = guide_legend(title.position="top", title = "Legend:GT='REF'+'ALT'"), shape = guide_legend(override.aes=list(size=10))) + scale_y_log10(breaks

I am working on numpy and I have a number of arrays with the same size and shape like: a= [153 186 0 258] b=[156 136 156 0] c=[193 150 950 757] I want to have average of the arrays, but I want the program to ignore the zero values in the computation. So, the resulting array for this example will be: d=[167.333 157.333 553 507.5] this is the result of this computation: d=[(153+156+193)/3 (186+136+150)/3 (156+950)/2 (258+757)/2] . Is it possible to do that? >>> import numpy as np >>> a = np.array([153, 186, 0, 258]) >>> b = np.array([156, 136, 156, 0]) >>> c = np.array([193, 150, 950, 757]) >>>

I have a data.frame from this code: my_df = data.frame("read_time" = c("2010-02-15", "2010-02-15", "2010-02-16", "2010-02-16", "2010-02-16", "2010-02-17"), "OD" = c(0.1, 0.2, 0.1, 0.2, 0.4, 0.5) ) which produces this: > my_df read_time OD 1 2010-02-15 0.1 2 2010-02-15 0.2 3 2010-02-16 0.1 4 2010-02-16 0.2 5 2010-02-16 0.4 6 2010-02-17 0.5 I want to average the OD column over each distinct read_time (notice some are replicated others are not) and I also would like to calculate the standard deviation, producing a table like this: > my_df read_time OD stdev 1 2010-02-15 0.15 0.05 5 2010-02-16 0.3

My question is about how to write an SQL query to calculate the average time between successive events. I have a small table: event Name | Time stage 1 | 10:01 stage 2 | 10:03 stage 3 | 10:06 stage 1 | 10:10 stage 2 | 10:15 stage 3 | 10:21 stage 1 | 10:22 stage 2 | 10:23 stage 3 | 10:29 I want to build a query that get as an answer the average of the times between stage(i) and stage(i+1). For example, the average time between stage 2 and stage 3 is 5: (3+6+6)/3 = 5 Vilx- Aaaaand with a sprinkle of black magic: select a.eventName, b.eventName, AVG(DATEDIFF(MINUTE, a.[Time], b.[Time])) as

I have a table that tracks changes in stocks through time for some stores and products. The value is the absolute stock, but we only insert a new row when a change in stock occurs. This design was to keep the table small, because it is expected to grow rapidly. This is an example schema and some test data: CREATE TABLE stocks ( id serial NOT NULL, store_id integer NOT NULL, product_id integer NOT NULL, date date NOT NULL, value integer NOT NULL, CONSTRAINT stocks_pkey PRIMARY KEY (id), CONSTRAINT stocks_store_id_product_id_date_key UNIQUE (store_id, product_id, date) ); insert into stocks