I have the following data: structure(list(criteria = c("D_max", "ENT_max", "Q_max", "MDCT_min", "BOD_min", "GF_max", "VOL_min"), Mean_ME_opaco = c(2.31951219512195, 3.59547738693467, 3.88265306122449, 5.68948655256724, 6.3643216080402, 6.55635491606715, 6.7), Mean_ME_Transp = c(0.869565217391304, 10.8641975308642, 2.1264367816092, 1, 3.67901234567901, 3.70967741935484, 2.18840579710145), sd_ME_opaco = c(4.13735558695493, 4.39425771945945, 4.71884778320564, 6.86415183201686, 6.50095388599195, 6.43475495235123, 6.56397890542807), sd_ME_Transp = c(1.78154793465634, 6.37525114489569, 3.09088907846522, 2.09208307535884, 4.75086087065762, 4.41824494780745, 3.46102399977589)), row.names = c(NA, -7L), class… Read More pivoting for 2 columns of values

I have a SQL written as below: select year(recvd_date), (case when month(recvd_date)=1 then count(distinct(app_id)) else 0 end) as Jan, (case when month(recvd_date)=2 then count(distinct(app_id)) else 0 end) as Feb, (case when month(recvd_date)=3 then count(distinct(app_id)) else 0 end) as Mar, (case when month(recvd_date)=4 then count(distinct(app_id)) else 0 end) as Apr, (case when month(recvd_date)=5 then count(distinct(app_id)) else… Read More SQL Pivot – Months as Column headers

I have Pyspark Dataframe named df as below, I need to pivot the data based on ProducingMonth and classification column and need to produce the following output I am using the following pyspark code pivotDF = df.groupBy("WELL_ID","CLASSIFICATION").pivot("CLASSIFICATION") while I am displaying the data I am getting error "’GroupedData’ object has no attribute ‘display’" >Solution :… Read More pyspark pivot row without aggrefation

I have a table as follows: df <- data.table(ID = c(1,2,3,1,2,1,1,2,3), vlaue = c("A", "B", "C", "A", "B", "A", "A", "B", "C")) > df ID vlaue 1: 1 A 2: 2 B 3: 3 C 4: 1 A 5: 2 B 6: 1 A 7: 1 A 8: 2 B 9: 3 C I need… Read More Column values as column names and fill with another columns values

I have a SQL query where I’m calculating the number of players for a game based on UserType (only 1 counts as an actual player for the purposes of my calculation), and listing this info out for all games on a certain date: SELECT gm.[Id] ,SUM(CASE WHEN mbr.[UserType] = 1 THEN 1 ELSE 0 END)… Read More How to "catch all other values" FOR a column in a SQL PIVOT?

My dataframe is given below: code: df = Car measurements Before After amb_temp 30.268212 26.627491 engine_temp 41.812730 39.254255 engine_eff 15.963645 16.607557 avg_mile 0.700160 0.733307 cor_mile 0.761483 0.787538 Expected output: modified_df = index amb_temp_Before amb_temp_after engine_temp_Before engine_temp_after … 0 30.268212 26.627491 41.812730 39.254255 … Present output: print(df.pivot(columns=’index’,value=[‘Before’,’After’])) amb_temp engine_temp amb_temp 0 30.268212 NaN NaN NaN NaN… Read More Pandas convert mutil-row-column dataframe to single-row multi-column dataframe

I have a dataframe that looks something like Strike. Strike2 Price 15000. 15000. 100 15100. 15100. 150 15200. 15200. 170 I want to create a matrix like df such that the output looks like Strike. 15000. 15100. 15200. 15000. 0. 50. 120 15100. -50 0. 20 15200. -170. -20. 0 Its essentially looking to compute… Read More pivot table in pandas with a subtraction function on all possible combinations

I have a reference table that’s currently delimited with ‘-‘ that I’m needing to split the values out into multiple columns. Our version of SQL DB doesn’t support the string_split function. The first part of the script (multiple CTE) is returning the results into multiple rows, which I’m then wanting to pivot into columns. Is… Read More Assistance with PIVOT function

I want to pivot a table such that each row in the type column is now its own row. Each metric is then a row and the values are the intersection of the metric and type. There are a variable number of types and metrics.Example. I do not want to alter the values in any… Read More How to pivot without aggregation in python or sql

Sample Input: YearNum WeekNum DayNum Hours 2023 1 1 28.40 2023 1 3 33.09 2023 1 4 35.20 2023 1 5 32.77 2023 1 6 37.15 2023 1 7 40.18 2023 2 1 29.43 2023 2 3 19.43 2023 2 4 36.62 2023 2 5 34.81 2023 2 6 38.50 2023 2 7 41.98 2023… Read More Can you help me transpose this data from rows into columns?