transpose

This question already has an answer here: How to pivot Spark DataFrame? 9 answers I want to transpose a dataframe. This is just a small excerpt from my original dataframe - from pyspark.sql.functions import to_timestamp, date_format valuesCol = [('22','ABC Ltd','U.K.','class 1',102),('22','ABC Ltd','U.K.','class 2',73),('22','ABC Ltd','U.K.','class 3',92), ('51','Eric AB','Sweden','class 1',52),('51','Eric AB','Sweden','class 2',34),('51','Eric AB','Sweden','class 3',11)] df = sqlContext.createDataFrame(valuesCol,['ID','Firm','Country','Class','Revenue']) df.show() +---+-------+-------+-------

I have a data set in SAS that I need to transpose. It has the form id date type value and I need to convert it into id date valueoftype1 valueoftype2 ... Is there any efficient way of accomplishing this? My data is huuuge. For example; data one; input ID date type $ value; cards; 1 2001 A 2 1 2002 A 4 1 2001 B 3 2 2001 B 1 2 2002 A 5 2 2002 C 2 2 2003 C 5 3 2001 B 6 4 2002 B 8 4 2003 B 4 4 2001 A 2 ; I wish to convert it in to following form; (last three columns are valA, valB, valC) 1 2001 2 3 . 1 2002 4 . . 2 2001 . 1 . 2 2002 5 . 2 2 2003 . . 5 3 2001 . 6 . 4 2001 2 . . 4 2002 . 8 . 4 2003

I have a dataframe (simplified) that looks like this: Age, Kodiak, Banana, Banff, Montreal, Fairfax Age_1, 5, 6 , 7, 9, 2 Age_2, 7, 6, 4, 3, 2 Age_3, 5, 3, 8, 5, 9 I would like my dataframe to look more like this: Location, Age_1, Age_2, Age_3 Kodiak, 5, 7, 5 Banana, 6, 6, 3 Banff, 7, 4, 8 Montreal, 9, 3, 5 Fairfax, 2, 2, 9 I've looked at the transpose function but I'm a bit confused on how to switch both the columns and rows. I'm very new to R. There are two ways you can do this. The first one is using t to just transpose the dataframe as if it would be a matrix (indeed the result of t is a

Simply put I am trying to take a single column query result and output it into a 5 wide by × long table. This is how the main table is organized. On separate tabs, I want to list all of the caught and seen Pokemon on their own for easy search. While I can get it to output something like this with =query(NatDex, "Select C Where F <> ''",1) I would like it to output the data something like this for easy reading so it's not eventually 100+ entries long: Bonus points if you can give me formula/something to do it where I can vary how wide the second table is. But this is far less important to me. I

For A model in Excel I would like to copy and paste data from one sheet in a workbook to the other sheet in the workbook and transpose this data with the usage of a for next loop. Data from the workfile CME should be pasted in the workfile of RME. The total lines of data is 2420. However, since the ranges in the copy and paste different workbook are different and step sizes are different, I assume that i should use two different variables i and j. The first two lines of code is the manual way to do it without a loop When I try to run this code, it will give me an error. the first to codes are

I have a dataframe that has recurring columns (the interval is 5). my dataframe at the moment So this is how it looks: I have 5 type of columns and they repeat time over time. The recurring columns have a suffix in their name, this can be removed/renamed as well, so that they would all match. What I would like to do is to transpose these recurring columns to rows, so that I would have only 5 columns in the end (Dates, PX_LAST, PX_HIGH, PX_VOLUME, Name). Then I would be able to group the dataframe by Dates, Name etc and do many other things. I tried some manipulations with pipe operator %>%,

Please help me put the cells with a class that corresponds to the column header, in the appropriate column. The iteration should be done per column and then loop through the table.temp TD array and replace the first empty cell found with the first temp td found. The end result should look similar to the table found here . var $tempScanner = $('table.temp tr td'); var tempArry = []; $tempScanner.each(function() { tempArry.push($(this)); }); tempArry = tempArry.sort(); td, th { border: 1px solid #111; padding: 6px; } th { font-weight: 700; } span.pull-right { float: right; text-align: right; }

I have this datasate now: animal age count dogs 1 49 2 134 3 147 4 154 cats 1 189 2 254 3 259 4 261 I would like to convert age column to 4 age columns for each age: animal age1 age2 age3 age4 dogs 49 134 147 154 cats .................... I have tried df.T and df.transpose() but both of them return my original column. You could use pd.pivot : In [25]: result = df.pivot(index='animal', columns='age', values='count') In [26]: result Out[26]: age 1 2 3 4 animal cats 189 254 259 261 dogs 49 134 147 154 In [27]: result.columns = ['age{:d}'.format(col) for col in result.columns] In [28]: result Out