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I have a dataframe with counts of different items, in different years:
df <- data.frame(item = rep(c('a','b','c'), 3), year = rep(c('2010','2011','2012'), each=3), count = c(1,4,6,3,8,3,5,7,9)) And I would like to add a "year.rank" column, which gives an item's rank within a given year, where a higher count leads to a higher "rank". With the above, it would look like:
item year count year.rank 1 a 2010 1 3 2 b 2010 4 2 3 c 2010 6 1 4 a 2011 3 2 5 b 2011 8 1 6 c 2011 3 3 7 a 2012 5 3 8 b 2012 7 2 9 c 2012 9 1 I know I could do this for the whole data frame using order(df$count), but I'm not sure how I would do it by year.
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Pandas DataFrame: rank() functionThe rank() function is used to compute numerical data ranks (1 through n) along axis. By default, equal values are assigned a rank that is the average of the ranks of those values. Index to direct ranking.
The ranking of a variable in an R data frame can be done by using rank function. For example, if we have a data frame df that contains column x then rank of values in x can be found as rank(df$x).
To assign new columns to a DataFrame, use the Pandas assign() method. The assign() returns the new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten. The length of the newly assigned column must match the number of rows in the DataFrame.
You can create a conditional DataFrame column by checking multiple columns using numpy. select() function. The select() function is more capable than the previous methods. We can use it to give a set of conditions and a set of values.
There is a rank function to help you with that:
transform(df, year.rank = ave(count, year, FUN = function(x) rank(-x, ties.method = "first"))) item year count year.rank 1 a 2010 1 3 2 b 2010 4 2 3 c 2010 6 1 4 a 2011 3 2 5 b 2011 8 1 6 c 2011 3 3 7 a 2012 5 3 8 b 2012 7 2 9 c 2012 9 1 data.table version for practice:
library(data.table) DT <- as.data.table(df) DT[,yrrank:=rank(-count,ties.method="first"),by=year] item year count yrrank 1: a 2010 1 3 2: b 2010 4 2 3: c 2010 6 1 4: a 2011 3 2 5: b 2011 8 1 6: c 2011 3 3 7: a 2012 5 3 8: b 2012 7 2 9: c 2012 9 1 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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