of 7 variables: #> $ model : chr "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive". # create a new object so that we don't overwrite our original `mycars` data mycars_base 'ame': 32 obs. (In RMarkdown we could of course access the objects created in R from Python via the r object, but lets stick to csv files to make this reproducible for all users.) Īs as final step lets write both ame’s mycars and recode_df from R to two separate csv files, so that we can load them easily into Python later on. of 7 variables: #> $ model: chr "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive". Next, we apply the three conditions mentioned above (see code comments) and assign this new data to mycars. ![]() We take the mtcars data set and create lookup ame called recode_df based on the information from the documentation This post concludes by looking at how we would tackle the same problem in Python’s ‘pandas’ library. It is interesting to see how the three large paradigms in R, base R, ‘data.table’ and ‘dplyr’ compare in handling this problem. The latter almost never contains all the columns names of our originial data set. Especially, since we often use short column names in the analysis and just rename them in the final step when creating a report. In real world settings however, there are many cases where we have to rename columns under one or more of the above conditions. Without those three conditions partially renaming columns is actually not a big deal.
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