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Where can I find information on the differences between calling on a column within a data.frame via:
df <- data.frame(x=1:20,y=letters[1:20],z=20:1)
df$x
df["x"]
They both return the "same" results, but not necessarily in the same format. Another thing that I've noticed is that df$x returns a list. Whereas df["x"] returns a data.frame.
EDIT: However, knowing which one to use in which situation has become a challenge. Is there a best practice here or does it really come down to knowing what the command or function requires? So far I've just been cycling through them if my function doesn't work at first (trial and error).
713
When you write df["] you are basically accessing a set of number values, but when you use df[["]] you are getting a DataFrame object which is compatible with your code.
A data frame is used for storing data tables. It is a list of vectors of equal length. For example, the following variable df is a data frame containing three vectors n, s, b.
for setting, values, you need to use df['column'] = series . i think the subtle difference here is that when you set something with df['column'] = ser , pandas goes ahead and adds it to the columns/does some other stuff (i believe by overriding the functionality in __setitem__ . if you do df.
1. Tibble displays data along with data type while displaying whereas data frame does not. 2. Tibble fetches data using data source in its original form instead of data frame such factors, characters or numeric.
Another difference is that df$w returns NULL and df['w'] or df[['w']] gives an error with your example dataframe.
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answered Sep 22 '22 15:09
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