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When I try to remove the last row from a single column data frame, I get a vector back instead of a data frame:
> df = data.frame(a=1:10)
> df
a
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
> df[-(length(df[,1])),]
[1] 1 2 3 4 5 6 7 8 9
The behavior I'm looking for is what happens when I use this command on a two-column data frame:
> df = data.frame(a=1:10,b=11:20)
> df
a b
1 1 11
2 2 12
3 3 13
4 4 14
5 5 15
6 6 16
7 7 17
8 8 18
9 9 19
10 10 20
> df[-(length(df[,1])),]
a b
1 1 11
2 2 12
3 3 13
4 4 14
5 5 15
6 6 16
7 7 17
8 8 18
9 9 19
My code is general, and I don't know a priori whether the data frame will contain one or many columns. Is there an easy workaround for this problem that will let me remove the last row no matter how many columns exist?
891
To drop a row or column in a dataframe, you need to use the drop() method available in the dataframe. You can read more about the drop() method in the docs here. Rows are labelled using the index number starting with 0, by default. Columns are labelled using names.
Use drop() method to delete rows based on column value in pandas DataFrame, as part of the data cleansing, you would be required to drop rows from the DataFrame when a column value matches with a static value or on another column value.
To delete rows and columns from DataFrames, Pandas uses the “drop” function. To delete a column, or multiple columns, use the name of the column(s), and specify the “axis” as 1. Alternatively, as in the example below, the 'columns' parameter has been added in Pandas which cuts out the need for 'axis'.
To remove rows with an in R we can use the na. omit() and <code>drop_na()</code> (tidyr) functions. For example, na. omit(YourDataframe) will drop all rows with an.
Try adding the drop = FALSE option:
R> df[-(length(df[,1])), , drop = FALSE]
a
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
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