How to remove NA data in only one columns?

In case of Python we can use subset to define column/columns and inplace true is to make the changes in DF:- rounds2.dropna(subset=['company_permalink'],inplace=True)

Use is.na() on the relevant vector of data you wish to look for and index using the negated result. For exmaple:

R> data[!is.na(data$A), ]
        date A  B
1 2014-01-01 2  3
2 2014-01-02 5 NA
4 2014-01-04 7 11
R> data[!is.na(data$B), ]
        date A  B
1 2014-01-01 2  3
4 2014-01-04 7 11

is.na() returns TRUE for every element that is NA and FALSE otherwise. To index the rows of the data frame, we can use this logical vector, but we want its converse. Hence we use ! to imply the opposite (TRUE becomes FALSE and vice versa).

You can restrict which columns you return by adding an index for the columns after the , in [ , ], e.g.

R> data[!is.na(data$A), 1:2]
        date A
1 2014-01-01 2
2 2014-01-02 5
4 2014-01-04 7

Every column in a data frame must have the same number of elements, that is why NAs come in handy in the first place...

What you can do is

df.a <- df[!is.na(df$A), -3]
df.b <- df[!is.na(df$B), -2]