Extracting specific selected columns to new DataFrame as a copy

There is a way of doing this and it actually looks similar to R

new = old[['A', 'C', 'D']].copy()

Here you are just selecting the columns you want from the original data frame and creating a variable for those. If you want to modify the new dataframe at all you'll probably want to use .copy() to avoid a SettingWithCopyWarning.

An alternative method is to use filter which will create a copy by default:

new = old.filter(['A','B','D'], axis=1)

Finally, depending on the number of columns in your original dataframe, it might be more succinct to express this using a drop (this will also create a copy by default):

new = old.drop('B', axis=1)

The easiest way is

new = old[['A','C','D']]

.

Another simpler way seems to be:

new = pd.DataFrame([old.A, old.B, old.C]).transpose()

where old.column_name will give you a series. Make a list of all the column-series you want to retain and pass it to the DataFrame constructor. We need to do a transpose to adjust the shape.

In [14]:pd.DataFrame([old.A, old.B, old.C]).transpose()Out[14]:    A   B    C0  4  10  1001  5  20   50