what is the most elegant way to create a new dataframe from an existing dataframe, by 1. selecting only certain columns and 2. renaming them at the same time?
For instance I have the following dataframe, where I want to pick column B, D and F and rename them into X, Y, Z
base dataframe
A B C D E F
1 2 3 4 5 6
1 2 3 4 5 6
new dataframe
X Y Z
2 4 6
2 4 6
You can select and rename the columns in one line
df2=df[['B','D','F']].rename({'B':'X','D':'Y','F':'Z'}, axis=1)
Slightly more general selection of every other column:
df = pd.DataFrame({'A':[1,2,3], 'B':[4,5,6],
'C':[7,8,9], 'D':[10,11,12]})
df_half = df.iloc[:, ::2]
with df_half being:
A C
0 1 7
1 2 8
2 3 9
You can then use the rename method mentioned in the answer by @G. Anderson or directly assign to the columns:
df_half.columns = ['X','Y']
returning:
X Y
0 1 7
1 2 8
2 3 9
df.rename()to rename a certain subset of columns and then simultaneously index with the keys. Sod = {"B": "X", "D": "Y", "F": "Z"}and then simply do something likenew = df[list(d.keys())].rename(columns=d)