avoid repeating the dataframe name when operating on pandas columns

Question

Very much a beginner question, sorry: is there a way to avoid repeating the dataframe name when operating on pandas columns?

In R, data.table allows to operate on a column without repeating the dataframe name like this

very_long_dt_name = data.table::data.table(col1=c(1,2,3),col2=c(3,3,1))

# operate on the columns without repeating the dt name:

very_long_dt_name[,ratio:=round(col1/col2,2)]

I couldn't figure out how to do it with pandas in Python so I keep repeating the df name:

data = {'col1': [1,2,3], 'col2': [3, 3, 1]}
very_long_df_name = pd.DataFrame(data)

# operate on the columns requires repeating the df name

very_long_df_name['ratio'] = np.round(very_long_df_name['col1']/very_long_df_name['col2'],2)

I'm sure there's a way to avoid it but I can't find anything on Google. Any hint please? Thank you.

Answer 1

Try assign:

very_long_df_name.assign(ratio=lambda x: np.round(x.col1/x.col2,2))

Output:

    col1    col2    ratio
0   1       3       0.33
1   2       3       0.67
2   3       1       3.00

---

Edit: to reflect comments, tests on 1 million rows:

%%timeit
very_long_df_name.assign(ratio = lambda x:x.col1/x.col2)
# 18.6 ms ± 506 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

and

%%timeit
very_long_df_name['ratio'] = very_long_df_name['col1']/very_long_df_name['col2']
# 13.3 ms ± 359 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

And with np.round, assign

%%timeit
very_long_df_name.assign(ratio = lambda x: np.round(x.col1/x.col2,2))
# 64.8 ms ± 958 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

and not-assign:

%%timeit
very_long_df_name['ratio'] = np.round(very_long_df_name['col1']/very_long_df_name['col2'],2)
# 55.8 ms ± 2.43 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

SO it appears that assign is vectorized, just not as well tuned.