I know two ways of adding a new column to pandas dataframe
df_new = df.assign(new_column=default_value) and
df[new_column] = default_value The first one does not add columns inplace, but the second one does. So, which one is more efficient to use?
Apart from these two is there is any all the more efficient method than these?
I think second one, assign is used if want nice code witch chaining all functions - one line code:
df = pd.DataFrame({'A':np.random.rand(10000)}) default_value = 10 In [114]: %timeit df_new = df.assign(new_column=default_value) 228 µs ± 4.26 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) In [115]: %timeit df['new_column'] = default_value 86.1 µs ± 654 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) I use perfplot for ploting:
import perfplot default_value = 10 def chained(df): df = df.assign(new_column=default_value) return df def no_chained(df): df['new_column'] = default_value return df def make_df(n): df = pd.DataFrame({'A':np.random.rand(n)}) return df perfplot.show( setup=make_df, kernels=[chained, no_chained], n_range=[2**k for k in range(2, 25)], logx=True, logy=True, equality_check=False, xlabel='len(df)') 