同时导入 Pandas 和 Numpy？

Question

For years I've used Pandas on a daily basis and often (but not nearly as frequently) use Numpy. Most of the time I'll do something like:

import pandas as pd
import numpy as np

But there is also the option of using Numpy directly from Pandas:

df['value'] = pd.np.where(df['date'] > '2020-01-01', 1, 0)

Does anyone know if either one of these options is significantly more performant than the other?

Answer 1

Using pd.np is deprecated:

<ipython-input-631-4160e33c868a>:1: FutureWarning: The pandas.np module is
 deprecated and will be removed from pandas in a future version. 
Import numpy directly instead

You can check this is the same module:

https://github.com/pandas-dev/pandas/blob/master/pandas/__init__.py#L205-L216

Answer 2

Both are importing the same library. There should not be any performance differences. It is just an alias for the same code.

Eg np.array is preferable over pd.np.array because it saves you three characters to type.