如何将元素追加到DataFrame内部的列表中？ [英] How to append an element to a List inside a DataFrame?

问题描述

假设我有一个列表的数据框，

Suppose I have a DataFrame of lists,

 my_df = pd.DataFrame({'my_list':[[45,12,23],[20,46,78],[45,30,45]]})

会产生以下结果：

         my_list
0  [45, 12, 23]
1  [20, 46, 78]
2  [45, 30, 45]

我如何在每行 my_list 中添加一个元素，例如99吗？

How can I add an element, let's say 99, to my_list for each row ?

预期结果：

         my_list
0  [45, 12, 23, 99]
1  [20, 46, 78, 99]
2  [45, 30, 45, 99]

推荐答案

听起来很无聊，但直接遍历值-这样您就可以调用 append 并避免使用 + = 进行任何重新绑定，从而使处理速度大大提高。

Sounds awfully boring but just iterate over the values directly - this way you can call append and avoid whatever rebinding occurs with +=, making things significantly faster.

for val in my_df.my_list:
    val.append(99)

演示

>>> import timeit
>>> setup = '''
import pandas as pd; import numpy as np
df = pd.DataFrame({'my_list': np.random.randint(0, 100, (500, 500)).tolist()})
'''

>>> min(timeit.Timer('for val in df.my_list: val.append(90)', 
                     setup=setup).repeat(10, 1000))
0.05669815401779488

>>> min(timeit.Timer('df.my_list += [90]', 
                     setup=setup).repeat(10, 1000))
2.7741127769695595

---

当然，如果速度（或什至不是速度）对您很重要，您应该问自己是否确实需要在DataFrame中具有列表。考虑使用NumPy数组，直到需要Pandas实用程序并执行

---

Of course, if speed (or even if not speed) is important to you, you should question if you really need to have lists inside a DataFrame. Consider working on a NumPy array until you need Pandas utility and doing something like

np.c_[arr, np.full(arr.shape[0], 90)]

或至少将DataFrame中的列表拆分为单独的列并分配一个新列。

or at least splitting your lists inside the DataFrame to separate columns and assigning a new column .

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