How to append an element to a List inside a DataFrame?

Question

Suppose I have a DataFrame of lists,

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

which yields the following:

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

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

Expected result :

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

Answer 1

In [90]: my_df['my_list'] += [99]

In [91]: my_df
Out[91]:
            my_list
0  [45, 12, 23, 99]
1  [20, 46, 78, 99]
2  [45, 30, 45, 99]

Answer 2

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)

Demo

>>> 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

---

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)]

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