熊猫数据框将功能应用于整个列

Question

I have some functions that act on a list and return a list. I would like to create a column on a Pandas Dataframe such that the new column is the list returned by one of the functions acting on some other column of the dataframe.

In python-like pseudocode:

def function(parameter, list):
    ...
    return output_list

df['New Column'] = function(parameter, df['Old Column'])

I have tried different options including something like the code above, using .apply() method and others... with no success.

Is there a way to do this? Thank you!

EDIT: See Brian Pendleton's answer for the solution. Columns in a dataframe are pandas' Series objects. Just have to create a Series out of the desired list.

df['New_Column'] = pd.Series(data=function(parameter,list))

Answer 1

If you're sending df["old column"] to the function, then you're sending a pandas.Series object. Why not just operate with that series and return a new series of the same shape. Then you can just use your assignment to new column as you have it.

Answer 2

I think that you want to:

• apply a function that return a list
• store the result in a DataFrame
• split the list into different column

Here is an example.

def my_funct(parameter):
return (1,2,3) + parameter

df = pd.DataFrame(np.random.randint(low=1, high=10, size=3), columns=['my_funct'])
#df['New Column'] = function(parameter, df['Old Column'])
df['my_funct'] = df['my_funct'].apply(lambda x: my_funct(x))

# After the function call the resulting lists are stored in one column

       my_funct
0     [5, 6, 7]
1  [10, 11, 12]
2     [6, 7, 8]

# Here is how to split the list into several columns

df = df['my_funct'].apply(pd.Series)

    0   1   2
0   5   6   7
1  10  11  12
2   6   7   8