Similar to this question How to add an empty column to a dataframe?, I am interested in knowing the best way to add a column of empty lists to a DataFrame.
What I am trying to do is basically initialize a column and as I iterate over the rows to process some of them, then add a filled list in this new column to replace the initialized value.
For example, if below is my initial DataFrame:
df = pd.DataFrame(d = {'a': [1,2,3], 'b': [5,6,7]}) # Sample DataFrame
>>> df
a b
0 1 5
1 2 6
2 3 7
Then I want to ultimately end up with something like this, where each row has been processed separately (sample results shown):
>>> df
a b c
0 1 5 [5, 6]
1 2 6 [9, 0]
2 3 7 [1, 2, 3]
Of course, if I try to initialize like df['e'] = [] as I would with any other constant, it thinks I am trying to add a sequence of items with length 0, and hence fails.
If I try initializing a new column as None or NaN, I run in to the following issues when trying to assign a list to a location.
df['d'] = None
>>> df
a b d
0 1 5 None
1 2 6 None
2 3 7 None
Issue 1 (it would be perfect if I can get this approach to work! Maybe something trivial I am missing):
>>> df.loc[0,'d'] = [1,3]
...
ValueError: Must have equal len keys and value when setting with an iterable
Issue 2 (this one works, but not without a warning because it is not guaranteed to work as intended):
>>> df['d'][0] = [1,3]
C:\Python27\Scripts\ipython:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
Hence I resort to initializing with empty lists and extending them as needed. There are a couple of methods I can think of to initialize this way, but is there a more straightforward way?
Method 1:
df['empty_lists1'] = [list() for x in range(len(df.index))]
>>> df
a b empty_lists1
0 1 5 []
1 2 6 []
2 3 7 []
Method 2:
df['empty_lists2'] = df.apply(lambda x: [], axis=1)
>>> df
a b empty_lists1 empty_lists2
0 1 5 [] []
1 2 6 [] []
2 3 7 [] []
Summary of questions:
Is there any minor syntax change that can be addressed in Issue 1 that can allow a list to be assigned to a None/NaN initialized field?
If not, then what is the best way to initialize a new column with empty lists?
One more way is to use np.empty:
df['empty_list'] = np.empty((len(df), 0)).tolist()
You could also knock off .index in your "Method 1" when trying to find len of df.
df['empty_list'] = [[] for _ in range(len(df))]
Turns out, np.empty is faster...
In [1]: import pandas as pd
In [2]: df = pd.DataFrame(pd.np.random.rand(1000000, 5))
In [3]: timeit df['empty1'] = pd.np.empty((len(df), 0)).tolist()
10 loops, best of 3: 127 ms per loop
In [4]: timeit df['empty2'] = [[] for _ in range(len(df))]
10 loops, best of 3: 193 ms per loop
In [5]: timeit df['empty3'] = df.apply(lambda x: [], axis=1)
1 loops, best of 3: 5.89 s per loop