如何遍历两个数据框列并基于这两列中的值从列表中添加值

Question

I have a dataframe with three columns like this:

Subject{1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 4, ...} datetime{6/4/16 3:04:30, 6/5/16 6:02:15, ...} markers{}

It is sorted by subject then by datetime, and the markers column is empty

I also have a dictionary which maps subject numbers to lists of datetimes. These datetimes are not exactly the same as the ones already in the dataframe. I want to add all these datetimes to the markers column in their corresponding subject and date row for comparison purposes, so a dictionary with the key (subject) 1 with a list of values like {6/4/16 5:00:15, 6/5/16 6:10:30} would have its first value added to row 1 because the subject and date match and its second value added to row 2 for the same reason.

I thought of looping through each dictionary key and all it's corresponding datetimes, but then finding the corresponding row in the map for each datetime within the nested loops would be very inefficient. It would be something like this:

for subject in df.iloc[:, 0]:
    # go to subject in dictionary and loop through datetimes in corresponding list,
    # adding the matching datetime to the current row
    # O(n^2) time!

Is there a more efficient way to do this?

Thanks!

Answer 1

try this, you will have to customize the answer somewhat to meet your specific needs, but the logic is basically the same.

df = pd.DataFrame({'colA': [100,200],'colB': ['NaN','NaN']})
dict1 = {100: ['rat','cat','hat'], 200: ['hen','men','den']}
df = pd.concat([df['colA'],df['colA'].map(dict1).apply(pd.Series)], axis = 1)