Dataframe Manipulation Coping Rows and Removing Dates

[ashleysnl]

Hello,

I have a data frame that contains two dates in a single column. I would like to copy the column and split the dates. how can I achieve this. data set below.

What I have:

   Ticker          Date Added        Date Removed
0     TER  September 21, 2020   June 7, 2019 December 23, 2013
1     BMS        June 7, 2019    December 5, 2014
2      DD        June 3, 2019   September 1, 2017
3     DOW       April 2, 2019   September 1, 2017
4    MXIM    December 3, 2018  September 27, 2007

Notice how the first row has two dates in the Date Removed Column? I would like the to manipulate the data to be the below:

   Ticker          Date Added        Date Removed
0     TER  September 21, 2020   June 7, 2019
1     TER  September 21, 2020   December 23, 2013
2     BMS        June 7, 2019    December 5, 2014
3      DD        June 3, 2019   September 1, 2017
4     DOW       April 2, 2019   September 1, 2017
5    MXIM    December 3, 2018  September 27, 2007

I am currently doing most of my manipulation using Pandas. Any help would be greatly appreciated.

[nilamo]

Could you share a little code showing how the dataframe is generated? If I try to recreate it, my output is different from yours.

>>> data = [
... {"Ticker": "TER", "Date Added": "September 21, 2020", "Date Removed": ["June 7, 2019", "December 23, 2013"]},
... {"Ticker": "BMS", "Date Added": "June 7, 2019", "Date Removed": "December 5, 2014"},
... {"Ticker": "DD", "Date Added": "June 3, 2019", "Date Removed": "September 1, 2017"},
... {"Ticker": "DOW", "Date Added": "April 2, 2019", "Date Removed": "September 1, 2017"},
... {"Ticker": "MXIM", "Date Added": "December 3, 2018", "Date Removed": "September 27, 2017"},
... ]
>>> import pandas as pd
>>> df = pd.DataFrame(data)
>>> df
  Ticker          Date Added                       Date Removed
0    TER  September 21, 2020  [June 7, 2019, December 23, 2013]
1    BMS        June 7, 2019                   December 5, 2014
2     DD        June 3, 2019                  September 1, 2017
3    DOW       April 2, 2019                  September 1, 2017
4   MXIM    December 3, 2018                 September 27, 2017