Create a new column based on previous row value and delete the current row

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I have an input dataframe which can be generated from the code given below

  df = pd.DataFrame({'subjectID' :[1,1,2,2],'keys': 
  ['H1Date','H1','H2Date','H2'],'Values': 
  ['10/30/2006',4,'8/21/2006',6.4]})

The input dataframe looks like as shown below

enter image description here

This is what I did

s1 = df.set_index('subjectID').stack().reset_index()

s1.rename(columns={0:'values'}, 
             inplace=True)
d1 = s1[s1['level_1'].str.contains('Date')]
d2 = s1[~s1['level_1'].str.contains('Date')]

d1['g'] = d1.groupby('subjectID').cumcount()
d2['g'] = d2.groupby('subjectID').cumcount()

d3 = pd.merge(d1,d2,on=["subjectID", 'g'],how='left').drop(['g','level_1_x','level_1_y'], axis=1)

Though it works, I am afraid that this may not be the best approach. As we might have more than 200 columns and 50k RECORDS. Any help to improve my code further is very helpful.

I expect my output dataframe to look like as shown below

enter image description here

pandas dataframe for-loop python-3.x python
1 Answers

may be something like:

s=df.groupby(df['keys'].str.contains('Date').cumsum()).cumcount()+1

final=(df.assign(s=s.astype(str)).set_index(['subjectID','s']).
       unstack().sort_values(by='s',axis=1))
final.columns=final.columns.map(''.join)
print(final)

           keys1     Values1 keys2 Values2
subjectID                                  
1          H1Date  10/30/2006    H1       4
2          H2Date   8/21/2006    H2     6.4
user_98406370
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