I have a Pandas dataframe like the one below, where column A is a series of string values, and column B maintains a running total of the number of times the value in column A differs from the value of column A in the previous row.
A B
1 1
1 1
1b 2
1b 2
1b 2
1 3
Every time there is a change in the value of column A, I would like to duplicate the preceding row and assign it an incremented value of column B. For example, with the input dataframe as above, the output would look like:
A B
1 1
1 1
1 2
1b 2
1b 2
1b 2
1b 3
1 3
Any thoughts about how to go about this in an efficient way?
Filter last duplicated values by B, then shifting only B and assign back, remove last row and last join togehter by concat with sorting by index:
df1 = (df[df['B'].ne(df['B'].shift(-1))]
.assign(B = lambda x: x.B.shift(-1)).iloc[:-1].astype({'B':int}))
df = pd.concat([df, df1]).sort_index(ignore_index=True)
print (df)
A B
0 1 1
1 1 1
2 1 2
3 1b 2
4 1b 2
5 1b 2
6 1b 3
7 1 3
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