How to write to the last occurrence of loc?
Question:
I have a dataFrame with date column, and sometimes the date might appear twice.
When I write to a certain date, I would like to write to the last row that have this date, not the first.
Right now I use:
df.loc[df['date'] == date, columnA] = value
Which in the case of a df like this will write at index 1, not 2:
date columnA
0 17.4.2022
1 17.5.2022
2 17.5.2022 value #in the case of 17.5 write the data to this row.
3 17.6.2022
How to make sure I am writing to the last date all the time, and if there is one, so write into that one?
Answers:
You can chain mask for last duplicated date value by Series.duplicated:
print (df)
date columnA
0 17.4.2022 8
1 17.5.2022 1
2 17.5.2022 1
2 17.5.2022 1
3 17.6.2022 3
date = '17.5.2022'
df.loc[(df['date'] == date) & ~df['date'].duplicated(keep='last'), 'columnA'] = 100
print (df)
date columnA
0 17.4.2022 8
1 17.5.2022 1
2 17.5.2022 1
2 17.5.2022 100
3 17.6.2022 3
I have a dataFrame with date column, and sometimes the date might appear twice.
When I write to a certain date, I would like to write to the last row that have this date, not the first.
Right now I use:
df.loc[df['date'] == date, columnA] = value
Which in the case of a df like this will write at index 1, not 2:
date columnA
0 17.4.2022
1 17.5.2022
2 17.5.2022 value #in the case of 17.5 write the data to this row.
3 17.6.2022
How to make sure I am writing to the last date all the time, and if there is one, so write into that one?
You can chain mask for last duplicated date value by Series.duplicated:
print (df)
date columnA
0 17.4.2022 8
1 17.5.2022 1
2 17.5.2022 1
2 17.5.2022 1
3 17.6.2022 3
date = '17.5.2022'
df.loc[(df['date'] == date) & ~df['date'].duplicated(keep='last'), 'columnA'] = 100
print (df)
date columnA
0 17.4.2022 8
1 17.5.2022 1
2 17.5.2022 1
2 17.5.2022 100
3 17.6.2022 3