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I have a pandas dataframe with duplicate ids. Below is my dataframe
id nbr type count
7 21 High 4
7 21 Low 6
8 39 High 2
8 39 Low 3
9 13 High 5
9 13 Low 7
How to delete only the rows having the type Low
520
Remove All Duplicate Rows from Pandas DataFrame You can set 'keep=False' in the drop_duplicates() function to remove all the duplicate rows. For E.x, df. drop_duplicates(keep=False) .
How do I delete all alternate rows? Select all the filtered cells in the helper column (excluding the header) Right-click on any of the selected cells and click on 'Delete Row' In the dialog box that opens, click on OK. This will delete all the visible records and you will only see the header row as of now.
To select the rows, the syntax is df. loc[start:stop:step] ; where start is the name of the first-row label to take, stop is the name of the last row label to take, and step as the number of indices to advance after each extraction; for example, you can use it to select alternate rows.
The drop_duplicates() method removes duplicate rows. Use the subset parameter if only some specified columns should be considered when looking for duplicates.
You can also just slice your df using iloc:
df.iloc[::2]
This will step every 2 rows
142
Another possible solution is to use drop_duplicates
df = df.drop_duplicates('nbr')
print(df)
id nbr type count
0 7 21 High 4
2 8 39 High 2
4 9 13 High 5
You can also do:
df.drop_duplicates('nbr', inplace=True)
That way you don't have to reassign it.
34
You can try this way :
df = df[df.type != "Low"]
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