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I have a DataFrame that has duplicated rows. I'd like to get a DataFrame with a unique index and no duplicates. It's ok to discard the duplicated values. Is this possible? Would it be a done by groupby?
768
The unique function in pandas is used to find the unique values from a series. A series is a single column of a data frame. We can use the unique function on any possible set of elements in Python. It can be used on a series of strings, integers, tuples, or mixed elements.
unique. Return unique values based on a hash table. Uniques are returned in order of appearance.
And you can use the following syntax to select unique rows across specific columns in a pandas DataFrame: df = df. drop_duplicates(subset=['col1', 'col2', ...])
You can use the pandas unique() function to get the different unique values present in a column. It returns a numpy array of the unique values in the column.
In [29]: df.drop_duplicates()
Out[29]:
b c
1 2 3
3 4 0
7 5 9
Figured out one way to do it by reading the split-apply-combine documentation examples.
df = pandas.DataFrame({'b':[2,2,4,5], 'c': [3,3,0,9]}, index=[1,1,3,7])
df_unique = df.groupby(level=0).first()
df
b c
1 2 3
1 2 3
3 4 0
7 5 9
df_unique
b c
1 2 3
3 4 0
7 5 9
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