How to "Select Distinct" Across Multiple Data Frame Columns in Pandas?

I've tried different solutions. First was:

a_df=np.unique(df[['col1','col2']], axis=0)

and it works well for not object data Another way to do this and to avoid error (for object columns type) is to apply drop_duplicates()

a_df=df.drop_duplicates(['col1','col2'])[['col1','col2']]

You can also use SQL to do this, but it worked very slow in my case:

from pandasql import sqldf
q="""SELECT DISTINCT col1, col2 FROM df;"""
pysqldf = lambda q: sqldf(q, globals())
a_df = pysqldf(q)

To solve a similar problem, I'm using groupby:

print(f"Distinct entries: {len(df.groupby(['col1', 'col2']))}")

Whether that's appropriate will depend on what you want to do with the result, though (in my case, I just wanted the equivalent of COUNT DISTINCT as shown).

There is no unique method for a df, if the number of unique values for each column were the same then the following would work: df.apply(pd.Series.unique) but if not then you will get an error. Another approach would be to store the values in a dict which is keyed on the column name:

In [111]:
df = pd.DataFrame({'a':[0,1,2,2,4], 'b':[1,1,1,2,2]})
d={}
for col in df:
    d[col] = df[col].unique()
d

Out[111]:
{'a': array([0, 1, 2, 4], dtype=int64), 'b': array([1, 2], dtype=int64)}

I think use drop duplicate sometimes will not be so useful depending dataframe.

I found this:

[in] df['col_1'].unique()
[out] array(['A', 'B', 'C'], dtype=object)

And worked for me!

You can take the sets of the columns and just subtract the smaller set from the larger set:

distinct_values = set(df['a'])-set(df['b'])