Pandas, DataFrame unique values from few columns [duplicate]

Question

I am trying to count uniqiue values that are in few columns. My data frame looks like that:

Name      Name.1    Name.2    Name.3
x         z          c          y
y         p          q          x
q         p           a         y

Output should looks like below:

I used a groupby or count_values but couldn't get a correct output. Any ideas ? Thanks All !

you can simply stack and count i.e df.stack().value_counts() — Bharath M Shetty
– Bharath M Shetty, Commented Jan 27, 2019 at 13:21
Possible duplicate of Get total values_count from a dataframe with Python Pandas. Also contains timings which may be relevant for your object type DataFrame — ALollz
– ALollz, Commented Jan 27, 2019 at 17:01

kentwait · Accepted Answer · 2019-01-27 13:10:52Z

2

Seems you want to consider values regardless of their row or column location. In that case you should collapse the dataframe and just use Counter.

from collections import Counter

arr = np.array(df)
count = Counter(arr.reshape(arr.size))

answered Jan 27, 2019 at 13:05

kentwait

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7 Comments

It work's great. Thanks mate ! Do you have a idea how to show for instance 5 results that occurs the most often ?

sorted(count.items(), key=lambda x: x[1])

It returns empty list

Sorry which one? The sorted result or count?

You can do sorted(..., reverse=True)[:5] to get the top five by occurrence

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edesz · Accepted Answer · 2019-01-27 17:57:58Z

0

Another (Pandas-based) approach is to (Series) apply value_counts to multiple columns and then take the sum (column-wise)

df2 = df.apply(pd.Series.value_counts)
print(df2.sum(axis=1).astype(int)
a    1
c    1
p    2
q    2
x    2
y    3
z    1
dtype: int32

answered Jan 27, 2019 at 17:57

edesz

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