pandas unique values multiple columns


df = pd.DataFrame({'Col1': ['Bob', 'Joe', 'Bill', 'Mary', 'Joe'],
                   'Col2': ['Joe', 'Steve', 'Bob', 'Bob', 'Steve'],
                   'Col3': np.random.random(5)})

What is the best way to return the unique values of ‘Col1’ and ‘Col2’?

The desired output is

'Bob', 'Joe', 'Bill', 'Mary', 'Steve'
Asked By: user2333196



Non-pandas solution: using set().

import pandas as pd
import numpy as np

df = pd.DataFrame({'Col1' : ['Bob', 'Joe', 'Bill', 'Mary', 'Joe'],
              'Col2' : ['Joe', 'Steve', 'Bob', 'Bob', 'Steve'],
               'Col3' : np.random.random(5)})

print df

print set(df.Col1.append(df.Col2).values)


   Col1   Col2      Col3
0   Bob    Joe  0.201079
1   Joe  Steve  0.703279
2  Bill    Bob  0.722724
3  Mary    Bob  0.093912
4   Joe  Steve  0.766027
set(['Steve', 'Bob', 'Bill', 'Joe', 'Mary'])
Answered By: WGS

I have setup a DataFrame with a few simple strings in it’s columns:

>>> df
   a  b
0  a  g
1  b  h
2  d  a
3  e  e

You can concatenate the columns you are interested in and call unique function:

>>> pandas.concat([df['a'], df['b']]).unique()
array(['a', 'b', 'd', 'e', 'g', 'h'], dtype=object)
Answered By: Mike
In [5]: set(df.Col1).union(set(df.Col2))
Out[5]: {'Bill', 'Bob', 'Joe', 'Mary', 'Steve'}


set(df.Col1) | set(df.Col2)
Answered By: James Little

pd.unique returns the unique values from an input array, or DataFrame column or index.

The input to this function needs to be one-dimensional, so multiple columns will need to be combined. The simplest way is to select the columns you want and then view the values in a flattened NumPy array. The whole operation looks like this:

>>> pd.unique(df[['Col1', 'Col2']].values.ravel('K'))
array(['Bob', 'Joe', 'Bill', 'Mary', 'Steve'], dtype=object)

Note that ravel() is an array method that returns a view (if possible) of a multidimensional array. The argument 'K' tells the method to flatten the array in the order the elements are stored in the memory (pandas typically stores underlying arrays in Fortran-contiguous order; columns before rows). This can be significantly faster than using the method’s default ‘C’ order.

An alternative way is to select the columns and pass them to np.unique:

>>> np.unique(df[['Col1', 'Col2']].values)
array(['Bill', 'Bob', 'Joe', 'Mary', 'Steve'], dtype=object)

There is no need to use ravel() here as the method handles multidimensional arrays. Even so, this is likely to be slower than pd.unique as it uses a sort-based algorithm rather than a hashtable to identify unique values.

The difference in speed is significant for larger DataFrames (especially if there are only a handful of unique values):

>>> df1 = pd.concat([df]*100000, ignore_index=True) # DataFrame with 500000 rows
>>> %timeit np.unique(df1[['Col1', 'Col2']].values)
1 loop, best of 3: 1.12 s per loop

>>> %timeit pd.unique(df1[['Col1', 'Col2']].values.ravel('K'))
10 loops, best of 3: 38.9 ms per loop

>>> %timeit pd.unique(df1[['Col1', 'Col2']].values.ravel()) # ravel using C order
10 loops, best of 3: 49.9 ms per loop
Answered By: Alex Riley

An updated solution using numpy v1.13+ requires specifying the axis in np.unique if using multiple columns, otherwise the array is implicitly flattened.

import numpy as np

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

This change was introduced Nov 2016:

Answered By: erikreed

for those of us that love all things pandas, apply, and of course lambda functions:

df['Col3'] = df[['Col1', 'Col2']].apply(lambda x: ''.join(x), axis=1)
Answered By: Lisle
list(set(df[['Col1', 'Col2']].as_matrix().reshape((1,-1)).tolist()[0]))

The output will be
[‘Mary’, ‘Joe’, ‘Steve’, ‘Bob’, ‘Bill’]

Answered By: smishra

here’s another way

import numpy as np
Answered By: muon

Get a list of unique values given a list of column names:

cols = ['col1','col2','col3','col4']
unique_l = pd.concat([df[col] for col in cols]).unique()
Answered By: BSalita
import pandas as pd
df= pd.DataFrame({'col1':["a","a","b","c","c","d"],'col2': 

output is

  col1 col2 col3
0   a   x   1
1   a   x   2
2   b   y   2
3   c   y   3
4   c   z   4
5   d   w   2

to get the unique values from all the columns

    for i in range(df.shape[1]) :
        a[j] = df.iloc[:,i].unique()

   for p,q in a.items():
       print( f"unique value in {p} are {list(q)} ")

ouput is

    unique value in col1 are ['a', 'b', 'c', 'd'] 
    unique value in col2 are ['x', 'y', 'z', 'w'] 
    unique value in col3 are [1, 2, 3, 4] 
Answered By: S abaranji
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