Numpy remove unique rows based on column

Question

I am trying to get an array where it would remove all unique rows based on the first column. My array works looks like this,

[['Aaple' 'Red']
 ['Aaple' '0.0']
 ['Banana' 'Yellow']
 ['Banana' '0.0']
 ['Orange' 'Orange']
 ['Pear' 'Yellow']
 ['Pear' '0.0']
 ['Strawberry' 'Red']]

I want it to look like this,

[['Aaple' 'Red']
 ['Aaple' '0.0']
 ['Banana' 'Yellow']
 ['Banana' '0.0']
 ['Pear' 'Yellow']
 ['Pear' '0.0']]

Where it would remove the unique values from column one. My current code looks like this,

arr = np.array(["Aaple", "Pear", "Banana"])

arr2 = np.array([["Strawberry", "Red"], ["Aaple", "Red"], ["Orange", "Orange"], ["Pear", "Yellow"], ["Banana", "Yellow"]])


arr = arr.reshape(-1,1)
zero_arr = np.zeros((len(arr), 1))
arr = np.column_stack((arr, zero_arr))
combine = np.vstack((arr2, arr))
sort = combine[combine[:,0].argsort()]
#Where the first array printed is sort

I was able to get ['Aaple' 'Banana' 'Pear'], the rows I want to keep by adding x = sort[:-1][sort[1:] == sort[:-1]], what would be the next steps?

Stef · Accepted Answer · 2020-05-30 21:13:33Z

1

It may be easier to use pandas:

df = pd.DataFrame(sort, columns=list('ab'))
df[df.groupby('a').a.transform('count')>1].values

Result:

array([['Aaple', 'Red'],
       ['Aaple', '0.0'],
       ['Banana', 'Yellow'],
       ['Banana', '0.0'],
       ['Pear', 'Yellow'],
       ['Pear', '0.0']], dtype=object)

answered May 30, 2020 at 21:13

Stef

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2 Comments

Awitz Over a year ago

I am trying to do this all in Numpy, I have a large dataset and using pandas takes too long, is it not possible using Numpy? @stef

Stef Over a year ago

@AndrewHorowitz it's probably possible doing it in pure numpy as pandas uses numpy under the hood, but I don't think that it'll so much faster than pandas as the involved operations here are being carried out on numpy arrays (maybe I'm wrong)

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