CODEWITHSUNDEEP
pythonnumpyscipy
cat
cat

Reputation: 91

filter by array numpy

I am trying to filter my ndarray by another array I have collected (with the same values)

My main ndarray looks like

[['Name' 'Col1' 'Count']
 ['test' '' '413']
 ['erd' ' ' '60']
 ..., 
 ['Td1' 'f' '904']
 ['Td2' 'K' '953']
 ['Td3' 'r' '111']]

I have another list with various matching names

names = ['Td1','test','erd']

What I'd Like to Do

I'd like to use the list names as a filter against the ndarray above?

What I've Tried

name_filter = main_ndarray[:,0] == names

This does not work

What I'd Expect

[['Name' 'Col1' 'Count']
 ['test' '' '413']
 ['erd' ' ' '60']
 ['Td1' 'f' '904']]

Upvotes: 3

Views: 2819

Answers (3)

Cleb
Cleb

Reputation: 25997

I would also go with @YXD's Pandas solution but just for the sake of completeness I also provide a simple solution based on list comprehension:

data = [['Name', 'Col1', 'Count'],
 ['test', '', '413'],
 ['erd', ' ', '60'],
 ['Td1', 'f', '904'],
 ['Td2', 'K', '953'],
 ['Td3', 'r', '111']]

names = ['Td1', 'test', 'erd']

# select all sublist of data
res = [l for l in data if l[0] in names]

# insert the first row of data
res.insert(0, data[0])

which then gives you the desired output:

[['Name', 'Col1', 'Count'],
 ['test', '', '413'],
 ['erd', ' ', '60'],
 ['Td1', 'f', '904']]

Upvotes: 1

YXD
YXD

Reputation: 32511

Consider using Pandas for this kind of data:

import pandas as pd

data = [['Name', 'Col1', 'Count'],
        ['test', '', '413'],
        ['erd', ' ', '60'],
        ['Td1', 'f', '904'],
        ['Td2', 'K', '953'],
        ['Td3', 'r', '111']]

df = pd.DataFrame(data[1:], columns=data[0])
names = ['Td1','test','erd']
result = df[df.Name.isin(names)]

Results:

>>> df
   Name Col1 Count
0  test        413
1   erd         60
2   Td1    f   904
3   Td2    K   953
4   Td3    r   111
>>> result
   Name Col1 Count
0  test        413
1   erd         60
2   Td1    f   904
>>>

References

Upvotes: 3

Nitish
Nitish

Reputation: 7116

You can use the filter function too. 

cats_array = numpy.array(
 [['Name' ,'Col1', 'Count'],
 ['test', '' ,'413'],
 ['erd' ,' ' ,'60'],
 ['Td1' ,'f' ,'904'],
 ['Td2' ,'K' ,'953'],
 ['Td3' ,'r', '111']]
 )

 names = ['Td1','test','erd']

 filter(lambda x: x[0] in names, cats_array)

gives:

[array(['test', '', '413'],
       dtype='|S5'), array(['erd', ' ', '60'],
       dtype='|S5'), array(['Td1', 'f', '904'],
       dtype='|S5')]

Upvotes: 1

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