seth127 seth127 - 1 month ago 8
Python Question

check if pair of values is in pair of columns in pandas

Basically, I have latitude and longitude (on a grid) in two different columns. I am getting fed two-element lists (could be numpy arrays) of a new coordinate set and I want to check if it is a duplicate before I add it.

For example, my data:

df = pd.DataFrame([[4,8, 'wolf', 'Predator', 10],
[5,6,'cow', 'Prey', 10],
[8, 2, 'rabbit', 'Prey', 10],
[5, 3, 'rabbit', 'Prey', 10],
[3, 2, 'cow', 'Prey', 10],
[7, 5, 'rabbit', 'Prey', 10]],
columns = ['lat', 'long', 'name', 'kingdom', 'energy'])

newcoords1 = [4,4]
newcoords2 = [7,5]

Is it possible to write one
statement to tell me whether there is already a row with that latitude and longitude. In pseudo code:

if newcoords1 in df['lat', 'long']:
print('yes! ' + str(newcoords1))

(In the example,
should be
should be

(newcoords1[0] in df['lat']) & (newcoords1[1] in df['long'])
doesn't work because that checks them independently, but I need to know if that combination appears in a single row.

Thank you in advance!


you can do it this way:

In [140]: df.query('@newcoords2[0] == lat and @newcoords2[1] == long')
   lat  long    name kingdom  energy
5    7     5  rabbit    Prey      10

In [146]: df.query('@newcoords2[0] == lat and @newcoords2[1] == long').empty
Out[146]: False

the following line will return a number of found rows:

In [147]: df.query('@newcoords2[0] == lat and @newcoords2[1] == long').shape[0]
Out[147]: 1

or using NumPy approach:

In [103]: df[(df[['lat','long']].values == newcoords2).all(axis=1)]
   lat  long    name kingdom  energy
5    7     5  rabbit    Prey      10

this will show whether at least one row has been found:

In [113]: (df[['lat','long']].values == newcoords2).all(axis=1).any()
Out[113]: True

In [114]: (df[['lat','long']].values == newcoords1).all(axis=1).any()
Out[114]: False


In [104]: df[['lat','long']].values == newcoords2
array([[False, False],
       [False, False],
       [False, False],
       [False, False],
       [False, False],
       [ True,  True]], dtype=bool)

In [105]: (df[['lat','long']].values == newcoords2).all(axis=1)
Out[105]: array([False, False, False, False, False,  True], dtype=bool)