Python: Binning based on 2 columns in Pandas

Question

Looking for a quick and elegant way to bin based on 2 columns in Pandas.

Here's my data frame

                              filename  height   width
0        shopfronts_23092017_3_285.jpg   750.0   560.0
1                   shopfronts_200.jpg   4395.0  6020.0
2  shopfronts_25092017_eateries_98.jpg   414.0   621.0
3                   shopfronts_101.jpg   480.0   640.0
4                   shopfronts_138.jpg   3733.0  8498.0
5  shopfronts_25092017_eateries_95.jpg   187.0   250.0
6      shopfronts_25092017_neon_33.jpg   100.0   200.0
7                   shopfronts_322.jpg   682.0  1024.0
8                   shopfronts_171.jpg   800.0   600.0
9         shopfronts_23092017_3_35.jpg   120.0   210.0

I need to bin the records based on 2 columns height & width (image resolutions)

I'm looking for something like this

                              filename  height   width    group
0        shopfronts_23092017_3_285.jpg   750.0   560.0       g3 
1                   shopfronts_200.jpg   4395.0  6020.0      g4  
2  shopfronts_25092017_eateries_98.jpg   414.0   621.0   others
3                   shopfronts_101.jpg   480.0   640.0   others
4                   shopfronts_138.jpg   3733.0  8498.0      g4
5  shopfronts_25092017_eateries_95.jpg   187.0   250.0       g1
6      shopfronts_25092017_neon_33.jpg   100.0   200.0       g1
7                   shopfronts_322.jpg   682.0  1024.0   others
8                   shopfronts_171.jpg   800.0   600.0       g3
9         shopfronts_23092017_3_35.jpg   120.0   210.0       g1

where 

g1: <= 400x300]
g2: (400x300, 640x480]
g3: (640x480, 800x600]
g4: > 800x600
others: If they don't comply to the requirement (Ex: records 7,2,3 - either height or width will fall in the categories defined but not both)

Looking to get the frequency count using group column. If this is not the best way to go about it and if there is a better way, kindly let me know.

Answer 1

You can use dual pd.cut i.e

bins = [0,400,640,800,np.inf]
df['group'] = pd.cut(df['height'].values, bins,labels=["g1","g2","g3",'g4'])

nbin = [0,300,480,600,np.inf]
t = pd.cut(df['width'].values, nbin,labels=["g1","g2","g3",'g4'])

df['group'] =np.where(df['group'] == t,df['group'],'others')

                              filename  height   width  group
0        shopfronts_23092017_3_285.jpg   750.0   560.0      g3
1                   shopfronts_200.jpg  4395.0  6020.0      g4
2  shopfronts_25092017_eateries_98.jpg   414.0   621.0  others
3                   shopfronts_101.jpg   480.0   640.0  others
4                   shopfronts_138.jpg  3733.0  8498.0      g4
5  shopfronts_25092017_eateries_95.jpg   187.0   250.0      g1
6      shopfronts_25092017_neon_33.jpg   100.0   200.0      g1
7                   shopfronts_322.jpg   682.0  1024.0  others
8                   shopfronts_171.jpg   800.0   600.0      g3
9         shopfronts_23092017_3_35.jpg   120.0   210.0      g1