finding duplicates and adding ID as attribute pandas-CodePudding

I'm working in geopandas with a large number (around 4.5 million) objects, where each has a unique ID number ('PARCEL_SPI') and also another code ('PC_PLANNO').

What I would like to do is write some code that, for each object, finds all other objects with the same PLANNO and adds their ID number as a list in a new attribute, say 'Same_code' for the object. The df is called spine_copy.

Here's a quick sample of what I have:

PARCEL_SPI	PC_PLANNO
23908	LP12345
90435	LP12345
329048	LP90803
6409	LP2399
34534	LP90803
092824	LP12345

and what I want out:

PARCEL_SPI	PC_PLANNO	Same_code
23908	LP12345	[90435, 092824]
90435	LP12345	[23908,092824]
329048	LP90803	34534
6409	LP2399	None
34534	LP90803	329048
092824	LP12345	[23908, 90435]

I'm not too sure how to do this, but here's my attempt using groupby:

spine_copy.groupby('PC_PLANNO')['PARCEL_SPI'].apply(list)

However, this doesn't add the list as a new attribute for each object, and I'm unsure how to do this.

Thanks in advance!

CodePudding user response：

Here converting to list is not necessary - filter duplciated rows by Series.duplicated and for it use GroupBy.transform with invert mask passed to numpy.where:

m = spine_copy['PC_PLANNO'].duplicated(keep=False)
s = spine_copy.groupby('PC_PLANNO')['PARCEL_SPI'].transform(lambda x: x.to_numpy()[::-1])
spine_copy['Same_code'] = np.where(m, s, None)
print (spine_copy)
   PARCEL_SPI PC_PLANNO Same_code
0       23908   LP12345     90435
1       90435   LP12345     23908
2      329048   LP90803     34534
3        6409    LP2399      None
4       34534   LP90803    329048

EDIT: with new data:

m = spine_copy['PC_PLANNO'].duplicated(keep=False)

new = spine_copy.groupby('PC_PLANNO')['PARCEL_SPI'].apply(list).rename('Same_code')
vals = spine_copy.join(new, on='PC_PLANNO')[['PARCEL_SPI','Same_code']]
s = [[z for z in y if z != x] for x, y in vals.to_numpy()]

spine_copy['Same_code'] = np.where(m, s, None)
print (spine_copy)
   PARCEL_SPI PC_PLANNO       Same_code
0       23908   LP12345  [90435, 92824]
1       90435   LP12345  [23908, 92824]
2      329048   LP90803         [34534]
3        6409    LP2399            None
4       34534   LP90803        [329048]
5       92824   LP12345  [23908, 90435]

CodePudding user response：

May be you can try:

other = df.groupby('PC_PLANNO')['PARCEL_SPI'].apply(lambda x: x.tolist()).reset_index()
df = df.merge(other.rename(columns={'PARCEL_SPI':'Same_code'}), how='left', on=['PC_PLANNO'])
df['Same_code'] = df[['PARCEL_SPI', 'Same_code']].apply(lambda x: list(set(x['Same_code']) - set([x['PARCEL_SPI']])), axis=1)

OUTPUT:

   PARCEL_SPI PC_PLANNO Same_code
0       23908   LP12345   [90435]
1       90435   LP12345   [23908]
2      329048   LP90803   [34534]
3        6409    LP2399        []
4       34534   LP90803  [329048]