Mfcttrf

I am trying to figure out the fastest way to perform search and sort on a pandas dataframe. Below are before and after dataframes of what I am trying to accomplish.

Before:

flightTo flightFrom toNum fromNum toCode fromCode
 ABC DEF 123 456 8000 8000
 DEF XYZ 456 893 9999 9999
 AAA BBB 473 917 5555 5555
 BBB CCC 917 341 5555 5555

After search/sort:

flightTo flightFrom toNum fromNum toCode fromCode
 ABC XYZ 123 893 8000 9999
 AAA CCC 473 341 5555 5555

In this example I am essentially trying to filter out 'flights' that exist in between end destinations. This should be done by using some sort of drop duplicates method but what leaves me confused is how to handle all of the columns. Would a binary search be the best way to accomplish this? Hints appreciated, trying hard to figure this out.

possible edge case:

What if the data is switched up and our end connections are in the same column?

flight1 flight2 1Num 2Num 1Code 2Code
 ABC DEF 123 456 8000 8000
 XYZ DEF 893 456 9999 9999

After search/sort:

flight1 flight2 1Num 2Num 1Code 2Code
 ABC XYZ 123 893 8000 9999

This case logically shouldn't happen. After all how can you go DEF-ABC and DEF-XYZ? You can't, but the 'endpoints' would still be ABC-XYZ

This is network problem , so we using networkx , notice , here you can have more than two stops , which means you can have some case like NY-DC-WA-NC

import networkx as nx
G=nx.from_pandas_edgelist(df, 'flightTo', 'flightFrom')

# create the nx object from pandas dataframe

l=list(nx.connected_components(G))

# then we get the list of components which as tied to each other , 
# in a net work graph , they are linked 
L=[dict.fromkeys(y,x) for x, y in enumerate(l)]

# then from the above we can create our map dict , 
# since every components connected to each other , 
# then we just need to pick of of them as key , then map with others

d=k: v for d in L for k, v in d.items()

# create the dict for groupby , since we need _from as first item and _to as last item 
grouppd=dict(zip(df.columns.tolist(),['first','last']*3))
df.groupby(df.flightTo.map(d)).agg(grouppd) # then using agg with dict yield your output 

Out[22]: 
 flightTo flightFrom toNum fromNum toCode fromCode
flightTo 
0 ABC XYZ 123 893 8000 9999
1 AAA CCC 473 341 5555 5555

Installation networkx

• 
  Pip: pip install networkx
  


• 
  Anaconda: conda install -c anaconda networkx
  

Here's a NumPy solution, which might be convenient in the case performance is relevant:

def remove_middle_dest(df):
 x = df.to_numpy()
 # obtain a flat numpy array from both columns
 b = x[:,0:2].ravel()
 _, ix, inv = np.unique(b, return_index=True, return_inverse=True)
 # Index of duplicate values in b
 ixs_drop = np.setdiff1d(np.arange(len(b)), ix) 
 # Indices to be used to replace the content in the columns
 replace_at = (inv[:,None] == inv[ixs_drop]).argmax(0) 
 # Col index of where duplicate value is, 0 or 1
 col = (ixs_drop % 2) ^ 1
 # 2d array to index and replace values in the df
 # index to obtain values with which to replace
 t = np.tile([3,5], len(col)).reshape(len(col), -1)
 ixs = np.concatenate([col[:,None], t], 1)
 # translate indices to row indices
 rows_drop, rows_replace = (ixs_drop // 2), (replace_at // 2)
 c = np.empty((len(col), 5), dtype=x.dtype)
 c[:,::2] = x[rows_drop[:,None], ixs]
 c[:,1::2] = x[rows_replace[:,None], [2,4]]
 # update dataframe and drop rows
 df.iloc[rows_replace, 1:] = c
 return df.drop(rows_drop)

Which fo the proposed dataframe yields the expected output:

print(df)
 flightTo flightFrom toNum fromNum toCode fromCode
0 ABC XYZ 123 893 8000 9999
1 DEF XYZ 456 893 9999 9999
2 AAA CCC 473 341 5555 5555
3 BBB CCC 917 341 5555 5555

remove_middle_dest(df)

 flightTo flightFrom toNum fromNum toCode fromCode
0 ABC XYZ 123 893 8000 9999
2 AAA CCC 473 341 5555 5555

This approach does not assume any particular order in terms of the rows where the duplicate is, but neither the column on which it is (to cover the edge case described in the question). If we use for instance the following dataframe:

 flightTo flightFrom toNum fromNum toCode fromCode
0 ABC DEF 123 456 8000 8000
1 XYZ DEF 893 456 9999 9999
2 AAA BBB 473 917 5555 5555
3 BBB CCC 917 341 5555 5555

remove_middle_dest(df)

flightTo flightFrom toNum fromNum toCode fromCode
0 ABC XYZ 123 456 8000 9999
2 AAA CCC 473 341 5555 5555