find reciprocal rows in pandas Dataframe
I have this dataframe, and need to retain only those lines having a reciprocal values for 2 columns (numA and numB here).
gpm = pd.DataFrame(data={
'id':[1,2,3,4,5,6,7,8,9],
'time':[150315,150315,150315,150315,150315,150315,150315,150315,150315],
'numA':['A','D','C','B','A','C','A','E','D'],
'numB':['B','C','B','A','B','D','B','A','A'],
'antA':['MSPDV','VIELU','RMPC1','MJCIH','PALT2','M2PV3','MACIF','MACIF','VIELU'],
'antB':['BPDV8','0GRI3','SSFDJ','SSFDJ','SSFDJ','CCPG1','0GRI3','SSFDJ','SSFDJ']
})
I only want lines in which columns numA and numB are reciprocal. That is, retaining al lines where the pairs (A,B), (B,A) and (C,D),(D,C) occur.
My solution, for now, involves making a list of all unique identifiers and going through each line looking whether the actual partner is in the list of partners
it is extremely slow.... (and perhaps incorrect!)
## here's my code
parties = {}
nums = gpm['numA']+gpm['numB']
for i in nums.unique():
parties[i] = gpm['numB'][gpm['numA'] == i]
parties[i] = gpm['numA'][gpm['numB'] == i]
new_d = gpm.iloc[[0]]
for i in np.arange(1,gpm.shape[0]):
numa = gpm.iloc[i]['numA']
if gpm.iloc[i]['numB'] in parties[numa]:
new_d.append(gpm.iloc[[i]])
any savvy coder that could help speed this up? The actual file to parse is a ~15GB csv.
Thanks
python pandas large-data
asked Nov 14 '18 at 12:45
HoracioHoracio
335
add a comment |
I have this dataframe, and need to retain only those lines having a reciprocal values for 2 columns (numA and numB here).
gpm = pd.DataFrame(data={
'id':[1,2,3,4,5,6,7,8,9],
'time':[150315,150315,150315,150315,150315,150315,150315,150315,150315],
'numA':['A','D','C','B','A','C','A','E','D'],
'numB':['B','C','B','A','B','D','B','A','A'],
'antA':['MSPDV','VIELU','RMPC1','MJCIH','PALT2','M2PV3','MACIF','MACIF','VIELU'],
'antB':['BPDV8','0GRI3','SSFDJ','SSFDJ','SSFDJ','CCPG1','0GRI3','SSFDJ','SSFDJ']
})
I only want lines in which columns numA and numB are reciprocal. That is, retaining al lines where the pairs (A,B), (B,A) and (C,D),(D,C) occur.
My solution, for now, involves making a list of all unique identifiers and going through each line looking whether the actual partner is in the list of partners
it is extremely slow.... (and perhaps incorrect!)
## here's my code
parties = {}
nums = gpm['numA']+gpm['numB']
for i in nums.unique():
parties[i] = gpm['numB'][gpm['numA'] == i]
parties[i] = gpm['numA'][gpm['numB'] == i]
new_d = gpm.iloc[[0]]
for i in np.arange(1,gpm.shape[0]):
numa = gpm.iloc[i]['numA']
if gpm.iloc[i]['numB'] in parties[numa]:
new_d.append(gpm.iloc[[i]])
any savvy coder that could help speed this up? The actual file to parse is a ~15GB csv.
Thanks
python pandas large-data
asked Nov 14 '18 at 12:45
HoracioHoracio
335
add a comment |
I have this dataframe, and need to retain only those lines having a reciprocal values for 2 columns (numA and numB here).
gpm = pd.DataFrame(data={
'id':[1,2,3,4,5,6,7,8,9],
'time':[150315,150315,150315,150315,150315,150315,150315,150315,150315],
'numA':['A','D','C','B','A','C','A','E','D'],
'numB':['B','C','B','A','B','D','B','A','A'],
'antA':['MSPDV','VIELU','RMPC1','MJCIH','PALT2','M2PV3','MACIF','MACIF','VIELU'],
'antB':['BPDV8','0GRI3','SSFDJ','SSFDJ','SSFDJ','CCPG1','0GRI3','SSFDJ','SSFDJ']
})
I only want lines in which columns numA and numB are reciprocal. That is, retaining al lines where the pairs (A,B), (B,A) and (C,D),(D,C) occur.
My solution, for now, involves making a list of all unique identifiers and going through each line looking whether the actual partner is in the list of partners
it is extremely slow.... (and perhaps incorrect!)
## here's my code
parties = {}
nums = gpm['numA']+gpm['numB']
for i in nums.unique():
parties[i] = gpm['numB'][gpm['numA'] == i]
parties[i] = gpm['numA'][gpm['numB'] == i]
new_d = gpm.iloc[[0]]
for i in np.arange(1,gpm.shape[0]):
numa = gpm.iloc[i]['numA']
if gpm.iloc[i]['numB'] in parties[numa]:
new_d.append(gpm.iloc[[i]])
any savvy coder that could help speed this up? The actual file to parse is a ~15GB csv.
Thanks
python pandas large-data
asked Nov 14 '18 at 12:45
HoracioHoracio
335
I have this dataframe, and need to retain only those lines having a reciprocal values for 2 columns (numA and numB here).
gpm = pd.DataFrame(data={
'id':[1,2,3,4,5,6,7,8,9],
'time':[150315,150315,150315,150315,150315,150315,150315,150315,150315],
'numA':['A','D','C','B','A','C','A','E','D'],
'numB':['B','C','B','A','B','D','B','A','A'],
'antA':['MSPDV','VIELU','RMPC1','MJCIH','PALT2','M2PV3','MACIF','MACIF','VIELU'],
'antB':['BPDV8','0GRI3','SSFDJ','SSFDJ','SSFDJ','CCPG1','0GRI3','SSFDJ','SSFDJ']
})
I only want lines in which columns numA and numB are reciprocal. That is, retaining al lines where the pairs (A,B), (B,A) and (C,D),(D,C) occur.
My solution, for now, involves making a list of all unique identifiers and going through each line looking whether the actual partner is in the list of partners
it is extremely slow.... (and perhaps incorrect!)
## here's my code
parties = {}
nums = gpm['numA']+gpm['numB']
for i in nums.unique():
parties[i] = gpm['numB'][gpm['numA'] == i]
parties[i] = gpm['numA'][gpm['numB'] == i]
new_d = gpm.iloc[[0]]
for i in np.arange(1,gpm.shape[0]):
numa = gpm.iloc[i]['numA']
if gpm.iloc[i]['numB'] in parties[numa]:
new_d.append(gpm.iloc[[i]])
any savvy coder that could help speed this up? The actual file to parse is a ~15GB csv.
Thanks
python pandas large-data
python pandas large-data
asked Nov 14 '18 at 12:45
HoracioHoracio
335
asked Nov 14 '18 at 12:45
HoracioHoracio
335
asked Nov 14 '18 at 12:45
HoracioHoracio
335
asked Nov 14 '18 at 12:45
HoracioHoracio
335
asked Nov 14 '18 at 12:45
HoracioHoracio
335
335
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
In your example, I assume the rows with id=3, 8 & 9, which are (C, B), (E, A) and (D, A), are unwanted? If so, here's a standard way to select by comparing the values in numA and numB for specific acceptable combinations:
In [5]: gpm[((gpm['numA'] == 'A') & (gpm['numB'] == 'B')) |
...: ((gpm['numA'] == 'B') & (gpm['numB'] == 'A')) |
...: ((gpm['numA'] == 'C') & (gpm['numB'] == 'D')) |
...: ((gpm['numA'] == 'D') & (gpm['numB'] == 'C'))
...: ]
Out[5]:
id time numA numB antA antB
0 1 150315 A B MSPDV BPDV8
1 2 150315 D C VIELU 0GRI3
3 4 150315 B A MJCIH SSFDJ
4 5 150315 A B PALT2 SSFDJ
5 6 150315 C D M2PV3 CCPG1
6 7 150315 A B MACIF 0GRI3
(assign the result of that to new_d)
answered Nov 14 '18 at 14:25
aneroidaneroid
6,99922742
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
In your example, I assume the rows with id=3, 8 & 9, which are (C, B), (E, A) and (D, A), are unwanted? If so, here's a standard way to select by comparing the values in numA and numB for specific acceptable combinations:
In [5]: gpm[((gpm['numA'] == 'A') & (gpm['numB'] == 'B')) |
...: ((gpm['numA'] == 'B') & (gpm['numB'] == 'A')) |
...: ((gpm['numA'] == 'C') & (gpm['numB'] == 'D')) |
...: ((gpm['numA'] == 'D') & (gpm['numB'] == 'C'))
...: ]
Out[5]:
id time numA numB antA antB
0 1 150315 A B MSPDV BPDV8
1 2 150315 D C VIELU 0GRI3
3 4 150315 B A MJCIH SSFDJ
4 5 150315 A B PALT2 SSFDJ
5 6 150315 C D M2PV3 CCPG1
6 7 150315 A B MACIF 0GRI3
(assign the result of that to new_d)
answered Nov 14 '18 at 14:25
aneroidaneroid
6,99922742
add a comment |
In your example, I assume the rows with id=3, 8 & 9, which are (C, B), (E, A) and (D, A), are unwanted? If so, here's a standard way to select by comparing the values in numA and numB for specific acceptable combinations:
In [5]: gpm[((gpm['numA'] == 'A') & (gpm['numB'] == 'B')) |
...: ((gpm['numA'] == 'B') & (gpm['numB'] == 'A')) |
...: ((gpm['numA'] == 'C') & (gpm['numB'] == 'D')) |
...: ((gpm['numA'] == 'D') & (gpm['numB'] == 'C'))
...: ]
Out[5]:
id time numA numB antA antB
0 1 150315 A B MSPDV BPDV8
1 2 150315 D C VIELU 0GRI3
3 4 150315 B A MJCIH SSFDJ
4 5 150315 A B PALT2 SSFDJ
5 6 150315 C D M2PV3 CCPG1
6 7 150315 A B MACIF 0GRI3
(assign the result of that to new_d)
answered Nov 14 '18 at 14:25
aneroidaneroid
6,99922742
add a comment |
In your example, I assume the rows with id=3, 8 & 9, which are (C, B), (E, A) and (D, A), are unwanted? If so, here's a standard way to select by comparing the values in numA and numB for specific acceptable combinations:
In [5]: gpm[((gpm['numA'] == 'A') & (gpm['numB'] == 'B')) |
...: ((gpm['numA'] == 'B') & (gpm['numB'] == 'A')) |
...: ((gpm['numA'] == 'C') & (gpm['numB'] == 'D')) |
...: ((gpm['numA'] == 'D') & (gpm['numB'] == 'C'))
...: ]
Out[5]:
id time numA numB antA antB
0 1 150315 A B MSPDV BPDV8
1 2 150315 D C VIELU 0GRI3
3 4 150315 B A MJCIH SSFDJ
4 5 150315 A B PALT2 SSFDJ
5 6 150315 C D M2PV3 CCPG1
6 7 150315 A B MACIF 0GRI3
(assign the result of that to new_d)
answered Nov 14 '18 at 14:25
aneroidaneroid
6,99922742
In your example, I assume the rows with id=3, 8 & 9, which are (C, B), (E, A) and (D, A), are unwanted? If so, here's a standard way to select by comparing the values in numA and numB for specific acceptable combinations:
In [5]: gpm[((gpm['numA'] == 'A') & (gpm['numB'] == 'B')) |
...: ((gpm['numA'] == 'B') & (gpm['numB'] == 'A')) |
...: ((gpm['numA'] == 'C') & (gpm['numB'] == 'D')) |
...: ((gpm['numA'] == 'D') & (gpm['numB'] == 'C'))
...: ]
Out[5]:
id time numA numB antA antB
0 1 150315 A B MSPDV BPDV8
1 2 150315 D C VIELU 0GRI3
3 4 150315 B A MJCIH SSFDJ
4 5 150315 A B PALT2 SSFDJ
5 6 150315 C D M2PV3 CCPG1
6 7 150315 A B MACIF 0GRI3
(assign the result of that to new_d)
answered Nov 14 '18 at 14:25
aneroidaneroid
6,99922742
answered Nov 14 '18 at 14:25
aneroidaneroid
6,99922742
answered Nov 14 '18 at 14:25
aneroidaneroid
6,99922742
answered Nov 14 '18 at 14:25
aneroidaneroid
6,99922742
6,99922742
add a comment |
add a comment |
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