TypeError: unsupported operand type(s) for &: 'str' and 'bool'
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ height:90px;width:728px;box-sizing:border-box;
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All,
I have below Pandas dataframe, and I am trying to filter my dataframe such that my output displays country name along with the year 1989 column whose number is >1000000.For this I am using below code, but it is returning me below error.
{'Country': {0: 'Austria', 1: 'Belgium', 2: 'Denmark', 3: 'Finland', 4: 'France', 5: 'Germany', 6: 'Iceland', 7: 'Ireland', 8: 'Italy', 9: 'Luxemburg', 10: 'Netherland', 11: 'Norway', 12: 'Portugal', 13: 'Spain', 14: 'Sweden', 15: 'Switzerland', 16: 'United Kingdom'}, 'y1989': {0: 7602431, 1: 9927600, 2: 5129800, 3: 4954359, 4: 56269800, 5: 61715000, 6: 253500, 7: 3526600, 8: 57504700, 9: 374900, 10: 14805240, 11: 4226901, 12: 10304700, 13: 38851900, 14: 8458890, 15: 6619973, 16: 57236200}, 'y1990': {0: 7660345.0, 1: 9947800.0, 2: 5135400.0, 3: 4974383.0, 4: 0.0, 5: 62678000.0, 6: 255708.0, 7: 3505500.0, 8: 57576400.0, 9: 379300.0, 10: 14892574.0, 11: 4241473.0, 12: 0.0, 13: 38924500.0, 14: 8527040.0, 15: 6673850.0, 16: 57410600.0}, 'y1991': {0: 7790957, 1: 9987000, 2: 5146500, 3: 4998478, 4: 56893000, 5: 79753000, 6: 259577, 7: 3519000, 8: 57746200, 9: 384400, 10: 15010445, 11: 4261930, 12: 9858500, 13: 38993800, 14: 8590630, 15: 6750693, 16: 57649200}, 'y1992': {0: 7860800, 1: 10068319, 2: 5162100, 3: 5029300, 4: 57217500, 5: 80238000, 6: 262193, 7: 3542000, 8: 57788200, 9: 389800, 10: 15129200, 11: 4273634, 12: 9846000, 13: 39055900, 14: 8644100, 15: 6831900, 16: 58888800}, 'y1993': {0: 7909575, 1: 10100631, 2: 5180614, 3: 5054982, 4: 57529577, 5: 81338000, 6: 264922, 7: 3559985, 8: 57114161, 9: 395200, 10: 15354000, 11: 4324577, 12: 9987500, 13: 39790955, 14: 8700000, 15: 6871500, 16: 58191230}, 'y1994': {0: 7943652, 1: 10130574, 2: 5191000, 3: 5098754, 4: 57847000, 5: 81353000, 6: 266783, 7: 3570700, 8: 57201800, 9: 400000, 10: 15341553, 11: 4348410, 12: 9776000, 13: 39177400, 14: 8749000, 15: 7021200, 16: 58380000}, 'y1995': {0: 8054800, 1: 10143047, 2: 5251027, 3: 5116800, 4: 58265400, 5: 81845000, 6: 267806, 7: 3591200, 8: 57268578, 9: 412800, 10: 15492800, 11: 4370000, 12: 9920800, 13: 39241900, 14: 8837000, 15: 7060400, 16: 58684000}}
My code
df[(df.Country)& (df.y1989>1000000)]
Error:
TypeError: unsupported operand type(s) for &: 'str' and 'bool'
I am not sure what could be the reason, being a newbie to python if you could provide explanation for the error that will be greatly appreciated.
Thanks in advance,
python pandas indexing
edited Nov 23 '18 at 17:36
jpp
103k2166116
asked Nov 23 '18 at 17:28
biggboss2019biggboss2019
9519
add a comment |
All,
I have below Pandas dataframe, and I am trying to filter my dataframe such that my output displays country name along with the year 1989 column whose number is >1000000.For this I am using below code, but it is returning me below error.
{'Country': {0: 'Austria', 1: 'Belgium', 2: 'Denmark', 3: 'Finland', 4: 'France', 5: 'Germany', 6: 'Iceland', 7: 'Ireland', 8: 'Italy', 9: 'Luxemburg', 10: 'Netherland', 11: 'Norway', 12: 'Portugal', 13: 'Spain', 14: 'Sweden', 15: 'Switzerland', 16: 'United Kingdom'}, 'y1989': {0: 7602431, 1: 9927600, 2: 5129800, 3: 4954359, 4: 56269800, 5: 61715000, 6: 253500, 7: 3526600, 8: 57504700, 9: 374900, 10: 14805240, 11: 4226901, 12: 10304700, 13: 38851900, 14: 8458890, 15: 6619973, 16: 57236200}, 'y1990': {0: 7660345.0, 1: 9947800.0, 2: 5135400.0, 3: 4974383.0, 4: 0.0, 5: 62678000.0, 6: 255708.0, 7: 3505500.0, 8: 57576400.0, 9: 379300.0, 10: 14892574.0, 11: 4241473.0, 12: 0.0, 13: 38924500.0, 14: 8527040.0, 15: 6673850.0, 16: 57410600.0}, 'y1991': {0: 7790957, 1: 9987000, 2: 5146500, 3: 4998478, 4: 56893000, 5: 79753000, 6: 259577, 7: 3519000, 8: 57746200, 9: 384400, 10: 15010445, 11: 4261930, 12: 9858500, 13: 38993800, 14: 8590630, 15: 6750693, 16: 57649200}, 'y1992': {0: 7860800, 1: 10068319, 2: 5162100, 3: 5029300, 4: 57217500, 5: 80238000, 6: 262193, 7: 3542000, 8: 57788200, 9: 389800, 10: 15129200, 11: 4273634, 12: 9846000, 13: 39055900, 14: 8644100, 15: 6831900, 16: 58888800}, 'y1993': {0: 7909575, 1: 10100631, 2: 5180614, 3: 5054982, 4: 57529577, 5: 81338000, 6: 264922, 7: 3559985, 8: 57114161, 9: 395200, 10: 15354000, 11: 4324577, 12: 9987500, 13: 39790955, 14: 8700000, 15: 6871500, 16: 58191230}, 'y1994': {0: 7943652, 1: 10130574, 2: 5191000, 3: 5098754, 4: 57847000, 5: 81353000, 6: 266783, 7: 3570700, 8: 57201800, 9: 400000, 10: 15341553, 11: 4348410, 12: 9776000, 13: 39177400, 14: 8749000, 15: 7021200, 16: 58380000}, 'y1995': {0: 8054800, 1: 10143047, 2: 5251027, 3: 5116800, 4: 58265400, 5: 81845000, 6: 267806, 7: 3591200, 8: 57268578, 9: 412800, 10: 15492800, 11: 4370000, 12: 9920800, 13: 39241900, 14: 8837000, 15: 7060400, 16: 58684000}}
My code
df[(df.Country)& (df.y1989>1000000)]
Error:
TypeError: unsupported operand type(s) for &: 'str' and 'bool'
I am not sure what could be the reason, being a newbie to python if you could provide explanation for the error that will be greatly appreciated.
Thanks in advance,
python pandas indexing
edited Nov 23 '18 at 17:36
jpp
103k2166116
asked Nov 23 '18 at 17:28
biggboss2019biggboss2019
9519
add a comment |
All,
I have below Pandas dataframe, and I am trying to filter my dataframe such that my output displays country name along with the year 1989 column whose number is >1000000.For this I am using below code, but it is returning me below error.
{'Country': {0: 'Austria', 1: 'Belgium', 2: 'Denmark', 3: 'Finland', 4: 'France', 5: 'Germany', 6: 'Iceland', 7: 'Ireland', 8: 'Italy', 9: 'Luxemburg', 10: 'Netherland', 11: 'Norway', 12: 'Portugal', 13: 'Spain', 14: 'Sweden', 15: 'Switzerland', 16: 'United Kingdom'}, 'y1989': {0: 7602431, 1: 9927600, 2: 5129800, 3: 4954359, 4: 56269800, 5: 61715000, 6: 253500, 7: 3526600, 8: 57504700, 9: 374900, 10: 14805240, 11: 4226901, 12: 10304700, 13: 38851900, 14: 8458890, 15: 6619973, 16: 57236200}, 'y1990': {0: 7660345.0, 1: 9947800.0, 2: 5135400.0, 3: 4974383.0, 4: 0.0, 5: 62678000.0, 6: 255708.0, 7: 3505500.0, 8: 57576400.0, 9: 379300.0, 10: 14892574.0, 11: 4241473.0, 12: 0.0, 13: 38924500.0, 14: 8527040.0, 15: 6673850.0, 16: 57410600.0}, 'y1991': {0: 7790957, 1: 9987000, 2: 5146500, 3: 4998478, 4: 56893000, 5: 79753000, 6: 259577, 7: 3519000, 8: 57746200, 9: 384400, 10: 15010445, 11: 4261930, 12: 9858500, 13: 38993800, 14: 8590630, 15: 6750693, 16: 57649200}, 'y1992': {0: 7860800, 1: 10068319, 2: 5162100, 3: 5029300, 4: 57217500, 5: 80238000, 6: 262193, 7: 3542000, 8: 57788200, 9: 389800, 10: 15129200, 11: 4273634, 12: 9846000, 13: 39055900, 14: 8644100, 15: 6831900, 16: 58888800}, 'y1993': {0: 7909575, 1: 10100631, 2: 5180614, 3: 5054982, 4: 57529577, 5: 81338000, 6: 264922, 7: 3559985, 8: 57114161, 9: 395200, 10: 15354000, 11: 4324577, 12: 9987500, 13: 39790955, 14: 8700000, 15: 6871500, 16: 58191230}, 'y1994': {0: 7943652, 1: 10130574, 2: 5191000, 3: 5098754, 4: 57847000, 5: 81353000, 6: 266783, 7: 3570700, 8: 57201800, 9: 400000, 10: 15341553, 11: 4348410, 12: 9776000, 13: 39177400, 14: 8749000, 15: 7021200, 16: 58380000}, 'y1995': {0: 8054800, 1: 10143047, 2: 5251027, 3: 5116800, 4: 58265400, 5: 81845000, 6: 267806, 7: 3591200, 8: 57268578, 9: 412800, 10: 15492800, 11: 4370000, 12: 9920800, 13: 39241900, 14: 8837000, 15: 7060400, 16: 58684000}}
My code
df[(df.Country)& (df.y1989>1000000)]
Error:
TypeError: unsupported operand type(s) for &: 'str' and 'bool'
I am not sure what could be the reason, being a newbie to python if you could provide explanation for the error that will be greatly appreciated.
Thanks in advance,
python pandas indexing
edited Nov 23 '18 at 17:36
jpp
103k2166116
asked Nov 23 '18 at 17:28
biggboss2019biggboss2019
9519
All,
I have below Pandas dataframe, and I am trying to filter my dataframe such that my output displays country name along with the year 1989 column whose number is >1000000.For this I am using below code, but it is returning me below error.
{'Country': {0: 'Austria', 1: 'Belgium', 2: 'Denmark', 3: 'Finland', 4: 'France', 5: 'Germany', 6: 'Iceland', 7: 'Ireland', 8: 'Italy', 9: 'Luxemburg', 10: 'Netherland', 11: 'Norway', 12: 'Portugal', 13: 'Spain', 14: 'Sweden', 15: 'Switzerland', 16: 'United Kingdom'}, 'y1989': {0: 7602431, 1: 9927600, 2: 5129800, 3: 4954359, 4: 56269800, 5: 61715000, 6: 253500, 7: 3526600, 8: 57504700, 9: 374900, 10: 14805240, 11: 4226901, 12: 10304700, 13: 38851900, 14: 8458890, 15: 6619973, 16: 57236200}, 'y1990': {0: 7660345.0, 1: 9947800.0, 2: 5135400.0, 3: 4974383.0, 4: 0.0, 5: 62678000.0, 6: 255708.0, 7: 3505500.0, 8: 57576400.0, 9: 379300.0, 10: 14892574.0, 11: 4241473.0, 12: 0.0, 13: 38924500.0, 14: 8527040.0, 15: 6673850.0, 16: 57410600.0}, 'y1991': {0: 7790957, 1: 9987000, 2: 5146500, 3: 4998478, 4: 56893000, 5: 79753000, 6: 259577, 7: 3519000, 8: 57746200, 9: 384400, 10: 15010445, 11: 4261930, 12: 9858500, 13: 38993800, 14: 8590630, 15: 6750693, 16: 57649200}, 'y1992': {0: 7860800, 1: 10068319, 2: 5162100, 3: 5029300, 4: 57217500, 5: 80238000, 6: 262193, 7: 3542000, 8: 57788200, 9: 389800, 10: 15129200, 11: 4273634, 12: 9846000, 13: 39055900, 14: 8644100, 15: 6831900, 16: 58888800}, 'y1993': {0: 7909575, 1: 10100631, 2: 5180614, 3: 5054982, 4: 57529577, 5: 81338000, 6: 264922, 7: 3559985, 8: 57114161, 9: 395200, 10: 15354000, 11: 4324577, 12: 9987500, 13: 39790955, 14: 8700000, 15: 6871500, 16: 58191230}, 'y1994': {0: 7943652, 1: 10130574, 2: 5191000, 3: 5098754, 4: 57847000, 5: 81353000, 6: 266783, 7: 3570700, 8: 57201800, 9: 400000, 10: 15341553, 11: 4348410, 12: 9776000, 13: 39177400, 14: 8749000, 15: 7021200, 16: 58380000}, 'y1995': {0: 8054800, 1: 10143047, 2: 5251027, 3: 5116800, 4: 58265400, 5: 81845000, 6: 267806, 7: 3591200, 8: 57268578, 9: 412800, 10: 15492800, 11: 4370000, 12: 9920800, 13: 39241900, 14: 8837000, 15: 7060400, 16: 58684000}}
My code
df[(df.Country)& (df.y1989>1000000)]
Error:
TypeError: unsupported operand type(s) for &: 'str' and 'bool'
I am not sure what could be the reason, being a newbie to python if you could provide explanation for the error that will be greatly appreciated.
Thanks in advance,
python pandas indexing
python pandas indexing
edited Nov 23 '18 at 17:36
jpp
103k2166116
asked Nov 23 '18 at 17:28
biggboss2019biggboss2019
9519
edited Nov 23 '18 at 17:36
jpp
103k2166116
asked Nov 23 '18 at 17:28
biggboss2019biggboss2019
9519
edited Nov 23 '18 at 17:36
jpp
103k2166116
edited Nov 23 '18 at 17:36
jpp
103k2166116
edited Nov 23 '18 at 17:36
jpp
103k2166116
103k2166116
asked Nov 23 '18 at 17:28
biggboss2019biggboss2019
9519
asked Nov 23 '18 at 17:28
biggboss2019biggboss2019
9519
asked Nov 23 '18 at 17:28
biggboss2019biggboss2019
9519
9519
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
'Country' doesn't form part of your filtering criteria, so don't use it to form your Boolean indexer. Instead, use the loc accessor to give a Boolean condition and specify necessary columns separately:
res = df.loc[df['y1989'] > 1000000, ['Country','y1989']]
Under no circumstances use chained assignment, e.g. via df[df['y1989']>1000000][['Country','y1989']], as this is ambiguous and explicitly discouraged in the docs.
answered Nov 23 '18 at 17:33
jppjpp
103k2166116
1
Thanks for sharing why its actually not as efficient
– yatu
Nov 23 '18 at 17:40
@jpp.. Thanks! Just so I understand correctly. Since, 'Country' doesn't have to meet boolean condtion. It is safe to use .loc ?
– biggboss2019
Nov 23 '18 at 17:43
1
@biggboss2019, Yes, that's correct.
– jpp
Nov 23 '18 at 18:10
add a comment |
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1 Answer
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1 Answer
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'Country' doesn't form part of your filtering criteria, so don't use it to form your Boolean indexer. Instead, use the loc accessor to give a Boolean condition and specify necessary columns separately:
res = df.loc[df['y1989'] > 1000000, ['Country','y1989']]
Under no circumstances use chained assignment, e.g. via df[df['y1989']>1000000][['Country','y1989']], as this is ambiguous and explicitly discouraged in the docs.
answered Nov 23 '18 at 17:33
jppjpp
103k2166116
1
Thanks for sharing why its actually not as efficient
– yatu
Nov 23 '18 at 17:40
@jpp.. Thanks! Just so I understand correctly. Since, 'Country' doesn't have to meet boolean condtion. It is safe to use .loc ?
– biggboss2019
Nov 23 '18 at 17:43
1
@biggboss2019, Yes, that's correct.
– jpp
Nov 23 '18 at 18:10
add a comment |
'Country' doesn't form part of your filtering criteria, so don't use it to form your Boolean indexer. Instead, use the loc accessor to give a Boolean condition and specify necessary columns separately:
res = df.loc[df['y1989'] > 1000000, ['Country','y1989']]
Under no circumstances use chained assignment, e.g. via df[df['y1989']>1000000][['Country','y1989']], as this is ambiguous and explicitly discouraged in the docs.
answered Nov 23 '18 at 17:33
jppjpp
103k2166116
1
Thanks for sharing why its actually not as efficient
– yatu
Nov 23 '18 at 17:40
@jpp.. Thanks! Just so I understand correctly. Since, 'Country' doesn't have to meet boolean condtion. It is safe to use .loc ?
– biggboss2019
Nov 23 '18 at 17:43
1
@biggboss2019, Yes, that's correct.
– jpp
Nov 23 '18 at 18:10
add a comment |
'Country' doesn't form part of your filtering criteria, so don't use it to form your Boolean indexer. Instead, use the loc accessor to give a Boolean condition and specify necessary columns separately:
res = df.loc[df['y1989'] > 1000000, ['Country','y1989']]
Under no circumstances use chained assignment, e.g. via df[df['y1989']>1000000][['Country','y1989']], as this is ambiguous and explicitly discouraged in the docs.
answered Nov 23 '18 at 17:33
jppjpp
103k2166116
'Country' doesn't form part of your filtering criteria, so don't use it to form your Boolean indexer. Instead, use the loc accessor to give a Boolean condition and specify necessary columns separately:
res = df.loc[df['y1989'] > 1000000, ['Country','y1989']]
Under no circumstances use chained assignment, e.g. via df[df['y1989']>1000000][['Country','y1989']], as this is ambiguous and explicitly discouraged in the docs.
answered Nov 23 '18 at 17:33
jppjpp
103k2166116
answered Nov 23 '18 at 17:33
jppjpp
103k2166116
answered Nov 23 '18 at 17:33
jppjpp
103k2166116
answered Nov 23 '18 at 17:33
jppjpp
103k2166116
103k2166116
1
Thanks for sharing why its actually not as efficient
– yatu
Nov 23 '18 at 17:40
@jpp.. Thanks! Just so I understand correctly. Since, 'Country' doesn't have to meet boolean condtion. It is safe to use .loc ?
– biggboss2019
Nov 23 '18 at 17:43
1
@biggboss2019, Yes, that's correct.
– jpp
Nov 23 '18 at 18:10
add a comment |
1
Thanks for sharing why its actually not as efficient
– yatu
Nov 23 '18 at 17:40
@jpp.. Thanks! Just so I understand correctly. Since, 'Country' doesn't have to meet boolean condtion. It is safe to use .loc ?
– biggboss2019
Nov 23 '18 at 17:43
1
@biggboss2019, Yes, that's correct.
– jpp
Nov 23 '18 at 18:10
1
1
Thanks for sharing why its actually not as efficient
– yatu
Nov 23 '18 at 17:40
Thanks for sharing why its actually not as efficient
– yatu
Nov 23 '18 at 17:40
@jpp.. Thanks! Just so I understand correctly. Since, 'Country' doesn't have to meet boolean condtion. It is safe to use .loc ?
– biggboss2019
Nov 23 '18 at 17:43
@jpp.. Thanks! Just so I understand correctly. Since, 'Country' doesn't have to meet boolean condtion. It is safe to use .loc ?
– biggboss2019
Nov 23 '18 at 17:43
1
1
@biggboss2019, Yes, that's correct.
– jpp
Nov 23 '18 at 18:10
@biggboss2019, Yes, that's correct.
– jpp
Nov 23 '18 at 18:10
add a comment |
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