Remove string from dataframe index values
0
I want to remove strings from my index values:
df.index.get_values().str.replace("and over", "").astype(int)
Doing this returns the following error:
AttributeError: 'numpy.ndarray' object has no attribute 'str'
I've tried to find a similar function used by numpy to achieve this but I can't seem to find any. Here are my index values:
['0' '1' '2' '3' '4' '5' '6' '7' '8' '9' '10' '11' '12' '13' '14' '15'
'16' '17' '18' '19' '20' '21' '22' '23' '24' '25' '26' '27' '28' '29'
'30' '31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43'
'44' '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57'
'58' '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71'
'72' '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85'
'86' '87' '88' '89' '90 and over' 'All ages']
numpy
asked Nov 20 at 18:45
Rotav
284
add a comment |
0
I want to remove strings from my index values:
df.index.get_values().str.replace("and over", "").astype(int)
Doing this returns the following error:
AttributeError: 'numpy.ndarray' object has no attribute 'str'
I've tried to find a similar function used by numpy to achieve this but I can't seem to find any. Here are my index values:
['0' '1' '2' '3' '4' '5' '6' '7' '8' '9' '10' '11' '12' '13' '14' '15'
'16' '17' '18' '19' '20' '21' '22' '23' '24' '25' '26' '27' '28' '29'
'30' '31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43'
'44' '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57'
'58' '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71'
'72' '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85'
'86' '87' '88' '89' '90 and over' 'All ages']
numpy
asked Nov 20 at 18:45
Rotav
284
Move the methodget_valuesafter thereplacesuch asdf.index.str.replace("and over", "").get_values()should not throw an error. But theastypemight not work as you also have 'All ages' that can't be change toint
– Ben.T
Nov 20 at 19:53
@Ben.T If the All ages string was replaced to be an empty string would it be possible to loop through the index changing each value to an integer?
– Rotav
Nov 20 at 20:26
Don't think so, you can try to replace by-1and it should work. If you explain more why you want to change the type and the final application, it may bring other solution
– Ben.T
Nov 20 at 20:46
@Ben.T The dataframe contains the population of an area broken down by age. Using the data, I am creating a line graph with matplotlib to show how the population (y axis) changes for the area by age (x axis). Because there are so many index values, they overlap on the x axis. To solve this I thought it may help if the index was recognised by the file as numerical rather than as strings, which would remove the overlap automatically. I'll also be using the data to produce other graphs and thus wanted the problem to be solved with one solution.
– Rotav
Nov 20 at 21:37
add a comment |
0
0
0
I want to remove strings from my index values:
df.index.get_values().str.replace("and over", "").astype(int)
Doing this returns the following error:
AttributeError: 'numpy.ndarray' object has no attribute 'str'
I've tried to find a similar function used by numpy to achieve this but I can't seem to find any. Here are my index values:
['0' '1' '2' '3' '4' '5' '6' '7' '8' '9' '10' '11' '12' '13' '14' '15'
'16' '17' '18' '19' '20' '21' '22' '23' '24' '25' '26' '27' '28' '29'
'30' '31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43'
'44' '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57'
'58' '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71'
'72' '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85'
'86' '87' '88' '89' '90 and over' 'All ages']
numpy
asked Nov 20 at 18:45
Rotav
284
I want to remove strings from my index values:
df.index.get_values().str.replace("and over", "").astype(int)
Doing this returns the following error:
AttributeError: 'numpy.ndarray' object has no attribute 'str'
I've tried to find a similar function used by numpy to achieve this but I can't seem to find any. Here are my index values:
['0' '1' '2' '3' '4' '5' '6' '7' '8' '9' '10' '11' '12' '13' '14' '15'
'16' '17' '18' '19' '20' '21' '22' '23' '24' '25' '26' '27' '28' '29'
'30' '31' '32' '33' '34' '35' '36' '37' '38' '39' '40' '41' '42' '43'
'44' '45' '46' '47' '48' '49' '50' '51' '52' '53' '54' '55' '56' '57'
'58' '59' '60' '61' '62' '63' '64' '65' '66' '67' '68' '69' '70' '71'
'72' '73' '74' '75' '76' '77' '78' '79' '80' '81' '82' '83' '84' '85'
'86' '87' '88' '89' '90 and over' 'All ages']
numpy
numpy
asked Nov 20 at 18:45
Rotav
284
asked Nov 20 at 18:45
Rotav
284
asked Nov 20 at 18:45
Rotav
284
asked Nov 20 at 18:45
Rotav
284
asked Nov 20 at 18:45
Rotav
284
284
Move the methodget_valuesafter thereplacesuch asdf.index.str.replace("and over", "").get_values()should not throw an error. But theastypemight not work as you also have 'All ages' that can't be change toint
– Ben.T
Nov 20 at 19:53
@Ben.T If the All ages string was replaced to be an empty string would it be possible to loop through the index changing each value to an integer?
– Rotav
Nov 20 at 20:26
Don't think so, you can try to replace by-1and it should work. If you explain more why you want to change the type and the final application, it may bring other solution
– Ben.T
Nov 20 at 20:46
@Ben.T The dataframe contains the population of an area broken down by age. Using the data, I am creating a line graph with matplotlib to show how the population (y axis) changes for the area by age (x axis). Because there are so many index values, they overlap on the x axis. To solve this I thought it may help if the index was recognised by the file as numerical rather than as strings, which would remove the overlap automatically. I'll also be using the data to produce other graphs and thus wanted the problem to be solved with one solution.
– Rotav
Nov 20 at 21:37
add a comment |
Move the methodget_valuesafter thereplacesuch asdf.index.str.replace("and over", "").get_values()should not throw an error. But theastypemight not work as you also have 'All ages' that can't be change toint
– Ben.T
Nov 20 at 19:53
@Ben.T If the All ages string was replaced to be an empty string would it be possible to loop through the index changing each value to an integer?
– Rotav
Nov 20 at 20:26
Don't think so, you can try to replace by-1and it should work. If you explain more why you want to change the type and the final application, it may bring other solution
– Ben.T
Nov 20 at 20:46
@Ben.T The dataframe contains the population of an area broken down by age. Using the data, I am creating a line graph with matplotlib to show how the population (y axis) changes for the area by age (x axis). Because there are so many index values, they overlap on the x axis. To solve this I thought it may help if the index was recognised by the file as numerical rather than as strings, which would remove the overlap automatically. I'll also be using the data to produce other graphs and thus wanted the problem to be solved with one solution.
– Rotav
Nov 20 at 21:37
Move the method
get_values after the replace such as df.index.str.replace("and over", "").get_values() should not throw an error. But the astype might not work as you also have 'All ages' that can't be change to int– Ben.T
Nov 20 at 19:53
Move the method
get_values after the replace such as df.index.str.replace("and over", "").get_values() should not throw an error. But the astype might not work as you also have 'All ages' that can't be change to int– Ben.T
Nov 20 at 19:53
@Ben.T If the All ages string was replaced to be an empty string would it be possible to loop through the index changing each value to an integer?
– Rotav
Nov 20 at 20:26
@Ben.T If the All ages string was replaced to be an empty string would it be possible to loop through the index changing each value to an integer?
– Rotav
Nov 20 at 20:26
Don't think so, you can try to replace by
-1 and it should work. If you explain more why you want to change the type and the final application, it may bring other solution– Ben.T
Nov 20 at 20:46
Don't think so, you can try to replace by
-1 and it should work. If you explain more why you want to change the type and the final application, it may bring other solution– Ben.T
Nov 20 at 20:46
@Ben.T The dataframe contains the population of an area broken down by age. Using the data, I am creating a line graph with matplotlib to show how the population (y axis) changes for the area by age (x axis). Because there are so many index values, they overlap on the x axis. To solve this I thought it may help if the index was recognised by the file as numerical rather than as strings, which would remove the overlap automatically. I'll also be using the data to produce other graphs and thus wanted the problem to be solved with one solution.
– Rotav
Nov 20 at 21:37
@Ben.T The dataframe contains the population of an area broken down by age. Using the data, I am creating a line graph with matplotlib to show how the population (y axis) changes for the area by age (x axis). Because there are so many index values, they overlap on the x axis. To solve this I thought it may help if the index was recognised by the file as numerical rather than as strings, which would remove the overlap automatically. I'll also be using the data to produce other graphs and thus wanted the problem to be solved with one solution.
– Rotav
Nov 20 at 21:37
add a comment |
1 Answer
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Something like the following code should work. But first, you would have to delete the 'All ages' entry.
arr = np.array([x.replace(' and over', '') for x in arr]).astype(int)
edited Nov 21 at 3:09
Pang
6,8601563101
answered Nov 20 at 21:05
Esteban Quiros
1015
add a comment |
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Something like the following code should work. But first, you would have to delete the 'All ages' entry.
arr = np.array([x.replace(' and over', '') for x in arr]).astype(int)
edited Nov 21 at 3:09
Pang
6,8601563101
answered Nov 20 at 21:05
Esteban Quiros
1015
add a comment |
0
Something like the following code should work. But first, you would have to delete the 'All ages' entry.
arr = np.array([x.replace(' and over', '') for x in arr]).astype(int)
edited Nov 21 at 3:09
Pang
6,8601563101
answered Nov 20 at 21:05
Esteban Quiros
1015
add a comment |
0
0
0
Something like the following code should work. But first, you would have to delete the 'All ages' entry.
arr = np.array([x.replace(' and over', '') for x in arr]).astype(int)
edited Nov 21 at 3:09
Pang
6,8601563101
answered Nov 20 at 21:05
Esteban Quiros
1015
Something like the following code should work. But first, you would have to delete the 'All ages' entry.
arr = np.array([x.replace(' and over', '') for x in arr]).astype(int)
edited Nov 21 at 3:09
Pang
6,8601563101
answered Nov 20 at 21:05
Esteban Quiros
1015
edited Nov 21 at 3:09
Pang
6,8601563101
edited Nov 21 at 3:09
Pang
6,8601563101
edited Nov 21 at 3:09
Pang
6,8601563101
6,8601563101
answered Nov 20 at 21:05
Esteban Quiros
1015
answered Nov 20 at 21:05
Esteban Quiros
1015
answered Nov 20 at 21:05
Esteban Quiros
1015
1015
add a comment |
add a comment |
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Move the method
get_valuesafter thereplacesuch asdf.index.str.replace("and over", "").get_values()should not throw an error. But theastypemight not work as you also have 'All ages' that can't be change toint– Ben.T
Nov 20 at 19:53
@Ben.T If the All ages string was replaced to be an empty string would it be possible to loop through the index changing each value to an integer?
– Rotav
Nov 20 at 20:26
Don't think so, you can try to replace by
-1and it should work. If you explain more why you want to change the type and the final application, it may bring other solution– Ben.T
Nov 20 at 20:46
@Ben.T The dataframe contains the population of an area broken down by age. Using the data, I am creating a line graph with matplotlib to show how the population (y axis) changes for the area by age (x axis). Because there are so many index values, they overlap on the x axis. To solve this I thought it may help if the index was recognised by the file as numerical rather than as strings, which would remove the overlap automatically. I'll also be using the data to produce other graphs and thus wanted the problem to be solved with one solution.
– Rotav
Nov 20 at 21:37