Save structured numpy array using np.savetxt with header
1
I have a structure array in the form of
output = np.zeros(names.size, dtype=[('name', 'U32'), ('r', float),('m',float)])
Then I tried to save it into a csv file using np.savetxt. I am wondering if there is way I could also save the label of each column as the header of the csv file?
Thank you in advance.
python numpy save structured-array
asked Jun 25 '18 at 15:51
somebodyzhsomebodyzh
61
add a comment |
1
I have a structure array in the form of
output = np.zeros(names.size, dtype=[('name', 'U32'), ('r', float),('m',float)])
Then I tried to save it into a csv file using np.savetxt. I am wondering if there is way I could also save the label of each column as the header of the csv file?
Thank you in advance.
python numpy save structured-array
asked Jun 25 '18 at 15:51
somebodyzhsomebodyzh
61
output.dtype.namesis a list of those field names. You could use that format a header line, e.g.' '.join(output.dtype.names)
– hpaulj
Jun 25 '18 at 16:12
@hpaulj Wouldn't that just gives me three column where all my data will appear in the first column?
– somebodyzh
Jun 26 '18 at 16:08
The header line doesn't affect the layout of the data. That's handled by thefmtas specified in the answer. Tosavetxt, the header is just a string it writes along with the comment character.
– hpaulj
Jun 26 '18 at 16:18
add a comment |
1
1
1
I have a structure array in the form of
output = np.zeros(names.size, dtype=[('name', 'U32'), ('r', float),('m',float)])
Then I tried to save it into a csv file using np.savetxt. I am wondering if there is way I could also save the label of each column as the header of the csv file?
Thank you in advance.
python numpy save structured-array
asked Jun 25 '18 at 15:51
somebodyzhsomebodyzh
61
I have a structure array in the form of
output = np.zeros(names.size, dtype=[('name', 'U32'), ('r', float),('m',float)])
Then I tried to save it into a csv file using np.savetxt. I am wondering if there is way I could also save the label of each column as the header of the csv file?
Thank you in advance.
python numpy save structured-array
python numpy save structured-array
asked Jun 25 '18 at 15:51
somebodyzhsomebodyzh
61
asked Jun 25 '18 at 15:51
somebodyzhsomebodyzh
61
asked Jun 25 '18 at 15:51
somebodyzhsomebodyzh
61
asked Jun 25 '18 at 15:51
somebodyzhsomebodyzh
61
asked Jun 25 '18 at 15:51
somebodyzhsomebodyzh
61
61
output.dtype.namesis a list of those field names. You could use that format a header line, e.g.' '.join(output.dtype.names)
– hpaulj
Jun 25 '18 at 16:12
@hpaulj Wouldn't that just gives me three column where all my data will appear in the first column?
– somebodyzh
Jun 26 '18 at 16:08
The header line doesn't affect the layout of the data. That's handled by thefmtas specified in the answer. Tosavetxt, the header is just a string it writes along with the comment character.
– hpaulj
Jun 26 '18 at 16:18
add a comment |
output.dtype.namesis a list of those field names. You could use that format a header line, e.g.' '.join(output.dtype.names)
– hpaulj
Jun 25 '18 at 16:12
@hpaulj Wouldn't that just gives me three column where all my data will appear in the first column?
– somebodyzh
Jun 26 '18 at 16:08
The header line doesn't affect the layout of the data. That's handled by thefmtas specified in the answer. Tosavetxt, the header is just a string it writes along with the comment character.
– hpaulj
Jun 26 '18 at 16:18
output.dtype.names is a list of those field names. You could use that format a header line, e.g. ' '.join(output.dtype.names)– hpaulj
Jun 25 '18 at 16:12
output.dtype.names is a list of those field names. You could use that format a header line, e.g. ' '.join(output.dtype.names)– hpaulj
Jun 25 '18 at 16:12
@hpaulj Wouldn't that just gives me three column where all my data will appear in the first column?
– somebodyzh
Jun 26 '18 at 16:08
@hpaulj Wouldn't that just gives me three column where all my data will appear in the first column?
– somebodyzh
Jun 26 '18 at 16:08
The header line doesn't affect the layout of the data. That's handled by the
fmt as specified in the answer. To savetxt, the header is just a string it writes along with the comment character.– hpaulj
Jun 26 '18 at 16:18
The header line doesn't affect the layout of the data. That's handled by the
fmt as specified in the answer. To savetxt, the header is just a string it writes along with the comment character.– hpaulj
Jun 26 '18 at 16:18
add a comment |
2 Answers
2
active
oldest
votes
0
Below is sample code:
output = np.zeros(names.size, dtype=[('name', 'U32'), ('r', float),('m',float)])
np.savetxt("foo.csv", output, delimiter=",", header="name,r,m", fmt="%s,%f,%f", comments='')
As documented here.
answered Jun 25 '18 at 16:03
chifu linchifu lin
665
It gives me 3 columns this way, but all the data appears to be in the first column. I am guessing it is because structured array has each element in the form of 'name float float'. Is there a way to seperate each element into 3 columns?
– somebodyzh
Jun 26 '18 at 16:04
add a comment |
0
You could try a solution similar to this SO answer to pivot the data
dtypes = [('name', 'U32'), ('r', float),('m',float)]
a = np.zeros(5, dtype=dtypes)
b = numpy.vstack(map(list, a))
Where you map list over the recarray tuples, and then vertically stack them.
Then you can do the following...
names = [n for n, t in dtypes]
numpy.savetxt('test.csv', b, header=','.join(names), fmt=','.join(['%s']*b.shape[1]))
answered Nov 13 '18 at 11:52
ryanjdillonryanjdillon
7,04864974
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
Below is sample code:
output = np.zeros(names.size, dtype=[('name', 'U32'), ('r', float),('m',float)])
np.savetxt("foo.csv", output, delimiter=",", header="name,r,m", fmt="%s,%f,%f", comments='')
As documented here.
answered Jun 25 '18 at 16:03
chifu linchifu lin
665
It gives me 3 columns this way, but all the data appears to be in the first column. I am guessing it is because structured array has each element in the form of 'name float float'. Is there a way to seperate each element into 3 columns?
– somebodyzh
Jun 26 '18 at 16:04
add a comment |
0
Below is sample code:
output = np.zeros(names.size, dtype=[('name', 'U32'), ('r', float),('m',float)])
np.savetxt("foo.csv", output, delimiter=",", header="name,r,m", fmt="%s,%f,%f", comments='')
As documented here.
answered Jun 25 '18 at 16:03
chifu linchifu lin
665
It gives me 3 columns this way, but all the data appears to be in the first column. I am guessing it is because structured array has each element in the form of 'name float float'. Is there a way to seperate each element into 3 columns?
– somebodyzh
Jun 26 '18 at 16:04
add a comment |
0
0
0
Below is sample code:
output = np.zeros(names.size, dtype=[('name', 'U32'), ('r', float),('m',float)])
np.savetxt("foo.csv", output, delimiter=",", header="name,r,m", fmt="%s,%f,%f", comments='')
As documented here.
answered Jun 25 '18 at 16:03
chifu linchifu lin
665
Below is sample code:
output = np.zeros(names.size, dtype=[('name', 'U32'), ('r', float),('m',float)])
np.savetxt("foo.csv", output, delimiter=",", header="name,r,m", fmt="%s,%f,%f", comments='')
As documented here.
answered Jun 25 '18 at 16:03
chifu linchifu lin
665
answered Jun 25 '18 at 16:03
chifu linchifu lin
665
answered Jun 25 '18 at 16:03
chifu linchifu lin
665
answered Jun 25 '18 at 16:03
chifu linchifu lin
665
665
It gives me 3 columns this way, but all the data appears to be in the first column. I am guessing it is because structured array has each element in the form of 'name float float'. Is there a way to seperate each element into 3 columns?
– somebodyzh
Jun 26 '18 at 16:04
add a comment |
It gives me 3 columns this way, but all the data appears to be in the first column. I am guessing it is because structured array has each element in the form of 'name float float'. Is there a way to seperate each element into 3 columns?
– somebodyzh
Jun 26 '18 at 16:04
It gives me 3 columns this way, but all the data appears to be in the first column. I am guessing it is because structured array has each element in the form of 'name float float'. Is there a way to seperate each element into 3 columns?
– somebodyzh
Jun 26 '18 at 16:04
It gives me 3 columns this way, but all the data appears to be in the first column. I am guessing it is because structured array has each element in the form of 'name float float'. Is there a way to seperate each element into 3 columns?
– somebodyzh
Jun 26 '18 at 16:04
add a comment |
0
You could try a solution similar to this SO answer to pivot the data
dtypes = [('name', 'U32'), ('r', float),('m',float)]
a = np.zeros(5, dtype=dtypes)
b = numpy.vstack(map(list, a))
Where you map list over the recarray tuples, and then vertically stack them.
Then you can do the following...
names = [n for n, t in dtypes]
numpy.savetxt('test.csv', b, header=','.join(names), fmt=','.join(['%s']*b.shape[1]))
answered Nov 13 '18 at 11:52
ryanjdillonryanjdillon
7,04864974
add a comment |
0
You could try a solution similar to this SO answer to pivot the data
dtypes = [('name', 'U32'), ('r', float),('m',float)]
a = np.zeros(5, dtype=dtypes)
b = numpy.vstack(map(list, a))
Where you map list over the recarray tuples, and then vertically stack them.
Then you can do the following...
names = [n for n, t in dtypes]
numpy.savetxt('test.csv', b, header=','.join(names), fmt=','.join(['%s']*b.shape[1]))
answered Nov 13 '18 at 11:52
ryanjdillonryanjdillon
7,04864974
add a comment |
0
0
0
You could try a solution similar to this SO answer to pivot the data
dtypes = [('name', 'U32'), ('r', float),('m',float)]
a = np.zeros(5, dtype=dtypes)
b = numpy.vstack(map(list, a))
Where you map list over the recarray tuples, and then vertically stack them.
Then you can do the following...
names = [n for n, t in dtypes]
numpy.savetxt('test.csv', b, header=','.join(names), fmt=','.join(['%s']*b.shape[1]))
answered Nov 13 '18 at 11:52
ryanjdillonryanjdillon
7,04864974
You could try a solution similar to this SO answer to pivot the data
dtypes = [('name', 'U32'), ('r', float),('m',float)]
a = np.zeros(5, dtype=dtypes)
b = numpy.vstack(map(list, a))
Where you map list over the recarray tuples, and then vertically stack them.
Then you can do the following...
names = [n for n, t in dtypes]
numpy.savetxt('test.csv', b, header=','.join(names), fmt=','.join(['%s']*b.shape[1]))
answered Nov 13 '18 at 11:52
ryanjdillonryanjdillon
7,04864974
edited Nov 14 '18 at 8:57
answered Nov 13 '18 at 11:52
ryanjdillonryanjdillon
7,04864974
answered Nov 13 '18 at 11:52
ryanjdillonryanjdillon
7,04864974
answered Nov 13 '18 at 11:52
ryanjdillonryanjdillon
7,04864974
7,04864974
add a comment |
add a comment |
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output.dtype.namesis a list of those field names. You could use that format a header line, e.g.' '.join(output.dtype.names)– hpaulj
Jun 25 '18 at 16:12
@hpaulj Wouldn't that just gives me three column where all my data will appear in the first column?
– somebodyzh
Jun 26 '18 at 16:08
The header line doesn't affect the layout of the data. That's handled by the
fmtas specified in the answer. Tosavetxt, the header is just a string it writes along with the comment character.– hpaulj
Jun 26 '18 at 16:18