StackOverflow2013

Note that there are some explanatory texts on larger screens.

plurals

PO
primarykey
Id
14400915
data
AcceptedAnswerId
0
AnswerCount
0
ClosedDate
CommentCount
1
CommunityOwnedDate
CreationDate
2013-01-18T14:23:20.717
FavoriteCount
0
LastActivityDate
2013-01-18T17:58:02.710
LastEditDate
2013-01-18T17:58:02.710
LastEditorUserId
110026
OwnerUserId
110026
ParentId
14398868
PostTypeId
2
Score
1
ViewCount
0
LastEditorDisplayName
text
Body
I think the trick is to <code>reshape(-1, nbcols)</code> what you get from <code>np.genfromtxt</code>, so your assignment should look like: <pre><code>a[:, ind:ind + nbcols] = np.genfromtxt('file_1000x10.csv', usecols = range(nbcols)).reshape(-1, nbcols) </code></pre> On a separate note, looping over <code>ind</code>, and reading the file every time is unnecessary. You can do a little bit of higher dimensionality voodoo as follows: <pre class="lang-py prettyprint-override"><code>import numpy as np from StringIO import StringIO def make_data(rows, cols) : data = ((str(k + cols * j) for k in xrange(cols)) for j in xrange(rows)) data = '\n'.join(map(lambda x: ' '.join(x), data)) return StringIO(data) def read_data(f, rows, cols, nbcols) : a = np.zeros((rows, (cols + nbcols - 1) // nbcols, nbcols)) a[...] = np.genfromtxt(f, usecols=range(nbcols)).reshape(-1, 1, nbcols) return a.reshape(rows, -1)[:, :cols] >>> read_data(make_data(3, 6), 3, 6, 2) array([[ 0., 1., 0., 1., 0., 1.], [ 6., 7., 6., 7., 6., 7.], [ 12., 13., 12., 13., 12., 13.]]) >>> read_data(make_data(3, 6), 3, 6, 1) array([[ 0., 0., 0., 0., 0., 0.], [ 6., 6., 6., 6., 6., 6.], [ 12., 12., 12., 12., 12., 12.]]) >>> read_data(make_data(3, 6), 3, 6, 4) array([[ 0., 1., 2., 3., 0., 1.], [ 6., 7., 8., 9., 6., 7.], [ 12., 13., 14., 15., 12., 13.]]) </code></pre> <hr> ORIGINAL ANSWER You can add that extra dimension of size 1 to <code>your_array</code> using: <pre><code>your_array.reshape(your_array.shape + (1,)) </code></pre> or the equivalent <pre><code>your_array.reshape(-1, 1) </code></pre> The same can be achieved with <pre><code>your_array[..., np.newaxis] </code></pre> or the equivalent <pre><code>your_array[..., None] </code></pre>
Tags
Title
singulars
PostAcceptedAnswerId
1. This table or related slice is empty.
PostParentId
1. POforcing genfromtxt output to no-vector
 singulars
 PostTypePostTypeId
 PTQuestion
PostTypePostTypeId
1. PTAnswer
UserLastEditorUserId
1. USJaime
UserOwnerUserId
1. USJaime
plurals
PostLinksPostIdRelatedPostId
1. This table or related slice is empty.
PostLinksRelatedPostIdPostId
1. This table or related slice is empty.
PostsAcceptedAnswerId
1. POforcing genfromtxt output to no-vector
 singulars
 PostTypePostTypeId
 PTQuestion
PostsParentIdCreationDate
1. This table or related slice is empty.
VotesPostIdCreationDate
1. VO
 singulars
 PostPostId
 PO
 UserUserId
 This table or related slice is empty.
 VoteTypeVoteTypeId
 VTUpMod
2. VO
 singulars
 PostPostId
 PO
 UserUserId
 This table or related slice is empty.
 VoteTypeVoteTypeId
 VTAcceptedByOriginator
CommentsPostId
1. COyour suggestion with reshape is a good solution.
 singulars
 PostPostId
 PO
 UserUserId
 USuser1850133

Querying!

Guidance

A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.