[Numpy-discussion] How to convert a list into a structured array?
Stephen Simmons
mail@stevesimmons....
Wed May 6 00:46:54 CDT 2009
Wei Su wrote:
>
> Hi, Francesc:
>
> Thanks a lot for offering me help. My code is really simple as of now.
>
> **********************************************************************************
>
> from pyodbc import *
>
> from rpy import *
>
> cnxn = connect(/'DRIVER={SQL
> Server};SERVER=srdata01\\sql2k5;DATABASE=_Qai_;UID=;PWD='/)
>
> cursor = cnxn.cursor()
>
> cursor.execute(/"select IsrCode, MstrName from _qai_..qaiLinkBase"/)
>
> data = cursor.fetchall()
>
> cursor.close()
>
> ***************************************************
> The result, data, I got from the above code tends to be a giant list,
> which is very hard to handle. My goal is to to turn it into a record
> array so that i can access the field directly by name or by index. My
> data is typically numerical, character and datetime variables. no
> other complications.
>
> From the above code, you can also see that I used R for some time. But
> I have to switch to something else because I sometimes cannot even
> download all my data via R due to its memory limit under windows. I
> thought NumPy might be the solution. But I am not sure. Anybody can
> let me know whether Python has a memory limit? or can I use virtual
> memory by calling some Python module?
>
> Thanks in advance.
>
> Wei Su
>
>
>
Hi Wei Su,
Below is an example from the code I use to read text files into
recarrays. The same approach can be used for your SQL data by redefining
the inner iterator(path) function to execute your SQL query.
If your data is really big, you could also use the PyTables package
(written by Francesc actually) to store SQL extracts as numpy-compatible
HDF tables. The HDF format can compress the data transparently, so the
resulting data files are 1/10 the size of an equivalent text dump. You
can then read any or all rows into memory for subsequent process using
table.read[row_from, row_to], thereby avoiding running out of memory if
your dataset is really big. PyTables/HDF is also really fast for
reading. As an example, my three year old laptop with slow hard drive
achieves up to 250,000 row per second speeds on GROUP BY-style
subtotals. This uses PyTables for storing the data and numpy's
bincount() function for doing the aggregation.
Stephen
def text_file_to_sorted_numpy_array(path, dtype, row_fn, max_rows=None,
header=None,
order_by=None,
min_row_length=None):
"""
Read a database extract into a numpy recarray, which is possibly
sorted then returned.
path Path to the text file.
dtype String giving column names and numpy data types
e.g. 'COL1,S8 COL2,i4'
row_fn Optional function splitting a row into a list
that is
compatible with the numpy array's dtype. The
function
can indicate the row should be skipped by returning
None. If not given, the row has leading and trailing
whitespace removed and then is split on '|'.
order_by Optional list of column names used to sort the
array.
header Optional prefix for a header line. If given, there
must be a line with this prefix within the first
20 lines.
Any leading whitespace is removed before checking.
max_rows Optional maximum number of rows that a file will
contain.
min_row_length Optional length of row in text file, used to
estimate
upper bound on size of final array. One or both of
max_rows and min_row_length must be given.
"""
# Create a numpy array large enough to hold the entire file in memory
if min_row_length:
file_size = os.stat(path).st_size
num_rows_upper_bound = file_size/min_row_length
else:
num_rows_upper_bound = max_rows
if num_rows_upper_bound is None:
raise ValueError('No information given about size of the final
array')
if max_rows and num_rows_upper_bound>max_rows:
raise ValueError("'%s' is %d bytes long, too large to fit in
memory" % (os.path.basename(path), file_size))
# Define an iterator that reads the data file
def iterator(path):
# Read the file
with file(path, 'rU') as fh:
ftype, prefix =
os.path.splitext(os.path.basename(path))[0].split('-', 2)
pb = ProgressBar(prefix=prefix)
# Read the data lines
ctr = idx = 0
for s in fh:
s = s.strip()
if s in ('\x1A', '-', '') or s.startswith('-------'):
# Empty lines after end of real data
continue
res = row_fn(s)
if res:
yield res
ctr+=1
if ctr%1000==0:
total_rows = float(file_size*ctr)/float(fh.tell())
pb(ctr, total=total_rows)
pb(ctr, last=True)
# Create an empty array to hold all data, then fill in blocks of
5000 rows
# Doing this by blocks is 4x faster than adding one row at a time.
dtype = list( tuple(x.split(',')) for x in dtype.split() )
arr = numpy.zeros(num_rows_upper_bound, dtype)
def block_iterator(iterator, blk_size):
"Group iterator into lists with blk_size elements"
res = []
for i in iterator:
res.append(i)
if len(res)==blk_size:
yield res
res = []
if res:
yield res
# Now fill the array
i = 0
try:
for blk in block_iterator(iterator(path), 5000):
b = len(blk)
tmp = numpy.rec.fromrecords(blk, dtype=dtype, shape=b)
arr[i:i+b] = tmp
i+=b
except KeyboardInterrupt:
pass
arr = arr[:i] # Remove unused rows at the end of the array
# Sort array if required
if order_by:
print " Sorting %d-row array on %r" % (len(arr), order_by)
arr.sort(order=order_by)
# Return the final array
return arr
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