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python Programming Glossary: float64major memory problems reading in a csv file using numpy http://stackoverflow.com/questions/10264739/major-memory-problems-reading-in-a-csv-file-using-numpy open filename 'r' as infile data np.fromiter items infile float64 1 data data.reshape 1 num_cols np.save filename data return..
Image transformation in OpenCV http://stackoverflow.com/questions/10364201/image-transformation-in-opencv to the values to float32 as OpenCV complains about the float64 type. Please let me know how it goes. Update If you don't want..
Using multiprocessing.Manager.list instead of a real list makes the calculation take ages http://stackoverflow.com/questions/13121790/using-multiprocessing-manager-list-instead-of-a-real-list-makes-the-calculation of free memory. After allocating a 15000x15000 array of float64 s there was 763248 KB free. This roughly makes sense since 15000..
How to trouble-shoot HDFStore Exception: cannot find the correct atom type http://stackoverflow.com/questions/15488809/how-to-trouble-shoot-hdfstore-exception-cannot-find-the-correct-atom-type values Steering_Controls 401125 non null values dtypes float64 2 int64 6 object 45 Code to store the dataframe In 30 store..
HDF5 and SQLite. Concurrency, compression & I/O performance [closed] http://stackoverflow.com/questions/16628329/hdf5-and-sqlite-concurrency-compression-i-o-performance A 1000000 non null values B 1000000 non null values dtypes float64 2 def test_sql_write df if os.path.exists 'test.sql' os.remove..
What is the difference between NaN and None? http://stackoverflow.com/questions/17534106/what-is-the-difference-between-nan-and-none to use NaN over None is that it can be stored with numpy's float64 dtype rather than the less efficient object dtype see NA type.. Out 13 dtype 'O' In 14 s_good.dtype Out 14 dtype 'float64' Saying that many operations may still work just as well with..
Can i set float128 as the standard float-array in numpy http://stackoverflow.com/questions/5350342/can-i-set-float128-as-the-standard-float-array-in-numpy dtype 'float128' all over the place. My arrays are all float64 but i never explicitly wrote dtype 'float64' so i was hoping.. arrays are all float64 but i never explicitly wrote dtype 'float64' so i was hoping there was a way to change this default behaviour...
numpy float: 10x slower than builtin in arithmetic operations? http://stackoverflow.com/questions/5956783/numpy-float-10x-slower-than-builtin-in-arithmetic-operations the following lines is uncommented before execution #s np.float64 1 #s np.float32 1 #s 1.0 for i in range 10000000 s s 8 s 2399232.. s print 'Runtime ' datetime.now START_TIME The timings are float64 16.1s float32 16.1s float 3.2s Now both np floats either 64.. code perhaps why the code below runs 10 times slow with float64 than with float . EDIT numpy.float64 is 10 times slower than..
append a numpy array to a numpy array http://stackoverflow.com/questions/9775297/append-a-numpy-array-to-a-numpy-array do the following without any luck M np.array M array dtype float64 M.append a axis 0 Traceback most recent call last File stdin..
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