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PONumpy set dtype=None, cannot splice columns and set dtype=object cannot set dtype.names
 

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  1. PONumpy set dtype=None, cannot splice columns and set dtype=object cannot set dtype.names
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    <p>I am running Python 2.6. I have the following example where I am trying to concatenate the date and time string columns from a csv file. Based on the dtype I set (None vs object), I am seeing some differences in behavior that I cannot explained, see Question 1 and 2 at the end of the post. The exception returned is not too descriptive, and the dtype documentation doesn't mention any specific behavior to expect when dtype is set to object.</p> <p>Here is the snippet:</p> <pre><code>#! /usr/bin/python import numpy as np # simulate a csv file from StringIO import StringIO data = StringIO(""" Title Date,Time,Speed ,,(m/s) 2012-04-01,00:10, 85 2012-04-02,00:20, 86 2012-04-03,00:30, 87 """.strip()) # (Fail) case 1: dtype=None splicing a column fails next(data) # eat away the title line header = [item.strip() for item in next(data).split(',')] # get the headers arr1 = np.genfromtxt(data, dtype=None, delimiter=',',skiprows=1)# skiprows=1 for the row with units arr1.dtype.names = header # assign the header to names # so we can do y=arr['Speed'] y1 = arr1['Speed'] # Q1 IndexError: invalid index #a1 = arr1[:,0] #print a1 # EDIT1: print "arr1.shape " print arr1.shape # (3,) # Fails as expected TypeError: unsupported operand type(s) for +: 'numpy.ndarray' and 'numpy.ndarray' # z1 = arr1['Date'] + arr1['Time'] # This can be workaround by specifying dtype=object, which leads to case 2 data.seek(0) # resets # (Fail) case 2: dtype=object assign header fails next(data) # eat away the title line header = [item.strip() for item in next(data).split(',')] # get the headers arr2 = np.genfromtxt(data, dtype=object, delimiter=',',skiprows=1) # skiprows=1 for the row with units # Q2 ValueError: there are no fields define #arr2.dtype.names = header # assign the header to names. so we can use it to do indexing # ie y=arr['Speed'] # y2 = arr['Date'] + arr['Time'] # column headings were assigned previously by arr.dtype.names = header data.seek(0) # resets # (Good) case 3: dtype=object but don't assign headers next(data) # eat away the title line header = [item.strip() for item in next(data).split(',')] # get the headers arr3 = np.genfromtxt(data, dtype=object, delimiter=',',skiprows=1) # skiprows=1 for the row with units y3 = arr3[:,0] + arr3[:,1] # slice the columns print y3 # case 4: dtype=None, all data are ints, array dimension 2-D # simulate a csv file from StringIO import StringIO data2 = StringIO(""" Title Date,Time,Speed ,,(m/s) 45,46,85 12,13,86 50,46,87 """.strip()) next(data2) # eat away the title line header = [item.strip() for item in next(data2).split(',')] # get the headers arr4 = np.genfromtxt(data2, dtype=None, delimiter=',',skiprows=1)# skiprows=1 for the row with units #arr4.dtype.names = header # Value error print "arr4.shape " print arr4.shape # (3,3) data2.seek(0) # resets </code></pre> <p><strong>Question 1:</strong> At comment Q1, why can I not slice a column, when dtype=None? This could be avoided by a) arr1=np-genfromtxt... was initialized with dtype=object like case 3, b) arr1.dtype.names=... wascommented out to avoid the Value error in case 2</p> <p><strong>Question 2:</strong> At comment Q2, why can I not set the dtype.names when dtype=object?</p> <p><strong>EDIT1:</strong></p> <p>Added a case 4 that shows when the dimension of the array would be 2-D if the values in the simulated csv files are all ints instead. One can slice the column, but assigning the dtype.names would still fail.</p> <p>Update the term 'splice' to 'slice'.</p>
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