Why does this Python code get the name error? [duplicate] - python

This question already has answers here:
Accessing class variables from a list comprehension in the class definition
(8 answers)
Closed 7 years ago.
I want to have attribute that is shared by and accessible to all instances of a class.
Sample code:
class Test:
code = [ 1, 2, 3, 5, 6, 7, 8, 9, 10]
code2d = [ [ code[j*3 + i] for i in range(3) ] for j in range(3) ]
def __init__(self):
pass
However, I get error:
NameError: name 'code' is not defined
If I put the lines with code and code2d into init method, all is working properly.
Why this code throws the error?


code is a class variable , and hence when accessing it you need to call - Test.code , you cannot access them using code .
Also, even if you do access them using Test.code , it would still not work, because the value for class variables (default value) is calculated when the class is being defined, so when you try to access Test.code , you would not be able to access Test as it has not been created yet. Example of that -
>>> class Test:
... code = [ 1, 2, 3, 5, 6, 7, 8, 9, 10]
... code2d = [ [ Test.code[j*3 + i] for i in range(3) ] for j in range(3) ]
... def __init__(self):
... pass
...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 3, in Test
File "<stdin>", line 3, in <listcomp>
File "<stdin>", line 3, in <listcomp>
NameError: name 'Test' is not defined
I am guessing when you put them in the __init__() you are putting them as -
class Test:
def __init__(self):
code = [ 1, 2, 3, 5, 6, 7, 8, 9, 10]
code2d = [ [ code[j*3 + i] for i in range(3) ] for j in range(3) ]
This would work, because code here is a local variable and hence can be accesed by the other local variables in the __init__() method , though they would not be accessible outside this function.
Maybe, you do not need them as class variables , if you want to have code and code2d for all instances of the class (objects of the class) , you should create instance variable as-
>>> class Test:
... def __init__(self):
... self.code = [ 1, 2, 3, 5, 6, 7, 8, 9, 10]
... self.code2d = [ [ self.code[j*3 + i] for i in range(3) ] for j in range(3) ]
If you really want code and code2d to be class variables , one way to do it would be to define code2d outside the class Test , example -
class Test:
code = [ 1, 2, 3, 5, 6, 7, 8, 9, 10]
def __init__(self):
pass
Test.code2d = [ [ Test.code[j*3 + i] for i in range(3) ] for j in range(3) ]

Related

class wise object detection product count in tensorflow 2

Can we count detected objects in Tensorflow 2 object detection API class wise?
as i am new to this so having hard time in manipulating the output of object detection model according to my use case described below
Say you have two classes tomato and potato in a super market shelf stock, I would like to count each object class wise
for example; potato_count:5 tomato_count:3.
for reference the following output i have is like this, just to give an idea:-
{'raw_detection_scores': array([[3.8237274e-03, 3.1729043e-03, 5.1983595e-03, ..., 1.0126382e-02,4.1468740e-03, 3.5721064e-03],
[3.7932396e-03, 1.9723773e-03, 2.3661852e-03, ..., 3.4036636e-03,
9.3266368e-04, 4.3996871e-03],
[3.2063425e-03, 2.9956400e-03, 3.9784312e-03, ..., 5.5939257e-03,
2.3936033e-03, 2.7040839e-03],
...,
[4.1239262e-03, 3.9246678e-04, 4.5044391e-05, ..., 1.2922287e-04,
2.8958917e-04, 2.2355914e-03],
[1.4656782e-03, 4.8859119e-03, 1.4899671e-03, ..., 2.8479993e-03,
2.8250813e-03, 2.1298528e-03],
[1.8135607e-03, 2.2478402e-03, 1.1820495e-03, ..., 9.5197558e-04,
1.3802052e-03, 2.2761822e-03]], dtype=float32), 'detection_anchor_indices': array([44692., 44710., 44728., 44818., 39652., 44674., 39670., 40036.,
40018., 44800., 39634., 39371., 44830., 38090., 44728., 44731.,
44728., 44710., 10078., 39796., 27838., 37604., 16933., 24833.,
39778., 44659., 45058., 38084., 44791., 44710., 44692., 30244.,
5284., 38090., 37604., 38204., 33593., 38192., 37982., 39796.,
44692., 6635., 33118., 24389., 37604., 44910., 33112., 39601.,
16133., 3845., 39918., 48370., 19204., 44740., 39778., 16792.,
6629., 25763., 38150., 48187., 15839., 38180., 23524., 44914.,
40036., 1438., 25763., 38078., 35992., 38012., 39888., 38084.,
44578., 40018., 51075., 38204., 15833., 37976., 40258., 37604.,
48751., 39906., 31684., 16453., 38054., 5140., 42568., 36484.,
38012., 38202., 37946., 14024., 2404., 40002., 5764., 39870.,
48823., 26878., 38198., 39430.], dtype=float32), 'detection_multiclass_scores': array([[1.6726255e-03, 3.2217724e-07, 2.8865278e-02, ..., 2.6032329e-04,
2.0583175e-05, 4.5886636e-04],
[1.0796189e-03, 5.8811463e-07, 3.6984652e-02, ..., 6.6033006e-04,
3.2279208e-05, 4.3705106e-04],
[6.1860681e-04, 2.4921978e-06, 6.0835034e-02, ..., 2.0631850e-03,
5.2474130e-05, 3.7664175e-04],
...,
[2.0163953e-03, 1.0121465e-03, 1.6601086e-03, ..., 5.1327944e-03,
1.6998947e-03, 9.6607208e-04],
[1.2855232e-03, 5.4006279e-03, 1.0573506e-02, ..., 1.3051391e-02,
1.0753423e-02, 1.3659596e-03],
[8.1962347e-04, 9.5169604e-01, 1.5044212e-03, ..., 5.1358938e-03,
8.4767938e-03, 3.2877922e-04]], dtype=float32),
'detection_classes': array([4, 4, 4, 4, 1, 4, 1, 1, 1, 4, 1, 1, 4, 6, 6, 4, 2, 6, 7, 7, 4, 6,
1, 6, 7, 4, 7, 9, 4, 2, 6, 7, 1, 3, 9, 6, 1, 6, 6, 3, 2, 5, 1, 2,
3, 6, 1, 1, 8, 1, 1, 1, 7, 4, 3, 1, 9, 6, 6, 8, 9, 6, 8, 2, 8, 7,
2, 6, 1, 6, 9, 5, 2, 9, 9, 3, 1, 9, 8, 5, 9, 9, 3, 5, 6, 1, 6, 6,
9, 6, 6, 1, 2, 1, 2, 9, 9, 4, 4, 7]), 'detection_boxes': array([[5.92004418e-01, 1.69490814e-01, 7.45701075e-01, 2.46565759e-01],
[5.89631081e-01, 2.46157080e-01, 7.39599228e-01, 3.18454713e-01],
[5.87109149e-01, 3.14503819e-01, 7.36972034e-01, 3.85336846e-01],
[5.87837219e-01, 7.05797434e-01, 7.28340387e-01, 7.74214983e-01],
[6.35630414e-02, 1.69735521e-01, 2.04962432e-01, 2.47269154e-01],
[5.92036664e-01, 9.53008384e-02, 7.46890843e-01, 1.73706189e-01],
[6.85142130e-02, 2.45773658e-01, 2.10277155e-01, 3.21099281e-01],
[9.23785418e-02, 7.77337551e-01, 2.25108251e-01, 8.45668435e-01],
[9.24619362e-02, 7.06092656e-01, 2.26126671e-01, 7.77547657e-01],
[5.85118353e-01, 6.37673438e-01, 7.26098835e-01, 7.05848277e-01],
[5.94619289e-02, 9.38070714e-02, 2.03622460e-01, 1.72308475e-01],
[3.44502553e-02, 7.54546002e-03, 2.08990484e-01, 9.39401984e-02],
[5.87913811e-01, 7.74250209e-01, 7.28712976e-01, 8.34733903e-01],
[9.84132528e-01, 3.18221241e-01, 9.96858120e-01, 3.95583898e-01],
[5.87109149e-01, 3.14503819e-01, 7.36972034e-01, 3.85336846e-01],
[5.89539468e-01, 3.49524260e-01, 7.35065162e-01, 4.21008408e-01],
[5.87109149e-01, 3.14503819e-01, 7.36972034e-01, 3.85336846e-01],
[5.89631081e-01, 2.46157080e-01, 7.39599228e-01, 3.18454713e-01],
[1.87163889e-01, 9.88169909e-01, 3.71130943e-01, 9.98176932e-01],
[9.36717317e-02, 7.77330160e-01, 2.24804163e-01, 8.45728278e-01],
[6.63008153e-01, 9.89469707e-01, 8.10642183e-01, 1.00000000e+00],
[9.70665693e-01, 3.16653520e-01, 9.95440483e-01, 3.85887355e-01],
[3.70503038e-01, 2.54840344e-01, 4.76123840e-01, 3.14984292e-01],
[5.87433934e-01, 7.05650687e-01, 7.27492571e-01, 7.73511648e-01],
[9.28397924e-02, 7.06507027e-01, 2.26004675e-01, 7.77664006e-01],
[5.82323313e-01, 1.54982358e-02, 7.39678025e-01, 1.03125945e-01],
[5.87077260e-01, 7.05565095e-01, 7.27602482e-01, 7.74259925e-01],
[9.83516991e-01, 3.11883837e-01, 9.97174442e-01, 3.89778942e-01],
[5.88727355e-01, 6.20116591e-01, 7.30183959e-01, 6.90428734e-01],
[5.89631081e-01, 2.46157080e-01, 7.39599228e-01, 3.18454713e-01],
[5.92004418e-01, 1.69490814e-01, 7.45701075e-01, 2.46565759e-01],
[7.42125034e-01, 0.00000000e+00, 8.81110668e-01, 8.84985179e-03],
[5.44907227e-02, 0.00000000e+00, 2.06223458e-01, 1.28744319e-02],
[9.84132528e-01, 3.18221241e-01, 9.96858120e-01, 3.95583898e-01],
[9.70665693e-01, 3.16653520e-01, 9.95440483e-01, 3.85887355e-01],
[9.84484971e-01, 5.55115521e-01, 9.97788608e-01, 6.32665694e-01],
[7.99783111e-01, 9.75158930e-01, 9.83929038e-01, 9.97444630e-01],
[9.85278428e-01, 5.28306305e-01, 9.97080624e-01, 6.08543932e-01],
[9.88123000e-01, 9.33963358e-02, 9.99226749e-01, 1.72955215e-01],
[9.36717317e-02, 7.77330160e-01, 2.24804163e-01, 8.45728278e-01],
[5.92004418e-01, 1.69490814e-01, 7.45701075e-01, 2.46565759e-01],
[9.08265784e-02, 7.78585851e-01, 2.25109786e-01, 8.43916476e-01],
[7.94785440e-01, 9.86553550e-01, 9.70936120e-01, 9.98435020e-01],
[5.84929466e-01, 7.75964439e-01, 7.24675894e-01, 8.34971726e-01],
[9.70665693e-01, 3.16653520e-01, 9.95440483e-01, 3.85887355e-01], 3.21474910e-01]],
dtype=float32), 'raw_detection_boxes': array([[-0.0132168 , -0.00798112, 0.03437265, 0.02366759],
[-0.01795438, -0.01333077, 0.04313567, 0.03091241],
[-0.00845873, -0.01297706, 0.02555573, 0.02979016],
[-0.01206583, -0.01901898, 0.03632494, 0.04061931],
[-0.01634497, -0.00570066, 0.04027664, 0.01987169],
[-0.02299639, -0.01094626, 0.05078602, 0.02601441],
[-0.01034649, -0.00047059, 0.03106559, 0.04336115],
[-0.01548673, -0.00679935, 0.03944379, 0.05214766],
[-0.00469762, -0.00637354, 0.02257038, 0.05068764],
[-0.00889431, -0.01532986, 0.03383063, 0.06445184],
[-0.01338234, 0.00258018, 0.03299785, 0.03899822],
[-0.02030504, -0.00274394, 0.04193052, 0.04610612],
[-0.0114202 , 0.00825354, 0.0315875 , 0.05609718],
[-0.01720474, 0.00155611, 0.03969076, 0.06473814],
[-0.0055348 , 0.00137738, 0.02347516, 0.06321988],
[-0.0093858 , -0.00954537, 0.03353771, 0.0789085 ],
[-0.01528691, 0.0120711 , 0.03230394, 0.05128276],
[-0.02242971, 0.00611713, 0.04139108, 0.0590462 ],
[-0.01265933, 0.01957938, 0.03226281, 0.06821183],
[-0.0190082 , 0.01264081, 0.04051029, 0.07676097],
[-0.00625486, 0.01262659, 0.02384217, 0.07535952],
[-0.01057751, 0.00036938, 0.03408406, 0.09211845],
[-0.01712188, 0.02387175, 0.03272626, 0.0631646 ],
[-0.02457684, 0.01729448, 0.04191976, 0.07130254],
[-0.01416131, 0.03209703, 0.03322188, 0.08013913],
[-0.02092581, 0.02524993, 0.04159252, 0.08845924],
[-0.00731821, 0.02507119, 0.02447667, 0.08743346],
[-0.01213621, 0.01294496, 0.03459452, 0.10395688],
[-0.01857999, 0.0361888 , 0.03388733, 0.07542843],
[-0.02637036, 0.02969162, 0.04293538, 0.08341235],
[-0.01507254, 0.04520991, 0.03351783, 0.09184141],
[-0.02222046, 0.03861695, 0.04212021, 0.10008947],
[-0.00780608, 0.03797973, 0.02448018, 0.09932629],
[-0.01303079, 0.02687315, 0.03459996, 0.1151351 ],
[-0.0191509 , 0.04890272, 0.03473954, 0.08777986],
[-0.02749499, 0.04277577, 0.04370061, 0.09534387],
[-0.01489433, 0.05867497, 0.03314201, 0.10344677],
[-0.02239214, 0.05207732, 0.04205906, 0.11197228],
[-0.00734611, 0.05139816, 0.02392033, 0.11116292],
[-0.01289164, 0.0412713 , 0.03449183, 0.12679553],
[-0.01872004, 0.06203329, 0.03483813, 0.09988385],
[-0.02761277, 0.05606709, 0.04412681, 0.10715124], 0.2496243 ]],
dtype=float32), 'detection_scores': array([0.9957284 , 0.9954956 , 0.9948391 , 0.9935589 , 0.9928843 ,
0.9922596 , 0.99091065, 0.9904872 , 0.9904753 , 0.9836049 ,
0.97076845, 0.76198786, 0.11483946, 0.08861226, 0.06485316,
0.06403089, 0.06083503, 0.05606595, 0.05304798, 0.05192479,
0.05068725, 0.0497607 , 0.04650801, 0.04170695, 0.04141748,
0.0396772 , 0.03875464, 0.03834933, 0.03700855, 0.03698465,
0.03656569, 0.03464538, 0.03429574, 0.03408125, 0.033981 ,
0.03356522, 0.03337869, 0.03140217, 0.03058183, 0.02957818,
0.02886528, 0.02712101, 0.02674139, 0.02655837, 0.02634463,
0.02611795, 0.02595255, 0.02580112, 0.0251711 , 0.02473494,
0.02423027, 0.02406707, 0.02352765, 0.02347961, 0.02342641,
0.02327773, 0.02312759, 0.0229713 , 0.02272761, 0.02240831,
0.02240023, 0.02203956, 0.02200234, 0.02167007, 0.02112213,
0.0210447 , 0.02079707, 0.02007249, 0.01999336, 0.01993376,
0.01986268, 0.0196887 , 0.01967749, 0.01877454, 0.01874545,
0.01856974, 0.01855248, 0.01853141, 0.01839408, 0.01838818,
0.01830906, 0.01829055, 0.01759666, 0.01758116, 0.01747909,
0.01745978, 0.01728415, 0.01719788, 0.0171611 , 0.01715598,
0.01704106, 0.01684934, 0.01672551, 0.01663077, 0.01645952,
0.01627839, 0.01607156, 0.01592609, 0.01579505, 0.01570672],
dtype=float32), 'num_detections': 100}
Please guyys help me out in this
thanks in advance

Check out his link: https://www.tensorflow.org/api_docs/python/tf/data/Dataset
Here, you can find how to iterate over Dataset using ".as_numpy_iterator()", but also how to use different methods to manipulate the input dataset.
Hope this will be useful.

Python How to hide traceback in unittest

guys, I have a unittest was where I don't want to throw exception when find one but wait until the end and output raise it at the end.:
from unittest import TestCase
class TestA(TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_lst(self):
a = [1, 2, 3, 4, 5]
b = [1, 3, 3, 5, 5]
total_errs_count = 0
total_errs_msg = []
for i in range(5):
try:
self.assertEqual(a[i], b[i])
except AssertionError:
total_errs_count += 1
total_errs_msg.append(f'Index {i}, Expected {a[i]}, Get {b[i]}')
if total_errs_count > 0:
for m in total_errs_msg:
print(m)
raise AssertionError("Test Failed")
test = TestA()
test.test_lst()
I got:
IOndex 1, Expected 2, Get 3
Number 3, Expected 4, Get 5
----------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-5-b70dc996c844> in <module>
27
28 test = TestA()
---> 29 test.test_lst()
<ipython-input-5-b70dc996c844> in test_lst(self)
24 for m in total_errs_msg:
25 print(m)
---> 26 raise AssertionError("Test Failed")
27
28 test = TestA()
AssertionError: Test Failed
However, the desired output is to hide the traceback:
Index 1, Expected 2, Get 3
Index 3, Expected 4, Get 5
----------------------------------------------------
AssertionError: Test Failed
How can I hide the traceback in this case? Another post suggested to catch the exception by unittest_exception = sys.exc_info(), but here I don't want to immediately throw the exceptions but wait for all test cases to finish.
Any suggestion ?
Thanks

Try this way
from unittest import TestCase
import unittest
class TestA(TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_lst(self):
a = [1, 2, 3, 4, 5]
b = [1, 3, 3, 5, 5]
for i in range(len(a)):
with self.subTest(i=i):
self.assertEqual(a[i], b[i])
if __name__ == '__main__':
unittest.main()

When I call a method inside a class it is not working but it works outside?

def lookup_cell(self, column, row):
return(self.puzzle[row-1][column-1])
def lookup_column(self, column):
output = []
for i in range(9):
output.append(self.lookup_cell(column, i+1))
return output
def check_puzzle(self):
valid = True
#check all the rows
for i in range(1,10):
row = self.lookup_row(i)
while 0 in row: row.remove(0)
for i in range(1,10):
if row.count(i) > 1:
valid = False
#check all the columns
for i in range(1,10):
print(i)
print(easy.lookup_column(i))
puzzle = '''0,9,0,7,5,1,0,2,3 /n
2,1,8,6,0,3,7,5,4 /n
0,0,0,4,0,2,0,0,0 /n
1,0,0,0,0,0,0,9,2 /n
0,0,0,5,0,0,3,8,0 /n
3,0,0,8,2,0,5,0,6 /n
0,0,0,0,7,0,0,4,8 /n
0,4,9,0,0,0,0,7,0 /n
0,2,0,0,0,5,6,3,1 '''
easy = Sudoku(puzzle)
Here the code works and prints the columns of the puzzle properly:
for i in range(1,10):
print(easy.lookup_column(i))
when this runs I get an error which I'll add below:
easy.check_puzzle()
1 [9, 2, 4, 1, 5, 3, 7, 4, 2] 2 [7, 1, 2, 9, 3, 8, 4, 9, 5] 3
Traceback (most recent call last):
File "/Users/ellis/Desktop/Sudoku.py", line 121, in <module>
easy.check_puzzle()
File "/Users/ellis/Desktop/Sudoku.py", line 81, in check_puzzle
print(easy.lookup_column(i))
File "/Users/ellis/Desktop/Sudoku.py", line 65, in lookup_column
output.append(self.lookup_cell(column, i+1))
File "/Users/ellis/Desktop/Sudoku.py", line 19, in lookup_cell
return(self.puzzle[row-1][column-1])
IndexError: list index out of range

Your code works outside of the class method because
for i in range(1,10):
print(easy.lookup_column(i))
is not calling
def lookup_cell(self, column, row):
return(self.puzzle[row-1][column-1]
The traceback you posted shows that the code above is causing the issue, where your accessing values in the object that's outside its bounds

I'm not exactly sure what you are trying to accomplish, but why not use a list for puzzle instead of a string?
Try this:
class Sudoku(object):
def __init__(self,puzzle):
self.puzzle = puzzle
print("Inside class; column lookup")
for i in range(1,10):
print('Column {0} ='.format(i),self.lookup_column(i))
self.check_puzzle()
def lookup_cell(self, column, row):
return self.puzzle[row-1][column-1]
def lookup_row(self,row):
output = []
for i in range(9):
output.append(self.lookup_cell(i+1, row))
return output
def lookup_column(self, column):
output = []
for i in range(9):
output.append(self.lookup_cell(column, i+1))
return output
def check_puzzle(self):
valid = True
#check all the rows
for i in range(1,10):
row = self.lookup_row(i)
while 0 in row: row.remove(0)
for i in range(1,10):
if row.count(i) > 1:
valid = False
puzzle = [
[0,9,0,7,5,1,0,2,3],
[2,1,8,6,0,3,7,5,4],
[0,0,0,4,0,2,0,0,0],
[1,0,0,0,0,0,0,9,2],
[0,0,0,5,0,0,3,8,0],
[3,0,0,8,2,0,5,0,6],
[0,0,0,0,7,0,0,4,8],
[0,4,9,0,0,0,0,7,0],
[0,2,0,0,0,5,6,3,1]]
easy = Sudoku(puzzle)
print("Outside class; column lookup")
for i in range(1,10):
print('Column {0} ='.format(i),easy.lookup_column(i))
easy.check_puzzle()

Trying to get a weighted average out of a dictionary of grades data

I am trying to return the weighted average of the student's grades based on the last definition. I have the dictionaries defined, but think my attempt to pull the numbers out is incorrect.
def Average(lst):
return sum(lst) / len(lst)
# Driver Code
lst = [1,2,3,4,5]
average = Average(lst)
print("Average of the list =", average)
def get_weighted_average(student):
return average('homework')*0.10 + average('quizzes')*0.30 + average('tests')*.60
#driver code
students = [steve, alice, tyler]
print(get_weighted_average('steve'))
How to get a weighted average out of a dictionary of grades above?

What is the primary source of your data? Text? Anyway, it looks like you have something like this in mind.
Imperative approach
1 - Your "database"
students_marks = {
'steve':{
'homework':[1,2,3,4,5],
'quizzes' :[5,4,3,2,1],
'tests' :[0,0,0,0,0],
},
'alice':{
'homework':[5,4,3,2,1],
'quizzes' :[0,0,0,0,0],
'tests' :[1,2,3,4,5],
},
}
use case:
>>> students_marks['steve']
{'homework': [1, 2, 3, 4, 5], 'quizzes': [5, 4, 3, 2, 1], 'tests': [0, 0, 0, 0, 0]}
>>> students_marks['steve']['homework']
[1, 2, 3, 4, 5]
2 - The definition of average and get_weighted_average
def average(lst):
return sum(lst)/len(lst) # Python3
#return sum(lst)/float(len(lst)) # Python2
def get_weighted_average(student_name):
student_marks = students_marks[student_name]
return round(
average(student_marks['homework'])*.1
+ average(student_marks['quizzes'])*.3
+ average(student_marks['tests'])*.6
, 2)
use case:
>>> get_weighted_average('steve')
1.2
>>> get_weighted_average('alice')
2.1
or using list
>>> students_names = ['steve', 'alice']
>>> [get_weighted_average(name) for name in students_names]
[1.2, 2.1]
or using dict
>>> {name:get_weighted_average(name) for name in students_names}
{'steve': 1.2, 'alice': 2.1}
Object-Oriented (OO) approach
All this being shown, what you want to do would probably be better done by programming in an OO manner. A quick example
class Student(object):
homeworks_weight = .1
quizzes_weight = .3
tests_weight = .6
def __init__(self, name, homeworks_marks, quizzes_marks, tests_marks):
self.name = name
self.homeworks_marks = homeworks_marks
self.quizzes_marks = quizzes_marks
self.tests_marks = tests_marks
#staticmethod
def average(marks):
return sum(marks)/len(marks)
def get_gpa(self, rd=2):
return round(
self.average(self.homeworks_marks)*self.homeworks_weight
+ average(self.quizzes_marks)*self.quizzes_weight
+ average(self.tests_marks)*self.tests_weight
, rd)
use case:
>>> steve = Student(
name = 'Steve',
homeworks_marks = [1,2,3,4,5],
quizzes_marks = [5,4,3,2,1],
tests_marks = [0,0,0,0,0]
)
>>> steve.get_gpa()
1.2
>>> steve.homeworks_marks
[1, 2, 3, 4, 5]

Outlook.OlSaveAsType.olMSG

reportPath = "C:\\Test\\"
oApp = win32com.client.Dispatch("Outlook.Application")
nSpace = oApp.GetNamespace("MAPI")
nSpace.Logon()
oInbox = nSpace.GetDefaultFolder(win32com.client.constants.olFolderInbox)
oItems = oInbox.Items
for i in range(oItems.Count):
oMsg = oItems.Item(i+1)
oMsg.SaveAs(reportPath+str(i)+".msg", Outlook.OlSaveAsType.olMSG)
oMsg.SaveAs fails as "Outlook.OlSaveAsType.olMSG" parameter invalid.
What is its equivalent in win32com?
Please help

Try to pass a numeric value (olMSG = 3) instead. Or just remove the second parameter, it is optional.

Thanks #Eugene Astafiev for driving it. Finally it worked. Steps following.
OlSaveAsType = {
"olTXT": 0,
"olRTF": 1,
"olTemplate": 2,
"olMSG": 3,
"olDoc": 4,
"olHTML": 5,
"olVCard": 6,
"olVCal": 7,
"olICal": 8
}
&
oMsg.SaveAs("C:\\Test\\1.msg", OlSaveAsType['olMSG'])

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