How to use simple_func method in pandera

Best Python code snippet using pandera_python

vectorization_test.py

Source:vectorization_test.py Github

copy

Full Screen

...104 mg = sparse_meshgrid(np.arange(5) - 3)105 val_1 = -1106 val_2 = 2107 @vectorize(otypes=['int'])108 def simple_func(x):109 return 0 if x < 0 else 1110 true_result_arr = [0, 0, 1, 1, 1]111 true_result_mg = [0, 0, 0, 1, 1]112 # Out-of-place113 out = simple_func(arr)114 assert isinstance(out, np.ndarray)115 assert out.dtype == np.dtype('int')116 assert out.shape == (5,)117 assert all_equal(out, true_result_arr)118 out = simple_func(mg)119 assert isinstance(out, np.ndarray)120 assert out.shape == (5,)121 assert out.dtype == np.dtype('int')122 assert all_equal(out, true_result_mg)123 assert simple_func(val_1) == 0124 assert simple_func(val_2) == 1125 # Python 2 really swallows this stuff in comparisons...126 bogus_input = [lambda x: x, object, Exception]127 if sys.version_info.major > 2:128 for b in bogus_input:129 with pytest.raises(TypeError):130 simple_func(b)131 # In-place132 out = np.empty(5, dtype='int')133 simple_func(arr, out=out)134 assert all_equal(out, true_result_arr)135 out = np.empty(5, dtype='int')136 simple_func(mg, out=out)137 assert all_equal(out, true_result_mg)138def test_vectorize_1d_lazy():139 # Test vectorization in 1d without data type --> lazy vectorization140 arr = (np.arange(5) - 2)[None, :]141 mg = sparse_meshgrid(np.arange(5) - 3)142 val_1 = -1143 val_2 = 2144 @vectorize145 def simple_func(x):146 return 0 if x < 0 else 1147 true_result_arr = [0, 0, 1, 1, 1]148 true_result_mg = [0, 0, 0, 1, 1]149 # Out-of-place150 out = simple_func(arr)151 assert isinstance(out, np.ndarray)152 assert is_int_dtype(out.dtype)153 assert out.shape == (5,)154 assert all_equal(out, true_result_arr)155 out = simple_func(mg)156 assert isinstance(out, np.ndarray)157 assert out.shape == (5,)158 assert is_int_dtype(out.dtype)159 assert all_equal(out, true_result_mg)160 assert simple_func(val_1) == 0161 assert simple_func(val_2) == 1162def test_vectorize_2d_dtype():163 # Test vectorization in 2d with given data type for output164 arr = np.empty((2, 5), dtype='int')165 arr[0] = ([-3, -2, -1, 0, 1])166 arr[1] = ([-1, 0, 1, 2, 3])167 mg = sparse_meshgrid([-3, -2, -1, 0, 1], [-1, 0, 1, 2, 3])168 val_1 = (-1, 1)169 val_2 = (2, 1)170 @vectorize(otypes=['int'])171 def simple_func(x):172 return 0 if x[0] < 0 and x[1] > 0 else 1173 true_result_arr = [1, 1, 0, 1, 1]174 true_result_mg = [[1, 1, 0, 0, 0],175 [1, 1, 0, 0, 0],176 [1, 1, 0, 0, 0],177 [1, 1, 1, 1, 1],178 [1, 1, 1, 1, 1]]179 # Out-of-place180 out = simple_func(arr)181 assert isinstance(out, np.ndarray)182 assert out.dtype == np.dtype('int')183 assert out.shape == (5,)184 assert all_equal(out, true_result_arr)185 out = simple_func(mg)186 assert isinstance(out, np.ndarray)187 assert out.dtype == np.dtype('int')188 assert out.shape == (5, 5)189 assert all_equal(out, true_result_mg)190 assert simple_func(val_1) == 0191 assert simple_func(val_2) == 1192 # In-place193 out = np.empty(5, dtype='int')194 simple_func(arr, out=out)195 assert all_equal(out, true_result_arr)196 out = np.empty((5, 5), dtype='int')197 simple_func(mg, out=out)198 assert all_equal(out, true_result_mg)199def test_vectorize_2d_lazy():200 # Test vectorization in 1d without data type --> lazy vectorization201 arr = np.empty((2, 5), dtype='int')202 arr[0] = ([-3, -2, -1, 0, 1])203 arr[1] = ([-1, 0, 1, 2, 3])204 mg = sparse_meshgrid([-3, -2, -1, 0, 1], [-1, 0, 1, 2, 3])205 val_1 = (-1, 1)206 val_2 = (2, 1)207 @vectorize208 def simple_func(x):209 return 0 if x[0] < 0 and x[1] > 0 else 1210 true_result_arr = [1, 1, 0, 1, 1]211 true_result_mg = [[1, 1, 0, 0, 0],212 [1, 1, 0, 0, 0],213 [1, 1, 0, 0, 0],214 [1, 1, 1, 1, 1],215 [1, 1, 1, 1, 1]]216 # Out-of-place217 out = simple_func(arr)218 assert isinstance(out, np.ndarray)219 assert is_int_dtype(out.dtype)220 assert out.shape == (5,)221 assert all_equal(out, true_result_arr)222 out = simple_func(mg)223 assert isinstance(out, np.ndarray)224 assert is_int_dtype(out.dtype)225 assert out.shape == (5, 5)226 assert all_equal(out, true_result_mg)227 assert simple_func(val_1) == 0228 assert simple_func(val_2) == 1229def test_vectorize_callable_class():230 # Test vectorization in 1d without data type --> lazy vectorization231 arr = [[-2, -1, 0, 1, 2]]232 mg = [[-3, -2, -1, 0, 1]]233 val_1 = -1234 val_2 = 2235 # Class with __call__ method236 class CallableClass(object):237 def __call__(self, x):238 return 0 if x < 0 else 1239 vectorized_call = vectorize(CallableClass())240 true_result_arr = [0, 0, 1, 1, 1]241 true_result_mg = [0, 0, 0, 1, 1]242 # Out-of-place...

Full Screen

Full Screen

testFunctionDef.py

Source:testFunctionDef.py Github

copy

Full Screen

1# foo before comment2def simple_func(foo, bar=5.0, mar=ble, *arg, **args): # on-line3 # this is a comment4 print "myfunc" # foo5def simple_func(foo, bar=5.0, mar=ble, *arg):6 # this is a comment7 print "myfunc" # foo8 9def simple_func(foo, bar=5.0, mar=ble):10 # this is a comment11 print "myfunc" # foo12 13def simple_func(foo, bar=5.0):14 # this is a comment15 print "myfunc" # foo16 17def simple_func(foo, bar):18 # this is a comment19 print "myfunc"20 21# a simple func22def simple_func(foo):23 # this is a comment24 print "myfunc" # foo25# a simple func26def simple_func(): # and a comment27 # this is a comment28 print "myfunc" # foo29 # last comment in simple_func30##r31# foo before comment32def simple_func(foo, bar=5.0, mar=ble, *arg, **args): # on-line33 # this is a comment34 print "myfunc" # foo35def simple_func(foo, bar=5.0, mar=ble, *arg):36 # this is a comment37 print "myfunc" # foo38def simple_func(foo, bar=5.0, mar=ble):39 # this is a comment40 print "myfunc" # foo41def simple_func(foo, bar=5.0):42 # this is a comment43 print "myfunc" # foo44def simple_func(foo, bar):45 # this is a comment46 print "myfunc"47# a simple func48def simple_func(foo):49 # this is a comment50 print "myfunc" # foo51# a simple func52def simple_func(): # and a comment53 # this is a comment54 print "myfunc" # foo55 # last comment in simple_func...

Full Screen

Full Screen

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.

LambdaTest Learning Hubs:

YouTube

You could also refer to video tutorials over LambdaTest YouTube channel to get step by step demonstration from industry experts.

Run pandera automation tests on LambdaTest cloud grid

Perform automation testing on 3000+ real desktop and mobile devices online.

Try LambdaTest Now !!

Get 100 minutes of automation test minutes FREE!!

Next-Gen App & Browser Testing Cloud

Was this article helpful?

Helpful

NotHelpful