- Python python smaller than or larer then
- Less than Or Greater Than Comparison as a Variable in Python
- Python Tutorials - Comparison Operators ( == != less than
- Python: numpy array larger and smaller than a value
- Python in VS Code is telling me a number is smaller than a smaller number
- Python recognizing 9 as larger than 27 in "if <this then this" string
- Python Code Example Demonstrating the Comparison of Sizes
- Which numbers in list 2 are bigger and smaller than each number in list 1
- NumPy: Create two arrays of size bigger and smaller than a given array
- NumPy: Array Object Exercise-123 with Solution
- Python: Find all the values in a list are greater than a specified number
- Python List: Exercise - 67 with Solution
- Visualize Python code execution:
- Finding first samples greater than a threshold value efficiently in Python (and MATLAB comparison)
Python python smaller than or larer then
Solution 2: It is the difference between comparing strings: And integers: Comparing different types is an error: To fix it, convert the string into an int: Python automatically accepts inputs as strings so convert it to an integer like this.
Less than Or Greater Than Comparison as a Variable in Python
You can use the operator module; it has functions that act as the comparison operators:
import operator if foobar: comp = operator.lt else: comp = operator.gt if comp(spam, eggs): This’ll use either test if spam is less then, or greater then eggs , depending on the truth of foobar .
This fits your comparison as a variable requirement exactly.
This is certainly what I’d use to avoid repeating myself; if you have two pieces of code that differ only in the comparison direction, use a function from operators to parameterize the comparison.
I had the same question when trying to build a module for unknown ML models. Some require a minimum score to perform, others a maximum score, depending on the metric used. I passed in a ‘greater than’ boolean variable and wrote a small function:
def is_better(gt,a,b): return a>b if gt else a I would not refactor this by making the operators dynamic in any way. I think the comparisons aren't that similar and forcing them to look more similar obfuscates the code. It's a bit of a red herring.
I would put the complex checks in a function, something like
def customer_trained_enough(customer_info): return ( customer_info['total_dls'] < min_training_actions or customer_info['percentage'] >train_perc_cutoff) def customer_tested_enough(test_info): return test_info['total_dls'] >= min_testing_actions def percentage_too_high(test_info): return test_info['percentage'] > test_perc_cutoff And then the code becomes:
if customer_trained_enough(customer_info): continue if (customer_id not in test_set or not customer_tested_enough(test_set[customer_id])): num_uncertain += 1 elif not percentage_too_high(test_set[customer_id]): num_correct += 1 else: num_incorrect += 1 I guessed some names for the functions, and had to negate the logic in two of them to make the names work.
How does a modulo operation work when the first number is smaller?, I'm messing with the modulo operation in python and I understand that it will spit back what the remainder is
Python Tutorials - Comparison Operators ( == != less than
Learn how to compare or relate two different variables, values, or even lists. The result will be a Duration: 5:39
Python: numpy array larger and smaller than a value
array([False, True, True, False, False], dtype=bool) You can do something like this :
import numpy as np c = np.array([2,3,4,5,6]) output = [(i and j) for i, j in zip(c>2, c<5)] [False, True, True, False, False] Python - Is there a difference between "==" and "is"?, is will return True if two variables point to the same object (in memory), == if the objects referred to by the variables are equal.
Python in VS Code is telling me a number is smaller than a smaller number
If you are using python 3, you need to add an int() around the input statement in order for python to know the user input should be a number, not a string:
try: Xmin = 100 print("X") X = int(input()) if X < Xmin: print("X is too small.") except: print('That is not an integer.') If you are using python 2, watch out! input() in python 2 is the equivalent of eval(input()) in python 3, and we all know that 'eval is evil'.
X = input() #takes input as string Use below code instead of above:
X = int(input()) #takes input as integer Confused with greater than and less than operators, The arrow only points to the smaller side. so "4
Python recognizing 9 as larger than 27 in "if <this then this" string
You're comparing strings when you should be comparing integers. Python automatically accepts inputs as strings so convert it to an integer like this.
temperature = int(input("What is the temperature in Celcius? ")) Pro tip: it's good practice to verify that your input is a number before you convert it to an integer to avoid errors. You tried to do that here:
But that statement does not set parameters for the temperature variable to only allow numbers between -30 and 55. Instead it just makes temperature equal to a range(-30,55) Use the isdigit() method and conditional statements to verify only the correct input will be received. For this you will need a while loop that asks for an input with specific criteria until one is accepted.
while True: temperature = input("What is the temperature in Celcius? ") if temperature.isdigit(): temperature = int(temperature) if temperature >= -30 and temperature Compare the integers like so. Notice how I don't use the str() method during comparison and have no duplicate conditional statements. I also make use of the else statement because all other cases have been covered by the temperature being less than 27 and greater than 32, so the only other option would be between 27 and 32.
if temperature < 27: print ("Plant is too cold") sum = 27 - temperature print("Recommended temperature increase:" + str(sum) + " degrees Celcius") print("Remember to keep your plant between 27 and 32 degrees!") elif temperature >32: print ("Plant is too warm") sum = temperature - 32 print("Recommended temperature decrease:" + str(sum) + " degrees Celcius") print("Remember to keep your plant between 27 and 32 degrees!") else: print ("Plant temperature is nominal") It is the difference between comparing strings:
Comparing different types is an error:
>>> '9'>27 Traceback (most recent call last): File "", line 1, in TypeError: '>' not supported between instances of 'str' and 'int' To fix it, convert the string into an int:
Python String Comparison, Python String comparison can be performed using equality (==) and comparison (<, >, !=, =) operators. There are no special methods to
Python Code Example Demonstrating the Comparison of Sizes
The Python Code Editor has multiple solutions for finding the positions of first Trues in a bool array. Solution 1 provides positions of first Trues, while a faster method of marking the first True is also given. Solution 2 explains why the code is slower than Matlab and presents an alternative code that returns indices matching the condition. Solution 3 suggests picking the best algorithm to optimize speed and offers a simple edge detector that prints the indices corresponding to the first entry in a sequence greater than the threshold.
Which numbers in list 2 are bigger and smaller than each number in list 1
This solution, which utilizes NumPy, is vectorized and does not contain any Python loops except for the final printing stage. This approach is expected to be highly efficient.
import numpy as np # set up fake data l1 = np.array([1.9, 2, 2.1]) # or whatever list you have l2 = np.array([1, 2, 5, 10]) # as above l2.sort() # remove this line if it's always sorted # the actual algorithm indexes = np.searchsorted(l2, l1, side='right') lower = l2[indexes - 1] upper = l2[indexes] diffs = upper - lower # print results for debugging for value, diff in zip(l1, diffs): print "value", value, "gap", diff This is the resulting output using the provided test data that has been hardcoded.
value 1.9 gap 1 value 2.0 gap 3 value 2.1 gap 3 The worst-case complexity for utilizing the bisect module is O(N * logN) .
import bisect lis1 = [4, 20, 26, 27, 30, 53, 57, 76, 89, 101] lis2 = [17, 21, 40, 49, 53, 53, 53, 53, 70, 80, 81, 95, 99] #this must be sorted #use lis2.sort() in case lis2 is not sorted for x in lis1: #returns the index where x can be placed in lis2, keeping lis2 sorted ind=bisect.bisect(lis2,x) if not (x >= lis2[-1] or x ".format(sm ,x, bi) Although 4 and 101 will not produce any results due to 4 being smaller than any element in lis2 and 101 being greater than any element in lis2, this issue can be resolved if necessary.
Your example is invalid code, or it doesn't perform the intended action.
In this scenario, the element being compared is denoted as i rather than the index. Therefore, the comparison should be between i and j, instead of list[i] and list[j].
List comprehensions ought to be made more user-friendly.
for i in list1: a = max([n for n in list2 if n < i]) b = min([n for n in list2 if n >i]) To ensure a and b exist, you may need to incorporate a couple of if statements, but the functionality should remain the same.
C - How to find number of elements greater/smaller, The simplest trick is sort the array first and count the number of elements in the array. So for example if you have 10 elements then your first element is less than 9 and likewise the second element is smaller than 8 and so on. The one exception is where you have two of the same items, there you have …
NumPy: Create two arrays of size bigger and smaller than a given array
NumPy: Array Object Exercise-123 with Solution
Come up with a NumPy code that generates two arrays larger and smaller than the given array's size.
Here's an example of a possible answer.
Python Code:
import numpy as np x = np.arange(16).reshape(4,4) print("Original arrays:") print(x) print("\nArray with size 2x2 from the said array:") new_array1 = np.resize(x,(2,2)) print(new_array1) print("\nArray with size 6x6 from the said array:") new_array2 = np.resize(x,(6,6)) print(new_array2) Original arrays: [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11] [12 13 14 15]]Array with size 2x2 from the said array: [[0 1] [2 3]]Array with size 6x6 from the said array: [[ 0 1 2 3 4 5] [ 6 7 8 9 10 11] [12 13 14 15 0 1] [ 2 3 4 5 6 7] [ 8 9 10 11 12 13] [14 15 0 1 2 3]]
Editor for Python-Numpy code:
Is there a greater than but less than function in python?, while 10 < a < 20: whatever. This doesn't work in most languages, but Python supports it. Note that you should probably be using a for loop: for a in range (11, 20): whatever. or if you just want to test a single number rather than looping, use an if: if 10 < a < 20: whatever. Be careful with the boundary … Code sampleif 10 < a < 20:whateverFeedback
Python: Find all the values in a list are greater than a specified number
Python List: Exercise - 67 with Solution
Develop a Python script that can identify every value within a list that exceeds a certain value.
A possible solution example is provided below:
Python Code:
list1 = [220, 330, 500] list2 = [12, 17, 21] print(all(x >= 200 for x in list1)) print(all(x >= 25 for x in list2)) 
Visualize Python code execution:
The tool provides a visual representation of the computer's execution process as it runs the specified program, showcasing each step as it occurs.
An editor for Python code.
Python - Comparing two lists using the greater than or, For example, when comparing a to b, the overall result will be the result of comparing 3 to 10. a < b ->True because 3 is less than 10. a > b -> False because 3 is not greater than 10. a == b …
Finding first samples greater than a threshold value efficiently in Python (and MATLAB comparison)
Instead of the other solutions that provide the positions of the first "True" values, you can opt for a faster option to obtain a boolean array that indicates the initial occurrence of "True" values.
import numpy as np signal = np.random.rand(1000000) th = signal > 0.5 th[1:][th[:-1] & th[1:]] = False In this article, the reasons behind your code's slower performance compared to Matlab are discussed.
import numpy as np threshold = 2 signal = np.array([1, 2, 3, 4, 4, 3, 2, 1, 0, 3, 2, 1, 0, 0, 1, 1, 4, 8, 7, 6, 5, 0]) indices_bigger_than_threshold = np.where(signal > threshold)[0] # get item print indices_bigger_than_threshold # [ 2 3 4 5 9 16 17 18 19 20] non_consecutive = np.where(np.diff(indices_bigger_than_threshold) != 1)[0]+1 # +1 for selecting the next print non_consecutive # [4 5] first_bigger_than_threshold1 = np.zeros_like(signal, dtype=np.bool) first_bigger_than_threshold1[indices_bigger_than_threshold[0]] = True # retain the first first_bigger_than_threshold1[indices_bigger_than_threshold[non_consecutive]] = True The function np.where provides the indices that meet a certain criteria.
The plan is to exceed the indices of threshold and eliminate the consecutive ones.
BTW, welcome to Python/Numpy world.
You can efficiently select the optimal algorithm by utilizing a basic edge detector, as it aligns with the idea that the quickest way to accelerate processes is by choosing the most suitable option.
import numpy signal = numpy.array([1, 2, 3, 4, 4, 3, 2, 1, 0, 3, 2, 1, 0, 0, 1, 1, 4, 8, 7, 6, 5, 0]) thresholded_data = signal > threshold threshold_edges = numpy.convolve([1, -1], thresholded_data, mode='same') thresholded_edge_indices = numpy.where(threshold_edges==1)[0] print(thresholded_edge_indices) The code [2 9 16] prints the indices of the initial element in a sequence that exceeds the threshold. This approach improves performance in both Matlab and Python (with Numpy), taking only 12ms on my system compared to the 4.5s it previously took.
The convolution in the code can be substituted with numpy.diff(thresholded_data) as suggested by @eickenberg. Although this method is simpler, it will require adding back 1 to the indices and converting thresholded_data to an integer array using thresholded_data.astype(int) . Both methods have the same speed performance.
Python - Why is the mean smaller than the minimum, I have an input array, which is a masked array. When I check the mean, I get a nonsensical number: less than the reported minimum value! So, raw array: numpy.mean(A) < numpy.min(A).Note A.dtype returns float32.. FIX: A3=A.astype(float).A3 is still a masked array, but now the mean lies between …