from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
nums = [1, 2, 3, 4] cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))] print(cumulative_sum)
Output: [1, 3, 6, 10]
The list comprehension iterates through the indices of the original list, slicing it up to the current index and using sum() to calculate the cumulative total.
Method 3: Using the accumulate Function from itertools
The accumulate() function from the itertools module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.
Here’s an example:
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)
print(cumulative_sum)
Output: [1, 3, 6, 10]
The code snippet above demonstrates creating a cumulative sum using a for loop. On each iteration, the current number is added to a running total and then appended to the cumulative_sum list.
Method 2: Using List Comprehension and Sum
This method leverages Python’s list comprehension feature combined with the sum() function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.
Here’s an example:
nums = [1, 2, 3, 4] cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))] print(cumulative_sum)
Output: [1, 3, 6, 10]
The list comprehension iterates through the indices of the original list, slicing it up to the current index and using sum() to calculate the cumulative total.
Method 3: Using the accumulate Function from itertools
The accumulate() function from the itertools module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.
Here’s an example:
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)
print(cumulative_sum)
Output: [1, 3, 6, 10]
The code snippet above demonstrates creating a cumulative sum using a for loop. On each iteration, the current number is added to a running total and then appended to the cumulative_sum list.
Method 2: Using List Comprehension and Sum
This method leverages Python’s list comprehension feature combined with the sum() function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.
Here’s an example:
nums = [1, 2, 3, 4] cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))] print(cumulative_sum)
Output: [1, 3, 6, 10]
The list comprehension iterates through the indices of the original list, slicing it up to the current index and using sum() to calculate the cumulative total.
Method 3: Using the accumulate Function from itertools
The accumulate() function from the itertools module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.
Here’s an example:
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)
print(cumulative_sum)
Output: [1, 3, 6, 10]
The code snippet above demonstrates creating a cumulative sum using a for loop. On each iteration, the current number is added to a running total and then appended to the cumulative_sum list.
Method 2: Using List Comprehension and Sum
This method leverages Python’s list comprehension feature combined with the sum() function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.
Here’s an example:
nums = [1, 2, 3, 4] cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))] print(cumulative_sum)
Output: [1, 3, 6, 10]
The list comprehension iterates through the indices of the original list, slicing it up to the current index and using sum() to calculate the cumulative total.
Method 3: Using the accumulate Function from itertools
The accumulate() function from the itertools module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.
Here’s an example:
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
nums = [1, 2, 3, 4] cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))] print(cumulative_sum)
Output: [1, 3, 6, 10]
The list comprehension iterates through the indices of the original list, slicing it up to the current index and using sum() to calculate the cumulative total.
Method 3: Using the accumulate Function from itertools
The accumulate() function from the itertools module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.
Here’s an example:
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)
print(cumulative_sum)
Output: [1, 3, 6, 10]
The code snippet above demonstrates creating a cumulative sum using a for loop. On each iteration, the current number is added to a running total and then appended to the cumulative_sum list.
Method 2: Using List Comprehension and Sum
This method leverages Python’s list comprehension feature combined with the sum() function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.
Here’s an example:
nums = [1, 2, 3, 4] cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))] print(cumulative_sum)
Output: [1, 3, 6, 10]
The list comprehension iterates through the indices of the original list, slicing it up to the current index and using sum() to calculate the cumulative total.
Method 3: Using the accumulate Function from itertools
The accumulate() function from the itertools module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.
Here’s an example:
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
nums = [1, 2, 3, 4] cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))] print(cumulative_sum)
Output: [1, 3, 6, 10]
The list comprehension iterates through the indices of the original list, slicing it up to the current index and using sum() to calculate the cumulative total.
Method 3: Using the accumulate Function from itertools
The accumulate() function from the itertools module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.
Here’s an example:
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)
print(cumulative_sum)
Output: [1, 3, 6, 10]
The code snippet above demonstrates creating a cumulative sum using a for loop. On each iteration, the current number is added to a running total and then appended to the cumulative_sum list.
Method 2: Using List Comprehension and Sum
This method leverages Python’s list comprehension feature combined with the sum() function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.
Here’s an example:
nums = [1, 2, 3, 4] cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))] print(cumulative_sum)
Output: [1, 3, 6, 10]
The list comprehension iterates through the indices of the original list, slicing it up to the current index and using sum() to calculate the cumulative total.
Method 3: Using the accumulate Function from itertools
The accumulate() function from the itertools module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.
Here’s an example:
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
nums = [1, 2, 3, 4] cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))] print(cumulative_sum)
Output: [1, 3, 6, 10]
The list comprehension iterates through the indices of the original list, slicing it up to the current index and using sum() to calculate the cumulative total.
Method 3: Using the accumulate Function from itertools
The accumulate() function from the itertools module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.
Here’s an example:
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)
print(cumulative_sum)
Output: [1, 3, 6, 10]
The code snippet above demonstrates creating a cumulative sum using a for loop. On each iteration, the current number is added to a running total and then appended to the cumulative_sum list.
Method 2: Using List Comprehension and Sum
This method leverages Python’s list comprehension feature combined with the sum() function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.
Here’s an example:
nums = [1, 2, 3, 4] cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))] print(cumulative_sum)
Output: [1, 3, 6, 10]
The list comprehension iterates through the indices of the original list, slicing it up to the current index and using sum() to calculate the cumulative total.
Method 3: Using the accumulate Function from itertools
The accumulate() function from the itertools module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.
Here’s an example:
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
nums = [1, 2, 3, 4] cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))] print(cumulative_sum)
Output: [1, 3, 6, 10]
The list comprehension iterates through the indices of the original list, slicing it up to the current index and using sum() to calculate the cumulative total.
Method 3: Using the accumulate Function from itertools
The accumulate() function from the itertools module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.
Here’s an example:
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)
print(cumulative_sum)
Output: [1, 3, 6, 10]
The code snippet above demonstrates creating a cumulative sum using a for loop. On each iteration, the current number is added to a running total and then appended to the cumulative_sum list.
Method 2: Using List Comprehension and Sum
This method leverages Python’s list comprehension feature combined with the sum() function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.
Here’s an example:
nums = [1, 2, 3, 4] cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))] print(cumulative_sum)
Output: [1, 3, 6, 10]
The list comprehension iterates through the indices of the original list, slicing it up to the current index and using sum() to calculate the cumulative total.
Method 3: Using the accumulate Function from itertools
The accumulate() function from the itertools module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.
Here’s an example:
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
💡 Problem Formulation: In Python, forming a cumulative sum list is a common operation that involves creating a new list where each element is the sum of the elements in the original list up to that index. For instance, given an input list [1, 2, 3, 4], the desired cumulative sum output would be [1, 3, 6, 10].
Method 1: Using a For Loop
This method involves iterating over the original list and adding each element to a running total, which is then appended to a new list. This approach is clear and illustrative, giving beginners a good comprehension of how cumulative sums are calculated.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
nums = [1, 2, 3, 4] cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))] print(cumulative_sum)
Output: [1, 3, 6, 10]
The list comprehension iterates through the indices of the original list, slicing it up to the current index and using sum() to calculate the cumulative total.
Method 3: Using the accumulate Function from itertools
The accumulate() function from the itertools module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.
Here’s an example:
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.
nums = [1, 2, 3, 4]
cumulative_sum = []
total = 0
for num in nums:
total += num
cumulative_sum.append(total)
print(cumulative_sum)
Output: [1, 3, 6, 10]
The code snippet above demonstrates creating a cumulative sum using a for loop. On each iteration, the current number is added to a running total and then appended to the cumulative_sum list.
Method 2: Using List Comprehension and Sum
This method leverages Python’s list comprehension feature combined with the sum() function. It’s an elegant and Pythonic way to calculate cumulative sums, utilizing the power of list comprehensions for conciseness.
Here’s an example:
nums = [1, 2, 3, 4] cumulative_sum = [sum(nums[:i+1]) for i in range(len(nums))] print(cumulative_sum)
Output: [1, 3, 6, 10]
The list comprehension iterates through the indices of the original list, slicing it up to the current index and using sum() to calculate the cumulative total.
Method 3: Using the accumulate Function from itertools
The accumulate() function from the itertools module provides a direct way to compute cumulative sums in Python. This function is a fast and memory-efficient tool for such tasks.
Here’s an example:
from itertools import accumulate nums = [1, 2, 3, 4] cumulative_sum = list(accumulate(nums)) print(cumulative_sum)
Output: [1, 3, 6, 10]
The code utilizes the accumulate() function, which takes an iterable as input and returns an iterator over the intermediate cumulative sum results, which is then converted into a list.
Method 4: Using NumPy’s cumsum Function
For those working within the scientific computing realm, NumPy’s cumsum() function provides a high-performance method to compute cumulative sums on numerical data.
Here’s an example:
import numpy as np nums = np.array([1, 2, 3, 4]) cumulative_sum = np.cumsum(nums) print(cumulative_sum)
Output: [1 3 6 10]
This snippet uses NumPy’s cumsum() function to efficiently compute the cumulative sum of an array, returning a NumPy array with the cumulative totals.
Bonus One-Liner Method 5: Using Reduce with Lambda
This elegance of functional programming is showcased using the reduce() function in combination with a lambda expression to achieve cumulative sum in a one-liner command.
Here’s an example:
from functools import reduce nums = [1, 2, 3, 4] cumulative_sum = reduce(lambda c, x: c + [c[-1] + x] if c else [x], nums, []) print(cumulative_sum)
Output: [1, 3, 6, 10]
This code uses reduce() to build the cumulative sum list. The lambda takes a cumulative list c and an element x, appends the sum of x and the last element in c to c, and continues this cumulatively through the list.
Summary/Discussion
- Method 1: For Loop. Easy to understand and implement. Not the most efficient for large datasets due to O(n) space and time complexity.
- Method 2: List Comprehension and Sum. Pythonic and compact. It can be less efficient than other methods, as sum is called repeatedly for each element.
- Method 3: accumulate Function. Pythonic and efficient, especially for iterator-based processing. It is a built-in function, but requires importing
itertools. - Method 4: NumPy’s cumsum. Highly efficient for numerical computations on large arrays. However, it requires NumPy, which is not part of Python’s standard library.
- Method 5: reduce with Lambda. Compact and functional programming style. May be less readable for those not familiar with
reduce()or lambda functions.