How numpy cumsum works in Python? Best example

How numpy cumsum works in Python? Best example
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If you’ve ever worked with numerical data in Python, you probably know how useful numpy is. One of its lesser-known but incredibly handy functions is numpy.cumsum(). In this article, I’ll break down exactly how numpy.cumsum() works, how it can be applied to different data structures, and why it’s so useful. Let’s dive in!

What Is numpy.cumsum()?

In short, numpy.cumsum() stands for “cumulative sum.” It takes an array and returns an array where each element is the sum of all previous elements plus the current one. Sounds simple, right? Well, there’s a lot more to it.

Basic Syntax of numpy.cumsum()

The function follows a straightforward syntax:

numpy.cumsum(a, axis=None, dtype=None, out=None)

Here’s what each argument does:

  • a: The input array.
  • axis: The axis along which to perform the cumulative sum. The default is None, meaning it flattens the array first.
  • dtype: The desired data type of the output array.
  • out: An optional array to place the result.

Best Example of numpy.cumsum() in Python

Let’s start with a simple example where we calculate the cumulative sum on a 1D array:

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
cumulative_sum = np.cumsum(arr)

print(cumulative_sum)  # Output: [ 1  3  6 10 15 ]

Here, each element in the result is the sum of all elements up to that position.

Using numpy.cumsum() on 2D Arrays

Now, let’s see how it behaves on a 2D array with different axis values.

arr_2d = np.array([[1, 2, 3], [4, 5, 6]])

# Cumulative sum along rows (axis=1)
print(np.cumsum(arr_2d, axis=1))  
# Output:
# [[ 1  3  6]
#  [ 4  9 15]]

# Cumulative sum along columns (axis=0)
print(np.cumsum(arr_2d, axis=0))  
# Output:
# [[ 1  2  3]
#  [ 5  7  9]]

Practical Applications of numpy.cumsum()

Here are some real-world use cases for numpy.cumsum():

  1. Running Totals: In financial analysis, it helps compute cumulative profits or losses.
  2. Moving Averages: Can be used as part of a moving average algorithm.
  3. Signal Processing: In digital signal processing, cumulative sums are sometimes needed for integration.

Working with Different Data Types

You can control the output type by specifying dtype. For instance:

arr = np.array([1, 2, 3], dtype=np.int32)
cumsum_float = np.cumsum(arr, dtype=np.float64)

print(cumsum_float)  # Output: [ 1.  3.  6. ]

This ensures precision when dealing with large numbers.

Table: Comparison of numpy.cumsum() with Similar Functions

Function Description Example Output
numpy.cumsum() Returns the cumulative sum of elements [1, 3, 6, 10, 15]
numpy.sum() Returns the total sum of elements 15
itertools.accumulate() Provides a similar cumulative sum [1, 3, 6, 10, 15]

Key Takeaways

  • numpy.cumsum() calculates cumulative sums efficiently.
  • It works with both 1D and multi-dimensional arrays.
  • Specifying axis changes how summation is applied.
  • It’s useful for financial analysis, data science, and signal processing.

And that’s everything you need to know about how numpy.cumsum() works in Python. Now, go ahead and use it in your projects!

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