Mastering Cumulative Sums: A Game-Changer for Data Analysis

Unlock the Power of Cumulative Sums

When working with arrays, calculating the cumulative sum of elements can be a game-changer. This is where the cumsum() function comes in – a powerful tool that helps you achieve just that.

Understanding the Syntax

The cumsum() function takes in several arguments, including:

• array: the input array
• axis (optional): the axis along which the cumulative sum is calculated
• dtype (optional): the data type of the returned cumulative sum
• out (optional): the output array where the result will be stored

Flexible Calculation Options

The axis argument gives you flexibility in how you calculate the cumulative sum. For instance:

• If axis = None, the array is flattened and the cumulative sum of the flattened array is returned.
• If axis = 0, the cumulative sum is calculated column-wise.
• If axis = 1, the cumulative sum is calculated row-wise.

Real-World Examples

Let’s see the cumsum() function in action:

Example 1: 2-D Array

In this example, we’ll calculate the cumulative sum of a 2-D array. The axis argument defines how we calculate the sum of elements.

Output

Example 2: Specifying Data Type

Here, we’ll use the dtype argument to specify the data type of the resulting cumulative sum. By setting dtype=float, we ensure that the resulting cumulative sum has elements of type float.

Output

Example 3: Storing Results

In this example, we’ll use the out argument to store the result of the cumulative sum in a separate array.

Output

As you can see, the cumsum() function is a versatile tool that can help you tackle a range of tasks. By understanding its syntax and options, you can unlock new insights and possibilities in your data analysis.