NumPy is one of the most popular scientific computing libraries in Python. It is widely used for mathematical and numerical computations, data analysis, and manipulation.
NumPy cumsum is a feature that is quite useful in NumPy. In this tutorial, we will take a closer look at NumPy cumsum, both in definition and purpose, as well as the various types of cumulative sums that can be applied in NumPy. With a better understanding of NumPy cumsum, we can perform more advanced data manipulation and analysis tasks with ease. What is NumPy cumsum?
Cumulative sum is a mathematical operation that involves finding the sum of all previous numbers in a sequence. A cumulative sum of a sequence starting from the first element computes the sum of the first element, then the sum of the first two elements, then the sum of the first three elements, and so on.
In NumPy, cumsum() is a method that computes the cumulative sum of elements in an array. One of the primary purposes of the cumsum() method in NumPy is to calculate the cumulative sum of an array along with various axes.
This can be useful for several applications, including finance, stock market analysis, physics, and many more. Cumulative sum is also useful in calculating the running total of a time series, for example, the cumulative sum of daily sales.
Types of Cumulative Sum in NumPy:
There are three types of cumulative sum that can be applied in NumPy:

Cumulative Sum of a Flattened Array:
This type of cumulative sum is the simplest and most straightforward.
A flattened array is a onedimensional array that contains all the elements of the original array. Therefore, the cumulative sum of a flattened array is performed as a onedimensional operation.
For example, suppose we have an array [3, 6, 9]. The cumulative sum of this array can be calculated as follows:
Copyarr = np.array([3, 6, 9]) print(np.cumsum(arr)) # Output: [ 3 9 18]

Cumulative Sum of Rows:
Another type of cumulative sum in NumPy is along the rows of a twodimensional array. In this case, the cumulative sum is performed across the columns of each row.
For example, let’s consider the following twodimensional array:
Copyarr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
The cumulative sum of each row can be calculated using the axis parameter of cumsum() as follows:
Copyprint(np.cumsum(arr, axis=1)) # Output: array([[ 1, 3, 6], [ 4, 9, 15], [ 7, 15, 24]])

Cumulative Sum of Columns:
The third type of cumulative sum in NumPy is along the columns of a twodimensional array.
In this case, the cumulative sum is performed across the rows of each column. For example, consider the same twodimensional array from above:
Copyarr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
The cumulative sum of each column can be calculated using the axis parameter of cumsum() as follows:
Copyprint(np.cumsum(arr, axis=0)) # Output: array([[ 1, 2, 3], [ 5, 7, 9], [12, 15, 18]])
Conclusion:
In this tutorial, we have learned about NumPy cumsum, its definition, purpose, and types of cumulative sum that can be applied in NumPy. NumPy cumsum is a powerful tool that can be used for several applications, including finance, stock market analysis, physics, and many more.
With a better understanding of NumPy cumsum, we can perform more advanced data manipulation and analysis tasks with ease. NumPy cumsum is a useful tool that computes the cumulative sum of elements in a NumPy array. It is a powerful tool for performing mathematical and statistical computations.
In this tutorial, we will delve deeper into the syntax and parameters of NumPy cumsum, as well as provide several examples to further demonstrate its functionality. Syntax of NumPy cumsum:
The syntax of NumPy cumsum function is as follows:
numpy.cumsum(a, axis=None, dtype=None, out=None)
Here, ‘a’ is the input array, ‘axis’ is the axis along which the cumulative sum is performed, ‘dtype’ is the data type of the output, and ‘out’ is the output array.
Parameters and Their Descriptions:

a: This is the input array.
It can be a onedimensional, twodimensional, or ndimensional array.

axis: This is an optional parameter that specifies the axis along which the cumulative sum is performed. It can take two values, 0 or 1.
If axis is None, then the cumulative sum is computed across the flattened array.

dtype: This is an optional parameter that specifies the data type of the output. If the input array is of integer data type, then the default output data type is also integer.
However, if we want an output array with a different data type, we can specify it using this parameter.

out: This is an optional parameter that specifies the output array. The dimensions of the output array must match those of the input array.
Examples:

Cumulative sum of a single element:
Consider a NumPy array containing a single element.
In this case, the cumulative sum of the element is the same as the element itself.
Copyimport numpy as np x = np.array([2]) print(np.cumsum(x))
Output:
Copy[2]

Cumulative sum of an empty array:
If the input array is empty, the output array will be empty as well.
Copyimport numpy as np x = np.array([]) print(np.cumsum(x))
Output:
Copy[]

Cumulative sum of a 1dimensional array:
Let’s consider a NumPy array with 5 elements.
We can calculate the cumulative sum of the array using the cumsum method.
Copyimport numpy as np x = np.array([1, 2, 3, 4, 5]) print(np.cumsum(x))
Output:
Copy[ 1 3 6 10 15]

Cumulative sum of a 2dimensional array:
Now, let’s consider a 2dimensional array, where each row contains 3 elements.
We can calculate the cumulative sum of each row using the axis parameter.
Copyimport numpy as np x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) print(np.cumsum(x, axis=1))
Output:
Copy[[ 1 3 6] [ 4 9 15] [ 7 15 24]]

Return Numpy.cumsum() of the array as float data type:
By default, the cumulative sum method returns an array of the same data type as the input array.
However, if we want the output to be of a different data type, we can specify it using the dtype parameter.
Copyimport numpy as np x = np.array([1, 2, 3], dtype=int) print(np.cumsum(x, dtype=float))
Output:
Copy[1. 3. 6.]

Cumulative sum along the axis(axis=0, axis=1):
We can calculate the cumulative sum along the axis using the axis parameter.
If axis is not provided or is None, cumulative sum is computed over the flattened array.
Copyimport numpy as np x = np.array([[1, 2], [3, 4]]) print(np.cumsum(x, axis=0))
Output:
Copy[[1 2] [4 6]]
Copyimport numpy as np x = np.array([[1, 2], [3, 4]]) print(np.cumsum(x, axis=1))
Output:
Copy[[1 3] [3 7]]
Conclusion:
In this tutorial, we have explored NumPy cumsum in detail. We have discussed its definition, purpose, syntax, and parameters, as well as provided several examples to demonstrate its functionality.
NumPy cumsum is a powerful tool that can be used for many mathematical and statistical computations, such as finding running totals of time series data. By better understanding the workings of cumsum(), we can perform advanced data manipulation and analysis tasks with ease. NumPy cumsum is a powerful tool in Python for performing mathematical and statistical computations for data manipulation and analysis.
In this tutorial, we have learned about what NumPy cumsum is, its definition, purpose, syntax, and parameters. We have also provided several examples that demonstrated its functionality.
In this final section of our tutorial, we will summarize what we have learned in this tutorial. Summary:
In conclusion, let us recap what we have learned about NumPy cumsum.
Cumulative sum is a mathematical operation that computes the sum of the previous elements in a sequence. NumPy cumsum is the cumulative sum operation performed in NumPy for various mathematical and statistical computations.
It has three types of cumulative sum that can be applied to any NumPy array, which includes flattened array, rows, and columns. We have learned about the definition, purpose, syntax, and parameters of NumPy cumsum.
The syntax of NumPy cumsum function involves the input array ‘a’, an optional parameter ‘axis’, an optional parameter ‘dtype’, and an optional parameter ‘out’. The input array can be a onedimensional, twodimensional, or ndimensional array.
The parameter ‘axis’ specifies the axis along which the cumulative sum is performed, while ‘dtype’ specifies the data type of the output. The parameter ‘out’ specifies the output array.
The cumulative sum of an array can be computed by using the cumsum() method, and it is a useful tool for performing various mathematical and statistical computations. Through the examples provided in this tutorial, we have learned how to compute the cumulative sum of a single element, empty array, onedimensional array, and twodimensional array.
We have also learned how to specify the data type of the output array as float and how to compute the cumulative sum along any desired axis. Some potential applications of NumPy cumsum include calculating the running total of timeseries data, finance, stock market analysis, and physics.
By understanding the power and functionality of NumPy cumsum, we can improve our data manipulation and analysis capabilities. Overall, NumPy cumsum is a valuable tool for any developer working with data in Python.
In conclusion, we hope that this tutorial has provided a substantial understanding of NumPy cumsum, and its applications. We encourage readers to try out the examples provided in this tutorial and experiment with different parameters and inputs to gain a broader understanding of how the tool works in practice.
NumPy cumsum is only one of many features in NumPy, and we hope this tutorial acts as a steppingstone towards more advanced data analysis techniques in Python. In conclusion, NumPy cumsum is a valuable tool for any developer who needs to perform mathematical or statistical computations for data manipulation and analysis in Python.
Through this tutorial, we have learned about the definition, purpose, syntax, and parameters related to NumPy cumsum. Additionally, we have examined several examples that demonstrate how to compute the cumulative sum of different types of arrays.
NumPy cumsum can help identify patterns or trends in data which is a significant takeaway for data analysis. By using this tool, developers can efficiently manipulate data and obtain essential insights relevant to their applications.
Understanding NumPy cumsum is a stepping stone towards utilizing more advanced data analysis techniques available in Python, which makes it a topic of relevance and importance in modern data science.