# np.diff() function in Python [5 Examples]

Do you want to do some mathematical calculations through NumPy in Python? In this NumPy tutorial, I will explain the np.diff() function in Python, its syntax, the parameters required, and the return values with some examples.

To understand the np.diff() function in Python, it’s essential to recognize that it calculates the difference between successive elements of an array. By adjusting its parameters, users can specify the number of times this operation is performed (n), the axis along which the differences are calculated (axis), and optionally prepend or append values.

## np.diff() function in Python

The np.diff() function in Python NumPy library calculates the discrete difference between consecutive elements of an array. An input array computes the output as a[i+1] – a[i] for each element i, where i ranges over the array’s length minus one.

Here’s a brief overview of how np.diff() function in Python works:

### NumPy diff() function syntax

The basic syntax of the np.diff() function in Python is as follows:

``numpy.diff(arr, n=1, axis=-1, prepend=<no value>, append=<no value>)``

Here,

### Python diff() function in NumPy return value

The np.diff() function in Python returns a new array of the same type as a, except along the specified axis where the dimension is smaller by n units. The returned array holds the calculated differences.

## np.diff Python usecases

Let’s see different use cases for the np.diff() function in Python:

### 1. np diff function in Python basic use

Calculates the difference between each pair of consecutive elements in a one-dimensional array in Python. For example:

``````import numpy as np

temperatures = np.array([58, 60, 62, 65, 63, 66, 68])
temperature_change = np.diff(temperatures)
print(temperature_change)``````

Output:

``[ 2  2  3 -2  3  2]``

The output from running the code in PyCharm is visually represented in the screenshot below.

### 2. numpy.diff function in Python with n Parameter

Computes the second-order difference, which is the difference of the consecutive differences in the array in Python.

``````import numpy as np

stock_prices = np.array([120, 125, 123, 130, 128])
price_change = np.diff(stock_prices, n=2)
print(price_change)``````

Output:

``[-7  9 -9]``

Displayed below is a screenshot capturing the outcome of the code execution in the PyCharm editor.

### 3. diff NumPy function in a multidimensional array

Finds the difference along the default last axis (axis=-1) in a multidimensional array in Python, effectively performing the operation on each sub-array.

``````import numpy as np

rainfall = np.array([[0.1, 0.2, 0.0, 0.3],
[0.3, 0.4, 0.5, 0.2],
[0.0, 0.0, 0.1, 0.2]])
daily_change = np.diff(rainfall)
print(daily_change)``````

Output:

``````[[ 0.1 -0.2  0.3]
[ 0.1  0.1 -0.3]
[ 0.   0.1  0.1]]``````

The following screenshot illustrates the results obtained from executing the code in the PyCharm editor.

### 4. Python NumPy diff() function with axis=0

Computes the difference between elements along the vertical axis (row-wise) in a multidimensional Python array.

``````import numpy as np

rainfall = np.array([[0.1, 0.2, 0.0, 0.3],
[0.3, 0.4, 0.5, 0.2],
[0.0, 0.0, 0.1, 0.2]])
city_change = np.diff(rainfall, axis=0)
print(city_change)``````

Output:

``````[[ 0.2  0.2  0.5 -0.1]
[-0.3 -0.4 -0.4  0. ]]``````

After executing the code in Pycharm, one can see the output in the below screenshot.

### 5. np.diff in Python with axis=1

Calculates the difference between elements along the horizontal axis (column-wise) in a multidimensional array in Python.

``````import numpy as np

rainfall = np.array([[0.1, 0.2, 0.0, 0.3],
[0.3, 0.4, 0.5, 0.2],
[0.0, 0.0, 0.1, 0.2]])
city_change = np.diff(rainfall, axis=1)
print(city_change)``````

Output:

``````[[ 0.1 -0.2  0.3]
[ 0.1  0.1 -0.3]
[ 0.   0.1  0.1]]``````

After implementing the code in the Pycharm editor, the screenshot is mentioned below.

## Conclusion

Understanding The np.diff() function in Python, that computes differences. And its flexibility with parameters like n and axis makes it an essential function for data analysis, signal processing, and scientific computing.

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