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You can use one of the following methods to calculate the rank of items in a NumPy array:

**Method 1: Use argsort() from NumPy**

**import numpy as np
ranks = np.array(my_array).argsort().argsort()
**

**Method 2: Use rankdata() from SciPy**

**from scipy.stats import rankdata
ranks = rankdata(my_array)
**

The following examples show how to use each method in practice with the following NumPy array:

**import numpy as np
#define array of values
my_array = np.array([3, 5, 2, 1, 9, 9])
#view array
print(my_array)
[3 5 2 1 9 9]**

**Example 1: Rank Items in NumPy Array Using argsort()**

The following code shows how to use the **argsort()** function from NumPy to rank the items in the array:

**#calculate rank of each item in array
ranks = np.array(my_array).argsort().argsort()
#view ranks
print(ranks)
[2 3 1 0 4 5]
**

The results show the rank of each item in the original array, with **0** representing the smallest value.

The benefit of this approach is that you donâ€™t have to load any extra modules, but the drawback is that **argsort()** only has one method for handling ties.

By default, **argsort()** uses an ordinal method for handling ties which means the tied value that occurs first is automatically given the lower rank.

**Example 2: Rank Items in NumPy Array Using rankdata()**

The following code shows how to use the **rankdata()** function from SciPy to rank the items in the array:

**from scipy.stats import rankdata **
#calculate rank of each item in array
**ranks = rankdata(my_array)**
#view ranks
print(ranks)
array([3. , 4. , 2. , 1. , 5.5, 5.5])

The results show the rank of each item in the original array, with **1** representing the smallest value.

If youâ€™d like **0** to represent the smallest value, simply subtract 1 from each value:

**from scipy.stats import rankdata **
#calculate rank of each item in array
ranks = rankdata(my_array) - 1
#view ranks
print(ranks)
[2. 3. 1. 0. 4.5 4.5]

By default, the **rankdata()** function assigns average ranks to any values that have ties.

However, you can use the method argument to handle ties in a different way.

For example, the following code shows how to use **ordinal**Â as the method for handling ties:

**from scipy.stats import rankdata **
#calculate rank of each item in array
ranks = rankdata(my_array, method='ordinal') - 1
#view ranks
print(ranks)
[2 3 1 0 4 5]

This produces the same results as the **argsort()** method from NumPy.

Other methods for handling ties include **min**, **max**, and **dense**.

Read about each method in the SciPy documentation.

**Additional Resources**

The following tutorials explain how to perform other common tasks in NumPy:

How to Remove Duplicate Elements from NumPy Array

How to Convert NumPy Array of Floats into Integers

How to Convert NumPy Matrix to Array