In this post, you will learn about some of the 5 most popular or useful set of **unary** **universal functions** (* ufuncs*) provided by Python Numpy library. As data scientists, it will be useful to learn these unary functions by heart as it will help in performing arithmetic operations on sequential-like objects. These functions can also be termed as vectorized wrapper functions which are used to perform element-wise operations.

The following represents different set of popular functions:

- Basic arithmetic operations
- Summary statistics
- Sorting
- Minimum / maximum
- Array equality

### Basic Arithmetic Operations

The following are some of the unary functions whichc an be used to perform arithmetic operations:

- add, subtract, multiply, divide, exp
- add.reduce
- sum

Here is the sample code demonstrating the usage of the above functions:

```
import numpy as np
#
# Create an array of 5 numbers between 5 and 10
#
arr = np.linspace(5, 10, 5)
#
# Print array
#
print(arr)
#
# Perform arithmetic operations
#
np.add(arr, 1), np.subtract(arr, 1), np.multiply(arr, 2), np.divide(arr, 2)
```

Here is how the output would look like:

In case, you want to add all the numbers in a row or column and get the output as matrix, functions such as add.reduce or sum is used with axis. In the code sample given below, a 2 x 5 matrix is reduced by rows (axis=1) and columns (axis=0)

### Summary Statistics

The following are some of the methods which can be used for calculating summary statistics:

**mean, median**: Finds the mean and median of the data sample respectively.**std**: Finds the standard deviation of the data**var**: Finds the variance of the data

Here is the code demonstrating the usage of above functions with SKlearn IRIS dataset.

```
import numpy as np
from sklearn import datasets
iris = datasets.load_iris()
np.mean(iris.data[:,0]), np.std(iris.data[:,0]), np.var(iris.data[:,0]), np.median(iris.data[:,0])
```

Here is how the output will look like:

### Sorting

The following represents the Numpy unary functions which can be used for sorting the array:

- sort: Sort the data
- argsort: Finds the indices of the sorted data

Here is the code demonstrating the usage of above functions with SKlearn IRIS dataset:

```
import numpy as np
from sklearn import datasets
iris = datasets.load_iris()
iris.data[0:10, 0]
np.sort(iris.data[0:10, 0])
np.argsort(iris.data[0:10, 0])
```

Here is how the output will look like:

### Finding Maximum / Minimum

The following are some of the methods which can be used for finding maximum and minimum value from a data array:

- min: Finds the minimum of the array
- max: Finds the maximum of the array
- argmin: Finds the index of the minimum of the array
- argmax: Finds the index of the maximum of the array

Here is the code demonstrating the usage of above functions with SKlearn IRIS dataset:

```
import numpy as np
from sklearn import datasets
iris = datasets.load_iris()
iris.data[0:10, 0]
np.min(iris.data[0:10, 0]), np.argmin(iris.data[0:10, 0])
np.max(iris.data[0:10, 0]), np.argmax(iris.data[0:10, 0])
```

Here is how the output will look like:

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