Among its numerous features, PySpark provides a comprehensive set of mathematical functions that are essential for data analysis. In this article, we focus on two such functions: `cos()`

and `cosh()`

. We’ll explore their applications, differences, and how to use them effectively in real-world data scenarios.

## Understanding cos() and cosh() in PySpark

### The cos() Function

**Definition**: The`cos()`

function in PySpark computes the cosine of a given angle, expressed in radians.**Usage**: Commonly used in trigonometric calculations, which are pivotal in fields such as physics, engineering, and even finance.

### The cosh() Function

**Definition**: The`cosh()`

function calculates the hyperbolic cosine of a given number.**Usage**: It’s essential in higher-level mathematics and physics, particularly in dealing with hyperbolic geometries and complex analysis.

#### Examples

To illustrate the use of `cos()`

and `cosh()`

in PySpark, let’s consider a dataset containing a range of values for which we want to calculate the cosine and hyperbolic cosine.

### Setting Up the Environment

Ensure you have PySpark installed and configured in your environment. Begin by importing the necessary modules:

```
from pyspark.sql import SparkSession
from pyspark.sql.functions import cos, cosh
```

```
# Initialize Spark session
spark = SparkSession.builder.appName("cos_cosh_example").getOrCreate()
# Sample data
data = [(0.0,), (1.0,), (2.0,), (3.14,)]
# Creating a DataFrame
df = spark.createDataFrame(data, ["angle"])
df.show()
```

### Applying cos() and cosh()

```
# Calculating cosine and hyperbolic cosine
df = df.withColumn("cosine", cos(df["angle"]))
df = df.withColumn("hyperbolic_cosine", cosh(df["angle"]))
df.show()
```

The `cos()`

and `cosh()`

functions in PySpark are potent tools for mathematical computations in big data analytics. Understanding their applications and differences is crucial for data professionals. Through practical examples, we demonstrated how these functions can be implemented to derive meaningful insights from data.

Spark important urls to refer