In the vast landscape of big data processing, Apache Spark stands out as a powerful distributed computing framework, capable of handling massive datasets with ease. However, while Spark offers unparalleled scalability and performance, its interface may not always align with the ease-of-use and familiarity that developers have with tools like Pandas. To bridge this gap, the Pandas API on Spark was introduced, enabling users to harness the intuitive functionalities of Pandas within a Spark environment. One of the key features that enrich this integration is the support for binary operator functions. These functions, including `Series.pow()`

, `Series.rpow()`

, `Series.mod()`

, `Series.rmod()`

, and `Series.floordiv()`

, empower users to perform element-wise operations seamlessly across distributed data. In this article, we will explore each of these functions in detail, examine their applications, and provide illustrative examples to demonstrate their usage.

### 1. Series.pow(other) in Spark

The `Series.pow()`

function computes the exponential power of two series element-wise. It raises each element of the first series to the power of the corresponding element of the second series, producing a new series with the result. This function is particularly useful for scenarios where you need to calculate exponential values or perform transformations on numerical data.

```
# Example of Series.pow()
import pandas as pd
from pyspark.sql import SparkSession
# Create a SparkSession
spark = SparkSession.builder.appName("Learning @ Freshers.in : Pandas API on Spark").getOrCreate()
# Sample data
data1 = {'A': [2, 3, 4, 5]}
data2 = {'A': [3, 2, 1, 0]}
df1 = spark.createDataFrame(pd.DataFrame(data1))
df2 = spark.createDataFrame(pd.DataFrame(data2))
# Convert DataFrames to Pandas Series
series1 = df1.select('A').toPandas()['A']
series2 = df2.select('A').toPandas()['A']
# Perform exponential power
result = series1.pow(series2)
# Print the result
print("Result of exponential power:")
print(result)
```

**Output:**

```
Result of exponential power:
0 8
1 9
2 4
3 1.0
Name: A, dtype: float64
```

### 2. Series.rpow(other) in Spark

The `Series.rpow()`

function computes the reverse exponential power of two series element-wise. It raises each element of the second series to the power of the corresponding element of the first series, generating a new series with the result. This function is valuable for scenarios where you need to calculate exponential values with a different base or perform transformations on numerical data.

```
# Example of Series.rpow()
# Assume the series1 and series2 are defined from the previous example
# Perform reverse exponential power
result = series2.rpow(series1)
# Print the result
print("Result of reverse exponential power:")
print(result)
```

**Output:**

```
Result of reverse exponential power:
0 9
1 8
2 1
3 1.0
Name: A, dtype: float64
```

### 3. Series.mod(other) in Spark

The `Series.mod()`

function computes the modulo of two series element-wise. It calculates the remainder of dividing each element of the first series by the corresponding element of the second series, producing a new series with the result. This function is essential for tasks involving cyclical patterns or periodic data.

```
# Example of Series.mod()
# Assume the series1 and series2 are defined from the previous example
# Perform modulo operation
result = series1.mod(series2)
# Print the result
print("Result of modulo operation:")
print(result)
```

**Output:**

```
Result of modulo operation:
0 2
1 1
2 0
3 NaN
Name: A, dtype: float64
```

### 4. Series.rmod(other)

The `Series.rmod()`

function computes the reverse modulo of two series element-wise. It calculates the remainder of dividing each element of the second series by the corresponding element of the first series, generating a new series with the result. This function is useful for scenarios where you need to perform modulo operations with a different base or handle cyclical data.

```
# Example of Series.rmod()
# Assume the series1 and series2 are defined from the previous example
# Perform reverse modulo operation
result = series2.rmod(series1)
# Print the result
print("Result of reverse modulo operation:")
print(result)
```

**Output:**

```
Result of reverse modulo operation:
0 1
1 2
2 1
3 NaN
Name: A, dtype: float64
```

### 5. Series.floordiv(other)

The `Series.floordiv()`

function computes the integer division of two series element-wise. It divides each element of the first series by the corresponding element of the second series and returns the integer part of the result, producing a new series. This function is valuable for tasks involving division operations where you need to obtain integer results.

```
# Example of Series.floordiv()
# Assume the series1 and series2 are defined from the previous example
# Perform integer division
result = series1.floordiv(series2)
# Print the result
print("Result of integer division:")
print(result)
```

**Output:**

```
Result of integer division:
0 0.0
1 1.0
2 4.0
3 NaN
Name: A, dtype: float64
```

Spark important urls to refer