In the realm of big data processing, the integration of Pandas API with Apache Spark brings forth a powerful combination of intuitive data manipulation tools and scalable distributed computing capabilities. Among the array of functionalities offered by the Pandas API on Spark, binary operator functions play a pivotal role in performing element-wise operations efficiently across distributed datasets. In this comprehensive guide, we will delve into advanced binary operator functions, including `Series.rfloordiv()`

, `Series.divmod()`

, `Series.rdivmod()`

, and `Series.combine_first()`

. Through detailed explanations and illustrative examples, we will unravel the potential of these functions and demonstrate their utility in real-world scenarios.

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

The `Series.rfloordiv()`

function computes the reverse integer division of two series element-wise. It divides each element of the second series by the corresponding element of the first series and returns the integer part of the result. This function is valuable for scenarios where you need to perform integer division operations with a different base or handle numerical data.

```
# Example of Series.rfloordiv()
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': [10, 20, 30, 40]}
data2 = {'A': [2, 3, 4, 5]}
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 reverse integer division
result = series2.rfloordiv(series1)
# Print the result
print("Result of reverse integer division:")
print(result)
```

**Output:**

```
Result of reverse integer division:
0 0
1 0
2 0
3 0
Name: A, dtype: int64
```

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

The `Series.divmod()`

function computes the integer division and modulo of two series element-wise. It divides each element of the first series by the corresponding element of the second series and returns a tuple containing the quotient and remainder. This function is useful for scenarios where you need to perform both division and modulo operations simultaneously.

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

**Output:**

```
Result of divmod operation:
A
0 (5, 0)
1 (6, 2)
2 (7, 2)
3 (8, 0)
```

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

The `Series.rdivmod()`

function computes the reverse integer division and modulo of two series element-wise. It divides each element of the second series by the corresponding element of the first series and returns a tuple containing the quotient and remainder. This function is beneficial for scenarios where you need to perform reverse division and modulo operations with a different base.

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

**Output:**

```
Result of reverse divmod operation:
A
0 (0, 2)
1 (0, 1)
2 (0, 2)
3 (0, 0)
```

### 4. Series.combine_first(other)

The `Series.combine_first()`

function combines the values of two series, choosing the calling series’s values first. It fills in missing values in the calling series with corresponding non-missing values from the other series. This function is useful for scenarios where you need to merge datasets while prioritizing the values from one series over the other.

```
# Example of Series.combine_first()
# Sample data
data1 = {'A': [1, 2, None, 4]}
data2 = {'A': [10, None, 30, 40]}
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']
# Combine series values
result = series1.combine_first(series2)
# Print the result
print("Result of combining series values:")
print(result)
```

**Output:**

```
Result of combining series values:
0 1.0
1 2.0
2 30.0
3 4.0
Name: A, dtype: float64
```

Spark important urls to refer