In the dynamic landscape of big data analytics, the fusion of Pandas API with Apache Spark has revolutionized the way developers manipulate and analyze large-scale datasets. Among the plethora of functionalities offered by the Pandas API on Spark, binary operator functions stand out as powerful tools for performing element-wise comparisons efficiently across distributed data. In this comprehensive article, we will delve into the intricacies of binary operator functions, focusing on `Series.lt()`

, `Series.gt()`

, `Series.le()`

, `Series.ge()`

, `Series.ne()`

, and `Series.eq()`

. Through detailed explanations and illustrative examples, we will explore the utility of these functions in real-world scenarios, empowering users to unleash the full potential of data comparison in Spark environments.

### 1. Series.lt(other) in Pandas API on Spark

The `Series.lt()`

function compares each element of the series with the corresponding element of another series or scalar value, returning `True`

if the current value is less than the other and `False`

otherwise. This function is invaluable for scenarios where you need to identify elements that are smaller than a given threshold.

```
# Example of Series.lt()
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': [1, 2, 3, 4]}
data2 = {'A': [3, 2, 1, 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 less than comparison
result = series1.lt(series2)
# Print the result
print("Result of less than comparison:")
print(result)
```

**Output:**

```
Result of less than comparison:
0 True
1 False
2 False
3 True
Name: A, dtype: bool
```

### 2. Series.gt(other) in Pandas API on Spark

The `Series.gt()`

function compares each element of the series with the corresponding element of another series or scalar value and returns a boolean series indicating whether each element is greater than the other.

```
# Example of Series.gt()
# Assume the series1 and series2 are defined from the previous example
# Compare series values
result = series1.gt(series2)
# Print the result
print("Result of greater than comparison:")
print(result)
```

**Output:**

```
Result of greater than comparison:
0 False
1 False
2 True
3 False
Name: A, dtype: bool
```

### 3. Series.le(other) in Pandas API on Spark

The `Series.le()`

function compares each element of the series with the corresponding element of another series or scalar value and returns a boolean series indicating whether each element is less than or equal to the other.

```
# Example of Series.le()
# Assume the series1 and series2 are defined from the previous example
# Compare series values
result = series1.le(series2)
# Print the result
print("Result of less than or equal to comparison:")
print(result)
```

**Output:**

```
Result of less than or equal to comparison:
0 True
1 True
2 False
3 True
Name: A, dtype: bool
```

### 4. Series.ge(other)

The `Series.ge()`

function compares each element of the series with the corresponding element of another series or scalar value and returns a boolean series indicating whether each element is greater than or equal to the other.

```
# Example of Series.ge()
# Assume the series1 and series2 are defined from the previous example
# Compare series values
result = series1.ge(series2)
# Print the result
print("Result of greater than or equal to comparison:")
print(result)
```

**Output:**

```
Result of greater than or equal to comparison:
0 False
1 True
2 True
3 False
Name: A, dtype: bool
```

### 5. Series.ne(other)

The `Series.ne()`

function compares each element of the series with the corresponding element of another series or scalar value and returns a boolean series indicating whether each element is not equal to the other.

```
# Example of Series.ne()
# Assume the series1 and series2 are defined from the previous example
# Compare series values
result = series1.ne(series2)
# Print the result
print("Result of not equal to comparison:")
print(result)
```

**Output:**

```
Result of not equal to comparison:
0 True
1 False
2 True
3 True
Name: A, dtype: bool
```

### 6. Series.eq(other)

The `Series.eq()`

function compares each element of the series with the corresponding element of another series or scalar value and returns a boolean series indicating whether each element is equal to the other.

```
# Example of Series.eq()
# Assume the series1 and series2 are defined from the previous example
# Compare series values
result = series1.eq(series2)
# Print the result
print("Result of equal to comparison:")
print(result)
```

**Output:**

```
Result of equal to comparison:
0 False
1 True
2 False
3 False
Name: A, dtype: bool
```

Spark important urls to refer