To use an asynchronous lambda function to filter rows of a Pandas DataFrame, you can use the `apply()` method with an `async` lambda function.

Here's a Python program to understand better:

import pandas as pd
import asyncio
import numpy as np
# Define an asynchronous lambda function to filter rows
async def filter_rows(row):
    # Perform some asynchronous operations on the row
    await asyncio.sleep(1)
    return row['column'] > 5
# Create a random data frame with a 'column' containing random integers between 1 and 10
np.random.seed(42)  # For reproducibility
data = {'column': np.random.randint(1, 11, 10)}
df = pd.DataFrame(data)
# Filter rows using the asynchronous lambda function
filtered_df = df[df.apply(lambda row: asyncio.run(filter_rows(row)), axis=1)]
print(filtered_df)

Explanation of the code:
* The code imports the required libraries: pandas, numpy, and asyncio.
* An asynchronous lambda function named `filter_rows` is defined that takes a row of data as input and returns `True` if the 'column' value of the row is greater than 5. The function simulates an asynchronous task by sleeping for one second using `asyncio.sleep(1)`.
* numpy is used to generate a DataFrame with a 'column' containing ten random integers between 1 and 10, ensuring reproducibility through a set seed value.
* The `apply()` method is used to apply the asynchronous filter_rows function to each row of the DataFrame concurrently using `asyncio.run`. The apply method returns a Boolean Series that indicates which rows satisfy the filtering condition.
* The Boolean Series is used to filter the original DataFrame using boolean indexing. The filtered DataFrame is stored in the filtered_df variable.
* The filtered_df DataFrame is printed to the console.
Note that `asyncio.sleep(x)` is used here to simulate an asynchronous operation. The argument `x` signifies the number of seconds the compiler needs to wait before performing the next operation. In real-world scenarios, you would use asyncio for I/O-bound operations like making network requests or reading/writing files where you need to wait for external resources. For CPU-bound operations, asyncio may not provide significant benefits and can add complexity.