To time I/O and computation using Pandas, we can use the time module in Python. Here's an example of how to time the reading of a CSV file into a Pandas DataFrame and the subsequent computation of a simple aggregation:

import pandas as pd
import time
 
start = time.time()
df = pd.read_csv('mydata.csv')
end = time.time()
 
print("Time taken to read CSV file: {:.2f} seconds".format(end - start))
 
start = time.time()
result = df.groupby('column').mean()
end = time.time()
 
print("Time taken to compute aggregation: {:.2f} seconds".format(end - start))

In this example, we use the time.time() function to record the start and end times of the I/O and computation steps. We first time the reading of a CSV file into a Pandas DataFrame using the pd.read_csv() function, and then time the computation of a simple aggregation using the groupby() and mean() functions.

The start and end times are subtracted to get the elapsed time for each step, which is then printed out in seconds using the format() method.

Note that the performance of Pandas can be highly dependent on the size of the dataset being processed, as well as the complexity of the computation being performed. It's important to benchmark Pandas operations on representative datasets and workloads to get an accurate sense of the performance characteristics.