Writing Python code to practice good data quality
Ensuring good data quality involves employing techniques to cleanse, validate, and manage data efficiently.
In the next section, three Python examples are presented to demonstrate different ways to achieve better data quality in datasets. These examples include data cleansing, validation, and handling missing values using popular Python libraries such as pandas and numPy.
Example 1 – data cleansing with pandas
This example will demonstrate how to remove duplicates and handle erroneous entries in a dataset using pandas:
import pandas as pd
# Sample data
# It contains features that can be present in a phishing detection dataset
data = {
    'Phishing_detect_Name': ['Alice', 'Bob', 'Charlie', 'David', 'Alice'],
    'Phishing_detect_Age': [25, 30, 35, 40, 25],
    'Phishing_detect_Email...
