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Sure! Let's write a step-by-step solution for creating the `writeBin` function that processes a list of employees, then writes the information of those employees who are over 40 years old into a binary file named "employees.dat".
### Step-by-Step Solution:
1. Define the Function:
- Begin by defining the function `writeBin` which takes one parameter `List`. This `List` contains details of employees, where each employee is represented by a tuple consisting of their name, age, and address.
2. Filter Employees by Age:
- Within the function body, filter the list to keep only those employees whose age is greater than 40.
3. Write to a Binary File:
- Using the `pickle` module, write the filtered list of employees into a binary file named "employees.dat".
Here is the detailed code for the function:
```python
import pickle
def writeBin(List):
"""
This function accepts a list of employees with their name, age, and address.
It writes records of employees whose age is more than 40 into a binary file named "employees.dat".
"""
# Step 2: Filter employees where age is more than 40
filtered_employees = [employee for employee in List if employee[1] > 40]
# Step 3: Write filtered employees to a binary file
with open("employees.dat", "wb") as file:
pickle.dump(filtered_employees, file)
# Example usage:
employees = [
("Alice", 45, "123 Maple St."),
("Bob", 39, "456 Oak St."),
("Carol", 50, "789 Pine St."),
("David", 42, "101 Elm St."),
("Eve", 38, "202 Birch St."),
]
# Call the function with the example list
writeBin(employees)
```
### Explanation:
1. Importing the `pickle` Module:
- The `pickle` module is used to serialize and deserialize Python objects. Here, we use it to write the filtered list to a binary file.
2. Filtering the List:
- The list comprehension `[employee for employee in List if employee[1] > 40]` iterates through each employee in the list, checking if the age (which is the second element, `employee[1]`) is greater than 40. If so, that employee is included in the `filtered_employees` list.
3. Writing to the Binary File:
- The `with open("employees.dat", "wb") as file:` statement opens (or creates) a file named "employees.dat" in write-binary mode. The `pickle.dump(filtered_employees, file)` function call writes the `filtered_employees` list to this file in binary format.
4. Example Usage:
- An example list of employees is provided, and the `writeBin` function is called with this list to demonstrate how it works.
Using this function, you can efficiently filter and store employee records meeting specific age criteria into a binary file. This solution ensures that only relevant data is stored, optimizing both storage and retrieval processes.
### Step-by-Step Solution:
1. Define the Function:
- Begin by defining the function `writeBin` which takes one parameter `List`. This `List` contains details of employees, where each employee is represented by a tuple consisting of their name, age, and address.
2. Filter Employees by Age:
- Within the function body, filter the list to keep only those employees whose age is greater than 40.
3. Write to a Binary File:
- Using the `pickle` module, write the filtered list of employees into a binary file named "employees.dat".
Here is the detailed code for the function:
```python
import pickle
def writeBin(List):
"""
This function accepts a list of employees with their name, age, and address.
It writes records of employees whose age is more than 40 into a binary file named "employees.dat".
"""
# Step 2: Filter employees where age is more than 40
filtered_employees = [employee for employee in List if employee[1] > 40]
# Step 3: Write filtered employees to a binary file
with open("employees.dat", "wb") as file:
pickle.dump(filtered_employees, file)
# Example usage:
employees = [
("Alice", 45, "123 Maple St."),
("Bob", 39, "456 Oak St."),
("Carol", 50, "789 Pine St."),
("David", 42, "101 Elm St."),
("Eve", 38, "202 Birch St."),
]
# Call the function with the example list
writeBin(employees)
```
### Explanation:
1. Importing the `pickle` Module:
- The `pickle` module is used to serialize and deserialize Python objects. Here, we use it to write the filtered list to a binary file.
2. Filtering the List:
- The list comprehension `[employee for employee in List if employee[1] > 40]` iterates through each employee in the list, checking if the age (which is the second element, `employee[1]`) is greater than 40. If so, that employee is included in the `filtered_employees` list.
3. Writing to the Binary File:
- The `with open("employees.dat", "wb") as file:` statement opens (or creates) a file named "employees.dat" in write-binary mode. The `pickle.dump(filtered_employees, file)` function call writes the `filtered_employees` list to this file in binary format.
4. Example Usage:
- An example list of employees is provided, and the `writeBin` function is called with this list to demonstrate how it works.
Using this function, you can efficiently filter and store employee records meeting specific age criteria into a binary file. This solution ensures that only relevant data is stored, optimizing both storage and retrieval processes.
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