Scenario-based Exercises for Data Manipulation

Topic Overview and Importance

Hello and welcome! Today, we're exploring practical data manipulation techniques in Python. We'll use Python lists to represent our data stream and perform projection, filtering, and aggregation. And here's the star of the show: our operations will be neatly packaged within a Python class! No mess, all clean code.

Introduction to Data Manipulation

Data manipulation is akin to being a sculptor but for data. We chisel and shape our data to get the desired structure. Python lists are perfect for this, and our operations will be conveniently bundled inside a Python class. So, let's get our toolbox ready! Here's a simple Python class, DataStream, that will serve as our toolbox: Python class DataStream: def __init__(self, data): self.data = data

Data Projection in Practice

Our first stop is data projection. Think of it like capturing a photo of our desired features. Suppose we have data about people. If we're only interested in names and ages, we project our data to include just these details. We'll extend our DataStream class with a project_data method for this: Pythonclass DataStream: def __init__(self, data): self.data = data def project_data(self, keys): projected_data = [{key: d.get(key, None) for key in keys} for d in self.data] return projected_data # Let's use it! ds = DataStream([ {'name': 'Alice', 'age': 25, 'profession': 'Engineer'}, {'name': 'Bob', 'age': 30, 'profession': 'Doctor'}, ]) projected_ds = ds.project_data(['name', 'age']) print(projected_ds) # Outputs: [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 30}] As you can see, we now have a new list with just the names and ages!

Data Filtering in Practice

Next, we have data filtering, which is like cherry-picking our preferred data entries. We'll extend our DataStream class with a filter_data method that uses a "test" function to filter data: Pythonclass DataStream: def __init__(self, data): self.data = data #... other methods ... def filter_data(self, test_func): filtered_data = list(filter(test_func, self.data)) return filtered_data # Applying it: ds = DataStream([ {'name': 'Alice', 'age': 25, 'profession': 'Engineer'}, {'name': 'Bob', 'age': 30, 'profession': 'Doctor'}, ]) age_test = lambda x: x['age'] > 26 # our "test" function filtered_ds = ds.filter_data(age_test) print(filtered_ds) # Outputs: [{'name': 'Bob', 'age': 30, 'profession': 'Doctor'}] With the filter method, our output is a list with only Bob’s data, as he's the only one who passes the 'age over 26' test.

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Last is data aggregation, where we condense our data into a summary. We will add an aggregate_data method to our DataStream class for this:

Python

class DataStream:
    def __init__(self, data):
        self.data = data

    #... other methods ... 

    def aggregate_data(self, key, agg_func):
        values = [d.get(key, None) for d in self.data]
        return agg_func(values)

# Let's put it to use
ds = DataStream([
             {'name': 'Alice', 'age': 25, 'profession': 'Engineer'},
             {'name': 'Bob', 'age': 30, 'profession': 'Doctor'},
        ])
average_age = ds.aggregate_data('age', lambda ages: sum(ages) / len(ages))
print(average_age)  # Outputs: 27.5

class DataStream:
    def __init__(self, data):
        self.data = data

    #... other methods ... 

    def aggregate_data(self, key, agg_func):
        values = [d.get(key, None) for d in self.data]
        return agg_func(values)

# Let's put it to use
ds = DataStream([
             {'name': 'Alice', 'age': 25, 'profession': 'Engineer'},
             {'name': 'Bob', 'age': 30, 'profession': 'Doctor'},
        ])
average_age = ds.aggregate_data('age', lambda ages: sum(ages) / len(ages))
print(average_age)  # Outputs: 27.5

With this script, we get the average age of Alice and Bob, which is 27.5.

Combining Projection, Filtering, and Aggregation

Now, let's combine projection, filtering, and aggregation to see the collective power of these techniques. We'll extend our example to demonstrate this flow:

Data Projection: Choose only the desired fields.
Data Filtering: Filter the data based on certain conditions.
Data Aggregation: Summarize the filtered data.

We'll modify our DataStream class to include all the methods and then use them together in a workflow. On top of that, projection and filtering methods will now return an instance of DataStream, not a list as before, so that we can chain these methods when calling them:

Python

class DataStream:
    def __init__(self, data):
        self.data = data

    def project_data(self, keys):
        projected_data = [{key: d.get(key, None) for key in keys} for d in self.data]
        return DataStream(projected_data)  # Return a new DataStream object for chaining

    def filter_data(self, test_func):
        filtered_data = list(filter(test_func, self.data))
        return DataStream(filtered_data)  # Return a new DataStream object for chaining

    def aggregate_data(self, key, agg_func):
        values = [d.get(key, None) for d in self.data]
        return agg_func(values)

# Example usage
ds = DataStream([
    {'name': 'Alice', 'age': 25, 'profession': 'Engineer', 'salary': 70000},
    {'name': 'Bob', 'age': 30, 'profession': 'Doctor', 'salary': 120000},
    {'name': 'Carol', 'age': 35, 'profession': 'Artist', 'salary': 50000},
    {'name': 'David', 'age': 40, 'profession': 'Engineer', 'salary': 90000},
])

# Step 1: Project the data to include only 'name', 'age', and 'salary'
projected_ds = ds.project_data(['name', 'age', 'salary'])

# Step 2: Filter the projected data to include only those with age > 30
filtered_ds = projected_ds.filter_data(lambda x: x['age'] > 30)

# Step 3: Aggregate the filtered data to compute the average salary
average_salary = filtered_ds.aggregate_data('salary', lambda salaries: sum(salaries) / len(salaries))
print(average_salary)  # Outputs: 70000.0

class DataStream:
    def __init__(self, data):
        self.data = data

    def project_data(self, keys):
        projected_data = [{key: d.get(key, None) for key in keys} for d in self.data]
        return DataStream(projected_data)  # Return a new DataStream object for chaining

    def filter_data(self, test_func):
        filtered_data = list(filter(test_func, self.data))
        return DataStream(filtered_data)  # Return a new DataStream object for chaining

    def aggregate_data(self, key, agg_func):
        values = [d.get(key, None) for d in self.data]
        return agg_func(values)

# Example usage
ds = DataStream([
    {'name': 'Alice', 'age': 25, 'profession': 'Engineer', 'salary': 70000},
    {'name': 'Bob', 'age': 30, 'profession': 'Doctor', 'salary': 120000},
    {'name': 'Carol', 'age': 35, 'profession': 'Artist', 'salary': 50000},
    {'name': 'David', 'age': 40, 'profession': 'Engineer', 'salary': 90000},
])

# Step 1: Project the data to include only 'name', 'age', and 'salary'
projected_ds = ds.project_data(['name', 'age', 'salary'])

# Step 2: Filter the projected data to include only those with age > 30
filtered_ds = projected_ds.filter_data(lambda x: x['age'] > 30)

# Step 3: Aggregate the filtered data to compute the average salary
average_salary = filtered_ds.aggregate_data('salary', lambda salaries: sum(salaries) / len(salaries))
print(average_salary)  # Outputs: 70000.0

Here:

Projection: We choose only the name, age, and salary fields from our data. The project_data method now returns a DataStream object, allowing us to chain multiple operations.
Filtering: We filter the projected data to include only those persons whose age is greater than 30. The filter_data method also returns a DataStream object for chaining.
Aggregation: We calculate the average salary of the filtered data. The final output shows the average salary for those aged over 30, which is 70,000.

By combining these methods, our data manipulation becomes both powerful and concise. Try experimenting and see what you can create!

Brilliant job! You've now grasped the basics of data projection, filtering, and aggregation on Python lists. Plus, you've learned to package these operations in a Python class — a neat bundle of reusable code magic!

Now, why not try applying these fresh skills with some practice exercises? They're just around the corner. Ready? Let's dive into more fun with data manipulation!