Leer Filtering and Sorting Business Data | Business Data Manipulation

In business analysis, you often need to focus on just a slice of your data to answer specific questions or make decisions. Filtering and sorting are essential techniques that help you zero in on what matters most. For instance, you might want to see only top-selling products, identify transactions where sales exceeded a certain threshold, or rank products by their revenue to spot best performers. These operations let you quickly extract insights from large datasets, making your analysis more targeted and actionable.


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import pandas as pd

# Sample sales dataset
data = {
    "Product": ["A", "B", "C", "D"],
    "Revenue": [1500, 800, 2200, 950],
    "Units Sold": [10, 5, 15, 7]
}
df = pd.DataFrame(data)

# Filter to include only records where Revenue > 1000
high_revenue = df[df["Revenue"] > 1000]
print(high_revenue)

Python provides powerful built-in tools for filtering and sorting data. The filter function lets you select elements from a sequence that meet a certain condition, while sorted arranges items based on a chosen key. Both functions often use lambda functions—a way to write quick, anonymous functions for custom logic. For example, you might use a lambda to filter all sales above $1,000, or sort products based on total revenue. Understanding how to use these tools gives you flexibility to adapt your analysis to any business scenario.


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# Suppose you have a summary dictionary of products and their total revenue
product_revenue = {
    "A": 1500,
    "B": 800,
    "C": 2200,
    "D": 950
}

# Sort products by total revenue in descending order
sorted_products = sorted(product_revenue.items(), key=lambda x: x[1], reverse=True)
print(sorted_products)

1. Why might a business analyst want to filter sales data by revenue?

2. What does the key parameter in the `sorted` function allow you to do?

3. Fill in the blanks: To filter records, you can use a expression inside a comprehension.

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              12345678910111213
            
import pandas as pd

# Sample sales dataset
data = {
    "Product": ["A", "B", "C", "D"],
    "Revenue": [1500, 800, 2200, 950],
    "Units Sold": [10, 5, 15, 7]
}
df = pd.DataFrame(data)

# Filter to include only records where Revenue > 1000
high_revenue = df[df["Revenue"] > 1000]
print(high_revenue)


              1234567891011
            
# Suppose you have a summary dictionary of products and their total revenue
product_revenue = {
    "A": 1500,
    "B": 800,
    "C": 2200,
    "D": 950
}

# Sort products by total revenue in descending order
sorted_products = sorted(product_revenue.items(), key=lambda x: x[1], reverse=True)
print(sorted_products)

1. Why might a business analyst want to filter sales data by revenue?

2. What does the key parameter in the `sorted` function allow you to do?

3. Fill in the blanks: To filter records, you can use a expression inside a comprehension.

Was alles duidelijk?

Bedankt voor je feedback!

Sectie 1. Hoofdstuk 6

Filtering and Sorting Business Data

1. Why might a business analyst want to filter sales data by revenue?

2. What does the key parameter in the sorted function allow you to do?

3. Fill in the blanks: To filter records, you can use a ____ expression inside a ____ comprehension.

Filtering and Sorting Business Data

1. Why might a business analyst want to filter sales data by revenue?

2. What does the key parameter in the sorted function allow you to do?

3. Fill in the blanks: To filter records, you can use a ____ expression inside a ____ comprehension.

2. What does the key parameter in the `sorted` function allow you to do?

3. Fill in the blanks: To filter records, you can use a expression inside a comprehension.

2. What does the key parameter in the `sorted` function allow you to do?

3. Fill in the blanks: To filter records, you can use a expression inside a comprehension.