Pipe for method chaining in Data Analysis Python - Mini Project: Build & Apply

Using Pipe for Method Chaining in Data Analysis

📖 Scenario: Imagine you work in a small bakery. You have sales data for different types of bread sold each day. You want to clean and analyze this data step-by-step in a clear and simple way.

🎯 Goal: You will create a small sales data table, set a filter value, use pipe to chain methods for filtering and calculating total sales, and finally print the result.

📋 What You'll Learn

Create a pandas DataFrame with exact sales data

Create a filter threshold variable

Use pipe method chaining to filter and sum sales

Print the final total sales value

💡 Why This Matters

🌍 Real World

Data analysts often clean and transform data step-by-step. Using pipe helps keep the code readable and easy to follow.

💼 Career

Knowing how to use method chaining with pipe is useful for data scientists and analysts to write clean, maintainable data processing code.

1

Create the sales DataFrame

Create a pandas DataFrame called sales_data with these exact columns and rows:
Product: 'Sourdough', 'Baguette', 'Rye', 'Focaccia'
Sales: 150, 200, 90, 120

Data Analysis Python

import pandas as pd
# Create the sales_data DataFrame with Product and Sales columns
# Your code here

Hint

Use pd.DataFrame with a dictionary containing lists for 'Product' and 'Sales'.

2

Set the sales filter threshold

Create a variable called min_sales and set it to 100 to filter products with sales above this number.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Sourdough', 'Baguette', 'Rye', 'Focaccia'],
    'Sales': [150, 200, 90, 120]
})
# Create min_sales variable with value 100
# Your code here

Hint

Just assign the number 100 to the variable min_sales.

3

Use pipe to filter and sum sales

Use the pipe method on sales_data to filter rows where Sales is greater than min_sales, then calculate the sum of the Sales column. Store the result in a variable called total_sales. Use a lambda function inside pipe.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Sourdough', 'Baguette', 'Rye', 'Focaccia'],
    'Sales': [150, 200, 90, 120]
})
min_sales = 100
# Use pipe to filter sales_data and sum Sales column
# Your code here

Hint

Inside pipe, use a lambda that filters rows with Sales greater than min_sales. Then sum the Sales column.

4

Print the total sales

Print the variable total_sales to show the sum of sales for products with sales above min_sales.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Sourdough', 'Baguette', 'Rye', 'Focaccia'],
    'Sales': [150, 200, 90, 120]
})
min_sales = 100
total_sales = sales_data.pipe(lambda df: df[df['Sales'] > min_sales])['Sales'].sum()
# Print total_sales
# Your code here

Hint

Use print(total_sales) to show the result.

Practice

1. What is the main purpose of using pipe() in pandas method chaining?

easy

A. To create plots directly from the DataFrame

B. To convert a DataFrame into a NumPy array

C. To save the DataFrame to a CSV file

D. To pass the DataFrame through custom functions in a clear, readable way

Solution

1. Step 1: Understand the role of pipe()

   pipe() allows passing the DataFrame through custom functions, making code cleaner and easier to read.

2. Step 2: Compare with other options

   Options B, C, and D describe unrelated DataFrame operations, not the purpose of pipe().

3. Final Answer:

   To pass the DataFrame through custom functions in a clear, readable way -> Option D

4. Quick Check:

   pipe() = pass DataFrame through functions [OK]

Hint: pipe() chains functions clearly by passing DataFrame [OK]

Common Mistakes:

• Thinking pipe() changes DataFrame format
• Confusing pipe() with saving or plotting
• Assuming pipe() modifies data without functions

2. Which of the following is the correct syntax to use pipe() with a custom function clean_data on a DataFrame df?

easy

A. df.pipe(clean_data)

B. df.pipe(clean_data())

C. df.pipe = clean_data()

D. pipe(df, clean_data)

Solution

1. Step 1: Recall correct pipe() usage

   The function name is passed without parentheses to pipe() so it can be called with the DataFrame as argument.

2. Step 2: Evaluate options

   df.pipe(clean_data()) calls the function immediately, which is incorrect. df.pipe = clean_data() assigns instead of calling. pipe(df, clean_data) is not valid pandas syntax.

3. Final Answer:

   df.pipe(clean_data) -> Option A

4. Quick Check:

   pipe() needs function name without () [OK]

Hint: Pass function name without parentheses to pipe() [OK]

Common Mistakes:

• Adding parentheses after function name in pipe()
• Assigning pipe instead of calling it
• Using pipe() as a standalone function

3. Given the code below, what will be the output?

import pandas as pd
def add_column(df):
    df['C'] = df['A'] + df['B']
    return df

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
result = df.pipe(add_column)
print(result)

medium

A. DataFrame with column C only

B. DataFrame with columns A, B, and C where C = A + B

C. Error because add_column does not return anything

D. DataFrame with columns A and B only

Solution

1. Step 1: Analyze the function add_column

   The function adds a new column 'C' as sum of 'A' and 'B', then returns the modified DataFrame.

2. Step 2: Understand pipe usage

   Using pipe(add_column) passes the DataFrame to the function and returns the updated DataFrame with the new column.

3. Final Answer:

   DataFrame with columns A, B, and C where C = A + B -> Option B

4. Quick Check:

   pipe() returns DataFrame with new column C [OK]

Hint: pipe() returns DataFrame after function modifies it [OK]

Common Mistakes:

• Forgetting to return DataFrame inside function
• Expecting pipe() to modify DataFrame without return
• Assuming pipe() creates new DataFrame without function

4. What is wrong with the following code snippet?

def drop_na(df):
    df.dropna()
    return df

df = pd.DataFrame({'A': [1, None, 3], 'B': [4, 5, None]})
result = df.pipe(drop_na)
print(result)

medium

A. Function drop_na should not return df

B. pipe() cannot be used with dropna()

C. dropna() is not called correctly; it needs inplace=True or assignment

D. DataFrame df is empty before pipe()

Solution

1. Step 1: Check dropna() usage inside function

   dropna() returns a new DataFrame by default and does not modify df unless inplace=True or reassigned.

2. Step 2: Identify the problem in function

   The function calls dropna() but ignores its result, returning the original df unchanged.

3. Final Answer:

   dropna() is not called correctly; it needs inplace=True or assignment -> Option C

4. Quick Check:

   dropna() must modify or assign result [OK]

Hint: dropna() returns new DataFrame; assign or use inplace=True [OK]

Common Mistakes:

• Ignoring return value of dropna()
• Assuming dropna() modifies DataFrame in place by default
• Returning original DataFrame without changes

5. You want to apply two custom functions scale_data and filter_data sequentially on a DataFrame df using pipe(). Which of the following is the correct way to chain these functions?

hard

A. df.pipe(scale_data).pipe(filter_data)

B. df.pipe(scale_data, filter_data)

C. df.pipe(scale_data(filter_data))

D. pipe(df).scale_data().filter_data()

Solution

1. Step 1: Understand chaining with pipe()

   Each pipe() call passes the DataFrame through one function, so chaining calls applies functions in order.

2. Step 2: Evaluate options

   df.pipe(scale_data).pipe(filter_data) correctly chains two functions with separate pipe() calls. Options B and C misuse pipe syntax. pipe(df).scale_data().filter_data() is invalid pandas syntax.

3. Final Answer:

   df.pipe(scale_data).pipe(filter_data) -> Option A

4. Quick Check:

   Chain multiple functions by chaining pipe() calls [OK]

Hint: Chain multiple functions by chaining pipe() calls [OK]

Common Mistakes:

• Passing multiple functions in one pipe() call
• Calling functions inside pipe() incorrectly
• Using non-existent pipe syntax