Building cleaning pipelines with pipe() in Pandas - Mini Project: Build & Apply

Building cleaning pipelines with pipe()

📖 Scenario: You work as a data analyst for a small online store. You receive raw sales data that needs cleaning before analysis. The data has some missing values and inconsistent formatting.Using pandas, you will clean this data step-by-step by building a pipeline with the pipe() method. This method helps you apply multiple cleaning functions in a clear and organized way.

🎯 Goal: Create a pandas DataFrame with raw sales data, define cleaning functions, and use pipe() to apply these functions in a pipeline. Finally, display the cleaned DataFrame.

📋 What You'll Learn

Create a pandas DataFrame with given sales data

Define a function to fill missing values

Define a function to standardize product names

Use pipe() to apply cleaning functions in sequence

Print the cleaned DataFrame

💡 Why This Matters

🌍 Real World

Cleaning data is a common first step in data science projects. Using pipelines with <code>pipe()</code> helps keep your code organized and easy to read.

💼 Career

Data analysts and scientists often clean messy data before analysis. Knowing how to build pipelines with <code>pipe()</code> is a valuable skill for writing clean, maintainable code.

1

Create the raw sales DataFrame

Create a pandas DataFrame called sales with these exact columns and data:
Product: ['apple', 'Banana', None, 'orange', 'banana']
Quantity: [10, 5, 8, None, 7]
Price: [1.2, 0.5, 0.8, 1.0, None]

Pandas

# Create the DataFrame called sales with the exact data
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary of lists for columns.

2

Define cleaning functions

Define two functions:
1. fill_missing(df) that fills missing values in Quantity and Price columns with 0.
2. standardize_product(df) that converts all Product names to lowercase strings and fills missing Product values with 'unknown'.

Pandas

import pandas as pd

sales = pd.DataFrame({
    'Product': ['apple', 'Banana', None, 'orange', 'banana'],
    'Quantity': [10, 5, 8, None, 7],
    'Price': [1.2, 0.5, 0.8, 1.0, None]
})

# Define fill_missing(df) to fill missing Quantity and Price with 0
# Define standardize_product(df) to lowercase Product and fill missing with 'unknown'
# Your code here

Need a hint?

Use fillna(0) for missing numbers and fillna('unknown') plus str.lower() for product names.

3

Build the cleaning pipeline with pipe()

Use the pipe() method on sales to apply fill_missing first, then standardize_product. Save the result in a new variable called cleaned_sales.

Pandas

import pandas as pd

def fill_missing(df):
    df['Quantity'] = df['Quantity'].fillna(0)
    df['Price'] = df['Price'].fillna(0)
    return df

def standardize_product(df):
    df['Product'] = df['Product'].fillna('unknown').str.lower()
    return df

sales = pd.DataFrame({
    'Product': ['apple', 'Banana', None, 'orange', 'banana'],
    'Quantity': [10, 5, 8, None, 7],
    'Price': [1.2, 0.5, 0.8, 1.0, None]
})

# Use pipe() on sales to apply fill_missing then standardize_product
# Save result in cleaned_sales
# Your code here

Need a hint?

Chain pipe() calls: sales.pipe(fill_missing).pipe(standardize_product)

4

Display the cleaned DataFrame

Print the cleaned_sales DataFrame to see the cleaned data.

Pandas

import pandas as pd

def fill_missing(df):
    df['Quantity'] = df['Quantity'].fillna(0)
    df['Price'] = df['Price'].fillna(0)
    return df

def standardize_product(df):
    df['Product'] = df['Product'].fillna('unknown').str.lower()
    return df

sales = pd.DataFrame({
    'Product': ['apple', 'Banana', None, 'orange', 'banana'],
    'Quantity': [10, 5, 8, None, 7],
    'Price': [1.2, 0.5, 0.8, 1.0, None]
})

cleaned_sales = sales.pipe(fill_missing).pipe(standardize_product)

# Print cleaned_sales
# Your code here

Need a hint?

Use print(cleaned_sales) to show the cleaned DataFrame.