Pipe for method chaining in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of method chaining with pipe

What is the output of this code using pipe for method chaining on a pandas DataFrame?

Data Analysis Python

import pandas as pd

def add_one(df):
    return df + 1

def multiply_two(df):
    return df * 2

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

A

   A   B
0  2   6
1  4   8

B

   A   B
0  2   4
1  3   5

C

   A   B
0  3   7
1  5   9

D

   A   B
0  4   8
1  6  10

💡 Hint

Remember that pipe passes the DataFrame to the function and returns the result, allowing chaining.

First, add_one adds 1 to each element, so the DataFrame becomes [[2,4],[3,5]]. Then multiply_two multiplies each element by 2, resulting in [[4,8],[6,10]].

❓ data_output

intermediate

Resulting DataFrame shape after pipe chaining

Given the following code, what is the shape of the resulting DataFrame?

Data Analysis Python

import pandas as pd

def filter_even(df):
    return df[df['num'] % 2 == 0]

def add_square(df):
    df['square'] = df['num'] ** 2
    return df

df = pd.DataFrame({'num': [1, 2, 3, 4, 5]})
result = df.pipe(filter_even).pipe(add_square)
print(result.shape)

A (3, 2)

B (2, 2)

C (2, 1)

D (5, 2)

💡 Hint

Check how many numbers are even and how many columns are in the final DataFrame.

The filter keeps only even numbers: 2 and 4, so 2 rows. The add_square function adds a new column, so total 2 columns.

🔧 Debug

advanced

Identify the error in pipe chaining

What error will this code raise when using pipe for method chaining?

Data Analysis Python

import pandas as pd

def add_column(df):
    df['new'] = df['A'] + 1
    return df

def wrong_func(df, x):
    return df + x

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

A TypeError: wrong_func() missing 1 required positional argument: 'x'

B TypeError: unsupported operand type(s) for +: 'DataFrame' and 'int'

C KeyError: 'x'

D TypeError: unsupported operand type(s) for +: 'DataFrame' and 'function'

💡 Hint

Check the definition of wrong_func and how pipe passes arguments.

The pipe method passes only the DataFrame by default. wrong_func requires two arguments but only one is given, causing a TypeError about missing argument 'x'.

🚀 Application

advanced

Using pipe to apply multiple transformations

Which option correctly uses pipe to apply these transformations in order: drop rows with NaN, reset index, and rename column 'old' to 'new'?

Data Analysis Python

import pandas as pd

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

def reset_idx(df):
    return df.reset_index(drop=True)

def rename_col(df):
    return df.rename(columns={'old': 'new'})

df = pd.DataFrame({'old': [1, None, 3], 'val': [4, 5, None]})

A df.pipe(drop_na).pipe(rename_col).pipe(reset_idx)

B df.pipe(reset_idx).pipe(drop_na).pipe(rename_col)

C df.pipe(drop_na).pipe(reset_idx).pipe(rename_col)

D df.pipe(rename_col).pipe(drop_na).pipe(reset_idx)

💡 Hint

Think about the order: first remove NaNs, then reset index, then rename columns.

Dropping NaNs first removes rows with missing data. Resetting index after ensures index is continuous. Renaming columns last changes column names correctly.

🧠 Conceptual

expert

Why use pipe in method chaining?

Which statement best explains the main advantage of using pipe in pandas method chaining?

A It allows applying functions that take the DataFrame as the first argument, enabling clean chaining of custom functions.

B It speeds up computation by parallelizing function calls.

C It automatically modifies the original DataFrame without needing assignment.

D It converts DataFrames into NumPy arrays for faster processing.

💡 Hint

Think about how pipe helps when using your own functions in a chain.

pipe passes the DataFrame as the first argument to any function, making it easy to chain custom functions without breaking the flow.

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