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.