applymap() for DataFrame-wide operations in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to apply a function to each element of the DataFrame.

Pandas

df = df.[1](lambda x: x * 2)

Drag options to blanks, or click blank then click option'

Aapply

Bapplymap

Cmap

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply() instead of applymap() which applies function to rows or columns, not elements.

Using map() which is for Series, not DataFrames.

The applymap() method applies a function to every element in the DataFrame.

2fill in blank

medium

Complete the code to convert all DataFrame elements to strings using applymap().

Pandas

df = df.[1](str)

Drag options to blanks, or click blank then click option'

Amap

Bapply

Capplymap

Dastype

Attempts:

3 left

💡 Hint

Common Mistakes

Using astype('str') which works but is different from applymap.

Using apply() which applies function to rows or columns.

applymap(str) converts each element to a string by applying str to every element.

3fill in blank

hard

Fix the error in the code to apply a function element-wise to the DataFrame.

Pandas

df = df.[1](lambda x: x + 1)

Drag options to blanks, or click blank then click option'

Aapplymap

Btransform

Capply

Dmap

Attempts:

3 left

💡 Hint

Common Mistakes

Using map() which is for Series, not DataFrames.

Using apply() which applies function to rows or columns.

applymap() is the correct method to apply a function to each element of a DataFrame.

4fill in blank

hard

Fill both blanks to create a DataFrame with doubled values using applymap().

Pandas

df = df.[1](lambda x: x [2] 2)

Drag options to blanks, or click blank then click option'

Aapplymap

B*

C+

Dapply

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply() instead of applymap() for element-wise operation.

Using + instead of * which adds 2 instead of doubling.

Use applymap to apply the lambda function element-wise, and * to multiply each element by 2.

5fill in blank

hard

Fill all three blanks to create a new DataFrame with squared values plus one using applymap().

Pandas

new_df = df.[1](lambda x: (x [2] 2) [3] 1)

Drag options to blanks, or click blank then click option'

Aapplymap

B**

C+

Dapply

Attempts:

3 left

💡 Hint

Common Mistakes

Using apply() instead of applymap().

Using * instead of ** for squaring.

Using - instead of + for adding one.

applymap applies the lambda function element-wise; ** squares the element; + adds one.

Practice

1. What does the applymap() function do in a pandas DataFrame?

easy

A. Applies a function to each column separately.

B. Applies a function to every single cell in the DataFrame.

C. Applies a function to each row separately.

D. Applies a function only to numeric columns.

Solution

1. Step 1: Understand the purpose of applymap()

   applymap() is designed to apply a function to every individual cell in a DataFrame, not just rows or columns.

2. Step 2: Compare with other pandas functions

   Functions like apply() work on rows or columns, but applymap() works cell-by-cell.

3. Final Answer:

   Applies a function to every single cell in the DataFrame. -> Option B

4. Quick Check:

   applymap() = cell-wise function application [OK]

Hint: Remember: applymap() works on each cell, not rows or columns [OK]

Common Mistakes:

• Confusing applymap() with apply()
• Thinking applymap() works on Series
• Assuming applymap() only works on numeric data

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

easy

A. df.applymap(func)

B. df.applymap = func()

C. df.applymap[func]

D. applymap(df, func)

Solution

1. Step 1: Recall the method call format

   In pandas, applymap() is a method of DataFrame objects and is called as df.applymap(func).

2. Step 2: Check other options for syntax errors

   Options B, C, and D do not follow valid Python or pandas syntax for method calls.

3. Final Answer:

   df.applymap(func) -> Option A

4. Quick Check:

   Method call syntax = df.applymap(func) [OK]

Hint: Use dot notation and parentheses: df.applymap(func) [OK]

Common Mistakes:

• Using assignment instead of method call
• Using square brackets instead of parentheses
• Calling applymap as a standalone function

3. Given the DataFrame df below:

   A  B
0  1  4
1  2  5
2  3  6

What is the output of df.applymap(lambda x: x * 2)?

medium

A. A B
0 1 4
1 2 5
2 3 6

B. A B
0 1 8
1 4 10
2 6 12

C. A B
0 2 8
1 4 10
2 6 12

D. Error: lambda function not supported

Solution

1. Step 1: Understand the lambda function

   The lambda function doubles each value: lambda x: x * 2.

2. Step 2: Apply the function to each cell

   Each cell in the DataFrame is multiplied by 2, so 1 becomes 2, 4 becomes 8, etc.

3. Final Answer:

   A B 0 2 8 1 4 10 2 6 12 -> Option C

4. Quick Check:

   Each cell doubled = output B [OK]

Hint: applymap applies function cell-wise; multiply each cell by 2 [OK]

Common Mistakes:

• Thinking applymap works column-wise
• Expecting original DataFrame unchanged
• Assuming lambda functions cause errors

4. What is wrong with this code snippet?

import pandas as pd
df = pd.DataFrame({'A':[1,2], 'B':[3,4]})
result = df.applymap(lambda x: x + 1)

Why might result be the same as df?

medium

A. The DataFrame contains non-numeric data, so applymap fails silently.

B. The lambda function is not applied because applymap was not called correctly.

C. Nothing is wrong; result will be different from df.

D. The code is correct, but the result is not assigned back to df.

Solution

1. Step 1: Check the code correctness

   The code correctly creates a DataFrame and applies applymap() with a lambda that adds 1.

2. Step 2: Understand the result assignment

   The result is stored in result, which will be different from df because each cell is incremented by 1.

3. Final Answer:

   The code is correct, but the result is not assigned back to df. -> Option D

4. Quick Check:

   applymap() works and returns new DataFrame [OK]

Hint: applymap returns new DataFrame; original stays unchanged [OK]

Common Mistakes:

• Expecting applymap to modify df in place
• Thinking applymap fails on numeric data
• Confusing assignment of result with in-place change

5. You have a DataFrame df with mixed data types:

   A     B
0  1   foo
1  2   bar
2  3   baz

You want to convert all numeric cells to their squares but leave non-numeric cells unchanged. Which applymap() function achieves this?

hard

A. df.applymap(lambda x: x**2 if isinstance(x, (int, float)) else x)

B. df.applymap(lambda x: x**2)

C. df.applymap(lambda x: str(x))

D. df.applymap(lambda x: x**2 if isinstance(x, str) else x)

Solution

1. Step 1: Identify the requirement

   We want to square numeric values only and keep strings unchanged.

2. Step 2: Analyze each option

   df.applymap(lambda x: x**2 if isinstance(x, (int, float)) else x) checks if the cell is int or float, then squares it; else returns original. df.applymap(lambda x: x**2) squares all cells, causing error on strings. df.applymap(lambda x: str(x)) converts all to strings, losing numeric info. df.applymap(lambda x: x**2 if isinstance(x, str) else x) squares strings (causing TypeError) and leaves numerics unchanged.

3. Final Answer:

   df.applymap(lambda x: x**2 if isinstance(x, (int, float)) else x) -> Option A

4. Quick Check:

   Use type check inside applymap to selectively transform [OK]

Hint: Use isinstance check inside applymap to handle mixed types [OK]

Common Mistakes:

• Applying math operations to strings causing errors
• Not checking data type before transformation
• Converting all data to string losing numeric info