info() for column types and nulls in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of info() on a DataFrame with mixed types and nulls

What is the output of the df.info() call on the following DataFrame?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'A': [1, 2, 3, None],
    'B': ['x', 'y', None, 'z'],
    'C': [1.1, 2.2, 3.3, 4.4],
    'D': pd.to_datetime(['2023-01-01', None, '2023-01-03', '2023-01-04'])
})
df.info()

A

RangeIndex: 4 entries, 0 to 3
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype         
---  ------  --------------  -----         
 0   A       4 non-null      int64         
 1   B       4 non-null      object        
 2   C       4 non-null      float64       
 3   D       4 non-null      datetime64[ns]

memory usage: 256.0 bytes

B

RangeIndex: 4 entries, 0 to 3
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype         
---  ------  --------------  -----         
 0   A       3 non-null      float64       
 1   B       3 non-null      object        
 2   C       4 non-null      float64       
 3   D       3 non-null      datetime64[ns]

memory usage: 256.0 bytes

C

RangeIndex: 4 entries, 0 to 3
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype         
---  ------  --------------  -----         
 0   A       3 non-null      int64         
 1   B       3 non-null      object        
 2   C       4 non-null      float64       
 3   D       3 non-null      object        

memory usage: 256.0 bytes

D

RangeIndex: 4 entries, 0 to 3
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype         
---  ------  --------------  -----         
 0   A       3 non-null      float64       
 1   B       4 non-null      object        
 2   C       4 non-null      float64       
 3   D       3 non-null      datetime64[ns]

memory usage: 256.0 bytes

💡 Hint

Remember that null in numeric columns converts the column to float64 with nulls counted.

The column 'A' has one null, so it becomes float64 with 3 non-null values. 'B' is object with 3 non-null because of one null. 'C' is float64 with no nulls. 'D' is datetime64 with one null, so 3 non-null.

❓ data_output

intermediate

Count of non-null values per column from info()

Given the DataFrame below, how many non-null values does column 'X' have according to df.info()?

Pandas

import pandas as pd
df = pd.DataFrame({
    'X': [10, None, 30, None, 50],
    'Y': ['a', 'b', 'c', 'd', 'e']
})
df.info()

A 3

B 2

C 5

D 4

💡 Hint

Count how many values in 'X' are not null.

Column 'X' has values 10, null, 30, null, 50. The non-null count is 3.

🔧 Debug

advanced

Identify the error in info() output interpretation

Which statement about the output of df.info() is incorrect?

Pandas

import pandas as pd
df = pd.DataFrame({
    'A': [1, 2, None],
    'B': ['x', None, 'z']
})
df.info()

A The 'B' column has 3 non-null values.

B The 'A' column has dtype 'float64' because of the null value.

C The 'A' column has 2 non-null values.

D The 'B' column has dtype 'object'.

💡 Hint

Check how many non-null values are in column 'B'.

Column 'B' has one null, so non-null count is 2, not 3.

🧠 Conceptual

advanced

Understanding memory usage in info() with nulls

Why does df.info() show memory usage even when columns have null values?

A Because memory usage only counts non-null values.

B Because null values are ignored and do not consume memory.

C Because pandas stores nulls as special values within the column's dtype, so memory is allocated for all rows.

D Because info() does not calculate memory usage when nulls are present.

💡 Hint

Think about how pandas stores data internally.

Pandas uses special markers (like NaN) to represent nulls but still allocates memory for all rows in the column.

🚀 Application

expert

Using info() to detect columns with missing data

You have a DataFrame df with many columns. You want to quickly find which columns have missing values using df.info(). Which approach below correctly identifies columns with missing data?

A Look for columns where the memory usage is zero.

B Look for columns where the 'Non-Null Count' equals the total number of rows.

C Look for columns with dtype 'object' because only those can have missing values.

D Look for columns where the 'Non-Null Count' is less than the total number of rows.

💡 Hint

Compare non-null counts to total rows.

If a column's non-null count is less than total rows, it has missing values.

Practice

1. What does the info() method in pandas primarily show about a DataFrame?

easy

A. The number of non-null values and data types of each column

B. The first five rows of the DataFrame

C. The summary statistics like mean and median

D. The unique values in each column

Solution

1. Step 1: Understand what info() displays

   The info() method shows the count of non-null entries and the data type for each column in a DataFrame.

2. Step 2: Compare with other options

   The first five rows are shown by head(), summary statistics like mean and median by describe(), and unique values by methods like unique(), not info().

3. Final Answer:

   The number of non-null values and data types of each column -> Option A

4. Quick Check:

   info() = non-null counts + dtypes [OK]

Hint: Remember: info() shows counts and types, not data samples [OK]

Common Mistakes:

• Confusing info() with head() or describe()
• Thinking info() shows unique values
• Assuming info() shows data content

2. Which of the following is the correct way to call the info() method on a pandas DataFrame named df?

easy

A. df.info()

B. info(df)

C. df.info

D. df.info[]

Solution

1. Step 1: Recall method call syntax in pandas

   Methods are called with parentheses after the method name, so df.info() is correct.

2. Step 2: Check other options for syntax errors

   info(df) treats info as a function, which it is not. df.info misses parentheses, so it returns the method object, not the output. df.info[] uses invalid syntax with brackets.

3. Final Answer:

   df.info() -> Option A

4. Quick Check:

   Method call needs parentheses: df.info() [OK]

Hint: Call methods with parentheses: df.info() not df.info [OK]

Common Mistakes:

• Forgetting parentheses when calling methods
• Using function syntax instead of method syntax
• Using brackets [] instead of parentheses ()

3. Given the DataFrame df below, what will df.info() show for the column Age?

import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, None, 30]}
df = pd.DataFrame(data)
df.info()

medium

A. Age column has 3 non-null values and dtype float64

B. Age column has 2 non-null values and dtype int64

C. Age column has 2 non-null values and dtype float64

D. Age column has 3 non-null values and dtype int64

Solution

1. Step 1: Count non-null values in Age column

   The Age column has values 25, None, and 30. None counts as null, so non-null count is 2.

2. Step 2: Determine data type of Age column

   Because of the None, pandas converts the Age column to float64 to accommodate NaN values.

3. Final Answer:

   Age column has 2 non-null values and dtype float64 -> Option C

4. Quick Check:

   Nulls reduce count; None makes dtype float64 [OK]

Hint: Nulls reduce non-null count; mixed types become float [OK]

Common Mistakes:

• Counting None as non-null
• Assuming dtype stays int64 despite nulls
• Ignoring dtype conversion due to nulls

4. You run df.info() but get an error: AttributeError: 'DataFrame' object has no attribute 'info'. What is the most likely cause?

medium

A. The DataFrame is empty

B. You forgot to import pandas as pd

C. You called info without parentheses

D. You overwrote the variable name df with a non-DataFrame object

Solution

1. Step 1: Understand the error meaning

   The error says df has no attribute info, meaning df is not a DataFrame.

2. Step 2: Identify common cause

   This usually happens if df was reassigned to something else, like a list or string, losing DataFrame methods.

3. Final Answer:

   You overwrote the variable name df with a non-DataFrame object -> Option D

4. Quick Check:

   AttributeError means wrong object type [OK]

Hint: Check if df is still a DataFrame before calling info() [OK]

Common Mistakes:

• Assuming empty DataFrame causes this error
• Thinking missing parentheses cause AttributeError
• Ignoring variable reassignment issues

5. You have a DataFrame df with columns ['A', 'B', 'C']. Column B has many null values. You want to quickly check which columns have missing data and their types. Which code snippet best helps you decide what cleaning is needed?

hard

A. df.isnull().sum()

B. df.info()

C. df.describe()

D. df.head()

Solution

1. Step 1: Identify what each method shows

   df.info() shows non-null counts and data types, helping spot missing data and column types.

2. Step 2: Compare with other options

   df.describe() summarizes numeric stats but not null counts. df.isnull().sum() shows null counts but not types. df.head() shows sample rows but not summary info.

3. Final Answer:

   df.info() -> Option B

4. Quick Check:

   info() = null counts + types [OK]

Hint: Use info() to see null counts and types in one step [OK]

Common Mistakes:

• Using describe() expecting null info
• Using isnull().sum() but ignoring types
• Using head() which shows no summary