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.