info() for column types and nulls in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

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

The info() method shows the number of rows, columns, data types of each column, and how many non-null values each column has.

beginner

How can you tell if a column has missing values using info()?

If the number of non-null values is less than the total number of rows, it means the column has missing (null) values.

beginner

What data types does info() display for DataFrame columns?

info() shows data types like int64, float64, object (for text), bool, and others.

beginner

Why is it useful to check column types and null counts with info() before analysis?

It helps you understand your data structure, find missing data, and decide how to clean or prepare data for analysis.

intermediate

What is the difference between info() and describe() in pandas?

info() shows data types and null counts, while describe() gives summary statistics like mean, min, max for numeric columns.

What does the info() method NOT show about a DataFrame?

A Number of non-null values per column

B Data types of columns

C Summary statistics like mean and median

D Total number of rows and columns

info() does not show summary statistics; that is done by describe().

If a column has 100 rows but info() shows 90 non-null values, what does this mean?

A The column has 10 missing values

B The column has 10 extra values

C The column is fully complete

D The column has 90 missing values

Non-null count less than total rows means missing values exist.

Which data type shown by info() usually means text data?

A bool

B float64

C int64

D object

object type in pandas usually means string or mixed data.

Why is it important to check for null values with info() before analysis?

A To identify missing data that may affect results

B To know the exact number of columns

C To change data types automatically

D To sort the DataFrame

Missing data can cause errors or bias, so identifying it early is important.

What is the default output of df.info()?

A A DataFrame with summary statistics

B A printout showing index range, columns, non-null counts, and data types

C A list of column names only

D A plot of missing values

info() prints a summary including index, columns, non-null counts, and data types.

Explain how you can use info() to find columns with missing data in a DataFrame.

Look at the non-null count vs total rows for each column.

Describe what information about column types and nulls you get from info() and why it matters.

Think about how data types affect operations and how missing data can impact results.

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