info() for column types and nulls in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to show the summary info of the DataFrame.

Pandas

df.[1]()

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

Adescribe

Btail

Chead

Dinfo

Attempts:

3 left

💡 Hint

Common Mistakes

Using describe() instead of info()

Using head() or tail() which show rows, not info

The info() method shows column types and null counts in a DataFrame.

2fill in blank

medium

Complete the code to import pandas with the common alias.

Pandas

import [1] as pd

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

Anumpy

Bpandas

Cmatplotlib

Dseaborn

Attempts:

3 left

💡 Hint

Common Mistakes

Importing numpy instead of pandas

Using wrong alias

pandas is imported as pd by convention.

3fill in blank

hard

Fix the error in the code to show info of DataFrame named data.

Pandas

data.[1]

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

Ainfo()

Binfo[]

CInfo()

Dinfo

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting parentheses after info

Using wrong case or brackets

The info() method must be called with parentheses to execute.

4fill in blank

hard

Fill both blanks to create a DataFrame and show its info.

Pandas

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

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

Apd

Bnp

Cpandas

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using different names for import and DataFrame

Using numpy alias instead of pandas

We import pandas as pd and use pd.DataFrame to create the DataFrame.

5fill in blank

hard

Fill all three blanks to create a DataFrame, add a column, and show info.

Pandas

import [1] as pd
df = pd.DataFrame({'col1': [1, 2, 3]})
df['[2]'] = [4, 5, 6]
df.[3]()

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

Apandas

Bnew_col

Cinfo

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong import alias

Forgetting quotes around new column name

Not calling info() as a method

Import pandas as pd, add a new column named new_col, then call info() to see the summary.

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