info() for column types in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Data Analysis Python

df.[1]()

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

Ainfo

Bdescribe

Chead

Dtail

Attempts:

3 left

💡 Hint

Common Mistakes

Using describe() which shows statistics, not column types.

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

The info() method shows a summary of the DataFrame including column types.

2fill in blank

medium

Complete the code to print the data types of all columns in the DataFrame.

Data Analysis Python

print(df.[1])

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

Acolumns

Bdtypes

Cshape

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Using df.columns which lists column names, not types.

Using df.shape which shows DataFrame size.

The dtypes attribute shows the data type of each column.

3fill in blank

hard

Fix the error in the code to show DataFrame info with memory usage.

Data Analysis Python

df.info(memory_[1]=True)

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

Ause

Bdisplay

Cshow

Dusage

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect parameter names like memory_use or memory_show.

Forgetting the underscore between memory and usage.

The correct parameter name is memory_usage to show memory details.

4fill in blank

hard

Fill both blanks to create a dictionary of column names and their data types.

Data Analysis Python

col_types = {col: df[col].[1] for col in df.[2]

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

Adtype

Bcolumns

Cdtypes

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Using df.dtypes in the loop which returns a Series, not column names.

Using df.index which lists row labels, not columns.

Use dtype to get a column's type and columns to loop over column names.

5fill in blank

hard

Fill all three blanks to filter columns with numeric data types using info() output.

Data Analysis Python

numeric_cols = [col for col in df.[1] if df.[2][col] in ['int64', [3]]]

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

Acolumns

Bdtypes

C'float64'

D'object'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'object' instead of 'float64' for numeric types.

Looping over df.dtypes which gives types, not column names.

Loop over columns, check each column's dtypes, and include 'float64' as numeric type.

Practice

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

easy

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

B. The first 5 rows of the dataset

C. The summary statistics like mean and median

D. The unique values in each column

Solution

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

   The info() method is used to display concise summary of a DataFrame including data types and non-null counts.

2. Step 2: Compare with other methods

   Methods like head() show rows, describe() shows statistics, and unique() shows unique values, so they are not correct here.

3. Final Answer:

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

4. Quick Check:

   info() = data types + counts [OK]

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

Common Mistakes:

• Confusing info() with head()
• Thinking info() shows statistics
• Assuming info() lists unique values

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

easy

A. info(df)

B. df.info[]

C. df.info

D. df.info()

Solution

1. Step 1: Recall method call syntax in Python

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

2. Step 2: Check other options

   info(df) is incorrect because info is not a standalone function; df.info misses parentheses and returns the method object; df.info[] is invalid syntax.

3. Final Answer:

   df.info() -> Option D

4. Quick Check:

   Method call needs parentheses [OK]

Hint: Call methods with () after the name [OK]

Common Mistakes:

• Forgetting parentheses when calling methods
• Using info() as a standalone function
• Using brackets [] instead of parentheses ()

3. Given the following DataFrame df and its info() output, what is the data type of the column Age?

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'Score': [85.5, 90.0, 88.0]}
df = pd.DataFrame(data)
df.info()

Output:

 <class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Name    3 non-null      object 
 1   Age     3 non-null      int64  
 2   Score   3 non-null      float64
dtypes: float64(1), int64(1), object(1)
memory usage: 200.0 bytes

medium

A. float64

B. object

C. int64

D. bool

Solution

1. Step 1: Locate the column Age in the info output

   The info output shows Age has 3 non-null values and data type int64.

2. Step 2: Match the data type to options

   Among the options, int64 matches the data type shown for Age.

3. Final Answer:

   int64 -> Option C

4. Quick Check:

   Age column type = int64 [OK]

Hint: Look for column name and dtype in info() output [OK]

Common Mistakes:

• Confusing object with string
• Mixing float64 and int64 types
• Ignoring the dtype column in info()

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

medium

A. You forgot to import pandas

B. You called info() on a list, not a DataFrame

C. You used info() without parentheses

D. The DataFrame is empty

Solution

1. Step 1: Analyze the error message

   The error says a 'list' object has no attribute 'info', meaning the variable is a list, not a DataFrame.

2. Step 2: Identify the cause

   Only DataFrames have the info() method. Calling it on a list causes this error.

3. Final Answer:

   You called info() on a list, not a DataFrame -> Option B

4. Quick Check:

   info() needs DataFrame, not list [OK]

Hint: Check variable type before calling info() [OK]

Common Mistakes:

• Assuming info() works on lists
• Ignoring error message details
• Thinking empty DataFrame causes this error

5. You have a DataFrame df with 200 columns. Column 'B' has some missing values. You want to quickly check which columns have missing data and their data types. Which code snippet using info() helps you do this?

hard

A. df.info(verbose=True)

B. df.info(null_counts=True)

C. df.info(show_missing=True)

D. df.info()

Solution

1. Step 1: Recall info() behavior on wide DataFrames

   With 200 columns (> pandas default max_info_columns=100), df.info() truncates the column list, hiding non-null counts and dtypes for most columns including potentially 'B'.

2. Step 2: Check options

   df.info(verbose=True) forces full display of all columns' non-null counts and dtypes. Other parameters like null_counts and show_missing are invalid.

3. Final Answer:

   df.info(verbose=True) -> Option A

4. Quick Check:

   verbose=True for full column info on large DFs [OK]

Hint: df.info(verbose=True) for full missing counts/types on wide DFs [OK]

Common Mistakes:

• Using df.info() on wide DFs (truncates columns)
• Using invalid parameters with info()
• Confusing verbose with missing data display