info() for column types in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the info() method show about a DataFrame's columns?

The info() method shows the number of non-null values, the data type of each column, and the memory usage of the DataFrame.

beginner

How can info() help you understand missing data in your dataset?

By showing the count of non-null values per column, info() helps identify columns with missing data because the count will be less than the total rows.

beginner

What are some common data types you might see in the output of info()?

Common data types include int64 for integers, float64 for decimals, object for text or mixed types, and bool for True/False values.

intermediate

Why is it useful to check the memory usage shown by info()?

Checking memory usage helps you understand how much RAM your DataFrame uses, which is important for working efficiently with large datasets.

beginner

How do you call info() on a DataFrame named df?

You call it by typing df.info(). This will print the summary of the DataFrame's columns, types, and memory.

What does df.info() NOT show?

A Statistical summary like mean or median

B Data types of columns

C Memory usage of the DataFrame

D Number of non-null values per column

info() does not show statistics like mean or median; it shows data types, non-null counts, and memory usage.

If a column has fewer non-null values than total rows, what does it mean?

A The column has no missing values

B The column is numeric

C The column has missing values

D The column is categorical

Fewer non-null values than total rows means some data is missing in that column.

Which data type in info() output usually represents text data?

A int64

B float64

C bool

D object

The object type usually means text or mixed types in pandas.

How do you get more detailed memory usage info from info()?

A Use <code>df.describe()</code>

B Use <code>df.info(memory_usage='deep')</code>

C Use <code>df.head()</code>

D Use <code>df.memory()</code>

Passing memory_usage='deep' to info() gives a more accurate memory usage.

What is the main purpose of using info() on a DataFrame?

A To get a quick overview of data types and missing values

B To plot graphs

C To sort the data

D To change column names

info() is used to quickly see data types, non-null counts, and memory usage.

Explain how the info() method helps you understand the structure of a DataFrame.

Think about what you want to know before analyzing data.

Describe how you can identify missing data in a DataFrame using info().

Missing data means some entries are empty or null.

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