info() for column types in Data Analysis Python

Introduction

The info() function helps you quickly see the types of data in each column of a table. This is useful to understand what kind of data you have.

When you want to check if columns have numbers or words.

Before cleaning data, to find columns with missing values.

To understand the size and memory use of your data table.

When you want to confirm the data types after loading a file.

Syntax

Data Analysis Python

DataFrame.info(verbose=True, buf=None, max_cols=None, memory_usage=None, show_counts=None)

DataFrame is your table of data.

The info() method shows summary including column types.

Examples

Shows summary of all columns with their data types and counts.

Data Analysis Python

df.info()

Shows only summary without detailed column info.

Data Analysis Python

df.info(verbose=False)

Shows detailed memory usage of each column.

Data Analysis Python

df.info(memory_usage='deep')

Sample Program

This code creates a small table with names, ages, heights, and membership status. Then it prints the info summary showing column types.

Data Analysis Python

import pandas as pd

data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'Height': [5.5, 6.0, 5.8],
    'Member': [True, False, True]
}
df = pd.DataFrame(data)

print('DataFrame info:')
df.info()

Important Notes

The info() method is very fast and works on large data tables.

It helps spot missing data by showing non-null counts.

Column types like object usually mean text data.

Summary

info() shows column data types and counts.

Use it to understand your data quickly.

It helps find missing values and memory use.

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