info() for column types in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ data_output

intermediate

Output of info() on DataFrame with mixed types

Given the following DataFrame, what will be the output of df.info()?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': ['x', 'y', 'z'],
    'C': [1.1, 2.2, 3.3],
    'D': [True, False, True]
})
df.info()

A Data columns (total 4 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 A 3 non-null int64 \n 1 B 3 non-null object \n 2 C 3 non-null float64\n 3 D 3 non-null bool \ndtypes: bool(1), float64(1), int64(1), object(1)\nmemory usage: 311.0 bytes

B <class 'pandas.core.frame.DataFrame'>\nRangeIndex: 3 entries, 0 to 2\nData columns (total 4 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 A 3 non-null int64 \n 1 B 3 non-null object \n 2 C 3 non-null float64\n 3 D 3 non-null bool \ndtypes: bool(1), float64(1), int64(1), object(1)\nmemory usage: 311.0 bytes

C RangeIndex: 3 entries, 0 to 2\nData columns (total 4 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 A 3 non-null int64 \n 1 B 3 non-null object \n 2 C 3 non-null float64\n 3 D 3 non-null bool \ndtypes: bool(1), float64(1), int64(1), object(1)\nmemory usage: 311.0 bytes

D RangeIndex: 3 entries, 0 to 2\nData columns (total 4 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 A 3 non-null int64 \n 1 B 3 non-null string \n 2 C 3 non-null float64\n 3 D 3 non-null bool \ndtypes: bool(1), float64(1), int64(1), string(1)\nmemory usage: 311.0 bytes

💡 Hint

Look carefully at the default data types pandas assigns to each column based on the data.

Pandas assigns int64 to integer columns, object to string columns, float64 to float columns, and bool to boolean columns by default. The info() method shows these types along with counts and memory usage.

🧠 Conceptual

intermediate

Understanding info() output for missing data

If a DataFrame column has some missing values, how does info() display the count for that column?

A It shows the count of non-null (non-missing) values in that column.

B It shows the total number of rows regardless of missing values.

C It shows the count of missing values in that column.

D It shows the percentage of missing values in that column.

💡 Hint

Think about what 'Non-Null Count' means in the info() output.

The info() method displays the number of non-null values per column, which means it counts only the entries that are not missing.

❓ Predict Output

advanced

Output of info() after type conversion

What will be the output of df.info() after converting column 'B' to category type?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'A': [10, 20, 30],
    'B': ['cat', 'dog', 'cat']
})
df['B'] = df['B'].astype('category')
df.info()

A RangeIndex: 3 entries, 0 to 2\nData columns (total 2 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 A 3 non-null int64 \n 1 B 3 non-null object \ndtypes: int64(1), object(1)\nmemory usage: 311.0 bytes

B RangeIndex: 3 entries, 0 to 2\nData columns (total 2 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 A 3 non-null int32 \n 1 B 3 non-null category\ndtypes: category(1), int32(1)\nmemory usage: 653.0 bytes

C RangeIndex: 3 entries, 0 to 2\nData columns (total 2 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 A 3 non-null int64 \n 1 B 3 non-null string \ndtypes: int64(1), string(1)\nmemory usage: 311.0 bytes

D RangeIndex: 3 entries, 0 to 2\nData columns (total 2 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 A 3 non-null int64 \n 1 B 3 non-null category\ndtypes: category(1), int64(1)\nmemory usage: 653.0 bytes

💡 Hint

Check what happens when you convert a column to 'category' dtype in pandas.

Converting a column to 'category' changes its dtype shown in info() to 'category' and increases memory usage slightly.

🔧 Debug

advanced

Identify the error in info() usage

What error will occur if you try to call df.info without parentheses?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'X': [1, 2, 3]})
print(df.info)

A No error, it prints the method object representation

B TypeError: 'method' object is not callable

C AttributeError: 'DataFrame' object has no attribute 'info'

D SyntaxError: Missing parentheses in call to 'info'

💡 Hint

Think about what happens when you print a method without calling it.

Printing df.info without parentheses prints the method object itself, not the info output.

🚀 Application

expert

Using info() to find columns with missing data

You want to find which columns in a DataFrame have missing values by using info(). Which approach below correctly identifies columns with missing data?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'A': [1, None, 3],
    'B': ['x', 'y', 'z'],
    'C': [None, None, 3.3]
})

# Which code snippet below helps identify columns with missing values using info() output?

A Check columns where 'Dtype' is 'object' in info() output.

B Check columns where memory usage is higher in info() output.

C Check columns where 'Non-Null Count' is less than total rows in info() output.

D Check columns where 'Non-Null Count' equals total rows in info() output.

💡 Hint

Missing values reduce the non-null count shown by info().

Columns with missing data have fewer non-null entries than total rows, so comparing these counts reveals missing data.