dtypes for column data types in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this dtype check?

Given the following pandas DataFrame, what will be the output of df.dtypes?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [1.5, 2.5, 3.5],
    'C': ['x', 'y', 'z'],
    'D': [True, False, True]
})
print(df.dtypes)

A

A      int32
B    float64
C     object
D       bool
dtype: object

B

A      float64
B    float64
C     object
D       bool
dtype: object

C

A      int64
B    float64
C     object
D       bool
dtype: object

D

A      int64
B    float64
C     string
D       bool
dtype: object

💡 Hint

Check the default integer type pandas uses on your system and how strings are stored.

Column 'A' contains integers, so pandas uses int64 by default. Column 'B' has floats, so float64. Column 'C' has strings, stored as object dtype. Column 'D' has booleans, so bool dtype.

❓ data_output

intermediate

Count columns with numeric dtypes

Using pandas, how many columns in this DataFrame have numeric data types?

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'num1': [1, 2, 3],
    'num2': [1.1, 2.2, 3.3],
    'text': ['a', 'b', 'c'],
    'flag': [True, False, True],
    'date': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03'])
})
numeric_cols = df.select_dtypes(include=[np.number]).columns
print(len(numeric_cols))

A 2

B 3

C 4

D 1

💡 Hint

Numeric types include int and float, but not boolean or datetime.

Only 'num1' (int) and 'num2' (float) are numeric. 'flag' is boolean, 'text' is object, 'date' is datetime64, so not counted.

🔧 Debug

advanced

Identify the error in dtype conversion

What error will this code raise when trying to convert column 'A' to integer dtype?

Pandas

import pandas as pd

df = pd.DataFrame({'A': ['1', '2', 'three']})
df['A'] = df['A'].astype(int)

A ValueError: invalid literal for int() with base 10: 'three'

B TypeError: cannot convert float NaN to integer

C KeyError: 'A'

D No error, conversion succeeds

💡 Hint

Check if all strings can be converted to integers.

The string 'three' cannot be converted to an integer, causing a ValueError.

🚀 Application

advanced

Select columns with object dtype

Given a DataFrame, which code snippet correctly selects all columns with object dtype?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2],
    'B': ['x', 'y'],
    'C': [3.5, 4.5],
    'D': ['foo', 'bar']
})

A df.select_dtypes(exclude=['object'])

B df.select_dtypes(include=['object'])

C df.select_dtypes(include=['int64'])

D df.select_dtypes(include=['bool'])

💡 Hint

Object dtype usually stores strings or mixed types.

Only option B selects columns with object dtype, which are 'B' and 'D'.

🧠 Conceptual

expert

Understanding pandas dtype memory optimization

Which pandas dtype conversion reduces memory usage the most for a column of integers ranging from 0 to 127?

A Convert to 'object' dtype

B Convert to 'int64' dtype

C Convert to 'float32' dtype

D Convert to 'int8' dtype

💡 Hint

Consider the smallest integer dtype that can hold values 0 to 127.

int8 can hold values from -128 to 127, sufficient for 0 to 127. It uses 1 byte per value, less than float32 (4 bytes), int64 (8 bytes), or object (most).