dtypes and data type checking in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this dtype check?

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

Pandas

import pandas as pd

data = {'A': [1, 2, 3], 'B': [1.1, 2.2, 3.3], 'C': ['x', 'y', 'z']}
df = pd.DataFrame(data)
print(df.dtypes)

A

A      int64
B      int64
C     object
dtype: object

B

A    float64
B    float64
C    object
dtype: object

C

A      int64
B    float64
C     object
dtype: object

D

A      int32
B    float32
C     object
dtype: object

💡 Hint

Check the default integer and float types pandas uses on your system.

Pandas uses 64-bit integers and floats by default on most systems. Strings are stored as 'object' dtype.

❓ data_output

intermediate

How many columns are numeric in this DataFrame?

Using the DataFrame below, how many columns will df.select_dtypes(include=['number']).shape[1] return?

Pandas

import pandas as pd
import numpy as np

data = {'X': [1, 2, 3], 'Y': [4.5, 5.5, 6.5], 'Z': ['a', 'b', 'c'], 'W': [True, False, True]}
df = pd.DataFrame(data)
print(df.select_dtypes(include=['number']).shape[1])

A 2

B 4

C 1

D 3

💡 Hint

Boolean columns are considered numeric by default in pandas.

Columns 'X' (int), 'Y' (float), and 'W' (boolean) are numeric. Booleans are included as numeric in pandas.

🔧 Debug

advanced

Why does this dtype conversion raise an error?

Consider this code snippet. Why does df['col'] = df['col'].astype(int) raise an error?

Pandas

import pandas as pd

data = {'col': ['1', '2', 'three', '4']}
df = pd.DataFrame(data)
df['col'] = df['col'].astype(int)

A Because the string 'three' cannot be converted to an integer, causing a ValueError.

B Because pandas does not allow converting strings to integers directly, causing a TypeError.

C Because the column contains mixed types, causing a KeyError.

D Because the DataFrame is missing a required index, causing an IndexError.

💡 Hint

Check the values in the column carefully for non-numeric strings.

The string 'three' cannot be converted to int, so pandas raises a ValueError.

🧠 Conceptual

advanced

Which dtype is best for storing categorical data efficiently?

You have a column with repeated string values representing categories. Which dtype should you use to save memory and speed up operations?

A object

B category

C string

D int64

💡 Hint

Think about pandas special dtype for repeated categories.

The 'category' dtype stores repeated values efficiently and speeds up comparisons.

🚀 Application

expert

Identify the dtype of each column after this operation

After running the code below, what are the dtypes of columns 'A', 'B', and 'C'?

Pandas

import pandas as pd
import numpy as np

data = {'A': [1, 2, 3], 'B': [1.0, 2.0, 3.0], 'C': ['a', 'b', 'c']}
df = pd.DataFrame(data)
df['A'] = df['A'].astype('float32')
df['B'] = df['B'].astype('int32')
df['C'] = df['C'].astype('category')
print(df.dtypes)

A

A    float64
B      int32
C    category
dtype: object

B

A    float64
B      int64
C     object
dtype: object

C

A      int32
B    float32
C     object
dtype: object

D

A    float32
B      int32
C    category
dtype: object

💡 Hint

Check the explicit astype conversions carefully.

Column 'A' is converted to float32, 'B' to int32, and 'C' to category dtype.