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.

Practice

1. What does the dtypes attribute in a pandas DataFrame tell you?

easy

A. The type of data stored in each column

B. The number of rows in the DataFrame

C. The names of the columns

D. The total memory used by the DataFrame

Solution

1. Step 1: Understand what dtypes represents

   The dtypes attribute shows the data type of each column in a DataFrame, such as integer, float, or object (text).

2. Step 2: Compare with other DataFrame attributes

   Other attributes like shape give row and column counts, and columns give column names, but dtypes specifically shows data types.

3. Final Answer:

   The type of data stored in each column -> Option A

4. Quick Check:

   dtypes = data types per column [OK]

Hint: Remember: dtypes = data types of columns [OK]

Common Mistakes:

• Confusing dtypes with number of rows
• Thinking dtypes shows column names
• Assuming dtypes shows memory usage

2. Which of the following is the correct way to check the data type of a single column named 'age' in a pandas DataFrame df?

easy

A. df.dtypes('age')

B. df['age'].dtype

C. df.dtype['age']

D. df.get_dtype('age')

Solution

1. Step 1: Recall syntax for accessing column dtype

   To get the data type of a single column, use df['column_name'].dtype.

2. Step 2: Check other options for correctness

   Options B, C, and D use incorrect syntax or methods that do not exist in pandas.

3. Final Answer:

   df['age'].dtype -> Option B

4. Quick Check:

   Single column dtype = df['column'].dtype [OK]

Hint: Use df['column'].dtype to check one column's type [OK]

Common Mistakes:

• Using parentheses with dtypes like a function
• Trying to access dtype as a dictionary
• Using non-existent methods like get_dtype

3. Given the following code:

import pandas as pd
data = {'name': ['Anna', 'Bob'], 'score': [85, 92]}
df = pd.DataFrame(data)
print(df.dtypes)

What will be the output?

medium

A. name object
score object
dtype: object

B. name int64
score int64
dtype: object

C. name float64
score int64
dtype: object

D. name object
score int64
dtype: object

Solution

1. Step 1: Identify data types of each column

   The 'name' column contains text, so pandas assigns it the 'object' dtype. The 'score' column contains integers, so it gets 'int64'.

2. Step 2: Understand pandas output format

   Printing df.dtypes shows each column name with its dtype, followed by 'dtype: object' indicating the Series type.

3. Final Answer:

   name object score int64 dtype: object -> Option D

4. Quick Check:

   Text = object, integers = int64 [OK]

Hint: Text columns show as object, numbers as int64 or float64 [OK]

Common Mistakes:

• Assuming text columns show as string dtype
• Confusing int64 with float64 for integers
• Expecting score to be object dtype

4. You run this code:

import pandas as pd
data = {'A': [1, 2], 'B': ['x', 'y']}
df = pd.DataFrame(data)
print(df['A'].dtypes())

What error will you get?

medium

A. AttributeError: 'Series' object has no attribute 'dtypes'

B. TypeError: 'dtype' object is not callable

C. SyntaxError: invalid syntax

D. No error, prints int64

Solution

1. Step 1: Identify the issue with the code

   A pandas Series (df['A']) has a .dtype attribute (singular), not .dtypes (plural, which is for DataFrames).

2. Step 2: Understand the error caused

   Accessing .dtypes on a Series raises AttributeError: 'Series' object has no attribute 'dtypes'. The parentheses would cause further issues if the attribute existed.

3. Final Answer:

   AttributeError: 'Series' object has no attribute 'dtypes' -> Option A

4. Quick Check:

   Series: .dtype | DataFrame: .dtypes [OK]

Hint: Series: .dtype | DataFrame: .dtypes [OK]

Common Mistakes:

• Confusing Series .dtype with DataFrame .dtypes
• Adding parentheses after attributes
• Expecting no error from wrong attribute

5. You have a DataFrame df with columns ['id', 'value', 'date']. The value column is stored as object dtype but contains numbers as strings. Which pandas command will convert value to numeric dtype safely, turning invalid parsing into NaN?

hard

A. df['value'] = df['value'].astype('float64')

B. df['value'] = df['value'].astype(int)

C. df['value'] = pd.to_numeric(df['value'], errors='coerce')

D. df['value'] = df['value'].convert_dtypes()

Solution

1. Step 1: Understand the problem with object dtype

   The 'value' column has numbers stored as strings, so converting directly with astype(int) or astype(float64) may fail if there are invalid strings.

2. Step 2: Use pd.to_numeric with errors='coerce'

   This function converts strings to numbers and replaces invalid parsing with NaN, preventing errors.

3. Step 3: Check other options

   df['value'] = df['value'].convert_dtypes() uses convert_dtypes() which infers types but does not coerce invalid strings safely.

4. Final Answer:

   df['value'] = pd.to_numeric(df['value'], errors='coerce') -> Option C

5. Quick Check:

   Use pd.to_numeric with errors='coerce' for safe conversion [OK]

Hint: Use pd.to_numeric with errors='coerce' to convert safely [OK]

Common Mistakes:

• Using astype(int) on strings causing errors
• Assuming convert_dtypes fixes string numbers
• Not handling invalid strings causing crashes