Checking data types in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of type checking with pandas

What is the output of this code snippet that checks data types of a pandas DataFrame?

Data Analysis Python

import pandas as pd

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

A

A      int64
B    int64
C     object
dtype: object

B

A      float64
B    float64
C     object
dtype: object

C

A      int32
B    float32
C     string
dtype: object

D

A      int64
B    float64
C     object
dtype: object

💡 Hint

Remember that integers default to int64 and floats to float64 in pandas.

The DataFrame columns have types int64 for integers, float64 for floats, and object for strings.

❓ data_output

intermediate

Count of data types in a DataFrame

What is the output of this code that counts the number of columns by data type in a pandas DataFrame?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'num1': [1, 2, 3],
    'num2': [4.0, 5.0, 6.0],
    'cat': ['a', 'b', 'c'],
    'flag': [True, False, True]
})
print(df.dtypes.value_counts())

A

int64      1
float64    2
object     1
dtype: int64

B

int64      2
float64    1
object     1
dtype: int64

C

int64      1
float64    1
object     1
bool       1
dtype: int64

D

int64      1
float64    1
object     2
dtype: int64

💡 Hint

Check the data types of each column carefully, including booleans.

The DataFrame has one int64 column ('num1'), one float64 column ('num2'), one object column ('cat'), and one bool column ('flag').

🔧 Debug

advanced

Identify the error in type checking code

What error does this code raise when trying to check data types of a list?

Data Analysis Python

data = [1, 2, 3, 4]
print(data.dtypes)

A TypeError: unsupported operand type(s) for dtypes

B AttributeError: 'list' object has no attribute 'dtypes'

C NameError: name 'dtypes' is not defined

D No error, prints the data types of list elements

💡 Hint

Check if Python lists have a dtypes attribute.

Python lists do not have a dtypes attribute; only pandas DataFrames or Series have it.

🚀 Application

advanced

Determine data types after conversion

After converting a pandas Series to numeric with errors='coerce', what is the data type of the resulting Series?

Data Analysis Python

import pandas as pd

s = pd.Series(['10', '20', 'abc', '30'])
s_num = pd.to_numeric(s, errors='coerce')
print(s_num.dtype)

A float64

B int64

C object

D string

💡 Hint

Non-convertible values become NaN, which forces float type.

Because 'abc' cannot be converted to a number, it becomes NaN, which is a float, so the whole Series is float64.

🧠 Conceptual

expert

Understanding data types in mixed-type pandas DataFrame

Given a pandas DataFrame with columns of mixed types, which statement about the dtypes attribute is true?

A Each column's dtype reflects the most specific type that can hold all its values.

B The dtypes attribute returns a list of Python built-in types for each column.

C All columns are automatically converted to the same dtype for consistency.

D Columns with mixed types are stored as int64 by default.

💡 Hint

Think about how pandas stores data efficiently per column.

Pandas assigns each column a dtype that can hold all its values, such as object for mixed types or specific numeric types.

Practice

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

easy

A. The data type of each column in the DataFrame

B. The number of rows in the DataFrame

C. The summary statistics of numeric columns

D. The first five rows of the DataFrame

Solution

1. Step 1: Understand what dtypes means

   The dtypes attribute in pandas shows the data type of each column in a DataFrame.

2. Step 2: Compare options with the definition

   Only The data type of each column in the DataFrame ('The data type of each column in the DataFrame') correctly describes this: it shows the data type of each column.

3. Final Answer:

   The data type of each column in the DataFrame -> Option A

4. Quick Check:

   DataFrame.dtypes = column types [OK]

Hint: Remember: dtypes means data types per column [OK]

Common Mistakes:

• Confusing dtypes with DataFrame shape
• Thinking dtypes shows data values
• Mixing dtypes with summary statistics

2. Which of the following is the correct way to check data types of columns in a pandas DataFrame named df?

easy

A. df.types()

B. df.dtypes

C. df.type()

D. df.get_types()

Solution

1. Step 1: Recall pandas syntax for data types

   In pandas, dtypes is an attribute (not a method), so it is accessed without parentheses.

2. Step 2: Evaluate each option

   df.dtypes uses df.dtypes correctly. Options A, B, and C use incorrect method names or parentheses.

3. Final Answer:

   df.dtypes -> Option B

4. Quick Check:

   Use attribute dtypes without parentheses [OK]

Hint: Use df.dtypes without parentheses to check types [OK]

Common Mistakes:

• Adding parentheses after dtypes
• Using incorrect method names
• Confusing dtypes with type() function

3. Given the following code:

import pandas as pd
data = {'age': [25, 30], 'name': ['Alice', 'Bob'], 'score': [88.5, 92.0]}
df = pd.DataFrame(data)
print(df.dtypes)

What will be the output?

medium

A. age int64
name object
score float64
dtype: object

B. age float64
name object
score int64
dtype: object

C. age int32
name string
score float32
dtype: object

D. age object
name object
score object
dtype: object

Solution

1. Step 1: Check data types of each column in the dictionary

   Column 'age' has integers (25, 30) so type is int64 by default. 'name' has strings, so type is object. 'score' has floats (88.5, 92.0), so type is float64.

2. Step 2: Confirm pandas default data types

   Pandas uses int64 for integers, object for strings, and float64 for floats by default.

3. Final Answer:

   age int64 name object score float64 dtype: object -> Option A

4. Quick Check:

   Integers=int64, Strings=object, Floats=float64 [OK]

Hint: Integers=int64, strings=object, floats=float64 in pandas [OK]

Common Mistakes:

• Assuming strings have type 'string' instead of 'object'
• Mixing int and float types
• Confusing int32 with int64 default

4. You run this code:

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

But it gives an error. What is the problem?

medium

A. The DataFrame is empty

B. You need to import numpy first

C. The column names must be integers

D. You should use df.dtypes without parentheses

Solution

1. Step 1: Identify the error cause

   The code uses df.dtypes() with parentheses, but dtypes is an attribute, not a method.

2. Step 2: Correct the syntax

   Remove parentheses and use df.dtypes to get the data types without error.

3. Final Answer:

   You should use df.dtypes without parentheses -> Option D

4. Quick Check:

   dtypes is attribute, not method [OK]

Hint: No parentheses after dtypes attribute [OK]

Common Mistakes:

• Adding parentheses after dtypes
• Thinking DataFrame is empty causes error
• Believing numpy import is needed for dtypes

5. You have a DataFrame df with columns: ['id', 'value', 'flag']. The flag column contains only 0 and 1 but is stored as integers. You want to convert flag to a boolean type for better memory use. Which code correctly changes the data type?

hard

A. df['flag'] = df['flag'].to_bool()

B. df['flag'] = bool(df['flag'])

C. df['flag'] = df['flag'].astype(bool)

D. df['flag'] = df['flag'].convert(bool)

Solution

1. Step 1: Understand how to convert column types in pandas

   Use the astype() method to convert a column to a different data type.

2. Step 2: Check each option's validity

   df['flag'] = df['flag'].astype(bool) uses astype(bool) correctly. df['flag'] = bool(df['flag']) tries to convert the whole Series to bool, which returns a single True/False, not a column. Options C and D use non-existent methods.

3. Final Answer:

   df['flag'] = df['flag'].astype(bool) -> Option C

4. Quick Check:

   Use astype(bool) to convert column to boolean [OK]

Hint: Use astype(bool) to convert column to boolean type [OK]

Common Mistakes:

• Using bool() on entire Series instead of astype
• Trying non-existent methods like to_bool()
• Confusing convert() with astype()