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.