Checking data types in Data Analysis Python - Mini Project: Build & Apply

Checking data types

📖 Scenario: You are working with a small dataset of different types of information collected from a survey. You want to check the type of each data item to understand what kind of data you have.

🎯 Goal: Build a simple Python program that stores different data items in a dictionary and then checks the data type of each item.

📋 What You'll Learn

Create a dictionary with exact keys and values as specified

Create a variable to hold the keys of the dictionary

Use a for loop to iterate over the keys and check the type of each value

Print the key and its data type in each iteration

💡 Why This Matters

🌍 Real World

Checking data types is important when cleaning and preparing data for analysis to avoid errors and understand the data better.

💼 Career

Data scientists and analysts often need to verify data types to ensure correct processing and analysis in their projects.

1

Create the data dictionary

Create a dictionary called survey_data with these exact entries: 'age': 30, 'name': 'Alice', 'height': 5.5, 'is_student': False

Data Analysis Python

# Create the dictionary survey_data with the given entries
# Your code here

Hint

Use curly braces {} to create a dictionary. Separate keys and values with a colon, and separate items with commas.

2

Create a list of keys

Create a variable called keys that stores the list of keys from the survey_data dictionary using the keys() method.

Data Analysis Python

survey_data = {'age': 30, 'name': 'Alice', 'height': 5.5, 'is_student': False}
# Create a variable keys that holds the keys of survey_data
# Your code here

Hint

Use list() to convert the keys view to a list.

3

Check data types with a loop

Use a for loop with variable key to iterate over keys. Inside the loop, get the value from survey_data using key and create a variable value_type that stores the type of this value using the type() function.

Data Analysis Python

survey_data = {'age': 30, 'name': 'Alice', 'height': 5.5, 'is_student': False}
keys = list(survey_data.keys())
# Use a for loop to iterate over keys and get the type of each value
# Your code here

Hint

Use for key in keys: to loop. Use type() to find the data type.

4

Print the key and its data type

Inside the for loop, add a print() statement that shows the key and the value_type separated by a colon and a space. For example: age: <class 'int'>

Data Analysis Python

survey_data = {'age': 30, 'name': 'Alice', 'height': 5.5, 'is_student': False}
keys = list(survey_data.keys())
for key in keys:
    value = survey_data[key]
    value_type = type(value)
    # Print the key and its data type
    # Your code here

Hint

Use an f-string in print() to format the output as key: value_type.

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()