Checking data types in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check the data type of the variable 'x'.

Data Analysis Python

x = 10
print(type([1]))

Drag options to blanks, or click blank then click option'

A10

B'x'

Cx

Dtype

Attempts:

3 left

💡 Hint

Common Mistakes

Putting the variable name in quotes, which checks the type of a string.

Passing the value directly instead of the variable.

Using type(x) returns the data type of the variable x.

2fill in blank

medium

Complete the code to check if the variable 'data' is a list.

Data Analysis Python

data = [1, 2, 3]
print(isinstance(data, [1]))

Drag options to blanks, or click blank then click option'

Aset

Btuple

Cdict

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using dict or tuple instead of list.

Confusing type() with isinstance().

isinstance(data, list) checks if data is a list.

3fill in blank

hard

Fix the error in the code to correctly check if 'value' is a string.

Data Analysis Python

value = 'hello'
if isinstance(value, [1]):
    print('It is a string')

Drag options to blanks, or click blank then click option'

AStr

Bstr

Cstring

DString

Attempts:

3 left

💡 Hint

Common Mistakes

Using capitalized or incorrect type names like String or string.

Forgetting that Python types are case-sensitive.

The correct Python type for strings is str (all lowercase).

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Data Analysis Python

words = ['apple', 'bat', 'car', 'door']
lengths = {word: [1] for word in words if len(word) [2] 3 }

Drag options to blanks, or click blank then click option'

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong comparison operator like < instead of >.

Using the word itself instead of its length.

The dictionary comprehension uses len(word) to get length and filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values for words longer than 3 characters.

Data Analysis Python

words = ['apple', 'bat', 'car', 'door']
result = { [1]: [2] for w in words if len(w) [3] 3 }

Drag options to blanks, or click blank then click option'

Aw.upper()

Blen(w)

C>

Dw

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word instead of uppercase for keys.

Using wrong comparison operators.

Mixing variable names.

The dictionary comprehension uses w.upper() as keys, len(w) as values, and filters words with length greater than 3 using >.

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()