Identifying missing values (isnull, isna) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check for missing values in the DataFrame column 'Age'.

Data Analysis Python

missing_ages = df['Age'].[1]()

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

Amean

Bsum

Cisnull

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() instead of isnull() to identify missing values.

Using count() which counts non-missing values.

Using mean() which calculates average, not missing values.

The isnull() method returns a boolean Series indicating which values are missing (NaN).

2fill in blank

medium

Complete the code to count how many missing values are in the 'Salary' column.

Data Analysis Python

missing_salary_count = df['Salary'].[1]().sum()

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

Aisna

Bcount

Cmean

Dfillna

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() which counts non-missing values.

Using mean() which calculates average, not missing values.

Using fillna() which replaces missing values instead of detecting them.

The isna() method returns True for missing values. Summing these True values counts the missing entries.

3fill in blank

hard

Fix the error in the code to correctly identify missing values in the DataFrame 'df'.

Data Analysis Python

missing_data = df.[1]()

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

Aisnull

Bisna

Cmissing

Dnull

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'missing' or 'null' which are not pandas methods.

Confusing isna() and isnull() - both work but isnull() is more common.

The correct method to identify missing values is isnull(). The options 'missing' and 'null' are not valid pandas methods.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths only if length is greater than 3.

Data Analysis Python

lengths = {word: [1] for word in words if [2]

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

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word > 3' which compares string to number and causes error.

Using 'word' as value instead of length.

Not filtering words by length.

The dictionary comprehension uses len(word) as the value and filters words where length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values only if value is greater than 0.

Data Analysis Python

result = {{ [1]: [2] for k, v in data.items() if v [3] 0 }}

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper() for keys.

Using '<' instead of '>' in condition.

Using k or v incorrectly in keys or values.

The dictionary comprehension uses uppercase keys with k.upper(), values as v, and filters where value is greater than 0.

Practice

1. What do the isnull() and isna() functions do in pandas?

easy

A. They identify missing or empty values in a DataFrame or Series.

B. They fill missing values with zeros.

C. They remove rows with missing values.

D. They convert data types to numeric.

Solution

1. Step 1: Understand the purpose of isnull() and isna()

   Both functions check for missing data and return True where data is missing.

2. Step 2: Differentiate from other functions

   They do not fill, remove, or convert data but only detect missing values.

3. Final Answer:

   They identify missing or empty values in a DataFrame or Series. -> Option A

4. Quick Check:

   Missing data detection = C [OK]

Hint: Think: True means missing, False means present [OK]

Common Mistakes:

• Confusing detection with filling missing values
• Thinking they remove data instead of marking it
• Assuming they change data types

2. Which of the following is the correct way to check for missing values in a pandas DataFrame named df?

easy

A. df.isempty()

B. df.checknull()

C. df.isnull()

D. df.missing()

Solution

1. Step 1: Recall pandas function names for missing data

   The correct pandas function to detect missing values is isnull().

2. Step 2: Verify other options

   Functions like checknull(), missing(), and isempty() do not exist in pandas for this purpose.

3. Final Answer:

   df.isnull() -> Option C

4. Quick Check:

   Correct pandas method = D [OK]

Hint: Remember: pandas uses isnull() or isna() [OK]

Common Mistakes:

• Using non-existent function names
• Confusing with methods that fill or drop data
• Misspelling function names

3. Given the following code, what is the output?

import pandas as pd
data = {'A': [1, None, 3], 'B': [4, 5, None]}
df = pd.DataFrame(data)
print(df.isna())

medium

A. A DataFrame with all False values.

B. A DataFrame showing True where values are missing and False otherwise.

C. A DataFrame with all True values.

D. An error because isna() is not a valid function.

Solution

1. Step 1: Understand the input DataFrame

   The DataFrame has missing values at df['A'][1] and df['B'][2].

2. Step 2: Apply isna() method

   This method returns True at missing positions and False elsewhere.

3. Final Answer:

   A DataFrame showing True where values are missing and False otherwise. -> Option B

4. Quick Check:

   Missing spots = True, others = False [OK]

Hint: Look for None or NaN spots turning True [OK]

Common Mistakes:

• Thinking isna() fills missing values
• Expecting all True or all False output
• Confusing isna() with dropna()

4. What is wrong with this code snippet to detect missing values?

import pandas as pd
data = {'X': [10, 20, None]}
df = pd.DataFrame(data)
print(df.isnull)

medium

A. The variable name df is invalid.

B. The DataFrame is not created correctly.

C. The isnull function does not exist in pandas.

D. The code is missing parentheses after isnull to call the function.

Solution

1. Step 1: Check the usage of isnull

   isnull is a function and needs parentheses to execute, like df.isnull().

2. Step 2: Verify other parts of the code

   DataFrame creation and variable names are correct; isnull exists in pandas.

3. Final Answer:

   The code is missing parentheses after isnull to call the function. -> Option D

4. Quick Check:

   Function call needs () = A [OK]

Hint: Remember to add () to call functions [OK]

Common Mistakes:

• Forgetting parentheses on function calls
• Assuming isnull is an attribute, not a method
• Misnaming variables or functions

5. You have a DataFrame df with columns ['Name', 'Age', 'Score']. Some Age and Score values are missing. How do you create a new column 'MissingCount' that counts how many missing values each row has?

hard

A. df['MissingCount'] = df.isnull().sum(axis=1)

B. df['MissingCount'] = df.isnull().sum()

C. df['MissingCount'] = df.isnull().count(axis=1)

D. df['MissingCount'] = df.isnull().mean(axis=1)

Solution

1. Step 1: Use isnull() to detect missing values

   This returns True/False for each cell indicating missing data.

2. Step 2: Sum missing values per row with sum(axis=1)

   Summing True values (counted as 1) across columns gives missing count per row.

3. Final Answer:

   df['MissingCount'] = df.isnull().sum(axis=1) -> Option A

4. Quick Check:

   Sum True per row = B [OK]

Hint: Sum isnull() across columns with axis=1 [OK]

Common Mistakes:

• Summing without axis=1 sums all missing in DataFrame
• Using count() instead of sum() to count missing
• Using mean() which gives fraction, not count