Handling missing values in Series in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of dropna() on a Series with missing values

What is the output of this code?

import pandas as pd
s = pd.Series([1, None, 3, None, 5])
result = s.dropna()
print(result)

Data Analysis Python

import pandas as pd
s = pd.Series([1, None, 3, None, 5])
result = s.dropna()
print(result)

A

0    1.0
2    3.0
4    5.0
dtype: float64

B

0    1
1    NaN
2    3
3    NaN
4    5
dtype: object

C

0    1.0
1    NaN
2    3.0
3    NaN
4    5.0
dtype: float64

D

0    1
2    3
4    5
dtype: int64

💡 Hint

dropna() removes all missing (NaN) values from the Series.

The dropna() method removes all missing values (None or NaN) from the Series and returns a new Series with only the non-missing values and their original indices.

❓ data_output

intermediate

Count of missing values in a Series

Given this Series, what is the output of s.isna().sum()?

Data Analysis Python

import pandas as pd
s = pd.Series([10, None, 20, None, 30, None])
print(s.isna().sum())

A None

B 0

C 6

D 3

💡 Hint

isna() returns True for missing values, sum counts them.

The Series has three None values which are treated as missing (NaN). isna() marks them True, sum counts how many True values exist.

🔧 Debug

advanced

Identify the error in filling missing values

What error will this code raise?

import pandas as pd
s = pd.Series([1, None, 3, None, 5])
result = s.fillna(method='backward')
print(result)

Data Analysis Python

import pandas as pd
s = pd.Series([1, None, 3, None, 5])
result = s.fillna(method='backward')
print(result)

A TypeError: fillna() got an unexpected keyword argument 'method'

B ValueError: Invalid fill method 'backward'

C AttributeError: 'Series' object has no attribute 'fillna'

D No error, prints the Series with missing values filled backward

💡 Hint

Check the valid method names for fillna() in pandas.

The fillna() method accepts 'ffill' or 'bfill' as method values. 'backward' is not valid and causes a ValueError.

🚀 Application

advanced

Replacing missing values with mean

You have this Series with missing values. Which option correctly replaces missing values with the mean of the non-missing values?

import pandas as pd
s = pd.Series([2, None, 4, None, 6])
mean_val = s.mean()

Data Analysis Python

import pandas as pd
s = pd.Series([2, None, 4, None, 6])
mean_val = s.mean()

A

s.fillna(mean_val, inplace=True)
print(s)

B

s.fillna(method='mean')
print(s)

C

s.replace(None, mean_val)
print(s)

D

s.fillna(s.mean())
print(s)

💡 Hint

fillna() can replace missing values with a scalar value; inplace modifies the original Series.

Option A correctly uses fillna() with the mean value and inplace=True to update s. Option A returns a new Series but does not update s, so print(s) shows original with missing values.

🧠 Conceptual

expert

Effect of dropna() on Series index

After applying dropna() on a Series with missing values, what happens to the index of the resulting Series?

A The index is dropped and replaced by default integer index

B The index is reset to a continuous range starting at 0

C The original index values of non-missing entries are preserved

D The index becomes a MultiIndex with missing value positions

💡 Hint

Think about whether dropna() changes the index or just removes rows.

dropna() removes rows with missing values but keeps the original index labels of the remaining entries unchanged.

Practice

1. What does the isna() method do when applied to a pandas Series?

easy

A. It replaces missing values with zeros.

B. It removes all missing values from the Series.

C. It counts the number of missing values in the Series.

D. It returns a Series of booleans indicating which values are missing (NaN).

Solution

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

   The isna() method checks each element in the Series and returns True if the value is missing (NaN), otherwise False.

2. Step 2: Compare with other options

   Removing values is done by dropna(), replacing by fillna(), and counting missing values requires sum() on the boolean Series.

3. Final Answer:

   It returns a Series of booleans indicating which values are missing (NaN). -> Option D

4. Quick Check:

   isna() = boolean mask [OK]

Hint: Remember: isna() marks missing values as True [OK]

Common Mistakes:

• Confusing isna() with dropna()
• Thinking isna() replaces values
• Assuming isna() counts missing values

2. Which of the following is the correct syntax to remove missing values from a pandas Series named data?

easy

A. data.dropna()

B. data.isna()

C. data.fillna()

D. data.remove_na()

Solution

1. Step 1: Identify the method to remove missing values

   The method dropna() removes missing values from a Series.

2. Step 2: Check other options for correctness

   isna() finds missing values, fillna() replaces them, and remove_na() is not a valid pandas method.

3. Final Answer:

   data.dropna() -> Option A

4. Quick Check:

   dropna() removes NaNs [OK]

Hint: Use dropna() to delete missing values from Series [OK]

Common Mistakes:

• Using fillna() instead of dropna() to remove values
• Calling a non-existent method remove_na()
• Confusing isna() with dropna()

3. Given the Series import pandas as pd s = pd.Series([1, None, 3, None, 5]) result = s.fillna(0), what is the value of result?

medium

A. [1, 0, 3, 0, 5]

B. [1, None, 3, None, 5]

C. [0, 0, 0, 0, 0]

D. [1, 1, 3, 3, 5]

Solution

1. Step 1: Understand fillna(0) on the Series

   The fillna(0) replaces all missing values (None/NaN) with 0.

2. Step 2: Apply fillna(0) to the Series

   Original Series: [1, None, 3, None, 5]. After fillna(0): [1, 0, 3, 0, 5].

3. Final Answer:

   [1, 0, 3, 0, 5] -> Option A

4. Quick Check:

   fillna(0) replaces NaN with 0 [OK]

Hint: fillna(value) replaces NaNs with value [OK]

Common Mistakes:

• Expecting fillna() to remove values
• Thinking fillna(0) replaces all values with 0
• Confusing None with zero already present

4. What is wrong with this code snippet to remove missing values from Series s?

clean_s = s.dropna(inplace=True)
print(clean_s)

medium

A. dropna() does not have an inplace parameter.

B. print(clean_s) will print the original Series with NaNs.

C. dropna(inplace=True) returns None, so clean_s is None.

D. dropna(inplace=True) modifies a copy, not the original Series.

Solution

1. Step 1: Understand dropna(inplace=True) behavior

   When inplace=True is used, dropna() modifies the Series in place and returns None.

2. Step 2: Analyze assignment and print

   Assigning clean_s = s.dropna(inplace=True) sets clean_s to None. Printing it shows None, not the cleaned Series.

3. Final Answer:

   dropna(inplace=True) returns None, so clean_s is None. -> Option C

4. Quick Check:

   inplace=True returns None [OK]

Hint: dropna(inplace=True) returns None, don't assign it [OK]

Common Mistakes:

• Assigning dropna(inplace=True) to a variable
• Expecting dropna() to return cleaned Series with inplace=True
• Confusing inplace behavior with default dropna()

5. You have a Series s = pd.Series([1, None, 2, None, 100]). You want to replace missing values with the mean of the non-missing values. Which code correctly does this?

hard

A. s.replace(None, s.mean())

B. s.fillna(s.mean())

C. s.dropna().mean()

D. s.fillna(s.median())

Solution

1. Step 1: Calculate mean of non-missing values

   Using s.mean() calculates the average ignoring missing values.

2. Step 2: Replace missing values with mean

   fillna(s.mean()) replaces NaNs with the calculated mean.

3. Step 3: Check other options

   fillna(s.median()) replaces with median, not mean. dropna().mean() calculates mean but does not fill. replace(None, s.mean()) does not work properly for NaN.

4. Final Answer:

   s.fillna(s.mean()) -> Option B

5. Quick Check:

   fillna(mean) replaces NaNs with average [OK]

Hint: Use fillna(s.mean()) to replace NaNs with average [OK]

Common Mistakes:

• Using median instead of mean
• Calculating mean but not filling missing values
• Using replace() which doesn't handle NaN properly