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.