Counting missing values in Pandas - Time & Space Complexity

Time Complexity: Counting missing values

O(n x m)

Understanding Time Complexity

We want to know how long it takes to count missing values in a table as the table grows.

How does the time needed change when we have more rows or columns?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, None, 4],
    'B': [None, 2, 3, 4],
    'C': [1, None, None, 4]
})

missing_count = df.isna().sum().sum()

This code counts all missing values in the whole table by first marking missing spots, then summing them up.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Checking each cell to see if it is missing (NaN).
• How many times: Once for every cell in the table (rows x columns).

How Execution Grows With Input

As the table gets bigger, the time to count missing values grows with the total number of cells.

Input Size (rows x columns)	Approx. Operations
10 x 5 = 50	About 50 checks
100 x 5 = 500	About 500 checks
1000 x 5 = 5000	About 5000 checks

Pattern observation: The time grows directly with the number of cells; doubling rows or columns roughly doubles the work.

Final Time Complexity

Time Complexity: O(n * m)

This means the time needed grows proportionally with the total number of cells in the table.

Common Mistake

[X] Wrong: "Counting missing values only looks at columns, so time depends just on the number of columns."

[OK] Correct: The code checks every cell, so both rows and columns affect the time equally.

Interview Connect

Understanding how counting missing data scales helps you handle bigger datasets confidently and shows you can think about efficiency clearly.

Self-Check

"What if we only count missing values in one column instead of the whole table? How would the time complexity change?"

Practice

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

easy

A. Counts the total number of rows in the DataFrame

B. Counts the number of missing values in each column of the DataFrame

C. Replaces missing values with zero in the DataFrame

D. Returns the data types of each column in the DataFrame

Solution

1. Step 1: Understand df.isna()

   This method returns a DataFrame of the same shape as df with True where values are missing and False otherwise.

2. Step 2: Apply .sum() on the boolean DataFrame

   Summing True values (which count as 1) per column gives the count of missing values in each column.

3. Final Answer:

   Counts the number of missing values in each column of the DataFrame -> Option B

4. Quick Check:

   df.isna().sum() counts missing per column [OK]

Hint: Use isna() then sum() to count missing per column [OK]

Common Mistakes:

• Thinking sum() counts rows instead of missing values
• Confusing isna() with fillna()
• Assuming it replaces missing values

2. Which of the following is the correct syntax to count all missing values in a pandas DataFrame df?

easy

A. df.isna().count()

B. df.count().sum()

C. df.isna().sum().sum()

D. df.isnull().count()

Solution

1. Step 1: Use df.isna() or df.isnull() to find missing values

   Both return a boolean DataFrame marking missing values as True.

2. Step 2: Use .sum().sum() to count all missing values

   The first sum() sums missing per column, the second sums those counts to get total missing in the DataFrame.

3. Final Answer:

   df.isna().sum().sum() -> Option C

4. Quick Check:

   Total missing = df.isna().sum().sum() [OK]

Hint: Double sum() after isna() counts all missing values [OK]

Common Mistakes:

• Using count() which counts non-missing values
• Using isna().count() which counts all rows, not missing
• Confusing isna() with isnull().count()

3. Given the DataFrame df below:

import pandas as pd
data = {'A': [1, None, 3], 'B': [None, None, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)

What is the output of df.isna().sum()?

medium

A. A 1
B 1
C 1
dtype: int64

B. A 0
B 0
C 0
dtype: int64

C. A 2
B 1
C 0
dtype: int64

D. A 1
B 2
C 0
dtype: int64

Solution

1. Step 1: Identify missing values in each column

   Column A has one None (missing), column B has two None values, column C has none.

2. Step 2: Apply df.isna().sum()

   This counts missing per column: A=1, B=2, C=0.

3. Final Answer:

   A 1 B 2 C 0 dtype: int64 -> Option D

4. Quick Check:

   Count missing per column matches output [OK]

Hint: Count None or NaN per column to verify sums [OK]

Common Mistakes:

• Mixing counts of missing and non-missing
• Miscounting missing in column B
• Assuming None counts as zero

4. You run the code below but get an error:

import pandas as pd
data = {'X': [1, 2, None], 'Y': [None, 5, 6]}
df = pd.DataFrame(data)
missing = df.isna.sum()

What is the error and how to fix it?

medium

A. Missing parentheses after isna; fix with df.isna().sum()

B. DataFrame missing columns; add columns before counting

C. sum() is not a pandas method; use count() instead

D. None values cause error; replace with zeros first

Solution

1. Step 1: Identify the error in method call

   The code uses df.isna.sum() missing parentheses after isna, which is a method.

2. Step 2: Correct the syntax

   Use df.isna().sum() to call the method and then sum missing values.

3. Final Answer:

   Missing parentheses after isna; fix with df.isna().sum() -> Option A

4. Quick Check:

   Method calls need parentheses [OK]

Hint: Remember to add () after isna to call the method [OK]

Common Mistakes:

• Forgetting parentheses on isna()
• Using sum() instead of count() incorrectly
• Thinking None causes errors in counting

5. You have a DataFrame df with columns ['age', 'income', 'score']. You want to find the percentage of missing values in each column. Which code snippet correctly calculates this?

hard

A. df.isna().sum() / len(df) * 100

B. df.isna().count() / len(df) * 100

C. df.count() / df.isna().sum() * 100

D. df.isna().sum() * 100

Solution

1. Step 1: Count missing values per column

   Use df.isna().sum() to get missing counts per column.

2. Step 2: Calculate percentage of missing values

   Divide missing counts by total rows len(df) and multiply by 100 to get percentage.

3. Final Answer:

   df.isna().sum() / len(df) * 100 -> Option A

4. Quick Check:

   Missing % = (missing count / total rows) * 100 [OK]

Hint: Divide missing count by total rows, multiply by 100 [OK]

Common Mistakes:

• Using count() which counts non-missing values
• Multiplying missing count by 100 without dividing
• Dividing count by missing count incorrectly