Data validation checks in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check if there are any missing values in the DataFrame.

Pandas

missing = df.[1]()

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

Afillna

Bdropna

Cisnull

Dhead

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna() removes rows instead of checking for missing values.

Using fillna() replaces missing values but does not check for them.

The isnull() function returns a DataFrame of booleans indicating missing values.

2fill in blank

medium

Complete the code to count the number of missing values in each column.

Pandas

missing_counts = df.isnull().[1]()

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

Asum

Bcount

Cmean

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() counts non-missing values, not missing ones.

Using mean() gives proportion, not count.

The sum() function adds up True values (missing) per column.

3fill in blank

hard

Fix the error in the code to check if all values in column 'age' are positive.

Pandas

all_positive = (df['age'] [1] 0).all()

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

A<

B<=

C==

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' checks for negative values.

Using '==' checks for equality, not positivity.

We want to check if all ages are greater than 0, so use the '>' operator.

4fill in blank

hard

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

Pandas

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 '<' includes shorter words, not longer.

Using 'word' as value stores the word, not its length.

We want the length of each word (len(word)) and only words longer than 3 (len(word) > 3).

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase keys and values greater than zero.

Pandas

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() changes keys to lowercase.

Using '<' filters for negative values.

Keys are uppercase (k.upper()), values are v, and filter values greater than zero (v > 0).

Practice

1. What does the pandas function isnull() check in a DataFrame?

easy

A. It checks for missing or null values in the data.

B. It checks for duplicate rows in the DataFrame.

C. It returns the data types of each column.

D. It sorts the DataFrame by a column.

Solution

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

   The isnull() function in pandas returns a DataFrame of the same shape with True where values are missing (NaN) and False otherwise.

2. Step 2: Compare with other functions

   Functions like duplicated() check duplicates, and dtypes shows data types, so they do not check for missing values.

3. Final Answer:

   It checks for missing or null values in the data. -> Option A

4. Quick Check:

   Missing data = isnull() [OK]

Hint: Missing values? Use isnull() to find them fast [OK]

Common Mistakes:

• Confusing isnull() with duplicated()
• Thinking isnull() returns data types
• Assuming isnull() sorts data

2. Which of the following is the correct syntax to check for duplicate rows in a pandas DataFrame named df?

easy

A. df.isnull()

B. df.check_duplicates()

C. df.duplicated()

D. df.find_duplicates()

Solution

1. Step 1: Recall pandas method for duplicates

   The correct pandas method to find duplicate rows is duplicated(), called as df.duplicated().

2. Step 2: Verify other options

   isnull() checks missing data, and check_duplicates() and find_duplicates() are not valid pandas methods.

3. Final Answer:

   df.duplicated() -> Option C

4. Quick Check:

   Duplicates = duplicated() [OK]

Hint: Duplicates? Use df.duplicated() to spot them [OK]

Common Mistakes:

• Using non-existent methods like check_duplicates()
• Mixing isnull() with duplicate checks
• Forgetting parentheses after duplicated

3. Given the DataFrame df below, what will df.isnull().sum() output?

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

medium

A. {'A': 1, 'B': 2, 'C': 1}

B. {'A': 0, 'B': 0, 'C': 0}

C. {'A': 2, 'B': 1, 'C': 0}

D. {'A': 1, 'B': 1, 'C': 0}

Solution

1. Step 1: Identify missing values in each column

   Column 'A' has one None (missing), 'B' has one None, and 'C' has no missing values.

2. Step 2: Understand what isnull().sum() does

   isnull() marks missing as True, and sum() counts True values per column.

3. Final Answer:

   {'A': 1, 'B': 1, 'C': 0} -> Option D

4. Quick Check:

   Count missing per column = isnull().sum() [OK]

Hint: Sum isnull() to count missing per column [OK]

Common Mistakes:

• Counting total missing instead of per column
• Confusing None with zero
• Misreading DataFrame values

4. The following code is intended to find duplicate rows in df. What is the error?

duplicates = df.duplicate()

medium

A. The method name should be duplicated(), not duplicate().

B. The DataFrame df is not defined.

C. The method duplicated() requires an argument.

D. The code should use df.isnull() to find duplicates.

Solution

1. Step 1: Check method name correctness

   The correct pandas method to find duplicates is duplicated(), not duplicate().

2. Step 2: Verify other options

   df is assumed defined, duplicated() does not require arguments, and isnull() checks missing data, not duplicates.

3. Final Answer:

   The method name should be duplicated(), not duplicate(). -> Option A

4. Quick Check:

   Correct method name = duplicated() [OK]

Hint: Remember: duplicated() has a 'd' at the end [OK]

Common Mistakes:

• Misspelling duplicated() as duplicate()
• Confusing duplicated() with isnull()
• Thinking duplicated() needs arguments

5. You have a DataFrame df with columns ['id', 'age', 'score']. You want to validate that age has no missing values and score has no duplicates. Which code correctly performs both checks?

hard

A. missing_age = df.isnull().sum()['age']
duplicate_score = df['score'].isnull().any()

B. missing_age = df['age'].isnull().sum()
duplicate_score = df['score'].duplicated().any()

C. missing_age = df['age'].duplicated().sum()
duplicate_score = df['score'].duplicated().sum()

D. missing_age = df['age'].duplicated().any()
duplicate_score = df['score'].isnull().sum()

Solution

1. Step 1: Check missing values in 'age'

   df['age'].isnull().sum() counts missing values correctly.

   missing_age = df['age'].isnull().sum()
   duplicate_score = df['score'].duplicated().any()

   uses this.

2. Step 2: Check duplicates in 'score'

   df['score'].duplicated().any() returns True if any duplicates exist, which is a good validation check.

3. Step 3: Review other options

   missing_age = df.isnull().sum()['age']
   duplicate_score = df['score'].isnull().any()

   wrongly checks duplicates with isnull(),

   missing_age = df['age'].duplicated().sum()
   duplicate_score = df['score'].duplicated().sum()

   uses duplicated() on 'age' which is wrong,

   missing_age = df['age'].duplicated().any()
   duplicate_score = df['score'].isnull().sum()

   wrongly uses duplicated() for missing and isnull() for duplicates.

4. Final Answer:

   missing_age = df['age'].isnull().sum(); duplicate_score = df['score'].duplicated().any() -> Option B

5. Quick Check:

   Missing count + duplicate check = correct validation [OK]

Hint: Sum isnull() for missing; use duplicated().any() for duplicates [OK]

Common Mistakes:

• Using duplicated() to count missing values
• Using isnull() to check duplicates
• Mixing sum() and any() incorrectly