Handling inconsistent values in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to replace all occurrences of 'N/A' with NaN in the DataFrame df.

Pandas

df = df.replace([1], np.nan)

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

A'None'

B'null'

C'missing'

D'N/A'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'None' or 'null' instead of 'N/A' which does not match the data.

Not using np.nan to represent missing values.

We use replace to change all 'N/A' strings to np.nan, which pandas recognizes as missing values.

2fill in blank

medium

Complete the code to drop rows from df that contain any NaN values.

Pandas

df_clean = df.[1](axis=0)

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

Afillna

Bdropna

Cisnull

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using fillna instead of dropna, which fills missing values instead of dropping rows.

Using isnull which only returns a boolean mask.

The dropna method removes rows or columns with missing values. Here, axis=0 means rows.

3fill in blank

hard

Fix the error in the code to convert the 'age' column to numeric, coercing errors to NaN.

Pandas

df['age'] = pd.to_numeric(df['age'], errors=[1])

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

A'coerce'

B'ignore'

C'raise'

D'convert'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'raise' which causes an error on invalid values.

Using 'ignore' which leaves invalid values unchanged.

The errors='coerce' argument converts invalid parsing to NaN instead of raising an error.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Pandas

{word: [1] for word in words if [2] > 3}

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

Alen(word)

Bword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Checking the word instead of its length in the condition.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase keys to values only if value is positive.

Pandas

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

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

Ak.upper()

Bv

Ck

Dv.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using the value as key or vice versa.

Not converting keys to uppercase.

The comprehension uses k.upper() as keys, v as values, and iterates over k, v pairs from data.items(). It filters values greater than zero.

Practice

1. What is the main purpose of handling inconsistent values in a pandas DataFrame?

easy

A. To clean data so analysis results are accurate and reliable

B. To make the DataFrame larger by adding more rows

C. To convert all numbers to strings

D. To sort the DataFrame by index

Solution

1. Step 1: Understand inconsistent values

   Inconsistent values are errors or variations in data that can cause wrong analysis results.

2. Step 2: Purpose of handling them

   Cleaning these values ensures the data is uniform and trustworthy for analysis.

3. Final Answer:

   To clean data so analysis results are accurate and reliable -> Option A

4. Quick Check:

   Cleaning data = Accurate analysis [OK]

Hint: Cleaning inconsistent data ensures correct analysis results [OK]

Common Mistakes:

• Thinking inconsistent values add more data
• Confusing cleaning with sorting
• Assuming conversion to string fixes inconsistencies

2. Which pandas method is commonly used to replace inconsistent values in a DataFrame column?

easy

A. df.sort_values()

B. df.dropna()

C. df.replace()

D. df.groupby()

Solution

1. Step 1: Identify method for replacing values

   df.replace() is designed to substitute specified values with new ones.

2. Step 2: Confirm other methods' purposes

   df.dropna() removes missing data, df.sort_values() sorts data, df.groupby() groups data; none replace values.

3. Final Answer:

   df.replace() -> Option C

4. Quick Check:

   Replace values = df.replace() [OK]

Hint: Use df.replace() to swap inconsistent values [OK]

Common Mistakes:

• Using dropna() to replace values
• Confusing sorting with replacing
• Trying to group data to fix values

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'color': ['red', 'Red', 'blue', 'Blue', 'RED']})
df['color'] = df['color'].str.lower()

What will be the unique values in df['color'] after this code runs?

medium

A. ['red', 'blue']

B. ['Red', 'Blue', 'RED']

C. ['red', 'Red', 'blue', 'Blue', 'RED']

D. ['RED', 'BLUE']

Solution

1. Step 1: Understand str.lower() effect

   str.lower() converts all strings in the column to lowercase.

2. Step 2: Apply to values and find unique

   All 'red', 'Red', 'RED' become 'red'; 'blue', 'Blue' become 'blue'. Unique values are ['red', 'blue'].

3. Final Answer:

   ['red', 'blue'] -> Option A

4. Quick Check:

   Lowercase unique values = ['red', 'blue'] [OK]

Hint: str.lower() unifies case to find unique values [OK]

Common Mistakes:

• Thinking original case stays
• Assuming unique keeps all original variants
• Confusing str.lower() with str.upper()

4. What is wrong with this code snippet for fixing inconsistent 'gender' values?

df['gender'].replace({'M': 'Male', 'F': 'Female'})

Why might it not update the DataFrame as expected?

medium

A. Because replace() cannot handle dictionaries

B. Because replace() needs inplace=true or assignment to update df

C. Because 'M' and 'F' are not valid keys

D. Because replace() only works on numeric columns

Solution

1. Step 1: Check replace() behavior

   By default, replace() returns a new Series and does not change df unless inplace=true or assigned.

2. Step 2: Identify missing update step

   The code does not assign back or use inplace=true, so df['gender'] remains unchanged.

3. Final Answer:

   Because replace() needs inplace=true or assignment to update df -> Option B

4. Quick Check:

   replace() without assignment = no change [OK]

Hint: Always assign or use inplace=true with replace() [OK]

Common Mistakes:

• Assuming replace() changes df without assignment
• Thinking replace() can't use dict
• Believing replace() only works on numbers

5. You have a DataFrame with a 'status' column containing inconsistent values like 'Active', 'active', 'ACTIVE', and some misspelled as 'Actve'. Which sequence of pandas operations best cleans this column to have only 'Active' or 'Inactive' values?

hard

A. df['status'] = df['status'].str.strip().replace({'Actve': 'Inactive'})"

B. df['status'] = df['status'].replace({'Active': 'active', 'ACTIVE': 'active'}).str.upper()"

C. df['status'] = df['status'].fillna('Inactive').replace({'active': 'Active'})"

D. df['status'] = df['status'].str.lower().replace({'actve': 'active'}).str.capitalize()"

Solution

1. Step 1: Normalize case to lowercase

   Using str.lower() converts all variations like 'Active', 'ACTIVE' to 'active'.

2. Step 2: Fix misspelled value

   replace({'actve': 'active'}) corrects the misspelled 'actve' to 'active'.

3. Step 3: Capitalize for consistent formatting

   str.capitalize() changes 'active' to 'Active' for uniform appearance.

4. Final Answer:

   df['status'] = df['status'].str.lower().replace({'actve': 'active'}).str.capitalize() -> Option D

5. Quick Check:

   Lowercase + replace typo + capitalize = clean status [OK]

Hint: Lowercase, fix typos, then capitalize for consistent values [OK]

Common Mistakes:

• Not fixing misspelled values
• Skipping case normalization
• Replacing with wrong values