Handling inconsistent values in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of filling missing values with mode

What is the output DataFrame after filling missing values in the 'color' column with the mode of that column?

Pandas

import pandas as pd

df = pd.DataFrame({'color': ['red', 'blue', None, 'blue', None, 'red']})
mode_color = df['color'].mode()[0]
df['color'] = df['color'].fillna(mode_color)
print(df)

A

   color
0    red
1   blue
2   blue
3   blue
4   blue
5    red

B

   color
0    red
1   blue
2    red
3   blue
4    red
5    red

C

   color
0    red
1   blue
2  None
3   blue
4  None
5    red

D

   color
0    red
1   blue
2   blue
3   blue
4  None
5    red

💡 Hint

The mode is the most frequent value in the column. Filling missing values replaces NaNs with this value.

The column has two modes ('red' and 'blue', both appearing twice); mode()[0] selects 'red' (first appearing). Filling missing values replaces None with 'red'.

❓ data_output

intermediate

Count unique values after standardizing text

After converting all entries in the 'fruit' column to lowercase, how many unique values remain?

Pandas

import pandas as pd

df = pd.DataFrame({'fruit': ['Apple', 'apple', 'Banana', 'BANANA', 'banana', 'Cherry']})
df['fruit'] = df['fruit'].str.lower()
unique_count = df['fruit'].nunique()
print(unique_count)

A 4

B 6

C 3

D 5

💡 Hint

Lowercasing makes 'Apple' and 'apple' the same value.

After lowercasing, the unique fruits are 'apple', 'banana', and 'cherry', totaling 3.

🔧 Debug

advanced

Identify error in replacing inconsistent categorical values

What error does this code raise when trying to replace inconsistent values in the 'status' column?

Pandas

import pandas as pd

df = pd.DataFrame({'status': ['Active', 'active', 'Inactive', 'inactive', 'Pending']})
df['status'] = df['status'].replace({'active': 'Active', 'inactive': 'Inactive'})
print(df)

A ValueError: cannot replace with different length

B KeyError: 'active' not found in axis

C TypeError: unhashable type: 'dict'

D

No error; output is:
  status
0  Active
1  Active
2  Inactive
3  Inactive
4  Pending

💡 Hint

Check if replace is case sensitive and if keys exist in the column.

The replace method works fine and replaces 'active' with 'Active' and 'inactive' with 'Inactive'. No error occurs.

❓ visualization

advanced

Visualizing inconsistent data before and after cleaning

Which plot correctly shows the count of each category in the 'department' column before and after fixing inconsistent capitalization?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({'department': ['Sales', 'sales', 'HR', 'hr', 'IT', 'it', 'IT']})
df['department_clean'] = df['department'].str.upper()
counts_before = df['department'].value_counts()
counts_after = df['department_clean'].value_counts()

plt.figure(figsize=(8,4))
plt.subplot(1,2,1)
counts_before.plot(kind='bar', title='Before Cleaning')
plt.subplot(1,2,2)
counts_after.plot(kind='bar', title='After Cleaning')
plt.tight_layout()
plt.show()

A Left plot shows 2 bars for 'Sales' and 'sales'; right plot shows 1 bar for 'SALES'.

B Left plot shows 1 bar for 'Sales'; right plot shows 2 bars for 'SALES' and 'IT'.

C Left plot shows 3 bars for 'Sales', 'HR', 'IT'; right plot shows 3 bars for 'SALES', 'HR', 'IT'.

D Left plot shows 6 bars for each entry; right plot shows 1 bar for all combined.

💡 Hint

Check how value_counts counts exact matches before and after uppercasing.

Before cleaning, 'Sales' and 'sales' are counted separately. After cleaning, both become 'SALES' and counted together.

🚀 Application

expert

Handling mixed data types in a column for analysis

Given a DataFrame column 'age' with mixed types (integers, strings, and NaNs), which option correctly converts all valid ages to integers and replaces invalid entries with the median age?

Pandas

import pandas as pd
import numpy as np

df = pd.DataFrame({'age': [25, '30', 'unknown', np.nan, 22, '27']})

# Your task: convert 'age' to integers, replace invalid with median

# median calculation should ignore invalid and NaN values

# Which option achieves this correctly?

A

df['age'] = pd.to_numeric(df['age'], errors='coerce')
median_age = int(df['age'].median())
df['age'] = df['age'].fillna(median_age).astype(int)
print(df)

B

df['age'] = df['age'].astype(int)
median_age = df['age'].median()
df['age'] = df['age'].fillna(median_age)
print(df)

C

df['age'] = df['age'].replace('unknown', 0).astype(int)
median_age = df['age'].median()
df['age'] = df['age'].fillna(median_age)
print(df)

D

df['age'] = df['age'].apply(lambda x: int(x) if x.isdigit() else np.nan)
median_age = df['age'].median()
df['age'] = df['age'].fillna(median_age).astype(int)
print(df)

💡 Hint

Use pd.to_numeric with errors='coerce' to convert and set invalid to NaN.

Option A converts all valid numbers to numeric, invalid to NaN, calculates median ignoring NaN, then fills NaN with median and converts to int.

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