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.