Exploratory data analysis workflow in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of basic data summary

What is the output of the following code snippet that summarizes a DataFrame?

Pandas

import pandas as pd

data = {'Age': [25, 30, 22, 40, 28], 'Salary': [50000, 60000, 45000, 80000, 52000]}
df = pd.DataFrame(data)
summary = df.describe()
print(summary)

A {'Age': {'count': 5.0, 'mean': 29.0, 'std': 7.071, 'min': 22.0, '25%': 25.0, '50%': 28.0, '75%': 30.0, 'max': 40.0}, 'Salary': {'count': 5.0, 'mean': 57400.0, 'std': 13435.028, 'min': 45000.0, '25%': 50000.0, '50%': 52000.0, '75%': 60000.0, 'max': 80000.0}}

B

   Age  Salary
0   25   50000
1   30   60000
2   22   45000
3   40   80000
4   28   52000

C

       Age        Salary
count   5.0      5.000000
mean   29.0  57400.000000
std     7.071 13435.028
min    22.0  45000.000000
25%    25.0  50000.000000
50%    28.0  52000.000000
75%    30.0  60000.000000
max    40.0  80000.000000

D

Age       29.0
Salary  57400.0
dtype: float64

💡 Hint

Look at the output of pandas DataFrame describe() method.

The describe() method returns a DataFrame with count, mean, std, min, quartiles, and max for each numeric column.

❓ data_output

intermediate

Count of missing values per column

Given the DataFrame below, what is the output of counting missing values per column?

Pandas

import pandas as pd
import numpy as np

data = {'Name': ['Alice', 'Bob', None, 'David'], 'Age': [25, None, 22, 40], 'Salary': [50000, 60000, 45000, None]}
df = pd.DataFrame(data)
missing_counts = df.isnull().sum()
print(missing_counts)

A

Name      1
Age       1
Salary    1
dtype: int64

B

Name      1
Age       0
Salary    1
dtype: int64

C

Name      0
Age       1
Salary    1
dtype: int64

D

Name      1
Age       1
Salary    0
dtype: int64

💡 Hint

Check which values are None or np.nan in each column.

The isnull() method marks None as missing. Each column has exactly one missing value.

❓ visualization

advanced

Correct histogram plot code

Which option produces a histogram plot of the 'Age' column with 5 bins using matplotlib?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'Age': [25, 30, 22, 40, 28, 35, 33, 31, 29, 27]}
df = pd.DataFrame(data)

# Choose the correct code to plot histogram

A

df.plot.hist('Age', bins=5)
plt.show()

B

df['Age'].plot.hist(bins=5)
plt.show()

C

plt.plot(df['Age'], bins=5)
plt.show()

D

plt.hist(df['Age'], bins=5)
plt.show()

💡 Hint

matplotlib's hist function takes data and bins as arguments.

plt.hist() is the correct matplotlib function to plot a histogram. Option D uses pandas plotting but requires plt.show(). Option D is invalid because plt.plot does not accept bins. Option D is invalid because df.plot.hist expects no argument or column name as parameter.

🔧 Debug

advanced

Identify error in groupby aggregation

What error does the following code raise?

Pandas

import pandas as pd

data = {'Category': ['A', 'B', 'A', 'B'], 'Value': [10, 20, 15, 25]}
df = pd.DataFrame(data)
result = df.groupby('Category').agg({'Value': 'sum', 'NonExistent': 'mean'})
print(result)

A KeyError: 'NonExistent'

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C ValueError: No numeric types to aggregate

D No error, prints grouped sums and means

💡 Hint

Check if all keys in agg dictionary exist in DataFrame columns.

The aggregation dictionary references a column 'NonExistent' which is not in the DataFrame, causing a KeyError.

🚀 Application

expert

Calculate correlation matrix and interpret output

Given the DataFrame below, what is the correlation coefficient between 'X' and 'Y'?

Pandas

import pandas as pd
import numpy as np

data = {'X': [1, 2, 3, 4, 5], 'Y': [2, 4, 6, 8, 10], 'Z': [5, 4, 3, 2, 1]}
df = pd.DataFrame(data)
corr_matrix = df.corr()
print(round(corr_matrix.loc['X', 'Y'], 2))

A -1.00

B 1.00

C 0.00

D 0.50

💡 Hint

Check if Y is a perfect linear function of X.

Y is exactly 2 times X, so correlation is perfect positive 1.00.

Practice

1. Which step is usually done first in an exploratory data analysis workflow?

easy

A. Viewing the first few rows of the dataset

B. Creating complex machine learning models

C. Removing all missing values immediately

D. Generating final reports without checking data

Solution

1. Step 1: Understand the initial data

   Exploratory data analysis starts by looking at the first few rows to get a feel for the data structure and values.

2. Step 2: Avoid premature actions

   Jumping to modeling or removing missing data without understanding can cause mistakes.

3. Final Answer:

   Viewing the first few rows of the dataset -> Option A

4. Quick Check:

   First step = View data sample [OK]

Hint: Always start by looking at data samples [OK]

Common Mistakes:

• Starting with modeling before understanding data
• Removing missing values without checking their impact
• Ignoring data overview and jumping to conclusions

2. Which pandas command correctly shows summary statistics of numeric columns in a DataFrame df?

easy

A. df.head()

B. df.info()

C. df.describe()

D. df.sample()

Solution

1. Step 1: Identify summary statistics function

   df.describe() returns count, mean, std, min, max, and quartiles for numeric columns.

2. Step 2: Differentiate other commands

   df.head() shows first rows, df.info() shows data types, and df.sample() shows random rows.

3. Final Answer:

   df.describe() -> Option C

4. Quick Check:

   Summary stats = df.describe() [OK]

Hint: Use df.describe() for quick numeric summaries [OK]

Common Mistakes:

• Using df.head() to get statistics
• Confusing df.info() with statistical summary
• Expecting df.sample() to summarize data

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

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': 1, 'C': 0}

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

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

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

Solution

1. Step 1: Check missing values per column

   Column 'A' has one None, 'B' has one None, 'C' has none.

2. Step 2: Use isnull().sum() to count missing

   This method counts missing values per column, so output is {'A': 1, 'B': 1, 'C': 0}.

3. Final Answer:

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

4. Quick Check:

   Missing counts = {'A':1,'B':1,'C':0} [OK]

Hint: Count missing with df.isnull().sum() [OK]

Common Mistakes:

• Counting total missing instead of per column
• Confusing None with zero or empty string
• Misreading which columns have missing values

4. You run df.info() but get an error: AttributeError: 'DataFrame' object has no attribute 'infoo'. What is the likely cause?

medium

A. The DataFrame is empty

B. You typed the method name incorrectly

C. The pandas library is not imported

D. The DataFrame has no columns

Solution

1. Step 1: Check the error message

   The error says 'infoo' attribute not found, which suggests a typo in method name.

2. Step 2: Confirm correct method name

   The correct method is df.info() with one 'o'.

3. Final Answer:

   You typed the method name incorrectly -> Option B

4. Quick Check:

   Typo in method name = error [OK]

Hint: Check spelling of method names carefully [OK]

Common Mistakes:

• Assuming empty DataFrame causes this error
• Forgetting to import pandas
• Thinking DataFrame must have columns to use info()

5. You have a DataFrame df with columns ['Age', 'Salary', 'Department']. You want to find the average salary per department but only for employees older than 30. Which pandas workflow is correct?

hard

A. df.groupby('Department')['Salary'].mean()

B. df['Salary'].mean().groupby('Department')[df['Age'] > 30]

C. df.groupby('Department').mean()['Salary'][df['Age'] > 30]

D. df[df['Age'] > 30].groupby('Department')['Salary'].mean()

Solution

1. Step 1: Filter rows where Age > 30

   Use df[df['Age'] > 30] to keep only employees older than 30.

2. Step 2: Group filtered data by Department and calculate mean Salary

   Apply .groupby('Department')['Salary'].mean() on filtered data to get average salary per department.

3. Final Answer:

   df[df['Age'] > 30].groupby('Department')['Salary'].mean() -> Option D

4. Quick Check:

   Filter then group and mean = df[df['Age'] > 30].groupby('Department')['Salary'].mean() [OK]

Hint: Filter first, then group and aggregate [OK]

Common Mistakes:

• Grouping before filtering data
• Trying to filter after aggregation
• Using mean() without grouping