Exploratory Data Analysis (EDA) template in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code snippet for missing value count?

Given a DataFrame df with some missing values, what does the following code output?

Data Analysis Python

import pandas as pd
import numpy as np
data = {'A': [1, 2, np.nan, 4], 'B': [np.nan, 2, 3, 4]}
df = pd.DataFrame(data)
missing_counts = df.isnull().sum()
print(missing_counts)

A

A    2
B    2
dtype: int64

B

A    0
B    0
dtype: int64

C

A    1
B    2
dtype: int64

D

A    1
B    1
dtype: int64

💡 Hint

Use isnull() to find missing values, then sum() counts them per column.

The isnull() method marks missing values as True. Summing counts Trues per column. Column 'A' has 1 missing, 'B' has 1 missing.

❓ data_output

intermediate

What is the shape of the DataFrame after filtering?

Consider this DataFrame df. After filtering rows where column 'Age' is greater than 30, what is the shape of the resulting DataFrame?

Data Analysis Python

import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'], 'Age': [25, 35, 30, 40]}
df = pd.DataFrame(data)
filtered_df = df[df['Age'] > 30]
print(filtered_df.shape)

A (3, 2)

B (2, 2)

C (1, 2)

D (4, 2)

💡 Hint

Count rows where 'Age' > 30.

Only Bob (35) and David (40) have Age > 30, so 2 rows remain with 2 columns.

❓ visualization

advanced

Which plot shows the distribution of 'Salary' correctly?

You have a DataFrame df with a 'Salary' column. Which code snippet produces a histogram of 'Salary' with 10 bins?

A df['Salary'].hist(bins=10)

B df.hist(column='Salary', bins=10)

C df['Salary'].plot(kind='hist', bins=10)

D All of the above

💡 Hint

All these methods create histograms with 10 bins.

Each option uses pandas plotting to create a histogram of 'Salary' with 10 bins. All are valid and produce similar plots.

🔧 Debug

advanced

What error does this code raise?

What error occurs when running this code?

Data Analysis Python

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)
mean_val = df['C'].mean()

A KeyError

B AttributeError

C TypeError

D No error, returns NaN

💡 Hint

Check if column 'C' exists in the DataFrame.

Accessing a non-existent column 'C' raises a KeyError in pandas.

🚀 Application

expert

Which code snippet correctly calculates the correlation matrix?

Given a DataFrame df with numeric columns, which code correctly computes the correlation matrix?

Data Analysis Python

import pandas as pd
data = {'X': [1, 2, 3, 4], 'Y': [4, 3, 2, 1], 'Z': [10, 20, 30, 40]}
df = pd.DataFrame(data)

A

corr_matrix = df.corr()
print(corr_matrix)

B

corr_matrix = pd.corr(df)
print(corr_matrix)

C

corr_matrix = df.corrcoef()
print(corr_matrix)

D

corr_matrix = df.correlation()
print(corr_matrix)

💡 Hint

Use the pandas method for correlation matrix.

The correct pandas method is corr(). Other options are invalid or do not exist.