Why exploratory inspection guides analysis in Data Analysis Python - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why is exploratory data inspection important before analysis?

Which of the following best explains why we perform exploratory data inspection before starting formal analysis?

A To finalize conclusions without looking at the data distribution.

B To understand data quality, spot missing values, and detect unusual patterns that affect analysis.

C To immediately apply machine learning models without checking data.

D To skip data cleaning and jump directly to visualization.

💡 Hint

Think about what problems might exist in raw data that could mislead your analysis.

Exploratory inspection helps identify data issues like missing values or outliers early. This ensures analysis is based on clean, reliable data.

❓ data_output

intermediate

Output of basic exploratory commands on a dataset

Given the dataset below, what is the output of df.describe()?

import pandas as pd
data = {'age': [25, 30, 22, 40, 28], 'income': [50000, 60000, 45000, 80000, 52000]}
df = pd.DataFrame(data)
print(df.describe())

A

       age        income
count   5.0      5.000000
mean   29.0  57400.000000
std     7.0  13629.969289
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

B

       age        income
count   4.0      4.000000
mean   29.0  57400.000000
std     7.0  13629.969289
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

C

       age        income
count   5.0      5.000000
mean   30.0  57400.000000
std     7.0  13629.969289
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        income
count   5.0      5.000000
mean   29.0  57000.000000
std     7.0  13629.969289
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

💡 Hint

Check the count and mean values carefully for both columns.

The describe() method shows count, mean, std, min, quartiles, and max for numeric columns. The count is 5 for both columns since there are 5 rows.

❓ visualization

advanced

Identifying outliers with boxplot visualization

Which boxplot below correctly shows an outlier in the dataset [10, 12, 12, 13, 14, 15, 100]?

Data Analysis Python

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

data = [10, 12, 12, 13, 14, 15, 100]
df = pd.DataFrame({'values': data})
sns.boxplot(x='values', data=df)
plt.show()

A Boxplot with whiskers extending beyond 100.

B Boxplot showing multiple outliers below 10.

C Boxplot with a single point far above the upper whisker at 100, indicating an outlier.

D Boxplot with no points outside whiskers, all data within range 10 to 100.

💡 Hint

Outliers appear as points outside the whiskers in a boxplot.

The value 100 is much larger than the rest and appears as a single outlier point above the whisker in the boxplot.

🔧 Debug

advanced

Error in inspecting missing data with pandas

What error does the following code produce?

import pandas as pd
data = {'A': [1, 2, None], 'B': [4, None, 6]}
df = pd.DataFrame(data)
print(df.isnull().sum())
print(df.missing())

A No error, prints counts of missing values

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

C KeyError: 'missing'

D AttributeError: 'DataFrame' object has no attribute 'missing'

💡 Hint

Check if missing() is a valid pandas DataFrame method.

There is no missing() method in pandas DataFrame. The correct method to check missing values is isnull().

🚀 Application

expert

Choosing next steps after exploratory inspection

You have a dataset with many missing values in some columns and a few extreme outliers in others. After exploratory inspection, what is the best next step?

A Remove or impute missing values and consider transforming or removing outliers before analysis.

B Ignore missing values and outliers and proceed with analysis as is.

C Only visualize data without cleaning or transformation.

D Delete entire dataset and start over with new data.

💡 Hint

Think about how missing data and outliers affect analysis results.

Cleaning missing data and handling outliers ensures more accurate and reliable analysis results.