Why end-to-end analysis matters in Pandas - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

Understanding data flow in end-to-end analysis

What is the output of the following code that simulates a simple end-to-end data analysis pipeline?

Pandas

import pandas as pd

data = {'sales': [100, 200, 300, 400], 'cost': [50, 80, 120, 150]}
df = pd.DataFrame(data)
df['profit'] = df['sales'] - df['cost']
total_profit = df['profit'].sum()
print(total_profit)

A 600

B 850

C 1000

D 900

💡 Hint

Calculate profit for each row, then add all profits.

The profit column is sales minus cost: [50, 120, 180, 250]. Summing these gives 600.

❓ data_output

intermediate

Result of filtering and aggregation in analysis

Given the DataFrame below, what is the output after filtering rows where 'age' > 30 and calculating the mean of 'income'?

Pandas

import pandas as pd

data = {'name': ['Anna', 'Bob', 'Cara', 'Dan'], 'age': [25, 35, 45, 28], 'income': [50000, 60000, 70000, 55000]}
df = pd.DataFrame(data)
filtered = df[df['age'] > 30]
mean_income = filtered['income'].mean()
print(mean_income)

A 70000.0

B 60000.0

C 65000.0

D 57500.0

💡 Hint

Only include rows with age above 30, then average their income.

Rows with age > 30 are Bob (60000) and Cara (70000). Their average is (60000 + 70000) / 2 = 65000.

❓ visualization

advanced

Identifying trends with visualization in end-to-end analysis

Which plot best shows the trend of monthly sales over time from the DataFrame below?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'month': ['Jan', 'Feb', 'Mar', 'Apr'], 'sales': [200, 220, 250, 270]}
df = pd.DataFrame(data)
plt.plot(df['month'], df['sales'])
plt.title('Monthly Sales Trend')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.show()

A Bar plot showing sales decreasing from Jan to Apr

B Pie chart showing sales distribution by month

C Scatter plot showing random sales values

D Line plot showing sales increasing from Jan to Apr

💡 Hint

Look for a plot that connects points to show change over time.

A line plot connects points in order, showing the upward trend in sales from January to April.

🔧 Debug

advanced

Finding the error in a data cleaning step

What error does the following code raise when trying to remove rows with missing values?

Pandas

import pandas as pd

data = {'A': [1, 2, None], 'B': [4, None, 6]}
df = pd.DataFrame(data)
df_clean = df.dropna(inplace=True)
print(df_clean)

A None (prints None because dropna with inplace=True returns None)

B AttributeError because dropna is not a DataFrame method

C TypeError because inplace must be False

D KeyError because 'inplace' is not a valid argument

💡 Hint

Check what dropna returns when inplace=True is used.

dropna with inplace=True modifies df in place and returns None. So df_clean is None and printing it shows None.

🚀 Application

expert

Why end-to-end analysis is crucial for decision making

You have sales data with missing values and outliers. Which approach best represents an end-to-end analysis to prepare data for reliable insights?

A Visualize raw data without cleaning to avoid bias

B Clean missing data, remove outliers, analyze trends, and visualize results

C Analyze trends first, then clean missing data and remove outliers

D Remove outliers only, ignoring missing data and analysis

💡 Hint

Think about the logical order of preparing data before analysis and visualization.

End-to-end analysis means cleaning data first (missing values, outliers), then analyzing and visualizing to get reliable insights.