This is often how exam questions ask you to interpret code snippets.
import pandas as pd
import matplotlib.pyplot as plt
# 1. Loading Data
df = pd.read_csv('sales_data.csv')
# 2. Inspecting Data
print(df.head()) # Shows the first 5 rows
print(df.describe()) # Shows statistical summary (mean, max, min)
# 3. Data Cleaning
# Fill missing values in the 'price' column with the average price
df['price'] = df['price'].fillna(df['price'].mean())
# 4. Analysis
# Group data by 'region' and sum the 'sales'
region_sales = df.groupby('region')['sales'].sum()
# 5. Visualization
region_sales.plot(kind='bar')
plt.title('Total Sales by Region')
plt.xlabel('Region')
plt.ylabel('Total Sales')
plt.show()
Type: Presumed gene / protein / sequence identifier (SCDV10168)
Assumption: You want an engaging, structured feature summary for a biological identifier; no additional context provided — I’ll treat this as a gene/protein locus or sequence entry and produce a general, reusable feature profile. scdv10168
Purpose: This module introduces students to the fundamental concepts of Data Science, the data lifecycle, and the tools used to analyze and visualize data. It bridges the gap between raw data and actionable insights. This is often how exam questions ask you
Learning Outcomes: