Apprendre Challenge: Investigate Article Performance | Data Analysis and Visualization for Media

To make informed editorial decisions, you need to understand not just what content is published, but how it performs. Data analysis can help you uncover which types of articles engage your audience most. By investigating relationships between article characteristics—such as length or publication time—and engagement metrics, you can identify patterns that guide content planning and strategy.


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import pandas as pd

# Example DataFrame with article data
data = {
    "article_length": [500, 1500, 800, 2000, 1200],
    "engagement": [120, 340, 150, 410, 220]
}
df = pd.DataFrame(data)

# Calculate correlation between article length and engagement
correlation = df["article_length"].corr(df["engagement"])
print("Correlation between article length and engagement:", correlation)

# Interpret the correlation
if correlation > 0:
    interpretation = "Longer articles tend to get more engagement."
elif correlation < 0:
    interpretation = "Longer articles tend to get less engagement."
else:
    interpretation = "There is no relationship between article length and engagement."
print("Interpretation:", interpretation)

Insights from such analyses can directly inform your content strategy. If you discover that longer articles drive more engagement, you might prioritize in-depth reporting. Conversely, if shorter pieces perform better, you could focus on concise updates. Using data in this way allows you to align editorial choices with audience interests and maximize your newsroom's impact.

Tâche

Swipe to start coding

Write a script to analyze article performance using the provided DataFrame. Your script must:

Calculate the correlation between article_length and engagement.
Plot a scatter plot of article_length versus engagement.
Print an interpretation of whether longer articles tend to get more or less engagement, based on the correlation value.

Solution

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              123456789101112131415161718192021
            
import pandas as pd

# Example DataFrame with article data
data = {
    "article_length": [500, 1500, 800, 2000, 1200],
    "engagement": [120, 340, 150, 410, 220]
}
df = pd.DataFrame(data)

# Calculate correlation between article length and engagement
correlation = df["article_length"].corr(df["engagement"])
print("Correlation between article length and engagement:", correlation)

# Interpret the correlation
if correlation > 0:
    interpretation = "Longer articles tend to get more engagement."
elif correlation < 0:
    interpretation = "Longer articles tend to get less engagement."
else:
    interpretation = "There is no relationship between article length and engagement."
print("Interpretation:", interpretation)