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ADS1001 - Data challenges 1 - S1 2025
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Which of the following best describes how pd.concat() behaves by default?
Which best describes aggregation in data analysis?
After grouping and aggregating, what typically happens to the number of rows in the dataset?
What will be the output of the following code?
import pandas as pd
data = {'Category': ['A', 'A', 'B', 'B', 'C'], 'Value': [5, 15, 25, 35, 45], 'Score': [2, 4, 6, 8, 10]}
df = pd.DataFrame(data)
result = df.groupby('Category').agg({'Value': 'sum', 'Score': 'mean'})
print(result)
What will be the output of the following code?
import pandas as pd
data = {'Category': ['A', 'A', 'B', 'B', 'C'], 'Subcategory': ['X', 'Y', 'X', 'Y', 'X'], 'Value': [10, 20, 30, 40, 50]}
df = pd.DataFrame(data)
result = df.pivot(index='Category', columns='Subcategory', values='Value')
print(result)
Which statement best describes the relationship between grouping and aggregation in data analysis?
What is Seaborn?
What will this code produce?
import seaborn as snssns.pairplot(sns.load_dataset("iris"), hue="species")
Which of the following best describes the purpose of the hue parameter in Seaborn plots?
Which one of the following statements is true based on this heatmap of correlations?