Data Science with Python Exploratory Data Analysis Questions and Answers

Question 1

A data scientist wants to get a quick overview of the central tendency, dispersion, and shape of the distribution of all numerical columns in a pandas DataFrame called `df_numeric`.
Which of the following methods is most direct and conventional for this task?

Accepted Answer

df_numeric.describe()

Answer

df_numeric.info()

Answer

df_numeric.mean()

Answer

df_numeric.hist()

Question 2

When performing EDA on a DataFrame with a categorical column 'Status', which pandas method is most suitable for quickly determining the number of occurrences for each unique category?

Accepted Answer

df['Status'].value_counts()

Answer

df['Status'].unique()

Answer

df['Status'].count()

Answer

df.groupby('Status').count()

Question 3

A data analyst is investigating the relationship between two continuous variables, 'engine_size' and 'co2_emissions'. Which type of plot is the most appropriate and standard choice for visualizing this relationship?

Accepted Answer

Scatter Plot

Answer

Histogram

Answer

Box Plot

Answer

Bar Chart

Question 4

You are analyzing a correlation heatmap generated using Seaborn to understand the relationships between numerical features in a dataset. What does a cell with a bright, dark red color and an annotation of `0.85` typically signify (using a standard 'coolwarm' or similar diverging colormap)?

Accepted Answer

A strong positive correlation between the two variables.

Answer

A weak negative correlation between the two variables.

Answer

No correlation between the two variables.

Answer

A perfect negative correlation between the two variables.

Question 5

Which of the following visualizations is most effective for summarizing the distribution of a single numerical variable by showing its median, quartiles, and specifically highlighting potential outliers?

Accepted Answer

Box Plot

Answer

Line Plot

Answer

Pie Chart

Answer

Scatter Plot

Question 6

A data scientist wants to visualize the frequency distribution of a single continuous variable, `age`, from a pandas DataFrame. Which Seaborn function is primarily used for this purpose?

Accepted Answer

seaborn.histplot(data=df, x='age')

Answer

seaborn.boxplot(x=df['age'])

Answer

seaborn.scatterplot(x=df['age'], y=df['income'])

Answer

seaborn.countplot(data=df, x='age')