Data Science with Python Exploratory Data Analysis Flashcards
7 cards from real Data Science with Python Certification practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
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Which pandas method returns the number of non-null entries for each column in a DataFrame?
Answer: df.count()
df.count() returns the count of non-null values for each column by default.
What does a box plot's IQR represent?
Answer: The middle 50% of the data between Q1 and Q3
The IQR (Interquartile Range) is the span between the first quartile (Q1) and third quartile (Q3), covering the middle 50% of data.
In pandas, what does df.duplicated().sum() compute?
Answer: The total number of duplicate rows in the DataFrame
df.duplicated() returns a boolean Series marking duplicate rows, and .sum() counts how many are True.
Which seaborn plot is best for visualizing the relationship between two continuous variables along with their marginal distributions?
Answer: sns.jointplot()
sns.jointplot() shows a scatter plot of two variables plus their individual marginal histograms or KDEs.
What is the primary purpose of using df.describe() on a DataFrame containing both numeric and object columns when include='all' is passed?
Answer: It provides summary statistics for both numeric and categorical columns
Passing include='all' forces describe() to include object columns, showing count, unique, top, and freq alongside numeric stats.
Which transformation is most appropriate to reduce right skewness in a continuous variable before EDA visualization?
Answer: Applying a log transformation
A log transformation compresses large values and expands small ones, which reduces right (positive) skew.
When using pandas, what does df.groupby('category')['value'].agg(['mean','std']) return?
Answer: A DataFrame with mean and std for each category group
agg() with a list of functions returns a DataFrame where each row is a group and each column is an aggregated statistic.
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