University:
American Baptist College
Course:
CSCI 1534 | Data Analysis and Visualization
Academic year:

2023
Views:

290

Pages:

3
Author:

Muthia M.

Answer this question. 1. To get a quick overview of the DataFrame, including the number of non-null entries in each column, which command would you use? A. df.info() B. df.describe() C. df.summary() D. df.overview() 2. To examine the unique values in the 'gender' column of this DataFrame, which of the following commands would you use? A. df['gender'].values() B. df['gender'].unique() C. df['gender'].distinct() D. df.unique('gender') 3. How would you group the dataset by 'race/ethnicity' and find the mean of 'math score', 'reading score', and 'writing score' for each group? A. df.groupby('race/ethnicity').mean() B. df.group_by('race/ethnicity').mean() C. df.groupby('race/ethnicity').average() D. df.mean().groupby('race/ethnicity') 4. If you want to create a bar chart of 'parental level of education' counts using Seaborn, which command would you use? A. sns.plot(data=df, x='parental level of education') B. sns.barplot(data=df, x='parental level of education') C. sns.countplot(data=df, x='parental level of education') D. sns.histplot(data=df, x='parental level of education') 5. How can you determine the correlation between 'math score' and 'writing score'? A. df['math score'].corr(df['writing score']) B. df.corr()['math score', 'writing score'] C. df['math score'].correlation('writing score') D. df['math score', 'writing score'].corr()