Quiz 11 Python for Data Analyst

Answer this question. 1. If you want to replace all 'math score' values below 50 with the median values, which of the following commands would you use? A. df['math score'] = df['math score'].apply('math score'.median()) B. df.loc[df['math score'] < 50, 'math score'] = df['math score'].median() C. df['math score'] < 50 = df['math score'].median() D. df['math score'].replace(df['math score'] < 50, df['math score'].median()) 2. How would you find the Interquartile Range (IQR) for 'math score' using Pandas? A. np.percentile(df['math score'], [75, 25]) B. df['math score'].describe() C. df['math score'].quantile(0.75) - df['math score'].quantile(0.25)

D. df['math score'].quantile([0.25, 0.75]) 3. Should you want to visualize the relationship between 'reading score' and 'writing score' for each 'gender' category, which Seaborn command would be most appropriate? A. sns.scatterplot(x='reading score', y='writing score', hue='gender', data=df) B. sns.pairplot(df, hue='gender') C. sns.relplot(x='reading score', y='writing score', hue='gender', data=df) D. sns.boxplot(x='reading score', y='writing score', hue='gender', data=df) 4. How can you display the 'math score' distribution for each 'gender' category using Seaborn? A. sns.kdeplot(data=df, x='math score', hue='gender') B. sns.distplot(df['math score'], hue=df['gender']) C. sns.boxplot(x='gender', y='math score', data=df) D. sns.violinplot(x='gender', y='math score', data=df)