Which Pandas function is used to read a CSV file into a DataFrame?, df.read_csv(), pd.load_csv(), pd.read_csv(), pd.open_csv(), Which statement about df.head() is correct?, It returns the first few rows of a DataFrame., It removes the first few rows., It returns only the column names., It calculates summary statistics., Which method is used to identify the number of missing values in each column?, df.dropna(), df.isnull().sum(), df.fillna(), df.describe(), Identify the INCORRECT statement., df.shape returns the number of rows and columns., df.info() provides information about data types and non-null counts., df.describe() provides summary statistics for numeric columns., df.columns() is a method used to calculate column statistics., Which method is most appropriate for finding the frequency of each category in a column such as Pclass?, df['Pclass'].mean(), df['Pclass'].describe(), df['Pclass'].value_counts(), df['Pclass'].shape, True or False: df.loc[2:8] and df.iloc[2:8] necessarily return exactly the same rows., True, False, Which method is used to replace missing values with a specified value such as the mean or mode?, fillna(), dropna(), drop_duplicates(), value_counts(), Which visualization is most suitable for identifying the distribution, skewness, and spread of a numeric variable such as Age?, sns.countplot(), sns.scatterplot(), sns.histplot(), sns.heatmap(), Consider the following code: df.query('Pclass > 2 and Fare > 10 and Fare < 50')[['Ticket', 'Name', 'Fare']] What is the primary purpose of this statement?, It calculates the average Fare for all passengers., It selects passengers satisfying multiple conditions and returns selected columns., It removes passengers whose Fare is outside the given range., It creates a correlation matrix between Pclass and Fare., A dataset contains missing Age values. The analyst wants to replace them with the mean age. Which sequence is most appropriate?, mean_age = df['Age'].mode(); df['Age'].dropna(mean_age), mean_age = df['Age'].mean(); df['Age'].fillna(mean_age, inplace=True), mean_age = df['Age'].value_counts(); df['Age'].ffill(mean_age), mean_age = df['Age'].describe(); df['Age'].drop_duplicates(mean_age), A dataset contains numeric features Age and Fare with very different scales. Which pairing correctly describes the scaling methods?, MinMaxScaler → zero mean and unit variance; StandardScaler → [0,1] range, MinMaxScaler → removes missing values; StandardScaler → removes duplicates, MinMaxScaler → [0,1] range; StandardScaler → zero mean and unit variance, MinMaxScaler → categorical encoding; StandardScaler → correlation analysis, A student wants to study the relationship between Age and Fare, while using Survived to distinguish groups visually. Which approach is most appropriate?, sns.countplot(x='Age', hue='Fare', data=df), sns.histplot(df['Survived']), sns.boxplot(x='Age', y='Fare', data=df), sns.scatterplot(x='Age', y='Fare', hue='Survived', data=df)

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