Data Cleaning and Preparation Flashcard

Front

Which of the following is NOT a part of data cleaning? Handling missing values, Removing duplicates, Correcting inconsistent data

Back

Applying machine learning algorithms

Front

What is the main disadvantage of dropping rows with missing values?

Back

It reduces the size of the dataset, potentially leading to data loss

Front

Which of the following methods is best suited for handling categorical variables with many unique values? Label encoding, One-hot encoding, Frequency encoding, Min-max normalization

Back

Frequency encoding

Front

Why is feature scaling important in data preparation?

Back

It ensures that numerical features contribute equally to distance-based models

Front

If a dataset has a column with typos such as ["Male", "male", "M", "FEMALE", "female", "F"], which data cleaning technique should be applied?

Back

Convert all values to lowercase and map variations to standard categories

Front

Given the following DataFrame, how do you replace all missing values in the "Age" column with the median value? Options: df['Age'].fillna(df['Age'].mean(), inplace=True), df['Age'].fillna(df['Age'].median(), inplace=True), df['Age'].replace(np.nan, 0, inplace=True), df.dropna(subset=['Age'], inplace=True)

Back

df['Age'].fillna(df['Age'].median(), inplace=True)

Front

How can you remove duplicate rows from a DataFrame while keeping only the last occurrence?

Back

df.drop_duplicates(keep='last', inplace=True)

Front

What is the correct way to standardize the "Price" column in the following DataFrame using z-score normalization? Options: df['Price'] = (df['Price'] - df['Price'].mean()) / df['Price'].std(), df['Price'] = (df['Price'] - df['Price'].min()) / (df['Price'].max() - df['Price'].min()), df['Price'] = df['Price'] / df['Price'].mean(), df['Price'] = df['Price'] - df['Price'].median()

Back

df['Price'] = (df['Price'] - df['Price'].mean()) / df['Price'].std()

Front

What is the correct way to convert the "Category" column into one-hot encoding? Options: df = pd.get_dummies(df, columns=['Category']), df['Category'] = df['Category'].astype('category').cat.codes, df['Category'] = df.replace({'A': 0, 'B': 1, 'C': 2}), df['Category'] = df.apply(lambda x: hash(x['Category']), axis=1)

Back

df = pd.get_dummies(df, columns=['Category'])

Front

Given a column "Salary", what is the correct way to remove outliers using the Interquartile Range (IQR) method?

Back

Q1 = df['Salary'].quantile(0.25)