Exploring Python Dataframes

What is a DataFrame in Python?

• A DataFrame is a type of database in SQL.

• A DataFrame is a one-dimensional array in NumPy.

• A DataFrame is a two-dimensional labeled data structure in pandas.

• A DataFrame is a graphical representation of data in matplotlib.

How do you create a DataFrame using pandas?

• You can create a DataFrame by importing it from a CSV file directly.

• Use pd.DataFrame(data) where data can be a dictionary, list, or NumPy array.

• DataFrame(data) is the correct syntax in pandas.

• Use pd.create_dataframe(data) for creating a DataFrame.

What function is used to read a CSV file into a DataFrame?

• pandas.load_csv

• pandas.import_csv

• pandas.read_csv

• pandas.read_file

How can you display the first five rows of a DataFrame?

• df.first(5)

• df.head(5)

• df.take(5)

• df.slice(0, 5)

What method would you use to get the shape of a DataFrame?

• df.size

• df.describe()

• df.info()

• df.shape

How do you select a specific column from a DataFrame?

• Use df.select('column_name') to get a column.

• Use df['column_name'] or df.column_name to select a specific column.

• Access df.column('column_name') to retrieve a column.

• Call df.get_column('column_name') for selection.

What is the purpose of the 'head()' method in a DataFrame?

• To return the last n rows of a DataFrame.

• To return a summary of the DataFrame's statistics.

• To return the first n rows of a DataFrame.

• To filter rows based on a specific condition.

How can you filter rows in a DataFrame based on a condition?

• Invoke df.rows(condition) to filter the DataFrame.

• Use df[df['column_name'] condition] to filter rows.

• Apply df.select(condition) to get the desired rows.

• Use df.filter('column_name', condition) to filter rows.

How can you rename columns in a DataFrame?

• Use df.set_column_names(['new_name1', 'new_name2'])

• Use df.change_columns({'old_name': 'new_name'})

• Use df.update_columns(['new_name1', 'new_name2'])

• Use df.rename(columns={'old_name': 'new_name'}) or df.columns = ['new_name1', 'new_name2', ...]

How do you handle missing values in a DataFrame?

• Delete the entire DataFrame if it contains any missing values.

• Replace missing values with the mean of the entire DataFrame.

• Use methods like dropna(), fillna(), or interpolation to handle missing values.

• Ignore missing values and proceed with analysis.