Mastering Data Preparation with Pandas

What is the purpose of data cleaning in pandas?

• To automate data entry processes in pandas.

• To reduce the size of the dataset in pandas.

• The purpose of data cleaning in pandas is to improve data quality by correcting inaccuracies and inconsistencies.

• To enhance data visualization in pandas.

Name two common techniques for data cleaning in pandas.

• Encoding categorical variables and feature scaling

• Visualizing data and creating reports

• Handling missing values and removing duplicates

• Normalizing data and aggregating values

How can you convert a column's data type in a DataFrame?

• Use the 'convert()' function on the DataFrame.

• Change the data type in the DataFrame settings.

• Use the 'astype()' method on the DataFrame column.

• Apply the 'transform()' method to the DataFrame column.

How do you merge two DataFrames on a common column?

• Join df1 and df2 using df1.append(df2)

• Combine df1 and df2 with df1 + df2

• Use pd.merge(df1, df2, on='common_column')

• Use df1.concat(df2, axis=1)

How can you reset the index of a DataFrame after a groupby operation?

• df.agg('function').groupby('column_name').reset()

• df.reset_index().groupby('column_name')

• df.groupby('column_name').agg('function').reset_index()

• df.groupby('column_name').sum()

What function would you use to concatenate two DataFrames vertically?

• Use df1 + df2

• Use pd.concat([df1, df2])

• Use df1.merge(df2)

• Use df1.append(df2)

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

• Use df[df['column'] == value]

• Use df.select('condition')

• Use df.query('condition')

• Use df.filter('condition')

What method would you use to group data by multiple columns in a DataFrame?

• Use df.groupby(['column1', 'column2'])

• Use df.group(['column1', 'column2'])

• Use df.aggregate(['column1', 'column2'])

• Use df.split(['column1', 'column2'])

What function can be used to remove duplicate rows from a DataFrame?

• Use df.drop_duplicates()

• Use df.unique_rows()

• Use df.delete_duplicates()

• Use df.remove_duplicates()

How can you check for missing values in a DataFrame?

• Use df.has_nulls()

• Use df.missing_values()

• Use df.check_missing()

• Use df.isnull().sum()