EDA_DataCleaning

Which of the following methods gives a quick statistical summary of Statistical columns in a DataFrame?

• df.head()

• df.describe()

• df.info()

• df.value_counts()

Which method removes all rows that contain at least one NaN value?

• df.remove_na()

• df.dropna()

• df.clean()

• df.fillna(0)

Which of the following fills all missing values in a column with 0?

• df.fillna(0)

• df.dropna(0)

• df.replace(0)

• df.nullfill(0)

What will df.isnull().sum() return?

• Boolean DataFrame

• Number of nulls per row

• Number of nulls per column

• Total null count in the DataFrame

How to fill missing values in column 'age' with the column’s median?

• df['age'].fillna('median')

• df['age'].fillna(df['age'].mean())

• df['age'].fillna(df['age'].median())

• df.fill_missing(df['age'])

Which command gives total number of missing values in entire DataFrame?

• df.countna()

• df.isnull().sum()

• df.isnull().sum().sum()

• df.check().na()

Which method forward fills missing values?

• df.fillna(method='backward')

• df.fillna(method='forward')

• df.fillna(method='ffill')

• df.ffill

  ()

Which method backward fills missing values?

• df.fillna(method='bfill')

• df.backfill()

• df.bfill()

• df.reversefill()

What does df.dropna(how='all') do?

• Drops rows where any value is missing

• Drops rows where all values are missing

• Drops columns where any value is missing

• Drops all missing values only in numeric columns

To remove columns that contain any null values:

• df.drop(columns_with_null=True)

• df.dropna(axis=1)

• df.drop(axis=0)

• df.columns.dropna()