This quiz is to check your understanding for strategies we've covered for detecting and dealing with missing data.

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?: Which of following is a problem that missing data could cause?

( ) Some visualizations will not work with missing data (X) Some types of data cleaning steps won't work with null values ( ) Machine learning models will break when they encounter null values ( ) All of the above

?: How does pandas represent missing values when displaying a DataFrame or Series object?

(X)

  NaN

( )

Null

( )

None

( )

"Missing Value"

?: What will the following code snippet return?

some_dataframe.isna()

( ) A count of the missing values in each column ( ) A count of the missing values in each row ( ) A single boolean value True if the DataFrame contains one or more missing values, or False if there are none.
(X) A corresponding dataframe of boolean values, with True in cells that contain missing values, and False in cells that contain valid data

?: Which of the following code snippets returns a count of the missing values in each column of a DataFrame?

(X) some_dataframe.isna().sum() ( ) some_dataframe.dropna() ( ) some_dataframe.is_na().sum() ( ) some_dataframe.value_counts.is_na()

?: Select all the correct code snippets that returns the unique values for a given column.

[ ] some_dataframe.get_unique_values() [ ] some_dataframe.unique() [X] some_dataframe['some_column'].unique() [X] some_dataframe.some_column.unique()

?: You're cleaning up a DataFrame with ~1000 observations. You notice that one categorical column contains 512 missing values. What strategy should you employ to deal with these missing values?

( ) Drop all rows with missing values (X) Drop the column entirely ( ) Replace all missing values with the column mean ( ) Replace all missing values with randomly sampled values from this column

?: True or False: some_dataframe.dropna() will drop all rows containing 2 or more missing values.

( ) True (X) False

?: Select all valid strategies for dealing with null values in a column containing numerical data, assuming that we can't afford to lose any data.

[X] Replace missing values with the column median [ ] Replace missing values with the column mode [X] Convert the column to categorical format using Coarse Classification ('Binning') [ ] Leave the Null values as is

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