Missing data strategies decision in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check for missing values in the DataFrame.

Pandas

missing_counts = df.isnull().[1]()

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Asum

Bcount

Cmean

Dfillna

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() which counts non-missing values instead of missing ones.

Using fillna() which replaces missing values instead of counting them.

The sum() function counts the number of missing values (NaNs) in each column.

2fill in blank

medium

Complete the code to drop rows with any missing values.

Pandas

clean_df = df.[1](axis=0, how='any')

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Afillna

Breplace

Cisnull

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using fillna() which fills missing values instead of dropping rows.

Using isnull() which only detects missing values but does not remove them.

The dropna() function removes rows or columns with missing values. Here, axis=0 means rows.

3fill in blank

hard

Fix the error in the code to fill missing values with the column mean.

Pandas

df_filled = df.fillna(df.[1]())

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Amean

Bmode

Cmedian

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using median() or mode() which are different statistics.

Using sum() which adds values but does not calculate average.

The mean() function calculates the average of each column, which is used to fill missing values.

4fill in blank

hard

Fill both blanks to create a dictionary for filling missing values: fill numeric columns with mean and categorical with 'Unknown'.

Pandas

fill_values = {col: (df[col].[1]() if df[col].dtype == 'float64' else [2]) for col in df.columns}

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Amean

B'Unknown'

C'NaN'

Dmedian

Attempts:

3 left

💡 Hint

Common Mistakes

Using median() instead of mean() for numeric columns.

Using 'NaN' string instead of 'Unknown' for categorical columns.

Numeric columns use mean() to fill missing values. Categorical columns are filled with the string 'Unknown'.

5fill in blank

hard

Fill all three blanks to create a new DataFrame with missing values filled: numeric columns with median, categorical with mode, and others with 'Missing'.

Pandas

fill_map = {col: (df[col].[1]() if df[col].dtype == 'float64' else (df[col].[2]()[0] if df[col].dtype == 'object' else [3])) for col in df.columns}
df_filled = df.fillna(fill_map)

Drag options to blanks, or click blank then click option'

Amedian

Bmode

C'Missing'

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of median() for numeric columns.

Not selecting the first mode value with [0].

Using 'Unknown' instead of 'Missing' for other types.

Numeric columns use median(), categorical columns use the first value of mode(), and other types are filled with the string 'Missing'.