Handling inconsistent values in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to replace all occurrences of 'N/A' with NaN in the DataFrame df.

Pandas

df = df.replace([1], np.nan)

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

A'None'

B'null'

C'missing'

D'N/A'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'None' or 'null' instead of 'N/A' which does not match the data.

Not using np.nan to represent missing values.

We use replace to change all 'N/A' strings to np.nan, which pandas recognizes as missing values.

2fill in blank

medium

Complete the code to drop rows from df that contain any NaN values.

Pandas

df_clean = df.[1](axis=0)

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

Afillna

Bdropna

Cisnull

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using fillna instead of dropna, which fills missing values instead of dropping rows.

Using isnull which only returns a boolean mask.

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

3fill in blank

hard

Fix the error in the code to convert the 'age' column to numeric, coercing errors to NaN.

Pandas

df['age'] = pd.to_numeric(df['age'], errors=[1])

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

A'coerce'

B'ignore'

C'raise'

D'convert'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'raise' which causes an error on invalid values.

Using 'ignore' which leaves invalid values unchanged.

The errors='coerce' argument converts invalid parsing to NaN instead of raising an error.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Pandas

{word: [1] for word in words if [2] > 3}

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

Alen(word)

Bword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Checking the word instead of its length in the condition.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase keys to values only if value is positive.

Pandas

{ [1]: [2] for [3], v in data.items() if v > 0 }

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

Ak.upper()

Bv

Ck

Dv.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using the value as key or vice versa.

Not converting keys to uppercase.

The comprehension uses k.upper() as keys, v as values, and iterates over k, v pairs from data.items(). It filters values greater than zero.