1. Question 1

Identify number of duplicate rows:

• ❌ df.sum_duplicates()

• ❌ df.duplicates().sum()

• ✅ df.duplicated().sum()

• ❌ df.find_duplicates()

Explanation:

df.duplicated() returns True/False for duplicate rows → .sum() counts them.

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2. Question 2

Remove duplicates based on specific columns:

• ✅ df.drop_duplicates(subset=[‘ResponseId’, ‘MainBranch’])

• ❌ df.drop_duplicates()

• ❌ df.drop_duplicates([‘ResponseId’, ‘MainBranch’])

• ❌ df.drop_duplicates(columns=…)

Explanation:

Use subset= to specify columns.

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3. Question 3

Identify columns with same values in duplicate rows:

• ❌ df.loc[df.duplicated(keep=False)].nunique()

• ❌ duplicated().unique()

• ❌ nunique(axis=1)

• ✅ df.loc[df.duplicated(keep=False)].nunique(axis=0)

Explanation:

Check nunique per column (axis=0) among duplicated rows.

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4. Question 4

Verify duplicates were removed:

• ❌ Compare row counts after dropna()

• ❌ Check df.drop_duplicates() returns zero

• ✅ Re-run df.duplicated().sum() and ensure it equals zero

• ❌ Check isnull

Explanation:

If no duplicates remain → .sum() will be 0.

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5. Question 5

Replace missing values with the most frequent value:

• ❌ fillna(0)

• ✅ df[‘column’].fillna(df[‘column’].mode()[0])

• ❌ replace(0)

• ❌ fillna(mean)

Explanation:

Mode returns the most frequent value. Use .mode()[0].

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6. Question 6

Purpose of df.describe(include='all'):

• ❌ Remove duplicates

• ❌ Count missing values

• ❌ Identify missing values

• ✅ Display summary statistics for all columns including categorical

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7. Question 7

Fill missing values with most frequent value:

• ❌ df.fillna(mode())

• ✅ df[‘column’].fillna(df[‘column’].mode()[0])

• ❌ Random text

• ❌ df.mode().fillna()

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8. Question 8

Replace NaN in ‘RemoteWork’ with a specific value:

• ✅ df[‘RemoteWork’].fillna(‘value’, inplace=True)

• ❌ dropna

• ❌ fillna(df.mean()

• ❌ replace()

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9. Question 9

Function of MinMaxScaler:

• ✅ Scales data to be between 0 and 1

• ❌ Drops duplicates

• ❌ Replaces NaN

• ❌ Normalizes around mean 0

Explanation:

MinMaxScaler transforms:

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10. Question 10

Function that provides summary statistics (mean, count, etc.):

• ❌ isnull()

• ❌ dropna()

• ❌ fillna()

• ✅ df.describe()

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🧾 Summary Table

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