Python Data Science Fundamentals Mastery Hub: The Industry F

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Q1Domain Verified

s based on "The Complete Python Data Wrangling & Pandas Course 2026: From Zero to Expert!" for a course on "Python Data Science Fundamentals Mastery Hub: The Industry Foundation": Question: When performing a merge operation in Pandas between two DataFrames, `df1` and `df2`, using `pd.merge(df1, df2, on='common_key', how='outer')`, what is the primary implication for rows that exist in `df1` but not in `df2`, and vice-versa?

Rows unique to `df1` will be dropped, and rows unique to `df2` will be kept with NaN values for `df1`'s columns.

D) Both rows unique to `df1` and `df2` will be dropped, as the merge requires a perfect match on 'common_key'.

Rows unique to `df1` will be kept with NaN values for `df2`'s columns, and rows unique to `df2` will be droppe

Rows unique to `df1` will be kept with NaN values for `df2`'s columns, and rows unique to `df2` will be kept with NaN values for `df1`'s columns.

Q2Domain Verified

Consider a Pandas DataFrame `df` with a MultiIndex. You want to select all rows where the first level index is 'A' and the second level index is greater than 5. Which of the following is the most efficient and idiomatic Pandas way to achieve this, assuming the index is sorted?

`df.loc['A'].loc[df.loc['A'].index > 5]`

`df.loc[pd.IndexSlice['A', 5:], how='gt']`

`df.loc[('A', slice(5, None))]`

`df.xs('A', level=0).loc[lambda x: x.index > 5]`

Q3Domain Verified

You are working with a large dataset and need to calculate the rolling mean of a specific column, 'value', with a window size of 10, but you want to exclude `NaN` values from the calculation for each window. Which Pandas function and its relevant parameter would you use?

`df['value'].rolling(window=10, min_periods=1).apply(lambda x: np.nanmean(x), raw=True)`

`df['value'].rolling(window=10, min_periods=1).mean(skipna=True)`

`df['value'].rolling(window=10, min_periods=1, center=True).mean()`

`df['value'].rolling(window=10, min_periods=1).mean(dropna=True)`

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Python Data Science Fundamentals Mastery Hub: The Industry F

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Elite Practice Intelligence

Consider a Pandas DataFrame `df` with a MultiIndex. You want to select all rows where the first level index is 'A' and the second level index is greater than 5. Which of the following is the most efficient and idiomatic Pandas way to achieve this, assuming the index is sorted?

You are working with a large dataset and need to calculate the rolling mean of a specific column, 'value', with a window size of 10, but you want to exclude `NaN` values from the calculation for each window. Which Pandas function and its relevant parameter would you use?

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Python Data Science Fundamentals Mastery Hub: The Industry F

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Elite Practice Intelligence

Consider a Pandas DataFrame `df` with a MultiIndex. You want to select all rows where the first level index is 'A' and the second level index is greater than 5. Which of the following is the most efficient and idiomatic Pandas way to achieve this, assuming the index is sorted?

You are working with a large dataset and need to calculate the rolling mean of a specific column, 'value', with a window size of 10, but you want to exclude `NaN` values from the calculation for each window. Which Pandas function and its relevant parameter would you use?

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2Consider a Pandas DataFrame `df` with a MultiIndex. You want to select all rows where the first level index is 'A' and the second level index is greater than 5. Which of the following is the most efficient and idiomatic Pandas way to achieve this, assuming the index is sorted?

3You are working with a large dataset and need to calculate the rolling mean of a specific column, 'value', with a window size of 10, but you want to exclude `NaN` values from the calculation for each window. Which Pandas function and its relevant parameter would you use?

Other Recommended Specializations

Mastery: Python

Python Fundamentals Mastery Hub: The Industry Foundation

Object-Oriented Python Mastery Hub: The Industry Foundation

Advanced Python Data Structures Mastery Hub: The Industry Foundation

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Python Core Syntax Mastery Hub: The Industry Foundation