How can you filter rows in a DataFrame `df` where column 'A' values are greater than 10?

What does the `df.describe()` method return?

If `series` is a Pandas Series, what will `series.value_counts()` do?

Which of the following methods is used to concatenate two DataFrames, `df1` and `df2`?

Which method is used to check the number of `None` observations across columns?

What does the following code return: `df.dtypes`?

Which method is used to drop duplicates from a DataFrame?

Which Pandas method is used to combine two DataFrames side by side?

Ready to try your hand at data science? This course is designed to challenge your existing knowledge and hands-on skills, ensuring you are fully prepared for any twists and turns a data science interview might present. We'll push your understanding of critical topics to the limit, assessing your readiness for real-life scenarios.

Let's take a look at what we'll be working with in this course. The first section will acquaint you with Python, a flexible and advanced programming language known for its clear syntax and readability.

NumPy is a fundamental library in Python that facilitates efficient numerical computations with powerful n-dimensional arrays and mathematical functions.

Pandas provides intuitive and versatile data structures for efficient data manipulation and analysis, streamlining the initial stages of the data science pipeline.

Matplotlib is a comprehensive Python library for creating static, animated, and interactive visualizations in Python.


Seaborn is a Python data visualization library based on Matplotlib that provides a high-level interface for creating informative and attractive statistical graphics.

Statistics provides data scientists with foundational techniques and tools to extract meaningful insights from data, allowing them to make informed decisions and predictions based on empirical evidence.

Scikit-learn is an open-source Python library that provides simple and efficient tools for data analysis and modeling, particularly for machine learning. Data scientists use it extensively for its comprehensive collection of algorithms and processing techniques, enabling them to quickly develop and deploy predictive models.

Theoretical Questions

1. How can you filter rows in a DataFrame `df` where column 'A' values are greater than 10?

2. What does the `df.describe()` method return?

3. If `series` is a Pandas Series, what will `series.value_counts()` do?

4. Which of the following methods is used to concatenate two DataFrames, `df1` and `df2`?

5. Which method is used to check the number of `None` observations across columns?

6. What does the following code return: `df.dtypes`?

7. Which method is used to drop duplicates from a DataFrame?

8. How can you transpose a DataFrame?

9. Which Pandas method is used to combine two DataFrames side by side?

10. What does `df.head(3)` return?

Theoretical Questions

1. How can you filter rows in a DataFrame df where column 'A' values are greater than 10?

2. What does the df.describe() method return?

3. If series is a Pandas Series, what will series.value_counts() do?

4. Which of the following methods is used to concatenate two DataFrames, df1 and df2?

5. Which method is used to check the number of None observations across columns?

6. What does the following code return: df.dtypes?