# Python Analytics - Advanced Analysis and ML (Data Analytics)

> groupby, merge, pivot_table, apply/map, Plotly, Scikit-Learn (regression, classification, clustering), train/test split, metrics, Jupyter, Google Colab

- 20 interview questions
- Senior
- [Interview Questions: Data Analytics](https://sharpskill.dev/en/technologies/data-analytics/interview-questions.md)

## 1. What is the main difference between apply() and map() methods on a Pandas Series?

**Answer**

The map() method is designed to map each value in a Series to a new value using a dictionary or function, and works only on Series. In contrast, apply() is more flexible: it can apply a function element-wise on a Series or row-wise/column-wise on a DataFrame. For simple value-to-value transformations on a Series, map() is generally faster and more readable.

## 2. Which Pandas method should be used to aggregate data with multiple aggregation functions on different columns simultaneously?

**Answer**

The agg() (or aggregate()) method allows applying different aggregation functions to different columns in a single operation. It accepts a dictionary where keys are column names and values are the functions to apply. This approach is more efficient and readable than chaining multiple groupby calls with individual functions.

## 3. What is the difference between merge() and join() in Pandas?

**Answer**

merge() is a more flexible function that joins two DataFrames on specific columns using the on, left_on/right_on parameters, or indexes. join() is a DataFrame method that joins on indexes by default and is more concise for simple index-based joins. For complex joins on non-index columns, merge() is preferable as it offers more control over join columns.

## 17 more questions available

- How to create a pivot table with pivot_table() specifying multiple aggregation functions?
- What is the purpose of transform() in a groupby() context compared to apply()?

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