Course Content

Data Science Interview Challenge

1. Python

Introduction Challenge 1: List Comprehension Challenge 2: String Manipulation Challenge 3: Dictionary Manipulation Challenge 4: Nested Loops Challenge 5: Classes Theoretical Questions

2. NumPy

Challenge 1: Array Creation Challenge 2: Array Manipulation Challenge 3: Statistical Insights Challenge 4: Handling Missing Values Challenge 5: Subarray Sorting Theoretical Questions

3. Pandas

Challenge 1: DataFrame Creation Challenge 2: Data Grouping Challenge 3: Indexing and MultiIndexing Challenge 4: Altering DataFrame Challenge 5: Iterating Over Data Theoretical Questions

4. Matplotlib

Challenge 1: Fundamentals of Plotting Challenge 2: Other Graph Types Challenge 3: Subplots Challenge 4: Customizing Plots Challenge 5: Adding Text to Plots Theoretical Questions

5. Seaborn

Challenge 1: Visualizing Distributions Challenge 2: Exploring Categorical Data Challenge 3: Relational Plots Challenge 4: Regression Plots Challenge 5: Matrix Plots Theoretical Questions

6. Statistics

Challenge 1: Probabilities and Distributions Challenge 2: Bayes' Theorem Challenge 3: Hypothesis Testing Challenge 4: Confidence Intervals Challenge 5: Correlation Theoretical Questions

7. Scikit-learn

Challenge 1: Data Scaling Challenge 2: Basic Model Creation Challenge 3: Pipelines Challenge 4: Cross-validation Challenge 5: Hyperparameter Tuning Theoretical Questions

Data Science Interview Challenge

Challenge 2: Data Grouping

Pandas, known for its comprehensive data analysis tools, offers a versatile grouping mechanism called the groupby method. This method is pivotal for aggregating data based on certain criteria, a process similar to the SQL GROUP BY statement. The benefits of using groupby are manifold:

Granularity Control: You can aggregate data at different levels of granularity, from high level (e.g., grouping by country) to fine-grained (e.g., grouping by individual timestamps).
Simplicity: The groupby syntax is concise and expressive, making it easy to chain operations and achieve complex aggregations.
Extensibility: With groupby, you can apply custom aggregation functions, not just the built-in ones, giving you the power to compute custom metrics for groups.

When diving into data exploration, the grouping capabilities of Pandas can reveal insightful patterns and trends by segmenting data into meaningful categories.

Task

Demonstrate data grouping in Pandas with the following tasks:

Group data by a single column A.
Sum all data grouped for column A using the built-in function.
Apply multiple aggregation functions simultaneously. Get sum aggregation for B column and mean for C column.
Group by multiple columns (A and B).

Code Description

grouped_A = df.groupby('A')

The groupby() method creates a GroupBy object which segments the original DataFrame based on unique values in column 'A'.

sum_grouped_A = grouped_A.sum()

Once data is grouped, you can apply aggregation functions. Here, we use the sum() method to compute the sum of columns 'B' and 'C' for each group in 'A'.

multi_aggregate = grouped_A.agg({'B': 'sum', 'C': 'mean'})

The agg() method allows multiple aggregation functions to be applied simultaneously. In this case, we're computing the sum of column B and the mean of column C for each group.

grouped_A_B = df.groupby(['A', 'B']).sum()

To group data based on multiple columns, you can pass a list of column names to the groupby() method. This creates a multi-level index in the resulting DataFrame.

Everything was clear?

Section 3. Chapter 2