Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Challenge 2: Data Grouping | Pandas
Data Science Interview Challenge
course content

Course Content

Data Science Interview Challenge

Data Science Interview Challenge

1. Python

IntroductionChallenge 1: List ComprehensionChallenge 2: String ManipulationChallenge 3: Dictionary ManipulationChallenge 4: Nested LoopsChallenge 5: ClassesTheoretical Questions

2. NumPy

Challenge 1: Array CreationChallenge 2: Array ManipulationChallenge 3: Statistical InsightsChallenge 4: Handling Missing ValuesChallenge 5: Subarray SortingTheoretical Questions

3. Pandas

Challenge 1: DataFrame CreationChallenge 2: Data GroupingChallenge 3: Indexing and MultiIndexingChallenge 4: Altering DataFrameChallenge 5: Iterating Over DataTheoretical Questions

4. Matplotlib

Challenge 1: Fundamentals of PlottingChallenge 2: Other Graph TypesChallenge 3: SubplotsChallenge 4: Customizing PlotsChallenge 5: Adding Text to PlotsTheoretical Questions

5. Seaborn

Challenge 1: Visualizing DistributionsChallenge 2: Exploring Categorical DataChallenge 3: Relational PlotsChallenge 4: Regression PlotsChallenge 5: Matrix PlotsTheoretical Questions

6. Statistics

Challenge 1: Probabilities and DistributionsChallenge 2: Bayes' TheoremChallenge 3: Hypothesis TestingChallenge 4: Confidence IntervalsChallenge 5: CorrelationTheoretical Questions

7. Scikit-learn

Challenge 1: Data ScalingChallenge 2: Basic Model CreationChallenge 3: PipelinesChallenge 4: Cross-validationChallenge 5: Hyperparameter TuningTheoretical Questions

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:

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

Task

Demonstrate data grouping in Pandas with the following tasks:

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

Everything was clear?

Section 3. Chapter 2
toggle bottom row

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:

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

Task

Demonstrate data grouping in Pandas with the following tasks:

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

Everything was clear?

Section 3. Chapter 2
toggle bottom row

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:

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

Task

Demonstrate data grouping in Pandas with the following tasks:

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

Everything was clear?

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:

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

Section 3. Chapter 2
Switch to desktop for real-world practiceContinue from where you are using one of the options below
We're sorry to hear that something went wrong. What happened?
some-alt