Course Content

# Data Science Interview Challenge

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