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.

Aufgabe

Swipe to start coding

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).

Lösung

import pandas as pd

# Sample DataFrame

df = pd.DataFrame({

'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar'],

'B': [1, 2, 1, 4, 5, 2],

'C': [2.5, 3.5, 4.5, 2.5, 3.5, 4.5]

})

# 1. Group data by a single column.

grouped_A = df.groupby('A')

# 2. Sum all data grouped for column `A` using the built-in function.

sum_grouped_A = grouped_A.sum()

display(sum_grouped_A)

print('-' * 20)

# 3. Apply multiple aggregation functions simultaneously.

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

display(multi_aggregate)

print('-' * 20)

# 4. Group by multiple columns.

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

display(grouped_A_B)

War alles klar?

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Abschnitt 3. Kapitel 2

import pandas as pd

# Sample DataFrame

df = pd.DataFrame({

'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar'],

'B': [1, 2, 1, 4, 5, 2],

'C': [2.5, 3.5, 4.5, 2.5, 3.5, 4.5]

})

# 1. Group data by a single column A.

grouped_A = ___

# 2. Sum all data grouped for column `A` using the built-in function.

sum_grouped_A = ___

display(sum_grouped_A)

print('-' * 20)

# 3. Apply multiple aggregation functions simultaneously.

multi_aggregate = ___

display(multi_aggregate)

print('-' * 20)

# 4. Group by multiple columns.

grouped_A_B = ___

display(grouped_A_B)