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EDA Tools Mastery Quiz

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EDA Tools Mastery Quiz

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Fast facts about Python EDA tools

Created by

DvSystem Tecnología
DvSystem Tecnología
Ecuador

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EDA Tools Mastery Quiz
 

EDA Tools Mastery QuizOnline version

Fast facts about Python EDA tools

by DvSystem Tecnología
1

Which two core data structures does Pandas offer for data manipulation?

2

Which Pandas method provides descriptive statistics like mean, std, min, and max?

3

Which library is described as the base for static, highly controllable data visuals in Python?

4

What is Seaborn primarily used for in the EDA toolkit?

5

Which library specializes in interactive, browser-based visuals?

6

How would you describe the code to get total sales by region using Pandas?

7

What is the 1D data structure in Pandas called?

8

What is the recommended first step in an Exploratory Data Analysis (EDA)?

9

Which Matplotlib visualization is used to show distribution of a numeric variable?

10

Which four tools are commonly combined in a typical EDA workflow according to the materials?

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Pandas focuses on Series (1D) and DataFrame (2D) for data handling.

describe() summarizes numerical columns by key statistics.

Matplotlib is the foundational plotting library referenced.

Seaborn creates elegant statistical plots built on top of Matplotlib.

Plotly supports interactive charts and dashboards.

Grouping by region then summing ventas yields region totals.

Series is the 1D labeled array in Pandas.

EDA starts with loading data and inspecting structure.

Histograms illustrate frequency distributions.

A typical EDA workflow combines Pandas, Matplotlib, Seaborn, and Plotly.

 
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