EDA Tools Mastery Quiz

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Fast facts about Python EDA tools

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EDA Tools Mastery Quiz


EDA Tools Mastery QuizVersión en línea Fast facts about Python EDA tools por DvSystem Tecnología 1 Which two core data structures does Pandas offer for data manipulation? a Dictionary and Set b List and Tuple c Array and Matrix d Series and DataFrame 2 Which Pandas method provides descriptive statistics like mean, std, min, and max? a summary() b stats() c summarize() d describe() 3 Which library is described as the base for static, highly controllable data visuals in Python? a Matplotlib b Seaborn c Plotly d Bokeh 4 What is Seaborn primarily used for in the EDA toolkit? a Interactive dashboards b Low-level plotting c Data cleaning d Statistical visualizations 5 Which library specializes in interactive, browser-based visuals? a Pandas b Seaborn c Plotly d Matplotlib 6 How would you describe the code to get total sales by region using Pandas? a df.groupby('region').mean('ventas') b df.sum('region') c df.pivot_table('region','ventas') d df.groupby('region')['ventas'].sum() 7 What is the 1D data structure in Pandas called? a Series b DataFrame c Panel d Index 8 What is the recommended first step in an Exploratory Data Analysis (EDA)? a Delete missing values b Immediately model the data c Read and understand the data structure d Create all plots first 9 Which Matplotlib visualization is used to show distribution of a numeric variable? a Box plot b Scatter plot c Line plot d Histogram 10 Which four tools are commonly combined in a typical EDA workflow according to the materials? a NumPy, SciPy, Bokeh, Altair b Pandas, NumPy, Scikit-learn, Plotly c SQL, Excel, Tableau, D3.js d Pandas, Matplotlib, Seaborn, Plotly Feedback 1 Pandas focuses on Series (1D) and DataFrame (2D) for data handling. 2 describe() summarizes numerical columns by key statistics. 3 Matplotlib is the foundational plotting library referenced. 4 Seaborn creates elegant statistical plots built on top of Matplotlib. 5 Plotly supports interactive charts and dashboards. 6 Grouping by region then summing ventas yields region totals. 7 Series is the 1D labeled array in Pandas. 8 EDA starts with loading data and inspecting structure. 9 Histograms illustrate frequency distributions. 10 A typical EDA workflow combines Pandas, Matplotlib, Seaborn, and Plotly.

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EDA Tools Mastery QuizVersión en línea

por DvSystem Tecnología

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

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

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

What is Seaborn primarily used for in the EDA toolkit?

Which library specializes in interactive, browser-based visuals?

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

What is the 1D data structure in Pandas called?

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

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

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

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