Programmatio Python - Visualisatio 2024
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Questions and Answers

Quid significat 'df.plot()' in contextu Pythonis?

  • Generare dataset random
  • Vulgare consilium graphici ex DataFrame (correct)
  • Exhibere descriptivam statisticam
  • Creare novam DataFrame

Quod tipo graphici non est in praebitis Pandas?

  • Area
  • Box
  • Hist
  • Stacked Bar (correct)

Quomodo possumus mutare columnas in DataFrame Pandas?

  • Per 'df.columns = new_columns' (correct)
  • Per 'df.add_columns(new_columns)'
  • Per 'df.update_columns(new_columns)'
  • Per 'df.rename(columns=new_names)' (correct)

Quod modulum visualisationis non est penitus in lista data visualization?

<p>tableau (A)</p> Signup and view all the answers

Quae res fornere non potest in grafico 1D ex Pandas?

<p>DataFrame (A)</p> Signup and view all the answers

Quae coloris ad chartam pertusam adhibentur in codice?

<p>#dde5b6, #adc178, #a98467, #6c584c (D)</p> Signup and view all the answers

Quid significat 'autopct' in functione 'plt.pie()'?

<p>Showing percentages (A)</p> Signup and view all the answers

Quoties usus est 'plt.bar()' in codice pro graphico verticali?

<p>Usus est semel (C)</p> Signup and view all the answers

Quod optionum in 'plt.bar()' non est modificandum?

<p>Height (D)</p> Signup and view all the answers

Quid significat 'plt.xticks()' in codice?

<p>Setting x-axis labels (D)</p> Signup and view all the answers

Quod genus chartae non potest creari cum pandas?

<p>scatter (A)</p> Signup and view all the answers

Quis est effectus invocationis df.cumsum() in dataframe?

<p>Calculat summam cumulativam valorum (A)</p> Signup and view all the answers

Quod color non est in lista colorum pro plotatione?

<p>yellow (C)</p> Signup and view all the answers

Quot elementos habet dataframe df in exemplo dato?

<p>4 (B)</p> Signup and view all the answers

Quale est exemplum stilorum linearium in matplotlib?

<p>bs (B)</p> Signup and view all the answers

Quid fit cum variabile t in codice dato?

<p>Generat series numerorum (C)</p> Signup and view all the answers

Quod est primum gradus ad mutandum indicem nominis in dataframe?

<p>df.set_index() (C)</p> Signup and view all the answers

Quod profectum datur cum plt.show() invocatur?

<p>Ostendit chartam (B)</p> Signup and view all the answers

Quid facit instructio 'plt.barh()' in matplotlib?

<p>Graphica data in forma horizontalis exhibet. (B)</p> Signup and view all the answers

Quot puncta sunt in variabili 'x' exemplari 'plt.scatter()'?

<p>50 (B)</p> Signup and view all the answers

Quod attributum in 'plt.scatter()' dat punctis diversae magnitudinis?

<p>s (D)</p> Signup and view all the answers

Quot et quales colores in graphica 'plt.scatter()' uti possunt?

<p>Varii colores ex numero random generantur. (B)</p> Signup and view all the answers

Quid facit instructio 'plt.text()' in bar chart?

<p>Textum dat baribus. (B)</p> Signup and view all the answers

Quid significat 'plt.yticks()' exemplario?

<p>Numeros in axem y ordinat. (C)</p> Signup and view all the answers

Quod attributum in 'plt.barh()' angularitatis colummorum regit?

<p>height (D)</p> Signup and view all the answers

Quot annos in variabili 'years' in bar chart exhibentur?

<p>Quinque (C)</p> Signup and view all the answers

Quod est munus ad faciendum graphum linearem in codice dato?

<p>plot_line() (D)</p> Signup and view all the answers

Quae optionis ad graphum areae creatio adhibetur?

<p>plt.fill_between() (D)</p> Signup and view all the answers

Quod color consilium 'viridis' in codice adhibetur?

<p>Ad graphum dispersum (C)</p> Signup and view all the answers

Quam multae columnas in DataFrame 'cp_df' sunt?

<p>5 (A)</p> Signup and view all the answers

Quod munus cum 'stacked=True' adhibetur?

<p>plot_area() (B)</p> Signup and view all the answers

Quomodo sunt valores in 'customer_purchase' generati?

<p>Ex valoribus randomicis (C)</p> Signup and view all the answers

Quod est objective 'plt.legend()'?

<p>Ad symbola describenda (C)</p> Signup and view all the answers

Quot species graphorum in functione 'draw_plots()' creantur?

<p>4 (D)</p> Signup and view all the answers

Quod attributum ad argumentum 'c' in 'plt.scatter()' adhibetur?

<p>Quantitate emptionis (D)</p> Signup and view all the answers

Quod argumentum ad definiendum ratio axes X adhibetur?

<p>xlabel (A)</p> Signup and view all the answers

Flashcards

Python Data Visualization

A method of representing data graphically in Python using libraries like matplotlib and seaborn.

Pandas DataFrames

Two-dimensional data structures in Python used for data manipulation and analysis.

Data Visualization Libraries

Python libraries (matplotlib, seaborn, plotnine, folium, plotly, pyecharts) that make creating graphs and charts easy.

Pandas Plotting Types

Different graph types available in Pandas for visualizing data (line, bar, barh, hist, box, area).

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Dataframe Plotting in Pandas

Using Pandas to generate graphs from DataFrames in Python.

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Pandas DataFrame index

A Pandas DataFrame index is used to uniquely identify each row in a data table.

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Pandas plot kind = 'barh'

Creates a horizontal bar chart of column data in a Pandas DataFrame.

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Pandas plot kind = 'hist'

Creates a histogram of a Pandas Series or DataFrame column.

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Pandas plot kind = 'bar'

Creates a vertical bar chart of column data in a Pandas DataFrame.

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Pandas plot kind = 'box'

Creates a box plot to show the distribution of numerical data.

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Matplotlib plot function

Used to create different types of plots like lines, scatter plots and more, using numerical data.

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NumPy array creation

Used to create matrices (2D tables) of random numbers using NumPy.

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Plotting color setting

Specifies colors used for various elements (lines, bars) of a chart.

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matplotlib.pyplot.pie() utilisatio

Graphicus circulus, qui sectiones diversas cum percentuali singulis proportionibus repraesentat.

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matplotlib.pyplot.bar() utilisatio

Graphicus tabulae verticales, qui in tempore valores comparat.

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argumentum 'my_colors'

Argumentum in graphicae functione ad colores adhibendos designandos.

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argumentum 'values' in graphico

Argumentum ad valores adhibendos in graphico.

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argumentum 'autopct' in graphico

Argumentum in graphico ad percentualem pro singulis sectionibus legendas addendas.

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plt.barh() optiones

Optiones pro "plt.barh()" functionem in matplotlib, quae chartas horizontales creant.

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plt.text() optiones

Optiones pro functionem "plt.text()" in matplotlib quae textum in chartas addit.

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Scatter diagramata: area

Optiones area pro functionem "plt.scatter()" in matplotlib quae magnitudinem punctorum in diagrammate determinat.

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Scatter diagramata: cmap

Optiones coloris pro functionem "plt.scatter()" in matplotlib quae palettam colorum pro punctis in diagrammate determinat.

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np.random.randint()

Function in modulo NumPy quae numeros integros randomicos intra limits specificos generat.

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"plt.xticks()" et "plt.yticks()"

Functiones in modulo matplotlib.pyplot quae labels pro axes horizontalem et verticalem chartarum determinant.

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Matplotlib: plt.plot()

Functio ad lineam in graphico generandum, cum optionibus pro colore, stylo lineae et latitudine.

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Numpy: np.arange()

Functio ad seriem numerorum generandum, inter initium, finem et incrementum datam.

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Matplotlib: plt.fill_between()

Functio ad aream inter duas curvas aut lineas replendum, cum optionibus pro colore et opacitate.

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plt.legend()

Functio ad legendam ad graphico addendum, explicandam significationes linearum aut arearum.

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Matplotlib: plt.title()

Functio ad titulum ad graphico addendum, describentem contentum graphici.

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plt.xlabel() & plt.ylabel()

Functiones ad inscriptiones ad axes x et y addendas, explicandas significantium variabilium.

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Matplotlib: plt.show()

Functio ad graphico visualem exhibendum in fenestra.

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cmap='viridis'

Optio in graphico ad applicationem colorum gradientum, adhibendo palletam colorum viridis, a claro ad obscuro mutandam.

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Pandas: df.plot()

Methodi ad creationem graphici ex dataframe, cum variis optionibus ad definiendum genus graphici.

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Pandas: df.transpose()

Methodi ad transpositionem dataframe, commutandos indices et columnas.

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Study Notes

Python Programming - Visualization

  • The presentation covers Python programming and visualization techniques.
  • The year is 2024, second semester.
  • The subject is software integration.

Data Visualization Modules

  • matplotlib: A fundamental module for creating static, interactive, and animated visualizations in Python.
  • Seaborn: Built on top of matplotlib, providing a high-level interface for visualizing statistical data.
  • plotnine: A Python library for creating grammar of graphics style plots that follows the aesthetics of the R package 'ggplot2'.
  • folium: A library for creating interactive maps within Python applications.
  • plotly: A powerful interactive visualization library for creating various chart types including plots and graphs.
  • pyecharts: A Python library that enables developers to create interactive charts (e.g., charts, graphs) in HTML format that can be viewed in a web browser.

Anatomy of a Figure

  • Title: A descriptive label for the figure.
  • Legend: A set of labels associated with different plots in the figure.
  • Grid: Provides a background grid structure to aid in visualizing data and plots.
  • Markers (Scatter Plot): Data points that are represented as symbols on a plot.
  • Axes (Line Plot): The axes (x and y) on a graph and the labeling of each axis.
  • Spines: The lines forming the borders of the figure.

Pandas Plotting

  • 'line': Creates line graphs, useful for time series data.
  • 'bar': Uses bars to represent categories.
  • 'barh': For horizontal bar graphs.
  • 'hist': Generates histograms, useful to visualize the distributions of data.
  • 'box': Produces box-and-whisker plots for visualizing data distribution.
  • 'area': Creates filled area charts that can be useful to highlight the cumulative effect of data over time.

Pandas Basic Graphing (1)

  • Displays example code for importing Pandas and NumPy libraries and creating a DataFrame.
  • Example uses np.random.randn to generate random data.
  • Shows how to plot the DataFrame using df.plot().

Pandas Basic Graphing (2)

  • Discusses generating plots with Pandas, adjusting dates, and changing data ranges.
  • Demonstrates setting color options with lists containing colors.

Matplotlib Module: Graphing (1)

  • Explains how to generate various types of plots using the Matplotlib library.
  • Shows how to use np.arange to generate a sequence of numbers.
  • Explains plotting a line graph using plt.plot(x, y, 'style').

Matplotlib Module: Pie Chart

  • Explains use of Matplotlib to create a pie chart.
  • Demonstrates using plt.pie for data visualization.
  • Explains how to set the colors and display percentages on the slices.

Matplotlib Module: Vertical Bar Graph

  • Shows how to use Matplotlib to create a vertical bar chart.
  • Explains using plt.bar() for creating vertical bar charts.
  • Detailed example that includes x-axis labels, titles, and data labeling within the charts.

Matplotlib Module: Horizontal Bar Graph

  • Demonstrates creating horizontal bar charts with Matplotlib.
  • Illustrates using plt.barh() to create horizontal bar graphs.
  • Shows how to include labels, titles, and detailed values within each bar.

Scatter Plot (1)

  • Describes creating scatter plots in Matplotlib with example code.
  • Provides a step-by-step explanation of the code and plotting method.
  • Shows how to adjust X and Y-axis labels and values, modify color schemes, and add custom ticks.

Scatter Plot (2)

  • Explains how to generate scatter plots with varying area sizes and different color schemes.
  • Shows how to control plot elements with size and cmap arguments.
  • Provides examples and instructions for modifying these parameters to customize the visualization.

Line Graph

  • Shows plotting a simple line graph in Matplotlib with example code.
  • Explains using plt.plot() to generate line graphs.
  • Describes how to customize the plot (e.g., line styles, colors).

Area Graph

  • Describes creating area graphs with Pandas and Matplotlib libraries.
  • Demonstrates data visualization with plt.fill_between().

Combining Plots / Styling

  • Details a method for creating a range of chart types (line, bar, area, pie) in sequence for a given data frame.
  • Explanations include the use of several functions and methods, and how to use variable data frames.

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Python Programming - Visualization PDF

Description

Haec praesentatio explorat technicas programmandi Python et visualisationis. In secundo semestri anni 2024, discimus de integratione software et diversis bibliothecis pro visualisatione data. Disce de matplotlib, Seaborn, plotly et aliis instrumentis.

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