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?

tableau Signup and view all the answers

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

DataFrame Signup and view all the answers

Quae coloris ad chartam pertusam adhibentur in codice?

#dde5b6, #adc178, #a98467, #6c584c Signup and view all the answers

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

Showing percentages Signup and view all the answers

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

Usus est semel Signup and view all the answers

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

Height Signup and view all the answers

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

Setting x-axis labels Signup and view all the answers

Quod genus chartae non potest creari cum pandas?

scatter Signup and view all the answers

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

Calculat summam cumulativam valorum Signup and view all the answers

Quod color non est in lista colorum pro plotatione?

yellow Signup and view all the answers

Quot elementos habet dataframe df in exemplo dato?

4 Signup and view all the answers

Quale est exemplum stilorum linearium in matplotlib?

bs Signup and view all the answers

Quid fit cum variabile t in codice dato?

Generat series numerorum Signup and view all the answers

Quod est primum gradus ad mutandum indicem nominis in dataframe?

df.set_index() Signup and view all the answers

Quod profectum datur cum plt.show() invocatur?

Ostendit chartam Signup and view all the answers

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

Graphica data in forma horizontalis exhibet. Signup and view all the answers

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

50 Signup and view all the answers

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

s Signup and view all the answers

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

Varii colores ex numero random generantur. Signup and view all the answers

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

Textum dat baribus. Signup and view all the answers

Quid significat 'plt.yticks()' exemplario?

Numeros in axem y ordinat. Signup and view all the answers

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

height Signup and view all the answers

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

Quinque Signup and view all the answers

Quod est munus ad faciendum graphum linearem in codice dato?

plot_line() Signup and view all the answers

Quae optionis ad graphum areae creatio adhibetur?

plt.fill_between() Signup and view all the answers

Quod color consilium 'viridis' in codice adhibetur?

Ad graphum dispersum Signup and view all the answers

Quam multae columnas in DataFrame 'cp_df' sunt?

5 Signup and view all the answers

Quod munus cum 'stacked=True' adhibetur?

plot_area() Signup and view all the answers

Quomodo sunt valores in 'customer_purchase' generati?

Ex valoribus randomicis Signup and view all the answers

Quod est objective 'plt.legend()'?

Ad symbola describenda Signup and view all the answers

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

4 Signup and view all the answers

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

Quantitate emptionis Signup and view all the answers

Quod argumentum ad definiendum ratio axes X adhibetur?

xlabel Signup and view all the answers

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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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.