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Matplotlib (portmanteau of MATLAB, plot, and library [3]) is a plotting library for the Python programming language and its numerical mathematics extension NumPy.It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.
Python and Matplotlib are cross-platform, and are therefore available for Windows, OS X, and the Unix-like operating systems like Linux and FreeBSD. Matplotlib can create plots in a variety of output formats, such as PNG and SVG. Matplotlib mainly does 2-D plots (such as line, contour, bar, scatter, etc.), but 3-D functionality is also available.
In Python using matplotlib ; The R programming language can be used for creating Wikipedia graphs. The Google Chart API allows a variety of graphs to be created. Livegap Charts creates line, bar, spider, polar-area and pie charts, and can export them as images without needing to download any tools.
English: This plot shows a Bar Chart of the installed Wind power capacity and generation in Italy. You find the Python code to regenerate this SVG below. Please update the data in the future ! Data from Wind_power_in_Italy and Énergie_éolienne_en_Italie
English: This plot shows a Bar Chart of the installed Wind power capacity and generation in Greece. You find the Python code to regenerate this SVG below. Please update the data in the future ! Data from Énergie_éolienne_en_Grèce
A bar graph shows comparisons among discrete categories. One axis of the chart shows the specific categories being compared, and the other axis represents a measured value. Some bar graphs present bars clustered in groups of more than one, showing the values of more than one measured variable. These clustered groups can be differentiated using ...
A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. A vertical bar chart is sometimes called a column chart and has been identified as the prototype of charts. [1]
Minitab, create an Empirical CDF; Mathwave, we can fit probability distribution to our data; Dataplot, we can plot Empirical CDF plot; Scipy, we can use scipy.stats.ecdf; Statsmodels, we can use statsmodels.distributions.empirical_distribution.ECDF; Matplotlib, using the matplotlib.pyplot.ecdf function (new in version 3.8.0) [7]