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To XOR a given scalar value with every element of a masked array, use the ma.MaskedArray.__rxor__() method in Python Numpy. A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and ... Read More
To XOR every element of a masked array by a given scalar value, use the ma.MaskedArray.__xor__() method in Python Numpy. A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and ... Read More
To change the attributes of a netwrokx/matplotlib graph drawing, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Initialize a graph with edges, name, or graph attributes.Add the graph's attributes. Add an edge between u and v.Get the edge attributes from the graph.Position the nodes with circles.Draw the graph G with Matplotlib.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import networkx as nx plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True G = nx.Graph() G.add_edge(0, 1, color='r', weight=2) G.add_edge(1, 2, color='g', weight=4) G.add_edge(2, 3, color='b', weight=6) G.add_edge(3, 4, ... Read More
To fill an area within a polygon in Python using matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Get an instance of a polygon.Get the generic collection of patches with iterable polygons.Add a 'collection' to the axes' collections; return the collection.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt from matplotlib.collections import PatchCollection from matplotlib.patches import Polygon import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots(1) polygon = Polygon(np.random.rand(6, 2), closed=True, alpha=1) ... Read More
To get data labels on a Seaborn pointplot, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create a dataframe, df, of two-dimensional, size-mutable, potentially heterogeneous tabular data.Create a pointplot.Get the axes patches and label; annotate with respective labels.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt import pandas as pd import seaborn as sns plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame({'a': [1, 3, 1, 2, 3, 1]}) ax = sns.pointplot(df["a"], order=df["a"].value_counts().index) for p, label in zip(ax.patches, df["a"].value_counts().index): ax.annotate(label, ... Read More
To draw a precision-recall curve with interpolation in Python, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create r, p and duplicate recall, i data points using numpy.Create a figure and a set of subplots.Plot the recall matrix in the range of r.shape.Plot the r and dup_r data points using plot() method.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True r = np.linspace(0.0, 1.0, num=10) p = np.random.rand(10) * (1. - r) dup_p = p.copy() i ... Read More
To plot additional points on the top of a scatter plot in matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Make a list of x and y data points.Create a scatter plot with x and y data points.Plot the additional points with marker='*'To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # List of data points x = [1, 2, 6, 4] y = [1, 5, 2, 3] # Scatter plot ... Read More
To make transparent error bars without affecting markers in matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Make lists x, y and z for data.Initialize a variable error_bar_width=5Plot y versus x as lines and/or markers with attached errorbars.Set the alpha value of bars and caps.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = [1, 3, 5, 7] y = [1, 3, 5, 7] z = [4, 5, 1, 4] error_bar_width = 5 markers, ... Read More
To set legend marker size and alpha in matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable N to store the number of sample data.Plot the x and y data points with marker="*".Place a legend on the figure.Set the marker size and alpha value of the marker.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True N = 10 x = np.random.rand(N) y = np.random.rand(N) line, = plt.plot(x, y, marker='*', markersize=20, markeredgecolor='black', ... Read More
To show points coordinate in a plot in Python, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Initilize a variable N and create x and y data points using numpy.Zip the x and y data points; iterate them and place coordinates.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True N = 5 x = np.random.rand(N) y = np.random.rand(N) plt.plot(x, y, 'r*') for xy in zip(x, y): plt.annotate('(%.2f, %.2f)' % xy, xy=xy) ... Read More