Let us understand the concepts of Jumpshare and SugarSync before learning the differences between them.JumpshareJumpshare was developed by Jumpshare Inc in the year 2012. It is a visual communication platform. By Using Jumpshare we can share our work and ideas instantly using shareable links. It combines the features like file sharing, screenshot capture, and video recording all in a single platform. It is available in MacOS, iOS, and Microsoft windows.Jumpshare offers free accounts with limited storage, and paid subscriptions with expanded storage and sharing options.FeaturesThe features of Jumpshare are as follows −Share the work visually in seconds.Delivering the work at ... Read More

Let us understand the concepts of Jumpshare and pCloud before learning the differences between them.JumpshareJumpshare was developed by Jumpshare Inc in the year 2012. It is a visual communication platform. By Using Jumpshare we can share our work and ideas instantly using shareable links. It combines the features like file sharing, screenshot capture, and video recording all in a single platform. It is available in MacOS, iOS, and Microsoft windows.Jumpshare offers free accounts with limited storage, and paid subscriptions with expanded storage and sharing options.FeaturesThe features of Jumpshare are as follows −Share the work visually in seconds.Delivering the work at ... Read More

Let us understand the concepts of Jumpshare and Wuala before learning the differences between them.JumpshareJumpshare was developed by Jumpshare Inc in the year 2012. It is a visual communication platform. By Using Jumpshare we can share our work and ideas instantly using shareable links. It combines the features like file sharing, screenshot capture, and video recording all in a single platform. It is available in MacOS, iOS, and Microsoft windows.Jumpshare offers free accounts with limited storage, and paid subscriptions with expanded storage and sharing options.FeaturesThe features of Jumpshare are as follows −Share the work visually in seconds.Delivering the work at ... 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