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Data Visualization Articles
Page 10 of 68
How to get data labels on a Seaborn pointplot?
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 MoreHow to draw a precision-recall curve with interpolation in Python Matplotlib?
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 MoreHow to plot additional points on the top of a scatter plot in Matplotlib?
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 MoreTransparent error bars without affecting the markers in Matplotlib
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 MoreHow to set legend marker size and alpha in Matplotlib?
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 MoreShowing points coordinate in a plot in Python Matplotlib
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 MoreHow to unset 'sharex' or 'sharey' from two axes in Matplotlib?
To inset sharex and sharey from two axes in matplotlib, we can use 'none', i.e., False or 'none'. Each subplot X- or Y-axis will be independent.StepsSet the figure size and adjust the padding between and around the subplots.Initialize two variables rows and cols.Create a figure and a set of subplots.Iterate the axes where rows=2 and cols=4.Plot the random data on the axis.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 rows = 2 cols = 4 fig, axes = plt.subplots(rows, cols, sharex='none', sharey='none', squeeze=False) ...
Read MoreHow to obtain 3D colored surface via Python?
To obtain 3D colored surface via Python, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Get 3D data, i.e., z.Create a new figure or activate an existing figure.Get the 3D axes.Create a surface plot.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 x = np.linspace(-3, 3, 100) y = np.cos(x) x, y = np.meshgrid(x, y) z = x ** 2 + y ** 2 - 2 ...
Read MoreScatter a 2D numpy array in matplotlib
To scatter a 2D numpy array in matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create random data of 100×3 dimension.Use the scatter() method to plot 2D numpy array, i.e., data.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Random data of 100×3 dimension data = np.array(np.random.random((100, 3))) # Scatter plot plt.scatter(data[:, 0], data[:, 1], c=data[:, 2], cmap='hot') # Display the plot plt.show()OutputIt will produce ...
Read MoreHow to avoid overlapping error bars in matplotlib?
To avoid overlapping error bars in matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create a list of names.Get the data points for y1 and y2, and errors ye1, ye2.Create a figure and a set of subplots.Create a mutable 2D affine transformation, trans1 and trans2.Plot y versus x as lines and/or markers with attached errorbars.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt from matplotlib.transforms import Affine2D plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = ['Jack', 'James', 'Tom', 'Garry'] y1, ...
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