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Data Visualization Articles
Page 40 of 68
Frequency plot in Python/Pandas DataFrame using Matplotlib
To show a frequency plot in Python/Pandas dataframe using Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Make a two-dimensional, size-mutable, potentially heterogeneous tabular data.Return a Series containing the counts of unique values.To display the figure, use show() method.Exampleimport pandas as pd from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() df = pd.DataFrame({'numbers': [2, 4, 1, 4, 3, 2, 1, 3, 2, 4]}) df['numbers'].value_counts().plot(ax=ax, kind='bar', xlabel='numbers', ylabel='frequency') plt.show()Output
Read MorePlotting power spectral density in Matplotlib
To plot Power Spectral Density in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, dt.Create t, nse , r, cnse, s, and r data points using numpyCreate a figure and a set of subplots.Plot t and s data using plot() method.Plot the power spectral density.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 dt = 0.01 t = np.arange(0, 10, dt) nse = np.random.randn(len(t)) r = np.exp(-t / 0.05) cnse = np.convolve(nse, ...
Read MoreHow do I print a Celsius symbol with Matplotlib?
To print Celsius symbol with Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, N.Create T and P data points using numpy.Plot T and P using plot() method.Set the label for the X-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 N = 10 T = np.random.rand(N) P = np.random.rand(N) plt.plot(T, P) plt.xlabel("$Temperature {^\circ}C$") plt.show()Output
Read MoreAutomated legend creation in Matplotlib
To automate legend creation in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, N, for number of sample data.Create x, y, c and s data using numpy.Create a figure and a set of subplots using subplots() method.Plot x and y data points with different colors and sizes.Place a legend on the axes.Add an artist to the figure.Create legend handles and labels for a PathCollection.Again, place a legend on the axes for sizes.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np ...
Read MoreHow to plot a nested pie chart in Matplotlib?
To plot a nested pie chart in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Initialize a variable size, create vals, cmap, outer_colors, inner_colors data using numpy.Use pie() function to make pie charts.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 fig, ax = plt.subplots() size = 0.3 vals = np.array([[60., 32.], [37., 40.], [29., 10.]]) cmap = plt.get_cmap("tab20c") outer_colors = cmap(np.arange(3)*4) inner_colors = cmap([1, 2, 5, 6, 9, ...
Read MoreHow to refresh text in Matplotlib?
To refresh text in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Add text to the axes.Write customized method to update text based on the keys "z" and "c".Bind the function action with key_press_event.Draw the canvas that contains the figure.Animate the figure with texts.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, animation plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots() text = ax.text(.5, .5, 'First Text') def action(event): if event.key == "z": ...
Read MoreHow to make xticks evenly spaced despite their values? (Matplotlib)
To make xticks evenly spaced despite their values, we can use set_ticks() and set_ticklabels() methods.StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Create a figure and a set of subplots using subplots() method.Plot x and y data points on axis 1.Set xticks using xaxis.set_ticks() method.Plot x and y data points on axis 2.Set xticks and ticklabels using xaxis.set_ticks() and xaxis.set_ticklabels() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.array([1, 1.5, ...
Read MoreStuffing a Pandas DataFrame.plot into a Matplotlib subplot
To stuff a Pandas dataframe plot into a Matplotlib subplot, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots, two axes.Create a Pandas dataframe using DataFrame.Use DataFrame.plot() method to plot.To display the figure, use show() method.Exampleimport pandas as pd import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, (ax1, ax2) = plt.subplots(2) df = pd.DataFrame(dict(name=["Joe", "James", "Jack"], age=[23, 34, 26])) df.set_index("name").plot(ax=ax1) df.set_index("name").plot(ax=ax2) plt.show()Output
Read MoreHow to set different opacity of edgecolor and facecolor of a patch in Matplotlib?
To set different opacity of edge and face color, we can use a color tuple and the 4th index of the tuple could set the opacity value of the colors.StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots using subplots() method.Set different values for edge and face color opacity.Add a rectangel patch using add_patch() method.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt, patches plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True figure, ax = plt.subplots() edge_color_opacity = 1 # 0
Read MoreDraw a parametrized curve using pyplot.plot() in Matplotlib
To draw a parametrized curve using pyplot.plot(), we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Initialize a variable, N, for number of samples.Create t, r, x and y data points using numpy.Create a figure and a set of subplots.Use plot() method to plot x and y data points.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 N = 400 t = np.linspace(0, 2 * np.pi, N) r = 0.5 + np.cos(t) x, y = r * ...
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