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To find the matplotlib style name, we can take the following steps −import matplotlib.pyplot as pltprint(plt.style.library)Exampleimport matplotlib.pyplot as plt print(plt.style.library)Output{'bmh': RcParams({'axes.edgecolor': '#bcbcbc', 'axes.facecolor': '#eeeeee', 'axes.grid': True, 'axes.labelsize': 'large', 'axes.prop_cycle': cycler('color', ['#348ABD', '#A60628', '#7A68A6', '#467821', '#D55E00', '#CC79A7', '#56B4E9', '#009E73', '#F0E442', '#0072B2']), 'axes.titlesize': 'x-large', 'grid.color': '#b2b2b2', 'grid.linestyle': '--', 'grid.linewidth': 0.5, 'legend.fancybox': True, 'lines.linewidth': 2.0, 'mathtext.fontset': 'cm', 'patch.antialiased': True, 'patch.edgecolor': '#eeeeee', 'patch.facecolor': 'blue', 'patch.linewidth': 0.5, 'text.hinting_factor': 8, 'xtick.direction': 'in', 'ytick.direction': 'in'}), 'classic': RcParams({'_internal.classic_mode': True, 'agg.path.chunksize': 0, ... Read More
To get an interactive plot of a pyplot when using PyCharm, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Set the background style.Plot the data on the axes.To display the figure, use show() method.Exampleimport matplotlib as mpl import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True mpl.use('Qt5Agg') plt.plot(range(10)) plt.show()Output
To increase the spacing between subplots with subplot2grid, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Add a grid layout to place subplots within a figure.Update the subplot parameters of the grid.Add a subplot to the current figure.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 ax = plt.GridSpec(2, 2) ax.update(wspace=0.5, hspace=0.5) ax1 = plt.subplot(ax[0, :]) ax2 = plt.subplot(ax[1, 0]) ax3 = plt.subplot(ax[1, 1]) plt.show()Output
To get multiple overlapping plots with independent scaling 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.Plot a list of data points using plot() method on a seperate Y-axis and overlapping X-axis.Create a twin Axes sharing the X-axis.Plot a list of data points using plot() method on a seperate Y-axis and overlapping X-axis.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax1 = plt.subplots() ax1.plot([1, 2, 3, 4, 5], color='red') ... Read More
RESTREST (Representational State Transfer) is a modern technique of enabling communication between two software systems. One such system is known as REST Client; the other is known as REST Server. It is an architectural technique based on a stateless communications protocol, such as HTTP. It organizes or structures data in XML, YAML, and other machine-readable formats. However, JSON is mostly used. REST is based on objectoriented programming model.REST is data-driven, unlike SOAP which is function-driven. REST is also referred to as RESTful APIs or RESTful web services. The description format of REST services does not follow a standard. REST service ... Read More
To display a sequence of images using Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a list of images that have to be drawn.Turn off the axes.Iterate the images and redraw over the axes.Take a pause after each draw.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True images = ['opera.jpg', 'mountain.jpg', '9.jpg'] plt.axis('off') img = None for f in images: im = plt.imread(f) if img is None: img = plt.imshow(im) plt.pause(0.5) else: ... Read More
To draw a filled arc 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 two variables, r, yoff.Create x and y data points using Numpy.Fill the area between x and y plots.Set the axis aspect and draw the figure canvas.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 fg, ax = plt.subplots(1, 1) r = 2. yoff = -1 x = np.arange(-1., 1.05, 0.05) y ... Read More
To create a surface plot from a grayscale image with matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create random data points using Numpy.Get the xx and yy data points from a 2d image data raster.Create a new figure or activate an existing figure.Get the current axis of the plot and make it 3d projection axes.Create a surface plot with cmap='gray'.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 data = np.random.rand(5, 5) xx, ... Read More
To remove the first and last ticks label of each Y-axis 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.Iterate the axes and set the first and last ticklabel's visible=False.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig, ax = plt.subplots(2, sharex=True) for a in ax: plt.setp(a.get_yticklabels()[0], visible=False) plt.setp(a.get_yticklabels()[-1], visible=False) plt.show()OutputRead More
To extract only the month and day from a datetime object in Python, we can use the DateFormatter() class.stepsSet the figure size and adjust the padding between and around the subplots.Make a dataframe, df, of two-dimensional, size-mutable, potentially heterogeneous tabular data.Create a figure and a set of subplots.Plot the dataframe using plot() method.Set the axis formatter, extract month and day.To display the figure, use show() method.Exampleimport numpy as np import pandas as pd from matplotlib import pyplot as plt, dates plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True df = pd.DataFrame(dict(time=list(pd.date_range("2021-01-01 12:00:00", periods=10)), speed=np.linspace(1, 10, 10))) fig, ax = ... Read More