Found 784 Articles for Data Visualization

To show a plot in Flask, we can take the following steps−Make a small application.To run Flask application, go to the current directory.$ export FLASK_APP=file.py$ flask runOpen the browser, hit url:http://127.0.0.1:5000/print-plot/To plot the figure, we can create data points for x and y using random.Plot data points, x and y, on the created axis.Write a figure into png figure format.Retrieve the entire contents of the BytesIO object.Exampleimport io from flask import Response from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas from matplotlib.figure import Figure from flask import Flask import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True app = Flask(__name__) ... Read More

To write unit test cases against a code, we can consider a plot that takes an array as x points and plot it as y=x^2. While testing, we would extract y_data for x data points.−StepsCreate a method, i.e., plot_sqr_curve(x) to plot x and x^2 using plot() method and return the plot.To test, use unittest.TestCase.Write test_curve_sqr_plot() method that includes the following statements.Create data points for x to plot the curve.Using the above x data points, create y data points.Using x and y data points, plot the curve.Using pt (from step 5), extract x and y data.Check whether the given expression is ... Read More

To store mouse event coordinates with matplotlib, we can use "button_press_event" event.−StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Plot a line in the range of 10Bind the function *onclick* to the event *button_press_event*.Print the x and y data of the event.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams['backend'] = 'TkAgg' plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Function to print mouse click event coordinates def onclick(event):    print([event.xdata, event.ydata]) # Create a figure and a set of subplots fig, ... Read More

To show mouse release event coordinates with 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 line in the range of 10.Bind the function *onclick* to the event *button_release_event*.Print event and its x and y data.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams['backend'] = 'TkAgg' plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True def onclick(event): print(event.button, event.xdata, event.ydata) fig, ax = plt.subplots() ax.plot(range(10)) fig.canvas.mpl_connect('button_release_event', onclick) plt.show()OutputMouseButton.LEFT 4.961566107601828 1.6644009000562534 MouseButton.LEFT 6.782345894140708 3.7026907931745727 MouseButton.LEFT 2.98552602918754 7.177807987999249Read More

To change the axis tick font in matplotlib when rendering using LaTeX, we can take the following Steps −Create x and y data points using numpy.Using subplot() method, add a subplot to the current figure.Set x and y ticks with data points x and y using set_xticks and set_yticks methods, respectively.Plot x and y using plot() method with color=red.To set bold font weight, we can use LaTeX representation.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = np.array([1, 2, 3, 4]) y = np.exp(x) ax1 = ... Read More

To plot a bar graph from a Pandas series in matplotlib, we can take the following Steps −Make a dictionary of different keys, between the range 1 to 10.Make a dataframe using Pandas data frame.Create a bar plot using plot() method with kind="bar".To display the figure, use show() method.Exampleimport pandas as pd from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True d = {'y=1/x': [1 / i for i in range(1, 10)],    'y=x': [i for i in range(1, 10)],    'y=x^2': [i * i for i in range(1, 10)],    'y=x^3': [i * i * ... Read More

To plot a circle with an edgecolor in matplotlib, we can take the following Steps −Create a new figure or activate an existing figure using figure() method.Add a subplot method to the current axis.Create a circle instance using Circle() class with an edgecolor and linewidth of the edge.Add a circle path on the plot.To place the text in the circle, we can use text() method.Scale the X and Y axes using xlim() and ylim() methods.To display the figure, use show() method.Exampleimport matplotlib from matplotlib import pyplot as plt, patches plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax ... Read More

To rotate tick labels in a subplot, we can use set_xticklabels() or set_yticklabels() with rotation argument in the method.Create a list of numbers (x) that can be used to tick the axes.Get the axis using subplot() that helps to add a subplot to the current figure.Set ticks on the X and Y axes using set_xticks and set_yticks methods, respectively, and the list x (from step 1).Set tick labels with label lists (["one", "two", "three", "four"]) and rotation=45 using set_xticklabels() and set_yticklabels().To add space between axes and tick labels, we can use tick_params() method with pad argument that helps to add ... Read More

To make longer subplot tick marks in matplotlib, we can use tick_params() method for minor and major ticks length and width.StepsAdd a subplot to the current figure using subplot() method.Plot a range(2) values for x and y data points.Turn the minor ticks of the colorbar ON without extruding into the "extend regions".Use tick_params for changing the appearance of ticks and tick labels.To display the figure, use show() method.Examplefrom matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True ax1 = plt.subplot() ax1.plot(range(2), range(2), linewidth=2) ax1.minorticks_on() ax1.tick_params('both', length=20, width=2, which='major') ax1.tick_params('both', length=10, width=1, which='minor') plt.show()OutputRead More

To make bold font weight LaTeX axes label in matplotlib, we can take the following steps−Create data points for x.Create data points for y, i.e., y=sin(x).Plot the curve x and y with LaTex representation.To activate the label, use legend() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt, font_manager as fm fprop = fm.FontProperties(fname='/usr/share/fonts/truetype/malayalam/Karumbi.ttf') plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True x = np.linspace(1, 10, 1000) y = np.sin(x) plt.plot(x, y, label=r'$\sin (x)$', c="red", lw=2) plt.title(label=r'$\sin (x)$', fontproperties=fprop) plt.show()OutputRead More