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Using plt.xticks, we can change the X-axis scale.StepsUsing plt.plot() method, we can create a line with two lists that are passed in its argument.Add text to the axes. Add the text *s* to the axes at location *x*, *y* in data coordinates, using plt.text() method, where the font size can be customized by changing the font-size value.Using xticks method, get or set the current tick locations and labels of the X-axis.To show the figure, use plt.show() method.Exampleimport matplotlib.pyplot as plt plt.plot([1, 2, 4], [1, 2, 4]) plt.text(2, 3, "y=x", color='red', fontsize=20) plt.xticks([1, 2, 3, 4, 5]) ... Read More
Using plt.text() method, we can increase the font size.StepsUsing plt.plot() method, we can create a line with two lists that are passed in its argument.Add text to the axes. Add the text *s* to the axes at location *x*, *y* in data coordinates, using plt.text() method. Font size can be customized by changing the font-size value.To show the figure, use plt.show() method.Exampleimport matplotlib.pyplot as plt plt.plot([1, 2, 4], [1, 2, 4]) plt.text(2, 3, "y=x", color='red', fontsize=20) # Increase fontsize by increasing value. plt.show()Output
We can use a user-defined method, autolabel, to annotate the axis value. Before that, we can initialize the fig and ax using plt.subplots() method.StepsCreate lists, labels, men_means, and women_means with different data elements.Return evenly spaced values within a given interval, using numpy.arrange() method.Set the width variable i.e., width=0.35.Create fig and ax variables using subplots method, where default nrows and ncols are 1.The bars are positioned at *x* with the given *align*\ment. Their dimensions are given by *height* and *width*. The vertical baseline is *bottom* (default 0), so create rect1 and rect2 using plt.bar() method.Set the Y-axis label using plt.ylabel() method.Set ... Read More
In this program, we can create a data frame and can plot a bar using df.plot.bar(x='lab', y='value', color='#5fba34') plot.StepsUsing Panda’s dataframe, we can create a data frame with the given dictionary, where the keys are lab and value, values of these keys are lists, respectively.Using Pandas plot.bar() method, we can create a vertical bar plot. A bar plot is a plot that presents categorical data with rectangular bars with lengths proportional to the values that they represent.To show the figure, use the plt.show() method.Exampleimport pandas as pd from matplotlib import pyplot as plt df = pd.DataFrame({'lab': ['A', 'B', 'C'], ... Read More
Using plt.grid(axis="x") method, we can plot vertical gridlines.StepsMake a list of numbers.Set the X-axis label using plt.xlabel() method.Set the Y-axis label using plt.ylabel() method.Toggle the gridlines, and optionally set the properties of the lines, using plt.grid() method.To show the figure, use the plt.show() method, where the argument axis can be “x”, “y” or “both”.Examplefrom matplotlib import pyplot as plt plt.plot([0, 5], [0, 5]) plt.ylabel("Y-axis ") plt.xlabel("X-axis ") plt.grid(axis="x") plt.show()Output
We can use the resolution value, i.e., dots per inch, and the image format to plot a high-resolution graph in Matplotlib.StepsCreate a dictionary with Column 1 and Column 2 as the keys and Values are like i and i*i, where i is from 0 to 10, respectively.Create a data frame using pd.DataFrame(d); d created in step 1.Plot the data frame with ‘o’ and ‘rx’ style.To save the file in pdf format, use savefig() method where the image name is myImagePDF.pdf, format="pdf".We can set the dpi value to get a high-quality image.Using the saving() method, we can save the image with ... Read More
To get interactive plots, we need to activate the figure. Using plt.ioff() and plt.ion(), we can perform interactive actions with plot.StepsCreate fig and ax variables using subplots method, where default nrows and ncols are 1.Draw a line, using plot() method.Set the color of the line, i.e., orange.Stopped the interaction, using plt.ioff() method.To make the interaction plots, change the color of the line coordinate.Start the interaction, using plt.ion() method.ExampleTo use interactive plot in Ipython -In [1]: %matplotlib auto Using matplotlib backend: GTK3Agg In [2]: import matplotlib.pyplot as plt In [3]: fig, ax = plt.subplots() # Diagram will ... Read More
First, we can initialize the dictionary with col1 and col2, convert it into a data frame. After that, we can plot this data with ‘o’ and ‘rx’ style.StepsCreate a dictionary with Column 1 and Column 2 as the keys and Values are like i and i*i, where i is from 0 to 10, respectively.Create a data frame using pd.DataFrame(d); d created in step 1.Plot the data frame with ‘o’ and ‘rx’ style.To show the plot, use plt.show().Exampleimport pandas as pd from matplotlib import pyplot as plt d = {'Column 1': [i for i in range(10)], 'Column 2': [i*i for ... Read More
Using matplotlib.get_backend(), we can get the backend value.StepsImport matplotlib.To return the name of the current backend, use the get_backend() method.Exampleimport matplotlib print("Backend used by matplotlib is: ", matplotlib.get_backend())OutputBackend used by matplotlib is: GTK3Agg
We can iterate a plot using display.clear_output(wait=True), display.display(pl.gcf()) and time.sleep() methods in a loop to get the exact output.StepsPlot a sample (or samples) from the "standard normal" distribution using pylab.randn().Clear the output of the current cell receiving output, wait=False(default value), wait to clear the output until new output is available to replace it.Display a Python object in all frontends. By default, all representations will be computed and sent to the frontends. Frontends can decide which representation is used and how, using the display() method. pl.gcf helps to get the current figure.To sleep for a while, use time.sleep() method.Exampleimport time import ... Read More