## Change values on matplotlib imshow() graph axis

Updated on 16-Mar-2021 10:21:58

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First, we can initialize an array matrix and pass it into the imshow method that can help to get the image for the given matrix.StepsCreate a 2D Array i.e., img.Using imshow() method, display the data as an image, i.e., on a 2D regular raster.Use plt.show() method to show the figure.Exampleimport matplotlib.pyplot as plt img = [[1, 2, 4, 5, 6, 7],       [11, 12, 14, 15, 16, 17],       [101, 12, 41, 51, 61, 71],       [111, 121, 141, 151, 161, 171]] plt.imshow(img, extent=[0, 5, 0, 5]) plt.show()Output

## How to make several plots on a single page using Matplotlib(Python)?

Updated on 16-Mar-2021 10:10:17

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Using plt.figure(), we can create a figure and can split that in parts using subplot(221) methods, where nrows=2, nlos=2, and (1, 2, 3, 4) represent the index position.StepsCreate a new figure, or activate an existing figure, using figure() method.Add a subplot to the current figure, using subplot() method, where nrows = 2, ncols = 2 and index = 1.Add a subplot to the current figure, using subplot() method, where nrows = 2, ncols = 2 and index = 2.Add a subplot to the current figure, using subplot() method, where nrows = 2, ncols = 2 and index = 3.Add a ... Read More

## Defining the midpoint of a colormap in Matplotlib

Updated on 16-Mar-2021 10:11:07

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Using plt.subplots(1, 1) method, we can create fig and axis. We can use fig.colorbar to make the color bar at the midpoint of the figure.StepsUsing mgrid() method, nd_grid instance which returns an open multi-dimensional "meshgrid".Create Z1, Z2 and Z data.Create fig and ax variables using subplots method, where default nrows and ncols are 1, using subplots() method.Create a colorbar for a ScalarMappable instance, *mappable*, using colorbar() method.Using plt.show(), we can show the figure.Exampleimport numpy as np import matplotlib.pyplot as plt import matplotlib.colors as colors N = 100 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)] Z1 = np.exp(-(X)**2 - ... Read More

## How to plot ROC curve in Python?

Updated on 16-Mar-2021 10:11:36

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ROC − Receiver operating characteristics (ROC) curve.Using metrics.plot_roc_curve(clf, X_test, y_test) method, we can draw the ROC curve.StepsGenerate a random n-class classification problem. This initially creates clusters of points normally distributed (std=1) about vertices of an n_informative-dimensional hypercube with sides of length 2*class_sep and assigns an equal number of clusters to each class.It introduces interdependence between these features and adds various types of further noise to the data. Use the make_classification() method.Split arrays or matrices into random trains, using train_test_split() method.Fit the SVM model according to the given training data, using fit() method.Plot Receiver operating characteristic (ROC) curve, using plot_roc_curve() method.To ... Read More

## Automatically run %matplotlib inline in IPython Notebook

Updated on 16-Mar-2021 20:15:30

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%matplotlib would return the backend value.%matplotlib auto would return the name of the backend, over Ipython shell.ExampleIn [1]: %matplotlib autoOutputUsing matplotlib backend: GTK3Agg

## How to make several plots on a single page using matplotlib in Python?

Updated on 16-Mar-2021 10:12:58

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Using Pandas, we can create a data frame and create a figure and axis. After that, we can use the scatter method to draw points.StepsCreate lists of students, marks obtained by them, and color codings for each score.Make a data frame using Panda’s DataFrame, with step 1 data.Create fig and ax variables using subplots method, where default nrows and ncols are 1.Set the X-axis label using plt.xlabel() method.Set the Y-axis label using plt.ylabel() method.A scatter plot of *y* vs. *x* with varying marker size and/or color.To show the figure, use plt.show() method.Examplefrom matplotlib import pyplot as plt import pandas as ... Read More

## How to make several plots on a single page using Matplotlib?

Updated on 16-Mar-2021 10:13:38

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We can create a figure using plt.figure() method and can create axes using fig.add_subplot(211) where nrow=2 and ncols=1. After that, we can plot the lines over both the plots in a figure.StepsUsing gcf() method, Get the current figure. If no current figure exists, a new one is created using ~.pyplot.figure().Add an ~.axes.Axes to the figure as part of a subplot arrangement, using add_subplot() method, where nrows = 2, ncols = 1 and position = 1.Plot the line using plt.plot() method at position 1.Add an ~.axes.Axes to the figure as part of a subplot arrangement, using add_subplot() method, where nrows = ... Read More

## What is the currently correct way to dynamically update plots in Jupyter/iPython?

Updated on 16-Mar-2021 10:15:11

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We can first activate the figure using plt.ion() method. Then, we can update the plot with different sets of values.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 line, i.e., orange.Activate the interaction, using plt.ion() method.To make the plots interactive, change the line coordinates.ExampleIn [1]: %matplotlib auto Using matplotlib backend: GTK3Agg In [2]: import matplotlib.pyplot as plt # Diagram will get popped up. Let’s update the diagram. In [3]: fig, ax = plt.subplots() # Drawing a line In [4]: ax.plot(range(5)) In ... Read More

## How can I set the 'backend' in matplotlib in Python?

Updated on 16-Mar-2021 10:08:06

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We can use matplotlib.rcParams['backend'] to override the backend value.StepsUsing get_backend() method, return the name of the current backend, i.e., default name.Now override the backend name.Using get_backend() method, return the name of the current backend, i.e., updated name.Exampleimport matplotlib print("Before, Backend used by matplotlib is: ", matplotlib.get_backend()) matplotlib.rcParams['backend'] = 'TkAgg' print("After, Backend used by matplotlib is: ", matplotlib.get_backend())OutputBefore, Backend used by matplotlib is: GTK3Agg After, Backend used by matplotlib is: TkAgg

## Scatter plot and Color mapping in Python

Updated on 16-Mar-2021 10:07:43

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We can create a scatter plot using the scatter() method and we can set the color for every data point.StepsCreate random values (for x and y) in a given shape, using np.random.rand() method.Create a scatter plot of *y* vs. *x* with varying marker size and/or color, using the scatter method where color range would be in the range of (0, 1000).Show the figure using plt.show().Exampleimport matplotlib.pyplot as plt import numpy as np x = np.random.rand(1000) y = np.random.rand(1000) plt.scatter(x, y, c=[i for i in range(1000)]) plt.show()OutputRead More