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To create vertical line for X variable at median in base R plot, we can follow the below steps −First of all, create two vectors and plot them.Create the vertical line at median using abline function.Create the vectors and plot themLet’s create two random vectors and plot them as shown below −Example Live Demox
The hist() method returns n, bins and patches in matplotlib. Patches are the containers of individual artists used to create the histogram or list of such containers if there are multiple input datasets. Bins define the number of equal-width bins in the range.Let's take an example to understand how it works.stepsSet the figure size and adjust the padding between and around the subplots.Create random data points using numpy.Make a Hist plot with 100 bins.Set a property on an artist object.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"] = ... Read More
To get all the legends from a plot in matplotlib, we can use the get_children() method to get all the properties of an axis, then iterate all the properties. If an item is an instance of a Legend, then get the legend texts.stepsSet the figure size and adjust the padding between and around the subplots.Create x data points using numpy.Create a figure and a set of subplots.Plot sin(x) and cos(x) using plot() method with different labels and colors.Get the children of the axis and get the texts of the legend.To display the figure, use show() method.Exampleimport numpy as np from ... Read More
To plot a multicolored line based on a condition in Python Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create y data points using numpy.Make l and u data points to differentiate the colors.Plot the u and l data points using plot() method, with different colors.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 y = np.sin(np.linspace(-10, 10, 100)) u = y.copy() l = y.copy() u[u = 0] = np.nan plt.plot(u, color='red') ... Read More
To create a Boxplot with Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a list of xticks.Plot a boxplot with xticks data.Set xticks and xtick labels with 45° rotation.To display the figure, use show() method.Exampleimport seaborn as sns from matplotlib import pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True xticks = [1, 4, 5, 2, 3] ax = sns.boxplot(xticks) ax.set_xticks(xticks) ax.set_xticklabels(["one", "two", "three", "four", "five"], rotation=45) plt.show()Output
To plot histogram bins interpreted with different bins, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a list of data to plot in histogram.Add a subplot to the current figure, nrows=1, ncols=3 and index=1.Plot a histogram with data; bins is a number.Add a subplot to the current figure, nrows=1, ncols=3 and index=2.Plot a histogram with data; bins is an array.Add a subplot to the current figure, nrows=1, ncols=3 and index=3.Plot a histogram with data, bins is a string.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True ... Read More
To annotate points from a Pandas dataframe in Matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a two-dimensional, size-mutable, potentially heterogeneous tabular data, with x, y and textc columns.Plot the columns x and y data points, using plot() method.Concatenate Pandas objects along a particular axis with optional set logic along the other axes.Iterate the Pandas object.Place text for each plotted points using text() method.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 df = ... Read More
To show a bar and line graph on the same plot in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a two-dimensional, size-mutable, potentially heterogeneous tabular data.Create a figure and a set of subplots.Plot the bar and line with the dataframe obtained from Step 2.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 df = pd.DataFrame(dict(data=[2, 4, 1, 5, 9, 6, 0, 7])) fig, ax = plt.subplots() df['data'].plot(kind='bar', color='red') df['data'].plot(kind='line', marker='*', color='black', ms=10) ... Read More
To plot blurred points in matplotlib, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing new figure.Add an ax1 to the figure as part of a subplot arrangement.First, we can make a marker, i.e., to be blurred.Set the X and Y axes scale, turn off the axes.Save the marker in a file, and load that image to be plotted after blurred.Close the previous figure, fig1.Create a new figure or activate an existing figure, fig2.Create random data points, x and y.Apply Gaussian filter, to ... Read More
To randomize column values of a data.table object for a set of columns in R, we can follow the below steps −First of all, create a data.table object.Then, use sample function for randomizationwith lapply while selecting the columns with SDcols.Create the data frameLet's create a data frame as shown below −Examplelibrary(data.table) ID