To plot multiple boxplots in one graph in Pandas or Matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Make a Pandas data frame with two columns.Plot the data frame using plot() method, with kind='boxplot'.To display the figure, use show() method.Exampleimport pandas as pd import numpy as np from matplotlib import pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Pandas dataframe data = pd.DataFrame({"Box1": np.random.rand(10), "Box2": np.random.rand(10)}) # Plot the dataframe ax = data[['Box1', 'Box2']].plot(kind='box', title='boxplot') # Display ... Read More

To plot a multivariate function in Python, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create random x, y and z data points using numpy.Create a figure and a set of subplots.Create a scatter plot with x, y and z data points.Create a colorbar for a ScalarMappable instance, s.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 def func(x, y):    return 3 * x + 4 * y - 2 + np.random.randn(30) x, y ... Read More

To make a polygon radar (spider) chart in Python, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create a Pandas dataframe with sports and values columns.Create a new figure or activate an existing figure.Add an 'ax' to the figure as part of a subplot arrangement.Based on data frame values, get the theta value.Get the values list of the data frame.Make a bar plot with theta and values data points.Fill the area between polygon.To display the figure, use show() method.Exampleimport pandas as pd import matplotlib.pyplot as plt import numpy as np ... Read More

To add a legend in a 3D scatterplot with scatter() in matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable N to store the number of sample data.Create x and y data points; make z1 and z2 data points list.Add a subplot to the current figure, with projection='3d'.Plot the x, y and z1 data points using plot() points on 2d axes, with marker *.Plot the x, y and z2 data points using plot() points on 2d axes, with marker o.Place legend on the figure.To display the figure ... Read More

To remove NaN values from a dataframe without filter or interpolate, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create an array to make a Pandas data frame.One-dimensional ndarray with axis labels (including time series).Plotting interpolation, 'index', 'values' − Use the actual numerical values of the index.To display the figure, use show() method.Exampleimport numpy as np import pandas as pd from matplotlib import pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Numpy array data = np.array([1., 1.2, 0.89, np.NAN,   ... Read More

To specify different colors for different bars in a matplotlib histogram, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create a figure and a set of subplots.Plot a histogram with random data with 100 sample data.Iterate in the range of number of bins and set random facecolor for each bar.To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt import random import string # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Figure and set of subplots fig, ax = ... Read More

To fill a region with only hatch (no background color) in matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable n to store the number of sample data.Create a figure and a set of subplots.Plot the x and y data points.Fill the area between x and y with circle hatches, edgecolor="blue".To display the figure, use show() method.Exampleimport numpy as np import matplotlib.pyplot as plt # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Number of sample data n = 256 ... Read More

To set a line color to orange, and specify line markers in matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create x and y data points using numpy.Plot the x and y data points with the attributes color='orange' and marker='*'.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # x and y data points x = np.linspace(-5, 5, 100) y = np.sin(x) # Plot the data points with color ... Read More

To deal with NaN value while plotting a boxplot using Python, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Initialize a variable N for data samples and for range.Next create the random spread, center's data, flier high and low, get the concatenated data, and the filtered data.Create a box plot using boxplot() method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # Data samples N = 10 # Random spread ... Read More

To plot a smooth line with matplotlib, we can take the following steps −StepsSet the figure size and adjust the padding between and around the subplots.Create a list of data points, x and y.Plot the x and y data points.Create x_new and bspline data points for smooth line.Get y_new data points. Compute the (coefficients of) interpolating B-spline.Plot x_new and y_new data points using plot() method.To display the figure, use show() method.Exampleimport numpy as np from matplotlib import pyplot as plt from scipy import interpolate # Set the figure size plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True # x ... Read More