matplotlib

问题 I am new to python and having some difficulties with plotting using pyplot . My goal is to plot a grid of plots in-line ( %pylab inline ) in Juypter Notebook. I programmed a function plot_CV which plots cross-validation erorr over the degree of polynomial of some x where across plots the degree of penalization (lambda) is supposed to vary. Ultimately there are 10 elements in lambda and they are controlled by the first argument in plot_CV . So fig = plt.figure() ax1 = fig.add_subplot(1,1,1)

问题 I am new to python and having some difficulties with plotting using pyplot . My goal is to plot a grid of plots in-line ( %pylab inline ) in Juypter Notebook. I programmed a function plot_CV which plots cross-validation erorr over the degree of polynomial of some x where across plots the degree of penalization (lambda) is supposed to vary. Ultimately there are 10 elements in lambda and they are controlled by the first argument in plot_CV . So fig = plt.figure() ax1 = fig.add_subplot(1,1,1)

问题 I am new to python and having some difficulties with plotting using pyplot . My goal is to plot a grid of plots in-line ( %pylab inline ) in Juypter Notebook. I programmed a function plot_CV which plots cross-validation erorr over the degree of polynomial of some x where across plots the degree of penalization (lambda) is supposed to vary. Ultimately there are 10 elements in lambda and they are controlled by the first argument in plot_CV . So fig = plt.figure() ax1 = fig.add_subplot(1,1,1)

问题 I am new to Pandas and Seaborn and trying to learn. I am trying to add a trend line and a bar plot on the same graph. I have some data that looks like Year Sample Size 2000 500 2001 3000 2003 10000 2004 20000 2004 23000 I am new to pandas and seaborn and I am attempting to draw a line through the bar plot showing a decreasing or an increasing trend but struggling to do it on the same graph. Till now, I have a bar plot. Below you can find the code. sampleSizes['Sample Size'] -> is the column I

问题 I'm trying to make a heatmap with the colour bar on the side using seaborn. However, in my real application case, I have long column names that I rotate. This requires the use of plt.subplots_adjust , otherwise the labels do not fit in the image: plt.setp(g.ax_heatmap.get_xticklabels(), rotation=90) plt.subplots_adjust(bottom=0.5) Using the following minimal example, I found out that this last command was cancelling the colour bar relocation: #!/usr/bin/env python3 import seaborn as sns

问题 As a minimal reproducible example, suppose I have the following multivariate normal distribution: import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from scipy.stats import multivariate_normal, gaussian_kde # Choose mean vector and variance-covariance matrix mu = np.array([0, 0]) sigma = np.array([[2, 0], [0, 3]]) # Create surface plot data x = np.linspace(-5, 5, 100) y = np.linspace(-5, 5, 100) X, Y = np.meshgrid(x, y) rv = multivariate_normal(mean=mu,

问题 As a minimal reproducible example, suppose I have the following multivariate normal distribution: import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from scipy.stats import multivariate_normal, gaussian_kde # Choose mean vector and variance-covariance matrix mu = np.array([0, 0]) sigma = np.array([[2, 0], [0, 3]]) # Create surface plot data x = np.linspace(-5, 5, 100) y = np.linspace(-5, 5, 100) X, Y = np.meshgrid(x, y) rv = multivariate_normal(mean=mu,

问题 As a minimal reproducible example, suppose I have the following multivariate normal distribution: import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from scipy.stats import multivariate_normal, gaussian_kde # Choose mean vector and variance-covariance matrix mu = np.array([0, 0]) sigma = np.array([[2, 0], [0, 3]]) # Create surface plot data x = np.linspace(-5, 5, 100) y = np.linspace(-5, 5, 100) X, Y = np.meshgrid(x, y) rv = multivariate_normal(mean=mu,

问题 I have a scatter plot where i am plotting 14 clusters, but each 2 clusters belong to the same class, they are all using the same markers. Every 50 rows is a cluster and every 100 rows is two clusters of the same class. What i want to do is change the markers for every 2 clusters or 100 rows. Link for the Data Frame import pandas as pd import numpy as np from matplotlib import pyplot as plt from matplotlib.pyplot import figure y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

问题 I have a scatter plot where i am plotting 14 clusters, but each 2 clusters belong to the same class, they are all using the same markers. Every 50 rows is a cluster and every 100 rows is two clusters of the same class. What i want to do is change the markers for every 2 clusters or 100 rows. Link for the Data Frame import pandas as pd import numpy as np from matplotlib import pyplot as plt from matplotlib.pyplot import figure y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,