I am not sure if this is the prettiest way to do it, but it does fix it with one line: import matplotlib.pyplot as plt import seaborn as sns import numpy as np import pandas as pd np.random.seed(0) fig = plt.figure() ax1 = fig.add_subplot(111) ax1.plot(pd.Series(np.random.uniform(0, 1, size=10))) ax2 = ax1.twinx() ax2.plot(pd.Series(np.random.uniform(10, 20, size=10)), color=”r”) # … Read more
Using the new pandas release (0.14.0 or later) the below code will work. To create the two axis I have manually created two matplotlib axes objects (ax and ax2) which will serve for both bar plots. When plotting a Dataframe you can choose the axes object using ax=…. Also in order to prevent the two … Read more
Actually both linked questions provide a way how to achieve the desired result. The easiest method is to create as many scatter plots as unique classes exist and give each a single color and legend entry. import matplotlib.pyplot as plt x=[1,2,3,4] y=[5,6,7,8] classes = [2,4,4,2] unique = list(set(classes)) colors = [plt.cm.jet(float(i)/max(unique)) for i in unique] … Read more
They all do different things, since matplotlib uses a hierarchical order in which a figure window contains a figure which may consist of many axes. Additionally, there are functions from the pyplot interface and there are methods on the Figure class. I will discuss both cases below. pyplot interface pyplot is a module that collects … Read more
You can do this with quiver, but it’s a little tricky to get the keyword arguments right. import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 2*np.pi, 10) y = np.sin(x) plt.figure() plt.quiver(x[:-1], y[:-1], x[1:]-x[:-1], y[1:]-y[:-1], scale_units=”xy”, angles=”xy”, scale=1) plt.show()
Translate from user data coordinate system to display coordinate system. and use edgecolors=”none” to plot faces with no outlines. import numpy as np fig = figure() ax = fig.add_subplot(111) dx_in_points = np.diff(ax.transData.transform(zip([0]*len(dx), dx))) scatter(x,y,c=z,s=dx_in_points**2,marker=”s”, edgecolors=”none”)
Try passing columns of the DataFrame directly to matplotlib, as in the examples below, instead of extracting them as numpy arrays. df = pd.DataFrame(np.random.randn(10,2), columns=[‘col1′,’col2’]) df[‘col3’] = np.arange(len(df))**2 * 100 + 100 In [5]: df Out[5]: col1 col2 col3 0 -1.000075 -0.759910 100 1 0.510382 0.972615 200 2 1.872067 -0.731010 500 3 0.131612 1.075142 1000 … Read more