seaborn

问题 If I have the following data and Seaborn Heatmap: import pandas as pd data = pd.DataFrame({'x':(1,2,3,4),'y':(1,2,3,4),'z':(14,15,23,2)}) sns.heatmap(data.pivot_table(index='y', columns='x', values='z')) How do I add a label to the colour bar? 回答1: You could set it afterwards after collecting it from an ax , or simply pass a label in cbar_kws like so. import seaborn as sns import pandas as pd data = pd.DataFrame({'x':(1,2,3,4),'y':(1,2,3,4),'z':(14,15,23,2)}) sns.heatmap(data.pivot_table

问题 I have plotted a Seaborn JointPlot from a set of "observed counts vs concentration" which are stored in a pandas DataFrame . I would like to overlay (on the same set of axes) a marginal (ie: univariate distribution) of the "expected counts" for each concentration on top of the existing marginal, so that the difference can be easily compared. This graph is very similar to what I want, although it will have different axes and only two datasets: Here is an example of how my data is laid out and

问题 I am wondering if there is a way to turn off the linear fit in seaborn's lmplot or if there is an equivalent function that just produces the scatterplot. Sure, I could also use matplotlib, however, I find the syntax and aesthetics in seaborn quite appealing. E.g,. I want to plot the following plot import seaborn as sns sns.set(style="ticks") df = sns.load_dataset("anscombe") sns.lmplot("x", "y", data=df, hue='dataset') Without the linear fit like so: from itertools import cycle import numpy

问题 I am plotting with seaborn a series of boxplots with sns.boxplot(full_array) where full_array contains 200 arrays. Therefore, I have 200 boxplots and ticks on the x-axis from 0 to 200. The xticks are too close to each other and I would like to show only some of them, for instance, a labeled xtick every 20, or so. I tried several solutions as those mentioned here but they did not work. Every time I sample the xticks, I get wrong labels for the ticks, as they get numbered from 0 to N, with unit

问题 I am plotting with seaborn a series of boxplots with sns.boxplot(full_array) where full_array contains 200 arrays. Therefore, I have 200 boxplots and ticks on the x-axis from 0 to 200. The xticks are too close to each other and I would like to show only some of them, for instance, a labeled xtick every 20, or so. I tried several solutions as those mentioned here but they did not work. Every time I sample the xticks, I get wrong labels for the ticks, as they get numbered from 0 to N, with unit

问题 I'm a bit struggling with seaborn Pairgrid. Let's say I have a Pairgrid like this: As you can see, the upper triangle plots mirror the lower triangle ones. I'd like to be able to plot only the lower triangle plots, but I found no easy way so far to do it. Can you help me? 回答1: Probably should be an easier way, but this works import numpy as np import seaborn as sns iris = sns.load_dataset("iris") g = sns.pairplot(iris) for i, j in zip(*np.triu_indices_from(g.axes, 1)): g.axes[i, j].set

问题 i have this plot of a dataframe with seaborn's facetgrid: import seaborn as sns import matplotlib.pylab as plt import pandas import numpy as np plt.figure() df = pandas.DataFrame({"a": map(str, np.arange(1001, 1001 + 30)), "l": ["A"] * 15 + ["B"] * 15, "v": np.random.rand(30)}) g = sns.FacetGrid(row="l", data=df) g.map(sns.pointplot, "a", "v") plt.show() seaborn plots all the xtick labels instead of just picking a few and it looks horrible: Is there a way to customize it so that it plots

问题 I'm plotting two data series with Pandas with seaborn imported. Ideally I would like the horizontal grid lines shared between both the left and the right y-axis, but I'm under the impression that this is hard to do. As a compromise I would like to remove the grid lines all together. The following code however produces the horizontal gridlines for the secondary y-axis. import pandas as pd import numpy as np import seaborn as sns data = pd.DataFrame(np.cumsum(np.random.normal(size=(100,2)),axis

问题 I am trying to change the size of the lmplot markers in seaborn. I have tried passing 's' or 'size' as arguments and neither of them work. lm = sns.lmplot(x="totalX",y="NormI", hue="Data Type", data=df, palette="Set1", legend_out=False, S=20) I have tried "s", "markersize", "size" I get no effect. I want to make the data points larger on the plot. Any help is much appreciated. 回答1: You want to use scatter_kws={"s": 100} As in: lm = sns.lmplot(x = "totalX", y = "NormI", hue = "Data Type", data

问题 Seaborn allows defining color palettes that contain multiple colors, useful for charts with many lines. However, when setting the palette to one with multiple colors, only the first six are used, after which colors recycle, making it hard to distinguish lines. This can be overridden by explicitly calling the palette, but that's not convenient. Is there a way to force the Seaborn current palette not to recycle colors, when more than 6 are defined? Example: from matplotlib import pyplot as plt