pandas 和 seaborn 的堆积密度图 [英] Stacked density plots with pandas and seaborn

问题描述

我正在尝试从 Pandas 数据框中获取以下图.

I am trying to obtain the following plot from a pandas data frame.

我不确定如何将 seaborn 与 Pandas 结合起来完成这项任务.

I am not sure how to combine seaborn with pandas for that task.

这是我要使用的数据框:

This is the dataframe I want to use:

import pandas as pd

data = pd.DataFrame({'a': np.random.randn(1000) + 1,
              'b': np.random.randn(1000),
              'c': np.random.rand(1000) + 10},        
             columns=['a', 'b', 'c'])

data.a[data.a.sample(100).index] = np.NaN
data.b[data.b.sample(800).index] = np.NaN

请注意，频率需要归一化(直方图的高度)，因为数据点和分布的数量存在显着差异，并且分布具有不同的y 尺度".

Notice that the frequency will need to be normalized (height of the histogram), as the number of data points and distributions differ significantly and the distributions will have different 'y scales'.

data.plot.hist();

这是生成我一开始用的图的seaborn的代码.

This is the code of seaborn that generates the figure I used in the beginning.

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})

# Create the data
rs = np.random.RandomState(1979)
x = rs.randn(150)
g = np.tile(list("ABC"), 50)
df = pd.DataFrame(dict(x=x, g=g))
m = df.g.map(ord)

# Initialize the FacetGrid object
pal = sns.cubehelix_palette(10, rot=-.25, light=.7)
g = sns.FacetGrid(df, row="g", hue="g", aspect=5, height=1, palette=pal)

# Draw the densities in a few steps
g.map(sns.kdeplot, "x", clip_on=False, shade=True, alpha=1, lw=1.5, bw=.2)
g.map(sns.kdeplot, "x", clip_on=False, color="w", lw=2, bw=.2)
g.map(plt.axhline, y=0, lw=2, clip_on=False)


# Define and use a simple function to label the plot in axes coordinates
def label(x, color, label):
    ax = plt.gca()
    ax.text(0, .3, label, fontweight="bold", color=color,
            ha="left", va="center", transform=ax.transAxes)


g.map(label, "x")

# Set the subplots to overlap
g.fig.subplots_adjust(hspace=-.0025)

# Remove axes details that don't play well with overlap
g.set_titles("")
g.set(yticks=[])
g.despine(bottom=True, left=True)

推荐答案

这是一个创建 kde 图(joyplot")网格的函数，每个数据框列一个图.

Here is a function to create a grid of kde plots ("joyplot") with one plot per dataframe column.

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gaussian_kde


def joyplot_from_dataframe(data, cmap=None):
    mi, ma = np.nanmin(data.values), np.nanmax(data.values)
    minx = mi - (ma-mi)/5
    maxx = ma + (ma-mi)/5
    x = np.linspace(minx,maxx, 1000)

    n = len(data.columns)

    if not cmap:
        cmap = plt.cm.get_cmap("Blues")
    colors = cmap(np.linspace(.2,1,n))

    fig, axes = plt.subplots(nrows = n, sharex=True)

    for c, ax, color in zip(data.columns, axes, colors):
        y = data[c].values
        y = y[~np.isnan(y)]
        kde = gaussian_kde(y)
        ax.fill_between(x, kde(x), color=color)
        ax.yaxis.set_visible(False)
        for spine in ["left", "right", "top"]:
            ax.spines[spine].set_visible(False)
        ax.spines["bottom"].set_linewidth(2)
        ax.spines["bottom"].set_color(color)
        ax.margins(y=0)
        ax.tick_params(bottom=False)

    return fig, axes

将其用作

import pandas as pd

data = pd.DataFrame({'a': np.random.randn(1000) + 1,
              'b': np.random.randn(1000),
              'c': np.random.rand(1000) + 10},        
             columns=['a', 'b', 'c'])

data.a[data.a.sample(100).index] = np.NaN
data.b[data.b.sample(800).index] = np.NaN


joyplot_from_dataframe(data)    
plt.show()

这篇关于pandas 和 seaborn 的堆积密度图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！