[英]Trouble Plotting a Ridge Plot in Seaborn
I have a dataframe hour_dist that shows the hour a customer showed up to a particular location.我有一个数据框 hour_dist 显示客户出现在特定位置的小时数。
hour_dist.sample(5)
Location Hour
88131 1233000000000000 21
111274 1233000000000000 0
81126 2991000000000000 23
104181 1232000000000000 22
55719 1232000000000000 15
I'm trying to plot this data with Seaborn to visualize a ridgeline plot ( https://seaborn.pydata.org/examples/kde_ridgeplot.html ).我正在尝试使用 Seaborn 绘制此数据以可视化山脊线图 ( https://seaborn.pydata.org/examples/kde_ridgeplot.html )。
It should essentially show the hour distribution by each location.它应该基本上显示每个位置的小时分布。 Here's an example of what it looks like:
下面是它的外观示例:
With hour_dist, I've been trying, unsuccessfully, to plot the locations on the y axis and the hour on the x axis.使用hour_dist,我一直在尝试绘制y 轴上的位置和x 轴上的小时,但没有成功。
For me working change g
to Location
and x
to Hour
, but if many unique Location
values there should be many plots with real data:对我来说,将
g
更改为Location
并将x
更改为Hour
,但是如果有许多唯一的Location
值,那么应该有很多带有真实数据的图:
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)})
# Initialize the FacetGrid object
pal = sns.cubehelix_palette(10, rot=-.25, light=.7)
g = sns.FacetGrid(df, row="Location", hue="Location", aspect=15, height=.5, palette=pal)
If need plot by percentage:如果需要按百分比绘制:
#df['pct'] = df['Location'].div(df.groupby('Hour')['Location'].transform('sum'))
#g = sns.FacetGrid(df, row="pct", hue="pct", aspect=15, height=.5, palette=pal)
# Draw the densities in a few steps
g.map(sns.kdeplot, "Hour", clip_on=False, shade=True, alpha=1, lw=1.5, bw=.2)
g.map(sns.kdeplot, "Hour", 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, .2, label, fontweight="bold", color=color,
ha="left", va="center", transform=ax.transAxes)
g.map(label, "Hour")
# Set the subplots to overlap
g.fig.subplots_adjust(hspace=-.25)
# Remove axes details that don't play well with overlap
g.set_titles("")
g.set(yticks=[])
g.despine(bottom=True, left=True)
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