覆盖 pandas KDE 和直方图时正确渲染 y 轴

Question

Similar questions to this have been asked before but not using these exact two plotting functions together so here we are:

I have a column from a pandas DataFrame that I am plotting both a histogram and the KDE. However, when I plot them, the y-axis is using the raw data value range instead of discrete number of samples/bin (what I want). How can I fix this? The actual plot is perfect, but the y-axis is wrong.

Data:

t2 = [140547476703.0, 113395471484.0, 158360225172.0, 105497674121.0, 186457736557.0, 153705359063.0, 36826568371.0, 200653068740.0, 190761317478.0, 126529980843.0, 98776029557.0, 132773701862.0, 14780432449.0, 167507656251.0, 121353262386.0, 136377019007.0, 134190768743.0, 218619462126.0, 07912778721.0, 215628911255.0, 147024833865.0, 94136343562.0, 135685803096.0, 165901502129.0, 45476074790.0, 125195690010.0, 113910844263.0, 123134290987.0, 112028565305.0, 93448218430.0, 07341012378.0, 93146854494.0, 132958913610.0, 102326700019.0, 196826471714.0, 122045354980.0, 76591131961.0, 134694468251.0, 120212625727.0, 108456858852.0, 106363042112.0, 193367024628.0, 39578667378.0, 178075400604.0, 155513974664.0, 132834624567.0, 137336282646.0, 125379267464.0]

Code:

fig = plt.figure()
# plot hist + kde
t2[t2.columns[0]].plot.kde(color = "maroon", label = "_nolegend_")
t2[t2.columns[0]].plot.hist(density = True, edgecolor = "grey", color = "tomato", title = t2.columns[0])

# plot mean/stdev
m = t2[t2.columns[0]].mean()
stdev = t2[t2.columns[0]].std()
plt.axvline(m, color = "black", ymax = 0.05, label = "mean")
plt.axvline(m-2*stdev, color = "black", ymax = 0.05, linestyle = ":", label = "+/- 2*Stdev")
plt.axvline(m+2*stdev, color = "black", ymax = 0.05, linestyle = ":")
plt.legend()

What it looks like now:

Answer 1

If you want the real counts, the you'll need to scale the KDE up by the width of the bins multiplied by the number of observations. The trickiest part is accessing the data pandas uses to plot the KDE. (I've removed parts related to the legend to simplify the problem at hand).

import matplotlib.pyplot as plt
import numpy as np

# Calculate KDE, get data
axis = t2[t2.columns[0]].plot.kde(color = "maroon", label = "_nolegend_")
xdata = axis.get_children()[0]._x
ydata = axis.get_children()[0]._y
plt.clf()


# Real figure
fig, ax = plt.subplots(figsize=(7,5))
# Plot Histogram, no density.
x = ax.hist(t2[t2.columns[0]], edgecolor = "grey", color = "tomato")

# size of the bins * N obs
scale = np.diff(x[1])[0]*len(t2)

# Plot scaled KDE
ax.plot(xdata, ydata*scale, color='blue')
ax.set_ylabel('N observations')

plt.show()