you can get cdf easily. so pdf via cdf
import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate
import scipy.stats
def setGridLine(ax):
#http://jonathansoma.com/lede/data-studio/matplotlib/adding-grid-lines-to-a-matplotlib-chart/
ax.set_axisbelow(True)
ax.minorticks_on()
ax.grid(which='major', linestyle='-', linewidth=0.5, color='grey')
ax.grid(which='minor', linestyle=':', linewidth=0.5, color='#a6a6a6')
ax.tick_params(which='both', # Options for both major and minor ticks
top=False, # turn off top ticks
left=False, # turn off left ticks
right=False, # turn off right ticks
bottom=False) # turn off bottom ticks
data1 = np.random.normal(0,1,1000000)
x=np.sort(data1)
y=np.arange(x.shape[0])/(x.shape[0]+1)
f2 = scipy.interpolate.interp1d(x, y,kind='linear')
x2 = np.linspace(x[0],x[-1],1001)
y2 = f2(x2)
y2b = np.diff(y2)/np.diff(x2)
x2b=(x2[1:]+x2[:-1])/2.
f3 = scipy.interpolate.interp1d(x, y,kind='cubic')
x3 = np.linspace(x[0],x[-1],1001)
y3 = f3(x3)
y3b = np.diff(y3)/np.diff(x3)
x3b=(x3[1:]+x3[:-1])/2.
bins=np.arange(-4,4,0.1)
bins_centers=0.5*(bins[1:]+bins[:-1])
cdf = scipy.stats.norm.cdf(bins_centers)
pdf = scipy.stats.norm.pdf(bins_centers)
plt.rcParams["font.size"] = 18
fig, ax = plt.subplots(3,1,figsize=(10,16))
ax[0].set_title("cdf")
ax[0].plot(x,y,label="data")
ax[0].plot(x2,y2,label="linear")
ax[0].plot(x3,y3,label="cubic")
ax[0].plot(bins_centers,cdf,label="ans")
ax[1].set_title("pdf:linear")
ax[1].plot(x2b,y2b,label="linear")
ax[1].plot(bins_centers,pdf,label="ans")
ax[2].set_title("pdf:cubic")
ax[2].plot(x3b,y3b,label="cubic")
ax[2].plot(bins_centers,pdf,label="ans")
for idx in range(3):
ax[idx].legend()
setGridLine(ax[idx])
plt.show()
plt.clf()
plt.close()