I have a 1-minutely OHLC dataset indexed by time as follows:
df_ohlc
Out[2]:
open high low close index week
Date
2011-09-13 09:53:00 5.8 6.0 5.8 6.0 1 1
2011-09-13 09:54:00 6.0 6.0 6.0 6.0 2 1
2011-09-13 09:55:00 6.0 6.0 6.0 6.0 3 1
2011-09-13 09:56:00 6.0 6.0 6.0 6.0 4 1
2011-09-13 09:57:00 6.0 6.0 6.0 6.0 5 1
...
2017-07-17 18:19:00 2176.99 2176.99 2176.50 2176.50 3073467 305
2017-07-17 18:20:00 2175.00 2177.65 2175.00 2176.99 3073468 305
2017-07-17 18:21:00 2177.80 2177.84 2173.71 2177.61 3073469 305
2017-07-17 18:22:00 2177.50 2177.50 2175.04 2175.04 3073470 305
2017-07-17 18:23:00 2177.30 2177.30 2175.00 2175.00 3073471 305
In Python,
for i in range(1,len(df_ohlc)+1):
plt.clf()
kde_est.iloc[i] = df_ohlc['close'][df_ohlc['week']==i].plot.kde()
plt.show()
generates the Kernel Density Estimate (a smooth histogram essentially) for each week's closing prices of the dataset. In other words, it generates 305 individual KDE plots for this dataset.
How would I plot all these KDEs over time on one 3-Dimensional surface?
For example, right now each KDE plot is [Close Price] x [Probability Density]. I'd like to introduce a new variable (z = time) so we can see the changes in KDE over time, [Close Price] x [Probability Density] x [Week]