The kdeplot function in the hana_ml.visualizers.eda module displays a kernel density estimate plot for a SAP HANA DataFrame, with parameters to specify the data, key, features, KDE calculation, number of points for plotting, and whether to use plotly instead of matplotlib.
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Here is a Python code template based on the provided documentation:

```python
# Import necessary modules
from hana_ml.visualizers.eda import kdeplot
from hana_ml.algorithms.pal.kernel_density import KDE
import matplotlib.pyplot as plt

# Assuming 'df' is your HANA DataFrame

# Single feature KDE plot using matplotlib
f = plt.figure(figsize=(19, 10))
ax = kdeplot(data=df, key="PASSENGER_ID", features=["AGE"])
ax.grid()
plt.show()

# Two features KDE plot using matplotlib
f = plt.figure(figsize=(19, 10))
ax, surf = kdeplot(data=df, key="PASSENGER_ID", features=["AGE", "FARE"])
ax.grid()
plt.show()

# Single feature KDE plot using plotly
fig = kdeplot(data=df.filter("SURVIVED = 1"), key="PASSENGER_ID", features=["AGE"], enable_plotly=True, width=600, height=600)
fig.show()

# Two features KDE plot using plotly
fig = kdeplot(data=df, key="PASSENGER_ID", features=["AGE", "FARE"], enable_plotly=True, width=600, height=600)
fig.show()
```

Please replace 'df' with your actual HANA DataFrame and adjust the 'key' and 'features' parameters as per your data.