Let’s bring one more Python package into the mix. Seaborn has a `displot()`

function that plots the histogram and KDE for a univariate distribution in one step. Let’s use the NumPy array `d`

from ealier:

```
import seaborn as sns
sns.set_style('darkgrid')
sns.distplot(d)
```

The call above produces a KDE. There is also optionality to fit a specific distribution to the data. This is different than a KDE and consists of parameter estimation for generic data and a specified distribution name:

```
sns.distplot(d, fit=stats.laplace, kde=False)
```

Again, note the slight difference. In the first case, you’re estimating some unknown PDF. In the second, you’re taking a known distribution and finding what parameters best describe it given the empirical data.

Michalon Dec. 9, 2020Using

`distplot`

now raises FutureWarning:`distplot`

is a deprecated function and will be removed in a future version. Please adapt your code to use either`displot`

(a figure-level function with similar flexibility) or`histplot`

(an axes-level function for histograms).(seaborn 0.11.0)

However, neither

`displot`

nor`histplot`

accept the`fit`

argument.